Deliverable D2.1 Working papers on Assessment Methodspractice-netweb.eu/sites/default/files/root/... · Ecosystems and Human Well-being: Desertification Synthesis. World Resources

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Deliverable D2.1 Working papers on Assessment Methods

Project: PRACTICE Prevention and Restoration Actions to Combat Desertification.

An Integrated Assessment Coordinator: V.R. Ramón Vallejo.

CEAM Foundation, Spain Grant Agreement no.: 226818

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This Deliverable includes four Working papers on Assessment Methods and Indicators to Evaluate Actions to Combat Desertification. The Working papers have been prepared by the PRACTICE Expert-Assessment Board (partners: UA, ABDN, CEAM, UTRIER, and CMCC), in the framework of WP2 (Task 2.1) and have been presented for discussion and refinement to the PRACTICE Workshop on Assessment Methods and Indicators to Evaluate Actions to Combat Desertification, held in Sardinia in October 2010.

PRACTICE (October 2010) [email protected]

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Table of contents Preface 5 I. Coupled Human-Environment Indicators for the Assessment of Dryland

Restoration Stephen Whitfield and Helmut J. Geist

7 1. introduction 9 2. Developing a human-environment framework 9

2.1. Ecosystem services for defining dryland degradation 10 2.2. Baskets and trade-offs in ecosystem services 11 2.3. The value of ecosystem as an indicator of resilience 13 2.4. Multiple stakeholders and multiple values 14 2.5. Value as a problematic concept 14

3. Dryland ecosystem services 15 4. Monitoring the value of ecosystem services 22

4.1. Valuing ecosystem services 22 4.2. Food, forage, fibre and fuelwood 24 4.3. Carbon sequestration 28 4.4. Water provision and conservation 29 4.5. Cultural heritage and landscapes 30

5. Conclusion 31 6. References 32

II. Ground-based Methods and Indicators for the Assessment of Desertification Prevention and Restoration Actions

Susana Bautista and Ángeles G. Mayor

41 1. Introduction 43 2. Evaluation approaches 43 3. Selection of attributes and indicators for evaluation 46

3.1. Evaluation frameworks 46 3.2. Number, domain and scale of application of the assessment indicators

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3.2.1. Evaluation approaches based on few holistic indicators

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3.2.2. 3.2.2 Evaluation approaches based on large suites of indicators

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4. Common indicators for assessing dryland management and restoration

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4.1. An evaluation approach based on the comparative assessment of the provision of ecosystem services

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4.2. Common indicators for assessing dryland management and restoration

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5. Conclusions 55 6. References 56

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III. Remote Sensing Based Options for Assessment of Mitigation and Restoration Actions to Combat Desertification

Achim Röder, Marion Stellmes, Joachim Hill, Thomas Udelhoven

62 1. Introduction 64 2. Coarse scale 67

2.1. Data sources 67 2.2. Diachronic analyses 69 2.3. Vegetation Indices 70 2.4. Time series analysis using hypertemporal satellite archives 72

2.4.1. Preprocessing 73 2.4.2. Assessment of seasonal components 74 2.4.3. Transitional components 77

2.5. Integrated interpretation concepts 77 2.5.1. Mapping functional vegetation types 77 2.5.2. Syndrome-based mapping approaches 78 2.5.3. Integrated land condition assessment 79

3. Fine scale 80 3.1. Data sources and time series setup 80 3.2. Standardisation requirements 83

3.2.1. Geometry 83 3.2.2. Radiometry 84

3.3. Derivation of vegetation-related information 88 3.4. Fine-scale temporal analysis 92 3.5. Land use / land cover classification 94 3.6. Integrated interpretation concepts 96

4. Plot scale 97 5. References 98

IV. A Methodological Framework for Assessing and Valuating Ecosystem Services and Mitigation Strategies against Desertification

Laura Onofri, Andrea Ghermandi, and P.A.K.L. Nunes

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1. Introduction 117 2. Some taxonomy 118 3. Total economic value and ecosystem services economic tools 119

3.1. A survey of economic valuation techniques 121 3.2. Advantages, disadvantages and applicability of ecosystem services valuation techniques

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4. Valuing drylands ecosystem services and anti-desertification policies 127 4.1 Evaluation of anti-desertification policies through socio-economic indicators

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4.2. Desertification policy assessment: A cost-effective meta indicator

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5. Conclusions 137 6. References 138

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Preface There is a consensus on the need for the evaluation of management actions to combat desertification, which ultimately can provide essential inputs for decision-making (see, for example, UNCCD 20091). It is increasingly recognised that the assessment of land condition must be based on both biophysical and socio-economic attributes (MA 20052), which also applies to the evaluation of restoration and management actions (SER 20043, Zucca et al. 20094). The participation of stakeholders and the incorporation of local knowledge in the assessment of environmental problems and potential solutions have also been increasingly demanded by international institutions.

PRACTICE addresses these needs and demands by linking the evaluation of management and restoration actions with knowledge exchange and social learning through a participatory process. PRACTICE assumes the mutual human-environment interactions in land-use change and simultaneously considers both biophysical and socio-economic attributes (Fig. 1).

Figure 1. PRACTICE framework for the assessment of actions to combat desertification.

1 UNCCD. 2009c. UNCCD 1st Scientific Conference: Synthesis and recommendations. Note by the secretariat. ICCD/COP(9)/CST/INF.3. Bonn: UN Convention to Combat Desertification (http://www.unccd.int/php/document2.php?ref=ICCD/COP%289%29/CST/INF.3) 2 MA (Millennium Ecosystem Assessment). 2005. Ecosystems and Human Well-being: Desertification Synthesis. World Resources Institute: Washington, DC. 3 SER (Society for Ecological Restoration, Science & Policy Working Group). 2004. The SER Primer on Ecological Restoration (http://www.ser.org/). 4 Zucca, C., Bautista, S., Previtali, F. 2009. Desertification: Prevention and restoration. In R. Lal (ed.), Encyclopedia of Soil Science, Second Edition. Taylor & Francis Group, New York, US.

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PRACTICE protocol is based on (1) key common indicators that represent overall human-ecological system functioning, (2) site-specific indicators identified by local stakeholders that are relevant to the objectives and the particular context conditions, and (3) stakeholder perspectives.

The selection of the key common indicators and assessment methods has been based on a series of working papers (WPs) produced by the PRACTICE Expert Assessment Board within the framework of PRACTICE Work Package 2 (WP2. Assessment methodology). The WPs were presented and discussed and further refined during the PRACTICE Workshop on Assessment Methods for Prevention and Restoration Actions to Combat Desertification (PRACTICE Deliverable D2.2), held in Sardinia in October 2010.

This report presents the refined versions of the working papers, which define the conceptual framework that underlies the assessment methodology (WP I), and review and propose ground-based bio-physical indicators and assessment methods (WP II), remote-sensing approaches (WP III), and socio-economic analysis to assess prevention and restoration actions (WP IV).

Susana Bautista

PRACTICE WP2 Coordinator

Coupled Human-Environment Indicators for the Assessment of Dryland Restoration

Department of Geography and the Environment

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I. Coupled Human-Environment Indicators for the Assessment of Dryland Restoration

Working Paper for the PRACTICE Project

Stephen Whitfield

Prof. Helmut J. Geist

S.Whitfield and H. J.Geist


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Front cover photo source: Focx Photography (Flickr)

CC: BY NC SA

Stephen Whitfield and Helmut J. Geist (August 2010)

Department of Geography and Environment

University of Aberdeen

Elphinstone Road Aberdeen AB24 3UF Scotland, UK

[email protected]



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1 Introduction

A study of the human-environment system is a fundamental component of EU PRACTICE project, which, through WP2, aims to ‘develop integrated evaluation tools to assess practices to combat desertification’ (PRACTICE, 2009) and apply and evaluate these tools within long-term monitoring sites (LTEMs). The project has a unique opportunity to build on the progress made by research groups such as REACTION, DESIRE, WOCAT and the Dryland Science for Development (DSD) Consortium, by synthesizing a large body of theoretical and case study literature on the human-environment aspects of dryland management and develop tools that will facilitate the application of this knowledge in the practical assessment of dryland management and restoration.

This paper will outline a conceptual framework for the PRACTICE approach from a human-environment perspective. The overarching concept of ecosystem services is used as a basis for describing the aims of dryland restoration and discussing indicators and methods for monitoring the progress of restoration projects.

2 Developing a human-environment framework

The monitoring and feedback components of development projects are often left neglected due to constraints on resources and short-sighted project planning (Dipper, 1998). However, because of the dynamic and adaptive nature of socio-ecological dryland systems (Holling, 1973; Folke, 2006), monitoring and evaluating restoration efforts are essential for implementing strategies that successfully adapt to changing conditions and stakeholder needs. Indicators of sustainability provide a simple metric for evaluating the success of a restoration project or mitigation action and are therefore necessary for facilitating the monitoring and learning components of a project, which is often neglected, particularly where resources are limited. Indicators are most effective when they are directly linked to pre-defined aims of a project, such that a change in indicators represents measurable progression towards (or regression from) a desired end point or along a desired pathway.

Dryland restoration projects might set-out with any number of specific goals such as increasing the populations of specific vegetation species (Gao et al, 2002); reducing soil erosion (Sivanappan, 1995); increasing agricultural productivity (Zaal and Oostendorp, 2002); improving access to water (Cotula, 2006); or conserving cultural landscapes (Moreira et al, 2006). Under the umbrella of ‘dryland restoration’ might be included reforestation projects,



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land-use conversion, protected area designation, water conservation strategies and property right regime shifts (Bainbridge, 2007). As such, any discussion of restoration as a whole needs to be equally broad in scope, and indicators must be devised in such a way that they can be interpreted in line with specific project aims and be adaptable to the changing dynamics of interacting social and ecological spheres.

In this chapter, a brief overview of the large body of literature on socio-ecological system dynamics will be linked to a discussion of the merits of using the value of ecosystem services as an indicator for monitoring dryland restoration.

2.1 Ecosystem services for defining dryland degradation

‘Ecosystem services’ is a utilitarian device for describing the valuable functions of an ecosystem to humans and as such it fits well within a framework designed to be responsive to the changing dynamics of a social-ecological system. Daily et al (1997) recognised that human societies derive a variety of goods from natural ecosystems, a concept that has subsequently been widely used in putting a value on ecosystems (e.g. Costanza et al, 1997) and evaluating the impacts of environmental change (e.g. Worm et al., 2006). The Millennium Ecosystem Assessment (MA) advanced the concept of ecosystem services by providing a framework for categorising ecosystems services and drawing links between ecosystem services and human well-being (MA, 2005). The MA defines ecosystem services as:

‘The benefits people obtain from an ecosystem, including provisioning services such as food, water, timber, and fibre; regulating services that affect climate, floods, disease, wastes, and water quality; cultural services that provide recreational, aesthetic, and spiritual benefits; and supporting services such as soil formation, photosynthesis, and nutrient cycling.’ (MA, 2005: xiii)

These benefits contribute to a holistic understanding of the well-being of stakeholders in the ecosystem, which represents not only income and material needs, but also health, social relations, security, and freedom of choice and actions (MA, 2005). The well-being of different stakeholders in an ecosystem depends in part, but not entirely, on the relative cultural, economic, social, and spiritual benefits that each derive from the environment. The dependency of livelihoods on the services provided by ecosystems is greater in drylands than in any other ecosystem (MA, 2005) and the marginal properties of these ecosystems is such that the balance between sustainability and degradation is a fine one (Geist and Lambin, 2004).

Defining dryland degradation as ‘a persistent reduction in the basket of services provided by a dryland ecosystem over an extended period’ (an



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adaptation of the MA 2005 definition), and therefore understanding effective management (restoration) as being represented by a long term increase in the relative value of ecosystem services’ has several benefits:

• A dryland ecosystem provides a range of services. By focusing on a basket of services (DSD WG1), rather than individual components of a dryland ecosystem, adaptation strategies in land management can be considered. The Millennium Ecosystem Assessment (2005) recognised that trade-offs between services may be strategically made in adapting to changing pressures and maintaining well-being – it therefore fits comfortably with the concept of resilience.

• Considering interactions and trade-offs between ecosystem services is a useful way of defining and identifying stakeholders at all scales as those who derive direct or indirect benefits from services provided. Reynolds and Stafford Smith (2002) explain that the unsustainable use of ecosystem services at the local level can place increased pressure on ecosystems (and affecting stakeholders) elsewhere. For practical purposes, however, it is necessary to define the boundaries of an ecosystem at the local scale and work outwards (Fisher et al, 2009).

• It is a common language that can be applied in land management from the perspectives of multiple stakeholders at multiple levels.

• By concentrating on a persistent reduction (or long-term increase), the definition makes a clear distinction between long-term trends and short-term fluctuations, which create noise in the observation of trends in ecosystem development (Scholes, 2009). There is a consensus within literature on dryland degradation monitoring and sustainable management assessment that attention should be primarily focused on slow variables (Reynolds and Stafford-Smith, 2002).

2.2 Baskets and trade-offs in ecosystem services

Interactions between ecosystem services are complex and often not well-defined (MA, 2005; van Jaarsveld et al, 2005), particularly where they result from human interactions within the ecosystem. The MA (2005) recognised that trade-offs between services may be strategically made by stakeholders in adapting to changing system pressures and maintaining well-being. There is a wealth of literature on local environmental knowledge in drylands that highlights examples of such adaptive strategies (see Reed et al, 2007). Furthermore, ecosystem services are strongly interlinked; the exploitation of a particular ecosystem service can have implications for the whole basket of services (De Fries et al., 2004), a change to the system of resource management may increase certain ecosystem services whilst degrading others (Reyers et al., 2009), and as such, a sustainable system may be one that moves between several stable states (or baskets) (Folke et al, 2004). It is



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for these reasons that it is necessary to take a holistic approach to monitoring drylands from an ecosystem services perspective, considering a whole ‘basket’ of ecosystem services rather than individual services.

The resilience alliance (Holling, 2001) combined several social-ecological system dynamics – threshold effects and non-linearity (Scheffer et al., 2001), adaptation through feedbacks (Walker and Meyers, 2004), legacy effects and time lags (Liu et al, 2007) – in describing the sustainability of a system in terms of resilience. The concepts of resilience and sustainability are somewhat interchangeable, in both cases we can consider a resilient, or sustainable, system as one with the capacity to avoid going beyond critical thresholds (tipping points) or recover from surpassing non-critical thresholds (turning points) through adaptive strategies. This concept might be extended to describe dryland restoration as actions to rebuild the adaptive capacity of systems that have passed critical thresholds.

Table 1: A description of social-ecological system dynamics illustrated with examples from the literature

Socio-Eco-System Property

Description Human-environment examples in the literature

Thresholds A point reached in a linear trend that causes a shift in the system state

Kinzig et al (2006) identify a soil salinity threshold between productive agricultural land and non productive land in the Western Australian wheatbelt

Non-linearity Dependent factors within a system that do not respond to each other with proportional magnitude

Koch et al (2009) describe non-linearity in coastal protection (an ecosystem service) provided by marshes, mangroves and coral reefs.

Adaptation through

feedbacks

A response to a system change that acts to increase (positive feedback) or decrease (negative feedback) its effects – i.e. adaptation through exploiting ecosystem services

Gordon (2007) explores feedbacks in the management of socio-ecological systems in the Great Barrier Reef catchments, describing links between upstream land management, soil erosion, downstream sediment deposition and reef degradation Lambin and Meyfroidt (2010) describe socio-ecological feedbacks that slow down deforestation in Vietnam

Legacy effects and time lags

The effects of a defined system event continue beyond, or may not be realised until sometime after, the event.

Liu et al (2007) show how the introduction of a keystone species in the Wisconsin Highland Lake District continues to restructure fish populations for decades afterwards



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Case studies of adaptation in drylands show how land users or dryland stakeholders substitute the utilisation of certain services for that of others in order to maintain or improve their well-being in response to changing system properties (e.g. Enfors and Gordon, 2007). Adeel and Safriel (2008), for example, describe the adaptation of a community at the edge of the Cholistan Desert in Pakistan, who have adapted to a reduction in ground water availability by switching their predominant income source from cattle grazing to aquaculture (breeding carp), which, through controlled conditions and careful management of surface water, requires less water investment. Adaptation through ecosystem service substitution also takes place in response to all manner of system changes, from climate change to population growth and from cultural change to market fluctuation (Adger 2000). A community in the Dana Biosphere Reserve, Jordan, for example, have adapted to unsteady market conditions for their food produce by switching to production of a non-food, high-end product – olive oil soap, and on the basis of this unusual industry they have also attracted additional tourism (Adeel and Safriel, 2008).

Importantly, within resilience thinking, a system is understood as being mobile between steady states rather than having just one. In the language of ecosystem services, several significantly different compositions of services within the basket may result from sustainable land management (SLM) and systems are capable of moving between baskets through adaptation to system processes (Carpenter and Brock, 2004; Janssen and Carpenter, 1999). Restoration is essentially about building the resilience of a system, utilising its ecosystems services in such a way that it doesn’t reduce the capacity of the system to adapt by altering its basket of services, or reduce the well-being of those with a stake in the ecosystem.

2.3 The value of ecosystem services as an indicator of resilience

The value of a broad basket of ecosystem services represents a strong indicator of the resilience of a dryland system as it reflects the potential pathways and substitution options for achieving an increase in the well-being of stakeholders (MA, 2005). Without prolonged and intensive data collection, which would contradict the rationale behind employing indicators, it is very difficult to identify and assess all of the ways in which each stakeholder derives different components of well-being from different services, furthermore, it is unrealistic to expect to weigh-up the relative importance of these different components. In this respect, valuing a basket of services is somewhat crude, but although it may smooth over some of the intricacies of the human-environment interactions within the system, it does strongly reflect the overall capacity of the ecosystem to contribute to the well-being of its stakeholders.



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The value of ecosystem services derived by stakeholders is partly indicative of the dependence of their well-being on the ecosystem, and the changing value of ecosystem services reflects a changing component of the well-being of stakeholders that is related to the environment. Whereas a biophysical approach might focus solely on the quantity of services provided (e.g. the amount of carbon sequestered by an ecosystem), a human-environmental perspective, which is concerned more with how ecosystem services translate into human well-being, necessarily focuses on the value of services to stakeholders.

2.4 Multiple stakeholders and multiple values

Different stakeholders, at different scales, exist within socio-ecological systems of different properties (Schwilch et al, 2009). The combination of countless social, economic, cultural and environmental factors, operating at multiple scales, which creates a local context for dryland degradation is such that each individual socio-ecological system is unique (Warren, 2002; Schwich et al, 2009) and is uniquely experienced by the range of people with a stake in it. This presents a problematic reality for the appropriateness of using generic tools in describing desertification and sustainable dryland management. A holistic assessment of dryland management requires multi-scale and multi-stakeholder analysis (Reyers et al, 2009). At the local scale, sustainable management can often appear as degradation because of a lack of understanding about how land managers prioritise and strategically substitute different ecosystem services, particularly those that represent cultural services (Jollands and Harmsworth, 2007). Incorporating local environmental knowledge within an assessment of dryland management is imperative for understanding how degradation is defined and experienced by those whose well-being is most directly linked to the services provided by a particular ecosystem (Danielsen et al, 2008). However, there is a significant challenge in attempting to monitor the value of ecosystem services across multiple stakeholders at multiple scales, particularly where there are significant differences in the benefits that different stakeholders receive from ecosystem services, such that a given scenario might result in an increase in value of ecosystem services for some stakeholder groups, but a decrease in value for others.

2.5 Value as a problematic concept

The mainstream approach to linking ecosystem services and well-being has been socio-economic or monetary valuation. The advantages of putting a monetary value on ecosystem services – those of comparability, quantitative analysis, and the potential avenues it opens for sustainability through payment (weak sustainability) – have been heralded and embraced within



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the literature (Reid, 2006; Costanza et al, 1997; Daily et al, 1997) as vociferously as they have been criticised by those who argue against the intrinsic failings of economic methodologies (Toman, 1998) and the moral and philosophical problems of putting monetary values on nature (McCauley, 2006). An economic valuation of the ecosystem has its limits, particularly with regards to considering the non-utility value of the system, for example its existence value and long-term sustainability (Spangenberg and Settele, 2010).

Value statements need not be expressed in monetary terms, qualitative description of value or semi-quantitative scoring and ranking systems (see DEFRA 2007) can generate informative and comparable information. It is suggested that monetary valuation of ecosystem services is avoided within monitoring and assessment because of the tendency it creates within participants to revert to a prioritisation of individual and market-related benefits. Discursive and qualitative approaches allow greater scope for participants to express values that relate to the sustainability of the system and the needs of future generations, and language-based analysis of discussions provides much richer information on the opinions of stakeholders.

3 Dryland ecosystem services

The Millennium Ecosystem Assessments’ (MA) ‘Desertification Synthesis’ report brings together work from the four working groups of the MA in an attempt to answer the following questions:

How has desertification affected ecosystems and human well-being? What are the main causes of desertification? Who is affected by desertification? How might desertification affect human well-being in the future? What options exist to avoid or reverse the negative impacts of desertification? And how can we improve our understanding of desertification and its impacts? (MA, 2005)

As such it represents a comprehensive summary of global dryland systems and provides a useful foundation on which to build on in an attempt to define indicators for the assessment of dryland restoration projects based on the value of ecosystem services. The MA outlines the following list of the key dryland ecosystem services, presented in accordance with its services framework:



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Table 2: Ecosystem services from drylands categorised in accordance with the MA schema. Source: Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-Being Desertification Synthesis

Provisioning Service

Goods produced or provided by ecosystems

• Provisions derived from biological productivity: food, fibre, forage, fuelwood, and biochemicals

• Fresh water

Regulating Service Benefits obtained from regulation of ecosystem processes

• Water purification and regulation • Pollination and seed dispersal • Climate regulation (local through

vegetation cover and global through carbon sequestration)

Cultural Service Nonmaterial benefits obtained from ecosystems

• Recreation and tourism • Cultural identity and diversity • Cultural landscapes and heritage values • Indigenous knowledge systems • Spiritual, aesthetic, and inspirational

services

Supporting Service Services that maintain the conditions for life on Earth

• Soil development (conservation, formation)

• Water conservation • Nutrient cycling

The basket of ecosystem services outlined by the MA is comprehensive, but in many cases is too large to translate into a practical assessment tool. The ecosystem services listed can be categorised in a number of ways, but depending on the vagueness of definition, this basket could reflect anything from four to twenty-six categories. Clearly, the more narrowly defined an indicator is, the more consistently it can be applied across research projects and locations; however the universality of indicators is compromised by specificity. Defining an assessment tool that is universally applicable, but practical, will require a limited suite of indicators that are well-defined, but contextually adaptable and representative of the broader basket of ecosystem services provided by the dryland system. The challenge taken up here is to select between three and five services that combined can adequately and universally reflect the full basket of ecosystem services provided by drylands and the variety of linkages that are made between this basket and the well-being of stakeholders.

In this chapter a wide-reaching literature review is drawn on in determining and defining those ecosystem services which, in combination, best reflect a common basket of ecosystem services provided by a dryland system of unspecified social, cultural, economic and biophysical properties. It is important to bear in mind is that the relative importance of each ecosystem



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service within the basket differs (spatially and temporally) across socio-ecological systems and across stakeholders, and as such what is presented here can only represent a guideline that should be altered and adapted to suit specific case studies.

The following criteria are used here as a basis for narrowing down the ecosystem services to a practical number for universal assessment purposes, they describe the desired properties of the services that should be selected.

• Interpretable and of measurable value at the local scale – such that stakeholders and researchers alike can draw clear conceptual links between the ecosystem function and the well-being that is derived from it

• Identifiable within peer-review case-study literature as being key services in all major global dryland regions – particularly as being relevant in both more- and less-economically developed regions

• Indicative of long term change rather than short-term variability (however, this is more of an issue for the valuation methodology; addressed in the next chapter)

And in combination, the group of services selected should:

• Cover the three broad categories of ecosystem services with direct links to human well-being: provisioning, regulatory and cultural (supporting services have indirect links with human well-being and their valuation can be methodologically problematic)

• Conceptually link to all four of the categories of human well-being that are directly related to ecosystem services: security, basic material for good life, health, good social relations

The following table summarizes literature, which reflects the potential of each of the ecosystem services identified by the MA to be used as indicators of the full basket of services. The categorisation of these services reflects that of the MA.



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Service Summary of available literature Food, fibre and forage

• A service of fundamental importance in all dryland regions and the main source of income or subsistence for the majority of land users (Zhen et al, 2010; Kerven et al, 2007; Russelle, 2007; Schulz et al, 2010; Luck et al, 2003; Pereira et al, 2006)

• Interpretable and adaptable at local scales in accordance with local and seasonal crops/livestock/land use (MA, 2005; Kerven t al, 2007)

• Closely linked to three fundamental components of human well-being: security (Nyariki et al, 2002); basic material for good life; and health (Corvalan et al, 2005)

Fuelwood • Wood is harvested for fuel in almost all dryland regions, but is of particular importance to the livelihoods of local land users in less economically developed regions (Zhen et al, 2010; Kerven et al, 2007; Schulz et al, 2010; Dubroeucq and Livenais, 2004; Tovey, 2008; Bautista et al, 2010)

• Ranges from subsistence harvesting ( Zhen et al, 2010) to large scale commercial production (Kakuru et al, 2004)

• Quantities and species used are locally and temporally-specific

• Closely linked to two fundamental components of human well-being: basic material for good life (Dovie et al, 2004); and health (Corvalan et al, 2005)

Biochemicals • Some commercial opportunities but a sparse literature cites few examples of biochemical extraction in drylands (e.g. Guaseelan, 2009)

Provisioning Services

Fresh water • A fundamental component of two components of human well-being: basic material for good life; and health (Corvalan et al, 2005)

• Typically low volumes of accessible fresh water in drylands means that it can represent a critical determinant/threshold in system properties (Huber-Sannwald et al, 2006; Reynolds et al, 2007) but is of little commercial significance in the majority of drylands

• May be of importance to a wide spectrum of stakeholders (Jiang et al, 2005; White et al, 2002)



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Water purification and regulation

• Because of the critical importance of freshwater in almost all dryland ecosystems, the capacity for purification of water is a key service (Carpenter et al, 2006), particularly where water used in agriculture contaminates groundwater (Scanlon et al, 2005)

• Complicated process that depends on many interacting system properties (climate, vegetation, soil structure, geology, water extraction technology, irrigation)

• May be of importance to a wide spectrum of stakeholders (Jiang et al, 2005; White et al, 2002)

• A fundamental component of two components of human well-being: basic material for good life; and health (Corvalan et al, 2005)

Pollination and seed dispersal

• An important ecosystem service in all major dryland regions (Kerven et al, 2007; Proctor et al, 2002; Bennett et al, 2009; Reyers et al, 2009; Safriel, 2006)

• In certain ecosystems the vast majority of vegetation may rely on seed dispersal by wind, this limits the usefulness of the value of pollinating services as an indicator of dryland health (Standish et al, 2007; Benayas et al, 2008)

• Closely linked to two components of human well-being: basic material for good life (MA, 2003; Luck et al, 2003; Balmford and Bond, 2005); and security (Lal, 2002)

Local climate regulation

• Very little literature on local climate regulation by dryland ecosystems, probably because the hydrological cycles associated with these systems are relatively unresponsive (Hope et al, 2004)

• Wind breaks offered by structural vegetation may be an important service in drylands at risk from wind-induced soil erosion (Mukhopadhyay, 2009; Masri et al, 2003)

• A complicated ecosystem service to monitor because of the difficulty of untangling local, ecosystem-related climate change from broader scale climate variations and trends (Stohlgren et al, 1998; Herrmann et al, 2005)

Regulating Services

Slope stabilisation

• Only relevant in mountainous and hilly dryland regions • Value of the ecosystem service is closely linked to the value

of the predominant land use (provisioning services), in few cases there may be a risk to lives, property and infrastructure associated with slope instability in drylands (Dregne, 2002; Wenlan et al, 2006)

• Slope stability depends strongly on climate events as well as the stabilisation that is offered by vegetation structure (Christanto et al, 2009)



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Regulating Services

Carbon sequestration

• The capacity for carbon sequestration and storage of dryland ecosystems is significant (Lal, 2002; 2004), but depends on the vegetation composition and soil properties, as such its significance as an indicator of dryland health is highly spatially variable (Lal, 2002; Kerven et al, 2007; Asner et al, 2003; Glenn et al, 1993; Carretero et al, 2007; Lal, 2004; Bautista et al, 2010)

• Implications for stakeholders on a global scale, and because of the fragile nature, and climate dependence, of drylands it will be of value of local land users (Dietz et al, 2004; IPCC, 2007), however valuation by land users will rely on their ability to make complex conceptual links between carbon storage and climatic change at multiple scales

• Connected to all four components of human well-being: security, basic material for good life, health, good social relations

Recreation and tourism

• Only of significant contemporary value where a tourism industry is established, although this may also be thought of in terms of potential value

• The commerciality of the tourist industry may depend on levels of investment and as such it may be of significance to a wide range of stakeholders (from large corporations to small informal entrepreneurs and including tourists themselves) at multiple scales (White et al, 2002; Portnov and Safriel, 2004)

• Significant primarily as a source of income and as such it has indirect links to all four components of human well-being: security, basic material for good life, health, good social relations (MA, 2005)

Cultural Services

Cultural identity and diversity

• In the majority of dryland ecosystems the livelihoods and lifestyle of local land users is intrinsically linked to the land and as such the ecosystem makes up some component of their cultural identity (Kerven et al, 2007; Moreira et al, 2006)

• In practical terms the value of this component of an ecosystem service may be very difficult to separate from the value put on other components such as provisioning services

• From the premise that cultural identity changes with the ecosystem, it becomes problematic to objectively value one identity relative to a previous (or to several alternative) identity(ies) (Kumar and Kumar, 2008).



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Cultural landscapes and heritage values

• A cultural landscape in dryland ecosystems is created by a history of land use (however long or short) and as such is a concept that is applicable within almost all dryland socio-ecological systems (Jiang, 2003; Kerven et al, 2007; Moreira et al, 2006; Schultz et al, 2010; Pereira et al, 2006)

• Because a cultural landscape is not static, this value should not be interpreted as the value of conserving ‘traditional’ practices. It is a value that is difficult to conceptualise, but perhaps should best be presented as the social-ecological system allowing users to continue to exercise choice in respecting cultural practices and self-defined tradition.

• It is a component of the ecosystem that should be defined and interpreted by local stakeholders

• Closely linked to three components of human well-being: security, basic material for good life, and good social relations (MA, 2003)

Indigenous knowledge systems

• Only appropriate in those dryland in which the land users consider themselves indigenous

• Knowledge is often passed down by land users through demonstration and practice, this ecosystem service represents the capacity of land users to continue to practice land use in a self-defined ‘traditional’ way (Alverson, 1984); thus it is affected not only the biophysical state of the ecosystem, but also the institutional , political and economic factors that affect the freedom of choice of land users (Geist and Lambin, 2004)


• Closely linked to three components of human well-being: security, basic material for good life (Berkes et al, 1995; Thomas et al, 2000), and good social relations (MA, 2005)

Cultural Services

Spiritual, aesthetic, and inspirational services

• Very sparse literature on spiritual and aesthetic services specific to dryland ecosystems (e.g. Upton, 2010). The relative importance of this ecosystem service will vary widely across different dryland locations


• Closely linked to three components of human well-being: security (Butler, 2006; MA, 2005), basic material for good life, and good social relations (MA, 2005)

Soil development

• Nitrogen content, salinity, soil depth, soil crust may all act as key constraints/supports of dryland ecosystem function.

• Particularly closely linked to the productivity of land

Supporting services

Water conservation

• The primary constraint on dryland system functioning • Indirectly linked to all components o human well-being and a

key constraint on all of the services listed above in most dryland environments



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In accordance with the criteria outlined, it is suggested that for a dryland system of unspecified social, cultural, political, economic and biophysical properties, the dryland ecosystem services basket focused on should include: the provision of food, fibre and forage, and fuelwood, carbon sequestration, water provision and conservation and cultural landscapes and heritage values. However a review of literature and participatory research within a particular dryland socio-ecological system may reveal that certain ecosystem services are of particular significance (or insignificance) to that context and the basket of services used should be altered accordingly. Appendix A presents a more detailed review of literature on two particular dryland contexts (the Kalahari and the Mongolia rangelands) and illustrates similarities and differences in the compositions of the basket of ecosystem services between the two.

The following chapter takes this basket of representative ecosystem services and considers in more detail how its value can be measured and monitored as an indicator of successful dryland restoration/degradation mitigation.

4 Monitoring the Value of Ecosystem Services

This chapter will outline some of the methodological issues involved in measuring the value of each of the ecosystem services within the indicator basket and it will make suggestions about how trends in these values might be most effectively monitored. For each ecosystem service an outline of the key stakeholders is given.

4.1 Valuing ecosystem services

A participatory approach to stakeholder valuation of ecosystem services is advocated here. We suggest that valuation should be done in a deliberative way, bringing together groups of stakeholders and experts such that they might share their knowledge and facilitate the adoption of alternative perspectives by stakeholders. Such an approach targets consensus about the value of ecosystem services, although in many cases this will be an ideal rather than a reality. The public nature of ecosystem services and the realisation that sustainability value can only be captured through an understanding of the values of non-human agents and future generations, means that it is necessary for those valuing ecosystem services to be able to achieve a perspective that looks beyond the utility that they derive as individuals (Owens 2000). Through engagement in a deliberative process that targets knowledge-transfer and consensus-based decision making, small groups of citizens are able to base judgements of value on communal benefits (Ostrom, 1990; Owens 2005). Deliberation involves the presentation of alternative viewpoints, including those of ecological experts, to a group of stakeholders, such that they might become better informed about the



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functions of the ecosystem and the ways in which utility is derived from it. From informed positions, stakeholders debate and hold each other to account for the opinions that are expressed. With an emphasis on learning, justification and logic, deliberation targets a consensual or democratic outcome (Baber and Bartlett 2005; Owens 2000). Pellizzoni (2001) highlights the civic virtue of a deliberative model; he explains that the opportunity to engage with alternative perspective and the responsibility that comes with it encourages individuals to be better citizens that are less prone to acting strategically to their own individual end. A civic valuation of ecosystem services has greater potential to be sensitive to the values of future generations and the inherent values of ecosystems (Parfit 1984), particularly where a case for sensitivity can be made within the deliberative process and stakeholders are able to learn more about the ecological functioning of the system through it.

Combining the multiple valuations of multiple stakeholders requires sensitivity to the social context of the environment being studied. It may be appropriate to place a social weighting function in combining the results of multiple stakeholders, such that the values of those whose livelihoods are most closely linked to the ecosystem services (i.e. local land users) are considered a more important determinant of sustainability than the values of those with an indirect stake in the ecosystem. This issue is contentious and must be resolved in a way that is appropriate to the specific location.

Tools available to the researcher concerned with monitoring, and particularly modelling, ecosystem services are becoming increasingly sophisticated. InVEST is a computer model that has been developed as part of the Natural Capital Project to map the distribution and value of ecosystem services within defined boundaries. Parameters within the model are capable of simulating the ecosystem services of carbon sequestration, storm peak management, soil conservation, pollinators, water quality and the market value of commodity production. It is capable of running scenario projections based on a relatively small suite of input variables and is therefore an ideal model to use for evaluating the supporting and regulating service components of dryland ecosystems. The outputs of models may be utilised in stakeholder-led discussions and valuations.

Whether or not a computer model is used, understanding and defining the parameters of the socio-ecological system is an important component of monitoring restoration efforts. Defining and mapping the boundaries of the social-ecological-system is a necessary first step, such that a frame of reference exists consistently for the long term and interdisciplinary monitoring process. Defining boundaries should be a participatory process involving discussions with key local informants (village elders, wildlife service administrators, local councillors, etc.) about important political, social, cultural and biophysical boundaries and should make use of remotely sensed maps (vegetation/soil properties). Categories for defining boundaries will inevitably depend on the characteristics of the social-ecological-ecosystem being



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studied. Identifying the ecosystem services that are derived from the drylands, and understanding what drives the decision of local people to make use of certain services in a particular place and time, is something that might be best achieved through close engagement with local stakeholders. The intricacies of social-ecological relationships at this local level do not lend themselves to quantitative definition, but aim here should be to qualitatively describe the socio-ecological system in terms of ecosystem services, with specific attention paid to thresholds, feedbacks, adaptation strategies and critical determinants of change. It is important to note that the dynamics of the socio-ecological system are changeable over time, and as evaluations are carried out periodically in monitoring management, it may also be appropriate to alter the parameters of the model.

One of the advantages of monitoring the value of ecosystem services over time is that it is based on relative rather than absolute values. As such, the significance of some of the issues over the accuracy of the economics are avoided. In fact, a purely comparative application of the value of ecosystem services may not require values to be expressed in monetary terms, but rather can be done through a retrospective or prospective consideration, and non-monetary description, of the changes in value over a given period. Such an approach, of course, is not immune to its own methodological pitfalls, particularly with regards to its accuracy and vulnerability to romantic/optimistic/pessimistic bias.

The following discussion will describe both absolute and relative approaches to the valuation of each of the considered ecosystem services. Combining the two approaches would provide the most reliable and accurate monitoring tool, but the extent to which either of both approaches can be applied will depend on the scope and capabilities of the research being undertaken. The DEFRA 2007 report, ‘an introductory guide to valuing ecosystem services’, describes a participatory approach to valuing ecosystem services which is advocated here.

4.2 Food, forage, fibre and fuelwood

The first step in measuring the value of provisioning services, such as food production, is to become familiar with all of the ways in which the land is used for cultivating, gathering and grazing and how this production translates into meeting the dietary needs of local people and, in some cases, contributes to the economic activity of land users. This is an ecosystem service that is potentially highly reactive to changes in the social-ecological-system; a balance between commercial and subsistence activity, for example, may alter in response to market or climatic changes, as may agricultural activity and the diets and dietary requirements of local people (Mortimore and Adams, 2001; Barbier, 1996; Watts, 1987).



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Monitoring the value of this ecosystem service is somewhat data-intensive, records of quantities of the full range of food products harvested, collected, hunted, gathered, cultivated and reared for a representative sample of land users need to be combined with information collected on dietary requirements and social and cultural connections to food. This is best done through the application of participatory research tools (such as Rapid Rural Appraisal described by Chambers (1994)). Remotely-sensed data can provide useful triangulation and a broad perspective on trends in agricultural trends by offering a visible and quantitatively comparable description of crop health.

Where there is a market for crops being produced, even if land users are purely subsistence farmers, the market value of what is being produced on the land can be a good measure of the social value of the ecosystem service (De Groot et al, 2002), because the alternative to producing the food is usually to purchase it. There are problems with such an opportunity cost approach though, particularly where local people’s food comes through a number of pathways (e.g. hunting, gathering, agriculture, market), as is most commonly the case in drylands (e.g. Owuor et al, 2005). It may be inappropriate to base calculations on a direct and linear relationship between quantity and value in production, above certain thresholds (e.g. production above the dietary requirements of local people in subsistence communities) the increase in value of production will begin to decline with increases in production. Food production may well come at a cost to other ecosystem services, particularly where it has involved the input of chemical fertilisers and pesticides, the clearing of trees/vegetation, or irrigation. These marginal costs may not be reflected value of food. Using simple market values, or even expressing values in monetary terms, is problematic in cases where local people are completely disconnected from the market economy and therefore have little concept of the value of money (Hein et al, 2006). The alternative is stakeholder led description of the value – use an impact pathways approach whereby stakeholders are asked to assess the severity of trends in a semi-quantitative way (Defra, 2007).

It is necessary to find a methodological approach that is context-appropriate and in most cases this will involve a combination of participatory tools, a mix of qualitative and quantitative descriptive tools and a consideration of both market values and meeting local needs in outlining a series of aim for restoration in terms of food production and determining some metrics of value (e.g. ability to meet local dietary needs; market share; balanced diets) using such metrics allows for consistency in the valuation process and are themselves consistent with the aims of the restoration.

Measuring the value of food production requires long term monitoring, because of its sensitivity to short term and seasonal variation. Averaging out annual food production is likely to provide the most appropriate unit for monitoring trends, ideally (but depending on data constraints) this production



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should be mapped across the ecosystem in order to identify areas of greatest change.

Similar to measuring the value of food production, the value of fuelwood will depend on the quantities harvested and the needs of the local people, which again may range from subsistence to commercial. The need for fuelwood is similarly sensitive to short-term and seasonal variations, thus requiring annual averaging, and the value may well be reflected in the market price in those locations where fuelwood is available to buy. There are a couple of challenges in measuring the value of fuelwood, both of which require an approach that is context-specific and participatory. The first is access and property rights, these can be quite complicated for non-timber forest products such as fuelwood, and it may be the case that there is dispute over the legality of taking deadwood from privately owned property or licences to extract fuelwood from communal property – this is particularly the case with fuelwood, as it may be taken as dead or live wood, which may fall under different legislation. Legislation often, especially in drylands, will stipulate a maximum level of extraction of fuelwood, although again whether or not this includes collecting deadwood may be contentious. The importance of legality in the extraction of fuelwood depends on the context and the strength of legislation within it. If one was looking solely at the ecosystem properties, then the quantity of fuelwood production would be a sole consideration, but in the social-ecological-system, legislation and social norms are an intrinsic component of the system, and as such legislatory limits may represent a threshold in the value of the commodity – the social cost of illegal harvesting might well be factored into calculations above this threshold and where fuelwood is harvested commercial an influx of illegally harvested fuelwood in the market will affect the market value of fuelwood.

The second complexity comes in considering the opportunity costs of fuelwood. Living trees can be assigned a whole host of alternative values (as timber, as carbon sinks, as cultural heritage, as wildlife habitats, as reproductive agents etc.). Different trees will be more or less valuable as different ‘service providers’ and the wood harvested as fuelwood may be done so because of it burns well or it may selected because it does not make good timber. In almost all cases, fuelwood could be valued as an alternative commodity. Ecologists would argue that even deadwood has important ecological value and should therefore not be considered a free commodity. The reality is that once fuelwood is harvested, chopped up, and put up for sale in the market, its market value is unlikely to reflect all of its alternative use values (especially those for which there is no market). Therefore basing valuations of fuelwood solely on market values, rather than on social value, may not adequately reflect the true value of this important ecosystem service.

Again, a mixed-method approach to drawing up aims for restoration in terms of fuelwood is an appropriate strategy here. An alternative metric of value for



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fuelwood might be the ability to meet local needs through sustainable extraction.

Measuring levels of fuelwood extraction can also be effectively achieved through combining participatory research tools with remotely sensed data. Although the quantities collected can be determined through simple household surveying, participatory research helps to improve understanding of how wood is selected for fuelwood, where it is taken from, and whether it is felled or gathered. Participatory approaches can target information about whether the needs of the local people are met by the ecosystem service and this service is being utilised in a sustainable way.

Key literature: Description Stakeholders Reference

Details dietary requirements, trends in food production quantities and market values, household consumption patterns, and sources of food and forage in Southern Africa.

Subsistence farmers; commercial agriculturalists; national and international markets; local consumers

Van Jaarsveld et al (2005) Measuring conditions and trends in ecosystem services at multiple scales: the Southern African Millennium Ecosystem Assessment (SAfMA) experience. Philosophical Transitions of the Royal Society of Biological Sciences 360(1454): 425-441

An insight into the challenges faced by commercial agri-business in South Africa’s semi-arid southwest, including a changing climate, competing demands for water and the trend towards intensive-input farming.

Commercial agriculturalists

Archer et al (2009) Climate change, groundwater and intensive commercial farming in the semi-arid northern Sandveld, South Africa. Journal of Integrative Environmental Sciences 6(2): 139-155

A consideration of the trade-offs between scarce water consumption and commercial food production in Tunisia’s arid agricultural lands. Discusses dietary requirements, demographic change and food security.

Food and water consumers at the national scale

Besbes et al (2010) Changing Water Resources and Food Supply in Arid Zones: Tunisia. Chapter 7 in Schneier-Madanes and Coure (eds.) Water and Sustainability in Arid Regions Bridging the Gap Between Physical and Social Sciences

A special edition of the journal Ecological Economics, entitled ‘Ecosystem Services and Agriculture’ considers the potential and challenges of incentivising the provisioning of ecosystem services, through markets that reflect their full value, for the overall welfare of society.

Local land users, but drawing links to the welfare of national and global society

Swinton et al (2007) Ecosystem services and agriculture: Cultivating agricultural ecosystems for diverse benefits. Introduction to a special edition of Ecological Economics 64(2)

A synthesis of perspectives on the links between fuelwood collection and the degradation of dryland forests and soil in Nigeria, and the discourses that define them.

Local land users, neighbouring ecosystem stakeholders, national and international community

Cline-Cole, R. (1998) Knowledge claims and landscape: alternative views of the fuelwood -- degradation nexus in northern Nigeria? Environment and Planning D: Society and Space 16(3) 311 – 346



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4.3 Carbon sequestration

Both soil and vegetation may represent large stores of carbon in a dryland ecosystem that can be reduced through deforestation and soil erosion. Valuing this ecosystem service will rely on complex scientific computations based on vegetation species, soil structure, dead organic matter, moisture content (and much more) (Nelson et al, 2009; Daily et al, 2000). Remote sensing again plays a useful role in monitoring many of these key variables, and increasingly sophisticated models, such as InVEST, can be utilised in computing carbon storage based on simplified data input needs.

Carbon sequestration is one of the ecosystem service values that the InVEST model is designed to map, and this provides a useful tool for monitoring changes in the relative carbon sequestration capacity of the ecosystem over time with minimal data input requirements. The key parameter on which this model is based is and LULC map and the input variables required for running the model are a categorisation of carbon pools (in accordance with InVEST categories), a description of harvest rates, and biophysical information on the carbon storage properties of predominant vegetation. The output maps of the InVEST model provide a useful tool for stakeholder- and expert-led discussion of trends and policy options.

Because drylands are marginal and vulnerable environments there is a direct cost associated with climate change, although it may be unreasonable/unreliable to expect local land users to draw conceptual links between vegetation at the local scale and large scale climatic trends, instead these links might be left to the researcher to conceive and communicate based on a participatory study – if it is appropriate. Again there is also a social cost issue in relation to alternative uses of wood (e.g. fuelwood and timber...). Because of the breadth of stakeholders in the carbon sequestration services of a dryland ecosystem, however, less emphasis should be placed here on local users valuing the ecosystem service (in comparison with things like food and fuelwood). A quantitative and globally interpretable metric for valuation, such as working in measurable quantities (e.g. Mg/ha) and monetary values, is more appropriate here.



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Based on the study of semi-arid agro-ecosystems in Sudan, they calculate that converting marginal agricultural land to rangeland will restore soil carbon content to 80% of savannah levels within 100 years and argue that this could be facilitated and incentivised by regulated markets

Local land users

International community

National and international institutions and markets

Olsson and Ardö (2002) Soil carbon sequestration in degraded semiarid agro-ecosystems – Perils and potentials. Ambio 31:471-477

Considers US semi-arid rangelands form a ecosystem services perspective and argues that market incentives for maintaining carbon sequestration services is a key strategy for sustainable management

Local land users

National and international community

Havstad et al (2007) Ecological services to and from rangelands of the United States. Ecological Economics 64(2): 261-268

Discusses the complexities and institutional and social challenges of accessing and profiting from carbon markets for low-income producers in Africa’s arid lands.

Local land-owners Perez et al (2007) Can carbon sequestration markets benefit low-income producers in semi-arid Africa? Potentials and challenges. Agricultural Systems 94(1): 2-12

Explains the biophysical process of carbon sequestration in dryland soils and the effects of land-use change and degradation on carbon storage capacity, and considers market opportunities provided by Kyoto’s CDM and World Bank’s BioCarbon Fund

Local land users and local communities

International institutions and markets

Lal, R. (2009) Sequestering carbon in soils of arid ecosystems. Land Degradation and Development 20(4): 441-454

4.4 Water Provision and Conservation

Water provision and conservation benefit stakeholders indirectly, through supporting almost all provisioning, cultural and regulating services. As a consequence it may be seen as an indicator that falls largely within the domain of ‘experts’. Scientists can apply ground-based tools for measuring the functioning of the soil in terms of infiltration and runoff and thus provide a strong indicator of the health of the ecosystem. However, these methodologies are imperfect and may be data intensive. Local ecological knowledge of the functioning of the ecosystem, rainfall patterns and indicators of soil moisture, and traditional practices of water conservation, for example, act as valuable sources of information in valuing these ecosystem services. Knowledge of this type can be shared amongst stakeholders and scientists through a deliberative approach and contribute to an informed valuation of the service.



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Key literature Description Stakeholders Reference

Explains the criticality of water conservation within drylands – a constraint on ecosystem resilience and a key element of trade-off in dryland management

Ecological system Bautista, S., B.J. Orr, et al. (2010). Evaluating the Restoration of Dryland Ecosystems in the Northern Mediterranean. Water and Sustainability in Arid Regions. G. Schneier-Madanes and M.-F. Courel, Springer Netherlands: 295-310.

Describes the importance of the knowledge of rangeland pastoralists in the Kalahari of vegetation and how it informs them about the functioning of the ecosystem

Local land users Thomas and Twyman (2004). Good or bad rangeland? Hybrid knowledge, science, and local understandings of vegetation dynamics in the Kalahari. Land Degradation and Development 15(3): 215-231

Landscape functional analysis approach to monitoring the properties of the ecosystem – infiltration and runoff of rainwater

Ecological system Tongway, D.J. and Hindley, N. (2004) Landscape Function Analysis: Procedures for Monitoring and Assessing Landscapes. CSIRO, Brisbane, Australia.

4.5 Cultural Landscape and Heritage

It is inappropriate to think in monetary terms for monitoring this ecosystem service, instead an approach that looks retrospectively and prospectively at trends, considering recent changes in the extent to which the social-ecological system allows users to continue to exercise choice in respecting cultural practices and self-defined tradition, is more appropriate.

Stakeholder-led discussions aimed at achieving an assessment of the cultural heritage of the landscape may aim to identify cultural ideals and aspirations and identify those factors that limit the freedom of stakeholders to live-out these ideals. Equally, asking stakeholders to give preferences (and explanations) for alternative environments may offer useful information about the relative values that they assign to cultural landscapes. Using such factors as a platform for discussion might help to generate targeted discussion about an otherwise intangible subject. Once again the skill of the researcher is in finding the most locally-appropriate methods for describing and monitoring the value of this ecosystem service.



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Describes the landscape of South-Western Niger as the product of a history of interactions between social and biophysical processes and the contemporary culture of economic diversification

Local land users

Batterbury, S. (2001) Landscapes of diversity: a local political ecology of livelihood diversification in South-Western Niger. Ecumene 8 (4): 437-464

Explains how the aims of landscape restoration differ from those of dryland ecosystem restoration, and discusses the challenges of balancing traditional techniques, nature conservation and the well-being of local groups

Local land users

Moreira et al (2006) Restoration principles applied to cultural landscapes. Journal for Nature Conservation 14(3-4): 217-224

Case studies of World Heritage cultural landscapes from around the world

Local indigenous populations

Rössler, M. (2006) World Heritage cultural landscapes: A UNESCO flagship programme 1992-2006. Landscape Research 31(4): 333-353

5 Conclusion

A coupled human-environment approach to monitoring dryland restoration requires a close connection with the social-ecological system being studied. Complex and multi-scale system dynamics mean that it is difficult to paint a generic picture of sustainable land management and it is similarly unlikely that a set of indicators, devised away from the context of study, will represent the most appropriate metric for dryland restoration success.

This paper has explored the idea of using the value of ecosystem services as overarching, but locally adaptable concept for monitoring dryland restoration and has highlighted some of the key ecosystem services, the values of which combined might be considered a good indicator for this purpose. Throughout, evidence from key literature and local case studies have been used to show how this generic indicator might be translated into a practical research focus in the field (and with the use of remotely sensed data).

The value of ecosystem services fits well within a coupled human-environmental approach because it focuses not solely on the changing biophysical properties of the ecosystem, but rather on the relationship that these changing properties have on interacting social, cultural and economic spheres, considering more precisely how the changing ecosystem affects the well-being of the ecosystem’s many stakeholders.

It is strongly suggested that the particular basket of ecosystem used as an indicator in any given study is adapted to be contextually-appropriate and



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closely compatible with pre-defined aims of restoration. Stakeholder-led and participatory research methodologies can help to ensure that this achieved. Similarly the ways in which value is measured should also be relevant to the aims of the restoration and appropriate to the situation of the stakeholders. Close collaboration between experts in social sciences and ecology and dryland stakeholders will allow for the sharing of knowledge and deliberation over the value of ecosystem services. The real strength of this approach to monitoring is in its holistic approach to understanding the many values of the ecosystem and in its emphasis on contextual system intricacies. It is about monitoring the ability of the ecosystem to meet the needs and contribute to the well-being of its stakeholders as well as sharing knowledge and incorporating consideration about how it might continue to meet stakeholder needs in the future.

6 References

Adger W.N.; Benjaminsen T.A.; Brown, K.; and Svarstad, H. (2001) ‘Advancing a political ecology of global environmental discourses’ Development and Change 32:681–715.

Adger WN, Hughes TP, Folke Carpenter SR, Rockstrom J (2005) Social ecological resilience to coastal disasters. Science 309:1036–1039.

Alverson,H. (1984) The wisdom of tradition in the development of dry-land farming: Botswana. Human Organisation 43(1): 1-8.

Archer et al (2009) Climate change, groundwater and intensive commercial farming in the semi-arid northern Sandveld, South Africa. Journal of Integrative Environmental Sciences 6(2): 139-155.

Asner, G.P.; Archer, S.; Hughes, R.F.; Ansley, R.J.; and Wessman, C.A. (2003) Net changes in regional woody vegetation cover and carbon storage in Texas Drylands, 1937–1999. Global Change Biology 9(3): 316-335.

Bainbridge, D.A. (2007) A guide for desert and dryland restoration: new hope for arid lands. Island Press: Washington.

Balmford, A. and Bond, W. (2005). Trends in the state of nature and implications for human well-being. Ecology Letters. 8: 1218-1234.

Barbier, B. (1996) Impact of market and population pressure on production, incomes and natural resources in the dryland savannas of West Africa: bioeconomic modelling at the village level. International Food Policy Research Institute.

Batterbury, S. (2001) Landscapes of diversity: a local political ecology of livelihood diversification in South-Western Niger. Ecumene 8 (4): 437-464.

Bautista, S.; Orr, B.J.; Alloza, J.A. and Vallejo, R.V. (2010) Evaluating the Restoration of Dryland Ecosystems in the Northern Mediterranean. In: G. Schneider-Madanes and M.F. Courel (eds.) Water and Sustainability in Arid Regions Bridging the Gap Between Physical and Social Sciences. Heidelberg: Springer.

Benayas, J.M.R; Bullock, J.M. and Newton, A.C. (2008) Creating woodland islets to reconcile ecological restoration, conservation, and agricultural land use. Frontiers in Ecology and the Environment. 6: 329—336.



33

Besbes et al (2010) Changing Water Resources and Food Supply in Arid Zones: Tunisia. Chapter 7 in Schneier-Madanes and Coure (eds.) Water and Sustainability in Arid Regions Bridging the Gap Between Physical and Social Sciences. Heidelberg: Springer.

Berkes F, Folke C, Gadgil M. (1995) Traditional Ecological Knowledge, Biodiversity, Resilience and Sustainability. Pages 281–299 in Perrings CA, Mahler KG, Folke C,Holling CS, Jansson B, eds. Biodiversity Conservation: Problems and Policies. Dordrecht (The Netherlands): Kluwer Academic.

Bennett, E.M.; Peterson, G.D.; Gordon, L.J. (2009) Understanding relationships among multiple ecosystem services Ecology Letters 12(2): 1394-1404.

Butler,C.D. and Oluoch-Kosura, W. (2006) Linking Future Ecosystem Services and Future Human Well-being Ecology and Society 11(1): 30.

Carpenter, S.R.; Bennett, E.M.; and Peterson, G.D. (2006) Scenarios for Ecosystem Services: An Overview. Ecology and Society 11(1): 29.

Carpenter, S. R., and W. A. Brock. (2004). Spatial complexity, resilience and policy diversity: fishing on lake-rich landscapes. Ecology and Society 9(1): 8.

Carretero,E.M.; Dalmasso, A.; and Trione, S. (2007) Carbon Storage in Larrea Divaricata andL. cuneifolia (Zygophyllaceae) in Drylands of Central-Western Argentina. Arid Land Research and Management 21(4): 273-285.

Chambers, R. 1994. Participatory Rural Appraisal (PRA): Analysis of Experience. World Development 22(9):1253-1268.

Christanto, N.; Hadmoko, D.S.; Westen, C.J.; Lavigne, F.; Sartohadi, J.; and Setiawan, M.A. (2009) Characteristic and Behavior of Rainfall Induced Landslides in Java Island, Indonesia : an Overview. Geophysical Research Abstracts 11.

Clewell, A.F., and Aronson, I. (2006) ‘Motivations for the restoration of ecosystems’ Conservation Biology 20 (2): 420–428.

Cline-Cole, R. (1998) Knowledge claims and landscape: alternative views of the fuelwood -- degradation nexus in northern Nigeria? Environment and Planning D: Society and Space 16(3) 311 –346.

Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R. V. O’Neill, J. Paruelo, R. G. Raskin, P. Sutton and M. van den Belt. 1997. The value of the world’s ecosystem services and natural capital. Nature 387:253–260.

Cotula, L. (ed), 2006, Land and Water Rights in the Sahel - Tenure Challenges of Improving Access to Water for Agriculture, London, IIED, Issue Paper 139, http://www.iied.org/pubs/pdf/full/12526IIED.pdf.

Daily, G.C.; Alexander, S.; Ehrlich, P.R.; Goulder, L.; Lubchenco, J.; Matson, P.A.; Mooney, H.A.; Postel, S.; Schneider, S.H.; Tilman, D.; and Woodwell, G.M. (1997). Ecosystem services: Benefits supplied to human societies by natural ecosystems. Issues in Ecology 2:1-16.

Daily, G.C.; Söderqvist , T.; Aniyar, S.; Arrow, K.; Dasgupta, P.; Erlich, P.R.; Folke, C.; Jansson, A; Jansson, B-G.; Kautsky, N.; Levin, S.; Lubchenco, J.; Mäler, K-G.; Simpson, D.; Starrett, D.; Tilman, D.; and Walker, B. (2000) The Value of Nature and the Nature of Value Science 289(5478): 395-396.

Danielsen, F., H. Beukema, N. D. Burgess, F. Parish, C. A. Brühl, P. F. Donald, D. Murdiyarso, B. Phalan, L. Reijnders, M. Struebig, and E. B. Fitzherbert. 2008. Biofuel plantations on forested lands: double jeopardy for biodiversity and climate. Conservation Biology 23(2):348–358.

Defra (2007) An introductory guide to valuing ecosystem services. www.defra.co.uk



34

Defries, R.S.; Foley, J.A.; and Asner, G.P. (2004) Land-use choices: balancing human needs and ecosystem function Frontiers in Ecology and the Environment 2: 249–257.

De Groot, R.S.; Wilson, M.A.; and Boumas, R.M.J. (2002) A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecological Economics 41(3): 393-408.

Dietz A.J.; Ruben, R.; and Verhagen, A. [eds](2004) The impact of climate change on drylands: with a focus on West Africa. Kluwer Academic Publishers

Dipper, B. (1998) Monitoring and Post-auditing in Environmental Impact Assessment: A Review. Journal of Environmental Planning and Management 41(6): 731-747

Dovie, D.B.K.; Witkowski, E.T.F.; Shackleton, C.M. (2004) The Fuelwood Crisis in Southern Africa – Relating Fuelwood Use to Livelihoods in a Rural Village. GeoJournal 60(2): 123-133

Dregne, H.E. (2002) Land Degradation in the Drylands. Arid Land Research and Management 16(2): 99-132

Dryland Science for Development Consortium Working Group One (DSD WG1, 2009) ‘Integrated methods for monitoring and assessing desertification/land degradation processes and drivers (land quality)’ White Paper of the DSD Working Group 1.

Dubroeucq, D. and Livenais, P. (2004) Land cover and land use changes in relation to social evolution – a case study of Northern Chile. Journal of Arid Environments 56(2): 193-211.

Farrow, A. and Winograd, M. (2001) Land use modelling at the regional scale: an input to rural sustainability indicators for Central America. Agriculture, Ecosystems and Environment 85: 249-268.

Fisher, B.; Turner, R.K.; and Morling, P. (2009) Defining and classifying ecosystem services for decision making. Ecological Economics 68(3): 643-653.

Folke, C. (2006) ‘Resilience: The emergence of a perspective for social-ecological systems analyses’ Global Environmental Change 16(3): 253-267.

Folke, C.; Carpenter, S.R.; Walker, B.; Scheffer, M.; Elmqvist, T.; Gunderson, L.; and Holling, C.S. (2004) ‘Regime Shifts, Resilience and Biodiversity in Ecosystem Management’ Annual Review of Ecology, Evolution, and Systematics 35: 557-581.

Gao, Y.; Qiu, G.Y.; Shimizu, H.; Tobe, K.; Sun, B.; and Wang, J. (2002) A 10-year study on techniques for vegetation restoration in a desertified salt lake area. Journal of Arid Environments 52(4): 483-497.

Geist, H.J. (2005) ‘The causes and progression of desertification (Ashgate Studies in Environmental Policy and Practice)’. Ashgate: Aldershot.

Geist H.J.and Lambin, E.F. (2004) ‘Dynamic causal patterns of desertification’. BioScience 54(9): 817-829.

Glenn, E.; Squires, V.; Olsen, M.; and Frye, R. (1993) Potential for carbon sequestration in the drylands. Water, Air and Soil Pollution 70(1-4): 341-355.

Gordon, I.J. (2007) Linking land to ocean: feedbacks in the management of socio-ecological systems in the Great Barrier Reef catchments. Hydrobiologia 591(1): 25-33

Guaseelan, V.N. (2009) Biomass estimates, characteristics, biochemical methane potential, kinetics and energy flow from Jatropha curcuson dry lands. Biomass and Bioenergy 33(4): 589-596.

Havstad et al (2007) Ecological services to and from rangelands of the United States. Ecological Economics 64(2): 261-268.



35

Hein, L.; van Koppen, K.; de Groot, R.S.; and van Ierland, E.C. (2006) Spatial scales, stakeholders and the valuation of ecosystem services. Ecological Economics 57(2): 209-228.

Herrmann, S.M.; Anyamba, A.; and Tucker, C.J. (2005) Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change Part A 15(4): 394-404.

Holling, C.S. (1973) ‘Resilience and stability of ecological systems’. Annual Review of Ecology and Systematics 4:1-23.

Holling, C.S. (2001) Understanding the complexity of economic, ecological, and social systems, Ecosystems 4: 390–405.

Hope, R.A.; Jewitt, G.P.W.; Gowing, J.W. (2004)Linking the hydrological cycle and rural livelihoods: a case study in the Luvuvhu catchment, South Africa. Physics and Chemistry of the Earth, Parts A/B/C 29(15-18): 1209-1217.

Huber-Sannwald, E; Maestre, F.T; Herrick, J.E.; and Reynolds, J.F.(2006) Ecohydrological feedbacks and linkages associated with land degradation: a case study from Mexico Hydrological Processes 20: 3395-3411.

IPCC (2007) IPCC, 2007: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 976 pp.

Illius, A.W. and O’Connor, T.G.O. (1999) On the relevance of nonequilibrium concepts to arid and semiarid grazing systems. Ecological Applications 9(3): 798-813.

Janssen, MA, and SR Carpenter. 1999. Managing the resilience of lakes: a multi-agent modelling approach. Conservation Ecology 3:1.

Jollands, N., Ruth, M., Golubiewski, N., and Bernier, C., 2007. The climate's long-term impact on New Zealand infrastructure (CLINZI) project – A case study of Hamilton City, New Zealand. Journal of Environmental Management 83(4):460–477.

Jiang, H (2003) Stories Remote Sensing Images Can Tell: Integrating Remote Sensing Analysis with Ethnographic Research in the Study of Cultural Landscapes. Human Ecology 31(2): 215-232.

Jiang, L.; Yufen, T.; Zhijie, Z.; Tianhong, L. And Jiankua, L. (2005) Water Resources, Land Exploration and Population Dynamics in Arid Areas - The Case of the Tarim River Basin in Xinjiang of China Population and Environment 26(6): 471-503

Kakuru, W.; Okia; C.; Okorio, J. (2004) Strategy for Agroforestry Development in Uganda’s Drylands. Paper presented at the Drylands Agroforestry Workshop 1st-3rd September. ICRAF Headquarters, Nairobi-Kenya.

Kerven, C. [eds] (2007) The Real Jewels of the Kalahari: Drylands Ecosystem Goods and Services in Kgalagadi South District, Botswana. IUCN Paper.

Kinzig, A. P., P. Ryan, M. Etienne, H. Allison, T. Elmqvist, and B. H. Walker. 2006. Resilience and regime shifts: assessing cascading effects. Ecology and Society 11(1): 20.

Koch, EW; Barbler, EB; Silliman, BR; Reed, DJ; Perillo, GM; Hacker, SD; Granek, EF; Primavera, JH; Muthlga, NI; Polasky, S; Halpern, BS; Kennedy, CJ; Kappel, CV; Wolanski, E (2009) Non-linearity in ecosystem services: temporal and spatial variability in coastal protection Frontiers in Ecology and the Environment 7(1): 29-37



36

Komatsu et al (2005) Evaluation of agricultural sustainability based on human carrying capacity in drylands - a case study in rural villages in Inner Mongolia, China. Agriculture, Ecosystems and Environment 108(1): 29-43

Kumar, M. and Kumar, P. (2008) Valuation of the ecosystem services: A psycho-cultural perspective. Ecological Economics 64(4): 808-819

Lal, R. (2002) Soil carbon sequestration in China through agricultural intensification, and restoration of degraded and desertified ecosystems. Land Degradation and Development 13(6): 469-478.

Lal, R. (2004) Carbon Sequestration in Dryland Ecosystems. Environmental Management 33(4): 528-544.

Lal, R. (2009) Sequestering carbon in soils of arid ecosystems. Land Degradation and Development 20(4): 441-454.

Lambin EF, Geist H, Reynolds JF, Stafford Smith DM (2009) Coupled human-environment system approaches to desertification: linking people to pixels In: Röder A, Hill J (eds). Recent advances in remote sensing and geoinformation processing for land degradation assessment (ISPRS Book Series). CRC Press/Balkema: Leiden, NL, 3-14 .

Lambin E.F. and Meyfroidt P. (2010). Land use transitions: Ecological feedback versus exogenous socio-economic dynamics, Land Use Policy, 27: 108-118.

Liu, J.; Dietz, T.; Carpenter, S.R.; Alberti, M.; Folke, C.; Moran, E.; Pell, A.N.; Deadman, P.; Kratz, T.; Lubchenco, J.; Ostrom, E.; Ouyang, Z.; Provencher, W.; Redman, C.L.; Schneider, S.H.; and Taylor, W.W. (2007) ‘Complexity of Coupled Human and Natural Systems’. Science 317: 1513 – 1516.

Luck, G.W.; Daily, G.C.; and Ehrlich, P.R. (2003) Population diversity and ecosystem services. Trends in Ecology and Evolution 18(7): 331-336.

McCauley, D.J. (2006) Selling out on Nature. Nature 443: 27-28.

Masri, Z.; Zöbisch, M.; Bruggeman, A.; Hayek, P.; and Kardous, M. (2003) Wind erosion in a marginal Mediterranean drylands area: a case study from the Khanasser Valley, Syria. Earth Surface Processes and Landforms 28(11): 1211-1222.

Millennium Ecosystem Assessment, (MA, 2005a). Ecosystems and Human Well-being: Synthesis. Island Press, Washington, DC.

Millennium Ecosystem Assessment (MA, 2005b) ‘Ecosystems and Human Well-being: Desertification Synthesis’. World Resources Institute: Washington, DC.

Moreira et al (2006) Restoration principles applied to cultural landscapes. Journal for Nature Conservation 14(3-4): 217-224

Moreira, F.; Queiroz, A.I.; and Aronson, J. (2006) Restoration principles applied to cultural landscapes. Journal for Nature Conservation 14(3-4): 217-224.

Mortimore, M.J. and Adams, W.M. (2001) Farmer adaptation, change and ‘crisis’ in the Sahel. Global Environmental Change 11(1): 49-57.

Mukhopadhyay, D. (2009) Indigenous Knowledge and Sustainable Natural Resource Management in the Indian Desert. The Future of Drylands 3: 161-170.

Nelson, E.; Mendoza, G.; Regetz, J.; Polasky, S.; Tallis, H.; Cameron, R.D.; Chan, K.M.A.; Daily, G.C.; Goldstein, J.; Kareiva, P.M.; Lonsdorf, E.; Naidoo, R.; Ricketts, T.H.; and Shaw, M.R. (2009) Modelling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and Environment 7(1): 4-11.



37

Nyariki,D.M.; Wiggins, S.L.; and Imungi, J.K. (2002) Levels and causes of household food and nutrition insecurity in dryland Kenya Ecology of Food and Nutrition 41(2): 155-176.

Olsson and Ardö (2002) Soil carbon sequestration in degraded semiarid agro-ecosystems – perils and potentials. Ambio 31: 471–477.

Owour, B.; Eriksen, S.; and Mauta, W. (2005) Adapting to Climate Change in a Dryland Mountain Environment in Kenya Mountain Research and Development 25(4):310-315.

Pereira, H.M.; Domingos, T.;and Vicente, L. (2006) Assessing Ecosystem Services at Different Scales in the Portugal Millennium Ecosystem Assessment. In Reid, W.V.; Berkes, F.; Wilbanks, T.; Capistrano, D. [eds] Concepts and Applications in Ecosystem Assessment. Island Press

Perez et al (2007) Can carbon sequestration markets benefit low-income producers in semi-arid Africa? Potentials and challenges. Agricultural Systems 94(1): 2-12.

Portnov, B.A. and Safriel, U.N.( 2004) Combating desertification in the Negev: dryland agriculture vs. dryland urbanization. Journal of Arid Environments 56(4): 659-680.

Proctor,W.; Cork, S.; Lamgridge, J.; Langston, A.; Abel, N.: Howden.; Anderies, m.; Parry, R.; Shelton, D. (2002) Assessing Ecosystem Services in Australia. Ecosystem Services Project.

Reed, M.S. and Dougill, A.J. (2010) Linking Degradation Assessment to Sustainable Land Management: a decision support system for Kalahari pastoralists. Journal of Arid Environments 74: 149–155.

Reed, M.S.; Fraser, E.D.G.; Dougill, A.J. (2006) ‘An adaptive learning process for developing and applying sustainability indicators with local communities’. Ecological Economics 59: 406–418.

Reid, W.V. (2006) Nature: the many benefits of ecosystem services. Nature 443: 749.

Reyers, B.; O'Farrell, P.J.; Cowling, R.M.; Egoh, B.N.; Le Maitre, D.C.; Vlok, J.H.J. (2009) Ecosystem services, land-cover change, and stakeholders: finding a sustainable foothold for a semiarid biodiversity hotspot. Ecology and Society 14(1): 23.

Reynolds, J.F.; Mark Stafford Smith, D.; Lambin, E.F.; Turner, II, B.L.; Mortimore, M.; Batterbury, S.P.J.; Downing, T.E.; Dowlatabadi, H.; Fernández, R.J.; Herrick, J.E.; Huber-Sannwald, E.; Jiang, H.; Leemans, R.; Lynam, T.; Maestre, F.T.; Ayarza, M.; and Walker, B. (2007) ‘Global desertification: Building a Science for Dryland Development’ Science 316(5826): 847-851.

Reynolds, J.F. and Stafford Smith, D.M. [eds] (2002) Global Desertification: Do Humans Cause Deserts? Dahlem Workshop Report 88, Dahlem University Press, Berlin.

Rössler, M. (2006) World Heritage cultural landscapes: A UNESCO flagship programme 1992-2006. Landscape Research 31(4): 333-353.

Russelle M.P.; Entz, M.H.; and Franzluebbers, A.J. (2007) Reconsidering Integrated Crop-Livestock Systems in North America. Agronomy Journal 99: 325-334.

Sá e Silva (2009) Use and knowledge of fuelwood in three rural caatinga (dryland) communities in NE Brazil. Environment, Development and Sustainability 11(4): 833-851.

Safriel, U.N. (2006) Dryland Development, Desertification and Security in the Mediterranean. Desertification in the Mediterranean Region. A Security Issues. NATO Security through Science Series 3: 227-250.

Safriel, U. (2009) ‘Deserts and desertification: Challenges but also opportunities’ Land degradation and development 20(4): 353-366.



38

Scanlon , B.R.; Reedy, R.C.; Stonestrom, D.A.; Prudic, D.E. and Dennehy, K.F. (2005) Impact of land use and land cover change on groundwater recharge and quality in the southwestern US. Global Change Biology 11(10): 1577-1593.

Scheffer, M.; Carpenter, S.; Foley, A.; Folke, C.; Walker, B. (2001) Catastrophic shifts in ecosystems. Nature 413: 591-596.

Scholes, R.J. (2009) Syndromes of dryland degradation in southern Africa. African Journal of Range and Forage Science 26(3): 113-125.

Schulz, J.J.; Cayuela, L.; Echeverria, C.; Salas, J.; and Benayas, J.M.R. (2010) Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008) Applied Geography 30(3): 436-447.

Schwilch, G., Bachmann, F. and Liniger, H. (2009), Appraising and selecting conservation measures to mitigate desertification and land degradation based on stakeholder participation and global best practices. Land Degradation and Development, 20: 308–326.

Sivanappan, R.K. (1995) Soil and water management in the dry lands of India. Land Use Policy 12(2): 165-175.

Spangenberg, J.H. (2002) Institutional sustainability indicators: an analysis of the institutions in Agenda 21 and a draft set of indicators for monitoring their effectivity. Sustainable Development 10(2): 103-115.

Standish,R.J.; Cramer, V.A.; Wild, S.L.; Hobbs, R.J. (2007) Seed dispersal and recruitment limitation are barriers to native recolonization of old-fields in western Australia. Journal of Applied Ecology 44(2): 435-445.

Stohlgren,T.J.; Chase, T.N.; Pielke, R.A.; Kittel. T.G.F.; Baron, J.S. (1998)Evidence that local land use practices influence regional climate, vegetation, and stream flow patterns in adjacent natural areas. Global Change Biology 4: 495-504.

Swinton et al (2007) Ecosystem services and agriculture: Cultivating agricultural ecosystems for diverse benefits. Introduction to a special edition of Ecological Economics 64(2).

Thomas, D.S.G.; Sporton, D.; and Perkins, J. (2000) The environmental impact of livestock ranches in the Kalahari, Botswana: natural resource use, ecological change and human response in a dynamic dryland system. Land Degradation and Development 11(4): 327-341.

Toman, M. (1998) Why not to calculate the value of the world’s ecosystem services and natural capital. Ecological Economics 25: 57-60.

Tomich, T.P.; Argumedo, A.; Baste, I.; Camac, E., Filer, C.; Garcia, K.; Garbach, K.; and Geist, H. (2010) Conceptual frameworks for ecosystem assessment: their development, ownership, and use In: Ash N [ed] Ecosystems and human well-being: a manual for assessment practitioners. Island Press: Washington, DC, 71-113.

Tovey, J.P. (2008) Whose rights and who’s right? Valuing ecosystem services in Victoria, Australia. Landscape Research 33(2): 197-209 .

Upton, C. (2010) Living off the land: Nature and nomadism in Mongolia . Geoforum (Article in Press).

van Jaarsveld, A.S.; Biggs, R.; Scholes, R.J.; Bohensky, E.; Reyers, B.; Lynam, T.; Musvoto, C.; and Fabricus, C. (2005) Measuring conditions and trends in ecosystem services at multiple scales: the Southern African Millennium Ecosystem Assessment (SAfMA) experience. Philosophical Transitions of the Royal Society of Biological Sciences 360(1454): 425-441.

Wack P. (1985). Scenarios: Uncharted waters ahead. Harvard Business Review 63 (5).



39

Walker, B.; Carpenter, S.; Anderies, J.; Abel, N.; Cumming, G.; Janssen, M.; Lebel, L.; Norberg, J.; Peterson, G.D.; and Pritchard, R. (2002) ‘Resilience management in social-ecological systems: a working hypothesis for a participatory approach’. Conservation Ecology 6(1): 14.

Walker, B. and Meyers, J.A. (2004) Thresholds in Ecological and Social–Ecological Systems: a Developing Database. Ecology and Society 9(2): 3.

Warren, A. (2002). Land degradation is contextual. Land Degradation and Development 13, 449-459.

Watts, M. (1987) Drought, environment and food security: some reflections on peasants, pastoralists and commoditization in dryland West Africa. In Glantz, M.H. [eds] Drought and hunger in Africa: denying famine a future. Cambridge University Press, Cambridge.

White, R.P.; Tunstall, D.; Henninger, N. (2002) Ecosystem approach to drylands- building support for new development. World Resources Institute.

Wenlan, F.; Qigang, Z.; Baolei, Z.; Wancun, Z.; Ainong, L.; Haizhen, Z.; and Wei, X. (2006) GIS-based spatial analysis and modeling for landslide hazard assessment: A case study in upper Minjiang River basin. Wuhan University Journal of Natural Sciences 11(4): 847-852.

Wollenberg E., D. Edmunds & L. Buck. (2000). Anticipating change: Scenarios as a tool for adaptive forest management. Bogor, Indonesia: Center for International Forestry Research.

Worm, B.; Barbier, E.B.; Beaumont, N.; Duffy, J.E.; Folke, C.; Halpern, B.S.; Jackson, J.B.C.; Lotze, H.K.; Micheli, F.; Palumbi, S.R.; Sala, E.; Selkoe, K.A.; Stachowicz, J.J.; and Watson, R. (2006) Impacts of Biodiversity Loss on Ocean Ecosystem Services. Science 314(5800): 787.

Young, O.R.; Lambin, E.F.; Alcock, F.; Haberl, H.; Karlsson, S.I.; McConnall, W.J.; Myint, T.; Pahl-Wostl, C.; Polsky, C.; Ramakrishnan, P.S.; Schroeder, H.; Scouvart, M.; and Verburg, P.H. (2006) ‘A portfolio approach to analysing complex human-environment interactions: Institutions and land change’ Ecology and Society 11(2): 31

Zaal, F. and Oostendorp, R.H. (2002) "Explaining a Miracle: Intensification and the Transition Towards Sustainable Small-scale Agriculture in Dryland Machakos and Kitui Districts, Kenya," World Development, 30(7): 1271-1287.

Zhen, L.; Ochribat, B.; Lv, Y.; Wei, Y.J.; Liu, X.L.; Chen, J.Q.; Yao, Z.J.; and Li, F.(2010) Comparing patterns of ecosystem service consumption and perceptions of range management between ethnic herders in Inner Mongolia and Mongolia. Environmental Research Letters 5(1).

Zucca, C.; Bautista, S.; and Previtali, F. (2009) ‘Desertification: Prevention and Restoration’. In: R. Lal (ed.) Encyclopedia of soil science (Second Edition). New York: Taylor & Francis Group.

Ground-based Assessment Methods and Indicators

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II. Ground-based Methods and Indicators for the Biophysical Assessment of Desertification

Prevention and Restoration Actions


Susana Bautista

Ángeles G. Mayor

S. Bautista and A. G. Mayor

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Susana Bautista1 and Ángeles G. Mayor2 (October 2010)

1 Department of Ecology University of Alicante

Apdo. 99 03080 Alicante Spain

[email protected] 2 CEAM Foundation Charles R. Darwin 14 Parque Tecnológico 46980 Paterna (Valencia) [email protected]


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1 Introduction

There is an increasing demand for the development and implementation of appropriate assessment methods to measure progress on combating desertification (UNCCD 2009). The adoption of best practices in prevention and restoration efforts requires a good use of the existing expertise and information, as well as improved understanding on the impacts of the strategies and techniques applied on the target socio-ecological systems. Both requirements can be met through the systematic evaluation of the actions applied and further dissemination of the results.

Evaluation is the key element linking practice and the advances in both science and technology. Moreover, monitoring and evaluation are critical components of an adaptive management approach. For any given prevention or restoration action, evaluation provides feedback for the fine-tuning of the treatments and techniques applied, and thereby helps address the uncertainty inherent to ecosystem dynamics. For the general practice of dryland management, evaluation helps managers, farmers, and other land users learn from past efforts and adapt management strategies and techniques in response to spatial and temporal variation in environmental and socio-economic conditions. Evaluation is also needed to establish cost-effective thresholds for the various management alternatives, and to identify priority areas where actions could be most effective.

Identifying the most appropriate conceptual framework, approach and indicator suite is crucial for a more widespread, harmonized, scientifically-sound, and effective monitoring and assessment of prevention and restoration actions. This working paper addresses this challenge by reviewing and discussing the state of the art on ground-based ecosystem assessment methods and approaches, and presenting a strategy and an associated suite of indicators and methods for the ground-based assessment of dryland management.

Although most of the approaches discussed here are applicable to a wide variety of management actions, this working paper focuses on the evaluation of prevention and restoration actions to combat degradation of dryland ecosystems.

2 Evaluation approaches

The approaches for evaluating prevention and restoration actions are many, including, among others, measuring the achievement of specific goals and stages (Zedler 1995, Zedler and Callaway 1999); comparisons between managed and non-managed areas, between management alternatives, or between restored and reference target areas (Brinson and Rheinhardt 1996, Gaboury and Wong 1999, Rey Benayas et al. 2009); comparisons with natural range of variability (Hobbs and Norton 1996, Parker and Pickett 1997, Allen et


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al. 2002); evaluation of the degree of (self)sustainability of the managed or restored ecosystem (SER 2004): and direct assessment of ecosystem quality according to a set of accepted standards. Though the different approaches partially overlap, there are particular pros and cons associated with the implementation of each of them that are discussed below.

The most obvious evaluation approach is to measure the degree of achievement of the proposed objectives. Ideally, based on the goals of the prevention or restoration action and on the knowledge and understanding of the ecology of the system, explicit predictions are made of expected responses by biotic and abiotic ecosystem components that will then be monitored for evaluation. In turn, designing the appropriate monitoring and evaluation program helps refine and explicitly state the project specific objectives. The fact that restored ecosystems are not static also points to the need for establishing the suitable time frame in which to assess the achievement of the various stages envisioned.

In some cases, expectations could be written as statements of testable hypotheses (Thayer et al. 2003). However, our limited understanding of the processes, factors, and biotic interactions that control ecosystem dynamics often limits the definition of the specific outcomes that could be expected, and therefore only broadly-defined objectives are established for most of the actions implemented. Moreover, both the knowledge framework and the social context are dynamic, and each influences management decisions and objectives. For example, since the 1990s, mitigating climate change has become a core objective of afforestation and reforestation programs worldwide. In the past, the main objectives of reforestation projects were wood production, soil protection from erosion, and flood control (Vallejo et al. 2006, Bautista et al. 2010); while in the last decades the objectives have shifted to other ecosystems goods and services such as improvement of water quality, recreation, improvement of wildlife habitats, fire prevention, biodiversity conservation, etc. Many projects that could be considered as highly successful in meeting originally established objectives, would meet none or very few of the current social demands regarding biodiversity conservation and ecosystem services.

Restoration ecologists usually advocate the use of target or model communities as reference systems to set restoration goals and evaluate restoration success (e.g., Aronson et al. 1993, Aronson and Le Floc’h 1996, Brinson and Rheinhardt 1996, White and Walker 1997, SER 2004, Ruiz-Jaen and Aide 2005). A reference system is any ecosystem or landscape showing the structure and function that is expected for an area to be deemed successfully restored or managed. A restoration process aimed at reconstructing a prior ecosystem and re-establishing former communities is, however, a very difficult task, particularly at the landscape level (Henry and Amoros 1995, Bradshaw 1997, van Diggelen et al. 2001). Several authors have called for an alternative approach based on evaluation criteria that focus on the functional aspects of the reference system, using specific services or certain functions as reference conditions (e.g., Brinson and Rheinhardt 1996,


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Choi 2004). Falk (2006) proposed to replace the more static concept of reference conditions by reference dynamics: a process-centred approach that places emphasis on ecological functions and ecosystem processes.

Historical data on pre-disturbed conditions or remnants of historic natural areas are common forms of target references (Holl and Cairns 2002, Hobbs and Harris 2001). However, given the dynamic nature of communities in a changing environment and socio-economic context, several studies point to the usefulness of using historical data as reference information (Pickett and Parker 1994, Hobbs and Norton 1996, Choi 2004). Some authors suggest that rather than focus on restoring to some primeval state, a more profitable approach would be to focus on repairing damaged systems to the extent possible, considering both the ecological potential for restoration and societal desires (Higgs 1997, Hobbs and Harris 2001). In this approach, the pre-restored, degraded system can be considered as the reference with which to evaluate restoration. Defining the degree of improvement that could be considered a success is the particular challenge of this approach.

Evaluation can be centred on the comparison of management alternatives. This approach does not rule out including either reference systems or degraded systems within the set of compared cases. Methods for generic functional analyses (e.g., Tongway and Hindley 2004, Herrick et al. 2005) are of particular interest for comparative evaluation, as they provide indices that can be directly comparable across sites differing in area or scale.

Finally, the outcomes of the actions implemented could be assessed through a variety of quality indicators that reflect current social demands, yet they may not be the original target outcomes considered by the management project. When no real, existing reference is available, or when there is no adequate information about previous degraded conditions, this approach can be the appropriate framework for evaluation. This approach is related to existing tools and methods for ecosystem monitoring and assessment that focus on the evaluation of ecosystem status and integrity. For example, WWF-World Wide Fund for Nature and IUCN-International Union for Conservation of Nature have developed an approach to landscape assessment of forest quality that can also be used to evaluate restored forests and woodlands. This method is based on the following criteria: (1) Authenticity, (2) Forest health, (3) Environmental benefits, and (4) Social and cultural values. The method relies on the assessment of a large suite of indicators that provide information on these four criteria (WWF 2002).

All the approaches above implicitly consider restoration evaluation as the evaluation of success (Hobbs and Harris 2001). However, success is a subjective and somehow unclear and elusive concept (Zedler 2007) that does not fully recognize and accommodate the many potential sources of variation and uncertainty concerning management and restoration outcomes, including the diverse, even contrasting perspectives among the various stakeholders. Rather than merely following a success vs failure approach, evaluation may be viewed as a process of creating information


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and knowledge on the restoration actions implemented, providing a more or less comprehensive and multifaceted description of the restoration outcomes. This approach considers evaluation as an information system that collects and provides useful data on ecosystem and landscape responses to restoration. It can therefore support any other approach to evaluation.

3 Selection of attributes and indicators for evaluation

Whatever the evaluation approach that is used, the selection of the actual attributes and indicators to be assessed is key to the evaluation process. The structural and functional attributes of ecosystems do not always linearly covary, (Cortina et al. 2006, Rey Benayas et al. 2009). Therefore, results may vary greatly depending on the variables considered.

In selecting the appropriate indicators, there are a number of key aspects to consider. First of all, an appropriate, scientifically justifiable framework must underlie the selection of the assessment indicators. Second, depending on the assessment goals and conceptual framework the appropriate number of indicators, their spatial and temporal scope of application, and the scale, methods, and resolution of the measurements can greatly vary. Finally, the selected indicators should be relevant and clearly linked to underlying primary processes, based on well-understood conceptual models, reliable, sensitive to important changes but robust against natural variability, and also be simple and measurable with a reasonable level of effort and cost (NRC 2000. Dale and Beyeler 2001, Jorgersen et al. 2005). Because of the large spatial and temporal variability of ecosystems, particularly in drylands, recent studies suggest focusing ecosystem assessment on ‘slow’ variables (Carpenter and Turner 2000), both biophysical and socio-economic (e.g., soil fertility, market access), as high variability in ‘fast’ variables may mask fundamental trends and long-term changes (Reynolds et al. 2007). Similarly, the variables used should have low spatial variability – outside of recognised gradients.

3.1 Evaluation frameworks

Evaluation criteria have evolved parallel to changes in conceptual frameworks and perspectives for restoration, which have in turn been reflected in the type of attributes and indicators selected for monitoring and assessment. Thus, traditional evaluation frameworks that commonly focused on technical aspects of compliance success (e.g., seedling survival rates in forest plantations; Alloza 2003), have given way to approaches that focus on ecosystem health and integrity (Xu et al. 2001, Ruiz-Jaen and Aide 2005).

The SER Primer (SER 2004) proposed a list of nine attributes that can be used as conceptual frameworks for designing ecological restoration projects and evaluating restoration success. It includes diversity and other structural properties (such as presence of indigenous species and presence of


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functional groups necessary for long-term stability), and general ecosystem functions (such as resilience to natural disturbances and self-sustainability).

As discussed by Whiffield and Geist (see Working paper I, this report), focus has recently shifted towards socio-ecological assessments, which recognise the coupled nature and dynamics between human and ecological systems. The Millennium Ecosystem Assessment provides a mechanism for integrating human and environmental systems through the concept of ecosystem services, defined as “the benefits people obtain from ecosystem” (MA 2005). The MA framework is commonly used in desertification research5, and has recently been used to assess potential impacts of ecological restoration (Rey Benayas 2009).

Finally, the need to base assessment of ecosystem health on stakeholder perspectives, and the need to integrate local and scientific knowledge are increasingly acknowledged (ICCD 2007, Reed et al. 2008). Addressing these needs would involve using a conceptual framework that facilitates the integration of scientifically developed indicators and locally identified indicators, capturing the variety of local perspectives and expertise through participatory approaches.

3.2 Number, domain and scale of application of the assessment indicators

There is no universal prescription for what to measure in order to describe the ecosystem response to management actions. In general, the spectrum of alternatives ranges from project-specific options to more broadly applicable selection of indicators, and from simple indicators of ecosystem integrity to indicator suites of a large variety of attributes (Fig. 1).

In making these choices of evaluation approaches and indicators, there are several tradeoffs to consider. On the one hand, the use of single indicators of ecosystem integrity can be a very cost-effective option that reduces monitoring effort, but it requires a profound knowledge of how well the indicator represents the structural and functional conditions expected for the restored system. Furthermore, the loss of information when many variables are integrated into a single index could mask real differences between management and restoration options. On the other hand, the more site- or project-specific the indicators, the more useful the resulting information for local managers to adjust restoration practice within an adaptive management framework. However, the evaluation results of such a tailored approach would apply only to the site and conditions under study, hindering the applicability to broad geographic areas and the comparison of restoration strategies across a variety of sites and regions.

Evaluation approaches based on few site-specific indicators would fit management actions with relatively straightforward, project-specific

5 See more on this topic in Chapter 1, this report.


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objectives or actions that are applied to a particular and relatively small piece of land over a limited period of time. For example, the local recovery of the population of certain species is often the primary goal of restoration, due either to ecological, economic and/or cultural reasons. The achievement of such a specific goal can be evaluated through the monitoring of few indicators related to the dynamics and sustainability of the target population (Bash and Ryan 2002). However, the selection of appropriate indicators is not so straightforward for projects with more general goals, such as those implemented to prevent desertification and restore degraded drylands. The ultimate goal of the actions implemented to combat desertification is to improve human well-being through the recovery of ecosystem health and integrity, and the conservation or enhancement of the provision of dryland ecosystem goods and services. Which metrics should be used to evaluate projects with such general objectives?

Figure 1. General range of alternatives for ecological evaluation of management and restoration projects as defined by the number of indicators and their domain of application (Adapted from Bautista and Alloza 2009).

4.2.1 Evaluation approaches based on few holistic indicators Simplification towards essential indicators that could characterise ecosystem recovery adequately is obviously a cost-effective approach to evaluation. For example, measures concerning indicator species, umbrella species, guilds, or assemblages of indicator species are often used as surrogates of ecosystem


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function and integrity (e.g., Williams 1993, Patten 1997). The structural and functional requirements of indicator species should reflect the conditions expected in the healthy ecological system. This approach requires the development of a conceptual model that outlines the structure of the community, including interrelations among ecosystem components (Block et al. 2001). Therefore, the accurate use of these indicators depends on a high level of knowledge of the target system.

Although evaluation protocols based on indicator species are relatively site-specific, when based on general taxa or guilds (e.g., bird populations, biological crusts) they could be applied to a broad range of sites and project types (Neckles et al. 2002, Bowker et al. 2006). General indicators of community structure, such as species richness, diversity, and evenness, can also be used for evaluating general ecosystem response to prevention and restoration actions (e.g., Reay and Norton 1999, Passell 2000, Gómez-Aparicio et al. 2009).

Vegetation cover is the most common single indicator used for evaluating management projects and restoration success, as it is often assumed that the recovery of fauna and ecological processes will follow the establishment of vegetation (Ruiz-Jaen and Aide 2005). Since vegetation cover is relatively easy to assess, it is commonly used as a surrogate of ecosystem functions and habitat quality (Reay and Norton 1999, Wilkins et al. 2003, Wildham et al. 2004). However, vegetation cover alone cannot always reflect how well an ecosystem is functioning, and, of course, do not take into account species identities and their potential role as keystone species, noxious weeds, or any other particular role played by single species or functional groups.

During the last decade, a variety of functional assessment approaches have been proposed (Tongway and Hindley 2004, Herrick et al. 2005). These approaches rely on few functional indices that commonly integrate a suite of metrics. For example, the “Landscape Functional Analysis” (LFA) methodology (Tongway and Hindley 1995, 2004) assesses ecosystem functional status through a set of easily recognizable soil and landscape features, from which indices of infiltration, stability and nutrient cycling are derived. These indices are expected to reflect the status of water conservation, soil conservation, and nutrient cycling processes in the target ecosystems.

Much of the recent scientific evidence suggests that ecosystems do not always undergo predictable and more or less gradual trajectories. Indeed, ecosystems can exhibit threshold dynamics, change between alternative metastable states that are then reinforced by internal feedbacks (Scheffer and Carpenter 2003, Rietkerk et al. 2004, Suding et al. 2004, Bestelmeyer 2006, Suding and Hobbs 2009). These facts have triggered recent and ongoing research on indicators that have the capacity for early warning of sudden changes, state shifts, and, in particular, of imminent dryland degradation (e.g., Kéfi et al. 2007). The general assumption of these approaches is that certain variables exhibit threshold values that can indicate ecosystem state


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shifts. Similarly, degraded systems that have shifted to a new state that is reinforced by internal feedbacks cannot be restored to the previous state unless certain thresholds are passed (Whisenant 1999, Suding et al. 2004). Due to hysteresis dynamics, thresholds for degradation and recovery paths are very different. Knowledge about restoration thresholds is still very scarce (Maestre et al. 2006), which limits the definition of reference target values for evaluation and the development of state-change indicators for evaluating restoration success. However, this is a hot topic in ecological indicator research and new insights and advances can be expected in the near future.

4.2.2 Evaluation approaches based on large suites of indicators Evaluation based on a relatively large suite of indicators (Fig. 1) aims to present a more comprehensive diagnosis of the restoration and management effects. An integrated suite of indicators may be specific for certain sites, problems or project types (Keddy and Drummond 1996, Davis and Muhlberg 2002, Palmer et al. 2005) or may mostly rely on general metrics that can be used for assessing a wide range of cases (SER 2004). Several authors have promoted approaches that combine both general and case-specific indicators (e.g., Neckles et al. 2002, Jorgersen et al. 2005).

The use of multiple indicators maximizes the amount and variety of information provided on the management impacts and is the best approach possible when there is not sufficient scientific knowledge to support the use of single holistic indicators as proxies for the function and integrity of the target dryland ecosystem. Numerous authors have proposed lists of attributes that can be used for evaluating management actions and restoration success (e.g., Aronson and Le Floc’h 1996, Hobbs and Norton 1996, SER 2004, Palmer et al. 2005, Ruiz-Jaen and Aide 2005).

According to this view, evaluation should rely on the widest range possible of attributes and perspectives, provided they are relevant. The challenge for this approach is to organize and integrate the profuse information in a harmonized way, allowing conclusions to be drawn, avoiding redundant information, and keeping the number of attributes assessed manageable in practical terms. For example, for evaluating forest restoration actions in northern Mediterranean drylands, a protocol based on a wide variety of indicators was recently developed by the REACTION project (Bautista et al. 2004). This protocol was not only conceived as an evaluation methodology, but also as an information system designed to compile and disseminate the information derived from an integrated assessment of the restoration projects evaluated. The large suite of indicators used were integrated into a small set of ecological, environmental, socio-economic, and cultural attributes that focused on both human and natural aspects of the systems evaluated (Bautista and Alloza 2009, Bautista et al. 2010).


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4 Common indicators and methods for the evaluation of prevention and restoration actions to combat desertification.

4.1 An evaluation approach based on the comparative assessment of the provision of ecosystem services

Management actions in degraded drylands may range from “managing as usual” approaches to adaptive management approaches that base improvement on the feedback provided by monitoring and assessment programs. Since the actions applied are choices from the many options available, assessment and evaluation can be based on a comparative analysis of the outcomes and impacts. There are cases in which objectives or reference values for the attributes of interest, as well as their acceptable range of variability, are precisely defined, and hence the evaluation approach should largely rely on measuring the achievement of those well-defined objectives and target values for any given single action. However, most prevention and restoration actions to combat dryland degradation share similar goals, such as the general improvement of ecosystem health, the enhancement of productivity and other ecosystem services, or the general improvement of people well-being. Therefore, we advocate here a comparative assessment of the various management actions considered in any given target area, including comparison with degraded (managing as usual) areas or with healthy reference areas, if appropriate.

We propose to base the comparative assessment of prevention and restoration actions on (1) key common indicators that represent overall functioning of dryland ecosystems, and (2) site-specific indicators identified by local stakeholders that are relevant to the objectives and the particular context conditions. By combining scientifically- and locally-derived indicators this approach aims at promoting the integration of scientific and local knowledge on dryland processes, and capturing both the specificities of the particular dryland systems assessed, and therefore the variation among regions, countries, and sites, and the impacts on key underlying processes that are common across drylands.

Following the rationale presented by Whitfield and Geist (2010, Working paper I, this report), the Millennium Assessment framework is adopted here for selecting and framing both common indicators and site-specific indicators. The ‘Ecosystem Services’ concept and categories provide a suitable framework for analysing in a harmonised and consistent way the information provided by the – a priori – wide variety of global and site-specific indicators that could be identified by experts and local stakeholders (see, for example Rey Benayas et al. 2009).

Next section will present and discuss a suite of common indicators for assessing the impacts of prevention and restoration actions on dryland ecosystems. The selected indicators focus on biophysical attributes and


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processes. The socio-economic assessment of actions to combat desertification is addressed in Working papers I and IV in this report.

4.2 Common indicators for assessing dryland management and restoration In selecting an appropriate common suite of criteria and indicators for the biophysical assessment of actions to combat desertification, a well-balanced basket of ecosystem services should be considered, covering the three broad categories of provisioning, regulatory and supporting services (the assessment of cultural services is addressed in Working papers I and IV, this report), and focusing on key services in drylands (MA 2005). Figure 2 summarises the criteria and suite of indicators proposed here, including potential metrics for their assessment. The selected criteria and indicator also seek to maximize possible synergies with global programmes pursuing ecosystem health and associated human well-being, such as the United Nation Framework on Climate Change (UNFCC) or the Convention on Biodiversity (CBD).

Prov

isio

ning

Se

rvic

e

Criteria Indicator Metrics

Indicator-specificGoods (food, fiber, timber, fuel wood,

fresh water)

C sequestration

Vascular plant ANPP

Standard SOC measures

Site-specific

Plant cover & patternBare-soil connectivity

Water regulation

Reg

ulat

ing

& S

uppo

rtin

g Se

rvic

e

Biodiversity Diversity of vascular plants

Species composition and abundance of vascular plants. Diversity indices

Soil Organic C (SOC)

Primary production

Soil conservation & Nutrient cycling Soil surface condition Infiltration & Nutrient

Cycling Indices (mLFA)

Above-ground biomass / Growth of key species

Plant cover

ANPP: Above-ground Net Primary Productivity SOC: Soil Organic Carbon mLFA: modified Landscape Functional Analyses (Tongway and Hindley 2004)

Figure 2. Common criteria, indicators and metrics for assessing prevention and restoration actions to combat dryland degradation


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The assessment of goods derived from biological productivity, such as food, fiber, fuel wood or timber, should be part of any common protocol to evaluate the impacts of prevention and restoration actions, as these provisions represent basic materials and quantifiable benefits for most dryland people. However, in spite of its relevance across drylands, the key goods provided and their relative importance vary between sites, countries and regions, including countries and regions where provision of goods is of small importance as compared with regulating, supporting or cultural services. Therefore, the suitable indicators, as well as the associated metrics, depend on the specific site context and should be selected by the local stakeholders and experts. Some general approaches for evaluating land productivity, in terms of provisioning services, are discussed by Onofri et al (Working paper IV, this report).

For those cases where the actions applied could have a direct impact on the provision of fresh water, this service should be assessed as part of the evaluation protocol. For instance, reforestation or afforestation actions can modify acquifer recharge or can prevent the rise of saline groundwater near the surface (Walsh et al. 2003, Calder 2007), and therefore the use of fresh water availability –measured, for instance, as the aquifer water level– as an indicator of the restoration impacts on ecosystem services is very much appropriate. However, as above, the suitability of this indicator is highly dependent on the site and the type of actions assessed. For a majority of management and restoration actions, the impacts on fresh water availability are indirect off-site impacts, resulting from changes in water regulation and conservation services (see below).

Primary Production is a key supporting service on which many other services depend. In drylands, the best indicator for assessing this service would be the Above-ground Net Primary Production (ANPP) of vascular plants. The ground-based assessement of total plant ANPP is however a challenging task. The assessment of changes in above-ground biomass or growth of few species or functional groups of particular interest could be a better approach for the ground-based assessment of primary production (Figure 2). When the spatial extent of the actions is large enough, remote sensing based methods probably are the most cost-effective approaches for assessing changes and long-term trends in primary production (see Working paper III, this report).

NPP in drylands is highly constrained by water availability, and large inter-annual climatic variations cause important fluctuations that could mask the impacts of the actions applied. Both ground and remote-sensing based approches must therefore adjust their metrics for rainfall variability. The ratio of net primary production to precipitation – the rain-use efficiency (commonly calculated from remotely sensed vegetation indices and rain gauge data) is often used as indicator of land degradation or recovery. Given the high variability and fast response of this service to changes in water inputs, it is important to focus the assessment on the ecosystem resilience in response to this variation: how much NPP is in step with rainfall? Does NPP recover rapidly following drought? (Prince et al. 2004). It is also important to assess long-term


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trends, and to consider the same time periods when comparing management different actions.

Appropriate land management practices can greatly increase both soil and vegetation carbon stocks and thereby contribute to global climate regulation. Soil organic carbon and above-ground biomass are proposed here as carbon sequestration indicators. These two indicators have been included within the Essential Climate Variables list, recently released by the UNFCCC (GCOS 2010), and therefore their use could strength the linkages among global environmental programmes.

The soil organic carbon pool is two times the size of the atmospheric pool and to and a half times the size of the biotic pool. Strategies to increase the soil carbon pool include soil restoration and woodland regeneration, no-till farming, cover crops, nutrient management, improved grazing, water conservation and harvesting, and agroforestry practices (Lal 2004). Soil organic carbon (SOC) is considered to be a ‘slow’ variable, and it can be asessed through well-known and widely applied available protocols. Ideally, the assessment of carbon sequestration rates would require the long-term monitoring of SOC. Furthermore, the rate of soil organic carbon sequestration depends on soil texture and structure, rainfall and temperature. However, for the comparative analysis of management strategies within a given site, total top-soil organic carbon, relative to the implementation time of the actions evaluated, can be a suitable metric for evaluation purposes.

Direct estimation of aboveground biomass through destructive harvesting can be used to quantify above-ground carbon stocks. However, there are available methods for converting simple ground and remote sensing based measurement into plant carbon stocks (Brown et al. 2005, Gibbs et al. 2007). For instance, in forest areas, measurements of diameter at breast height (DBH) alone or in combination with tree height can be converted to estimates of forest carbon stocks using allometric relationships. Actually, species-specific allometric relationships are not needed to generate reliable estimates of forest carbon stocks. Grouping all species together and using generalized allometric relationships, stratified by broad forest types or ecological zones, can be highly effective (Brown 2002).

Water regulation and conservation, frequently tied to the conservation of soil and nutrients trough runoff, is probably the most essential function in drylands (Whitford, 2002), and thus, its evaluation has a special interest for their management. However, measuring water fluxes is highly time consuming and costly, especially at larger scales. Vegetation cover is commonly used as a surrogate of water conservation potential, as its relationships with general hydrological functioning have been widely reported. However, a number of studies in semiarid areas have shown that not only cover but also shape, spatial orientation and arrangement of plant patches within a landscape can greatly influence hydrological functioning (e.g., Ludwig et al. 1999, Puigdefábregas 2005, Bautista et al. 2007). During the last decade, several functional indices that assume a tied relationship between the spatial pattern


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of the vegetation and the hydrological functioning of drylands have been proposed (Bastin et al. 2002, Imeson and Prinsen 2004, Ludwig et al. 2007, Mayor et al. 2008). The theoretical framework for these approaches considers that landscapes occur along a continuum of functionality from systems with low bare-soil connectivity that conserve all resources to those that have no plant patches and leak all resources (Ludwig and Tongway 2000). The index developed by Mayor et al. (2008), Flowlength, is a simple metric of bare-soil connectivity that integrates both plant cover and pattern, and which has shown strong positive relationships with hillslope runoff and sediment yields. It can be easily estimated from vegetation maps, aerial photos, or high-resolution satellite images, combined with elevation data.

Indicators and metrics for assessing water conservation are generally used to assess soil conservation as well. In addition, a number of indicators that specifically rely on soil and surface properties have been proposed for the assessment of soil functioning, in particular soil conservation and nutrient cycling (Tongway and Hindley, 2004, Herrick et al. 2005). For example, the “Landscape Functional Analysis” (LFA) methodology (Tongway and Hindley, 1995, 2004) assesses ecosystem functional status through a set of easily measured soil and landscape features, from which indices of infiltration, stability and nutrient cycling are derived. These indices are expected to reflect the status of water conservation, soil conservation, and nutrient cycling processes in the target ecosystems. The LFA approach has been applied in many dryland areas worldwide (e.g., Tongway and Hindley 2000, McR. Holm et al. 2002, Maestre and Puche 2009), where LFA indices have shown positive relationships with quantitative measures of (top)soil features such as aggregate stability, water infiltration capacity and nutrient content.

Biodiversity is involved in most services provided by dryland ecosystems (Blignaut and Aronson 2008). Recent results indicate that biodiversity is positively related to the ecological functions that support the provision of ecosystem services in restored areas (Rey Benayas et al. 2009). Biodiversity assessments typically focus on particular biota, ranging from general groups (e.g., plants, vertebrates, herbaceous species) to specific taxa or guilds (e.g., butterflies, resprouting shrubs), often resulting in a combined approach based on biodiversity and indicator taxa (Kerr et al. 2000). Here we propose to focus on the diversity of vascular plants as indicator of overall biodiversity. When certain species or functional groups play a key functional role within the drylands assesses, measuring the abundance of the these key species will contribute to information on the biodiversity status of the system.

5 Conclusions In summary, the approach advocated here for the ground-based biophysical assessment of prevention and restoration actions combines the use of few essential indicators that could characterise ecosystem recovery for a majority of drylands worldwide with a site-specific suite of indicators selected by local expert and stakeholders. Both common and site-specific indicators are


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expected to jointly represent a well-balanced basket of ecosystem services, with common indicators mostly focusing on water and soil conservation, nutrient cycling, carbon sequestration, and biological diversity, while site-specific indicators capture the provision of goods and fresh water, including the net primary production of key species or functional groups.

6 References

Allen, C.D., Savage, M., Falk, D.A., Suckling, K.F., Swetnam, T.W., Schulke, T., Stacey, P.B., Morgan, P., Hoffman, M., and Klingel, J.T. 2002. Ecological restoration of southwestern ponderosa pine ecosystems: a broad perspective. Ecological Applications 12(5): 1418-1433.

Alloza, J.A. 2003. Análisis de repoblaciones forestales en la Comunidad Valenciana. Desarrollo de criterios y procedimientos de evaluación. Ph.D. Thesis, Universitat Politècnica de València, Spain.

Aronson J. and Le Floc'h E. 1996. Vital landscape attributes: Missing tools for restoration ecology. Restoration Ecology 4(4): 377-387.

Aronson J., Floret, C., Le Floc'h, E., Ovalle, C., and Pontanier R. 1993. Restoration and rehabilitation of degraded ecosystems in arid and semi-arid lands. I. A view from the south. Restoration Ecology 1: 8-17.

Bash, J.S. and Ryan, C.M. 2002. Stream restoration and enhancement projects: Is anyone monitoring? Environmental Management 29(6): 877-885.

Bastin, G.N., Ludwig, J.A., Eager, R.W., Chewings, H., Liedloff, C., 2002. Indicators of landscape function: comparing patchiness metrics using remotely-sensed data from rangelands. Ecological Indicators 1: 247-260.

Bautista, S. and Alloza, J.A. 2009. Evaluation of forest restoration projects. In S. Bautista, J. Aronson, and J. Vallejo (eds.), Land Restoration to Combat Desertification. Innovative Approaches, Quality Control and Project Evaluation. CEAM Foundation, pp. 47-72.

Bautista, S., J.A. Alloza, and V.R. Vallejo. 2004. Conceptual framework, criteria, and methodology for the evaluation of restoration projects. The REACTION approach. Fundación CEAM, Valencia. (http://www.ceam.es/reaction/).

Bautista, S., Mayor, A. G., Bourakhouadar, J., Bellot, J., 2007. Plant spatial pattern predicts hillslope runoff and erosion in a semiarid Mediterranean landscape. Ecosystems 10(6): 987-998.

Bautista, S., Orr, B.J., Alloza, J.A., and Vallejo, V.R. 2010. Evaluation of the restoration of dryland ecosystems in the northern Mediterranean: Implications for practice. In M-F. Courel and G. Schneier-Madanes (eds.), Water in Arid and Semi-arid Zones. Advances in Global Change Research. Springer, Dordrecht, The Netherlands, pp. 295-310.

Bestelmeyer, B.T. 2006. Threshold concepts and their use in rangeland management and restoration: The good, the bad, and the insidious. Restoration Ecology 14: 325-329.

Blignaut, J. and Aronson, J. 2008. Getting serious about maintaining biodiversity. Conservation Letters 1: 12-17.

Block, W., Franklin, A., Ward, J., Ganey, J., and White, G., 2001. Design and implementation of monitoring studies to evaluate the success of ecological restoration on wildlife. Restoration Ecology 9(3): 293-303.

Bowker, M.A., Belnap, J., and Miller, M.E. 2006. Spatial modeling of biological soil crusts to support rangeland assessment and monitoring. Rangeland Ecology and Management 59: 519-529.


57

Bradshaw, A.D. 1997. What do we mean by restoration?. In K. Urbanska, N. Webb and P. Edwards (eds.), Restoration Ecology and Sustainable Development. Cambridge University Press, Cambridge, UK, pp. 8-14.

Brinson, M.M. and Rheinhardt, R. 1996. The role of reference wetlands in functional assessment and mitigation. Ecological Applications 6: 69-76.

Brown, S. 2002 Measuring carbon in forests: current status and future challenges Environmental Pollution 116: 363–72.

Brown, S., Pearson, T., Slaymaker, D., Ambagis, S., Moore, N., Novelo, D., and Sabido, W. 2005. Creating a virtual tropical forest from three-dimensional aerial imagery: application for estimating carbon stocks Ecological Application 15: 1083–95.

Calder, I. 2007. Forests and water—ensuring forest benefits outweigh water costs. Forest Ecology and Management 251:110–120.

Carpenter, S.R. and Turner, M.G. 2000. Hares and tortoises: Interactions of fast and slow variables in ecosystems. Ecosystems 3(6): 495-497.

Choi, Y.D. 2004. Theories for ecological restoration in changing environment: Toward 'futuristic' restoration. Ecological Research 19: 75-81.

Cortina, J., Maestre, F.T., Vallejo, V.R., Baeza, M.J., Valdecantos, A., and Pérez-Devesa, M. 2006. Ecosystem structure, function, and restoration success: Are they related? Journal for Nature Conservation 14: 152-160.

Dale, V.H. and Beyeler, S.C. 2001. Challenges in the development and use of ecological indicators. Ecological Indicators 1: 3-10.

Davis, J.C. and Muhlberg, G.A. 2002. The evaluation of wetland and riparian restoration projects. Anchorage, AK. Alaska Dep. of Fish and Game, Habitat and Restoration Division (http://www.state.ak.us/adfg/habitat/geninfo/publictn/riparianproj.pdf).

Falk, D.A. 2006. Process-centred restoration in a fire-adapted ponderosa pine forest. Journal for Nature Conservation 14: 140-151.

Gaboury, M. and Wong, R. 1999. A framework for conducting effectiveness evaluations of watershed restoration projects. Watershed Restoration Technical Circular 12. Ministry of Environment, Lands and Parks and Ministry of Forest. British Columbia.

GCOS (Global Climate Observing System) 2010. Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (August 2010 Update) GCOS-138 (GTOS-184, GTOS-76, WMO-TD/No.1523. Geneva: WMO GCOS. Available Online: http://www.wmo.int/pages/prog/gcos/Publications/gcos-138.pdf

Gibbs H.K., Brown S., Niles J.O., and J.A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2(4), 045023. doi:10.1088/1748-9326/2/4/045023.

Gómez-Aparicio, L., Zavala, M.A., Bonet, F.J., and Zamora, R. 2009. Are pine plantations valid tools for restoring Mediterranean forests? An assessment along abiotic and biotic gradients. Ecological Applications 19(8): 2124-2141.

Henry, C.P. and Amoros, C. 1995. Restoration ecology of riverine wetlands. I. A scientific base. Environmental Management 19(6): 891-902.

Herrick, J.E., van Zee, J.W., Havstad, K.M., and Whitford, W.G. 2005. Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems. USDA-ARS Jornada Experimental Range, Las Cruces, New Mexico. University of Arizona Press, Tucson.

Higgs, E.S. 1997. What is good ecological restoration? Conservation Biology 11: 338-348.

Hobbs, R.J. and Harris, J.A. 2001. Restoring ecology: Repairing the Earth's ecosystems in the new millennium. Restoration Ecology 9(2): 239-246.


58

Hobbs, R.J. and Norton, D.A. 1996. Towards a conceptual framework for restoration ecology. Restoration Ecology 4(2): 93-110.

Holl, K.D. and Cairns Jr, J. 2002. Monitoring and appraisal. In M.R. Perrow and A.J. Davy (eds.), Handbook of Ecological Restoration, I: Principles of Restoration. University of Anglia, Cambridge University Press, pp. 411-432.

ICCD/COP8/CST 2007. Report of the fifth meeting of the Group of Experts of the Committee on Science and Technology. Addendum: Methodologies for the Assessment of Desertification at Global, Regional and Local Levels. ICCD/COP(8)/CST/2/Add.6.

Imeson, A.C., Prinsen, H.A.M., 2004. Vegetation patterns as biological indicators for identifying runoff and sediment source and sink areas for semi-arid landscapes in Spain. Agriculture, Ecosystems and Environment 104 333-342.

Jorgersen, S.E., Xu, F-L., Salas, F., and Marques, J.C. 2005. Application of indicators for the assessment of ecosystem health. In S.E. Jorgensen, R. Costanza, and F-L. Xu (eds.), Handbook of Ecological Indicators for Assessment of Ecosystem Health. Lewis Publishers, Inc. USA, pp. 5-66.

Keddy, P.A. and Drummond, C.G. 1996. Ecological properties for the evaluation, management, and restoration of temperate deciduous forest ecosystems. Ecological Applications 6(3): 748- 762.

Kéfi, S., Rietkerk, M., Alados, C.L., Pueyo, Y., Papanastasis, V.P., ElAich, A., and de Ruiter, P.C. 2007. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449: 213-217.

Kerr, J.T., Sugar, A., and Packer, L. 2000. Indicator taxa, rapid biodiversity assessment, and nestedness in an endangered ecosystem. Conservation Biology 14(6): 1726-1734.

Lal, R. 2004. Soil carbon sequestration impacts on global climacte change and food security. Science 304: 1623-167.

Ludwig, J. A., Tongway, D., Freudenberger, D., Noble, J., Hodgkinson, K., 1997. Landscape Ecology, Function and Management: Principles from Australia’s Rangelands. CSIRO, vol. 158. CSIRO, Collingwood, Australia.

Ludwig, J.A., Bastin, G.N., Chewings, V.H., Eager, R.W., and Liedloff, A.C. 2007. Leakiness: A new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data. Ecological Indicators 7: 442-454.

Ludwig, J.A. and Tongway, D.J. 2000. Viewing rangelands as landscape systems. In O. Arnalds and S. Archer (eds.), Rangeland Desertification. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 39-52.

Ludwig, J.A., Tongway, D.J., and Marsden, S.G. 1999. Stripes, strands or stipples: modelling the influence of three landscape banding patterns on resource capture and productivity in semiarid woodlands, Australia. Catena 37: 257-273.

MA (Millennium Ecosystem Assessment). 2005. Ecosystems and Human Well-being: Desertification Synthesis. World Resources Institute: Washington, DC.

Maestre, F.T. and Puche, M.D. 2009. Indices based on surface indicators predict soil functioning in Mediterranean semi-arid steppes. Applied Soil Ecology 41: 342–350.

Maestre, F.T., Cortina, J., and Vallejo, V.R. 2006. Are Ecosystem composition, structure, and functional status related to restoration success? A test from semiarid Mediterranean steppes. Restoration Ecology 14(2): 258-266.

Mayor, A.G., S. Bautista, E. E. Small, M. Dixon, and J. Bellot (2008), Measurement of the connectivity of runoff source areas as determined by vegetation pattern and topography: A tool for assessing potential water and soil losses in drylands, Water Resour. Res., 44, W10423, doi:10.1029/2007WR006367.


59

McR. Holm, A., Bennet, L.T., Loneragan, W.A., Adams, M.A., 2002. Relationships between empirical and nominal índices of landscape function in the arid shrubland of Western Australia. Journal of Arid Environments 50: 1-21.

Neckles, H.A., Dionne, M.D., Burdick, D.M., Roman, C.T., Buchsbaum, R., and Hutchins, E. 2002. A monitoring protocol to assess tidal restoration of salt marshes on local and regional scales. Restoration Ecology, 10: 556-563.

NRC (U.S. National Research Council). 2000. Ecological Indicators for the Nation. Chapter 3: A Framework for Indicator Selection. Washington D.C.: The National Academies Press. 180 pp. Online: http://www.nap.edu/openbook.php?record_id=9720&page=51

Palmer, M.A., Bernhardt, E.S., Allan, J.D., Lake, P.S., Alexander, G., Brooks, S., Carr, J., Clayton, S., Dahm, C.N., Follstad-Shah, J., Galat, D.L., Loss, S.G., Goodwin, P., Hart, D.D., Hassett, B., Jenkinson, R., Kondolf, G.M., Lave, R., Meyer, J.L., O'donnell, T.K., Pagano, L., and Sudduth, E. 2005. Standards for ecologically successful river restoration. Journal of Applied Ecology 42(2): 208-217.

Parker, V.T. and Pickett, S.T.A. 1997. Restoration as an ecosystem process: implications of the modern ecological paradigm. In K. Urbanska, N.Webb, and P. Edwards (eds.), Restoration Ecology and Sustainable Development. Cambridge University Press, Cambridge, UK, pp. 17-32.

Passell, H.D. 2000. Recovery of bird species in minimally restored Indonesian thin strips mines. Restoration Ecology 8: 112-118.

Patten, M.A. 1997. Reestablishment of a rodent community in restored desert scrub. Restoration Ecology 5: 156-161.

Pickett, S.T.A. and Parker, V.T. 1994. Avoiding the old pitfalls: opportunities in a new discipline. Restoration Ecology 2: 75-79.

Prince, S.D., Brown De Colstoun, E., and Kravitz, L.L. (2004). Evidence from rain-use efficiencies does not indicate extensive Sahelian desertification. Global Change Biology 4(4): 359–374.DOI: 10.1046/j.1365-2486.1998.00158.x

Puigdefábregas, J. 2005. The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surface Processes and Landforms 30, 133-147.

Reay S.D. and Norton, D.A. 1999. Assessing the success of restoration plantings in a temperate New Zealand forest. Restoration Ecology 7: 298-308.

Reed, M.S., Dougill, A.J., Baker, T.R. 2008. Participatory indicator development: What can ecologists and local communities learn from each other? Ecological Applications 18(5): 1253–1269

Rey Benayas, J.M., Newton, A.C., Diaz, A., and Bullock, J.M. 2009. Enhancement of biodiversity and ecosystem services by ecological restoration: A meta-analysis. Science 325: 1121-1124. DOI: 10.1126/science.1172460.

Reynolds, J.F., Stafford Smith, D.M., Lambin, E.F., Turner II, B.L., Mortimore, M., Batterbury, S.P.J., Downing, T.E., Dowlatabadi, H., Fernández, R.J., Herrick, J.E., Huber-Sannwald, E., Jiang, H., Leemans, R., Lynam, T., Maestre, F.T., Ayarza, M., Walker, B. 2007. Global desertification: Building a science for dryland development. Science 316: 847-551. DOI: 10.1126/science.1131634.

Rietkerk, M., Dekker, S.C., de Ruiter, P.C., and van de Koppel, J. 2004. Self-organized patchiness and catastrophic shifts in ecosystems. Science 305: 1926-1929.

Ruiz-Jaen, M.C. and Aide, T.M. 2005. Restoration success: How is it being measured?. Restoration Ecology 13(3): 569-577.

Scheffer, M., and Carpenter, S.R. 2003. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends in Ecology and Evolution 18(12): 648-656.


60

SER (Society for Ecological Restoration, Science & Policy Working Group). 2004. The SER Primer on Ecological Restoration (http://www.ser.org/).

Suding, K.N. and Hobbs, R.J. 2009. Threshold models in restoration and conservation: a developing framework. Trends in Ecology and Evolution 24: 271-279.

Suding, K.N., Gross, K.L., and Houseman, G.R. 2004. Alternative states and positive feedbacks in restoration ecology. Trends in Ecology and Evolution 19(1): 46-53.

Thayer, G.W., McTigue, T.A., Bellmer, R.J., Burrows, F.M., Merkey, D.H., Nickens, A.D., Lozano, S.J., Gayaldo, P.F., Polmateer, P.J., and Pinit, P.T. 2003. Science-Based Restoration Monitoring of Coastal Habitats, I: A Framework for Monitoring Plans Under the Estuaries and Clean Waters Act of 2000 (Public Law 160-457). NOAA Coastal Ocean Program Decision Analysis Series No. 23. NOAA National Centers for Coastal Ocean Science, Silver Spring, MD.

Tongway, D.J. and Hindley, N. 2004. Landscape Function Analysis: Procedures for Monitoring and Assessing Landscapes. CSIRO Publishing, Brisbane.

Tongway, D.J., Hindley, N., 2000. Assessing and monitoring desertification with soil indicators. In: Arnalds, O., Archer, S. (Eds.), Rangeland Desertification, Advances in Vegetation Sciences, vol. 19. Kluwer Academic Publishers, London, UK, pp. 89-98.

Tongway, D.J. and Hindley, N. 1995. Assessment of soil condition of tropical grasslands. CSIRO Ecology and Wildlife, Canberra.

UNCCD 2009. UNCCD 1st Scientific Conference: Synthesis and recommendations. ICCD/COP(9)/CST/INF.3. UN Convention to Combat Desertification. Available Online: http://www.unccd.int/php/document2.php?ref=ICCD/COP%289%29/CST/INF.3

Vallejo, V.R., Aronson, J., Pausas, J.G., and Cortina, J. 2006. Restoration of Mediterranean woodlands. In J. van Andel and J. Aronson (eds.), Restoration Ecology: The New Frontier. Oxford, Blackwell Science, Oxford, pp. 193-209.

van Diggelen, R., Grootjans, Ab P., and Harris, J.A. 2001. Ecological restoration: State of the art or state of the science?. Restoration Ecology 9(2):115-118.

Walsh, P.G., C.V. Barton, and K.D. Montagu. 2003. Macquarie catchment pilot project, New South Wales, Australia: a cost-effective, market-based approach to reducing salinity through tree planting. Unasylva 54 (212): 37–39.

Whisenant, S.G. 1999. Repairing Damaged Wildlands. Cambridge University Press, Cambridge, UK.

White, P.S. and Walker, J.L. 1997. Approximating nature's variation: selecting and using reference sites and reference information in restoration ecology. Restoration Ecology 5: 338-249.

Whitford, W.G. 2002. Ecology of desert systems. Elsevier Science Ltd., London, England.

Windham, L., Laska, M.S., and Wollenberg, J. 2004. Evaluating urban wetland restorations: case studies for assessing connectivity and function. Urban Habitats 2(1): 130-146.

Wilkins, S., Keith, D.A., and Adam, P. 2003. Measuring success: evaluating the restoration of a grassy eucalypt woodland on the Cumberland Plain, Sydney, Australia. Restoration Ecology 11: 489-503.

Williams, K.S. 1993. Use of terrestrial arthropods to evaluate restored riparian woodlands. Restoration Ecology 1: 107-116.

WWF, 2002. Indicators for measuring progress towards forest landscape restoration. (http://www.panda.org/downloads/forests).


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Xu, F-L., Tao, S., Dawson, R.W., Li, P-L., and Cao, L. 2001. Lake ecosystem health assessment: indicators and methods. Water Research 13: 3157-3167.

Zedler, J.B. 2007. Success: An unclear, subjective descriptor of restoration outcomes. Ecological Restoration 25(3): 162-168.

Zedler, J.B. 1995. Salt marsh restoration: lessons from California. In J. Cairns (ed.), Rehabilitating Damaged Ecosystems. Lewis Publishers, Boca Raton. Florida, pp. 75-95.

Zedler, J.B. and Callaway, J.C. 1999. Tracking wetland restoration: Do mitigation sites follow desired trajectories?. Restoration Ecology 7(1): 69-73.

Remote Sensing Based Assessment

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III. Remote Sensing Based Options for

Assessment of Mitigation and Restoration Actions to Combat Desertification

Working paper for the practice project

Achim Röder

Marion Stellmes

Joachim Hill

Thomas Udelhoven

A. Röder, M. Stellmes, J. Hill, and Th. Udelhoven

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© 2010 by A. Röder, M. Stellmes, J. Hill, Th. Udelhoven

Remote Sensing Department

Trier University

D-54286 Trier, Germany

[email protected], [email protected], [email protected], [email protected]

www.feut.de


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1 Introduction

The capability of ecosystems to supply goods and services that are essential for human well-being are strongly impacted upon by many factors, desertification and land degradation being among the most important ones in dryland areas. This impact varies greatly, driven by the degree of aridity, the variability of precipitation and the pressure exerted by people on the resources.

A variety of measures are being devised that aim at a general recovery of degraded ecosystems, or the mitigation of negative development. Evidently, any such measure requires definition of an intended ecosystem state, of what a “healthy” ecosystem looks like, or what services and functions it needs to supply. In very general terms, restoration goals might be grouped according to “species”, “ecosystem functions” and “ecosystem services” (Harris and van Diggelen 2008).

In principle, there is no possibility to measure the success of restoration or mitigation actions from remote sensing data directly. Rather, approaches need to be designed to match the intervention scale of the action to be addressed, and the development goal pursued by this action. In that sense, remote sensing based options are closely linked to studies of land use / land cover change. Yet, in this case locations under management or intervention scenarios are known, and the task is to evaluate if the intended change (or no-change if desired) can actually be detected in a spatially explicit manner.

As a direct consequence of these basic statements, first considerations of which approach is best suited require a clear definition of

• The ecological problem being tackled

• The type of management intervention applied (including location and spatial extension)

• The intended management or development goal

This information defines the type of remote sensing data to be utilized as well as the indicators that might be employed to evaluate the degree of success of management actions.

Therefore, the remote sensing component of a protocol to assess the success of restoration and mitigation practices proposed by PRACTICE will draw from concepts and techniques established in the context of land use / land cover change assessments. Traditionally, these have been based on diachronic or multi-date assessments of land use / land cover (LULC) maps, and an evaluation of the changes between classes between two or more dates. Such analyses may involve characterizing LULC for different dates and interpreting change, or mapping change directly from diachronic data sets and attributing the type of change to apparent patterns (Coppin et al. 2004a; Lu et al. 2004). Accordingly, great importance is attributed to the selection of appropriate classification algorithms, as well as strategies of defining training and validation data. In recent years, there has been growing


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evidence that modifications of land cover, even within one broad LULC category, may be of equal importance than changes between classes (in this context termed transformations or conversions) (Lambin et al. 2006; Turner II et al. 2007). Implications of gradual changes within vegetated areas relate for instance to aspects as diverse as fire risk, soil erosion or carbon sequestration. Assessing modifications, particularly if slow processes are considered, necessitates a different approach that allows tracking gradual developments, i.e. time series analysis using long-term datasets (Hill et al. 2004).

It follows from these considerations that the analysis of restoration measures using remote sensing data relies on the derivation of suitable spatial information for the required dates. This information can either relate to LULC information or to the quantification of suitable indicators, such as proportional cover of surfaces types (vegetation, sand, soil,…) or the derivation of more specific biophysical indicators, i.e. leaf area index, leaf water content etc. While LULC information is commonly employed to characterize change between two or more dates, the temporal development of more specific indicators may be quantified using time series analysis techniques. To move towards a more comprehensive evaluation, information derived either way subsequently needs to be integrated with auxiliary spatial and non-spatial data, such as knowledge of applied management techniques, socio-economic data, climate data etc. The outcome of this integration may be an advanced evaluation, but could also serve to drive specific assessment models at the landscape scale.

Today, a wide range of sensor systems placed on space-based and airborne platforms are available. They are characterized by geometric resolution, spatial coverage, repetition rate and history of data acquisition. These need to be selected depending on target processes and areas, and on the type of information product desired, which is in turn dependent on potential end users. In addition, these systems are equipped with a largely varying number of spectral bands determining their information content. Figure 1 provides an overview of sensor systems grouped according to spectral and spatial resolution.

Besides these technical specifications, the history and repetition rate of image acquisition of the different missions are determining their potential for long-term analyses. Although various approaches for intercalibration across different sensors exist (Jiang et al. 2008; Röder et al. 2005; Steven et al. 2003), coherence of information is facilitated by the use of consistent datasets. In such setups, spectral and geometric sensor setups as well as orbit parameters allow for a direct integration of information derived from different sensors of the same family.

Most prominent examples of long-term, globally available data archives are the NOAA-AVHRR and Landsat systems, available since 1981 and 1972, respectively. In recent years they have been complemented by further relevant sensors, in particular the MODIS mission started in 1999.


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Figure 1. Important operational and experimental remote sensing systems grouped according to spectral and spatial resolution (Hill et al. 2004, modified)

These are the archetypical examples of the major categories of multi-temporal data: coarse scale imagery with reduced spatial (and often spectral) capabilities, but daily coverage allowing for the provision of different stabilized, multi-day composites on the one hand; and finer scale data with better spatial (and sometimes spectral) discrimination capabilities at the expense of revisit intervals. While these data can be considered largely operational and cost-efficient, commercial and experimental systems may provide excellent information for particular questions, but these are commonly covering only limited areas, and are either expensive (Very High Resolution (VHR) data such as Ikonos, Quickbird, World-View) or experimental in nature, such as airborne hyperspectral (Aviris, HyMap, Ares) or LiDAR systems, which can – as of now – only be acquired in the frame of specific campaigns (although different countries in Europe have already initiated country-wide LiDAR campaigns).

Besides their technical characteristics, the level of detail resolved by these systems, along with the area covered in total, largely determines their potential use. Coarse-scale, hypertemporal systems such as NOAA-AVHRR, Spot-VEGETATION or MODIS are highly relevant for policy makers and regional to national decisions makers. On the other hand, systems with better spatial


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resolution (e.g. Landsat, Aster, Spot) are more consistent with regional to local land management, and finally VHR data may help in tracking processes at the level of individual plots.

Given all these considerations, this document will focus on routinely available remote sensing data sources, and aim at maintaining a high degree of operational applicability in methods wherever possible. The techniques and methods presented will be structured according to the major scales described before, followed by some considerations on opportunities and limitations associated with different scales.

2 Coarse scale

2.1 Data sources

For detection of large-scale effects from regional to country and global levels, archives of satellite data with high temporal resolution provide a temporally intermittent view across large areas (Bastin et al. 1995; Hill et al. 1998; Hostert et al. 2001b). With their high repetition rate, they allow to distinguish long-term changes in surface reflectance from seasonal and/or annual oscillations (Duchemin et al. 1999; Erbrecht 2003; Senay and Elliott 2002). Since temporal resolution of remote sensing systems is typically enhanced at the expense of spatial resolution those systems operate on coarser scales.

In particular data archives derived from the Advanced Very High Resolution Radiometers (AVHRR-) onboard the National Oceanic and Atmospheric Administration (NOAA-) satellites provide invaluable and irreplaceable historical information about surface conditions (Tucker et al. 2005). Monitoring relative biomasses and greenness indicators such as NDVI reveals information about timing and distribution of phytophenological events such as greening-up, rate of biomass accumulation and onset of vegetation senescence. This is of paramount importance in different contexts, among others to study the influence of climatic conditions, the success of land management systems, and impacts of overgrazing, exploitation of natural resources, pests and diseases. Although new, global, land vegetation-sensing instruments such as SeaWiFS land data (launch October 1997), SPOT4/VEGETATION (launch in April 1998), MODIS from the Terra and Aqua platforms (launch in 2000 and 2002, respectively) or MERIS (onboard of Envisat; launch in March 2002) supply higher quality data compared to the AVHRR, their relatively short operation duration so far is the limiting factor for analyzing long-term surface conditions. For instance, at the time of MODIS launch, there was already nearly a 20-year NDVI global data set (1981 - 1999) available from the NOAA- AVHRR series. Thus, AVHRR data will remain a fundamental component regarding the need for the longest possible time series required for understanding long-term variability in the earth terrestrial biosphere (Cihlar et al. 2004).


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The existence of hypertemporal data archives enables the application of well-established methods in time-series analysis to assess changes in surface conditions and in addition to forecast trajectories to some degree, for instance to evaluate the performance of national drought policies or rangeland management programs in drylands. In addition, many time series analysis techniques, such as autoregressive and moving average models, Continuous Wavelet Transform or Empirical Mode Decomposition, require temporal equally sampled data which are usually not available from local scale systems.

By nature, results based on these data offer only explanations on the scale of observation, thereby, local scale processes and management practices are likely to remain undetected. At present, hypertemporal data archives with global coverage derive from the AVHRR, Spot VEGETATION, MODIS and MERIS sensors.

The most frequently used global AVHRR archives are compiled from the "afternoon" NOAA operational meteorological satellites (NOAA7, 9, 11, 14, 16 and 18) and include the Pathfinder AVHRR Land (PAL) data and the data from the global inventory monitoring and modeling studies (GIMMS).

The 8x8 km GIMMS set additionally includes data from the NOAA-16 and NOAA-17 sensor and comprises bimonthly NDVI composites (days 1-15 and 16-end of the month) covering the period 1981 to 2006. The data are available to the scientific community e.g. through the Global Land Cover Facility at the University of Maryland (http://glcf.umiacs.umd.edu/data/gimms). The data pre-processing scheme is described in Tucker et al. (2005).

The MEDOKADS poses an example for a Local Area Coverage (LAC) data set with higher spatial (1x1 km) and temporal (daily) resolution compared to the PAL and GIMMS data sets. The data are collected, processed and distributed by the Institute of Meteorology, Free University of Berlin, Germany and cover the period from 1989 until 2005. This data set comprises full resolution AVHRR channel data and collateral data in latitude-longitude representation, with a resolution of 0.01° in both directions (Friedrich and Koslowsky 2009).

The Spot VEGETATION Programme provides daily coverage of the entire earth at a spatial resolution of 1 km. The instrument and associated ground services for data archival, processing and distribution are operational since April 1998. The first VEGETATION instrument is part of the SPOT 4 satellite and a second payload, VEGETATION 2, is now operationally operated onboard SPOT 5. Data and product information are available at http://www.spot-vegetation.com.

MODIS was launched by NASA aboard the Earth Observing System (EOS) satellite Terra on December 18, 1999. A second MODIS instrument was launched May 4, 2002 on the EOS-Aqua platform. With both instruments in operation, MODIS provides a morning and afternoon view with global, near-daily repeat coverage, complementing the spectral, spatial, and temporal coverage of the other research instruments aboard each platform. Its detectors measure 36 spectral bands between 0.405 and 14.385 µm, and it


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acquires data at three spatial resolutions -- 250m, 500m, and 1,000m. As part of the MODIS mission, data provided range from surface reflectance and albedo data sets to derived vegetation-related indices (NDVI, EVI, FAPAR, LAI) and even LULC data provided at a yearly resolution. More detailed information on MODIS data products and algorithms can be found at http://modis.gsfc.nasa.gov/data/.

MERIS is a programmable imaging spectrometer on ESA’s ENVISAT platform with fifteen spectral bands in the solar reflective spectral range that can be selected by ground command. A Product Handbook that guides users to select and to use MERIS data and that explains the way these data are processed and organized is available online at http://envisat.esa.int/handbooks/meris/.

2.2 Diachronic analyses

There have been many efforts to apply various classification approaches to coarse-scale, hypertemporal data, commonly making use of the fact that these data are able to trace phenological dynamics of different vegetation units over the year. As a result, different LULC products derived from different sensor systems exist (Bartholome and Belward 2005; Friedl and Brodley 1997; Friedl et al. 2002; Friedl et al. 2010; Loveland et al. 2000). Provided their availability for suitable time steps, they can be employed to map LULC change either aggregated for larger units or at a per-pixel level (Coppin et al. 2004a). Alternatively, per-year differences in aggregated NDVI values (see below) that exceed certain thresholds were shown to be related to LULC change processes under specific assumptions (Lunetta et al. 2006).

Different limitations need be considered in this context. On a general level, the coarse resolution of 500 m (best resolution for MODIS LULC products; Globcover derived from MERIS provides 300 m but is only available for one date) or less involves considerable problems given the heterogeneous structure of many landscapes (Stellmes et al. 2010). In such cases, the problem of mixed pixels may cause erroneous classification of fine-grained landscape patterns. Globally applicable mapping approaches are frequently reported to be of highly variable quality, with best results commonly being achieved in regions where algorithms had been developed and calibrated (Cohen et al. 2003; Price 2003). In addition, maps of the same region derived from different sensor systems are not necessarily congruent, which is owed to differences in classification techniques and LULC definitions (Herold et al. 2008). As a result, it seems advisable to base diachronic analyses of coarse scale data on consistent data sets from the same sensor. This limits the use of MODIS data, as these are only available since 2000.


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2.3 Vegetation indices

Vegetation is considered a major indicator in remote sensing based approaches, because it is spectrally distinct from many other surface types and it can be characterized using different methods. Subsequently, such information can thematically be linked to biophysical parameters, such as leaf area index, biomass etc. and/or be interpreted in different thematic contexts, such as grazing, fire (and post-fire recovery) etc.

Using remote sensing data, ‘vegetation’ can be characterized at different scales and using a range of methodological approaches, depending on the target information required.

Traditionally, the derivation of vegetation-related information has been based on the calculation of simple spectral indices such has the Vegetation Index (VI) or Normalized Difference Vegetation Index (NDVI), which is commonly calculated as

RNIRRNIRNDVI

+−

=

where NIR and R denote reflectance of the near infrared and red portions of the wavelength spectrum, respectively. The red part is sensitive toward photosynthetically absorbed radiation, whereas surface reflectance increases more sharply in the near-infrared for green vegetation than for soils or senescent vegetation (Curran 1980). NDVI values vary between −1.0 to 1.0. Larger values are associated with higher levels of green vegetation density and abundance for a given plant, but spectral reflectance can generally be different for different plants. Negative values indicate non-vegetated features such as water, barren areas, ice, snow or clouds. It was shown that the NDVI partially compensates multiplicative noise effects in multiple bands such as changing illumination conditions, atmospheric attenuation, and viewing aspect-factors that strongly affect observed radiances in the reflective domain of the solar spectrum (Gutman 1991; Lyon et al. 1998). NDVI suffers from a number of drawbacks, such as the influence of soil background especially for bright soils and sparse vegetation canopies, or saturation effects for very dense vegetation layers (Baret and Guyot 1991; Huete et al. 1985; Verstraete 1994a). In addition, it does not eliminate differences that arise from intrinsic differences between the different AVHRR instruments including spectral response functions, sensor degradation and orbital drift effects (Cracknell 1997). Despite these limitations, NDVI registers spatial information about short- and long-term changes in their temporal contexts, even at the coarse scale.

Alternative measures related to vegetation were developed to account for the influence of background soil and atmosphere, including Tasselled Cap (Kauth and Thomas 1976), Perpendicular Vegetation Index (PVI, (Richardson and Wiegand 1977)), Weighted Difference Vegetation Index (WDVI, (Clevers 1988)), Soil Adjusted Vegetation Index (SAVI, (Major et al. 1990)), and non-linear Global Evironmental Monitoring Index (GEMI, (Pinty and Verstraete


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1992)). The computation of these indices requires knowledge of the spectral properties of the background soil, to define the so-called soil line. The distance of a vegetation pixel from the soil line is then interpreted as a measure for the amount of vegetation. However, since the reflectance properties of the soil are spatially variable, they cannot be generally defined and the NDVI often produces a comparable quality in estimating vegetation cover (Schmidt and Karnieli 2001).

Another promising measure for vegetation cover that overcomes some of the limitations of the NDVI is based on spectral mixture analysis (SMA). The classical SMA derives the fraction of vegetation cover on the basis of multi-spectral image data and also allows the characterization of different surface materials (Hill et al. 1998; Smith et al. 1994). A modified SMA technique that improves the vegetation cover estimation in low vegetated areas by eliminating the effect of the background signal on the NDVI was introduced by Sommer et al. (European Commission 1998) and further developed by Stellmes et al. (2006) and Weissteiner (2008). This approach is based on the assumption, that vegetation canopy should predominately control the position on an AVHRR land surface pixel within the feature space spanned up by NDVI and surface temperature. The amount of vegetation canopy is inversely proportional to surface temperature, due to a variety of factors including the lower heat capacity and thermal inertia of vegetation compared to soils and the latent heat transfer through evapotranspiration (Gates 1980; Goward and Hope 1989).

In the context of the MODIS mission, the Enhanced Vegetation index (EVI) was developed and incorporated into the MODIS products (Huete et al. 2002). It is defined as follows:

GBcRcNIRL

RNIREVI **2*1 −++

−=

The EVI utilizes the ratio of the reflectance in the near-infrared band (pNIR, MODIS 841-876 nm) and the red band (pRED, MODIS 620-670 nm). It also employs the blue band (pBLUE, MODIS 459-479 nm) to reduce atmospheric effects, the coefficients C1 and C2 of aerosol resistance, L for the background stabilization and the gain factor G.

Vegetation indices have been correlated with various vegetation parameters such as LAI, biomass, canopy cover, and fraction of absorbed photosynthetically active radiation (fAPAR). The EVI was found to have a more linear relationship with these parameters compared to the NDVI (Huete et al. 2002). In addition, specific MODIS LAI and FAPAR products are available based on a general reflectance transfer model (Myneni et al. 2002; Tian et al. 2002a, b). Yet, since image quality does often not support parameterization of this model, it is often replaced by a scaled NDVI, thus questioning its


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superiority over traditional vegetation indices (Yang et al. 2006a; Yang et al. 2006b).

Time-series analysis on daily raw hypertemporal images is problematic because the data are strongly contaminated with clouds and cloud-shadows. In order to produce nearly cloud free time-series with reduced atmospheric contamination such data are regularly aggregated using different compositing techniques, such as maximum-value compositing (MVC) (Holben 1986) or Best Index Slope Extraction (Viovy et al. 1992).

2.4 Time-series analysis using hypertemporal satellite archives

Hypertemporal remote sensing data provide the opportunity to discriminate between short-lived fluctuations in surface reflectance, several types of phenologies and long-term transient signals. These patterns might be related to degradation processes in drylands such as overgrazing. This chapter provides an overview about relevant time-series analysis (TSA-) techniques that are necessary to decouple transient, cyclic, and stochastic components in time records (Stellmes et al. 2010; Verbesselt et al. 2010). Figure 2 shows an example of how a (simulated) temporal NDVI profile can be decomposed into these individual trend components.

Figure 2: Different components of hypertemporal time series: (a) trend (transient), (b) cyclic, (c) noise (Udelhoven 2010)

TSA-methods are not explained in detail since they are subject of many textbooks (Chatfield 2004; Shumway and Stoffer 2006; Wei 1990).


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2.4.1 Preprocessing

As a first step the data archives should be cleaned from inconsistencies such as missing values and outliers and extraneous noise. Missing values and outliers can be replaced by (seasonal) averages of the affected time-series or by interpolation from surrounding data. A reliable tool for outlier detection is Chebyshev's Theorem, which states that a random variable, irrespective of its distribution, will take on a value within k standard deviations s of the mean with a probability (confidence level) of at least 1-(1/k2) (Lohninger 1999). The factor k can be determined according to a user defined confidence level. However, it is recommended to exercise care on outlier removal, since outliers sometimes represent true extreme values that may be more informative than the other values, for instance in land degradation questions. Extraneous noise that introduces high frequent components in the data can be are removed by Savitzky-Golay filtering (Savitzky and Golay 1964).

A widely used method to analyze climatic impacts on vegetation cover is to compare anomalies from a climatic time-series (temperature and/or rainfall) and the NDVI. There are several options to derive anomalies:

− Anomalies from the total mean are differences d of each measured values and the arithmetical mean of the respective series:

XXd t −=

The anomalies between two series can be directly compared to each other by calculating standardized anomalies, which are obtained by dividing d by the respective standard deviation of the series.

− Anomalies from seasonal mean Similar to a., but here global means are replaced by the respective seasonal parameters.

.seast XXd −=

Anomalies from long-term seasonal averages can be considered as differences of the observed quantities from the long-term seasonal reference state. The anomalies can be standardized by dividing d by the seasonal standard deviations. This removes seasonal components (e.g. the annual vegetation growth cycle) from the data: The resulting series has zero seasonal means and seasonal standard deviations are one, respectively. The removal of seasonal effects from a record is a precondition for significance assessment of trends, if ordinary-least-square regression is used as a trend model (see below). Figure 3 shows an example how the seasonal component is removed from the series by calculating (standardized) seasonal anomalies.


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Figure 3: NDVI series (left), differences from the seasonal mean (middle) and standardized seasonal differences (right). While in the original series the annual cycle is clearly visible, seasonal differences are eliminated in the pre-treated series.

2.4.2 Assessment of seasonal components

Seasonal components in a time-series are undesirable elements for trend-detection, yet, they bear important information about the phenological cycle. The most important methods to characterize the non-stationary behavior of time-series seasonal components are discrete Fourier transform, continuous wavelet transform (CWT), and empirical mode decomposition (EMD).

Discrete Fourier transform (DFT), that is in practice computed by the Fast Fourier transform (FFT), returns a two-sided spectrum F(λ) in complex form which is scaled and converted to polar form to obtain the power spectrum or periodogram I(λ).

N

FimaginaryFrealI

22 ))(())(()(

λλλ

+=

The power spectrum consists of the Fourier frequencies (Fλ) λ=0,…,N/2. (Schlittgen and & Streitberg 1999). I(λi) represents the magnitude of a frequency λi while the phase information is lost. Therefore, the original signal cannot be fully reconstructed from I(λ).


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Figure 4: A MEDOKADS monthly NDVI series (left) and raw periodogram of the series (right). Peaks in the periodogram correspond to the fundamental frequency (192 month), the annual cycle (12 months) and related harmonics (six and four months, respectively) which in many cases represent no independent frequency compounds if a cyclic phenomenon has non-sinusoidal shape.

Apart from the magnitude of a certain Fourier frequency λ the DFT as complex measure also bears information about the phase. The phase term for each λ is recovered from the real and imaginary part of F(λ) using the relation

{ } { }1( ) tan ( ) / ( )i i iP F Fλ λ λ− ⎡ ⎤= ℑ ℜ⎣ ⎦

Magnitudes and phases in NDVI data are closely linked to agro-biological phenomena, such as land cover conditions in response to seasonal pattern of rainfall and temperatures (Andres et al., 1994). Figure 4 provides an example where both measures were used to describe the spatial patterns of the annual vegetation cycle in PAL-NDVI data (1982 – 1999).

To account for local-frequency information the DFT must be modified. The windowed Fourier transform (WFT) uses a sliding rectangular window of length T that moves along the time-series with the sampling rate of ∆t and the length T = N∆t. For each window the FFT derives the Fourier frequencies T-1 to (2∆t)-1. The length of the window is selected such that the basic cyclic components of interest are represented by these frequencies. For instance, the development of the annual cycle’s amplitude in monthly temperature data can be assessed by selecting T as any integer multiple of 12. However, for complex signals with several mixed frequency components the selection of a proper window that balances time and frequency resolution is difficult or even impossible (Stefanovska and Bracic 1999). For those cases the CWT has been proven to be very useful in analyzing data with gradual frequency changes (Torrence and Compo 1998). It produces a two-dimensional


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scalogram for each time-series that keeps both time and frequency information assessable.

Figure 5: CWT output from TimeStats’s interactive CWT function, using a GIMMS NDVI series (1982-2003) from the western fringe of the Nile delta. The scale-averaged series is related to the annual cycle (mother wavelet: Morlet). The trend map (right) derives from a linear regression model applied to the corrected MEDOKADS and highlights the estimated NDVI changes in the observation period.

As an example figure 5 demonstrates the CWT decomposition for a detrended monthly GIMMS-NDVI series (1982-2003) from the western fringe of the Nile delta. The image shows the original series (a), the scalogram denoting the wavelet power spectrum at different scales (b), the scale-averaged series, which is associated in this example with the wavelet power of the annual growth cycle (c), and the global wavelet (d) that integrates the wavelet power at each scale over time. Crosshatched regions in the scalogram indicate the cone of influence (COI), where results are questionable since wavelet power is lowered by edge effects (Torrence and Compo 1998). Two striking features in the CWT results are a positive development in the strength of the annual vegetation growth cycle outside the COI and the onset of a semi-annual cycle (at the 0.5 year period in the scalogram) starting in the middle of the observation period. In addition a NDVI trend map (figure 5, right) suggests a positive development in vegetation cover along the fringes of the delta. This points to land reclamation activities by irrigation during the observation period.


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2.4.3 Transitional components

The detection of spatial trend patterns is an important issue in long-term environmental studies. A trend is characterized by its functional form, direction and magnitude and should be interpreted with respect to its statistical significance (Widmann and Schär 1997). There exist many techniques for trend detection, including (non-) linear regression analysis and non-parametric methods. In general, parametric tests are more powerful than nonparametric versions, provided the conditions for parametric significance testing are satisfied (Hirsch and Slack 1984). The distribution or density estimation in nonparametric trend models is data-driven and requires no a priori assumptions on the functional form. The relative robustness of nonparametric methods against missing values and irregularly spaced values is also higher than in case of the parametric models.

The simplest trend model is linear regression analysis: atimey +⋅= β

where the coefficient β determines the strength and direction of a trend. The linear model can easily be extended by the consideration of higher order terms (e.g. time2) or Fourier polynomials to account for non-linear and cyclic behaviour.

A powerful non-parametric methods is the modified seasonal Kendall (MSK-) test, which is suited for monotonic trends detection independent from the functional type (e.g. linear, quadratic) of the trend. In addition the MSK test accounts for seasonality and temporal autocorrelation in the series. Thus, there is no need to remove the annual cycle from the data to enable significance tests. The test statistics are derived individually for each season. The H0 assumes all observations in each season are randomly ordered, whereas the H1 expects a monotone trend (increasing or decreasing) in at least one season (Hirsch and Slack 1984).

2.5 Integrated interpretation concepts

Based on the time series components extracted from hypertemporal time series, advanced interpretation approaches may be implemented if sufficient background information and auxiliary data are available. Although these approaches have been developed to map changes in species composition or land use patterns, they might be adapted to characterize the long-term result of mitigation and restoration measures across large areas.

2.5.1 Mapping functional vegetation types

In dryland ecosystems, plant communities are effective indicators of land condition and are strongly related to land use intensities. For instance, plant composition reflects succession patterns after disturbances (e.g. clearing,


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fire), and has been shown to be linked to grazing intensities (Noy-Meir 1998; Quinn 1986; Trabaud 1981). A typical example is savannah ecosystems, where trees and greases interact in many ways (Jeltsch et al. 1996; Jeltsch et al. 2000; Scholes and Archer 2997), and where tree and shrub encroachment resulting from high stocking rates is frequently causing problems in terms of carbon and nitrogen cycles and wildfire dynamics (Gillson and Hoffman 2007; Skarpe 1992). In accordance with information on sensor characteristics given in section 1, different observation scales offer specific opportunities and limitations for assessing plant communities. At the coarse scale, geometric and spectral resolutions are limited, impeding identification of plant species. However, the high temporal resolution provides information on phenological cycles, which can in turn be used to map major groups of plant communities (i.e. functional types), such as grasslands, shrublands, forests etc. Such information is essential for large area management of grazing livestock or to predict phenological cycles of trees and greases for ecological applications (Archibald and Scholes 2007; Skarpe 1992). Various techniques exist to extract the required characteristics from time series data, which include multivariate exploratory methods such as principal components analysis (Eastman and Fulk 1993; Lobo and Maisongrande 2006), fitting of mathematical functions such as sigmoidal functions (Fischer 1994a, b; Zhang et al. 2003) or Gaussian functions (Jönsson and Eklundh 2002) to derive models representing vegetation phenological cycles, as well as methods of harmonic analysis and wavelet analysis (Li and Kafatos 2000). In particular, harmonic analysis has been shown by many studies to be useful in assessing vegetation dynamics (Azzali and Menenti 2000; Moody and Johnson 2001). Yet, in cases where subtle phenological differences exist in semi-arid rangeland vegetation, high order polynomials may be required to completely represent all features. For instance, (Geerken et al. 2005) employed five annual harmonics to differentiate functional vegetation types in a grazed steppe in Syria. Since incorporation of higher order Fourier coefficients tends to generate spurious oscillations in the modelled time series (Chen et al. 2004), higher order terms can be down-weighted or their amplitudes be tapered (Hermance 2007; Hermance et al. 2007). Alternatively, vegetation discrimination may be based on Fourier analysis which is used to quantify the similarity between the annual cycles of an individual pixel with a reference cycles (Evans and Geerken 2006; Geerken et al. 2005).

2.5.2 Syndrome-based mapping approaches

Human population and natural renewable resources are main components of human-environment systems, which can be affected by climatic or socio-economic disturbances. For instance, large areas of the European Mediterranean are believed to be affected by land degradation processes, mainly emerging from conflicts between past and present land uses, or between economic and ecological priorities (Brandt and Thornes 1996; Puigdefabregas and Mendizabal 1998). Although the different definitions of


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and perspectives on desertification illustrate the difficulty in identifying a unifying concept or explanation (Geist 2005; Geist and Lambin 2004), the concept of “syndromes”, developed in a wider global change concepts, may serve to provide an interpretation framework for land degradation and desertification. The basic idea behind it is the description of change archetypical, co-evolutionary patters of human-nature interactions. Downing and Lüdeke (2002) have supplied a first attempt at linking vulnerability concepts to degradation and identifying relevant global change syndromes.

Time series components as described before contain information that may be related to land degradation based on a clear, thematic definition of syndromes relevant in a specific area. For instance, trends in vegetation cover may be related to greening-up following land abandonment, often accompanied by increased water uptake and fire risk, while degradation due to overuse is characterized by negative trends. On the contrary, shifts in peaking times and the magnitude of the vegetation signal point to changes in land use, for instance to intensification of agriculture, often associated with excessive water consumption, use of fertilizers etc. Hill et al. (2008) have implemented an interpretation scheme based on the linear trend component, phase and phase shift, and change in magnitude of time series fluctuations to map major syndromes between 1989 to 2004 based on the MEDOKADS archive. They used upper and lower percentiles for each factor and developed a rule base to combine them into major groups of change patterns for the Iberian Peninsula. For natural and semi-natural areas they defined syndrome classes “rural exodus”, “disaster” and “overexploitation” and mapped these on a spatially explicit level. In order to understand if apparent changes are driven by climatic conditions (such as precipitation variability, droughts etc.) or if they are human-induced, rainfall data need to be integrated in a second step (Wessels 2007). As, depending on resilience and elasticity, this influence may manifest at different times after precipitation (or drought) events, specific models can be employed to quantify this effect and thus differentiate between human- and climate-induced changes, e.g. lag models (Udelhoven et al. 2009).

2.5.3 Integrated land condition assessment

Land condition is frequently assessed based on vegetation related information, one of the reasons being the possibility to derive this information in a spatially explicit manner from remote sensing data. In dryland ecosystems, vegetation abundance is strongly linked to water availability, and plants aim at maximizing the efficiency of water use through optimization processes. Based on this relation, Boer (1999) has proposed a theoretical framework for land degradation assessments relying on site water balance modeling, and integrating vegetation-related temporal information with rainfall to evapotranspiration ratios (Boer and Puigdefabregas 2003, 2005). Central to such approaches is the concept of rain use efficiency (RUE), i.e. the ratio of net primary productivity (NPP) to precipitation (Le Houérou 1984),


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and the assumption that land degradation is resulting in decreasing RUE values. Simplified approaches of land degradation mapping based on RUE derived from remote sensing time series and interpolated rainfall data were developed by Hountondjo et al. (2009) and Bai et al. (2008). Other authors argue that in more humid areas, rainfall is not a limiting factor to NPP. Accordingly, RUE gradients may merely reflect gradients in precipitation if viewed across large areas (Wessels 2009). The problem of applicability of land condition assessment across climatic zones has been addressed by the 2dRUE concept developed by del Barrio et al. (2010). It draws from the concept that any reduction of plant biomass and NPP below those of land which under equivalent environmental conditions is not desertified. Major ecosystem attributes required in the model are annual average biomass and seasonal or inter-annual growth peaks, both of which can be derived from climatically de-trended expressions of rain use efficiency, requiring time series of remotely sensed vegetation estimates and continuous fields of precipitation and temperature data. To mitigate the fact that in areas of varying climatic conditions, the driest areas tend to exhibit largest scores because of their very low precipitation values opposed to very high evpotranspiration estimates, a climatic de-trending was introduced. It is based on a spatially explicit calculation of aridity index using the expression of potential evapotranspiration given by Hargreaves & Samani (1982). The potential of this method has been demonstrated for the Iberian peninsula (Del Barrio et al. 2010) and for the Horqin Sandy Lands region in North Western China, which is located in the agro-pastoral zone between the Inner Mongolian Plateau and the Northeast Plains (Hill et al. 2010).

3 Fine scale

3.1 Data sources and time series setup

The analysis of landscape dynamics at fine scales may be based on different sensor systems.The longest continuity of data is provided by the Landsat mission, which was initiated in 1972 with the launch of Landsat-1 Multi Spectral Scanner (MSS). It acquires data at a spatial resolution of 79*79 m2 and in four spectral bands, i.e. the blue, green red bands in the visible (VIS) and in the near-infrared (NIR) wavelength range of the electromagnetic spectrum. In 1984, the Thematic Mapper (TM) replaced the MSS system, with an improvement in geometric, spectral and radiometric resolution (Richards and Jia 2005). At a spatial resolution of 30*30 m2, bands are positioned in the VIS (blue, green, red), the NIR, plus 2 bands in the mid-infrared section (MIR), thus providing better spectral discrimination capabilities. In addition, a thermal band is available at a 120*120 m2 spatial resolution. In 2001, the Enhanced Thematic Mapper (ETM+) was introduced onboard Landsat-7 (Goward et al. 2001; Masek et al. 2001). It has essentially the same technical specifications as Landsat-5 for optical bands, but a panchromatic band was added with 15*15 m2 geometric resolution, and two thermal bands (high gain and low gain)


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acquire data at 60*60 m2 resolution. Data from this system have provided excellent quality until 2003, when failure of the scan line corrector occurred, causing line dropouts particularly around the edges of the data frame (Markham et al. 2004). Although approaches for gap-filling have been developed (Maxwell et al. 2007; Pringle et al. 2009; Roy et al. 2008), this has caused a decline in the usability of Landsat-7 data, such that Landsat-5 was switched back on and continues to deliver information. Meanwhile the Landsat continuity mission has been approved and launch of Landsat-8 has been scheduled for December 2012 (Wulder et al. 2008). All Landsat-systems have a Nadir-viewing geometry with only limited scan-angles to the sides of 7.5°, which, together with the orbital characteristics, leads to revisit times of 16 days.

In addition to the Landsat data suite, a number of potential complementary data sources exist, such as the SPOT and ASTER sensors launched in 1986 and 1999, respectively (Kramer 2002).

The SPOT-4 and -5 (since 2002) HRV systems provide information in four bands, VIS (green, red), NIR and MIR, at 20*20 m2 (SPOT-4) and 10*10 m2 (SPOT-5), respectively. In addition, a panchromatic band at 5*5 m2 is available onboard SPOT-5. The SPOT system can be programmed to observe specific locations, and high revisit frequencies can be achieved by pointing the sensor to up to 23.5° off-nadir viewing positions. However, this acquisition mode has significant implications on image geometries and complicates geometric image registration (Richards and Jia 2005). SPOT data are acquired on demand and can be bought off-the-shelve later on. Yet, there is no regular revisit interval and acquisition depends on ordering status, such that no global coverage comparable to the Landsat system exists.

Besides these operational and commercial (SPOT) systems with long-term monitoring capabilities, other systems have been placed in orbit in recent years, such as the ASTER and Rapideye systems. Aster provides information in 15 spectral bands, with varying geometric resolutions according to the wavelength range covered (VIS/NIR: 15*15 m2, MIR: 30*30 m2, TIR: 90*90 m2). The ASTER sensor acquires images upon previous registration of test sites and ordering. Recorded images can be ordered off-the-shelve at low cost; however, no complete coverage of the globe is available (Richards and Jia 2005). In 2008, the Rapideye system has been launched, providing images in five bands (VIS Blue, Green, Red, VIS/NIR Red Edge, NIR) and a pixel size of 5*5 m2. Again, data need to be acquired for registered test sites for following acceptance of a research proposal, or bought from the Rapideye Company directly. Similar to other sensors, no global coverage is available.

In addition, further experimental sensors exist, such as multi-angle viewing CHRIS-PROBA, MISR etc. While all of these systems offer potentially higher information content due to specific acquisition techniques or improved spectral, spatial and temporal characteristics, they do not provide global coverage but only image registered test sites, so far only cover short periods of time, and mission continuity is not safeguarded for any of them. This


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accounts even more for experimental airborne systems, such as hyperspectral or LiDAR systems. Despite the enhanced spectral and geometric capabilities of these systems, they are largely scientific, such that proposals need to be submitted to attain imagery of specific test sites or monitoring areas. Given these considerations, monitoring or assessment approaches that need to incorporate the spatial domain should ideally be based on routinely available data that cover sufficient periods of time and are also collectable in a retrospective manner, rendering the Landsat system still the prime choice. This is particularly true given that a large amount of data is now made freely available through the USGS6. Yet, this does not account for all of the data covering western, central and southern Europe, which needs to be acquired through commercial providers at higher cost. Given their availability, higher level experimental data may still be incorporated to answer specific questions or complement other information.

The setup of the data base needs to be based upon a sound knowledge of the processes to be detected. In the case of assessing already implemented restoration measures, exact knowledge of applied measures might hence greatly facilitate this work, with implications for work load associated with processing and analysis of data etc. Following traditional change analysis concepts, data bases are frequently compiled of a set of multi-temporal data, i.e. a number of data sets within on specific observation date (e.g. one year). This ensures phenological information may be employed to map different types of natural, semi-natural and agricultural vegetation types. If this is repeated for different time steps, change in land use/land cover (LULC) classes may be inferred. In recent times, this concept of reducing “change” to alteration between broad land use categories has been questioned, since structural changes within one stable class may be equally important in different environmental contexts (e.g. increasing or decreasing vegetation cover within a “grassland” land use category). To address both potential types of change, the terms land use transformation (or conversion) and modification were introduced (Lambin et al. 2006). While LULC conversion can be assessed following the concept described before, modification issues may require a more densely populated time series enabling to apply time series analysis techniques such as those described in section 2. Although possible in rare cases (Scarth et al. 2010), hypertemporal analysis is usually impeded by the amount of processing involved, as well as the fact that clouds frequently prevent establishment of an appropriate, equally spaced time series. Therefore, time series are often set up in a way to acquire one image per year at comparable phenological dates and allow assessing the transient or linear trend component (Hostert et al. 2003b; Hostert et al. 2001b; Röder et al. 2008c).

If transformation and modification processes need to be addressed for a specific area, satellite image time series should incorporate (i) yearly or two-yearly data for a given seasonal window and (ii) multi-temporal data sets 6 www.glovis.usgs.gov


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within a year that are placed in relation to time frames potentially relevant for governing physical and socio-economic framework conditions, such as accession to political institutions, legal events, droughts etc. In case the required time series cannot be procured using a single sensor type, specific intercalibration approaches might be required to ensure quantitative consistency (compare section 3.2.2) (de Vries et al. 2007; Dinguirard and Slater 1999; Röder et al. 2005; Teillet et al. 2007).

3.2 Standardization requirements

Satellite imagery exhibits a number of unique properties, which result from the technical process of recording images of a part of the Earth’s surface while this is rotating, in combination with technical characteristics of the sensor and the satellite platform. This denies a direct comparison of images from different dates and sensors, although this is a mandatory component of any surveillance system.

3.2.1 Geometry

The interplay between sensor characteristics with movement of the satellite platform and the rotation of the Earth surface results in geometric distortions that prevent a direct integration with other spatial data bases.

As a result of the opto-mechanical image acquisition of the Landsat sensors and the rotation of the Earth, different sources of error emerge, which are generally accounted for by the system correction prior to delivery ([USGS] 1984; Richards and Jia 2005). In addition to these systematic or system-inherent errors, images acquired with opto-mechanical scanners show a typical cartographic representation of the Earth’s surface, with a parallel projection in along-track and a central projection in across-track viewing direction. The latter significantly influences cartographic comparability with the magnitude of distortion depending on the distance of the pixel from the satellite nadir and the elevation of the surface element. Consequently, especially in terrain with a strong relief gradient, it is crucial to adequately account for this terrain-induced distortion to ensure the consistency of satellite images with each other, as well as with other sources of spatial information (Itten and Meyer 1993; Pala and Pons 1995; Welch et al. 1985).

Finally, combining the resulting data set with additional information layers and producing maps requires transferring this planar space-oblique Mercator projection into a pre-defined reference cartographic projection system (Lillesand and Kiefer 2000). The type of reference system depends largely on the geometric resolution of data employed, the scale to be addressed by the analysis, and the spatial extension of target areas. For local to regional scale applications (1:250,000 or larger) and target areas in mid-latitudes, the UTM system is widely used. In addition, there is a range of locally adapted spheroids and datums, such that the UTM system can be well adapted to


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different areas. In areas at very high latitudes, other ortho-projected references systems with narrow zones may be more convenient.

Depending on the data provider, images may already have been rectified to a cartographic reference system. The USGS provides ortho-rectified Landsat imagery (Level 1T product) at excellent quality, which has been processed employing SRTM-based elevation information to account for non-systematic distortions. Yet, data from other providers may still require this essential processing step. Provided the availability of a suitable source of digital elevation information (e.g. DEMs from the Shuttle Radar Topography Mission, SRTM, or made available from the ASTER mission based on stereoscopic image pairs), ground control points are required to link pixel to geographic coordinates. The accuracy largely depends on the scale of reference maps and whether the images provide sufficient suitable features that can be identified with high confidence. More sophisticated methods are frequently based on window-based cross-correlation approaches. Using statistical correlations with search windows, they are able to exploit topographic features, and instead of maps geo-referenced digital elevation sources may be used as references to register one image as a master image, whereupon other images may be co-registered (Hill and Mehl 2003).

3.2.2 Radiometry

The envisaged data processing and analysis strategy generally determines the level of accuracy required from radiometric data pre-processing. For simple classification approaches, radiometric processing may be negligible or simple correction approaches may be sufficient (Song et al. 2001). However, with the development of quantitative analysis techniques, changes becoming apparent from multi-temporal analyses should only result from intrinsic surface properties (Hall et al. 1991; Vogelmann et al. 2001). The signal at the sensor is a result of solar irradiance received at the ground, which interacts with surface elements, and is partially reflected back towards the sensor. However, the direct analysis of multi-temporal data sets based on spectral properties of these surface elements is impeded by radiometric properties of satellite images, which cause them to deviate from “true” surface reflectance values and which can be summarized under two major components.

a) Sensor calibration: Incoming radiance is converted to a digital signal, which can be described by a calibration function. The further radiometric processing of satellite images requires physically meaningful values; hence it is necessary to calibrate the digital numbers (DN) in satellite images back to radiance values. Since the sensitivity of detectors is known to decay with sensor age, the same incoming radiance signal will not result in the same radiance value for different dates, and the corresponding error can be expected to propagate through the subsequent processing and interpretation chain (Teillet 1997; Thome et al. 1997). Calibration procedures


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are further complicated when different sensor systems need to be integrated within one multi-temporal data base.

b) Atmosphere- and topography-induced distortions: The signal recorded at the sensor is a result of solar radiance propagating through the Earth’s atmosphere, interacting with the surface, and being partially reflected back through the atmosphere towards the sensor. Besides surface properties, which are the target information required, there are a number of factors affecting this signal, such as the distance between Earth and Sun; geometric viewing constellation between sun, sensor and earth surface (including the slope and orientation of the surface); and the constitution of the atmosphere, as characterized by gaseous and aerosol components and their concentration. (Schott 1997; Tanre et al. 1990; Vermote et al. 1997).

In order to meet the criteria for a general monitoring framework formulated before, it is mandatory to remove these influences of topography, illumination, shadows, viewing directions and atmospheric properties to attain physical quantities (i.e. surface reflectance) that can then be considered to be primary indicators for further interpretation, (Du et al. 2002; Schott 1997). Different approaches to radiometric correction exist, with the most common ones being empirical correction approaches, or a full modeling of processes associated with transfer of radiation through the atmosphere.

Empirical line correction The empirical line method is an atmospheric correction technique that provides an alternative to radiative transfer modeling approaches. It offers a relatively simple means of surface reflectance calibration, providing that a series of calibration targets are available, that are invariant in time and for which measurements are available. This technique has been applied with variable success to both airborne data and coarser spatial resolution satellite sensor data. Earth observation satellite data have a spatial resolution (4- 30 m) which permits to identify a number of homogeneous targets with a range of contrasting reflectances that are suitable for the empirical calibration.

The approach is based on matching the image DNs and true reflectance of two (or more) targets found in the respective scene. "Correction" is accomplished by forcing the scene spectral data to match selected field reflectance spectra by calculating a linear regression for each spectral band to equate DN and reflectance, calculate as

DNaat 10 +=ρ

with tρ being the target reflectance, a0 and a1 offset and gain coefficients, and DN denoting the uncalibrated digital number as recorded by the sensor. The empirical line method is an efficient method to produce reflectance values for further interpretation at the expense of non-linear effects or topography-induced illumination variations (de Vries et al. 2007; Karpouzli and Malthus 2003).


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Radiative transfer correction Available radiative transfer codes make extensive use of analytical expressions and pre-selected atmospheric models to correct for atmospheric absorption, scattering and pixel adjacency effects, where diffusion and absorption processes are assumed to be independent. Upward and downward transmission coefficients are derived by introducing the auxiliary quantity of optical thickness τ which measures the total extinction of a light beam due to molecular and aerosol scattering when passing through an airmass. Multiple scattering is accounted for, and the absorbing atmospheric gases (H2O, O3, CO2, O2) are assumed to condense at the top of the atmosphere and at the top of the layer between the earth surface and the sensor altitude. In the absence of measurements of atmospheric properties, the scattering optical depth may also be estimated from scene data itself (Hill and Sturm 1991; Teillet and Fedosejevs 1995). The major problem in retrieving reflectance factors from operational satellites lies in the sensitivity to the absolute radiance calibration of the sensors. In-flight calibrations for Landsat-TM and SPOT data are conducted at various high-reflectance sites which is useful for determining at least an absolute radiometric calibration gain and monitoring the sensor degradation with time (Chander et al. 2004; Chander and Markham 2003; Helder et al. 2008; Teillet et al. 2001; Thome et al. 1997). However, it must be also noted that the successful use of radiative transfer calculations for high spectral resolution imagers depends on the availability of key parameters that adequately characterize atmospheric conditions during the flight (i.e. absorption optical depth, in particular that of water vapor).

Figure 6 illustrates the radiance paths that need to be accounted for in the context of radiative transfer modeling, although the influence of topography is not considered yet.

Figure 6: Simplified representation of the radiative transfer through atmospheres to be considered for correcting satellite measurements in the reflective domain.


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Rather than being expressed as radiance, the various quantities of the image forming equation are commonly described in terms of equivalent at-satellite reflectance which is defined as

ρ πµ

* =⋅

⋅ ⋅L

E d0 0

µ0 is the cosine of the solar zenith angle θ0; E0 denotes the solar irradiance at the top of the atmosphere [mW/cm2/µm], and L is the target radiance registered at the satellite, [mW/cm2/sr/µm]. The correction coefficient d accounts for the daily variation in the sun-to-earth distance and is obtained from d = 1/au2, where au denotes the average distance from sun to earth (149.6 x 106 km) in astronomical units (Markham and Barker, 1986).

The signal ρ* in reflective bandpasses is directly related to the target reflectance factor ρt which is affected from two major effects: absorption by atmospheric gases (mainly water vapor and ozone) and scattering by aerosols and molecules; emission at the normal atmospheric temperature is very small and can be ignored. For a uniform Lambertian surface these effects can be conveniently expressed as a series of radiation interactions in the ground-atmosphere system (Tanre et al. 1990; Vermote et al. 1997):

[ ]⎭⎬⎫

⎩⎨⎧

−+⋅=

⋅⟩⟨

⟩⟨⋅↑+⋅↑⋅↓↓↑

st std

atgastT

tρ

ρρρρ

1*

In this equation, ρt denotes the target reflectance factor, <ρ> the background contribution to the apparent reflectance of the target, and s is the spherical albedo; ρat is the intrinsic atmospheric signal component, T↓ is the total downward, td↑ the direct and ts↑ the scattered (i.e., diffuse) upward transmittance; tgas denotes the combined transmittance of the atmospheric gases.

The target reflectance ρt depends on the photometric properties of the surface and the distribution of the illumination which is a complex combination of attenuated solar irradiance arriving at the ground Eg, the diffuse sky light Es and the reflected ground radiance from the target's surroundings. Upward and downward transmission coefficients are derived by introducing the auxiliary quantity of optical thickness τ which measures the total extinction of a vertical light beam passing through an air mass. Optical depth largely depends on density and size distribution of particles (molecules and aerosols) in the atmosphere and on their scattering and absorption properties, which need to be taken into account in the modeling process (Aranuvachapun 1985).

In mountainous terrain, further improvements of the radiative transfer approach may be attained by considering topography-induced illumination variations, requiring the calculation of the angle between the normal to the terrain surface and incident irradiation (cos λ), a shade mask, a visible sky mask, and the cosine of the slope. Integrating these parameters with the properties derived from the atmosphere model allows separate treatise of direct and diffuse irradiance components (Hay and McKay 1985; Hill et al. 1995).

Various approaches exist for radiometric correction of Landsat-type imagery based on the 5S or 6S transfer codes. Hill & Mehl (2003) have proposed a


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geometric and radiometric processing chain that was successfully implemented in the context of different long-term monitoring studies (Hostert et al. 2003a; Röder et al. 2008a; Röder et al. 2008c). It is based on the 5S transfer code (Tanré 1990), while atmospheric transmission is characterized using the Modtran code (Berk et al. 1999) along with aerosol phase functions derived from Aranuvachapun (1985). Using the anisotropy index by Hay and McKay (1985), diffuse and direct irradiance terms are separately treated and differentiated into anisotropic and isotropic components to adequately represent global irradiation and attenuation by shading effects. Commonly, such models can be parameterized based on knowledge of atmospheric water vapor concentrations and optical thickness being represented by the Angstrøm coefficients (Angstrom 1964). If there are not atmospheric sounding data concurrent with satellite overpasses, such information can be derived from data bases such as AERONET (Holben et al. 1998; Sinyuk et al. 2007; Smirnov et al. 2000), based on dark targets such as clear water bodies, forests etc. (Teillet and Fedosejevs 1995), or be estimated using other sensors as references (Hill et al. 2010; Roy et al. 2008). Although such approaches are only an approximation of actual atmospheric conditions at the time of overpass, they have been shown to produce consistent time series supporting retrospective analyses.

3.3 Derivation of vegetation-related information

There are a number of indices that may be employed to characterize the state and ground cover of vegetation, and which may be related to other parameters such as LAI, biomass etc.

In this context, numerous studies have addressed the issue of sparse vegetation mapping. Traditionally, the derivation of vegetation-related information has been based on the calculation of simple spectral indices such as the Simple Vegetation Index (VI) or the Normalized Difference Vegetation Index (NDVI, compare section 2). While their application is simple and efficient, they suffer from a number of drawbacks, such as the influence of soil background especially for bright soils and sparse vegetation canopies, or saturation effects for very dense vegetation layers (Baret and Guyot 1991; Huete et al. 1985; Verstraete 1994b). Some authors suggested introducing a soil line as a reference, resulting in the Soil Adjusted Vegetation Index (SAVI), or the Perpendicular Soil Adjusted Vegetation Index (PSAVI) (Pinty and Verstraete 1992). These helped resolve some of the ambiguities, but for variable soils and lithological backgrounds the concept of a universal soil line cannot be maintained (Gilabert et al. 2002; Huete 1988; Rondeaux 1995; Verstraete 1994a). Other approaches include the Global Environmental Monitoring Index (GEMI) (Pinty and Verstraete 1992) or transformations of the feature space, such as the Tasseled Cap transformation (Crist and Kauth 1986; Kauth and Thomas 1976). Another problem common to these indices is the comparability of index values derived from remote sensing systems with different spectral characteristics. Nonetheless, vegetation indices are still


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widely used in manifold applications and are still being further developed (Gilabert et al. 2002; Turner et al. 1999), in particular in relation to hyperspectral data (Schlerf et al. 2005).

However, all of these indices remain confined to the pixel as the reference spatial entity, which poses different problems, in particular when heterogeneous environments are regarded. In these cases, the average size of homogeneous surface features frequently lies below the pixel size, such that the recorded values have to be considered mixtures of different materials (Cracknell 1998; Fisher 1997). Consequently, the pixel signal can be assumed to be a combination of the reflectance of a limited number of surface features. Provided the availability of information on these reference materials, the signal can be decomposed into proportions of these materials, which is known as ‘Spectral Mixture Analysis’ (SMA) (Adams et al. 1986; Smith et al. 1990; Smith et al. 1994). Elmore et al. (1993) have demonstrated the superiority of vegetation cover derived using SMA over NDVI for sparsely vegetated areas, and various authors have successfully applied the methodology to infer information on vegetation or soil cover in a quantitative way in different environmental settings (GarciaHaro et al. 1996; Hostert et al. 2003a; Roberts et al. 1993; Smith et al. 1994). An extension of the approach is represented by multi-date unmixing approaches (Kuemmerle et al. 2006; Rogan et al. 2002; Shoshany and Svoray 2002) or the establishment of multiple endmember setups (García-Haro et al. 2005; Okin et al. 1998). In many cases, it is aimed at relating information on vegetation cover to other structural parameters, such as LAI, above ground biomass etc. (Calvao and Palmeirim 2004; Fang et al. 2005; Lacaze 1996, 2005).

Beside vegetation, soil-related parameters have been successfully derived from remote sensing data using a variety of approaches. For instance, Hill et al. (1995) used SMA to relate soils and bedrock fractional cover to degradation status for a test site in Southern France. Hill & Schütt (1993) used an empirical function derived from soil samples and hyperspectral measurements to infer a linear relation between reflectance and soil organic matter, which was upscaled to Landsat-TM imagery using a forward-backward spectral unmixing approach to restore soil-related information.

Spectral mixture analysis (SMA) is based on the assumption that the majority of spectral variation encountered in multispectral imagery can be ascribed to a mixture of a limited number of surface materials, which can be characterized by reflectance spectra (Smith et al., 1990). Most frequently, these mixtures occur at the sub-pixel level, resulting in mixed-pixel spectra. SMA attempts to model the multispectral reflectance recorded in a pixel as a mixture of representative ‘prototype’ spectra, which are termed ‘spectral endmembers’ (EM). The approach computationally decomposes multispectral measurements into a finite number of pure spectral components by describing the recorded signal as a result of linear spectral mixing (Adams et al. 1986; Smith et al. 1990) and yields the relative abundance of the different endmembers in a pixel according to


90

∑=

+•=n

jiijji REFR

1

ε and ∑=

=n

jjF

1

1

with iR = reflectance of the mixed spectrum in band i

ijRE = reflectance of the endmember spectrum j in band i

jF = fraction of endmember j n = number of spectral endmembers

iε = residual error in band i This equation is solved through a least squares regression, while constraining the sum of fractions to one. A unique solution is possible as long as the number of spectral components (endmembers) does not exceed the number of bands plus one. However, it has been emphasized that the intrinsic dimensionality of the data is lower than the number of bands due to autocorrelation between bands (Settle and Drake 1993; Small 2004). For instance, Small (2004) gives a maximum of 4 EM for Landsat ETM+ data; however, this number has also been shown to depend on the spectral contrast of the selected endmembers in relation to the sensor sensitivity and the spectral variance within the image (Drake and Settle 1989).

Composition of the endmember sets is the crucial step in spectral unmixing. These may be derived from the image itself (Boardman et al. 1995; Garcia-Haro et al. 1999), or - given limitations in finding homogeneous pixels in moderate resolution image data (Bateson and Curtiss 1996) – from hyperspectral reflectance measurements of different surface materials (Hill et al. 1995; Lacaze 1996). In any case, establishment of an endmember set that encompasses the spectral dimensionality of the data set is a complicated task that largely determines the quality of results (Chang et al. 2006; Dennison et al. 2004; Dennison and Roberts 2003; Franke et al. 2009; García-Haro et al. 2005; Numata et al. 2007; Rogge et al. 2006).

In addition, an artificial shade endmember may be included to account for albedo differences between different pixels and to model shading effects (Adams et al. 1986; Roberts et al. 1993). Once a suitable endmember model is determined and the abundance estimates for the different components is calculated, a normalization of the results with respect to the shade component can be performed by proportionally assigning it to the other fractions. The required normalization is calculated according to:

)1(1

ShadeFf

−= and ∑

−

=

=•)1(

11

n

jj fF

with f = shade normalisation factor ShadeF = fraction estimate for the shade endmember

jF = fraction estimate for endmember j


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Fractions resulting from the unmixing process should range from 0 to 1. In addition, the root-mean squared error may be employed as a measure of the mathematical accuracy of the SMA model.

m

nRR

RMSE

m

k

n

jjkjk∑ ∑

= = ⎥⎥

⎦

⎤

⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛ ′−

=1 1

2

/

with jkR = modelled reflectance in band j and pixel k

′jkR = measured reflectance in band j and pixel k

n = number of spectral bands m = number of pixels

The RMSE provides a measure of the spectral variability explained by the model, and its calculation per pixel yields an RMSE image that allows a visual assessment which surface materials may not have been correctly presented in the model.

Once a mathematically sound result has been attained, the next crucial step in any vegetation-related analysis is to validate results. Here, significant scaling issues are major obstacles to direct comparison between ground-based and satellite-based results, since the way of acquiring data is often entirely different from a conceptual point of view (Brogaard and Olafsdottir 1997; Röder et al. 2008a). However, given ground-sampling schemes are set up to be consistent with remote sensing image acquisition, very good results have been demonstrated (Armston et al. 2008). As an alternative some authors employ high resolution imagery or aerial photography as an intermediate step between field measurements and moderate scale image data such as Landsat-TM/ETM+ (Hill et al. 2009; Kuemmerle et al. 2006).

Given the availability of hyperspectral imagery, enhanced vegetation-related information may be derived through empirical relations to ground-based observation, e.g. using logistic regression models, support vector regression models etc. Such approaches have been employed to map forest biophysical parameters (Schlerf et al. 2005), nutritional content (Asner and Martin 2009; Skidmore et al. 2010), or map native vs. invasive plant species (Asner et al. 2008a; Asner et al. 2008b).

A more complex approach to deriving vegetation related biophysical parameters at leaf-, tree- or canopy level is the application of reflectance models that model the reflectance signal as a function of these parameters, and utilizes model inversion or lookup strategies to conclude on target variables such as LAI, water content, leaf angle etc. (Kimes et al. 2000). To run such models at canopy level, models need to integrate leaf-, tree-, and geometric components to account for geometric properties of canopies. A proven combination is to use the PROSPECT model to account for leaf reflectance as a function of biophysical properties (chlorophyll, water concentration, leaf structure etc.) (Jacquemoud and Baret 1990) with a model to represent the full plant reflectance taking into account


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background, leaf angle, woody plant components etc. (e.g. SAIL) (Jacquemoud 1995; Verhoef 1984), and link this to a turbid medium model for the geometric configuration of the landscape in terms of trees/shrubs, interspersed areas with no vegetation or a grass understorey, and shading/illumination effects. One frequently employed model is the GeoSail model (Huemmrich 2001), but models and algorithms are continuously being further developed (Verhoef and Bach 2007) to accommodate new sensor systems, such as multi-angular systems etc. (Chopping et al. 2008). Alternative approaches are ray tracing models that represent the path of each individual photon within vegetation canopies (Govaerts and Verstraete 1998), which is becoming more attractive through the use of ground-based LiDAR systems (Loudermilk et al. 2009; Strahler et al. 2008). Yet, such model-based approaches are often very demanding in terms of input data (e.g. hyperspectral data) , and were found to be limited in complex, semi-arid environments (Hill et al. 2009; Röder et al. 2008b). Therefore, their application to such environments is a research issue that would need to be specifically addressed.

3.4 Fine-scale temporal analysis

There are different ways in which to assess temporal dynamics of vegetation-related information as described in section 3.3. One is to assess temporal trajectories for different positions based on the respective pixel values for different dates. This has frequently been done to assess post-fire dynamics (García-Haro et al. 2001; Riano et al. 2002; Viedma et al. 1997) or the impact of fire severity (Díaz-Delgado et al. 2003), for instance by comparing pre- and post-fire values and calculating the difference between recovered and pre-fire vegetation states. With the availability of long-term records with sufficient points in time, full trend analysis is possible and was successfully employed to map post-fire recovery rates (Röder et al. 2008a) or the impact of grazing schemes (Hostert et al. 2003b; Röder et al. 2008c; Scarth et al. 2010). Also, evenly spaced time series allow for the identification of singular disturbance events, e.g. fires, clear-cuts etc. by threshholding (Röder et al. 2008a) or by moving temporal window time series analysis (Verbesselt et al. 2010).

Fine-scale linear trend analysis of remotely sensed images is partially congruent with the approaches presented in section 2 for coarse-scale imagery. Therefore, principles and formula presented above are not repeated here. However, and beside rare exceptions (Scarth et al. 2010), limitations of data availability and processing efforts lead to fine scale time series can usually not be compiled in a manner to support analysis of frequency components. Rather, characterization of trends in rangelands that might indicate grazing induced degradation of resources has to be based on assessing the transient component by means of a linear trend analysis.

Hence, temporal analysis of fine scale imagery is confined to linear trend analysis of the target indicator by minimizing the sum of least squares


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between given and estimated values for each fix point in the series (Udelhoven 2010). The offset of the resulting function characterizes the level of vegetation cover at the start of the observation period, while the gain describes the direction and magnitude of the development. The statistical significance of the resulting function is assessed using a t-test, while the cyclic and the white noise component have to be considered sources of ambiguity in the resulting function. In order to reduce this ambiguity to a minimum, careful procurement of the data base is required. Ideally, it should be composed of one image per year, and image acquisition dates should be selected to represent comparable states of phenological cycles, such as maximum photosynthetic activity, or late summer conditions after senescence of herbaceous vegetation.

Figure 7 illustrates possible temporal profiles for a series of Landsat-TM and –EMT+ images, and indicates the corresponding statistical estimates (Röder 2005). While profiles a and b show significant negative and positive trends, profiles c and d are illustrative of stable situations, where no major change in vegetation cover can be noted except for minor fluctuations. Although these do not pertain to ‘trends’ in the true sense of the meaning, there is a high relevance to such profiles as they are indicative of stable ecological situations, in particular in grazing areas. Hence, the root-mean squared is another important factor of multi-temporal statistics, as it is independent of the magnitude and direction of change, such that stable conditions may be identified through low error (RMSE) estimates.

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tatio

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Gain = 1.416E-006RMSE/mean = 0.0297R2 = 0.0108not sig.

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Gain = 8.0134E-005RMSE/mean = 0.0833R2 = 0.8567sig.

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(a) (b)

(c)(d)

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Figure 7: Sample temporal profiles of green vegetation cover in the Lagadas area, illustrating negative (a), positive (b) and indifferent (c, d) temporal development as a result of different factors and how these ‘translate’ into statistical parameters; more specific explanations in the text.


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3.5 Land use / land cover change analysis

Trend analysis of vegetation-related parameters is a viable means of characterizing within-class changes, for instance caused by grazing of shrubland and grassland areas. However, understanding the often complex and interactive process regimes at the landscape level requires complementing such trends by information on changes in surface types and relating these to underlying socio-economic and political causes where possible (Geist and Lambin 2004; Stafford-Smith and Reynolds 2002; Turner II et al. 2007). Remote sensing data offer opportunities to provide spatially explicit information on processes of land change. On a fine scale, there are different strategies that can be employed (Coppin et al. 2004b; Lu et al. 2004), and which can principally be assigned to two categories: (i) a so-called post-classification can be performed, where the satellite images are classified first and next a change detection analysis is conducted based on the thematic classes derived before, (ii) the change detection analysis can be performed by spectral images itself since land use/land cover changes involve alterations of the spectral properties of a surface, e.g. by multi-temporal Principal Component Analysis (García-Haro et al. 2001), Change Vector Analysis (Johnson and Kasischke 1998) or image differencing (Rogan and Yool 2001). Contrary to a post-classification change analysis, the thematic assignment of these changes has to be done afterwards. This may be complicated in the absence of precise knowledge of the changes to be expected, and requires the availability of images covering comparable dates. In addition, land cover maps of different periods can serve as useful sources of information for a variety of assessment approaches, such that the focus of this overview will be on classifying individual data sets.

For land use classifications based on Landsat data, it is important to characterize individual time slices using a sequence of images, thus incorporating phenology information relevant to differentiate different types of (semi-)natural vegetation and crops (Dymond et al. 2002; Schriever and Congalton 1995). Again, two major options for classification may be considered: supervised and unsupervised classification. Supervised classification methods are effective in identifying complex land cover classes if detailed a-priori knowledge of the study area and good training data exist, for instance derived from field surveys or aerial photographs (Cihlar et al. 1998). Then, different classification approaches can be employed, such as minimum distance and parallel-epiped classifiers, artificial neural networks or maximum likelihood classifiers (Benediktsson et al. 1990; Richards and Jia 2005; Smits et al. 1999). Recently, support vector machine (SVM) classifiers have been introduced, which discriminate classes by fitting separating hyperplanes in the feature space based on training samples (Foody and Mathur 2006; Huang et al. 2002; Sanchez-Hernandez et al. 2007). SVMs are trained in an iterative and controlled manner, and represent an excellent alternative where land cover classes are spectrally similar or where only small training data sets are available. Where only little reference information is available, unsupervised classification approaches may be preferable (Bauer and


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Coppin 1994; Lark 1995) and they have been rated more robust and repeatable (Cihlar et al. 1998; Wulder et al. 2004). Unsupervised classification is based on a statistical clustering approach to map pixels to spectral classes without prior information on the number and types of classes. Using a minimum distance approach, each pixel is compared to a number of clusters in the spectral feature space, which are characterized by their class statistics. In an automated, iterative way classes are rearranged until maximum separability is achieved, and the user may interact by assigning thresholds and termination criteria. Further approaches to classification include object-oriented classification or rule-based, decision tree classification of different input layers (Blaschke et al. 2004; Friedl and Brodley 1997; San Miguel-Ayanz and Biging 1996; Shandley et al. 1996). Some authors also suggest combining the strengths of different classifiers by using decision fusion methods (Udelhoven et al. 2010) or by applying random forest classification based on various runs with different subsamples from the training sample, e.g. through bagging and boosting methods (Breiman 2001; Gislason et al. 2006; Lawrence et al. 2006; Pal 2007).

In order to exploit the strengths of the individual approaches in the best possible manner, hybrid approaches have been suggested that combine supervised and unsupervised classification strategies. Three strategies can be distinguished: first, unsupervised clustering is useful to stratify input images prior to subsequent supervised classifications (Hill et al. 2004; Tommervik et al. 2003); second, unsupervised methods can reveal spectrally homogenous areas for optimized training sample and ground truth collection (McCaffrey and Franklin 1993; Rees and Williams 1997); and third, manually collected training data can be clustered into spectrally homogeneous sub-classes for use in a subsequent supervised classification, for instance using Support Vector Machine classifiers (Bauer and Coppin 1994; Kuemmerle et al. 2009; Pal and Mather 2005; Stuckens et al. 2000).

Once classification of land use and land cover at discrete dates has been achieved, change may be characterized qualitatively in the form of a from-to matrix or through a spatial representation of major change classes (Coppin et al. 2004b). In any case, exact geometric co-registration of images is a prerequisite; also, it may prove necessary to reduce the number of identified classes to a limited set of target classes, thus facilitating a better spatial discrimination of change patterns.

A particular challenge is often encountered where large areas need to be classified, but reference information is only available for a limited area, One option to overcome such limitations was presented by Knorn et al. (2009), who propose a chain classification approach to propagate training data between adjacent images, thus enabling multi-scene classification across image tiles and seasonal differences.

Regardless of the methodology employed, it is essential to provide accuracy measures in order to enable users to evaluate the accuracy of results attained (Congalton and Green 1999; Foody 2004). Different sources of


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information can serve validation purposes, such as data collected in the field, aerial photography; recently, Google Earth ™ images have presented an alternative in the absence of other credible data sources (Knorn et al. 2009; Kuemmerle et al. 2009)

3.6 Integrated interpretation concepts

Once information on land use and cover and on vegetation-related attributes has been procured, it can be assimilated into assessment models in different manners. It is beyond the scope of this document to review all of these; therefore, some studies are briefly referenced for illustration purposes. In particular, any such approach needs to be tailored to the problem addressed.

Owing to their spatially explicit nature, remote sensing data serve as prime input for various data interpretation concepts.

Categorical maps derived from land use land cover classification can be employed to quantify landscape spatial patterns over time using aggregated and non-aggregated spatial metrics (McGarigal 2002; Ostapowicz et al. 2008; Soille and Vogt 2009; Vogt et al. 2009; Vogt et al. 2007). Such information may be linked to a variety of questions, i.e. mapping of habitats (Kuemmerle et al. 2010), assessing landscape connectivity in terms of fire propagation (Rodriguez Gonzalez et al. 2008), monitor protected areas (Townsend et al. 2009) etc.

Land use change models of varying nature, e.g. based on cellular automata or dynamic agent-based simulations etc. A review of methods and approaches is provided by Verburg et al. (2004) and Koomen et al. (2007). While remote sensing derived data are able to reveal different types of land use change, it is equally important to understand why stakeholders take their decisions on how to use resources, either to inform land management or policy making, or to employ such data in model based approaches. Therefore, integrating spatially explicit data with socio-economic information is an important interpretation issue. Various authors stress the importance of investigating coupled human-environment systems (Lambin et al. 2006; Rindfuss et al. 2004a; Rindfuss et al. 2004b) and advocate the integration of physical and socio-economic data at a spatially explicit level. Lorent et al. (2008) surveyed the income situation of individual households dwelling on livestock or agricultural production and related this information to land use change and vegetation trend information derived from remote sensing and GIS analyses. They could show the dependence of land use decisions on subsidies in a marginal area in Northern Greece, which is essential to establish enhanced land management and ultimately policy schemes.

In the context of grazing management, the integration of remotely sensed data with auxiliary data has been shown to be an efficient tool for modeling livestock dynamics and suggesting management interventions. Different studies have used time series of Landsat data to estimate the long-term


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impact of grazing based on an integrated evaluation of different time series parameters, e.g. by calculating degradation indices (Hostert et al. 2003b; Hostert et al. 2001a; Röder et al. 2008c). Harris & Asner (2003) et al. employed airborne spectrometry data to map grazing impact, while Röder et al. (2007) have adapted the grazing gradient approach introduced by Pickup & Chewings (1994) using cost surface modeling and identified spatially explicit gradients in grazing pressure in a heterogeneous Mediterranean rangeland in Northern Greece. Svoray et al. (2009) have coupled a grassland production model with climate and remote sensing data to model forage availability, and relate this to livestock grazing behavior acquired based on GPS collars.

One frequently used option is to calibrate and validate models using remote sensing based information from different time steps, i.e. by providing input data for early time periods, and employing remote sensing data acquired at a later stage for validation purposes. Duguy et al. (2007) have employed fuel maps derived from a time series of satellite data to parameterize a fire propagation model and model a particular fire event. Once the model had been established, they simulated the effect of different fuel treatments and suggested landscape interventions for the prevention of fire spread.

As stated in section 2.5, land condition is frequently assessed based on vegetation related information, since this may be efficiently derived using remote sensing data. Opposed to hypertemporal analyses, however, selection of appropriate dates is crucial in representing yearly phenological cycles. Following ecological optimization theories, vegetation abundance in dryland ecosystems is strongly linked to water availability. Boer (1999) has proposed a theoretical framework for land degradation assessments relying on site water balance modeling, and integrating vegetation-related temporal information with rainfall to evapotranspiration ratios (Boer and Puigdefabregas 2003, 2005). Central to such approaches is the concept of rain use efficiency (RUE), i.e. the ratio of net primary productivity to precipitation (Le Houérou 1984), and the assumption that land degradation is resulting in decreasing RUE values. Integrating different geospatial data layers, he employed Specht’s evaporative coefficient to predict vegetation index values under different local conditions, and used the difference between the predicted and actual values to map land degradation based on undegraded reference sites (Boer 1999).

4 Plot scale

As shown in figure 1, different sensors exist that allow addressing specific question at the plot scale with a very high geometric resolution (VHR), such as the Quickbird-, Ikonos- or WorldView-systems, with geometric resolution of 0.5*0.5 to 2.4*2.4 m2. Alternatively, aerial imagery may be utilized. The high spatial resolving capacity of these systems is commonly achieved at the expense of spectral resolution, such that these systems commonly are characterized by two to three bands in the visible portion of the wavelength


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spectrum, and one band in the near infra-red. In principle, this allows for application of all of the concepts presented in section 3. Yet, high acquisition costs most frequently impede acquisition of extensive VHR data sets. This is added to by high demands in processing capacities imposed by large data volumes and by the often complicated image geometry resulting from the combination of very high resolution and side-looking viewing properties. A review of image processing concepts for VHR imagery may be found in Toutin et al. (Toutin 2004) with a focus on satellite based VHR imagery, or Mikhail et al. (2001) for general photogrammetric principles and approaches, e.g. bundle block adjustment of stereo image pairs etc.

VHR image data are an excellent source of information at a high level of spatial detail. They are frequently employed for visual interpretation and to map specific surface features. Automatic and semi-automatic approaches include for instance visual delineation of surface features, threshold-based mapping of target features, or simple classification approaches.

Visual mapping is commonly employed to quickly produce emergency-related maps for disaster relief (rapid mapping, e.g. www.zki.dlr.de).

A combination of thresholds applied to the panchromatic band and to vegetation index values derived from the multi-spectral bands was employed to map woody cover in a rangeland ecosystem in Northern Greece (Hill et al. 2009; Röder et al. 2008b) and separate tree and grass vegetation in an African Savannah ecosystem (Archibald and Scholes 2007). Yet, some authors have successfully employed maximum likelihood classifiers to map invasive species (Laba et al. 2008) or mangrove stands (Wang et al. 2004), characterize biomass volumes (Leboeuf et al. 2007), or inventory forest resources (Song et al. 2010). In general, VHR data have been shown to be consistent with medium resolution imagery (e.g. Landsat, Spot) (Goward et al. 2007; Soudani et al. 2006a; Soudani et al. 2006b) They are therefore an excellent source of information to complement data covering larger areas, and may be used for calibration/validation purposes or to provide for intermediate explanation levels when upscaling from ground-based information to higher aggregation levels (Kuemmerle et al. 2006).

5 References

Adams, J.B., Smith, M.O., & Johnson, P.E. (1986). Spectral mixture modelling: a new analysis of rock and soil types at Viking Lander. Journal of Geophysical Research, 91, 8113-8125

Angstrom, A. (1964). The parameters of atmospheric turbidity. Tellus, 16, 64-75

Aranuvachapun, S. (1985). Satellite remote sensing of atmospherical optical depth spectrum. In E.M. Rollin, M.D. Steven & P.M. Mather (Eds.), Atmospheric Corrections for Remote Sensing. Proceedings of a Remote Sensing Workshop on Atmospheric Corrections, 22nd May 1985. Nottingham, UK: University of Nottingham

Archibald, S., & Scholes, R.J. (2007). Leaf green-up in a semi-arid African savanna - separating tree and grass response to environmental cues. Journal of Vegetation Science, 18, 583-594


99

Armston, J.D., Denham, R.J., Danaher, T.J., Scarth, P.F., & Moffiet, T.N. (2008). Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. In, 14th Australasian Remote Sensing and Photogrammetry Conference (pp. 1-28). Darwin, Northern Territory, Australia

Asner, G.P., Jones, M.I., Martin, R.E., Knapp, D.E., & Hughes, R.F. (2008a). Remote sensing of native and invasive species in Hawaiian forests. Remote Sensing of Environment, 112, 1912-1926

Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Jones, M.O., Martin, R.E., Boardman, J., & Hughes, R.F. (2008b). Invasive species detection in hawaiian raninforests using airborne imaging spectroscopy and LiDAR. Remote Sensing of Environment, 112, 1942-1955

Asner, G.P., & Martin, R.E. (2009). Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment, 7, 269-276

Azzali, S., & Menenti, M. (2000). Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHAA NDVI data. International Journal of Remote Sensing, 21, 973-996

Bai, Z.G., Dent, L.G., Olsson, L., & Schaepman, M.R. (2008). Proxy global assessment of land degradation. Soil Use and Management, 24, 223-234

Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161-173

Bartholome, E., & Belward, A. (2005). GLC2000: A new approach to global land cover mapping from earth observation data. International Journal of Remote Sensing, 26, 1959-1977

Bastin, G.N., Pickup, G., & Pearce, G. (1995). Utility of AVHRR data for land degradation assessment: a case study. International Journal of Remote Sensing, 16, 651-672

Bateson, A., & Curtiss, B. (1996). A method for manual endmember selection and spectral unmixing. Remote Sensing of Environment, 55, 229-243

Bauer, M.E., & Coppin, P.R. (1994). Processing of Multitemporal Landsat Tm Imagery to Optimize Extraction of Forest Cover Change Features. IEEE Transactions on Geoscience and Remote Sensing, 32, 918-927

Benediktsson, J.A., Swain, P.H., & Ersoy, O.K. (1990). Neural Network Approaches Versus Statistical-Methods in Classification of Multisource Remote-Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 28, 540-552

Berk, A., Anderson, G.P., Acharya, P.K., Chetwynd, J.H., Bernstein, L.S., Shettle, E.P., Matthew, M.W., & Adler-Golden, S.M. (1999). MODTRAN4 user’s manual. In. Hanscom AFB, MA 01731-3010: Air Force Research Laboratory, Space Vehicles Directorate, Air Force Material Command

Blaschke, T., Burnett, C., & Pekkarinen, A. (2004). Image Segmentation Methods for Object-based Analysis and Classification. In S.M.d. Jong & F.D.v.d. Meer (Eds.), Remote Sensing Image Analysis. Including the Spatial Domain. (pp. 211-236): Kluwer Academic Publishers

Boardman, J.W., Kruse, F.A., & Green, R.O. (1995). Mapping target signatures via partial unmixing of AVIRIS data. In, 5th annual JPL Airborne Earth Science Workshop (pp. 23-26)

Boer, M.M. (1999). Assessment of dryland degradation: linking theory and practice through site water balance modelling. Utrecht: Department of Physical Geography, Utrecht University


100

Boer, M.M., & Puigdefabregas, J. (2003). Predicting potential vegetation index values as a reference for the assessment and monitoring of dryland condition. International Journal of Remote Sensing, 24

Boer, M.M., & Puigdefabregas, J. (2005). Assessment of dryland condition using spatial anomalies of vegetation index values. International Journal of Remote Sensing, 26, 4045-4065

Brandt, J., & Thornes, J.B. (1996). Mediterranean desertification and land use. London: Wiley & Sons

Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32

Brogaard, S., & Olafsdottir, R. (1997). Ground-truths of Ground-lies? In, Lund Electronic Reports in Physical Geography

Calvao, T., & Palmeirim, J.M. (2004). Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. International Journal of Remote Sensing, 25, 3113-3126

Chander, G., Helder, D.L., Markham, B.L., Dewald, J.D., Kaita, E., Thome, K.J., Micijevic, E., & Ruggles, T.A. (2004). Landsat-5 TM reflective-band absolute radiornetric calibration. IEEE Transactions on Geoscience and Remote Sensing, 42, 2747-2760

Chander, G., & Markham, B. (2003). Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674-2677

Chang, C.-I., Wu, C.-C., Liu, W.-M., & Ouyang, Y.-C. (2006). A new growing method for simplex-based endmember extraction algorithm. IEEE Transactions on Geoscience and Remote Sensing, 44, 2804-2819

Chatfield, C. (2004). The analysis of time-series. An introduction. Boca Ration: CRC Press

Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., & Eklundh, L. (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91, 332-344

Chopping, M.J., Su, L., Rango, A., Martonchik, J.V., Peters, D.P.C., & Laliberte, A. (2008). Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric optical canopy reflectance model. Remote Sensing of Environment, 112, 19-34

Cihlar, J., Latifovice, R., Chen, J., Trishchenko, A., Du, Y., Fedosojevs, G., & Guindon, G. (2004). Systematic corrections of AVHRR image composites for temporal studies. Remote Sensing of Environment, 89, 217-233

Cihlar, J., Xia, Q.H., Chen, J., Beaubien, J., Fung, K., & Latifovic, R. (1998). Classification by progressive generalization: a new automated methodology for remote sensing multichannel data. International Journal of Remote Sensing, 19, 2685-2704

Clevers, J.G.P.W. (1988). The derivation of a simplified reflectance model for te estimation of leaf area index. Remote Sensing of Environment, 35, 53-70

Cohen, W.B., Maiersperger, T.K., Yang, Z., Gower, S.T., Turner, D.P., Ritts, W.D., Berterretche, M., & Running, S.W. (2003). Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sensing of Environment, 88, 233-255

Congalton, R.G., & Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data:Principles and Practices. Boca Raton: CRC Press

Coppin, P., Jonckheere, I., Nackaerts, K., & Muys, B. (2004a). change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25, 1565-1596


101

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004b). Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25, 1565-1596

Cracknell, A.P. (1997). The Advanced Very High Resolution Radiometer (AVHRR). London: Taylor & Francis

Cracknell, A.P. (1998). Synergy in remote sensing - what's in a pixel? International Journal of Remote Sensing, 19, 2025-2047

Crist, E.P., & Kauth, R.T. (1986). The Tasseled Cap De-Mystified. Photogrammetric Engineering & Remote Sensing, 52, 81-86

Curran, P. (1980). Multispectral remote sensing of vegetation amount. Progress in Physical Geography, 4, 315-341

de Vries, C., Danaher, T., Denham, R., Scarth, P., & Phinn, S. (2007). An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites. Remote Sensing of Environment, 107, 414-429

Del Barrio, G., Puigdefabregas, J., Sanjuan, M.E., Stellmes, M., & Ruiz, A. (2010). Assessment and monitoring of land condition in the Iberian Peninsula, 1989-2000. Remote Sensing of Environment, 114, 1817-1832

Dennison, P.E., Halligan, K.Q., & Roberts, D.A. (2004). A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sensing of Environment, 93, 359-367

Dennison, P.E., & Roberts, D.A. (2003). Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sensing of Environment, 87, 123-135

Díaz-Delgado, R., Lloret, F., & Pons, X. (2003). Influence of fire severity on plant regeneration through remote sensing imagery. International Journal of Remote Sensing, 24, 1751-1763

Dinguirard, M., & Slater, P.N. (1999). Calibration of Space-Multispectral Imaging Sensors: A Review. Remote Sensing of Environment, 68, 194-205

Downing, T.E., & Lüdeke, M.K.B. (2002). Social geographies of vulnerability and adaptation. In J.F. Reynolds & D.M. Stafford-Smith (Eds.), Global desertification. Do humans cause deserts? (pp. 233-252). Berlin: Dahlem University Press

Drake, N.A., & Settle, J.J. (1989). Linear mixture modelling of Thematic Mapper data of teh Peruvian Andes. Proceedings of the 9th EARSeL Symposium, Helsinki, Finland, 27th June - 1st July 1989, 490-495

Du, Y., Teillet, P.M., & Cihlar, J. (2002). Radiometric normalization of multitemporal high resolution satellite images with quality control for land cover change detection. Remote Sensing of Environment, 82, 123-134

Duchemin, B., Goubier, J., & Courrier, G. (1999). Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sensing of Environment, 76, 68-82

Duguy, B., Alloza, J.A., Röder, A., Vallejo, R., & Pastor, F. (2007). Modelling the effects of landscape fuel treatments on fire growth and behaviour in a Mediterranean landscape (eastern Spain). International Journal of Wildland Fire, 16, 619-632

Dymond, C.C., Mladenoff, D.J., & Radeloff, V.C. (2002). Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sensing of Environment, 80, 460-472


102

Eastman, R.J., & Fulk, M. (1993). Long sequence time series evaluation using standardized principla components. Photogrammetric Engineering & Remote Sensing, 59, 1307-1312

Erbrecht, T. (2003). Monitoring of land degradation and land use systems with high temporal resolution remote sensing data. Trier

European Commission (1998). An Integrated Approach to Assess and Monitor Desertification Processes in the Mediterranean Basin (DeMon-II). Final Report. In. Ispra, Italy: European Commission, Directorate General Joint Research Centre

Evans, J.P., & Geerken, R. (2006). Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure. Remote Sensing of Environment, 105, 1-8

Fang, H., Lian, S., McClaran, M.P., van Leeuwen, W.J.D., Drake, S., Marsh, S.E., Thomson, A.M., Izaurralde, R.C., & Rosenberg, N. (2005). Biophysical characterization and management effects on semiarid rangeland observed from Landsat ETM+ data. IEEE Transactions on Geoscience and Remote Sensing, 43, 125-134

Fischer, A. (1994a). A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract cop parameters. Remote Sensing of Environment, 48, 220-230

Fischer, A. (1994b). A simple model for the temporal variations of NDVI at regional scale over agricultural countries. Validation with ground radiometric measurements. International Journal of Remote Sensing, 15, 1421-1446

Fisher, P. (1997). The pixel: A snare and a delusion. International Journal of Remote Sensing, 18, 679-685

Foody, G.M. (2004). Thematic map comparison: evluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering & Remote Sensing, 70, 627-633

Foody, G.M., & Mathur, A. (2006). The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103, 179-189

Franke, J., Roberts, D.A., Halligan, K., & Menz, G. (2009). Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 113, 1712-1723

Friedl, M.A., & Brodley, C.E. (1997). Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, 399-409

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoeny, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., & Schaaf, C. (2002). Global land cover mappign from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287-302

Friedl, M.a., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168-182

Friedrich, K., & Koslowsky, D. (2009). Inter-comparison of MEDOKADS and NOAA/NASA pathfinder AVHRR land NDVI time series. In A. Röder & J. Hill (Eds.), Recent Advancesin Remote Sensing and Geoinformation Processing for Land Degradation Assessment (pp. 103-116). Boca Raton, New York, Abingdon: CRC Press/Balkema

Garcia-Haro, F.J., Gilabert, M.A., & Melia, J. (1999). Extraction of endmembers from spectral mixtures. Remote Sensing of Environment, 68, 237-253

García-Haro, F.J., Gilabert, M.A., & Melìa, J. (2001). Monitoring fire-affected areas using Thematic Mapper data. International Journal of Remote Sensing, 22, 533-549


103

García-Haro, F.J., Sommer, S., & Kemper, T. (2005). A new tool for variable multiple endmember spectral mixture analysis (VMESMA). International Journal of Remote Sensing, 26, 2135-2162

GarciaHaro, F.J., Gilabert, M.A., & Melia, J. (1996). Linear spectral mixture modelling to estimate vegetation amount from optical spectral data. International Journal of Remote Sensing, 17, 3373-3400

Gates, D.M. (1980). Biophysical Ecology. New York: Springer

Geerken, R., Zaitchicki, B., & Evans, J.P. (2005). Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. International Journal of Remote Sensing, 26, 5535-5554

Geist, H. (2005). The causes and progression of desertification. Aldershot: Ashgate Publishing Limited

Geist, H.J., & Lambin, E.F. (2004). Dynamic causal patterns of desertification. Bioscience, 54, 817-829

Gilabert, M.A., Gonzàles-Piqueras, J., GarciaHaro, F.J., & Melia, J. (2002). A generalized soil-adjusted vegetation index. Remote Sensing of Environment, 82

Gillson, L., & Hoffman, M.T. (2007). Rangeland ecology in a changing world. Science, 315, 53-54

Gislason, P.O., Benediktsson, J.A., & Sveinsson, J.R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27, 294-300

Govaerts, Y.M., & Verstraete, M.M. (1998). Raytran: A Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media. IEEE Transactions on Geoscience and Remote Sensing, 36, 493-505

Goward, S.N., Davis, P.E., Fleming, D., Miller, L., & Townshend, J.R.G. (2007). Empirical comparison of Landsat 7 and IKONOS multispectral measurements for selected Earth Observation Systems (EOS) validation sites. Remote Sensing of Environment, 88, 80-99

Goward, S.N., & Hope, A.S. (1989). Evapotranspiration from combined reflected solar and emitted terrestrial radiations: preliminary FIFE results from ACHRR data. Advances in Space Research, 9

Goward, S.N., Masek, J.G., Williams, D.J., Irons, J.R., & Thompson, R.J. (2001). The Landsat 7 mission. Terrestrial research and applications for the 21st century. Remote Sensing of Environment, 78, 3-12

Gutman, G. (1991). Vegetation indices from AVHRR: an update and future prospects. Remote Sensing of Environment, 35, 121-136

Hall, F.G., Strebel, D.E., Nickeson, J.E., & Goetz, S.J. (1991). Radiometric rectification - toward a common radiometric response among multi-date, multisensor images. Remote Sensing of Environment, 35, 11-27

Hargreaves, G.H., & Samani, Z.A. (1982). Estimating potential evapotransporation. Journal of the Irrigation and Drainage Division - ASCE, 108, 225-230

Harris, J.A., & Asner, G.P. (2003). Grazing gradient detection with airborne imaging spectroscopy on a semi-arid rangeland. Journal of Arid Environments, 55, 391-404

Harris, J.A., & van Diggelen, R. (2008). Ecological restoration as a project for global society. In J. van Andel & J. Aronson (Eds.), Restoration Ecology (pp. 3-15). Malden, Oxford, Carlton: Blackwell Publishing

Hay, J.E., & McKay, D.C. (1985). Estimating solar irradiance on inclined surfaces: A review and assessment of methodologies. International Journal of Solar Energy, 3, 203-240


104

Helder, D.L., Markham, B., Thome, K.J., Brarsi, J.A., Chander, G., & Malla, R. (2008). Updated radiometric calibration for the Landsat 5 Thematic Mapper reflective bands. IEEE Transactions on Geoscience and Remote Sensing, 46, 3309-3325

Hermance, J.F. (2007). Stabilizing high-order, non-classical harmonic analysis of NDVI data for average annual models by damping model roughness. International Journal of Remote Sensing, 28, 2801-2819

Hermance, J.F., Jacob, R.W., Bradley, B.A., & Mustard, J.F. (2007). Extracting phenological signals from multiyear AVHRR NDVI time series: ramework for applying high-order annual splines with roughness damping. IEEE Transactions on Geoscience and Remote Sensing, 45, 3264-3276

Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A., & Schmullius, C. (2008). Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112, 2538-2556

Hill, J., Dach, C., Del Barrio, G., Stellmes, M., Helldén, U., & Changyao, W. (2010). Integrating MODIS-EVI and Gridded Rainfall/Temperatur Fields for Assessing Land Degradation Dynamics in Horqin Sandy Lands (Inner Mongolia, China). In EARSeL (Ed.), 30th EARSeL Symposium 2010. Remote Sensing for Science, Education and Natural and Cultural Heritage. 31 May - 3 June, 2010, Paris, France. Paris

Hill, J., Hostert, P., & Röder, A. (2004). Long-term Observation of Mediterranean Ecosystems with Satellite Remote Sensing. In S. Mazzoleni, G. di Pasquale, M. Mulligan, P. di Martino & F. Rego (Eds.), Recent Dynamics of the Mediterranean Vegetation and Landscape (pp. 33-43). Chichester: John Wiley & Sons Ltd.

Hill, J., Hostert, P., Tsiourlis, G., Kasapidis, P., Udelhoven, T., & Diemer, C. (1998). Monitoring 20 years of increased grazing impact on the Greek island of Crete with earth observation satellites. Journal of Arid Environments, 39, 165-178

Hill, J., & Mehl, W. (2003). Geo- und radiometrische Aufbereitung multi- und hyperspektraler Daten zur Erzeugung langjähriger kalibrierter Zeitreihen. Photogrammetrie – Fernerkundung – Geoinformation, 1, 7-14.

Hill, J., Mehl, W., & Radeloff, V. (1995). Improved forest mapping by combining corrections of atmospheric and topographic effects. In J. Askne (Ed.) (pp. 143-151). Rotterdam, Brookfield: A.A. Balkema

Hill, J., Röder, A., Mehl, W., & Tsiourlis, G.M. (2009). Retrieving rangeland vegation characteristics through constrained inverse reflectance modelling of earth observation imagery. In A. Röder & J. Hill (Eds.), Recent Advances in Remote Sensing and Geoinformation Prcessing for Land Degradation Assessment (pp. 211-226). London: Taylor&Francis

Hill, J., Stellmes, M., Udelhoven, T., Röder, A., & Sommer, S. (2008). Mediterranean desertification and land degradation. Mapping related land use change syndromes based on satellite observations. Global and Planetary Change, 64, 146-157

Hill, J., & Sturm, B. (1991). Radiometric correction of multitemporal Thematic Mapper data for use in agricultural land-cover classification and vegetation monitoring. International Journal of Remote Sensing, 12, 1471-1491

Hirsch, R., & Slack, J. (1984). A nonparametric trend test for seasonal data with serial dependance. Water Resources Research, 20, 727-732

Holben, B.N. (1986). Characteristics of maximum value composite images for temporal AVHRR data. International Journal of Remote Sensing, 7, 1417-1437

Holben, B.N., Eck, T.F., Slutsker, I., Tanré, D., Buis, J.P., Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y.J., Nakajima, T., Lavenu, F., Jankowiak, I., & Smirnov, A. (1998). AERONET--A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sensing of Environment, 66, 1-16


105

Hostert, P., Roder, A., & Hill, J. (2003a). Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands. Remote Sensing of Environment, 87, 183-197

Hostert, P., Roder, A., Hill, J., Udelhoven, T., & Tsiourlis, G. (2003b). Retrospective studies of grazing-induced land degradation: a case study in central Crete, Greece. International Journal of Remote Sensing, 24, 4019-4034

Hostert, P., Roder, A., Jarmer, T., Udelhoven, T., & Hill, J. (2001a). The potential of remote sensing and GIS for desertification monitoring and assessment. Annals of Arid Zone, 40, 103-140

Hostert, P., Röder, A., Jarmer, T., Udelhoven, T., & Hill, J. (2001b). The potential of remote sensing and GIS for desertification monitoring and assessment. Annals of Arid Zone, 40, 103-140

Hountondji, Y.C., Sokpon, N., Nicolas, J., & Ozer, P. (2009). Ongoing desertification processes in the sahelian belt of West Africa: an evidence from the rain-use efficiency. In A. Röder & J. Hill (Eds.), Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment (pp. 173-186). London: Taylor & Francis

Huang, C., Davis, L.S., & Townshend, J.R.G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725-749

Huemmrich, K.F. (2001). The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sensing of Environment, 75, 423-431

Huete, A.R. (1988). A Soil-Adjusted Vegetation Index (Savi). Remote Sensing of Environment, 25, 295-309

Huete, A.R., Didan, K., Mirua, T., Rodriguez, E.P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195-213

Huete, A.R., Jackson, R.D., & Post, D.F. (1985). Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17, 37-53

Itten, K.I., & Meyer, P. (1993). Geometric and radiometric correction of TM data of mountainous forested areas. IEEE Transactions on Geoscience and Remote Sensing, 31, 764-770

Jacquemoud, G., & Baret, F. (1990). PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75-91

Jacquemoud, S. (1995). Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing of Environment, 52, 163-172

Jeltsch, F., Milton, S.J., Dean, W.R.J., & van Rooyen, N. (1996). Tree spacing and coexistence in semiarid savannas. Journal of Ecology, 84

Jeltsch, F., Weber, G.E., & Grimm, V. (2000). Ecological buffering mechanisms in savannas: A unifying theory of long-term tree-grass coexistence. Plant Ecology, 161

Jiang, L., Tarpley, J.D., Mitchell, K.E., Zhou, S., Kogan, F.N., & Guo, W. (2008). Adjusting for long-term anomalous trends in NOAA's global vegetation index data sets. IEEE Transactions on Geoscience and Remote Sensing, 46, 409-422

Johnson, R.D., & Kasischke, E.S. (1998). Change vector analysis: a technique for the multispectral monitoring of land cover and condition. International Journal of Remote Sensing, 19, 411-426

Jönsson, P., & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40, 1824-1832


106

Karpouzli, E., & Malthus, T. (2003). The empirical method for the atmospheric correction of IKONOS imagery. International Journal of Remote Sensing, 24, 1143-1150

Kauth, R.J., & Thomas, G.S. (1976). The Tasselled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as seen by Landsat. In, Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, Indiana, USA: The Laboratory for Applications of Remote Sensing, Purdue University

Kimes, D.S., Knyazikhin, Y., Privette, J.L., Abuelgasim, A.A., & Gao, F. (2000). Inversion methods for physically-based models. Remote Sensing Reviews, 18, 381-439

Knorn, J., Rabe, A., Radeloff, V.C., Kuemmerle, T., Kozak, J., & Hostert, P. (2009). Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sensing of Environment, 113, 957-964

Koomen, E., Stillwell, J., Bakema, A., & Scholten, H.J. (2007). Modelling land-use change. Progress and Applications. Dordrecht, The Netherlands: Springer

Kramer, H.J. (2002). Observation of the Earth and its environment. Survey of missions and sensors. Berlin, Heidelberg, New York: Springer

Kuemmerle, T., Chaskovskyy, O., Knorn, J., Radeloff, V.C., Keeton, W.S., & Hostert, P. (2009). Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sensing of Environment, 113, 1194-1207

Kuemmerle, T., Perzanowski, K., Chaskovskyy, O., Ostapowicz, K., Halada, L., Bashta, A.-T., Kruhlov, I., Hostert, P., Waller, D., & Radeloff, V.C. (2010). European Bison habitat in the Carpathian Mountains. Biological Conservation, in print

Kuemmerle, T., Röder, A., & Hill, J. (2006). Separating grassland and shrub vegetation by multidate pixel-adaptive spectral mixture analysis. International Journal of Remote Sensing, 27, 3251-3271

Laba, M., Downs, R., Smith, S., Welsh, S., Neider, C., White, S., Richmond, M., Philpot, W., & Baveye, P. (2008). Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery. Remote Sensing of Environment, 112, 286-300

Lacaze, B. (1996). Spectral characterisation of vegetation communities and practical approaches to vegetation cover changes monitoring. In J. Hill & D. Peter (Eds.), The use of remote sensing for land degradation and desertification monitoring in the Mediterranean basin - state of the art and future research. Proceedings of a workshop, jointly organized by JRC/IRSA and DGXII/D-2/D-4, Valencia, 13-15 June 1994 (pp. 149-166). Valencia: Office for Official Publications of the European Communities

Lacaze, B. (2005). Remotely-sensed optical and thermal indicators of land degradation. In M. Oluic (Ed.), New strategies for European Remote Sensing. Proceedings of the 24th Symposium of the European Association of Remote Sensing Laboratories, Dubrovnik, Croatia, 25-27 May 2004 (pp. 211-217)

Lambin, E.F., & Geist, H.J. (2006). Land Use and Land Cover Change. Local Processes and Global Impacts. Berlin, Heidelberg, New York: Springer Verlag

Lambin, E.F., Geist, H.J., & Rindfuss, R.R. (2006). Introduction: Local Processes with Global Impacts. In E.F. Lambin & H.J. Geist (Eds.), Land-Use and Land-Cover Change (pp. 1-8). Berlin, Heidelberg, New York: Springer

Lark, R.M. (1995). A reappraisal of unsupervised classification 1. Correspondence between spectral and conceptual classes. International Journal of Remote Sensing, 16, 1425-1443


107

Lawrence, R.L., Wood, S.D., & Sheley, R.L. (2006). Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (Random Forests). Remote Sensing of Environment, 100, 356-362

Le Houérou, N.H. (1984). Rain use efficiency: a unifying concept in arid land ecology. Journal of Arid Environments, 7, 1-15

Leboeuf, A., Beaudoin, A., Fournier, R.A., Guindon, L., Luther, J.E., & Lambert, M.C. (2007). A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery. Remote Sensing of Environment, 110, 488-500

Li, Z., & Kafatos, M. (2000). Interannual variability of vegetation in the United States and its relation to El Nino/southern oscillation. Remote Sensing of Environment, 71, 239-247

Lillesand, T.M., & Kiefer, R.W. (2000). Remote Sensing and Image Interpretation. New York, USA: John Wiley & Sons

Lobo, A., & Maisongrande, P. (2006). Searching for trends of change through exploratory data analysis of time series of remotely sensed images of SW Europe. In J.A. Sobrino (Ed.), Proceedings of the Second Conference on Recent Advances in Quantitative Remote Sensing, Valencia (pp. 534-537). Valencia

Lohninger, H. (1999). Teach/Me Data Analysis. Berlin: Springer

Lorent, H., Evangelou, C., Stellmes, M., Hill, J., Papanastasis, V.P., Tsiourlis, G., Röder, A., & Lambin, E.F.-. (2008). Land degradation and economic conditions of agricultural households in a marginal region of northern Greece. Global and Planetary Change, 64, 198-209

Loudermilk, E.L., Hiers, J.K., O'Brien, J.J., Mitchell, R.J., Singhania, A., Fernandez, J.C., Cropper, W.P.J., & Slattion, K.C. (2009). Ground-based LiDAR: a novel approach to quantify fine-scale fuelbed characteristics. International Journal of Wildland Fire, 18, 676-685

Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L., & Merchant, J.W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21, 1303-1330

Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25, 2365-2407

Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., & Worthy, L.D. (2006). Land-cover change detectio nusing multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105, 142-154

Lyon, J.G., Yuan, D., Lunetta, R.S., & Elvidge, C.D. (1998). A change detection experiment using vegetation indices. Photogrammetric Engineering & Remote Sensing, 64, 143-150

Major, D., Baret, F., & Guyot, G. (1990). A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing, 11, 727-740

Markham, B.L., Storey, J.C., Williams, D.J., & Irons, J.R. (2004). Landsat sensor performance: history and current status. IEEE Transactions on Geoscience and Remote Sensing, 42, 2691-2694

Masek, J.G., Honzak, M., Goward, S.N., Liu, P., & Pak, E. (2001). Landsat-7 ETM+ as an observatory for land cover. Initial radiometric and geometric comparisons with Landsat-5 Thematic Mapper. Remote Sensing of Environment, 78, 118-130

Maxwell, S.K., Schmidt, G.L., & Storey, J.C. (2007). A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of Remote Sensing, 28, 5339-5356

McCaffrey, T.M., & Franklin, S.E. (1993). Automated training site selection for large-area remote-sensing image-analysis. Computers & Geosciences, 19, 1413-1428


108

McGarigal, K. (2002). Landscape pattern metrics. In A.H. El-Shaarawi & W.W. Piegorsch (Eds.), Encyclopedia of environmetrics (pp. 1135-1142). Sussex: Wiley & Sons

Mikhail, E.M., Bethel, J.S., & McGlone, J.C. (2001). Introduction to Modern Photogrammetry. New York: John Wiley & Sons

Moody, A., & Johnson, D.M. (2001). Land surface phenologies from AVHRR using the discrete fourier transform. Remote Sensing of Environment, 73, 305-323

Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., & Running, S.W. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83, 214-231

Noy-Meir, I. (1998). Effects of grazing on Mediterranean grasslands: the community level. In V.P. Papanastasis & D. Peter (Eds.), Ecological basis of livestock grazing in Mediterranean ecosystems. Proceedings of the International Workshop held in Thessaloniki (Greece) on October 23-25, 1997 (pp. 27-39). Luxemburg: Office for Official Publications of the European Communities

Numata, I., Roberts, D.A., Chadwick, O.A., Schimel, J., Sampaio, F.R., Leonidas, F.C., & Soares, J.V. (2007). Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sensing of Environment, 109, 314-327

Okin, G.S., Okin, W.J., Roberts., D.A., & Murray, B. (1998). Multiple endmember spectral mixture analysis: application to an arid/semi-arid landscape. In R.O. Green (Ed.), 10th Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop. Pasadena

Ostapowicz, K., Vogt, P., Riitters, K.H., Kozak, J., & Estreguil, C. (2008). Impact of scale on morphological spatial pattern of forest. Landscape Ecology, 23, 1107-1117

Pal, M. (2007). Ensemble learning with decision tree for remote sensing classification. World Academy of Science, Engineering and Technology, 36, 258-260

Pal, M., & Mather, P.M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26

Pala, V., & Pons, X. (1995). Incorporation of relief in polynom-based geometric correction. Photogrammetric Engineering & Remote Sensing, 61, 935-944

Pickup, G., & Chewings, V.H. (1994). A grazing gradient approach to land degradation assessment in arid areas from remotely sensed data. International Journal of Remote Sensing, 15, 597-617

Pinty, B., & Verstraete, M.M. (1992). GEMI: A non-linear index to monitr global vegetation from satellites. Vegetatio, 1335-1372

Price, J.C. (2003). Comparing MODIS and ETM+ data for regional and global land classification. Remote Sensing of Environment, 86, 491-499

Pringle, M.J., Schmidt, M., & Muir, J.S. (2009). Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing, 1-11

Puigdefabregas, J., & Mendizabal, T. (1998). Perspectives on desertification: western Mediterranean. Journal of Arid Environments, 39, 209-224

Quinn, R.D. (1986). Mammalian herbivory and resilience in mediterranean-climate ecosystems. In B. Dell, A.J.M. Hopkins & B.B. Lamont (Eds.), Resilience in mediterranean-type ecosystems (pp. 113-128). Dordrecht, Boston, Lancaster: Kluwer Academic Publishers

Rees, W.G., & Williams, M. (1997). Monitoring Changes in land cover induced by atmospheric pollution in the Kola Peninsula, Russia, using Landsat-MSS data. International Journal of Remote Sensing, 18, 1703-1723


109

Riano, D., Chuvieco, E., Usting, Zomer, R., Dennison, P.E., & Roberts, D.A. (2002). Assessment of vegetation regeneration after fire through multi-temporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sensing of Environment, 79, 60-71

Richards, J.A., & Jia, X. (2005). Remote Sensing Digital Image Analysis. Berlin, Germany: Springer

Richardson, A.J., & Wiegand, C.L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing, 1541-1552

Rindfuss, R.R., Walsh, S.J., Turner, B.L., Fox, J., & Mishra, V. (2004a). Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences of the United States of America, 101, 13976-13981

Rindfuss, R.R., Walsh, S.J., Turner II, B.L., Moran, E.F., & Entwisle, B. (2004b). Linking people and pixels. In G. Gutman, A.C. Janetos, C.O. Justice, E.F. Moran, J.F. Mustard, R.R. Rindfuss, D. Skole, B.L. Turner Ii & M.A. Cochraine (Eds.), Land Change Science (pp. 379-394). Dordrecht: Kluwer Academic Publishers

Roberts, D.A., Smith, M.O., & Adams, J.B. (1993). Green Vegetation, Nonphotosynthetic Vegetation, and Soils in Aviris Data. Remote Sensing of Environment, 44, 255-269

Röder, A. (2005). A remote sensing based framework for monitoring and assessing Mediterranean rangelands. Case studies from two test sites in Spain and Greece. PhD Thesis, University of Trier, Germany

Röder, A., Duguy, B., Alloza, J.A., Vallejo, R., & Hill, J. (2008a). Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. Remote Sensing of Environment, 112 (1), 259-273

Röder, A., Hill, J., & Tsiourlis, G. (2008b). Assessing scale transitions in leaf area index estimation in a heterogeneous shrubland ecosystem. In, 14th Australasian Remote Sensing and Photogrammetry Conference (pp. 1-10). Darwin, Northern Territory, Australia

Röder, A., Kuemmerle, T., & Hill, J. (2005). Extension of retrospective datasets using multiple sensors. An approach to radiometric intercalibration of Landsat TM and MSS data. Remote Sensing of Environment, 95, 195-210

Röder, A., Kuemmerle, T., Hill, J., Papanastasis, V.P., & Tsiourlis, G.M. (2007). Adaptation of a grazing gradient concept to heterogeneous Mediterranean rangelands using cost surface modelling. Ecological Modelling, 204, 387-398

Röder, A., Udelhoven, T., Hill, J., Del Barrio, G., & Tsiourlis, G.M. (2008c). Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece. Remote Sensing of Environment, (in print)

Rodriguez Gonzalez, J., Del Barrio, G., & Duguy, B. (2008). Assessing functional landscape connectivity for disturbance propagaion on regional scales - A cost surface model approach applied to surface fire spread. Ecological Modelling, 211, 121-141

Rogan, J., Franklin, J., & Roberts, D.A. (2002). A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment, 80, 143-156

Rogan, J., & Yool, S.R. (2001). Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico. International Journal of Remote Sensing, 22, 3101-3121

Rogge, D.M., Rivard, B., Zhang, J., & Feng, J. (2006). Iterative spectral unmixing for optimizing per-pixel endmember sets. IEEE Transactions on Geoscience and Remote Sensing, 44, 3725-3736

Rondeaux, G. (1995). Vegetation monitoring by remote sensing: a review of biophysical indices. Photo-Interprétation, 95, 197-213


110

Roy, D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., & Lindquist, E. (2008). Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 112, 3112-3130

San Miguel-Ayanz, J., & Biging, G.S. (1996). An iterative classification approach for mapping natural resources from satellite imagery. International Journal of Remote Sensing, 17, 957-981

Sanchez-Hernandez, C., S., D., & Foody, G.M. (2007). Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats. Ecological Informatics, 2, 83-88

Savitzky, A., & Golay, M.J.E. (1964). Smoothing and differentiating of data by simplified least square procedure. Analytical Chemistry, 36, 1627-1638

Scarth, P., Schmidt, M., Röder, A., & Gill, T. (2010). Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis. In, 15th Australasian Remote Sensing and Photogrammetry Conference. Alice Springs, Australia

Schlerf, M., Atzberger, C., & Hill, J. (2005). Remote sensing of forest biophysical variabiles using HyMap imaging spectrometer data. Remote Sensing of Environment, 95, 177.194

Schlittgen, R., & & Streitberg, B.H.J. (1999). Zeitreihenanalyse. München, Wien: Oldenburg Verlag

Schmidt, H., & Karnieli, A. (2001). Sensitivity of vegetation indices to substrate brightness in hyper-arid environments: the Makhtesh Ramon Crater (Israel) case study. International Journal of Remote Sensing, 22, 3503-3520

Scholes, R.J., & Archer, S.R. (2997). Tree-grass interactions in savannas. Annual Review of Ecology and Systematics, 28

Schott, J.R. (1997). Remote Sensing. The image chain approach. Oxford, New York: Oxford University Press

Schriever, J.R., & Congalton, R.G. (1995). Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the Northeast. Photogrammetric Engineering and Remote Sensing, 61, 321-327

Senay, G.B., & Elliott, R.L. (2002). Capability of AVHRR data in discriminating rangeland cover mixtures. International Journal of Remote Sensing, 23, 299-312

Settle, J.J., & Drake, N.A. (1993). Linear Mixing and the Estimation of Ground Cover Proportions. International Journal of Remote Sensing, 14, 1159-1177

Shandley, J., Franklin, J., & White, T. (1996). Testing the Woodcock Harward image segmentation algorithm in an area of southern Californian chaparral and woodland vegetation. International Journal of Remote Sensing, 17, 983-1004

Shoshany, M., & Svoray, T. (2002). Multidate adaptive spectral unmixing and its application to analysis of ecosystem transition along a climatic gradient. In, Remote Sensing of Environment (pp. 5-20)

Shumway, R.H., & Stoffer, D.S. (2006). Time Series Analysis and Its Applications: With R Examples. New York: Springer

Sinyuk, A., Dubovik, O., Holben, B., Eck, T.F., Breon, F.-M., Martonchik, J., Kahn, R., Diner, D.J., Vermote, E.F., Roger, J.-C., Lapyonok, T., & Slutsker, I. (2007). Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and satellite data. Remote Sensing of Environment, 107, 90-108

Skarpe, C. (1992). Dynamics of savanna ecosystems. Journal of Vegetation Science, 3, 293-300


111

Skidmore, A., Ferwerda, J.G., Mutanga, O., Van Wieren, S., Peel, M., Grant, R.C., Prins, H.H.T., Bektas Balick, F., & Venus, V. (2010). Forage quality of savannas - Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery. Remote Sensing of Environment, 114, 64-72

Small, C. (2004). The Landsat ETM plus spectral mixing space. Remote Sensing of Environment, 93, 1-17

Smirnov, A., Holben, B.N., Eck, T.F., Dubovik, O., & Slutsker, I. (2000). Cloud-Screening and Quality Control Algorithms for the AERONET Database. Remote Sensing of Environment, 73, 337-349

Smith, M.O., Adams, J.B., & Gillespie, A.R. (1990). Reference Endmembers for Spectral Mixture Analysis. In, 5th Australasian Remote Sensing Conference. Perth, Australia

Smith, M.O., Adams, J.B., & Sabol, D.E. (1994). Spectral Mixture Analysis - New Strategies for the Analysis of Multispectral Data. In J. Hill & J. Mégier (Eds.), Imaging Spectrometry -- a Tool for Environmental Observations (pp. 125-143): Springer Netherland

Smits, P.C., Dellepiane, S.G., & Schowengerdt, R.A. (1999). Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20, 1461-1486

Soille, P., & Vogt, P. (2009). Morphological segmentation of binary patterns. Pattern Recognition Letters, 30, 456-459

Song, C., Dickinson, M.B., Su, L., Zhang, S., & Yaussey, D. (2010). Estimating average tree crown size using spatial information from Ikonos and QuickBird images: Across-sensor and across-site comparisons. Remote Sensing of Environment, 114, 1099-1107

Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., & Macomber, S.A. (2001). Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230-244

Soudani, K., François, C., le Maire, G., Le Dantec, V., & Dufrêne, E. (2006a). Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102, 161-175

Soudani, K., François, C., le Maire, G., Le Dantec, V., & Dufrêne, E. (2006b). Erratum to "Comparative analysis of IKONOS, SPOT, and ETM+ data leaf area index estimation in temperature coniferous and deciduous forest stands" [Remote Sensing of Environment 102 (2006) 161-175]. Remote Sensing of Environment, 103, 497-497

Stafford-Smith, D.M., & Reynolds, J.F. (2002). The Dahlem desertification paradigm: a new approach to an old problem. In J.F. Reynolds & M.D. Stafford-Smith (Eds.), Global desertification. Do humans cause deserts? (pp. 403-424). Berlin: Dahlem University Press

Stefanovska, A., & Bracic, M. (1999). Physics of the human cardiovascular system. Contemporary Physics, 40, 31-55

Stellmes, M., Udelhoven, T., Röder, A., Sonnenschein, R., & Hill, J. (2010). Dryland observation at local and regional scale - Comparison of Landsat TM/ETM+ and NOAA AVHRR time series. Remote Sensing of Environment, 114

Steven, M.D., Malthus, T.J., Baret, F., Xu, H., & Chopping, M.J. (2003). Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 88, 412-422

Strahler, A.H., Jupp, A.L.B., Woodcock, C.E., Schaaf, C., Yao, T., Zhao, F., Yang, X., Lovell, J., Culvenor, D., Newnham, G., Ni-Miester, W., & Boykin-Morris, W. (2008). Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna). Canadian Journal of Remote Sensing, 34, 426-440


112

Stuckens, J., Coppin, P.R., & Bauer, M.E. (2000). Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sensing of Environment, 71, 282-296

Svoray, T., Shafran-Nathan, R., Ungar, E.F., Arnon, A., & Perevolotsky, A. (2009). Integrating GPS technologies in dynamic spatio-temporal models to monitor grazing habitats in dry rangelands. In A. Röder & J. Hill (Eds.), Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment (pp. 301-322). London: Taylor&Francis

Tanré, D. (1990). Description of a computer code to simulate the satellite signal in the solar spectrum: 5S code. International Journal of Remote Sensing, 11, 659-668.

Tanre, D., Deroo, C., Duhaut, P., Herman, M., Morcrette, J.J., Perbos, J., & Deschamps, P.Y. (1990). Description of a computer code to simulate the satellite signal in the solar spectrum - the 5S Code. International Journal of Remote Sensing, 11, 659-668

Teillet, P.M. (1997). A status overview of Earth observation calibration/validation for terrestrial applications. Canadian Journal of Remote Sensing, 23, 291-298

Teillet, P.M., Barker, J.L., Markham, B.L., Irish, R.R., Fedosejevs, G., & Storey, J.C. (2001). Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets. Remote Sensing of Environment, 78, 39-54

Teillet, P.M., & Fedosejevs, G. (1995). On the dark target approach to atmospheric correction of remotely sensed data. Canadian Journal of Remote Sensing, 21, 374-387

Teillet, P.M., Fedosejevs, G., Thome, K.J., & Barker, J.L. (2007). Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain. Remote Sensing of Environment, 110, 393-409

Thome, K., Markham, B., Barker, J., Slater, P., & Biggar, S. (1997). Radiometric calibration of Landsat. Photogrammetric Engineering and Remote Sensing, 63, 853-858

Tian, Y., Woodcock, C.E., Wang, Y., Privette, J.L., Shabanov, N.V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Häme, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., & Myneni, R.B. (2002a). Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment. Remote Sensing of Environment, 83, 414-430

Tian, Y., Woodcock, C.E., Wang, Y., Privette, J.L., Shabanov, N.V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Häme, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., & Myneni, R.B. (2002b). Multiscale analysis and validation of the MODIS LAI product: II. Sampling strategy. Remote Sensing of Environment, 83, 431-441

Tommervik, H., Hogda, K.A., & Solheim, L. (2003). Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 85, 370-388

Torrence, C., & Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61-78

Toutin, T. (2004). Review article: Geometric processing of remote sensing images: models, algorithms and methods. International Journal of Remote Sensing, 25, 1893-1924

Townsend, P.A., Lookingbill, T.R., Kindon, C., & Gardner, R.H. (2009). Spatial pattern analysis for monitoring protected areas. Remote Sensing of Environment, 113, 1410-1420

Trabaud, L. (1981). Postfire plant community dynamics in the Mediterranean basin. In J.M. Moreno & W.C. Oechel (Eds.), The role of fire in Mediterranean-type ecosystems (pp. 1-15). New York, Berlin, Heidelberg: Springer

Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D., Pak, E.W., Mahoney, R., Vermote, E., & El-Saleous, N. (2005). An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT Vegetation NDVI data. International Journal of Remote Sensing, 26, 4485-4498


113

Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S., & Briggs, J.M. (1999). Relationships between leaf area index and Landsat TM spectral indices across three tempreate zone sites. Remote Sensing of Environment, 70, 52-68

Turner II, B.L., Lambin, E., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences of the United States of America, 104, 20666-20671

Udelhoven, T. (2010). TimeStats: A Software Tool for the Retrieval of Temporal Patterns from Global Satellite Archives. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-8

Udelhoven, T., Stellmes, M., Del Barrio, G., & Hill, J. (2009). Assessment of rainfall and NDVI anomalies in Spain (1989-1999) using distributed lag models. International Journal of Remote Sensing, 30, 1961-1976

Udelhoven, T., van der Linden, S., Waske, B., & Stellmes, M. (2010). Hypertemporal classification of large areas using decision fusion. IEEE Geoscience and Remote Sensing Letters, 6, 592-596

U.S.G.S. (1984). Landsat data user handbook. Alexandria

Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115

Verburg, P.H., Schot, P.P., Dijist, M.J., & Veldkamp, A. (2004). Land use change modelling: current practice and research priorities. GeoJournal, 61, 309-324

Verhoef, W. (1984). Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16, 125-141

Verhoef, W., & Bach, H. (2007). Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment, 109, 166-182

Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., & Morcrette, J.J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35, 675-686

Verstraete, M. (1994a). The contribution of remote sensing to montior vegetation and to evaluate its dynamic aspects. In F. Veroustrate & R. Ceulemans (Eds.), Vegetation, modeling and climage change effects (pp. 207-212). The Hague: SPB Academic Publishing

Verstraete, M. (1994b). Retrieving canopy properties from remote sensing measurements. In J. Hill & J. Mégier (Eds.), Imaging spectrometry - a tool for environmental observations (pp. 109-125). Dordrecht, Bostong, London: Kluwer Academic Publishers

Viedma, O., Melia, J., Segarra, D., & GarciaHaro, F.J. (1997). Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing of Environment, 61, 383-398

Viovy, N., Arino, O., & Belwards, A.S. (1992). The best index slope extraction (BISE): a method for reducing noise in NDVI time series. International Journal of Remote Sensing, 13

Vogelmann, J.E., Helder, D., Morfitt, R., Choate, M.J., Merchant, J.W., & Bulley, H. (2001). Effects of landsat 5 thematic mapper and Landsat 7 enhanced thematic mapper plus radiometric and geometric calibrations and corrections on landscape characterization. Remote Sensing of Environment, 78, 55-70

Vogt, P., Ferrari, J.R., Lookingbill, T.R., Gardner, R.H., Riitters, K.H., & Ostapowicz, K. (2009). Mapping functional connectivity. Ecological Indicators, 9, 64-71


114

Vogt, P., Riitters, K.H., Iwanowski, M., Estreguil, C., Kozak, J., & Soille, P. (2007). Mapping landscape corridors. Ecological Indicators, 7, 481-488

Wang, L., Sousa, W.P., Gong, P., & Biging, G.S. (2004). Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment, 91, 432-440

Wei, W. (1990). Time series analysis. Univariate and multivariate methods. : Addison-Wesley

Weissteiner, C., Böttcher, K., Mehl, W., Sommer, S., & Stellmes, M. (2008). Mediterranean-wide Green Vegetation Abundances for Land Degradation Asessment Derived from AVHRR NDVI and Surface Temperature 1989 - 2005. Luxembourg: European Commission

Welch, R., Jordan, T.R., & Ehlers, M. (1985). Comparative evaluations of the geodetic accuracy and cartographic potential of Landsat-4 and Landsat-5 Thematic Mapper Image Data. Photogrammetric Engineering & Remote Sensing, 51, 1249-1262

Wessels, K.J. (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68

Wessels, K.J. (2009). Letter to the Editor: Comments on Proxy global assessment of land degradation by Bai et al. (2008). Soil Use and Management, 25

Widmann, M., & Schär, C. (1997). A principal component and long-term trend analysis of daily precipitation in Switzerland. International Journal of Climatology, 17, 1333-1356

Wulder, M.A., Franklin, S.E., White, J.C., Cranny, M.M., & Dechka, J.A. (2004). Inclusion of topographic variables in an unsupervised classification of satellite imagery. Canadian Journal of Remote Sensing, 30, 137-149

Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, J., Loveland, T.R., & Woodcock, C.E. (2008). Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112, 955-969

Yang, W., Huang, D., Tan, B., Stroeve, J.C., Shabano, N.V., Knyazikhin, Y., & Nemani, R.R. (2006a). Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the Terra MODIS sensor: 2000-2005. IEEE Transactions on Geoscience and Remote Sensing, 44, 471-475

Yang, W., Tan, B., Huang, D., Rautiainen, M., Shabanov, N.V., Wang, Y., Privette, J.L., Huemmrich, K.F., Fensholt, R., Sandholt, I., Weiss, M., Ahl, D.E., Gower, S.T., Nemani, R.R., Knyazikhin, Y., & Myneni, R.B. (2006b). MODIS leaf area index products: from validation to algorithm improvement. IEEE Transactions on Geoscience and Remote Sensing, 44, 1885-1898

Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., & Huete, A.R. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84

Assessing and Valuating Ecosystem Services and Mitigation Strategies

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IV. A Methodological Framework for Assessing and Valuating Ecosystem Services

and Mitigation Strategies against Desertification


Laura Onofri

Andrea Ghermandi

Paulo A.L.D. Nunes

L. Onofri, A. Ghermandi, and P.A.L.D. Nunes

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Laura Onofri, Andrea Ghermandi and Paulo A.L.D. Nunes (November 2010) Department of Economics University of Venice Cà Foscari San Giobbe 823 30121 Venice Italy [email protected]


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1 Introduction

Drylands are territories where ecosystems, human economic activities and biodiversity coexist. Human activities and economic use of natural resources often imply a deterioration of natural, ecological and geo-physical resources. There is, in fact, an evident trade-off expressed in term of objectives. Economic productive activities generate income, profits or guarantee survival At the same time, economic productive activities can negatively affect delicate ecological equilibria. It is recently recognized that sustainable development requires the preservation and respect of ecological equilibria, when performing economic activities. It is also recently recognized that natural ecosystems provide themselves services that might be of support to human activities. These services are called ecosystem services. Therefore, the trade off does not imply anymore a corner solution, bur should be interpreted as an equilibrium condition. The choice does not focus anymore on the trade-off between economic growth vs. natural resources preservation, but it becomes a choice on how much natural resources should be used, so that economic growth costs (loss of natural resources) equal economic growth benefits at the margin Economists, in fact, study human behavior in order to valuate, explain and predict. In the economist’s framework, economic agents are rational and pursue a rational objective/perform choices in a constrained setting. Constrained choice implies that every choice has benefits and costs. Economists assess and measure, when feasible, the monetary value of benefits and costs. The measurement of a monetary value is straightforward when there is an institution (market), where demand and supply meet. The market value is defined by several indicators, the most important of which is the trade price. In addition, economists are able to compute consumers’ surplus, producer surplus, total surplus. Non-market values are more difficult to compute, since there is not an institution where trade occurs and no price exists. Therefore, in order to assess non-market values (benefits), it is important to adopt valuation methodologies that mimic market behavior, in order to elicit stakeholders’ preferences for the good, expressed in monetary terms (willingness-to-pay). Ecosystem services produce both market and non-market values. In order to assess and measure ecosystem services we need some classifications and a lot of data and information.

The paper is organized as follows. Section 2 provides some definitions and classifications. Section 3 surveys the most important valuation techniques adopted in economic analysis, by highlighting costs and benefits of each methodology Section 4 introduces, surveys and critically discusses socio-economic indicators for assessing ecosystem services and anti-desertification strategies. In addition, the section presents a meta-indicator that conveys synthesized information with some causality content. Section 5 concludes.


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2. Some taxonomy.

The paragraph introduces some basic taxonomy for defining and explaining technical economic and valuation concepts. It is thus effectively the application of ‘ecosystem valuation’ (7) by analysts to recognize and manage the dependence and impact of human activities upon ‘ecosystem services’. This is achieved through better understanding the economic value of ecosystem services, and determining which stakeholders derive those values. Such information can then be incorporated within the decision-making to help the stakeholders to enhance their objectives and/or implement sustainable development solutions. In this section we provide some taxonomy. ‘Ecosystem Services’ are ‘the benefits people obtain from ecosystems’. The benefits can be broken down into four categories that include:

– Provisioning services. The benefits that ecosystems provide in the form of ‘products’ or ‘goods’ that are consumed by humans or used in the production of other goods. They include things such as fish, nuts, timber, water and genetic resources;

– Regulating services. The benefits obtained from an ecosystem’s control of natural processes such as climate, disease, erosion, water quality and flows, and pollination, as well as protection from natural hazards such as storm and wave damage. “Regulating” in this context is a natural phenomenon and is not to be confused with government policies or regulations. They are ecosystem ‘functions’ and ‘regulatory processes’ that includes vegetation storing carbon, wetlands slowing down water flows and cleansing water, and coral reefs and mangroves protecting coastal infrastructure from erosion and storm damage;

– Cultural services: The non-material benefits people obtain from ecosystems such as recreation, spiritual values, and aesthetic enjoyment.

– Supporting services: The natural processes such as nutrient cycling and primary production that maintain the other services.

The value of supporting services is captured within the value of the above three services and so should NOT be valued separately. ‘Ecosystem Valuation’ is defined in this context as ‘the placing of monetary values on ecosystem services and associated changes in their quantity and or quality’. These changes may be positive or negative. It is important to recognize that this does not include the valuation of all ‘environmental externalities’8 only those (7) Often referred to as 'environmental valuation'. 8 ‘Environmental externalities’ are ‘environmental impacts caused by one party that affect another, but which are not accounted for financially by the party that caused the impact due to a lack of market price’. For example, a company emitting pollution will typically not take into account the costs that its pollution imposes on others in terms of damaged health or climate change costs. Environmental externalities include impacts to ecosystems, but also to other receptors such as humans, buildings and structures (including cultural assets) and economic activities. In addition, externalities can be either positive or negative. The impacts typically relate to adverse implications associated with air emissions, discharges, spills, land-take, noise, sedimentation and waste disposal etc. However, positive externalities occur when a company makes an environmental improvement (eg protecting a habitat) which they do not benefit from directly.


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specifically related to ecosystems. For example, this would include assessing the value that carbon emissions have on ecosystems (eg agriculture and environmental related tourism), but not the value of impacts to other economic activities, buildings or people directly.

3 Total economic value and ecosystem services economic tools

Ecosystem services are crucial to human well-being (MEA, 2000) and are associated to a wide range of benefits to human society. The value of ecosystem services can be assessed in terms of their impacts on the provision of inputs to production processes; in terms of their direct impact to human welfare and in terms of their impact on the regulation of the nature ecological functions relationship. Usually, market mechanisms that price the total value of ecosystem services, given their complexity, are lacking. Therefore, the economic valuation of ecosystem services require the use of special valuation tools, in particular it requires the use of a holistic, integrated assessment.

The paragraph surveys the most important valuation techniques adopted in economic analysis, by highlighting costs and benefits of each methodology. A well recognized and used framework for putting monetary values on ecosystem services is that of ‘Total Economic Value’. As illustrated in Figure 1, this categorizes the different ‘ecosystem services’ into the following type of economic value:

• Direct use values: These include raw materials and physical products that are used directly for production, consumption and sale such as those providing energy, shelter, foods, agricultural production, water supply, transport and recreational facilities. These are effectively the same as ‘provisioning services’ and recreation related ‘cultural services’.

• Indirect use values: These include the ecological functions that maintain and protect natural and human systems through services such as maintenance of water quality and flow, flood control and storm protection, nutrient retention and micro-climate stabilization, and the production and consumption activities they support. They are effectively equivalent to ‘regulatory services’.

• Option values: This is the ‘premium’ placed on maintaining a pool of habitats, species and genetic resources for future possible uses, some of which may not be known now, such as leisure, commercial, industrial, agricultural and pharmaceutical applications. This type of value potentially applies to each of the three main services.

• Non-use values: This is the value of ecosystems regardless of their current or future use, for cultural, spiritual, aesthetic, heritage and


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biodiversity reasons. They comprise ‘existence’, ‘altruistic’ and ‘bequest’ values. For example, people are willing to pay to protect whales and rainforests even though they may never use or see them in the wild themselves. Non-use values can be significant, particularly for maintaining unique ecosystems where large populations may be willing to pay to protect them.

Figure.1. Ecosystem Services and Total Economic Value

There are four main applications (or approaches) for ecosystem valuation (9)

each trying to answer different types of questions.

The most important and commonly used application is to evaluate ‘trade-offs’ between different options. This approach typically uses cost-benefit analysis to compare the full range of costs and benefits over time associated with different options. For example, one design option for a new development may result in a loss of 2 ha of wetlands, 1 ha of seagrass and 1 ha of sand dune.

Another option may result in the loss of 5ha of wetland. If all other things are equal, which is the best option? From a purely economic perspective, the answer depends on the difference in aggregated ‘marginal’ cost of losing each ha of habitat. Ecosystem valuation can help evaluate that trade-off through converting the impacts to a single unit of currency (money), although other factors may also influence the ultimate decision.

The other applications include: attempting to estimate the total value of an environmental asset; determining the distribution of benefits amongst stakeholders, and identifying potential sources of surplus based on who

(9) Pagiola, S,von Ritter, K, and Bishop, J (2004) Assessing the Economic Value of Ecosystem Conservation. The World Bank Environment Department. In collaboration with The nature Conservancy and IUCN.


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benefits (or loses). Assessing values for the latter tends to involve combining outputs from i) or ii), plus iii) below.

It is important to stress that there exist a hierarchy of valuation approaches. Ecosystem ‘valuations’ may be in qualitative, quantitative or monetary terms. Qualitative valuation typically involves describing the value as well as indicating whether the value is likely to be of high, medium or low economic value (eg a fishery value of a wetland may be medium whilst the non use value may be high). Quantitative valuation involves describing the nature of the value in terms of relevant quantitative information (eg the wetland is used by 10 fishermen who catch 5 tons of fish per year, and its existence in good quality is of significance to 50,000 local people).Monetary valuation actually involves placing a ‘monetary’ or ‘dollar value’ on the impact (eg the wetland generates US$ 2,000/year from its fish and the locals are willing to pay US$ 500,000/year for its continued protection).

3.1 A survey of economic valuation techniques

As a result of most ecosystem services and environmental impacts not having a monetary value expressed in a market-place/market-price, a diverse range of environmental valuation techniques have been developed over the past twenty to thirty years. This has historically been so that monetary values of environmental assets and impacts can be assessed alongside other economic and financial values for public decision-making purposes, to encourage more sustainable outcomes.

There are three main categories of valuation techniques: Revealed preference approaches; Cost-based approaches and Stated preference approaches. These are briefly summarized in the table below.

Table 2. Monetary Based Ecosystem Valuation Techniques. Source: adapted from ‘WBCSD Corporate Ecosystem Valuation Scoping Study’

Category Technique Description Example

Market prices Market prices

How much it costs to buy an ecosystem good or service, or what it is worth to sell.

The market price of timber, fish or water.

Production function approaches

Effect on production

Relates changes in the output of a marketed good or service to a measurable change in ecosystem goods.

The reduction in fishery output as a result of clearing a mangrove or saltmarsh.

Travel costs

Using information on the amount of time and money people spend visiting an ecosystem for recreation or leisure purposes to elicit a value per visit.

The transport and accommodation costs, entry fees and time spent to visit a National Park.

Revealed preference approaches: Look at the way in which people reveal their preferences for ecosystem services through market production and consumption

Surrogate market approaches

Hedonic pricing

The difference in property prices or wage rates that can be ascribed to the different ecosystem qualities or values.

The difference in prices between houses overlooking an area of natural beauty or water features compared to similar houses that do not.


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Category Technique Description Example

Replacement costs

The cost of replacing an ecosystem good or service with artificial or man-made products, infrastructure or technologies, in terms of expenditures saved.

The costs of flood protection infrastructure after the loss of catchment protection forest

Mitigative or avertive expenditures

Expenditures required to mitigate or avert the negative effects of the loss of ecosystem services (similar to replacement costs).

Additional purification infrastructure required to maintain water quality standards after the loss of natural wetlands

Cost-based approaches: Look at the market trade-offs or costs avoided of maintaining ecosystems for their goods and services

Damage costs avoided

The costs incurred to property, infrastructure and production when ecosystem services which protect economically valuable assets are lost, in terms of expenditures saved.

The damage to roads, bridges, farms and property resulting from increased flooding after the loss of catchment protection forest

Contingent valuation

Infer ecosystem values by asking people directly what is their willingness to pay (WTP) for them or their willingness to accept (WTA) compensation for their loss saved.

How much would you be willing to contribute towards a fund to clean up and conserve a river?

Stated preference approaches: Ask consumers to state their preference directly Choice

experiments

Presents a series of alternative resource or ecosystem use options, each defined by various attributes set at different levels (including price), and asks respondents to select which option (ie sets of attributes at different levels) they prefer.

Respondents’ preferences for conservation, recreational facilities and educational attributes of natural woodlands.

In addition, a number of other ‘valuation’ techniques can be used, as detailed in Table 3. These include ‘benefit transfer’ (or value transfer), which is becoming an increasingly common means of valuation due to its relative ease and cost-effectiveness. Benefit transfer is simply taking an economic value determined for a good in one context (e.g . improvement in environmental quality) and applying it to another site and context. Another technique, which is commonly used in determining biodiversity offsets, is ‘Habitat equivalency analysis’. This involves determining the quantity of new habitat required (ie restored or created) to offset the loss of goods and services from a damaged area of similar habitat.

Table 3. Other Ecosystem Valuation Techniques. Source: adapted from ‘WBCSD Corporate Ecosystem Valuation Scoping Study’

Category/ approach

Technique Description Example

Benefit (or Value) Transfer

Unit value (eg average willingness to pay values)

This technique applies average WTP values taken from other studies. Ideally these values are adjusted to account for key differences in context such as income levels and degree of impact.

The average willingness to pay of recreational visitors to one woodland in UK applied to another similar woodland in UK. If applied to a wood in Poland, converted using difference in GDP/capita factor.


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Category/ approach

Technique Description Example

WTP functions This technique uses the benefit function (or ‘bid’ function) which is the formula that explains the WTP value in terms of key characteristics (eg environmental and socio-economic such as incomes).

Insert specific site related variables (eg average income, level of education) into the WTP bid function for visitor s to a woodland determined for a similar woodland elsewhere.

Meta analysis This technique takes the results from a number of studies and analyses them in such a way that the variations in WTP in those studies can be explained.

Analysis of many WTP studies for woodlands to derive trends in the key variables affecting visitor WTP values for woodlands, to establish a suitable value or adjustments for the site to be assessed.

Habitat equivalency analysis

This techniques uses a scaling process to determine the amount of environmental compensation required based on units of habitat damaged and created.

The approach may determine that 3.5m2 of coral reef needs to be replaced for every 1m2 of coral reef damaged.

Resource equivalency analysis

The amount of environmental compensation required is based on units of resource damaged and created.

500 trout need to be replaced in a river damaged by a pollution incident.

Natural resource damage assessments and biodiversity offsetting

Value equivalency analysis

The amount of environmental compensation required is based on either ‘value to cost’ (where the cost of remediation measures is set to the value of damages) or ‘value to value’ (where the value of remediation measures is set to the value of damages).

The value of damages to corals based on a stated preference survey reveals a loss of US$ 1 million, which is then invested in coral remedial and compensatory measures that cost US$ 1 million or that provide US$ 1 million of benefits.

A number of other valuation techniques may be relevant for valuing environmental externality impacts to human health. These include for example, the cost of illness, loss of earnings, Quality Adjusted Life Years, but are outside the scope of these guidelines.

Finally, a variety of new ‘tools’ are being developed to assist with ecosystem valuation studies. These include various innovative web and Geographic Information System (GIS) based approaches. Some have been developed initially with public decision-making in mind (eg for governments assessing economic trade-offs comparing net benefits to society as a whole from different alternatives), and others with specific business applications. GIS can be extremely useful to map out the economic aspect and environmental features. It can help illustrate the assessment from a visual perspective and assist with calculations such as determining the precise area of habitat affected and the number of household or people within different distance


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bands from an impact. The latter can be especially useful when assessing non-use values.

3.2. Advantages, disadvantages and applicability of ecosystem services valuation techniques

This section provides guidance on selecting ecosystem valuation techniques and highlights some typical steps and issues associated with a number of the more common valuation techniques likely to be used in valuation exercises. The Table below indicates which valuation technique is more typically used to value which category of ecosystem service.

Table 4. Application of Valuation Techniques for Different Types of Ecosystem Services. Source: adapted from ‘WBCSD Corporate Ecosystem

Revealed preference Total Economic Value

Ecosystem Services Market prices

Effect on prod-uction

Travel costs

Hedonic pricing

Cost-based

Stated preference

Benefit transfer

Direct use Provisioning

Indirect use Regulating

Direct use Recreation

Non use Cultural

Aesthetic

However, in many situations there is a choice of technique, and it then depends on the study context and circumstances as to the preferred technique. For example, aspects such as accuracy required, and budget, data and time availability may play a role. Note that the budgets (including data costs), timelines, and skills required can vary significantly depending on data already available, nature of the issue and who is involved in undertaking the study.

Finally, Table 5 summarises some of the key features of the main ecosystem valuation techniques.


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Table 5. Comparison of Ecosystem Valuation Techniques. Source: adapted from ‘WBCSD Corporate Ecosystem Valuation Scoping Study’

Technique Advantages Disadvantages Data required

Market prices + A readily transparent and defensible method since based on market data. + It reflects an individual’s willingness to pay (WTP).

- Only applicable where a market exists for the ecosystem service and data is readily available.

Market price to buy an ecosystem product. The costs involved to process and bring the ecosystem product to market (e.g. processed timber).

Effect on production

+ If data is available, it is a relatively straightforward technique to apply.

- Necessary to recognise and understand the relationship between the ecosystem service and output of product. - Can be difficult to obtain data on both change in the ecosystem service and effect on production.

Data on changes in the output of a product. Data on cause and effect relationship (eg loss of fisheries due to loss of seagrass or coral habitat).

Travel costs + Based on actual behaviour (what people do) rather than a hypothetically stated WTP. + The results are relatively easy to interpret and explain.

- Approach is limited to direct use recreational benefits. - Difficulties in apportioning costs when trips are to multiple destinations or are for more than one purpose. - Considering travel costs alone ignores the opportunity cost of time whilst travelling (e.g. should a proportion of the individual’s hourly salary be added?) - Some individuals may have moved house to live closer to the site, reducing their travel costs and therefore under estimating their valuation of the site.

The amount of time and money that people spend visiting an ecosystem for recreation or leisure purposes. Motivations for travel.

Hedonic pricing + Readily transparent and defensible method since based on market data and WTP. + Property markets are generally very responsive so are good indicators of values.

- Approach is largely limited to benefits related to property. - The property market is affected by a number of factors in addition to environmental attributes, so these need to be identified and discounted.

Usually data relating to differences in property prices or wage rates that can be ascribed to the different ecosystem qualities (e.g. a landscape view, distance to water feature).

Replace-ment costs

+ Provides surrogate measures of value for regulatory services (which are difficult to value by other means). + A readily transparent and defensible method when based on market data.

- Can overestimate values. - The technique does not consider social preferences for services or behaviour in the absence of the services. - The replacement service probably only represents a proportion of the full range of services provided by the natural resource.

The replacement costs of a proxy for the ecosystem service. This needs to be at least as effective as the ecosystem service and be the least cost alternative. Society must also have demonstrated a willingness to pay for the project.


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Mitigative or avertive expenditures

+ Provides surrogate measures of value for regulatory services. + When goods are non-marketed, it can be easier to value the costs of producing benefits than the benefits themselves.

- Can overestimate values. Data on the expenditures required to mitigate or avert the negative effects of the loss of ecosystem services.

Damaged costs avoided

+ Provides surrogate measures of value for regulatory services that are difficult to value by other means (eg storm, flood and erosion control).

- The approach is largely limited to services related to properties, assets and economic activities. - Can overestimate values.

Data on costs incurred to property, infrastructure or production as a result of loss of ecosystem services. Damages under different scenarios including ‘with’ and ‘without’ regulatory service.

Contingent valuation

+ Enables values to be captured for both use and non-use values. + Extremely flexible since it can be used to estimate the economic value of virtually anything. + Will give a much more accurate outcome than benefit transfers.

- The results are subject to numerous different bias from respondents and hypothetical in nature. - E.g. respondents may express a positive willingness to pay to promote a ‘warm glow’ effect, overestimating valuations. - E.g. if the cost is perceived as a government tax, respondents may express a negative willingness to pay, underestimating valuations. - Non-use values derived can still be questionable - It is resource intensive.

Data on the amounts that people would be willing to pay for an ecosystem service, or conversely, what they would be willing to accept as compensation for loss of an ecosystem service. Obtained by asking individuals to state their preference directly.

Choice experiments

+ Enables values to be captured for both use and non-use values. + Provides theoretically more accurate values for marginal changes (eg values per percent increase in coral cover). + Will give a much more accurate outcome than benefit transfers.

- The results are subject to bias from respondents, is resource intensive and hypothetical in nature. - It is resource intensive. - Can be mentally challenging for respondents to truly weigh up the alternative choices given to them in the time available.

Data on the individual preferences of people when presented with a series of alternative resource or ecosystem use options.

Benefits transfer + Low cost and rapid method for estimating recreational and non-use values.

- The results can be questionable unless carefully applied.

Valuations from similar studies elsewhere. Data on key variables from different studies (eg GDP per person).


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4 Valuing drylands ecosystem services and anti-desertification policies

Within the context of the mounting global concern caused by land degradation in drylands (defined as desertification in the text of the United Nations Convention to Combat Desertification), desertification generates a persistent reduction in the services provided by drylands ecosystems, leading to unsustainable use of the drylands and their impaired development. ‘‘Desertification’’ means land degradation in arid, semiarid, and dry subhumid areas resulting from various factors, including climatic variations and human activities. Land degradation means reduction of or loss in the biological or economic productivity and complexity of rain-fed cropland, irrigated cropland, range, pasture, forest, or woodlands resulting from land uses or from processes arising from human activities and habitation patterns (UNCCD 1992). Drylands Ecosystem services might contribute (together with other exogenous factors, including political marginality, slow growth of health and education infrastructure, facilities and services) to directly affect human-well-being. The Millennium Assesment conceptual framework (MA 2003) broadly identifies five key components of human well-being.

• Basic materials for a good life. The low biological production of drylands, constrained by water, is the ecosystem condition that limits the provision of basic materials for a good standard of living. This also limits the livelihood opportunities in drylands and often leads to practices, such as intensified cultivation, that cannot be serviced due to low and further impaired nutrient cycling and water regulation and provision, requiring adjustments. in management practices or the import of nutrients and water provided by services of other ecosystems.

• Health. The two key factors contributing to poor health in drylands are malnutrition and limited access to clean drinking water, again reflecting their low biological production and water provision. In Asia, for example, the fraction of children under the age of five facing hunger is 36% in drylands compared with 15% in the forest and woodland system (averaged for the different subtypes of each of these systems) However, poor health is also exacerbated by poor health-related infrastructures.

• Good Social Relations. The quality of social relations can be gauged in terms of social strife (wars and political upheavals) and refugees. Environmental refugees leave their homes due to environmental degradation and lack of viable livelihoods. And the sale of stock, wage labor, borrowing of cash for food, and the sale of valuables all precede their migration. Other categories include people displaced for political reasons that may affect the availability of services in drylands to which they have been relocated. Thus demography and sociopolitical drivers, more than the direct condition of ecosystem services, contribute to the quality of social relations.

• Security. Food security is an essential element of human wellbeing in drylands and is related to socioeconomic marginalization, lack of proper


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infrastructure and social amenities, and often the lack of societal resilience. Climatic events like prolonged droughts and excessive floods also drive insecurity in drylands. But sociopolitical drivers like land tenure practices that relate to sharing and conservation of natural resources or that generate land cover change that may limit traditional pastoral livelihood opportunities can greatly affect food security.

• Freedom and Choice. Dryland people are not affected just by the unique condition of the ecosystem but are also further restricted by local and global political and economic factors, which can exert major limitations on their freedom and life choices. With the exception of OECD countries, dryland peoples are mostly politically marginalized; that is, their role in political decision-making processes is perceived as being insignificant. Consequently, market factors that determine dryland farmers’ decision-making and the effects on their well-being are often critical.

What needs to be assessed, in this case, is whether or not there are thresholds in the provision of services that, either independently or together with the exogenous factors, cause human well-being to drop to a level that crosses the poverty threshold or dramatically decrease welfare conditions. In addition, the recognized importance of ecosystem services on human activities has spurred to take action and starts implementing measures that limit desertification in drylands. This part of socio-economic analysis, valuation approach and technique choice should consider the impact of mitigation and adaptation strategies in dryland selected areas, in particular, those prevention and restoration actions that combat desertification in those areas. The analysis can vary according to available data and valuation approach and technique. However, the focus should be on comparing different situations before and prevention/restoration action and valuate costs and benefits of those actions. It is, therefore, important to valuate the socio-economic impacts of several dimensions:

1) The (total) economic benefit of a dryland systems with respect to the services they provide and the drivers that determine trends in their provision;

2) The economic loss/costs that desertification is causing to delicate dryland systems;

3) The economic impacts of mitigation and adaptation strategies to desertification.

In order to perform an economic-valuation exercise, it is important to gather data and select the proper valuation approach (qualitative, quantitative and/or monetary) and technique (as surveyed in the previous paragraph). Often the choice of the valuation approach and technique depends on data availability and on the possibility to “build” data (like WTP)10. Integrated assessment of ecosystem services in drylands before, during and after the 10 See Appendix 1.


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policy should be the preferred valuation methods. However, for the sake of simplicity, for the difficulty to gather information and for the costly management of economic valuation techniques, socio-economic indicators may represent an interesting tool for assessing anti-desertification policies performance.

4.1 Evaluation of anti-desertification policies through socio-economic indicators

In general, a policy evaluation process should evaluate the “success” (or failure) of a given policy (in a certain sense it is the performance measurement of public policies). However it is always important to find a solid causal nexus/relationship between policy/measures and observed facts/phenomena. For instance, suppose that in the period after the reforestation policies in a selected area, tourism (measured in terms of number of presences) has increased by 20%. Shall we conclude that the mitigation/anti desertification policy has been effective (on a socio-economic perspective) because a better environment has increased tourism? Is this statement correct? The answer is: it depends!

Suppose that a policy generates a (potential) Outcome A and an observed Outcome B. An evaluation comparing (potential) Outcome B and (observed) Outcome A is a counterfactual evaluation. An evaluation considering only Outcome B is a sort of “qualitative” evaluation; it is very common in the evaluation of European Cohesion Policy. It suffers from problems of causality. Describing observations via summaries, as in the previous example, is a critical part of most evaluation research. But it is often not the primary goal. The goal rather, is inference – the process using the facts we know to learn about facts we do not know. There are two types of inference: (1) descriptive and (2) causal11. To identify the impact of the policy on Y we need to consider also X and Z (e.g. in the tourism example, we should consider the general economic conditions). We need a counterfactual (i.e. observations without the policy).

When adopting socio-economic indicators for assessing the impact of policies it is also very important to stress that:

Correlation ≠ causality (in general, we need a theory or argument to link one variable to another! Or we need a deep evaluation of inter-correlated phenomena!).

11 (1) Descriptive inference is different than data summary. The aim is still to find out

“something we do not know” (i.e. a particular function, the impact of a given policy). By using case studies and data summary, researchers should prove the existence of “links” (see e.g. meta-analysis).

(2) In causal inference, researcher wants to know whether one factor or a set of factors leads to (or causes) some outcome. In general, causal inference is the difference between two descriptive inferences.


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Another point of policy economic evaluation refers to the timing of the evaluation exercise. We can distinguish three types of valuation linked to the timing:

a. Ex ante valuation (before the policy): cost benefit analysis

b. Ex post valuation (after the policy, success of the policy): effectiveness

c. In fieri valuation (during the policy): monitoring.

It is crucial to emphasise the importance of framing the socio-economic analysis within a holistic perspective, well represented by ecosystem services valuation (the before and after the implementation of anti-desertification practices). This approach (shortly summarized in Section 3) allows embracing all the multidimensional implications of the problem at issue, without giving up on its complexity or unduly broadening the scope of the analysis. However, adopting complex valuation techniques is a costly research activity. Therefore, another method to evaluate phenomena (desertification in the case at issue) is the adoption of socio-economic indicators.

Broadly speaking, “an indicator can be defined as the measurement of an objective to be met […]. An indicator produces quantified information with a view to helping actors concerned with public interventions to communicate, negotiate or make decisions. Within the framework of evaluation, the most important indicators are linked to the success criteria of public interventions” (EVALSED, p. 111).

In the various lists of desertification indicators that have been developed over the years, the characterization of the socio-economic context against which to evaluate the interventions to combat desertification is invariably acknowledged as a crucial component next to environmental indicators. This is natural if one considers that desertification is the result of both natural and anthropogenic pressures, which lead to environmental degradation and loss of biological and economic productivity (Rubio and Bochet, 1996). According to those authors, the main purposes of desertification indicators are the following:

(1) assess the current environmental and economic state;

(2) determine trends over time;

(3) detect detrimental stages in early stage to prevent irreversible damage; and

(4) identify causes in order to specify proper management actions.

The UN Convention to Combat Desertification (UNCCD) has identified desertification indicators as a necessary tool for both management and monitoring the implementation of strategies to combat desertification. Among the desirable characteristics of desertification indicator systems according to UNCCD is simplicity. Lists with a limited number of indicators that broadly apply to many stressors and sites are generally preferable, since they tend to be simpler and more ready to use by the parties concerned (UN,


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1997). At the same time, the indicators must be sensitive enough to detect important changes without being by natural variability (MEA, 2000). When extended lists are produced, a list of key indicators for common purposes should be indicated and it should be possible for final users to select among the remaining indicators only those that are most relevant for their purposes.

Reliability and objectivity of indicators are a crucial concern. According to Reed et al. (2006), indicators should be accurate and bias-free, reliable and consistent over space and time, scientifically robust and credible, verifiable and replicable. They should provide timely information and be sensitive to system stresses or the changes they are meant to indicate.

In the selection of the indicators, cost-effectiveness in the collection and analysis of the data is an important concern since they may require substantial resources in terms of finance, manpower, and time. Preferably, already available information concerning measures undertaken and their link with the rehabilitation of productive dryland ecosystems should be used.

Indicators characterized by measurability and ease to be operationalized should be clearly preferred over purely conceptual ones, and they should be related as much as possible to the existing onsite practices (Brandt et al., 2003). They should be able to reflect spatial change in land function and over time, i.e., to assess the present status as well as to identify time trends. A target level, baseline or threshold against which to measure them should be defined (Reed et al., 2006).

It is important to keep in mind that indicators are an aid to decision-making and not an end in themselves. Therefore their understandability by policy-makers is an important concern. Indicators should be clear and unambiguous, simplify complex phenomena and facilitate communication of information. They should measure what is important to stakeholders and possibly have social appeal and resonance (Reed et al., 2006).

Several international bodies have worked on the definition of environmental indicator systems, which bear importance in evaluating the extent, severity and socio-economic aspects of desertification. Such institutions include:

The UN Convention to Combat Desertification (UNCCD, http://www.eea.europa.eu/data-and-maps/indicators); The Organization for Economic Cooperation and Development (OECD, www.oecd.org/dataoecd/32/20/31558547.pdf); The United Nations Development Programme (UNDP, http://www.undp.org/fssd/crosscutting/sustdevmdg.html); The United Nations Environment Programme (UNEP, http://www.fao.org/docrep/w4745e/w4745e07.htm); The World Bank (http://web.worldbank.org); The European Environment Agency (EEA, http://www.eea.europa.eu/data-and-maps/indicators); The World Meteorological Organization (WMO, http://www.wmo.int);


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The UN Food and Agriculture Organization (FAO, http://www.fao.org/ag/agl/agll/lada). The investigation of the indicator systems put forward by such organizations or proposed by other EU projects such as Desertlinks (http://www.kcl.ac.uk/projects/desertlinks) and Medalus (http://www.kcl.ac.uk/projects/desertlinks), one can identify the common trends in socio-economic desertification indicators.

Although the actual indicator may differ according to the spatial scale of interest (e.g., global, national or project level), we can in general distinguish four main categories of socio-economic desertification indicators:

(1) Resource indicators provide information on the means used to implement programmes. For instance:

a. information about monetary investments for anti-desertification policies in €;

b. or information about other resources (e.g. number of workers);

c. time frame;

d. costs of inaction.

e. Opportunity cost of the policy

(2) Output indicators represent the product of the programme activity. Everything obtained in exchange for public expenditure. For instance:

a. hectares of reforested area or dryland;

b. information about productivity and profitability of the forest area/land;

∑∑∑== =

−=N

nn

N

n iin

ngserviceprovisionidryland LandAreaForesttTotalOutpuoductivity

11

7

1/Pr

Where i and n represent the type of forest/agricultural product and the number of countries/regions located in each dryland area12.

In particular, current productivity values of forests in terms of provisioning of the 7 types of Wood Forest Products (WFPs), i.e. industrial round wood, wood pulp and recover paper, other processed wood products, sawn wood, wood-

12 Forest/Land productivity refers to productivity calculation when the land/forest is used to produce marketable products (i.e agriculture, breeding). The same index can be calculated by considering data on Harvesting/Silvicultural practices, when the land/forest is used to produce non-marketable products, in order to highlight different resource productivity when human activities intervene or do not interve;


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based panels, paper and paperboard and wood fuel can be synthesized by the following:

∑∑∑== =

− =N

nn

N

n iin

ngserviceprovisioniaticregioncgeo ForestAreaeExportValuValueoductivity

11

7

1lim /Pr

Where i and n represent the type of forest product and the number of countries located in each geo-climatic area. This index is used by Fao, for the valuation of forests provisioning services. Another way to calculate the (land) productivity value is:

ucteoftheprodmarketpricoductivityValueoductivity *PrPr =

Where the land productivity (per hectare or squared meter) is multiplied by the market price of a selected product cultivated in the land.

Data on Forest/Land profitability (how much profit/income is produced per ha/squared meter, when the land is used for productive activities);

∑∑∑== =

−−=N

nn

N

n iin

ngserviceprovisionidryland LandAreaForestoftiseExportValuyofitabilit

11

7

1/PrPr

Where i and n represent the type of forest/agricultural product and the number of countries/regions located in each dryland area

c. soil water conservation measures;

d. hectares of land converted to agricultural use;

e. diversification of land-use and land-use intensity.

(3) Result indicators are the immediate advantages of the programme for the direct beneficiaries. For instance:

a. decrease in child mortality or people migration in sub/Saharian areas at risk of desertification;

b. unemployment rate;

c. net farm income;

d. water availability per capita;

e. population living above poverty level.


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(4) Impact indicators are the consequences of the programme beyond its direct and immediate interaction with the beneficiaries. For instance:

a. increase of the overall economy performance of the area (impact on GDP/GNP per capita; total and social investment as a percentage of GDP; development of new economic activities; tourism change and number of tourists);

b. impact on social context of the local population (changes in migration patterns and intensity; changes in population ageing patterns and age structure; family size; population density).

4.2 Desertification policy assessment: A cost-effective meta indicator

For the Practice Project purposes, given the heterogeneity of the many case studies and data sources and given the difficulties to gather the same kind of information, we propose a cost-effective indicator:

CEI = nactioneffectsofihepolicyeffectsoft

ontsofinactimonetaryicytsofthepolmonetary−− coscos

Let us analyse this “meta-indicator. On the numerator there is the difference between the monetary costs of the policy (i.e. Euros transferred for planting new trees, building irrigation systems/pipelines and so on) and the monetary costs of inaction, that (obviously assume to be zero. No policy, no money transfer, no public expenditure). It is very important to highlight that in this simplified ratio we do not account for opportunity costs13. The denominator is the difference between the (potential, expected) gain on the socio-economic milieu from a selected measure (GDP of the area; child mortality and many others) and the gains/losses, interpreted as the started benchmark.

Suppose that the costs of the mitigation policy are X units of money. So the numerator is positive. Suppose that we want to evaluate the effects of the policy in terms of impact on agriculture (the choice of the denominator is discretional to the researcher). Anti-desertification measures are assumed to improve agriculture. This might be done in terms of increased (if any) hectares of cultivable land or in terms of increased income (if any) from the agricultural

13 James Buchanan has defined opportunity costs as expressing "the basic relationship between scarcity and choice." The notion of opportunity cost plays a crucial part in ensuring that scarce resources are used efficiently. Thus, opportunity costs are not restricted to monetary or financial costs: the real cost of output forgone, lost time, pleasure or any other benefit that provides utility should also be considered opportunity costs. Opportunity cost is, therefore, the cost related to the next-best choice available to someone who has picked between several mutually exclusive choices. It is a key concept in economics


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sector in the selected area.; or in terms of increased productivity of land or farmers. Suppose that the numerator is always positive (money for the policy > 0 ⇒ inaction implies no money transfer for the policy). Suppose that our data (gathered information) show that the GDP per capita in the agricultural sector has increased with respect to the moment when no action was taken. We can/cannot infer causality, but this is not the purpose of our indicators, whose primary aim is “comparing” in a simple and synthetic informational vehicle (the indicator) phenomena occurring before and after the policy. Suppose that the denominator is positive because the gains from the policy are larger than the effects of inaction on the selected informative indicator (the agricultural sector). Since the numerator is always positive, this would generate a positive CEI. Suppose now that the denominator is positive because the gains from the policy are smaller than the effects of inaction on the selected informative indicator (the agricultural sector). For instance, after the policy action is taken the farmers’ GDP decreases. Since the numerator is always positive, this would generate a negative CEI. Finally, suppose that the denominator is zero because the gains from the policy equal the effects of inaction. In this case, the CEI is zero.

This kind of simple indicator is very flexible and allows for direct and straightforward comparability. In addition, if the different currencies are expressed by unique single, selected currency (i.e. Euros or U.S. dollars), the comparability can be possible also across different case studies.

5 Conclusion

Sustainable development requires the preservation and respect of ecological equilibria, when performing economic activities. Natural ecosystems provide themselves services that might be of support to human activities. These services are called ecosystem services. Desertification is a threat to the provision of ecosystem services and human well-being in drylands. For this reason, anti-desertification policies are designed and implemented in order to mitigate and adapt to climate changes. It is a challenging task, for economists, to value ecosystem services and impacts of anti-desertification policies. The valuation exercise can be performed through different valuation techniques that aim at targeting and measuring the value of ecosystem services (before and after the anti-desertification policy) in monetary terms. Alternatively the assessment of the anti-desertification policy can be performed through the adoption of indicators that convey synthesized socio-economic information about ecosystem services in selected areas and time periods. The first approach allows embracing all the multidimensional implications of the problem at issue, without giving up on its complexity or unduly broadening the scope of the analysis. However, adopting complex valuation techniques is a costly research activity. Therefore, another method to evaluate phenomena (desertification in the case at issue) is the adoption of socio-economic indicators. This paper critically surveys economic valuation techniques and socio-economic indicators for ecosystem services (and


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policies), highlighting limits and advantages of such important informational tools. The focus of the paper is on the survey, discussion and definition of key socio-economic indicators for assessing anti-desertification practices. In particular, the paper suggests a cost-effective meta-indicator, a flexible measure for anti-desertification policy assessment. Such measure is easy to compute; is not only a correlation measure, but synthesizes some causality among selected variables and can be used for comparability in different scenarios and selected areas. Further research should focus on the calculation of such indicators in order to assess its applicability.

6 References

Brandt, J., N. Geeson, and A. Imeson (2003) A desertification indicator system for Mediterranean Europe. Report of the DESERTLINKS project, available at: http://www.kcl.ac.uk/projects/desertlinks/ (accessed September 2010).

Chiabai A., Travisi C. , A. Markandya, H. Ding, P. A.L.D. Nunes (2010) Economic Assessment of Forest Ecosystem Services Losses: Cost of Policy Inaction," Working Papers 2010-13, BC3.

Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R. V. O’Neill, J. Paruelo, R. G. Raskin, P. Sutton and M. van den Belt. 1997. The Value of the World’s Ecosystem Services and Natural Capital. Nature 387:253–260.

De Groot, R.S.; Wilson, M.A.; and Boumas, R.M.J. (2002) A Typology for the Classification, Description and Valuation of Ecosystem Functions, Goods and Services. Ecological Economics 41(3): 393-408

Fisher B. and K. Turner (2008) Ecosystem Services: Classification for Valuation. Biological Conservation. 141. 1167-1169.

Fisher, B.; Turner, R.K.; and Morling, P. (2009) Defining and Classifying Ecosystem Services for Decision Making. Ecological Economics 68(3): 643-653

Garrod G. and K. Willis (2010) Economic Valuation of the Environment: Methods and Case Studies. Edward Elgar.

Geist, H.J. (2005) The Causes and Progression of Desertification. Ashgate Studies in Environmental Policy and Practice. Ashgate: Aldershot

Ghermandi A., J.C.J.M. van den Bergh, L.M. Brander, H.L.F. de Groot, P.A.L.D. Nunes, (2008). The Economic Value of Wetland Conservation and Creation: A Meta-Analysis, Working Papers 2008.79, Fondazione Eni Enrico Mattei.

Kumar, M. and Kumar, P. (2008) Valuation of the Ecosystem Services: A Psycho-Cultural Perspective. Ecological Economics 64(4): 808-819

MEA, Millennium Ecosystem Assessment (2000) Analytical Approaches for Assessing Ecosystem Condition and Human Wellbeing. World Resource Institute, Washington DC. p.50.

MEA, Millennium Ecosystem Assessment, (2005a). Ecosystems and Human Well-being: Synthesis.

Island Press, Washington, DC.

MEA, Millennium Ecosystem Assessment (2005b) ‘Ecosystems and Human Well-being: Desertification Synthesis’. World Resources Institute: Washington, DC.


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Nunes P.A.L.D. and J. C.J.M. van den Bergh (2001) Economic Valuation of Biodiversity: Sense or Nonsense? Ecological Economics 39: 203-222.

Nunes P. A.L.D., H. Ding, A. Markandya, (2009) The Economic Valuation of Marine Ecosystems, Working Papers 2009.68, Fondazione Eni Enrico Mattei.

Nunes P. A.L.D., R.Portela, N. Rao and S. Teelucksingh (2009). Recreational, Cultural and Aesthetic Services from Estuarine and Coastal Ecosystems, Working Papers 2009.121, Fondazione Eni Enrico Mattei

Onofri L, P.A.L.D. Nunes, H.Ding (2007). An Economic Model for Bioprospecting Contracts," Working Papers 2007.102, Fondazione Eni Enrico Mattei]

Onofri L. and P.A.L.D. Nunes (2004). The Profile of a “Warm-Glower”: A Note on Consumer’s Behavior and Public Policy Implications Working Papers 2004.113, Fondazione Eni Enrico Mattei.

Reed, M.S., E.D.G. Fraser, A.J. Dougill (2006) An Adaptive Learning Process for Developing and Applying Sustainability Indicators with Local Communities. Ecological Economics 59: 406-418.

Rubio, J.L., and E. Bochet (1998) Desertification indicators as diagnosis criteria for desertification risk assessment in Europe. Journal of Arid Environments 39: 113-120.

Turner K., I. Bateman,, W. N. Adger (2001) Economics of Coastal and Water Resources: Valuing Environmental Functions,. Kluwer

Turner K. , S. Morse-Jones and B. Fisher ( 2010) Ecosystem Valuation: A Sequential Decision Support System and Quality Assessment Issues. Annals of the New York Academy of Science. 79: 101-123.

UN (1997) Report on ongoing work being done on benchmarks and indicators. Intergovernmental negotiating committee for the elaboration of an international convention to combat desertification in those countries experiencing serious drought and/or desertification, particularly in Africa, 10th session, New York, 6-17 January 1997.


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APPENDIX 1: Required Information for Performing an Economic Valuation Study In order to perform an economic-valuation exercise, it is important to gather data and select the proper valuation approach (qualitative, quantitative and/or monetary) and technique (as surveyed in the previous paragraph). Often the choice of the valuation approach and technique depends on data availability and on the possibility to “build” data (like WTP).

The paragraph, therefore, is listing required information (minimum threshold) for economic valuation of drylands ecosystem services.

1) Drylands Supporting Services:

a) soil formation and soil conservation

b) Nutrient cycling supporting the service of soil development

c) Primary production

The information/data required for Supporting Services valuation are mostly geo-physical. We can highlight:

• Data and Information on above ground biomass:

• Data on dryland temperature in a selected time period:

• Data on dryland precipitation in a selected time period:

• Information about the composition in the land cover/land cover pattern in the area at study;

• Information/data about soil death and soil fertility:

• …

2) Drylands Regulating Services

a) water regulation;

b) climate regulation though carbon sequestration and/or surface reflectance and evaporation;

c) Pollination and seed dispersal

The information/data required for Regulating Services valuation are mostly geo-physical. We can highlight:

• Data on number of natural water basins and/or constructed/engineering based streams/water pipelines or other water infrastructure possibilities;


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• Data on Irrigation Costs:

• Data on Carbon Reserves (forestry map; extension of the dryland/forest area);

• …

3) Drylands Provisioning Services:

a) Provisions derived from Biological Production (foods and fiber, wood fuel, biochemicals; freshwater provisioning)

The information/data required for Regulating Services valuation are mostly economic. We can highlight:

Data on land productivity per squared meter/ha for the selected products cultivated in the land at desertification risk (e.g. x kg/corn/ha);

Data on domestic prices of products produced in drylands (for market purposes);

Information on the type and intensity of (market) economic activity in the selected dryland;

Information (qualitative) on the type and intensity of bioprospecting in the selected dryland;

Data on the number of water springs and/or water river basins and reservoir in all or selected locations in drylands

Data on domestic prices of selected products cultivated in the land at desertification risk (e.g. y euro/kg corn);

Data on land use patterns/composition in the land area at desertification risk, including forestry, grassland and the intensity of grazing activities, when applicable (e.g. for the case study Y we have a total of land area of Z ha, distributed in z1 for crop1, z2 crop2 and so on, or preferably a GIS map);

Data on production input (labour, water and land) costs/prices for the production of selected products cultivated in the land at desertification risk (e.g. irrigation costs);

Data on employment regarding the areas of study, including the contribution of the cultivated land at desertification risk on the creation of jobs/employment

….

4) Drylands Cultural Services:

a) Cultural Identity and diversity;


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b) Cultural landscape and heritage value;

c) Services Knowledge Systems;

d) Spiritual Services;

e) Aesthetic and Inspiration Services;

f) Recreation and Tourism

The information/data required for Cultural Services valuation are mostly economic. We can highlight:

• Data on Human Capital: (real GDP per capita/income of relevant population; number of inhabitants; relevant population in the nearby/and study site; level of human development (education level; employment/unemployment ratios; Innovation level; Public and private research and development investment; poverty indexes; time spent by household members collecting water and fuel wood; percent of rural population below the poverty line¸ children mortality and percent of children underweight. These indicators vary across selected area and depend on the level of economic and human development of selected areas. Indicators about poverty or R&D investments are calculated in different ways. Therefore, we just indicate the required information and leave the selection of data/specific indicator ta a casa-by-case selection ).

This kind of data is useful in order to define the profile of the relevant population living in the drylands and for defining proper policies and to perform at least a qualitative valuation.

• Data/Information on Cultural, Environmental and Aesthetic Values

I. number of Unesco sites, monuments, archeological sites; historic sites, cultural identity sites; natural protected areas;

II. data constructed through valuation techniques (see above paragraph 3);

III. Data/Information on in situ Education/Research Activities

IV. Data/information on particular economic, production skills developed through the site

• Data/Information on Governance Structures (information about the way the land/forest is organized and managed; information about property rights allocation (commons or privately owned land; for developing countries, information on common land, quantity of annual household consumption that is derived from common lands; information about the management of natural resources (associations, private companies; public authorities; information about trade habits (contracts; exchange; franchising)


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This kind of data is useful in order to define the governance structure, through which the resources that provide ecosystem services in drylands are managed.

Central or local government investments in infrastructure and accessibility to credit can also influence sustainability or vulnerability of dryland livelihoods as well as determine human well-being in these areas. Large-scale government-driven projects can facilitate the sustainable development of drylands, as seen in developed dryland areas in Israel, California, and Australia. But if inappropriately designed, implemented or managed, they can lead to desertification, as in the Aral Sea region. The failure of African governments, for instance, to devolve power to affected people and to link environmental degradation to economic policy has been seen by some as a significant drawback in combating desertification and drought As a result of these failings, many programs lack local support or are undermined by conflicting trade and agricultural policies pursued by governments.

• Data on Tourism (annual flows of visitors, characterization of the supply side, including the number and type of hotels and restaurants););

• anthropogenic pressure (e.g., some index of pollution);

• climatic data (e.g., temperature, precipitation);

• type and intensity of economic activity taking place in the area under consideration, including tourism;

• …

Biodiversity Services Species richness declines with decreasing primary productivity and vice versa, hence drylands species richness should decline with aridity. Indeed, the low number of flowering plant species in the hyperarid

subtype rapidly increases with reduced aridity (in agreement with an increase of between-‘‘broad biome’’ diversity) and peaks in the dry sub-humid subtype. However, contrary to expectation, species diversity declines in non-dryland temperate humid areas. But as might be expected, it is nearly half that of tropical areas. It is important to assess and measure the dimensions, structure and composition of dryland biodiversity, since biodiversity conservation plays a role in the provision of drylands ecosystem services. There are many dryland species that are directly involved in the provision of a range of ecosystem services. One such example is African acacia (Ashkenazi 1995), which provides for soil development and conservation (roots, canopy, and litter), forage (leaves and pods eaten by livestock), fuelwood (dead twigs), and food (edible gums). It is also involved in nutrient cycling (symbiosis with nitrogen-fixing bacteria), generates cultural services and it supports other biodiversity: a large number of animal species depend on it for shelter,


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shade, nest sites, and food. (Often this is of mutual benefit: wild and domestic mammals disperse the seeds, thus determining the spatial distribution of the species.)

The numerous dryland plant species of different growth forms jointly provide a package of services through their ground cover and structure, which provide the drylands’ most important services of water regulation and soil conservation as well as forage and fuel wood provision and climate regulation. In arid and semiarid areas, clumps of bushes and annuals embedded in the matrix of a biological soil crust—which consists of an assemblage of several species of cyano bacteria (that provide the added benefit of nitrogen fixation), microalgae, lichens, and mosses—jointly generate soil conservation and water regulation. In many arid and semiarid areas, this biodiversity of ‘‘vegetation cover’’ and biological soil crusts is linked to a diversity of arthropod species that process most of the living plant biomass, constituting the first link of nutrient cycling.

The loss of biodiversity from drylands is not likely to affect all services uniformly. Rather, primary production and the provisioning services derived from it, as well as water provision, will be more resilient than recreation and ecotourism. This is based on the observation that ecosystem services performed by top predators will be lost before those performed by decomposers. The service of supporting biodiversity (by generating and maintaining habitats of required value and sample size) is expected to be degraded faster than the service of provisioning biological products.

The direct threats to the service of supporting biodiversity include not only land degradation but also habitat loss and fragmentation, competition from invasive alien species, poaching, and the illegal trade in biodiversity products. Indirect threats include the losses of the drylands-specific ‘‘keystone’’ species and ‘‘ecosystem engineer’’ species (which modify the dryland environment for the benefit of other species) . Finally, not only losses but also addition of species may impair service provision. For example, Eucalyptus tree species introduced to southern Africa have invaded entire catchments of natural vegetation, causing large-scale changes in water balance and depriving water from lower catchments

Required data/information is both ecological and economic:

• Data/information on local Biodiversity (census of mammals, birds, plants…)

• Data/Information on Cultural, Environmental and Estethic Values of Biodiversity:

V. number of existing and protected species in dryland;

VI. data constructed through valuation techniques (see above paragraph 3);


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Adaptation and Mitigation Strategies

The recognized importance of ecosystem services on human activities has spurred to take action and starts implementing measures that limit desertification in drylands. This part of socio-economic analysis, valuation approach and technique choice should consider the impact of mitigation and adaptation strategies in selected areas, in particular, those prevention and restoration actions that combat desertification in selected area. The analysis can vary according to available data and valuation approach and technique. However, the focus should be on comparing different situations before and prevention/restoration action and valuate costs and benefits of those actions.

Required data/information is both ecological and economic:

• Information about restoration practices adopted in selected areas;

• Information/data about mitigation and adaptation strategies costs and investments (i.e. management costs and restoration/prevention technologies in drylands;

• Data/Information about (selected) stakeholders awareness and opinion on adaptation/mitigation strategies;

• data constructed through valuation techniques (see above paragraph 3);

• ….

Finally, Box 1 highlights an important point about data gathering: time frame and discounting rate.

Box 1: Selecting a study time-frame and a Discount Rate

An appropriate time-scale for the analysis is needed, for example relating to the expected life of the product, project or asset, or perhaps more arbitrarily set at a reasonable duration between say 25 to 100 years. This needs to account for longer term implications, but bear in mind that going too far into the future leads to i) considerable uncertainties and ii) future money flows becoming significantly reduced depending on the discount rate used.

A suitable discount rate should be adopted when considering different values over time. Depending on the nature of the assessment, this should be either a company commercial rate or an appropriate social rate (if using it for an economic analysis). It may be that several rates could be applied to show the sensitivity of the outcome. The UK government, for instance, adopts declining rate of discount 3.5% for first 30 years, 3% for years 31 to 75, 2.5% for 76 to 125, 2% for 126 to 200, 1.5% for 201 to 300 and 1% for over 300 years.


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APPENDIX 2: A Pilot Case Study of Valuation to combat desertification; the Piximanna Forest at Pula, Sardinia. “Forests in drylands” is not an oxymoron, because forests do occur in drylands. Eighteen percent of the area of the dryland system is occupied by the forest and woodland system, though the probability of encountering forests in drylands decreases with their aridity. In general, aridity increases inland, and the forest and woodland systems prevail along the coasts. The dry sub-humid dryland subtype that is adjacent to the forest and woodland system has the greatest amount of overlap between the two systems, as compared with other drylands subtypes. But the distribution of forest in dry sub-humid areas is patchier than it is within the forest and woodland system outside the drylands. Forests also occur in the much wider semiarid zone of Australia but are there mostly confined to the less dry seaward direction. Forests occur in the dry sub-humid subtype in Africa but are very scattered and rare in the semiarid zone. In China and India, with dry sub-humid areas wider than in Australia, forests penetrate deep into dry sub-humid areas. In Europe, where many dry subh-umid areas are surrounded by non-drylands, forests are scattered all over the dryland areas. In the Americas, forests are patchily distributed in dry sub-humid and semiarid regions.

The forest of Piximanna14, at Pula represents an ideal case-study for valuating measures to combat desertification, in the Practice framework. The forest has drylands characteristics and, since the 1950’s, represents was subject to mitigation actions, aiming at reforestation (from the 1950’s to the ‘70s) and implementing silvicultural practices, that improve structural complexity and ecosystem functions (from the ‘70s)15.

The valuation of (selected) ecosystem services and mitigation strategies at Pula will be designed in such a way, that the adopted methodology can be applied and/or extended to other Practice sites/partners, and will follow the following organizational guidelines:

14 For details about the forest and mitigation strategies we remand to the Practice official documents. 15 Anti desertification practice is mostly reforestation (30 years, in two stages)

1) MITIGATION ACTIONS > 50s, 60s, 70s: Reforestation actions (Pinus pinea L.,Pinus halepensis Mill.,Quercus suber L.) for water-sheds protection; > Last three decades: Silvicultural practices (selective thinning on conifer stands) to facilitate the natural introduction of late-successional hardwood (improvement of structural complexity and ecosystem functions) 2) MAIN DEGRADATION DRIVERS > Overgrazing by domestic animals (mainly goats untill‚‘60s) > Overexploitation of fuel - wood (untill ’50s) > Forest fires (Source: D’Angelo et. al. Practice Kick Off Meeting (2009)


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1) Define the Scope This step helps to scope out and define what to value and to define the boundaries of the valuation exercise.

2) Plan the Study This step goes into specific details as to how the valuation will actually be undertaken in terms of which stakeholders will be involved, to undertake what aspects, when, how long it may take and what it will cost.

3) Undertake the Valuation This step is the actual valuation itself. It comprises a process of other sub-steps that should be followed to undertake the valuation aspect of the ecosystem services. This involves aspects such as establishing the environmental baseline, determining the stakeholders impacts and dependencies, assessing which ecosystem services are affected, and valuing the changes. The valuation methodology can be developed along the main points in Box 2.

For the Piximanna study the scope of the valuation is defined and summarized by Table 3. We have selected 3 different application areas.

1) A “very restricted area characterised by the reforestation intervention;

2) A “restricted area” characterised by economic, ecological and geo-physical similarities with the “non reforested area”. That is we look for situations that would depict us the status quo of the dryland if no mitigation strategy had occurred

3) A “broad area” that covers the all forest district. The valuation of different ecosystem services under different scenarios (three different areas) and the related required information is summarized in Table 6.

Finally a list of potential stakeholders was defined. Next step is planning thee study scope and undertaking the valuation.


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Box 2. Ecosystem Services Valuation Methodology 1) Finalise agreed objectives and initial methodology. This step may be

necessary if an external organisation has submitted a Proposal to conduct the ecosystem valuation.

2) Undertake initial data collection. This could include a literature and database review, stakeholder consultation (internal and external), site visits and various surveys (eg biological, hydrological or physico-chemical). The aim is to determine what information gaps need filling and what information can be used for the valuation techniques.

3) Select the valuation approach. This depends on the data availability and on the objectives and methodology. Valuation can be qualitative, quantitative or monetary.

4) Design the valuation approach. This step could include designing a draft stated preference questionnaire, undertaking a pilot questionnaire survey for the stated preference surveys, designing sampling strategies and draft questionnaire surveys et cetera.

5) Implement the valuation. This could involve a further literature and database review to identify specific studies for benefit transfers, additional stakeholder consultation, and implementing questionnaire surveys.

6) Analyse the results. This should include assessing monetised and non-monetised costs and benefits, considering the time-scales, discount rate and undertaking econometric analysis.

7) Produce a report. This should set out the methodology, level of consultation, results, assumptions, sensitivity analysis, conclusions and potential applications etc.


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Table 6. Piximanna Case Study Scoping. Valuation of reforestation and Silviculture (anti-desertification mitigation policies) actions. The mitigation policies have been effective because contributed to improve ecosystem functions?

Vey Restricted Area

Restricted Area

Broad Area

Supporting + Biodiversity

Biomass Biodiversity (ecological-biophysical analysis) Transects in plot area

Land cover Valuation of biodiversity

Simplified Land cover; inter-temporal comparison (1955-1980-2010) Biodiversity (literature survey)

Regulating Carbon reserves

Irrigation Costs Number of water springs

Irrigation Costs? Number of water reservoirs

Provisioning Type and intensity of economic activity Productivity Profitability Prices (wood, honey, mushrooms, breeding)

Type and intensity of economic activity Productivity Profitability Prices Opportunity Cost Indicator Policy Valuation Meta-Indicator

Type and intensity of economic activity

Cultural Environmental and cultural sites…(census) Census of Knowledge Systems (relevant activities) Governance structure of forestal resource

Valuation of cultural, environmental and aesthetic value/WTP Valuation of Aesthetic and inspiration services Valution/Census of Knowledge Systems Valuation/census of recreationists and recreation-cultural system

Data on tourism (flows, returns) GDP, Number of Inhabitants, employment/unemployment rate ….. Governance structures (resources governance structure) Environmental and cultural sites…(mapping)


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Documents

Deliverable D2.1 Working papers on Assessment Methodspractice-netweb.eu/sites/default/files/root/... · Ecosystems and Human Well-being: Desertification Synthesis. World Resources