CHALLENGES IN OPERATIONALIZING REMOTE SENSING IN CLIMATE

CHANGE MITIGATION PROJECTS IN DEVELOPING COUNTRIES

Shijo Joseph1*, Martin Herold2, William D. Sunderlin1 and Louis V. Verchot1

1. Center for International Forestry Research, Jalan CIFOR, Bogor 16115, Indonesia.

2. Center for Geoinformation, Department of Environmental Science, Wageningen University, The Netherlands

*. For correspondence: s.joseph@cgiar.org / shijonrsa@gmail.com

ABSTRACT The present study assesses the remote sensing challenges experienced by the project developers for operational monitoring of REDD+ (Reducing Emission from Deforestation and Degradation) projects. The study was carried out at a sample of 20 REDD+ projects in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam using a questionnaire survey and regional workshops on MRV (Monitoring, Reporting and Verification). The assessment showed that eleven project developers (55%) showed high or very high remote sensing and GIS capacity, seven (35%) were ranked medium, and two (10%) were ranked low. At the regional level, capacity tended to be highest in the projects in Brazil and Peru and somewhat lower in Cameroon, Tanzania, Indonesia and Vietnam. The study calls for an increased investment and capacity building to meet the various remote sensing challenges in REDD+ projects.

Index Terms— forest carbon, deforestation, remote sensing, MRV, REDD+.

1. INTRODUCTION Climate change mitigation is one of the top global priorities. Estimates show that emissions from deforestation and forest degradation contribute about 12% of the total greenhouse gas emissions released into the atmosphere [1]. United Nations Framework Convention on Climate Change (UNFCCC) takes a lead role to reduce this emission under an umbrella program called REDD+ (Reducing Emission from Deforestation and Degradation). REDD+ will be implemented in a three phased approach: achieving readiness, establishing policy measures, and carrying out results-based action.

One of the major initiatives as part of the readiness phase was to establish climate change mitigation projects or REDD+ demonstration projects at the local/sub-national levels.  The key goal of these projects is to implement performance-based rewards for increments in carbon (additionality) against the baseline, while also achieving social and environmental co-benefits. Projects also aim to ensure that activities to reduce emissions in one place should not cause emissions in neighboring areas (leakage), and should also satisfy permanence criteria (i.e., no reversibility of sequestered carbon). As of now, about 340 REDD+ and other carbon projects have been initiated across 52 developing countries [2].

Establishing a functional monitoring, reporting and verification (MRV) system is a major step towards measuring the additionality in REDD+ projects [3]. Remote sensing is an important tool in the MRV system due to its capacity to provide spatially explicit multi-temporal observations. The role of remote sensing in measuring and monitoring forest area, and assessing its structural and functional attributes have been well documented [4,5]. However, the REDD+ projects are located in the tropical belt where a number of prevalent atmospheric and ground conditions such as cloud cover, haze and uneven topography often disrupts a sensor’s capacity to image good quality ground data. Moreover, spatial infrastructure, data accessing and technological expertise are also key determinants of remote sensing capacity in countries in the tropical belt. The recent assessment at the national scale indicates serious capacity gaps in the developing countries to operationalize remote sensing as a monitoring tool [6]. Here we address this challenge with respect to the REDD+ projects at the subnational level at selected countries in Latin America, Africa and Southeast Asia.

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2. MATERIALS AND METHODS

Twenty REDD+ project developers were selected in order to address the challenges in the remote sensing capacity. These projects were selected after careful examination of the REDD+ national context and subnational initiatives in six countries (for the full selection criteria, see Sunderlin et al 2010, pp. 17–20) [7]: seven in Latin America (five in Brazil and two in Peru), seven in Africa (five in Tanzania and two in Cameroon) and six in Southeast Asia (five in Indonesia and one in Vietnam). Fig. 1 shows the geographical distribution projects that participated in the assessment. The assessment was carried out from October, 2011 to September, 2012. The first step in the assessment was to carry out a questionnaire survey to assess the geospatial datasets and tools used by the project developers at the project sites. The survey was done by communicating directly with the key MRV expert in the respective project developer organizations. Further to this, three regional workshops were carried out where these experts from each region came together, interacted with the international experts and discussed the various challenges in operationalizing remote sensing based monitoring. Fig. 1 – Geographical distribution of project sites and principal project developer participated in the assessment on remote sensing challenges of REDD+ projects.

The assessment looked at three factors needed to match the requirements of international standards of carbon certification3. These are: geospatial data availability; tools and methods used for the analysis; and the implementation capacity of the project developers (Table 1). Specific criteria were formulated in each category and the performance indicators were assigned with an indicator value and score. For example, for the first criterion, availability of medium-resolution remote sensing data was divided into four performance indicators. If 100% spatial data coverage was available for more than three points in time (which is important for reliable estimation of the

deforestation rate and linear projection of the baseline), that indicator received an indicator value of ‘very high’ and a score of 3. A score of 2 was awarded for 100% data coverage in two to three time points, and 1 for those with one time point. Similar guidelines were followed for all the performance indicators. Thus a total of 28 performance indicators under seven criteria were assessed. The scores were summed up with respect to each category and were further relatively graded on a scale of 0-3 to get equal weighting for each category in the final capacity score. Thus the highest score a project developer could get is nine (i.e., three categories with maximum score of three) and the lowest was zero. Capacity was assessed as very high for a score greater than 8, high for a score of 6–7, medium for 4–5, and low for 3 or less.

3. RESULTS AND DISCUSSION Eleven of 20 project developers (55%) were rated high or very high remote sensing and GIS capacity, while seven (35%) were in the medium and 10% were in the low capacity category. In almost all cases, the project developers were equipped with medium resolution satellite data which was primarily Landsat series data available at free of cost. The other major sensors included SPOT series, IRS-LISS series and CBERS. About half of the developers had high resolution (>10 m) satellite data and in most cases with spatial coverage of more than 50% of the study area. At least one higher end data products (among LiDAR, aerial photos and SAR) were also accessible for eleven of the project developers. SAR data was primarily PALSAR which is also freely available. All organizations have many years of experience at their respective sites, and therefore the availability of ancillary GIS layers at the project, provincial and national scales was not a problem. Fig. 2 – The remote sensing and GIS capacity of project developers at a sample of REDD+ projects in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam. The total number of projects in the sample is 20, and the values in the diagram represent number of project developers with percentage in parentheses.

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In terms of processing softwares, five organizations showed no limitations, and were well equipped with adequate commercial softwares while nine organizations showed partial limitations with access to only two commercial softwares produts. However, these limitations were overcome by the open source software such as ‘R’ and altogether fourteen organizations used decision tree or multivariate iterative algorithms to execute image

classification and change analysis. On addressing the issue of in-house capacity versus outsourcing, eleven organizations showed ‘very good’ capacity with the main project implementation office, while five organizations outsourced their work to a specialized consulting firm and the rest carried out the work at their headquarters with inputs from their field project office.

Table 1 – The categories, subcategories and the criteria used for measuring the remote sensing challenges in REDD+ projects.

Category Criteria Performance indicator Indicator value Score Data Availability of medium resolution

remote sensing data with 100% spatial coverage over project site

> 3 time points Very high 3 2-3 time points High 2 1 time point Medium 1 0 time point Low 0

Availability of high resolution remote sensing data (<10m) in at least 1 time point with percentage cover

> 50 % Very high 3 20 – 50% High 2 < 20% Medium 1 No data Low 0

Access to other higher end monitoring tools such as LiDAR, Aerial, Hyperspectral & SAR

3 or more tools Very high 3 2 tools High 2 1 tool Medium 1 none Low 0

GIS data availability at the national, provincial and project scales

All 3 scales Very high 3 2 scales High 2 1 scale Medium 1 none Low 0

Tools and methods

Availability of RS & GIS processing softwares (ERDAS, ENVI, ArcGIS, IDRISI & open source)

3 or more programs Very high 3

2 programs High 2 1 program Medium 1 none Low 0

Use of higher end techniques for classification and change analysis

Use of multivariate iterative algorithms, decision tree & so on

Very high 3

Use of supervised/unsupervised techniques, & post classification change detection

High 2

Visual Medium 1 No/yet to start Low 0

Implementation capacity

Internal capacity for remote sensing and GIS work

In-house expertise within the main project implementation office

Very high 3

In-house expertise at headquarters with assistance from the field office

High 2

Partial outsourcing Medium 1 Complete outsourcing Low 0

Remote sensing and GIS capacity varied by region (Fig. 3). Six out of seven projects in Brazil and Peru showed high or very high capacity and readiness, and one was in the medium category. The projects in Tanzania and Cameroon showed more variability: two of seven (29%) were rated very high, and the remaining five (71%) were rated medium to low. None of the projects in Indonesia and Vietnam

showed very high capacity; three were rated high, two medium, and one low. With respect to high-resolution satellite data and other higher-end monitoring tools such as LiDAR, aerial photos and SAR, all three regions experienced limitations; these limitations were severe in Tanzania and Cameroon. African project capacity was further weakened by insufficient expertise and lack of

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software and processing infrastructure. In contrast, Indonesia experienced rapid progress in the acquisition of LiDAR in recent years together with improvement in data processing algorithms and facilities. Fig. 3. Region-wise remote sensing capacity of project developers at a sample of REDD+ projects in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam.

4. CONCLUSION AND RECOMMENDATIONS

The present assessment showed that remote sensing capacity tended to be highest in the projects in Brazil and Peru and somewhat lower in Cameroon, Tanzania, Indonesia and Vietnam. Further research and investment are needed to enable REDD+ to move forward to the operational (‘results-based action’) phase. Although the sample is too small to allow definitive conclusions, some tentative recommendations can be made.  

Improve Remote sensing data access: Landsat series data are the primary source of information for the project developers. However, the availability of time series data was a limitation for many of the project sites. This limitation is more severe for high-resolution satellite data and other higher-end monitoring tools like LiDAR and aerial photos. Moreover, cloud cover is a year-round problem at many sites. Possible ways to address this would be establishment of an Earth Observation Data Directory for REDD+ projects by international donors, and other key stakeholder organizations.

Method development: Many project developers expressed concern about the limitations of methods for measuring degradation drivers such as selective logging. Scale integration was another challenging task in larger projects.

Capacity building: The software and processing capacities of project developers varied considerably. In addition, human expertise was also a concern in many projects. Capacity development through collaboration should first be prioritized within the region and then extended to efforts like south-south cooperation where there is specific need. Also, it is important to organize more capacity building

training and workshops for development of human expertise in data processing.

5. ACKNOWLEDGEMENTS This study is part of a larger initiative, the Global Comparative Study on REDD+, undertaken by the Center for International Forestry Research with funding support from NORAD (Grant Agreement # QZA-10/0468) and AusAID (Grant Agreement # 46167). The work is part of the CGIAR Research Program on Forests Trees and Agroforestry. The authors would like to thank the national coordinators, field researchers and regional workshops participants for their valuable helps and constructive comments.

6. REFERENCES [1] G.R. van der Werf, D.C. Morton, R.S. DeFries, J.G.J. Olivier, P.S. Kasibhatla, R.B. Jackson, G.J. Collatz, and J.T. Randerson, CO2 emissions from forest loss, Nature Geoscience 2, 737-738, 2009. [2] M. Kshatriya, E.O. Sills, and L. Lin, Global Database of REDD+ and Other Forest Carbon Projects: Interactive Map. http://www.forestsclimatechange.org/redd-map/, 2012 (last accessed 15 January 2013). [3] M. Estrada, and S. Joseph, Baselines and monitoring in local REDD+ projects, in: Angelsen A, Brockhaus M, Sunderlin W D, Verchot L V, (Eds) Analysing REDD+: Challenges and Choices. Center for International Forestry Research, Bogor, Indonesia, pp. 247–260, 2012. [4] S. Joseph, M.S.R. Murthy, and A.P. Thomas, The progress on remote sensing technology in identifying tropical forest degradation: A synthesis of the present knowledge and future perspectives, Environmental Earth Sciences 64 731–741, 2011. [5] S. Joseph, G.A. Blackburn, B. Gharai, S. Sudhakar, A.P. Thomas, and M.S.R. Murthy, Monitoring conservation effectiveness in a global biodiversity hotspot: the contribution of land cover change assessment, Environmental Monitoring and Assessment 158, 169-179, 2009. [6] E. Romijn, M. Herold, L. Kooistra, D. Murdiyarso, and L. Verchot, Assessing capacities of non-Annex I countries for national forest monitoring in the context of REDD+, Environmental Science and Policy 19/20 33–48, 2012. [7] W.D. Sunderlin, A.M. Larson, A. Duchelle, E.O. Sills, C. Luttrell, P. Jagger, S. Pattanayak, P. Cronkleton, and A.D. Ekaputri, Technical Guidelines for Research on REDD+ Project Sites with Survey Instruments and Code Book. Center for International Forestry Research, Bogor, Indonesia, 2010.

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