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Carbon Assessment Tool for New Oil Palm Plantings

December 2012 Version 1

Document prepared by:

Surin Suksuwan

On behalf of the RSPO as requested by the P&C Review Taskforce.

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Table of Contents 1. Introduction ................................................................................................................................................................... 3

1.1 About This Tool ..................................................................................................................................................... 3

1.2 Objective of This Tool ......................................................................................................................................... 3

1.3 Tool Development ................................................................................................................................................ 3

2. Carbon Accounting within the RSPO ................................................................................................................... 4

3. HCV and carbon stocks .............................................................................................................................................. 6

3.1 HCV ............................................................................................................................................................................. 6

3.2 Carbon stocks ......................................................................................................................................................... 6

3.3 Linkages between HCV and carbon stocks ................................................................................................ 7

4. Using the Tool................................................................................................................................................................ 8

4.1 Pre-Screening ...................................................................................................................................................... 12

4.2 Remote-Sensing and further Stratification of Land Cover ............................................................... 15

4.3 Ground-truthing ................................................................................................................................................. 18

4.4 Biomass Sampling ............................................................................................................................................. 19

4.5 Carbon Density Calculation ........................................................................................................................... 21

4.6 Soil Sampling ....................................................................................................................................................... 24

5. References ................................................................................................................................................................... 26

Appendix 1: Case Study of Carbon Assessment Conducted by Golden Agri-Resources (GAR) and SMART ................................................................................................................................................................................ 30

Appendix 2: Limitations of the Tools .................................................................................................................... 34

Appendix 3: Reporting Framework ....................................................................................................................... 36

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1. Introduction

1.1 About This Tool

The Roundtable for Sustainable Palm Oil (RSPO) is an international multi-stakeholder and certification scheme for sustainable palm oil and its mission include advancing the production, procurement, finance and use of sustainable palm oil products; and to develop, implement, verify, assure and periodically review credible global standards for the entire supply chain of sustainable palm oil.

The Principles and Criteria (P&C) for sustainable palm oil is a framework developed by RSPO (2007) to define sustainable palm oil in practical and implementable terms that allows for palm oil to be certified as sustainable. There are eight Principles into total, of which Principle 7 is on the responsible development of new plantings.

In achieving its mission, the RSPO embraces the concept of continuous improvement and in line with this, the P&C is to be reviewed and improved upon every five years. The P&C is now currently being reviewed and a Review Taskforce has been formed to lead the process.

The P&C Review Taskforce has proposed a new Criterion within Principle 7 requiring the identification and estimation of potential sources of emission and sinks of carbon associated with new developments. It is also proposed to require that new developments must be designed to minimise emissions and maximise sequestration and carbon stock conservation.

The P&C review taskforce have requested the RSPO secretariat to produce a new tool incorporating practical methodologies for growers to use to help identify and estimate possible significant sources of emissions and sinks of carbon associated with new developments.

1.2 Objective of This Tool

This tool is designed to provide a methodology to growers for identifying and estimating carbon sources and sinks associated with new oil planting in a manner that is as practical and simple as possible and which fits current processes required under Principle 7 – primarily the soil survey, SEIA and HCV assessments.

This tool is also designed to be compatible with the Palm GHG tool (developed to account for and report emissions from existing operations).

At the request of the RSPO Secretariat it was also developed to be compatible with emerging guidance from the Biodiversity and HCV Working Group on how to undertake assessments of historic land use changes since 2005.

In practice, this tool details out the steps to be taken in assessing the carbon stock in the land area where new planting development is to take place, from the pre-screening process until the carbon values expressed in tonnes of carbon per hectare (tC/ha) are derived. The carbon stock values can then be plugged into the PalmGHG calculator, which would estimate the GHG balance for the entire planned palm oil production cycle.

1.3 Tool Development

The main steps involved in developing this tool were a review of literature related to carbon assessments for the forestry and agriculture sectors in tropical regions of the world (with a particular emphasis on Malaysia and Indonesia); and interviews with relevant people from oil

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palm producing companies, non-governmental organisations (NGOs), consultant, research institutions and remote sensing experts.

Progress with the development of the tool was presented at the RSPO’s Roundtable 10 (RT 10) in Singapore on 30th October 2012 during which useful feedback was obtained from participants of the RT10’s Preparatory Cluster 5 on Greenhouse Gases. There was also an information sharing session with the RSPO’s Biodiversity & HCV Working Group (BHCV-WG) sixth meeting of its Compensation Task Force in Kuala Lumpur on 28th November 2012, which was aimed at improving alignment between the HCV and carbon assessment processes.

Of particular relevance is the experience of Golden Agri-Resources (GAR) and its subsidiary, SMART, in conducting carbon stock assessments in relation its new oil palm concessions in Central and West Kalimantan, as it one of the few such pioneering initiatives by oil palm growers. This is documented as a case study in Appendix 1.

Appendix 2 discusses the limitations of the tool, gaps identified and suggested further work.

In the process of data gathering and developing the tool, emphasis was given to building upon current assessment processes related to oil palm development. Attention was given to HCV assessments and basic land categorisations generated by the HCV framework including the work of the RSPO’s Biodiversity & HCV Working Group (BHCV-WG) as part of a separate tool being developed to assess past land use changes.

Emphasis was also given to the use of widely available remote sensing technology (including radar and optical sensors mounted on satellite and aerial platforms) to stratify land cover that allow for biomass (and therefore carbon stock) estimated.

In conjunction with this tool, a basic reporting framework for projected emissions/sequestrations arising from new plantings has also been developed (see Appendix 3).

2. Carbon Accounting within the RSPO

In order to be achieve the proposed Criterion 7.x (essentially to plan new developments to minimise emissions and maximise sequestration) this information needs to be combined with a tool to ‘forecast’ the balance of emissions and sequestration associated with a proposed development.

The RSPO has developed its preferred GHG accounting tool, called the PalmGHG Calculator (Chase et al., 2012). Palm GHG, focuses partly on the emissions from the production of oil palm through the collection of agronomic data supplemented with estimates of emissions associated with land use change derived from default values provided for GHG emissions from the change of land cover from any one of 10 different land cover classes (or strata). Net GHG emissions over the full crop cycle (the default value is 25 years) are calculated by adding the emissions released during land clearing, crop production and crop processing, and subtracting from these emissions the sequestration of carbon in the standing crop and in any conservation areas as well as avoidance of emissions from operations such as methane capture and POME management and the maintenance of water tables in areas of peat under oil palm. The contribution of land clearing to GHG emissions in PalmGHG is averaged out over the full crop cycle, together with the emission and sequestration values from other aspects of palm oil production, so that the average emissions in any one year of this cycle can be estimated. The emissions are presented as t or g CO2 equivalents (CO2e), per hectare and per unit of product: i.e. per tonne of Crude Palm Oil (CPO) or per tonne of Crude Palm Kernel Oil (CPKO) (Chase et al., 2012).

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The PalmGHG Calculator recognises ten previous land cover1 classes and their respective default carbon stock values were defined in consultation with the scientific panel of the RSPO’s GHG WG2 (Working Stream 3) who conducted a thorough review of literature data and satellite images to identify land cover changes associated with oil palm plantations in Indonesia and Malaysia (Agus et al., in press). The nine previous land cover are: primary forest, logged forest, secondary re-growth (average of logged forest and food crops), shrub land, grassland, rubber, cocoa under shade, coconut, food crops (average of annual and perennial crops in Papua New Guinea) and oil palm.

It should be noted that the land cover classes are not clear cut, especially the term ‘logged’ which can cover a variety of situations, and that the values are provided as guidance in the absence of more specific measurements. As far as possible, more accurate values derived from field measurements should be used for the carbon stock values for previous land cover. Of the nine land cover classes, only five are considered to be relevant in the case of plantations on peat soils: logged forest, food crops, secondary re-growth, and oil palm were identified as relevant.

For any land cover, the total carbon stock could be divided into different “pools”. The standard division of carbon pools as defined by the IPCC are aboveground biomass, belowground biomass, dead wood, litter and soil organic matter (see Section 4 for more elaboration on these carbon pools).

1 In this document, a distinction is made between land use and land cover following Di Gregorio & Jansen (2000). The definition used for land cover is "the observed (bio)physical cover of the earth's surface", while for land use it is "the arrangements, activities, and inputs people undertake in a certain land cover type to produce, change or mantain it". However in most other documents, "land use" and "land cover" are used interchangeably.

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3. HCV and carbon stocks

3.1 HCV

The High Conservation Value (HCV) concept was first developed by the Forest Stewardship Council (FSC) in 1999 as Principle 9 in its Principle and Criteria for environmentally appropriate, socially beneficial and economically viable forest management. HCV was originally designed to help forest managers identify areas with exceptional social, cultural, or environmental attributes, in order for them to implement a management and monitoring system to maintain or enhance these attributes.

The central focus of the HCV concept is the identification and mapping of values that are considered to be of outstanding significance or critical importance. There are six types of high conservation value numbered as HCV1 to HCV6 which may be broken down further to more specific elements.

The RSPO has built up extensive experience, national interpretations and guidance of the use of HCV assessments in relation to new plantings and existing operations.

3.2 Carbon stocks

Compared with the HCV concept, the carbon stock concept is not so well-developed. Currently there is no standard definition for areas with ‘high’ carbon stocks nor is there a standard methodology for identifying such areas.

There is a mention of “high-carbon stock” in the REDD Methodological Module on “Estimation of baseline carbon stock changes and greenhouse gas emissions from unplanned deforestation” (Version 1.0)2 but there was no definition provided for the term.

A pioneering work in elaborating the High Carbon Stock (HCS) concept in an oil palm context is the study by the oil palm plantation company Golden Agri-Resources (GAR) and its subsidiary, SMART, in collaboration with The Forest Trust and Greenpeace (GAR & SMART, 2012). The objective of the study was to “develop a practical, scientifically robust and cost effective methodology to define and identify areas of HCS for conservation” (GAR & SMART, pg. 5). The methodology used is described as a case study in Appendix 1. A provisional definition of HCS forest was proposed as being greater than 35 tC/ha in living above ground biomass, which should be avoided when developing oil palm plantations.

GAR & SMART (2012) also observed that above ground biomass would accumulate through forest regeneration if a proposed new planting area is not converted to oil palm, and therefore rationalised that the concept of HCS within their study included a component of potential carbon sequestration.

The P&C review taskforce has not requested the RSPO to set a threshold for carbon stocks or a cut-off point above which conversion is not permitted to proceed. Instead this tool has been developed for members to identify the carbon stock changes and GHG emissions associated with a particular development, to plan to mitigate these possible impacts and to report on what the projected changes and emissions will be.

2 Available as a pdf document downloadable from: http://v-c-s.org/sites/v-c-s.org/files/9_BL-

UP_Baseline_unplanned_deforestation.pdf

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3.3 Linkages between HCV and carbon stocks

HCV areas are not necessarily high carbon stock areas or vice versa. Peatlands that are not forested, for example, have substantial amounts of carbon below ground in the organic peat soils but may have relatively little biodiversity or community value. Equally monoculture tree plantations may store substantial amounts of carbon but are often not of high conservation value. Conversely grasslands may have considerable high conservation value – but store little carbon either above or below ground.

Taking into consideration only one aspect of conservation value, i.e., biodiversity, it has however been shown that there is a strong congruence between biomass (above ground biomass and root biomass) and biodiversity at the global level (Strassburg et al., 2010). Within the southeast Asian region, Imai et al. (2009) compared the diversity of forest dwelling vertebrate species between two production forests in Sabah, Malaysia and found that there was higher carbon density and diversity of vertebrate species in a sustainably managed forest compared to the other areas which is a forest damaged by conventional logging. However, this relationship between biomass and biodiversity may not always hold at the sub-national level as argued by Paoli et al. (2010) who found that the total carbon stocks in lowland forests on peat soils in Indonesia are, on average, eight times higher than in lowland forests on mineral soils, while overall biodiversity levels in peat forest are substantially lower than in lowland forest on mineral soils.

Aside from biodiversity, there are other aspects of areas with high carbon stocks that could be considered as HCVs. In particular, many forests and peatlands contribute to HCV 4 as they provide critical water catchment functions, while peatlands can function as barriers to destructive fire, if left undrained. The Indonesian HCV Toolkit (HCV Consortium for Indonesia, 2009) notes in its section on HCV 4.1 that ecosystems referred to by HCV4.1 include peat swamp (especially swamp that is still forested), while the section on HCV 4.3 states that “densely forested regions and wetlands, when in good condition, have physical characteristics that make them resistant to fire, even during the dry season...” (pg. 98).

Because it is not always possible to say that an area identified as having high conservation values also has high carbon stocks this tool has been developed to supplement the existing requirement to undertake an HCV assessment prior to clearing with a parallel and connected carbon assessment.

Carbon sequestration by itself is an ecosystem service in which case areas with high carbon stocks could correspond to HCV3 which are “forest areas that provide basic services of nature in critical situations” as defined in the HCV global toolkit (Jennings et al., 2003). There is, however, a degree of subjectivity in deciding what would be a “critical situation” and the only general guidance provided in the toolkit is that “the consequence of a breakdown in these services would have a serious catastrophic or cumulative impact”.

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4. Using the Tool

This tool provides a methodology for how to assess the carbon stock in the land area where new planting development is proposed, so as to identify high carbon stock areas that should be avoided as well as opportunities for carbon sequestration, in fulfilment of the RSPO’s [proposed] Criterion 7.x. Using this data in combination with the Palm GHG tool allows the net GHG emissions of the proposed development to be estimated and appropriate avoidance and mitigation measures to be planned prior to the development occurring.

It should be noted that this document is not intended to reproduce in detail information that is already contained in existing manuals and other guidance documents. Detailed descriptions for designing and establishing sample plots and calculating biomass, for example, are well documented in other publications. However, this tool provides references to the recommended online or published resources wherever possible.

The flowchart in Figure 1 provides an overview of the steps required while the sections following this provide a more detailed description for each step in the process.

In deciding on the protocol for assessing carbon, much emphasis was given to minimising the resources that need to be mobilised, through aligning with other processes that are already mandatory under the RSPO’s Principle 7, particularly the social and environmental impact assessments (SEIA), the soil survey and the HCV assessment.

Although carbon sequestration is not explicitly identified as a HCV within existing national HCV toolkits, some of the steps included in this tool are already part of the responsible development of new plantings. Mapping is an integral component in identifying HCVs and the use of geographical information system (GIS) tools in conducting spatial analyses and producing maps is now widespread. These analyses usually incorporate a variety of GIS data layers including land use, land cover, soils, satellite and/or aerial imagery, and other related features. Carbon stocks could be added as another layer to these data sets.

In identifying HCVs, it is also standard practice to establish plots for the purpose of carrying out vegetation sampling, which may include the measurement of the diameter at breast height (dbh) of trees. Such plots and measurements can also be used to estimate above ground carbon stocks.

Similarly, soil surveys are part of routine agronomic practice in determining soil fertility prior to development.

However, in assessing the carbon stock in the new planting area, additional resources would have to be allocated for carrying out plot samplings in order to estimate biomass. More resources would also need to be allocated for conducting stratification of land cover using aerial or remote sensing data combined with GIS spatial analysis. In carrying out HCV assessments, it is beneficial to invest more resources for desktop review and GIS analysis as this would help greatly to reduce the number of days needed to be spent doing fieldwork (Gary Paoli, Daemeter, pers. comm.) and this should hold through for carbon assessments as well.

As mentioned earlier in Section 2, the total carbon stock in a particular land area is divided into five carbon pools. Table 2 below provides a summary of available methods at the planned plantation scale for measuring the different pools, and an analysis of pros and cons of each option. It is assumed that at least in the first place this tool applies to new plantings to be undertaken by plantation companies, and that the size of new planting areas is in the range of hundreds of hectares to tens of thousands of hectares3.

3 Interviews conducted by WRI in the process of developing its Suitability Mapper tool indicated that

the common minimum size preference expressed by companies was 5,000ha (Gingold et al., 2012).

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Table 2: Summary of Methods for Measuring Biomass in Different Carbon Pools

Carbon pool Method Relative amount of resources needed

Notes

Above ground (tree) biomass

1

Destructive sampling and direct measurement of biomass

High – labour intensive Destructive sampling is usually done at a very limited scale in order to produce allometric equations

2 that are more

specific to the particular area.

Comprehensive random plot sampling involving measurements of dbh and height (optional) of trees and use of allometry to estimate carbon stock.

Moderate to High depending on the size of the area to be covered, accessibility (terrain, availability of access road etc.) and the range of different land covers present.

Extensive ground reconnaissance has to be conducted in order to identify the different land covers present.

Sufficient number of plots need to be established in order to have statistically representative sampling

Measure tree height and crown area using very high resolution airborne remote optical sensors (e.g. aerial photo, 3D digital aerial imagery) or airplane-mounted laser remote sensor (e.g. LiDAR), and use allometry to estimate carbon stock.

High – cost of procuring images is high and method is technically demanding.

No allometric equations based on crown area are available.

Less accurate in complex canopies of mature tropical forest as signal saturates.

Field based measurement still needed for calibration and verification of carbon stock estimation.

Stratification of land cover using remote sensing/aerial survey and GIS analysis, followed by targeted plot sampling to verify default carbon stock values for different land cover types.

Moderate – cost of remote sensing and GIS analysis offset by lower number of plots required. Freely available satellite images or moderate resolution (e.g. Landsat) can be used.

Stratification of land cover allows for sampling plots to be established more accurately (targeted sampling).

Number of sampling plots greatly reduced compared to random sampling.

Below ground (root) biomass

Destructive sampling and direct measurement of biomass.

High – labour intensive. Destructive sampling is usually done at a very limited scale in order to produce allometric equations that are more specific to the particular area.

Use default value for calculating root biomass as a function of aboveground biomass.

Low – no sampling needed. Default values for calculating root biomass available from various sources.

Dead biomass Non-destructive sampling Low – sampling done in conjunction with above-

Optional – can be omitted if amount of dead biomass

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- for standing dead biomass, measure dbh and height (optional) as with tree biomass

- for lying dead biomass (fallen tree trunk), measure diameter (>10cm) using line-intersect method

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ground biomass sampling observed during field reconnaissance is found to be relatively low.

Litter Destructive sampling – litter collected within a 30x30cm frame within the above ground biomass sampling plot, weighed, oven-dried and mass calculated.

Low – sampling done in conjunction with above-ground biomass sampling. Post-sampling work required (oven-drying of litter samples)

Optional – usually omitted as the contribution to total biomass is low.

Soil carbon Three types of variables must be measured: depth; bulk density (calculated from the oven-dried weight of soil from a known volume of sampled material); and the concentrations of organic carbon within the sample.

Low – sampling done in conjunction with above-ground biomass sampling. Post-sampling work required (oven-drying of soil).

Compulsory for peat soils.

Optional for mineral soils due to uncertainty of how mineral soil is affected by land cover change.

Notes:

1Above ground biomass includes tree biomass as well as non-tree biomass including lianas, understorey plants and

epiphytes. Due to difficulties in estimating the non-tree components and their relatively small contribution to the above ground biomass, these components are usually excluded from field measurements of above ground biomass.

2Allometric equations are regression equations expressing the relationship between the dimension of a tree with its

biomass, and are used to estimate the biomass of trees.

3 See, for example, Pearson et al. (2005).

For above ground biomass measurements the preferred method for this tool is stratification of land cover followed by plot sampling in the field, for verification purposes. This method is selected based on the literature review (see for example Gibbs et al., 2007; Pearson et al., 2005; Quiñones et al., 2011) and the experience of GAR & SMART (2012) in the study they conducted on their concession areas in Kalimantan.

This tool is to be used in conjunction with the PalmGHG Calculator developed by RSPO (Chase et al., 2012). The PalmGHG Calculator allows for the estimation of the GHG balance for palm oil production from land clearing activities (land cover conversion) combined with GHG emissions associated with the subsequent production of crude palm oil (CPO) and palm kernel oil (PKO). Default values for the carbon stock of the previous land cover is provided in the PalmGHG Calculator and these are combined with emissions based on the input of agronomic data such as fertiliser, other inputs and fossil fuel use, etc.

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Figure 1: Flowchart for Assessing GHG Emission Balance for New Plantation Development

Use existing soils map if available in required

resolution to determine extent of peat soil in area

of interest

Use free remote sensing data (e.g. Landsat) to

stratify land cover in area of interest into at least 4

categories as proposed by the Compensation Task

Force (CTF)

Use available tools (e.g. WRI’s Forest Cover Analyzer and Suitability Mapper) to assess preliminary HCV and Carbon status and presence of peat soil in the area of interest.

PRE-SCREENING STAGE

Decide whether or not to proceed with new planting

RECONSIDER

NEW PLANTING PLANNING STAGE

PROCEED

All other requirements under RSPO’s New Plantings

Procedure still apply. This flowchart is only concerned

with C assessment related to new plantings.

Conduct soil survey for the plantation area as

part of agronomic assessment

Consider obtaining higher resolution remote sensing images (<30m resolution) for area of interest

Carry out GIS analysis of remote sensing data and stratify land cover into standardised categories (strata)

as recommended by the Science Panel

Carry out ground-truthing to verify accuracy of land stratification

If pockets of peat soil are present, determine

extent of peat area (in ha), average depth of

peat (m), and expected water table depth

Estimate above ground biomass in sample plots and use allometry to

calculate average carbon density of above ground and root

biomass(tonne/ha), for the different strata

Measure dbh and height of trees, use allometric equations and default values e.g. root-to-shoot ratio, biomass to C stock

conversion factor etc.

Compare calculated average carbon density values with default values for

different the land cover types present and verify that they are

within the same range

Reference values as recommended by the Science

Panel

Conduct additional plot samplings and check if stratification has been

done correctly

Are the calculated values within the reference values for specific land

cover types?

NO YES

Colour Key

Use PalmGHG to calculate GHG emission balance from new

plantation development based on different trajectories

Mandatory step

Optional step Formulate HCV and C management strategies including identification and

demarcation of set aside areas

Aimed at reducing net GHG emission

Decision needed

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4.1 Pre-Screening

Objective: to allow for a rapid identification (at minimal expense of resources) of areas with potentially high carbon stock in order to make an early decision whether or not to proceed with the new planting prior to undertaking HCV assessments, SEIA and FPIC processes.

If the new planting area is in Kalimantan, use WRI’s Forest Cover Analyzer and Suitability Mapper tools (see Box 1 below). For other areas, existing carbon stock map as developed as Saatchi et al. (2011) or Baccini et al. (2012), could be used to do a rough assessment of magnitude and variation in C stocks in the area of interest, where possible. Otherwise follow the steps below.

Key steps:

Procure freely available satellite

images (e.g. Landsat) for the area proposed

for new planting development

Carry out image processing and use GIS tools to stratify

land cover into 4 initial categories

(strata)

Using GIS tools, overlay boundary of new planting area

with the 4 land cover strata layers (refer to

Table 3)

Determine distribution of the different strata

within the new planting area

Use existing soils map if available in required

resolution to determine extent of peat soil in area of

interest

If substantial areas of Land Cover Class A or B or peat soil within new planting area, consider choosing a new site. Otherwise

proceed to next step.

Conduct initial reconnaissance to verify

if satellite images correspond to actual

conditions on the ground.

The RSPO’s Compensation Task Force (CTF)4 has drafted a land cover classification based on four classes (or strata), (see Table 3 below). This stratification can be done with GIS tools using freely available satellite imagery, such as Landsat. This tool proposes using the same classes for its initial stratification process:

Table 3: Draft Land Cover Classification by the CTF5 Land Cover

Class Status of vegetation in year 2005

A Multi-layered old growth forest (including primary forest) and secondary closed forest with uneven canopy

B Secondary closed forest with even canopy

C Other areas with trees that meet FAO’s forest definition (>10% canopy, >5m tall trees).

4 The Compensation Task Force was established in August 2011 to support the RSPO to develop a

guidance document on compensating for land cleared without prior HCV assessment. It was formed under the Biodiversity and High Conservation Value working group (BHCV WG) and all BHCV members are part of the CTF with additional invited experts. 5 This was the land cover classification discussed by the CTF at the time of writing. However,

discussions are ongoing and the classification and description of land cover are continuously evolving. As the RSPO Carbon Assessment Tool for New Oil Palm Plantings undergo further review and refinement during the implementation period, these changes where applicable, will also be reflected in later versions of this document. For the latest update on the CTF land cover classification, please refer to http://www.rspo.org/en/compensation_task_force

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D Areas that do not meet FAO’s forest definitions.

Preliminary stratification of land cover into these four strata and overlaying the boundaries of the proposed new planting area would allow for a rough assessment to be made on the general distribution of carbon stock within the proposed area. A substantial cover of Land Cover Class A and B strata within the proposed new planting area indicates that the new planting development has a high risk of clearing high carbon stock areas.

Soil maps can be procured from the relevant agencies. For example, in the case of Malaysia, the Department of Agriculture has a database of soil maps of various resolutions6, while for Indonesia, the Indonesia Center for Agricultural Land Resources Research and Development (ICALRRD) maintains a digital soil database management system. GIS spatial analysis using digital soil maps can be used to determine the presence and extent of peat soil in the proposed new planting area, and combined analyses with land cover information could also be conducted.

It is advisable for the pre-screening stage to also include an initial field reconnaissance (or “recce”) to verify conditions on the ground with the information provided by satellite images, particularly in determining whether the stratification of land cover is accurate. This field reconnaissance could also include a visual detection of possible presence of peatland within the proposed new planting area, to complement information gathered from soil maps.

If there is a substantial cover of Class A and B strata and/or peat soil in the proposed new planting area, a decision may be made (in conjunction with other considerations e.g. terrain and accessibility, social aspects, HCV assessments), to reconsider the location of the new planting.

For new planting areas within Kalimantan in Indonesia, the pre-screening could be done more easily and at virtually no cost using the Forest Cover Analyzer and Suitability Mapper online tools (see Box 1 below). In the Forest Cover Analyzer, forest cover classes are categorised into “Primary” forest (i.e. having characteristics similar to primary forest) and “Other forest” (likely degraded or secondary forest which likely still contain high conservation values) (WRI, 2012) which are comparable with Classes A and B respectively.

6 A list of available soil maps can be accessed at:

http://www.doa.gov.my/web/guest/senarai_peta_yang_disediakan_doa

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Box 1: WRI’s Forest Cover Analyzer and Suitability Mapper

The World Resources Institute (WRI)’s web-based tools, the Forest Cover Analyzer and Suitability Mapper, were launched at the RSPO’s Roundtable 10 in October 2012.

Forest Cover Analyzer

The Forest Cover Analyzer allows the user to determine if areas containing forest cover and peatland that are likely to contain high conservation values are present in their proposed new planting area so that these areas can be avoided or further assessed for appropriate management. The Forest Cover Analyzer is designed for a wide range of target audience, including oil palm growers who can upload or draw custom concession boundaries to be analysed. The tool is designed to provide only preliminary information which means that field assessments and additional due diligence activities are still required.

The Forest Cover Analyzer incorporates a 50m resolution dataset on Land Cover 2010 from SarVision with the dataset on peat extend and depths (1:250,000 scale, depth categories (in cm): 0, <50, 50-100; 100-200; 200-400; 400-800; 800-1200) sourced from Wetlands International. Forest cover classes are categorised into “Primary” forest (i.e. having characteristics similar to primary forest) and “Other forest” (likely degraded or secondary forest which likely still contain high conservation values). Other maps available on the Forest Cover Analyzer include land cover 2010 (50m resolution), aboveground biomass (in tonnes per hectare, 100m resolution) and legal classification.

The Forest Cover Analyzer can be accessed at http://www.wri.org/applications/maps/forest-cover-analyzer/index.html and requires Adobe Flash Player to operate.

Suitability Mapper

This tool assigns land within a province in Kalimantan to one of three suitability classes for sustainable oil palm expansion: high potential, potential, or not suitable. This suitability map is a combination of three thematic layers: carbon and biodiversity; soil and water protection; and crop productivity.”.

The layer on carbon and biodiversity acts as a proxy for “degraded land” following the Indonesian HCV guidance requiring that new plantations in Indonesia should use previously cleared and/or degraded land. The carbon and biodiversity layer indicates whether the conversion of an area to an oil palm plantation is likely to result in negative impacts on carbon stocks and biodiversity (HCV 1–3). This layer has three suitability indicators: (1) land cover, (2) peat, and (3) conservation areas with buffer zones. Any land cover with carbon stock of more than 35 tC/ha was classified as not suitable, including both primary and secondary forests.

Based on this suitability mapping, about 4.5 million ha (31 percent) of province of West Kalimantan were classified as potentially suitable (high potential or potential) while 3.3 million ha was potentially suitable for sustainable palm oil production in Central Kalimantan, about 21 percent of the province’s total land area.

The Suitability Mapper can be accessed at http://www.wri.org/applications/maps/suitability-mapper/

and requires Adobe Flash Player to operate. Currently both the Forest Cover Analyzer and Suitability Mapper are only available for Kalimantan, Indonesia, but efforts are under way to include other areas in the region.

Source: Forest Cover Analyzer – WRI (2012); Suitability Mapper – Gingold et al. (2012)

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4.2 Remote-Sensing and further Stratification of Land Cover

Objective: to further stratify land cover using remote sensing data into the ten strata to facilitate assignment of default carbon values as determined by the RSPO’s Science Panel (GHG WG2 Workstream3) Key steps:

Use satellite images from the Pre-Screening stage OR procure higher resolution images if needed, for the area proposed for new planting development

Procure associated maps (e.g. land use, topography, geology etc.) required for GIS analysis

Carry out image processing and use GIS tools to stratify land

cover into 10 strata as determined by the RSPO

Science Panel

Assign default carbon values (as determined by the RSPO

Science Panel) to the relevant land cover strata

Determine distribution of the different strata within the

new planting area

Using GIS tools, overlay boundary of new planting area with the 10 land cover strata

layers

The purpose of stratification is to divide land cover into relatively homogenous units so that the variation within each land cover type (stratum) is minimised at the expense of the variation between the strata. If the strata are well-defined the number of sampling plots required in field-based carbon estimation will be smaller than with random sampling (Hairiah et al., 2001). A key assumption in stratification in the context of carbon accounting is that areas falling under the same land cover stratum would have similar forest carbon stocks. Stratification of land cover can be done whether the concession area is in one large contiguous block of land or consists of many small parcels (Pearson et al., 2005).

Depending on the size of the proposed new planting area, its topography, land cover mix, availability of free satellite images and other factors, it may be necessary to procure higher resolution remote sensing data and for the stratification process. GAR & SMART (2012), for example,

As a guide for oil palm growers intending to purchase satellite images, Table 4 below provides a brief summary of the key attributes of the more commonly-used satellite data.

Table 4: Comparison of Satellite Data Sources

Satellite and

Sensor

Spatial Resolution

Availability Cost (per scene unless

otherwise stated)

Scale Output

Application Notes

Landsat TM (4,5) (optical)

30m Worldwide Free < 1: 100,000 ( medium scale)

Land cover Last acquisition data – 2011

Landsat 7 ETM (optical)

15m, 30 m Worldwide Free < 1: 100,000 ( medium scale)

Land cover, environmental change

After April 2003, images have strips of missing data. Suitable for HCV and carbon stock modelling (moderate)

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Modis (optical)

250m, 500m, 1,000m

Worldwide Free < 250,000 ( Small Scale)

Land cover (forest/non- forest cover)

Not suitable for oil palm monitoring

SPOT 4 HRV (optical)

15m Most of the world

USD500-750

< 1: 75,000 Land cover (more detailed)

Suitable for HCV and carbon stock modelling (moderate)

ALOS PRISM (optical)

2.5m On request Y 31,000 Land cover, disaster monitoring etc.

ALOS VNIR (optical)

10m On request Y 31,000 Land cover, disaster monitoring etc.

ALOS PALSAR (radar)

50 m, 10 m Most of the world

free, Y31,000

Land cover, disaster monitoring etc.

Able to penetrate cloud cover

SPOT 5 HRV (optical)

2.5m, 5m, 10m

On request USD 10.000

< 1: 10,000 Land cover use analysis, environmental change, land use planning

Suitable for HCV and carbon stock modelling (moderate)

SPOT 6 HRV (optical)

1.5m On request - > 1 : 10,000 Defence, agriculture, land cover, deforestation, environmental change, land use planning

(DETAILED) Suitable for oil palm plantation monitoring, HCV and carbon stock estimation

Ikonos (optical)

0.82m (Pan-chromatic), 3.25m (colour)

On request USD31/ km2

> 1: 10,000 Road planning, defence, agriculture, land cover, HCV assessment, environmental change, land use planning and monitoring

(DETAILED) Suitable for oil palm plantation monitoring, HCV and carbon stock estimation

World view 1 (optical)

0.50m (Pan-chromatic)

On request USD37/ km2

> 1: 10,000 Road planning, defence, agriculture, land cover, HCV assessment, environmental change, land use planning and monitoring

(DETAILED) Suitable for oil palm plantation monitoring, HCV and carbon stock estimation

World View 2 (optical)

0.46 m (Pan-chromatic), 1.84 m (Multi-spectral)

On request USD37/ km2

> 1: 10,000 Road planning, defence, agriculture, land cover, HCV assessment, environment change, land use planning and monitoring

(DETAILED) Suitable for oil palm plantation monitoring, HCV and carbon stock estimation

Source: Eko G. Manjela Eko Hartoyo, GIS Coordinator, Tropenbos (pers. comm.) with additional inputs by the author

In addition to remote sensing using satellite, there are emerging technologies which in the near future may be more readily available and at lower costs than at present. These new technologies may provide solutions to limitations of conventional remote sensing technology using sensors

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mounted on satellites. Signals from remote-sensing instruments tend to saturate quickly when used on tropical forests due to the high biomass and structurally complex ecosystem, and there is also a perennial problem with using optical sensors in the tropics as their signals are often blocked by cloud cover (Archard et al., 2007; Gibbs et al., 2007, Morel et al., 2011). Newer technologies relying on radar systems, for example, can penetrate clouds and provide data day and night (Asner, 2001). These new technologies are summarised in Box 2 below.

Box 2: Emerging Remote Sensing Options

Very high-resolution imagery The spatial detail (as fine as 10 cm pixels) obtained from airborne sensors can be used to directly measure tree height and crown area, allowing for tree carbon stocks to be calculated using allometric equations. These data are collected over areas of several thousands of hectares using an airplane-mounted system, collecting imagery that can be viewed in 3D. It can reduce costs of conducting forest inventories in sites that are highly variable, widely spaced or inaccessible.

Microwave or radar data Radar signals can penetrate ground cover and clouds to reveal the underlying terrain as well as the top of the canopy. The radar signals returned from the ground and tops of trees are used to estimate tree height, which are then converted to forest carbon stock estimates using allometry. The ALOS PALSAR sensor has the potential to improve estimates of carbon stocks in the tropics for degraded or young forests but will be less useful for mature, higher biomass forests.

LiDAR (light detection and ranging) LiDAR systems send out pulses of laser light and measure the signal return time to directly estimate the height and vertical structure of forests. Forest carbon stocks are estimated by applying allometric height–carbon relationships. Large-footprint LiDAR remote sensing far exceeds the capabilities of radar and optical sensors to estimate carbon stocks for all forest types. Currently, airplanemounted LiDAR instruments are too costly to be used for more than a small area.”

Source: Gibbs et al. (2007)

Once the relevant remote sensing images have been procured they need to undergo pre-processing and processing, which include radiometric and geometric correction and image enhancement before they are classified into different land cover strata. A detailed account of the processes involved in using remote sensing using Landsat satellite images to stratify land cover into distinct vegetation classes can be found in Widayati et al. (undated). GAR & SMART (2012) developed an interpretation key to aid the land cover stratification in their study.

Land cover in the proposed new planting area should be classified into ten land cover types in line with the PalmGHG Calculator (Chase et al, 2012). These ten land cover classification can be nested within the CTF’s vegetation cover classification in a hierarchical manner as shown in Table 5 below. The default carbon values in tonnes of carbon per hectare (tC/ha) for the nine land cover strata are also provided.

Table 5: Hierarchical Land Cover System Incorporating Land Cover Classifications of the CTF and the PalmGHG Calculator and Default Carbon Values1

CTF Class

Vegetation Cover Land Cover Classification in

PalmGHG Calculator

Default Carbon Density Value

(tC/ha)2

A Multi-layered old growth forest (including primary Primary forest 225

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forest) and secondary closed forest with uneven canopy

B Secondary closed closed forest with even canopy Logged forest 87

C Other areas with trees that meet FAO’s forest definition (>10% canopy, >5m tall trees).

Coconut 75

Cocoa under shade 70

Rubber 62

D Areas that do not meet FAO’s forest definitions. Oil palm 503

Shrub land 26

Secondary regrowth 48

Food crops 9

Grassland 5

Notes: 1These are broad comparisons based on carbon stock content and not an actual comparison of land cover types that fall

within FAO definitions 2

Carbon density values are for above-ground and below-ground (root) biomass 3Calculated with OPRODSIM and OPCABSIM models (Henson, 2005, 2009). Depends on the cycle length and growth type

(vigourous or average)

It should be noted that the above hierarchical land cover classification is a preliminary one as the CTF has yet to complete its work and the vegetation status assigned to each co-efficient may still see some changes. There is also a Science Panel paper by Fahmuddin Agus et al. (in prep.) on land cover types in Malaysian, Indonesia and Papua New Guinea, which is intended to inform the revision of the land cover classification and their corresponding default carbon density values in the PalmGHG Calculator after it has undergone peer review.

4.3 Ground-truthing

Objective: To verify the accuracy of land cover stratification (based on remote sensing data) and to prepare for field-based biomass measurements using sampling plots

Key steps:

Gather relevant information on

biophysical characteristics of the

proposed new planting area

Prepare base maps for ground-truthing

incorporating proposed location of

sampling plots

Conduct ground-truthing –verify accuracy of land

cover stratification

After ground-truthing review land cover stratification and

modify accordingly

In order to maximise the productivity of time spent in the field, adequate preparations should be made prior to the ground-truthing exercise. As much information as possible should be compiled about the biological and physical characteristics of the proposed new planting area. Good base maps should be prepared incorporating (where possible) the following features:

Road and trail network

River courses and other water bodies (hydrology)

Topography

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Administrative boundaries

Strata verification points

Routes to sampling and verification locations

Standard data sheet

During ground-truthing, hardcopies of the base maps should be brought along, as well as print-outs of the land cover map. Equipment in a standard survey toolkit includes GPS, compass, altimeter, clinometer and digital camera.

The key task to be undertaken during ground-truthing is to conduct a visual assessment to determine the accuracy of strata boundaries that were determined through satellite imagery. The verification points are usually located at the boundary of two strata or where the land cover could not be determined from remote sensing data (usually due to missing satellite data). At each verification point the location should be recorded using GPS and directional photographs should be taken in five directions i.e. north, south, east, west and skywards with GPS coordinates.

Other tasks that could be carried out during ground-truthing include the visual assessment of soil type with particular attention given to the presence of peat soil. Information from the ground-truthing exercise should be recorded in standard data sheet.

After the ground-truthing exercise, the land cover stratification should be reviewed based on information gathered from the field, and corrections made to any site that may have been misclassified. Strata boundaries may need to be adjusted to more accurately reflect conditions observed during ground-truthing.

4.4 Biomass Sampling

Objective: to measure above-ground biomass in sample plots randomly distributed within the different land cover strata Key steps:

Determine the number, location and design of

sampling plots within the different land cover strata

Conduct additional ground-truthing if necessary to

confirm location of sampling plots

Carry out field measurements of above-ground biomass (tree

dbh) in sample plots

In order to estimate the carbon stock of a proposed new planting area, it would be hugely impractical and prohibitively expensive to measure every single tree in the whole area. Sampling is therefore the only viable option.

Results obtained from sampling plots can be extrapolated to the whole area of interest. The carbon stock values calculated from sampling are an estimation of the actual values. Statistics can be used to give an indication of how close the estimation is to reality.

As described in Section 4 above, the preferred sampling approach in this tool is sampling targeted at the different land cover strata. However, within each stratum, sampling should still be random (Hairiah et al., 2001) with sampling lots located across the stratum in an unbiased way (Walker et al., 2012), i.e. the plots do not only fall in areas with the densest or least vegetation” (Hairiah et al., 2011).

There are many manuals and guidance available on determining the design (number, size and distribution) of sample plots and for calculating associated sampling errors including by Brown

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(1997), Pearson et al. (2005), Hairiah et al. (2011) and Walker et al. (2012). In deciding on the sample design, there will be a trade-off among accuracy, precision and resources needed for the sampling effort (Pearson et al., 2007; Walker et al., 2012). These documents should be studied in detail when before embarking on any sampling exercise.

Nested plots are recommended for land cover with a wide range of tree diameters and stem densities with an uneven size distribution (Pearson et al., 2007) such as in tropical forests. Nested plots could be rectangular or circular (see Figure 2 below) but some researchers prefer rectangular plots as they tend to include more of the within-plot heterogeneity, and thus will be more representative than square or circular plots of the same area (Hairiah et al., 2011). The most appropriate size and shape may also be dependent on the land cover found in the sampling area (Walker et al., 2012)

Figure 2: Schematic diagram showing a three-nest sampling plot in both circular and rectangular forms

Source: Pearson et al. (2005)

It is advisable to select a larger set of sampling locations than the actual number required, in order to provide alternatives in case of unexpected field conditions, such inaccessibility (Hairiah et al., 2011). Ground-truthing prior to the actual plot sampling is important to finalise the location of sampling plots and identify the most efficient routes to reach them.

Winrock International (2008) has developed an online Excel tool called the Winrock Terrestrial Sampling Calculator that helps in the calculation of the number of samples and the cost involved for base line studies as well as monitoring. This calculator is available at: http://www.winrock.org/Ecosystems/tools.asp.

Tree measurements are taken within the sampling plots. The most important measurement is the diameter at breast height (dbh) which is usually set at 1.3m above ground level. Detailed guidance on how to take dbh measurements and the equipment needed can be found in many publications including Brown (1997), Pearson et al. (2005), Hairiah et al. (2011) and Walker et al. (2012). In a nested plot, larger trees (e.g. dbh>50cm) are measured in the larger plot while the smaller plots are for measuring trees of smaller dbh classes (as illustrated in Figure 2 above).

Although the measuring both the dbh and height of a tree would provide a more accurate estimation of its biomass, measuring tree height can be time-consuming (Pearson et al., 2005)

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and often difficult because treetops are hidden by the canopy layer. A decision should be made during the planning phase of sampling – based on resources available, data gathered on the land cover and field conditions – whether or not to measure tree height. There are allometric equations available for estimating above ground biomass with or without height measurement.

Above-ground non-tree or understory biomass is only to be measured if it is a significant component, such as for grassland or shrubland where trees are only present at low densities (Pearson et al., 2005). For forested land cover, above-ground non-tree biomass is generally not a significant component.

As this tool is meant to be used in conjunction with the PalmGHG Calculator (which takes into consideration only the above ground biomass and root biomass for all land cover), there is no need to measure the dead biomass and litter pools. Root biomass can be derived from the above-ground biomass using allometry (see Section 4.5 below). Soil organic carbon can be omitted for mineral soils but not in the case of peat soil for which the PalmGHG Calculator makes use of water table depth as a proxy for GHG emission from peat.

4.5 Carbon Density Calculation

Objective: to calculate the carbon density values for above-ground and below-ground biomass for each land cover stratum Key Steps:

Calculate above ground and below-ground (root)

biomass for each strata

Calculate the carbon density (tC/ha) for each stratum

Compare measured carbon density values with default

values for the relevant strata as determined in the PalmGHG

Calculator

Finalise the values of carbon density for each stratum

If the measured vs. default carbon density values are not

comparable, conduct more sampling and/or review land

cover stratification.

Calculating above-ground biomass

Once the dbh measurements of the trees in a sampling plot have been obtained (Section 4.4), the above-ground biomass can be calculated using an allometric equation that relates tree biomass with the dbh, height (optional), and wood density.

There are generally two approaches in using allometry to convert dbh measurements into above ground biomass. If the trees can be identified up to species or at least genus level, and their respective wood density is known, species- or genus-specific allometric equations can be used to estimate the above-ground tree biomass. Average wood density values for a range of species or genus are available from Brown (1997), IPCC (2006) and the World Agroforestry Center’s Wood Density Database.

However, tree diversity in the tropics is very high with one hectare of tropical forest containing as many as 300 different species (de Oliveira & Mori, 1999), making species-specific allometry not practical (Chave et al., 2005). Instead, grouping all species together within a particular land cover strata and using generalised allometric equations, is highly effective for tropical regions

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because dbh alone accounts for more than 95% of the variation in above-ground tropical forest carbon stocks, even in highly diverse regions (Brown, 2002). Generalised allometric equations are based on large numbers of trees covering a wide range of diameters (Brown, 1997; Chave et al., 2005).

All allometric equations require dbh values. In addition to dbh, some allometric equations values for tree height and/or wood density (for generalised equations, a weighted average value for wood density is the norm). Brown (1997) provides an allometric equation for tropical moist forests using data collected from Kalimantan and other tropical regions while others have developed allometric equations for specific forest types e.g. lowland dipterocarp forests (Basuki et al., 2009). If wood density value is needed in an allometric equation, the range provided by Brown (1997) for tropical tree species in the Asian region is 0.40-0.69 g/cm3 while some other researchers have used a value of 0.67 for Borneo and the Amazon (Chave et al., 2006; Fearnside, 1997; Paoli et al., 2008) or 0.60 in Sumatra (Ketterings et al., 2001) and Sabah Morel et al. (2011).

Calculating below-ground (root) biomass

Measuring below-ground (root) biomass (coarse and fine roots) is time consuming so (Pearson et al., 2007) so the usual practice is to use a default ratio of below-ground biomass to above ground biomass (commonly referred to as root:shoot ratio). The ratio of the below-ground biomass to above-ground biomass varies depending on the vegetation type and local circumstances (Mokany et al., 2006). A mean ratio of 0.18 was derived by Germer & Saeurborn (2008) for Southeast Asian tropical rainforests based on an extensive literature review and this value was also supported by Niiyama et al. (2010) and Saner et al. (2012). However, a critical review by Mokany et al. (2006) of global data on root:shoot ratios for terrestrial biomes provided a more generalised value of 0.205 for tropical/subtropical moist forest/plantation. Mokany et al. (2006) also provided root:shoot values other vegetation categories including tropical/subtropical moist woodland (0.420) and tropical/subtropical grassland (1.887).

Calculating above- and below-ground biomass on a per hectare basis The above- and below-ground biomass values for all measured trees in a plot are added up to give a total for the plot. The value for the plot is then extrapolated to the full hectare area in order to derive the density of biomass expressed in tonnes per hectare (t/ha). The average above- and below-ground biomass density value for each stratum is calculated by adding the values for all plots in each stratum divided by the number of plots for that stratum.

Calculating above- and below-ground carbon density In order to convert above- and below-ground biomass density to carbon density (expressed in tC/ha), the carbon content of the biomass has to be estimated and it a value of about 0.50 (e.g. Westlake, 1966; Brown, 1997; Saner et al., 2012) is commonly used in carbon accounting studies. The default value for the carbon content of above- and below-ground biomass used in the PalmGHG Calculator is 0.45 (Chase et al., 2012) and it is recommended that this value be used for consistency.

The steps described above are summarised in Figure 3.

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Figure 3: Scaling up of dbh measurement to estimate biomass

Step Measurement output

(unit)

Items needed

Measure dbh Diameter of one tree (m)

Dbh value

Height (optional)

Wood density (weighted average values from published sources, optional)

Allometric equation (select appropriate one from published sources)

Use allometric equation to convert dbh measurement to above-ground biomass

Above-ground biomass of one tree (tonnes)

Repeat process for all measured trees

Total up biomass of all trees within a plot

Above ground biomass of one plot (tonnes)

Above-ground biomass value

General ratio of below-ground biomass to above-ground biomass (from published sources)

Calculate root biomass and add to above-ground

biomass

Above-ground and below-ground (root) biomass of one tree

(tonnes)

Size of each stratum (ha)

Total up biomass of all plots for each land cover

stratum and calculate average per hectare

Average biomass per hectare for each

stratum (tonnes/ha)

Average biomass per hectare value for each stratum

Carbon conversion factor (use default value)

Use carbon conversion factor to convert biomass

to carbon amount

Average carbon density for each stratum (tC/ha)

The average carbon density value for each stratum should be compared with the relevant default value for the stratum as determined by the RSPO’s Science Panel (see Table 5 above). If the two values are very different (e.g. the calculated value is close to the default value of another stratum), it is necessary to check if the land cover stratification has been done correctly and if the sampling plots are actually in the stratum that they are supposed to be. Independent

24

verification (Pearson et al., 2005) by a third party may also be considered. If the discrepancy in values remain after these additional efforts, the calculated value may be used instead of the default value if there is a high level of confidence in the robustness of the field sampling exercise, which is likely to yield more accurate results as compared to the default values which are average values that may not be applicable in all cases.

4.6 Soil Sampling

As discussed above, the carbon content of soil of mineral soils is not taken into consideration when calculating the carbon density in new planting areas. This is due to uncertainty on the affect of land cover change to soil as evidence concerning changes in soil carbon is limited and contradictory (Chase et al., 2012).

While the carbon content of peat soil is very significant and the evidence of GHG emission from oil palm cultivation on peat is well documented (see for example Hooijer et al., 2010), there is much debate on the actual amount of these emissions. As noted by Chase et al. (2012): “Research is still ongoing to determine the magnitude of these emissions and how they are affected by and related to factors such as drainage depth, peat subsidence and plantation age.”

Based on deliberations within the RSPO’s GHG WG2, it was decided that peat CO2 emissions due to peat cultivation will be calculated using an equation that relies on drainage depth of peatland (in cm) as the main variable (Chase et al., 2012). The extent of peat soil within the new planting area also needs to be determined during soil surveying conducted as a requirement under Principle 7.

As the method adopted by the RSPO for calculating emission from peat may change in future in light of new information arising from on-going research on peatlands, soil sampling conducted as part of the responsible new plantation development should still include measurements of the following parameters that may be used for calculating carbon stocks in peatlands (Agus et al., 2011):

Bulk density (g/cm3 or kg/dm3 or t/m3)

Organic carbon content (% by weight or g/g or kg/kg)

Peat thickness. If the samples consist of many layers, the thickness of each layer with its respective bulk density and organic carbon content needs to be measured (cm or m)

Area of land in which the carbon stock is to be estimated (ha or km2)

Detailed description on how to measure the above parameters is provided by Agus et al. (2012) and in a soon-to-be-published report commissioned by the RSPO’s Peatlands Working Group (GHG WG2 Workstream 2) on methods for determining greenhouse gas emissions and carbon stocks from oil palm plantations and their surroundings in tropical peatlansds (A. Schrier , in prep.).

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Estimating GHG Balance for the Palm Oil Production

Objective: to estimate the GHG balance for the planned palm oil production

Key steps:

Enter the carbon density values, and planned

drainage depth for peat areas (if applicable) into

PalmGHG Calculator

Enter other projected agronomic and mill data as required in the PalmGHG

Calculator and determine the net GHG balance for the

whole palm oil production cycle

Use scenarios within Palm GHG tool to formulate management

strategies to reduce GHG emission and maximise

sequestration opportunities from the new development

Once the carbon density of different land cover strata, and the planned drainage depth of peatland areas (if applicable) in the proposed area for new planting are known, the values can be used to calculate the land use change and soil GHG balance using the PalmGHG Calculator, in conjunction with other parameters related to the other aspects of palm oil production and milling. Guidance on how to use the PalmGHG Calculator is provided in the accompanying documentation of the calculator (Chase et al., 2012) and in a series of trainings available form the RSPO Secretariat.

The PalmGHG Calculator allows for the contribution of land cover change to the overall GHG balance from palm oil production to be calculated. Management, avoidance and mitigation strategies could then be formulated to reduce net GHG emissions and these could include implementing methane capture or minimisation methods, increasing the size of conservation blocks within the proposed new planting area and maintaining the optimum water table of any peat areas that may be planted. The PalmGHG has the required flexibility to allow for modelling of different scenarios associated with the new planting.

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Archard, F.A., R. DeFries, H. Eva, M. Hansen, P. Mayaux & H-J. Stibig. 2007. Pan-tropical monitoring of deforestation. Environmental Research Letters 2: 045022 (11pp.).

Asmara, D.H., N. Khasanah, F. Agus & M. van Noordwijk. 2012. Oil palm plantation carbon stock calculator. World Agroforestry Centre ICRAF-SEA Regional Programme and Indonesian Soil Research Institute.

Asner, G.P. 2001. Cloud cover in Landsat observations of the Brazilian Amazon. International Journal of Remote Sensing 22:3855–62.

Baccini, A., S.J. Goetz, W.S. Walker, N.T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P.S.A. Beck, R. Dubayah, M.A. Friedl, S. Samanta & R.A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182-185.

Basuki, T.M., P.E. van Laake, A.K. Skidmore, Y.A. Hussin. 2009. Allometric equations for estimating the above-ground biomass in tropical lowland dipterocarp forests. Forest Ecology and Management 257: 1684-1694.

Brown, S. 2002. Measuring carbon in forests: current status and future challenges. Environ. Pollut. 116: 363-72.

Brown, S. 1997. Estimating biomass and biomass change of tropical forests: a primer. FAO Forestry Paper no. 134. FAO, Rome, Italy.

Chase L.D.C., I.E. Henson, A.F.N. Abdul-Manan, F. Agus, C. Bessou, L. Milà i Canals & M. Sharma. 2012. The PalmGHG Calculator: The RSPO Greenhouse Gas Calculator for Oil palm Products, Beta-version. The Roundtable for Sustainable Palm Oil (RSPO), Kuala Lumpur, Malaysia.

Chave, J., C. Andalo, S. Brown, M.A. Cairns, J.Q. Chambers, D. Eamus, H. Fölster, F. Fromard, N. Higuchi, T. Kira, J.P. Lescure, B.W. Nelson, H. Ogawa, H.Puig, B. Riéra & T. Yamakura. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145(1):87-99.

Chave, J., H.C. Muller-Landau, T.R. Baker, T.A. Easdale, T.E.R Hans Steege, & C.O. Webb. 2006. Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecological Applications 16:2356-2367.

de Oliveira, A.A. & S.A. Mori. 1999. A central Amazonian terra firme forest I. High tree species richness on poor soils. Biodiversity Conservation 8:1219–1244.

Di Gregorio, A. & L.J.M. Jansen. 2000. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Version 1.0. FAO Land and Water Development Division, Environment and Natural Resources Service, Africover - East Africa Project, Nairobi, Kenya. Accessed at: http://www.fao.org/DOCREP/003/X0596E/X0596e00.htm#P-1_0

Fearnside, P.M.1997. Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecology and Management 90: 59-87.

GAR & SMART. 2012. High Carbon Stock Forest Study Report: Defining and Identifying High Carbon Stock Forest Areas for Possible Conservation. Golden Agri-Resources (GAR) and SMART in collaboration with The Forest Trust and Greenpeace, Singapore.

Germer, J. & J. Sauerborn. 2008. Estimation of the impact of oil palm plantation establishment on greenhouse gas balance. Environment, Development and Sustainability 10(6):697-716.

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Gibbs, H.K., S. Brown, J.O. Niles J.O. & J.A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD+ a reality. Environmental Research Letters 2: 045023 (13pp).

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Hairiah, K., S.M. Sitompul, M. van Noordwijk & C. Palm. 2001. ASB Lecture Note 4B: Methods for Sampling Carbon Stocks Above and Below Ground. World Agroforestry Centre (ICRAF), Bogor, Indonesia.

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Houghton, R.A. 2005. Tropical deforestation as a source of greenhouse gas emissions. In: Mutingo & Schwartzman (Eds.). 2005. Tropical Deforestation and Climate Change. Ipam, Belem, Brazil.

Houghton, J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell & C.A. Johnson (Eds.). 2001. Climate Change 2001: The Scientific Basis. Contribution to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, USA. 881 pp.

Imai, N., H. Samejima, A. Langner, R.C. Ong, S. Kita S, J. Titin, A.Y.C. Chung, P. Lagan, Y.F. Lee & K. Kitayama. 2009. Co-Benefits of sustainable forest management in biodiversity conservation and carbon sequestration. PLoS ONE 4(12): e8267.

IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme. Eggleston, H.S. , L. Buendia, K. Miwa, T. Ngara & K. Tanabe K. (eds). IGES, Japan.

Jennings, S., R. Nussbaum, N. Judd &. T. Evans. 2003. The High Conservation Value Forest Toolkit – Edition 1. Proforest, Oxford, UK.

Ketterings, Q.M., R. Coe, M. van Noordwijk, Y. Ambagau, C.A. Palm. 2001. Reducing uncertainty in the use of allometric biomass equations for predicting aboveground tree biomass in mixed secondary forest. Forest Ecology and Management 146: 199-209.

Khasanah, N., M. van Noordwijk, A. Ekadinata, S. Dewi, S. Rahayu, H. Ningsih, A. Setiawan, E. Dwiyanti, R. Octaviani. 2012. The carbon footprint of Indonesian palm oil production. Technical Brief No 25: Palm Oil Series. World Agroforestry Centre-ICRAF, SEA Regional Office, Bogor, Indonesia.

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Koh, L.P., J. Mittinen, S.C. Liew & J. Ghazoul. 2011. Remotely sensed evidence of tropical peatland conversion to oil palm. Proceedings of the National Academy of Science (PNAS). Accessed at www.pnas.org/cgi/doi/10.1073/pnas.1018776108

Mokany, K., R.J. Raison & A.S. Prokushkin. 2006. Critical analysis of root : shoot ratios in terrestrial biomes. Global Change Biology 11:1-13.

Morel, A.C., S.S. Saatchi, Y. Malhi, N.J. Berry, L. Banin, D. Burslem, R. Nilus & R. Ong. 2011. Estimating aboveground biomass in forest and oil palm plantations in Sabah, Malaysian Borneo using ALOS PALSAR data. Forest Ecology and Management 262:1786-1798.

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Widayati, A., A. Ekadinata & R. Syam. Undated. Carbon-stocks through land cover types and vegetation density. In: Lusiana, B, M van Noordwijk & S Rahayu (Eds.). Carbon Stocks in Nunukan, East Kalimantan: A Spatial Monitoring and Modelling Approach. A report from the Carbon Monitoring Team of the Forest Resources Management for Carbon Sequestration (FORMACS) Project. World Agroforestry Centre (ICRAF), Bogor, Indonesia.

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Appendix 1: Case Study of Carbon Assessment Conducted by Golden Agri-Resources (GAR) and SMART

GAR in collaboration with The Forest Trust (TFT) launched its Forest Conservation Policy (FCP) in Feb 2011 with a central focus on ensuring that GAR has no deforestation footprint. The FCP is adopted for all the plantations owned, managed or invested in (regardless of the stake) by GAR. The FCP underscores GAR’s commitment in ensuring that its oil palm plantations will not be developed on areas that have High Conservation Values (HCV), peat areas regardless of depth, and areas with high carbon stock (HCS).

As part of its commitment to the FCP, GAR, its subsidiary PT SMART Tbk (SMART), TFT and Greepeace collaborated in a study to develop a practical, scientifically robust and cost effective methodology to define and identify areas of HCS for conservation. The study was conducted in GAR’s concessions in Central and West There were no peat land in any of the concessions.

The methodology employed was based on the premise that there is a correlation between vegetation density and above ground living wood volume in trees greater than or equal to 5cm dbh. Carbon was measured indirectly using dbh of trees in the sampling plots as a proxy to calculate the carbon in the vegetation of the strata. This avoided the use of destructive sampling which is a more direct measurement technique.

The methodology comprised a combination of remote sensing data analysis with ground-based field data as summarised below.

A combination of Landsat 7 ETM images and medium-sized resolution images such as SPOT-4 (spatial resolution of 20m) and SPOT-5 (10m) were analysed and combined with data from field work, resulting in the stratification of vegetation cover into difference classes.

The canopy cover for 13 concessions were stratified using canopy cover and incorporating information from earlier aerial surveillance conducted by Greenpeace in some concessions. Initial field work in the fourth quarter of 2010 was conducted at eight sites that appear to contain more vegetation. It was found that most of the carbon in these forest areas was in larger trees and that it would not be cost effective to measure small trees of less than 5cm dbh.

Initial measurements were carried out in a number of field sites. In each of these sites, a main plot was identified, measuring 20m x 20m, in which all trees with dbh>20cm were measured. Within the main area, a sub plot of 10m by 10m were identified to measure all trees taller than 2m and below 20cm in dbh. The measurements obtained from the fieldwork were used to calculate the AGB using a generic Asia-wide formula where wood density of 600kg/m3 is generally accepted as an average density for Asian tropical tree species. AGB is then converted to tonnes of carbon per hectare (tC/ha) using a conversion of 0.47 as determined by the IPCC (2006). These initial measurements indicated that that satellite images with higher resolution

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were needed to identify HCS areas more accurately; more focus should be given to the stratification process of the satellite images so as to establish preliminary estimates of the range of carbon values; and a more structured approach was needed in conducting an AGB inventory using an appropriate allometric.

Through an adaptive approach, the stratification process was improved by reducing the number of strata from 16 to 6. The six strata of vegetation cover correlated with different average carbon stocks were identified as summarised in the table below.

Stratum Description Average C stock (tC/ha)

High Density Forest (HK3) Remnant forest or advanced secondary forest close to primary condition.

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Medium Density Forest (HK2) Remnant forest but more disturbed than High Density Forest.

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Low Density Forest (HK1) Appears to be remnant forest but highly disturbed and recovering (may contain platation/mixed garden).

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Old Scrub (BT) Mostly young re-growth forest, but with occasional patches of older forest within the stratum.

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Young Scrub (BM) Recently cleared areas, some woody regrowth and grass-like ground cover.

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Cleared/Open Land (LT) Very recently cleared land with mostly grass or crops, few woody plants.

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The sampling technique and plot selection process was also adjusted based on the results obtained from the first fieldwork. Plot samplings were conducted during fieldwork between the first and last quarter of 2011, involving 431 plots in the four concession areas with three located in West Kalimantan and one in Central Kalimantan. The size of the concessions range from 14,000ha to 20,000ha. These concessions were where new plantings were taking place and were designated as land for other uses (APL) under Indonesian land use planning regulations. The concessions were selected for ease of access to study locations and community engagement and contained large areas that were still covered with vegetation and were subjected to extensive human disturbances, including timber harvesting and swidden agriculture. The results of the sample plots were extrapolated to the rest of the concessions.

Using the results from the initial fieldwork, the coefficient of variance for the target strata was calculated using the Winrock Terrestrial Sampling Calculator with a 5% sampling error. A rectangular nested design was used for the sample plots where a smaller 10m x 10m subplot was nested within a larger 10m x 50m main plot. Trees with dbh greater than or equal to 5cm and less than 20cm were measured in the subplot while all trees with dbh≥20cm were measured in the main plot. Two different techniques were used in designing the 431 sampling plots – transect plots and random plots. In one of the concession areas, transect lines were used given the lack of baseline data. Subsequently the technique was refined and plots were identified randomly. For the transect method, plots were systematically located every 200m across transect lines drawn across the concession. The random plots were located randomly across the concessions and within targeted strata, although some random plots were not measured due to inaccessibility. A total of 114 anomalous plots removed from the final analysis because of uncertainty or inconsistency in allocating vegetation classification to specific plots.

The biomass of a particular tree was estimated from its dbh using a generic allometric for Tropical Moist Forests following Brown (1997) where:

Biomass = 42.69 – 12.800*dbh + 1.242*dbh2

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The allometric equation was chosen because it was derived from a dataset that included a large number of trees harvested from dipterocarp forests in Kalimantan. As with the initial sampling, a carbon conversion factor of 0.47 was used to convert biomass to tonnes of molecular carbon per tree. After the tree carbon weight was summed for each plot, the amount of carbon per plot was calculated and then extrapolated to a per hectare figure basis and expressed as tonnes per hectare. Carbon values for each stratum were calculated by averaging plot data to produce a mean carbon value for each stratum. A 90 percent confidence level was used to calculate the confidence level of the mean.

In plotting the weighted average carbon stock of the various strata it was discovered that some of the strata’s carbon values overlap as shown in the figure below.

To establish if there was any statistical difference between the weighted average carbon stocks across the strata, an analysis of variance (ANOVA) was conducted which indicated that there were indeed statistically distinct groups. An additional analysis, called the Scheffe test, was conducted as it allows the determination of simultaneous confidence intervals for groups only based on the number of groups (strata) and the number of observations (plots).

The results of this test showed that:

There were no significant differences between strata HK3 and HK2

There were no significant differences between BM and LT

Other pairs of strata were significantly different from each other.

In the study report, it was recommended that as HK2 and HK3 were not statistically distinct from each other, it may be practical for future work to group these two strata into one. Further studies were also recommended to investigate why the BM and LT strata were not statistically distinct or if it is more practical for them to be combined.

Among the key findings of the study were that:

Vegetation cover can be used broadly to estimate the level of carbon stocks.

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Vegetation cover can be stratified into different classes to broadly represent different carbon stocks.

There were similarities in the carbon stock of strata across the different concessions.

Across some of the different classes of vegetation cover, there are significant differences in the carbon stock.

A threshold level could be defined with the vegetation strata classified as HCS considered for conservation, while the strata with carbon stocks below the threshold could be considered as non-HCS and could potentially be cleared for new oil palm development, subject to further research that considers the regeneration potential.

The limitations of the study included the following:

The carbon stocks were underestimated as the methodology did not account for all AGB (trees with dbh<5cm and dead wood were excluded), and below-ground biomass

Field surveys were limited only to areas where permission was obtained from local communities, which could have led to biased results as a fully statistically valid sampling approach could not be completed.

Satellite images were of low to medium resolution which gave rise to the potential for human error being introduced in the course of their interpretation. While the boundaries between two very distinct strata could be differentiated and mapped, other strata were more difficult to differentiate as the boundaries between the two strata were not distinct, the use of high-resolution satellite or aerial imagery along with semi-automated processing could assist in addressing this limitation.

Satellite images of up to two years earlier were used due to the problems with their availability (especially cloud free images). This meant that subsequent disturbance resulting in changes to the vegetation cover may not have been detected.

Visual interpretation of satellite images of vegetation canopy cover cannot fully identify land use. Therefore stratification should be accompanied by extensive ground-truthing in order to improve the demarcation of boundaries between the different strata.

Source: GAR & SMART (2012)

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Appendix 2: Limitations of the Tools

Assumptions

It is assumed that the oil palm growers (who are the main users of this tool) have the resources required to conduct the land cover stratification using remote sensing data, and also the additional fieldwork (that is, in addition to the current new planting requirements) related to biomass estimation within their proposed new planting area. It is also assumed that if growers do not have the necessary human resources in-house, there is available external expertise that could be engaged to undertake these tasks.

In deciding on the scope and content of this tool, a key assumption is that it should not attempt to provide all the necessary information in order to conduct a carbon assessment for new planting areas but that it is aimed at providing enough guidance so that growers understand the purpose and key steps involved and would know where to get additional information where required. For some of these key steps, more comprehensive guidance is already easily available from published sources, as in the case of field sampling procedures for measuring biomass. While it is possible to incorporate more detailed information on land cover stratification and field sampling in this tool, this would make the tool cumbersome to use and intimidating to those who are in need of a simpler guidance or primer to help navigate them through a subject matter that is littered with technical jargon.

Availability of data

This tool was developed over a relatively short period of time (September to December 2012) and although considerable efforts were made to secure information from the relevant interviewees, in some cases the required information was not fully available. For example, the report of a carbon assessment conducted by REA in Kalimantan was not publically available during the development of this tool. In some cases, the methodology used for carbon stock assessment by the interviewees was not particularly relevant for the purpose of developing this tool, e.g. in cases where the carbon assessment conducted by the interviewees were for the purpose of REDD.

This tool was developed in parallel with other related tool development processes by the RSPO which meant that some of the components of other tools incorporated into this tool were in draft form and are likely to be further refined at a later date. For example, the review of CO2 emission from land use changes conducted by the RSPO’s Science Panel (GHGWG2 WS3) is on-going and the final outcome of this review will likely lead to an update of the default values for land cover changes currently used in the PalmGHG Calculator (which is referred to in Section 4.2 of this tool). There are also efforts by other parties to establish reference values for biomass or carbon stock for different land cover strata. For example, the Malaysian Ministry of Natural Resources and Environment together with the Forestry Department and the Forest Research Institute of Malaysia (FRIM) is in the midst of estimating biomass in different forest strata based on the forest type and logging history but their findings have yet to be made publically available.

Calculated values vs. default values

The tool requires that oil palm growers conduct targeted sampling in different land cover strata in order to derive carbon density (in tC/ha) for different land cover strata in their area of interest, and to compare these calculated values with the default values included in the PalmGHG Calculator. In its current form, the PalmGHG Calculator utilises preferred default values which are absolute values rather than a range of values. The review of CO2 emission from land use changes by the RSPO’s Science Panel may provide a range of values for each of the

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previous land cover but it is very likely that there will be a great deal of overlap between these ranges. Therefore, there is a high degree of subjectivity in deciding whether the calculated carbon value for a specific land cover stratum derived from field surveys is comparable with the default value for the same stratum as determined in the PalmGHG Calculator. In some instances, the oil palm grower may decide to use the calculated values rather than the default values.

The calculated values can differ substantially from calculated default values due to the following:

Subjectivity in land cover stratification – although there are GIS tools for interpreting remote sensing imagery there is still a need for supervision by remote sensing experts. In some cases, the boundary between two strata may not be very distinct. As such, a considerable amount of subjectivity is involved in deciding the number of distinct land cover strata in a particular area of interest and also in the nomenclature of the strata. In this tool, land cover is to be stratified initially into four strata (in line with the RSPO’s Compensation Task Force’s coefficients – see Section 4.1 of this tool) and then into nine strata according to the classification provided in the PalmGHG calculator. While the Compensation Task Force has provided a brief description of the different strata, the current version of the PalmGHG Calculator does not include standard definitions for the different land cover strata; more guidance on this may be available once the review of CO2 emissions from land use changes by the RSPO’s Science Panel is completed. Much of the subjectivity in land cover stratification is due to a lack of clarity on key terms used, particularly with regard to “disturbed forest”, “secondary forest” and “upland forest”. Even if these terms are clearly defined and used in a standardised manner, there is still much inherent variability within a particular land cover stratum that would make land cover stratification a challenging task. For example, GAR & SMART (2012) found that that in a stratum classified as open scrub dominated by younger vegetation, there were still some very large trees that represent elements of remnant forests.

Errors in estimating biomass – Chave et al. (2004) investigated uncertainties that could lead to statistical error in calculating above-ground biomass in tropical forests and described four types of uncertainty: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. They found that the most important source of error was related to the choice of the allometric model and suggested that more work should be done on improving the predictive power of allometric models for biomass. Morel et al. (2011) discussed the suitability of various allometric equations relevant to tropical rainforests.

Threshold value for High Carbon Stock (HCS)

There may be a need to define a threshold value for what is considered to be “high carbon stock”. A logical approach would be to use the average carbon density value for an oil palm plantation as a threshold value as the conversion from a previous land cover with a higher carbon density than an oil palm plantation would lead to a net emission of CO2. This threshold value for HCS would only be applicable in the context of palm oil certification and not as a generic HCS threshold value for other purposes, such as REDD. However, the assignment of a threshold value for HCS is beyond the scope of this tool development and it is recommended that such an exercise should involve an extensive literature review and stakeholder consultation process which could be tasked to a working group as with the normal practice within the RSPO.

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Appendix 3: Reporting Framework

This carbon assessment tool is to be used in conjunction with the proposed inclusion of carbon considerations into the RSPO’s Principle 7 and therefore the New Planting Procedure (NPP). There is a set of guidance documents related to the NPP, including on the format for reporting. As the proposed carbon assessment would be an addition to existing assessments required under the NPP, it would make sense to incorporate the reporting requirements for carbon assessment into the NPP reporting framework rather than to have a stand-alone reporting process for carbon assessment alone.

The available reporting formats related to the NPP are on the summary reports of Planning and Management, and on SEIA and HCV Assessments, which are reproduced below in Box A3.1 and Box A3.2 respectively. In both text boxes, the proposed amendments to the reporting formats to allow for the incorporation of carbon assessment reporting are in italics and in red.

Box A3.1: Incorporation of Carbon Considerations into the Format for the NPP Summary

Report of Planning and Management

Format for Summary Report of Planning and Management

1. Executive Summary

2. Reference Documents SEIA, HCV and carbon and GHG emissions assessment reports. List of Legal documents and regulatory permits related to the areas assessed. Location maps – both at landscape level and property level. Area of new plantings and time-plan for new plantings.

3. SEIA, HCV and GHG Management & Planning Personnel Organisational information and contact persons. Personnel involved in planning and implementation. Stakeholders to be involved.

4a. Summary of Management and Mitigation Plans (SEIA) Mitigation plans to minimise negative for socio-economic impacts. Management plans to enhance socio-economic contributions. Mitigation plans for negative environmental effects. Management plans to promote positive environmental effects.

4b. Summary of Management and Mitigation Plans (HCV) Plan for HCV monitoring and regular review of data. Management and mitigation plans for threats to HCV areas. Management plans to enhance or maintain conservation values of identified HCV areas.

4c. Summary of Management and Mitigation Plans (Carbon stocks and GHG emissions) Plan for carbon stock and GHG emissions monitoring and regular review of data. Management and mitigation plans for threats to carbon stocks within new planting areas. Management plans to enhance carbon sequestration in new planting areas. Management and mitigation plans to reduce GHG emissions from new plantings.

5. Internal responsibility Formal signing off of management and mitigation plans.

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Box A3.2: Incorporation of Carbon Considerations into the Format for the NPP Summary Report of SEIA and HCV Assessments

Format for Summary Report of SEIA, HCV and Carbon Assessments 1. Executive Summary As is mentioned in the document on “RSPO Detailed Process Flow for New Plantings Procedures” the summary must include reporting of the identification of all primary forest any area required to maintain or enhance one of more High Conservation Values

(HCVs) all areas of peat soils all areas of significant carbon stocks (current and projected) all likely significant sources of GHG emissions and sequestration local peoples’ lands

2. Scope of the SEIA, HCV and Carbon and GHG emissions Assessments: Organisational information and contact persons List of Legal documents, regulatory permits and property deeds related to the areas assessed Location maps – both at landscape level and property level Area of new plantings and time-plan for new plantings

3 Assessment process and procedures Assessors and their credentials Assessment methods (Data sources, data collection, dates, programme, places visited ) Stakeholder consultation (stakeholders contacted, consultation notices and dates) List of Legal, regulatory and other guidance referenced.

4a. Summary of assessment findings (for SEI assessments) Summary of key findings in respect of socio-economic impacts to country, region and local communities. Summary of key findings in respect of socio-economic impact in respect of emergent communities (workers, suppliers, etc.). Issues raised by stakeholders and assessors comments on each issue.

4b. Summary of assessment findings (For HCV assessments) Overall HCV identification and proposed measures to maintain and enhance those identified Documentation showing the Obtained Free, Prior and Informed Consent of any indigenous peoples affected by the development of the concession (part of RSPO requirements)

Data sources and quality Which HCV toolkits employed Decisions on HCV status and related mapping

4c. Summary of assessment findings (For Carbon and GHG assessments) Method used for conducting carbon and GHG assessments Land cover stratification (maps included) and estimated carbon density (tC/ha) for each land cover stratum Carbon emission values for each land cover stratum (tC02e) used in the PalmGHG Calculator (if different from default values) Green house gas (GHG) balance derived from the PalmGHG Calculator based on the proposed management regime for new plantings (projected over the length of crop cycle)

5. Internal responsibility Formal signing off by assessors and company Statement of acceptance of responsibility for assessments.

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