Carbon Assessment Tool for New Oil
Palm Plantings
Version: June 2014
Document prepared by:
Surin Suksuwan
On behalf of the RSPO as requested by the P&C Review Taskforce.
Version: June 2014
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 the production of 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 in 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 first P&C review
began with the initial public consultation in 2011 and the process continued throughout 2012
and early 2013 led by the P&C Review Taskforce. The revised P&C was endorsed by the RSPO
Executive Board and accepted at the Extraordinary General Assembly by RSPO members on
April 25th 2013.
The revised P&C (2013) has a new Criterion 7.8 requiring that new plantation developments
are designed to minimise net greenhouse gas (GHG) emissions. The indicators under this
criterion include the identification and estimation of potential sources of emission and sinks of
carbon associated with new developments. Another indicator is that new developments must
be designed to minimise GHG emissions which takes into account avoidance of land areas with
high carbon stocks and/or sequestration options.
As a parallel process to the P&C review, the P&C Review Taskforce requested the RSPO
secretariat to produce a new tool incorporating practical methodologies for growers to use to
estimate the carbon stock of the land area associated with new developments.
This tool is to be used in conjunction with the Palm GHG Calculator, henceforth referred to as
“PalmGHG”, developed by RSPO (Chase et al., 2012). PalmGHG allows for the estimation of the
greenhouse gas (GHG) balance for palm oil production from land clearing activities (land cover
change) 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 and these are combined with emissions based on the
input of agronomic data such as fertiliser, other inputs and fossil fuel use, etc.
1.2 Objective of This Tool
The objective of this tool is to provide a practical methodology to growers for estimating
the carbon stock of above- and below-ground biomass for land earmarked for new oil
palm development. Based on this, the corresponding expected GHG emission associated
with the resulting land cover change to oil palm can be estimated.
This methodology is intended to be compatible with current processes required under
Principle 7 – primarily the soil survey, SEIA and HCV assessments – and should be used in
conjunction with the PalmGHG Calculator (developed to account for and report emissions from
existing operations).
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 , which would estimate the GHG balance for the
entire planned palm oil production cycle.
Version: June 2014
By using 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
Criterion 7.8. Using this data in combination with Palm GHG 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.
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
palm producing companies, non-governmental organisations (NGOs), consultant companies,
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 at time of this tool’s development. This is documented as a case study in Appendix 1.
Appendix 2 discusses the limitations of the tool, gaps identified and opportunities.
In the process of data gathering and developing the tool, 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. Attention was also given to
the land cover categories generated bythe 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
(Gunarso et al., 2013).
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
emissions/sequestrations arising from new plantings has also been developed.
projected
There will be future revisions to this tool based on the outcome of implementation period
(ending 31st December 2016) for promoting best practices for reporting to the RSPO as stated in
Criterion 7.8 of the revised RSPO P&C (2013). The RSPO Emission Reduction Working Group
(ERWG), which was formed after the 10th General Assembly of the RSPO in Medan, Indonesia on
14th November 2013, will oversee these revisions.
Version: June 2014
2. Carbon Accounting within the RSPO
In order to comply with the recently-introduced Criterion 7.8, information on the carbon stock
in the proposed new planting area 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, i.e. the PalmGHG, which 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, 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).
PalmGHG provides default carbon stock values for previous land cover1 classes based on inputs
from the scientific panel of the RSPO’s GHG WG2 (Working Stream 3).. In the current version of
PalmGHG, previous land cover classes 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.
However, these land cover classes and their default carbon stock values are being re-evaluated
by the RSPO ERWG following a thorough review by Agus et al. (2013a) of literature data and
satellite images to identify land cover changes associated with oil palm plantations in Indonesia
and Malaysia. Depending on the decision of the RSPO ERWG, later versions of PalmGHG may
have different land cover classification and default values.
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).
Table 1 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 hectares2.
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.
2
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).
Version: June 2014
Table 1: Summary of Methods for Measuring Biomass in Different Carbon Pools
Carbon pool
Above ground
(tree)
1
biomass
Below ground
(root) biomass
Method
Relative amount of
resources needed
Notes
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
2
equations 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.
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.
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).
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 ratio or
allometric equation for
calculating root biomass
as a function of
aboveground biomass.
Low – no sampling needed.
 Root:shoot ratios and
allometric equations for
calculating root biomass
available from various
sources.
Version: June 2014
 Sufficient number of plots
need to be established in
order to have statistically
representative sampling
 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.
 Number of sampling plots
greatly reduced compared
to random sampling.
Table 1: Summary of Methods for Measuring Biomass (continued)
Carbon pool
Method
Dead biomass
Non-destructive sampling
- for standing dead
biomass, measure dbh
and height (optional) as
with tree biomass
Relative amount of
resources needed
Notes
Low – sampling done in
conjunction with aboveground biomass sampling
 Optional – can be omitted if
amount of dead biomass
observed during field
reconnaissance is found to
be relatively low.
- for lying dead biomass
(fallen tree trunk),
measure diameter
(>10cm) using line3
intersect method
Litter
Destructive sampling –
litter collected within a
30x30cm frame within the
above ground biomass
sampling plot, weighed,
oven-dried and mass
3
calculated.
Low – sampling done in
conjunction with aboveground 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 aboveground 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:
1
Above 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.
2
Allometric 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.
It should be noted that the land cover classes are not clear cut, for example the term ‘logged
forest’ which can cover a variety of situations, and that the values are provided as a practical
means for estimating carbon stock 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. This carbon assessment tool is not meant to be
prescriptive and therefore the ultimate decision on which option to use (using default values vs.
direct measurements) lies with the grower.
Version: June 2014
2.1 Carbon stocks
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)3 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.
3. Using the Tool
There are essentially three recommended options for estimating the carbon stocks within the
framework of this tool, which can be summarised as follows:
Option 1: Use remote sensing data to stratify land cover according to the classification as
specified in PalmGHG and use the relevant default values of carbon stocks for the different land
cover classes.
Option 2: Use remote sensing data to stratify land cover, carry out field sampling to estimate
carbon stocks and use the measured carbon stock values in PalmGHG instead of the default
values.
Option 3: Use LiDAR (or equivalent technology using very high resolution imagery) to estimate
carbon stocks and use the measured carbon stock values in PalmGHG instead of the default
values.
The flowchart in Figure 1 provides an overview of the steps required for Options 1 and 2 while
the sections following this provide a more detailed description for each step in the process.
Option 3 employs an emerging technology which is relatively expensive and not widely used at
this point in time and as such the detailed methodology for this option is not documented in this
3
Available as a pdf document downloadable from: http://v-c-s.org/sites/v-c-s.org/files/9_BLUP_Baseline_unplanned_deforestation.pdf
Version: June 2014
tool. However, this situation may change in future as the technology improves and this option
becomes more affordable. More information on emerging remote sensing technologies is
included in Table 2.
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.
Version: June 2014
Figure 1: Flowchart for Estimating Carbon Stock from Land Cover Change due to 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 and assess
extent of potential high
carbon stock areas, e.g.
forests.
Use available tools (e.g.
WRI’s Forest Cover Analyzer
and Suitability Mapper) to
estimate provisional carbon
stock and presence of peat
soil in the area of interest.
Decide whether or not to
proceed with new planting
PRE-SCREENING STAGE
RECONSIDER
NEW PLANTING PLANNING STAGE
PROCEED
Decide whether to use
Option 1 or 2 for biomass
estimation
Soil Carbon
Estimation
(for Peat)
Option 1
Biomass
Estimation
Option 2
Conduct soil survey for
the plantation area as
part of agronomic
assessment
Step 1: Consider procuring
higher resolution remote
sensing data (<30m
resolution) for area of
interest
Step 1: Consider procuring
higher resolution remote
sensing data (<30m
resolution) for area of
interest
If areas of peat soil are
present, determine
extent of peat area (in
ha), average depth of
peat (m), and expected
water table depth
Step 2a: Carry out GIS
analysis of remote sensing
data and stratify land cover
into standardised
categories (strata) as
specified in PalmGHG
Step 2b: Carry out GIS
analysis of remote sensing
data and stratify land cover
according to what best
captures the variability of the
area of interest
Step 3: Carry out groundtruthing to verify accuracy
of land stratification and
modify boundaries of land
strata accordingly
Step 3: Carry out groundtruthing to verify accuracy of
land stratification and modify
boundaries of land strata
accordingly
Step 4a: Use default values
for carbon stocks of aboveand below-ground biomass
(tonne/ha) for the different
strata
Step 4b: Estimate above
ground biomass in sample
plots and use allometry to
calculate carbon stocks of
above- and below-ground
biomass (tonne/ha), for the
different strata
Colour Key
Decision needed
Optional step
Mandatory step
Step 5:Use PalmGHG to
calculate GHG emission balance
from new plantation
development
Version: June 2014
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 the
magnitude and variation in C stocks in the area of interest, where possible. Otherwise follow the
steps below.
Key steps:
Procure satellite
images (e.g. Landsat)
for the area proposed
for new planting
development
Use existing soils map
if available in required
resolution to
determine extent of
peat soil in area of
interest
Carry out image
processing and use
GIS tools to stratify
land cover
Using GIS tools,
overlay boundary of
new planting area
with the land cover
strata layers
If there are substantial areas of potentially
high carbon stock (e.g. forests or peat soil)
within new planting area, consider choosing
a new site. Otherwise proceed to next step.
Determine distribution
of the different strata
within the new planting
area
Conduct initial
reconnaissance to verify if
satellite images
correspond to actual
conditions on the ground.
Preliminary stratification of land cover 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 forest cover 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 resolutions4, 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 high likelihood that there is substantial cover of high carbon stock areas within 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
4
A list of available soil maps can be accessed at:
http://www.doa.gov.my/web/guest/senarai_peta_yang_disediakan_doa
Version: June 2014
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).
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-coveranalyzer/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)
Version: June 2014
Step 1: Consider procuring higher-resolution remote-rensing data
Objective: to improve the land cover stratification using higher-resolution remote sensing data
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. 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.
As a guide for oil palm growers intending to purchase satellite images, Table 2 below provides a
brief summary of the key attributes of the more commonly-used satellite data.
Table 2: 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
Modis
(optical)
250m,
500m,
1,000m
Worldwide
Free
< 250,000 (
Small Scale)
Land cover
(forest/non- forest
cover)
After April 2003,
images have strips of
missing data.
Suitable for HCV and
carbon stock
modelling (moderate)
Not suitable for oil
palm monitoring
SPOT 4
HRV
(optical)
ALOS
PRISM
(optical)
ALOS VNIR
(optical)
15m
Most of the
world
USD500750
< 1: 75,000
Land cover
(more detailed)
2.5m
On request
Y 31,000
Land cover, disaster
monitoring etc.
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
Suitable for HCV and
carbon stock
modelling (moderate)
SPOT 6 HRV
(optical)
1.5m
On request
-
> 1 : 10,000
Land cover use
analysis,
environmental
change, land use
planning
Defence, agriculture,
land cover,
deforestation,
environmental
change, land use
planning
Version: June 2014
Suitable for HCV and
carbon stock
modelling (moderate)
(DETAILED) Suitable
for oil palm
plantation
monitoring, HCV and
carbon stock
estimation
Table 2: Comparison of Satellite Data Sources (continued)
Satellite
and
Sensor
Spatial
Resolution
Availability
Cost (per
scene
unless
otherwise
stated)
Scale
Output
Application
Notes
Ikonos
(optical)
0.82m (Panchromatic),
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 (Panchromatic)
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
(Panchromatic),
1.84 m
(Multispectral)
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
Step 2: Carry out GIS analysis of remote sensing data and stratify land cover
The relevant remote sensing images 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). Some guidance on land cover stratification is also
available from Gunarso et al. (2013) as well as GAR & SMART (2012).
Objective for Step 2a: to stratify land cover using remote sensing data in accordance to the
standardised categories as specified in PalmGHG
Land cover in the proposed new planting area should be stratified in line with the land cover
categorisation as specified in PalmGHG Chase et al., 2012). Table 3 below shows the land cover
strata and their default carbon stock values for the current version of PalmGHG.
Version: June 2014
Table 3: Land Cover Classifications in the Current Version of PalmGHG and Default Carbon Stock
Values
Land Cover
Default Carbon
Stock Value
1
(tC/ha)
Undisturbed forest
268
Disturbed forest
128
Shrubland
46
Grassland
5
Tree crops
75
2
Oil palm
50
Annual/Food crops
8.5
Notes:
1
Carbon density values are for above-ground and below-ground (root) biomass
2
Calculated with OPRODSIM and OPCABSIM models (Henson, 2005, 2009). Depends on the cycle
length and growth type (vigorous or average)
It should be noted that the above default carbon stock values are preliminary as the land cover
classification in the PalmGHG may see some changes based on the findings of the GHG WG2
science panel paper by Agus et al. (2013a) on land cover types in Malaysia, Indonesia and Papua
New Guinea.
Objective for Step 2b: to stratify land cover using remote sensing data in a way that best captures
the variability of the area of interest
This step is applicable for Option 2 in which field measurements will be taken in sampling plots
in order to generate carbon stock estimates rather than relying on default values. Unlike in the
case of Step 2a for Option 1, there is no strict requirement to classify the land cover according to
the standard categories identified in PalmGHG. Land cover stratification in this step should be
done based on what best captures the variability in land cover of the area proposed for new
planting. However, it is recommended that the land cover stratification should not diverge too
much from the land cover categories as included in PalmGHG, as much work has been done to
ensure that these land cover categories are representative of the main ecosystem types found in
tropical regions (specifically Southeast Asia).
Step 3: Ground-truthing
Objective: To verify the accuracy of land cover stratification (based on remote sensing data)
Key steps:
Gather relevant
information on
biophysical
characteristics of the
proposed new
planting area
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Prepare base maps for
ground-truthing
incorporating
proposed location of
sampling plots
Conduct groundtruthing – 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

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 printouts 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 the GIS analysis of
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 (e.g. due to missing
satellite data). At each verification point the location (coordinates) should be recorded using
GPS and photographs should be taken in five directions i.e. north, south, east, west and
skywards.
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 the boundary of the relevant
stratum in case of any misclassification.
Step 4a: Use default values for carbon stocks of above- and below-ground biomass
(tonne/ha) for the different strata
Objective: to avoid field sampling by using default values of carbon stock for different strata,
based on land cover stratification alone
For this step under Option 1, there is no additional action required once the land cover
stratification is finalised after ground-truthing (Step 3). The key data before proceeding to the
next step are the different land cover strata present in the area proposed for new planting, and
the area (hectarage) of each stratum present.
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Step 4b: Estimate above ground biomass in sample plots and use allometry to calculate
carbon stocks of above- and below-ground biomass (tonne/ha), for the different strata
Objective: to measure above-ground biomass in sample plots and calculate the carbon density
values for above-ground and below-ground biomass for each land cover stratum
Key steps:
Determine the number,
location and design of
sampling plots within the
different land cover strata
Carry out field measurements
of above-ground biomass
(tree dbh) in sample plots
Calculate above-ground and
below-ground (root) biomass
for each strata
Calculate the carbon density
(tC/ha) for each stratum
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.
The preferred approach is sampling targeted at the different land cover strata. However, within
each stratum, sampling should 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 documents available on determining the design
(number, size and distribution) of sample plots and for calculating associated sampling errors
including by Brown (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)
Version: June 2014
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 (which could be done in conjunction with Step 3) 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.
Estimating above-ground biomass
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 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)
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.
Once the dbh measurements of the trees in a sampling plot have been obtained, the aboveground biomass can be calculated using an allometric equation that relates tree biomass with
the dbh, height (optional), and wood density.
Version: June 2014
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
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
require 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). The RSPO Secretariat has compiled a database of relevant
allometric equations for a range of vegetation/ecosystem types and geographical regions and
this will be made available to interested parties. As a general guideline, allometric equations
should be chosen on the basis of similarities between the vegetation type that the particular
equation was developed and that of the proposed new planting area, and also the geographical
regions concerned. For example, if the proposed new planting area is a degraded secondary
forest in Papua New Guinea (PNG) it makes sense to select an allometric equation that was
developed for a similar area in Sulawesi if there is no equation available for PNG itself or
surrounding areas, rather than selecting an allometric equation developed for an area in Peru.
An alternative is to select allometric equations that were developed using data from more than
one region, as in the case of pan-tropical allometric equations developed by Brown (1997).
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).
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.
Calculating below-ground (root) biomass
Measuring below-ground (root) biomass (coarse and fine roots) is time consuming (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.
Version: June 2014
Mokany et al. (2006) also provided root:shoot values for other vegetation categories including
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 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 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.
As this tool is meant to be used in conjunction with PalmGHG (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 or root:shoot ratios (see below). Soil organic carbon can be omitted for mineral
soils but not in the case of peat soil for which PalmGHG makes use of water table depth as a
proxy for GHG emission from peat.
Version: June 2014
Figure 3: Scaling up of dbh measurement to estimate biomass
Step
Measurement output
(unit)
Measure dbh
Diameter of one tree
(m)
Use allometric equation to
convert dbh measurement
to above-ground biomass
Above-ground biomass
of one tree (tonnes)
Items
needed
 Dbh value
 Height (optional)
 Wood density
(weighted average
values from published
sources, optional)
 Allometric equation
(select appropriate one
from published sources)
Repeat process for all
measured trees
Total up biomass of all
trees within a plot
 Above-ground biomass
value
 General ratio of belowground biomass to
above-ground biomass
(from published
sources)
Above ground biomass
of one plot (tonnes)
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 included in PalmGHG(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 verification (Pearson
et al., 2005) by a third party may also be considered. If the discrepancy in values remain after
Version: June 2014
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.
Soil Carbon Estimation
As discussed above, the carbon content 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; Page et al.,
2011), there is much debate on the actual amount of these emissions (Chase, et al., 2012, Agus et
al., 2013b; Schrier-Uijl et al., 2013). 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.”
Conversion of above ground biomass forest to other land uses is a one-point emission in time,
while GHG emissions resulting from peat drainage are continuous processes. Emissions linked
to drainage and oxidation of peat soils are caused by the long-term effects of land use change on
the carbon store in peat soil and will occur for as long as the soil is drained (Schrier-Uijl et al.,
2013).
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; Schrier-Uijl & Anshari, 2013):

Bulk density (g/cm3 or kg/dm3 or t/m3)

Organic carbon content (% by weight or g/g or kg/kg)

Peat depth or 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 scientific review commissioned by the RSPO’s Peatlands Working Group (GHG WG2
Workstream 2) (Schrier-Uijl & Anshari, 2013).
Version: June 2014
Step 5: Use PalmGHG to calculate GHG emission balance from new plantation
development
Objective: to estimate the GHG balance for the proposed new oil palm development using
the carbon stock values from different land cover strata prior to conversion to oil palm
Key steps:
Enter the carbon density
values, and planned
drainage depth for peat
areas (if applicable) into
PalmGHG
Enter other projected
agronomic and mill data as
required in the PalmGHG and
determine the net GHG
balance for the whole palm oil
production cycle
Use scenarios within Palm GHG
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 PalmGHG, in conjunction
with other parameters related to the other aspects of palm oil production and milling. Guidance
on how to use the PalmGHG is provided in Chase et al. (2012) and in a series of training courses
available from the RSPO Secretariat.
PalmGHG 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.PalmGHG has the required flexibility to allow for modelling of different
scenarios associated with the new planting.
4. Integration with Existing Processes under New Planting Procedures
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 a 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 true for carbon assessments as well.
Version: June 2014
5. Reporting Framework
This carbon assessment tool is to be used for the purpose of the newly included C7.8 in the
RSPO P&C 2013. For practical purposes, the carbon assessment can be done in addition to
existing assessments required under the NPP.
Public reporting on C7.8 remains voluntary until 31st December 2016 when the implementation
period ends. However reporting to the ERWG is required via the RSPO Secretariat. It is
recommended that companies use the following reporting format to report on C7.8 to the ERWG
during the implementation period.
Box 2: Recommended format for summary report to the ERWG
Assessment process and procedures
 Assessors and their credentials
 Methods and procedures used for conducting carbon stock and GHG assessments
Summary of carbon stock and GHG assessment findings
 Location maps indicating area of new plantings at landscape level and property level
 Land cover stratification (including maps and ground-truthing report) and
estimated carbon density (tC/ha) for each land cover stratum
 All areas of significant carbon stocks (current and projected) including areas of peat
soils
 All likely significant sources of GHG emissions and sequestration
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.
 Green house gas (GHG) balance derived from the PalmGHG Calculator (or an RSPO
endorsed equivalent) based on the proposed management regime for new plantings
(projected over the length of crop cycle)
Internal responsibility
 Formal signing off of management and mitigation plans.
 Organisational information and contact persons.
 Personnel involved in planning and implementation
Version: June 2014
References
Agus, F, K. Hairiah, A. Mulyani. 2011. Measuring carbon stock in peat soils: practical guidelines.,
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Agricultural Land Resources Research and Development, Bogor and Jakarta, Indonesia. 60p.
Agus, F., I.E. Henson, B.H. Sahardjo, N. Harris, M. van Noordwijk & T.J. Killeen. 2013a. Review of
emission factors for assessment of CO2 emission from land use change to oil palm in Southeast
Asia. In: Killeen, T.J. & J. Goon (eds.). 2013. Reports from the Technical Panels of the 2nd
Greenhouse Gas Working Group of the Roundtable on Sustainable Palm Oil (RSPO). RSPO, Kuala
Lumpur, Malaysia.
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CO2 emisssions from land use and land use change from the oil palm industry in 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
were needed to identify HCS areas more accurately; more focus should be given to the
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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.
192
Medium Density Forest (HK2)
Remnant forest but more disturbed than High Density
Forest.
166
Low Density Forest (HK1)
Appears to be remnant forest but highly disturbed and
recovering (may contain platation/mixed garden).
107
Old Scrub (BT)
Mostly young re-growth forest, but with occasional
patches of older forest within the stratum.
60
Young Scrub (BM)
Recently cleared areas, some woody regrowth and
grass-like ground cover.
27
Cleared/Open Land (LT)
Very recently cleared land with mostly grass or crops,
few woody plants.
17
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 semiautomated 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, Gaps and Opportunities
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
The initial version of 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 CO 2
emission from land use changes conducted by the RSPO’s Science Panel (GHGWG2 WS3) was
on-going at the time of writing 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 . 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 provides oil palm growers with the option to conduct field sampling in order to derive
carbon density (in tC/ha) for different land cover strata in their area of interest, or to use
default values included in the PalmGHG. In its current form, PalmGHG utilises preferred default
values which are absolute values rather than a range of valuesThe calculated values can differ
substantially from 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.
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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.
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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Emerging Remote Sensing Technologies
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
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 the box below.
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, airplane-mounted LiDAR instruments are too costly to be used for more
than a small area.”
Source: Gibbs et al. (2007)
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