Report to EPA

December 2009

Land Use Change and Emission 

Factors:

Updates since the RFS Proposed Rule

Nancy Harris, Sean Grimland and Sandra Brown
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Contents

EXECUTIVE SUMMARY ...........................................................................................................................2

1.0 INTRODUCTION ...................................................................................................................................3

2.0 RESPONSES TO PEER REVIEW COMMENTS ...............................................................................4

3.0 METHODS USED FOR UPDATED ANALYSIS ................................................................................7

3.1 LAND COVER CHANGE ...........................................................................................................................7

3.2 UPDATED EMISSION FACTOR ANALYSIS.................................................................................................9

3.3 REVERSION FACTOR ANALYSIS ............................................................................................................18

4.0 UNCERTAINTY ANALYSIS ..............................................................................................................19

4.1 UNCERTAINTY ESTIMATION FOR INDIVIDUAL INPUT PARAMETERS .......................................................20

4.2 COMBINING UNCERTAINTIES: SIMPLE ERROR PROPAGATION APPROACH (IPCC TIER 1).....................23

4.2 COMBINING UNCERTAINTIES: MONTE CARLO APPROACH....................................................................25

5.0 RESULTS AND DISCUSSION............................................................................................................27

5.1 LAND COVER CHANGE .........................................................................................................................27

5.2 EMISSION FACTORS ..............................................................................................................................28

6.0 REFERENCES ......................................................................................................................................29

ACKNOWLEDGEMENTS

We thank Aaron Levy of EPA for 

guiding this work and providing 

useful comments.

Cite report as:

Harris, NL, S. Grimland and S. 

Brown. 2009. Land Use Change and 

Emission Factors: Updates since 

Proposed RFS Rule. Report submitted 

to EPA.
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EXECUTIVE SUMMARY

Winrock International has been working with US EPA to estimate the lifecycle greenhouse gas (GHG) emissions 

from indirect land use change associated with the increased production of biofuels under the 2007 Energy 

Security and Independence Act‟s Renewable Fuel Standard (RFS) program. In an initial analysis, Winrock 

estimated the extent of recent land cover change at national and sub-national scales for all countries using 

recent land cover products derived from satellite imagery and developed greenhouse gas emission factors for 

various land cover conversions using IPCC methodologies. Results from Winrock‟s initial analysis were 

incorporated into EPA‟s draft RFS rule, released in May 2009. 

Since the release of the draft RFS rule, EPA has been updating various components of its GHG lifecycle 

analysis. Winrock has incorporated new datasets on land cover change and carbon stocks into updated land 

cover change matrices and emission factor estimates for various countries. This document summarizes the 

changes made to the analysis and highlights responses to various comments received from peer reviewers on 

the initial work. 

The analysis for the proposed rule covered 314 regions across 35 key countries, with a weighted average 

applied to the rest of the world. We have since completed an analysis (with satellite data, emission factors, and 

uncertainty estimates) that covers nine land categories in more than 750 distinct regions across 160 countries.

Although the use of high resolution land cover data is the preferred approach for analysing land cover change at 

national and subnational scales, MODIS land cover maps were retained in the updated analysis because they 

remain the only consistent, global land cover dataset available for multiple years. However, since the initial 

Winrock analysis, the MOD12Q1 dataset has been expanded to cover additional years and the data products 

have improved in resolution from 1-km to 500-m. Therefore, for the updated analysis, Winrock extended the 

time period analysed for land cover change (2001 to 2007) and used the higher resolution datasets. Additional 

land cover classes („barren‟ and „wetland‟) were also included in the updated land cover change matrices.

Various new datasets were used to update emission factor estimates as well. New estimates of forest carbon 

stocks were derived for countries in Latin America and sub-Saharan Africa to reflect ongoing efforts by Winrock 

to produce a pantropical carbon stock map at 1-km resolution using an internally consistent and spatially explicit 

approach. The forest carbon stock map for these regions was derived using a combination of ground plots and 

remote sensing data (lidar and optical satellite data). Apart from tropical regions, many of the same data 

sources were used to estimate forest carbon stocks for other regions as in the initial analysis, but were 

corrected for errors in some cases. The approach for estimating grassland, savanna and shrubland carbon 

stocks remained unchanged since the initial analysis. To address the fact that sugarcane can store significantly 

more carbon than other annual croplands, separate emission factors were estimated for conversion to annual 

and perennial cropland. Foregone carbon sequestration rates in temperate and tropical forests were updated 

based on recent literature. Carbon stored in harvested wood products (such as woodbase panels and other 

industrial roundwood) was estimated by reviewing FAO timber volume estimates for 111 developing countries, 

published literature on the share of harvested timber used in wood products, and the oxidation rate of wood 

products. Consistent with the peer reviewers‟ statements, our analysis concluded that even in countries with 

high rates of harvested timber utilization, a very small share of harvested forest biomass would be sequestered 

in harvested wood products for longer than 30 years and therefore carbon storage in wood products remained 

excluded from the emission factor estimates. 

The updated analysis also includes the estimation of “reversion factors” (i.e., the opposite of emission factors), 

to reflect the carbon sequestration that occurs when cropland or pasture land is abandoned and reverts to forest 
3 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

or another land cover type with a higher relative carbon stock. Carbon accumulation in forests was 

conservatively assumed to be equivalent to the annual foregone sequestration rate over 20 years or the initial 

forest carbon stock whichever value was lower.

One large omission in the initial Winrock analysis was an estimation of uncertainty around emission factors. For 

the updated analysis, uncertainty in emission factors was estimated using two approaches: the IPCC Simple 

Propagation of Errors method and the Monte Carlo approach. Monte Carlo is the preferred approach due to its 

ability to handle various probability distributions around a given input parameters and its ability to deal with large 

uncertainties in individual parameters. The IPCC Simple Error Propagation resulted in an average uncertainty of 

approximately 30% around 30-year emission factors.

Results of the updated land cover change analysis using 500-m resolution MODIS products are more consistent 

with known land cover trends. For example, in the initial analysis the area of forest increased and the area of 

cropland decreased in Brazil between 2001 and 2004, a trend that was inconsistent with other country-level 

datasets. In the updated analysis covering the time period 2001 to 2007 using higher resolution remote sensing 

data, the trend was reversed such that Brazil lost forest area and gained cropland area, which was known to be 

the actual trend. This result highlights the importance of using higher resolution imagery and evaluating land 

cover products for accuracy. In future analyses, higher resolution datasets such as Landsat should be used to 

calibrate global MODIS products to correct for the land transitions that moderate resolution sensors are not able 

to detect. At a minimum, this should be done to detect changes between forest and non-forest area.

Additional detailed interpretation of land cover trends and emission factors are not discussed in this report, as 

the estimates reported here represent just two components of a larger and more complex framework of lifecycle 

GHG emissions that will be discussed in detail by EPA.

1.0 INTRODUCTION

Winrock International has been working with US EPA to estimate the lifecycle greenhouse gas (GHG) emissions 

from indirect land use change associated with the increased production of biofuels under the 2007 Energy 

Security and Independence Act‟s Renewable Fuel Standard (RFS) program. In an initial analysis, Winrock 

estimated the extent of recent land cover change at national and sub-national scales for all countries using 

recent land cover products derived from satellite imagery and developed greenhouse gas emission factors for 

various land cover conversions using IPCC methodologies. Results from Winrock‟s initial analysis were 

incorporated into EPA‟s draft RFS rule, released in May 2009. 

Since the release of the draft RFS rule, EPA has been updating various components of its GHG lifecycle 

analysis. Winrock has incorporated new datasets on land cover change and emission factor estimates to reflect 

the most current information available and to respond to peer review comments. This document summarizes the 

changes made to the analysis since the proposed rule and focuses mainly on the changes in methodology 

rather than a detailed discussion of results. 

In Section 2 below, several of the most significant peer review comments are addressed, and the changes 

made to the analysis to reflect these comments are described. In Section 3, changes to the methods used to 

estimate land cover change and emission factors between the initial and final analysis are discussed. 

Uncertainty analysis is covered in Section 4. Section 5 provides limited interpretation of the land use trends, but 

it should be noted that these estimates feed into a larger analysis by EPA and thus are not discussed in detail 
4 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

on their own. Emission factors and land cover change matrices for each region are given in the supporting 

material.

Note that in the following sections, 2 tons dry biomass = 1 ton carbon = 3.67 tons of CO2.

2.0 RESPONSES TO PEER REVIEW COMMENTS

The main concerns and suggestions of peer reviewers, as well as brief responses to each, are summarized 

briefly below. Additional details are also provided in Section 3 (Methods). Note that this list is not exhaustive, but 

reflect a summary of overarching themes that emerged from many of the comments received.

The spatial resolution of the land cover change analysis was too coarse. Various peer reviewers 

suggested the use of higher resolution data products (e.g., ASTER, ALOS/PALSAR) to estimate land 

cover change. However, land cover products derived from ASTER data are not currently available,1and

although radar imagery is useful due to its ability to penetrate clouds in tropical regions, the 

ALOS/PALSAR products are still relatively new and have existed only since 2006. Therefore, already 

processed land cover maps with global coverage spanning multiple years derived from ALOS/PALSAR 

imagery are not available. If alternative imagery were to be used, raw data would need to be processed 

into a land cover product and then assessed for accuracy. This has been done for select locations (e.g. 

Brazil), but not over large geographic regions. Creating a global land cover map from raw remote 

sensing data was beyond the time and resources intended for this effort. However, the latest MODIS 

land cover maps (MOD12Q1 version 5), produced at 500-m resolution, were used in the updated 

analysis (vs. the version 4 products with a spatial resolution of 1-km, used in the initial analysis). The 

new MODIS products at a resolution of 500 meters are likely to capture land transitions more accurately 

than the 1-km products that were used in the initial analysis. The change from 1-km to 500-m resolution 

essentially means that for every 1-km pixel classified in the version 4 imagery, version 5 classifies the 

same area as four pixels, which allows smaller land cover changes to be detected and represents an 

improvement in the final change matrices.

The time period of land cover change was too short. Several peer reviewers expressed concern 

about the 3-year time period (2001 to 2004) used in the initial land cover change analysis, as this was 

too short to capture the often gradual or sequential cropland expansion that often occurs in the tropics. 

The time period of the land cover change analysis was extended to 2007 and land cover trends are 

reported for the six-year time period 2001 to 2007 in the updated analysis. Although MODIS version 5 

land cover data have become available for 2008, the 2008 product was not released in time to be 

incorporated into the final analysis.

Wetlands should be included as a land cover category. Peer reviewers suggested that wetlands be 

included as a land cover category, and expressed concern about the trend of oil palm plantations 

expanding into peat swamp forests of southeast Asia. Wetlands were included as a new category in the 

updated land cover change analysis, but it should be noted that the peat swamp forests of southeast 

Asia are categorized in the MODIS land cover products as forests, not wetlands. The proportion of 

 

1 See https://lpdaac.usgs.gov/lpdaac/products/aster_products_table for a list of ASTER products.
5 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

forests on peat was calculated for Indonesia and Malaysia, and peat emissions were calculated 

accordingly in the emission factor estimates.

Change detection rather than time differencing should be used for estimating land cover 

change. The differencing method of analyzing land cover change was used in both the initial and 

updated analyses. The differencing method, known as post-classification change detection, involves 

simple comparison of two or more already processed land cover maps. The drawback of this approach 

is that individual classification errors are multiplicative when used in combination if it is assumed that 

the errors of one map are independent of errors in the other map, so if classification errors are large, 

true change between land cover classes is difficult to detect due to the background classification errors. 

A true change detection using spectral change analysis or principal component analysis would involve 

the interpretation of raw remote sensing imagery, but this type of analysis is not well suited for global 

applications unless spectral-temporal variabilities in a specific land cover type could be accounted for, 

which would necessitate large amounts of ground validation data. After change is detected, the change 

would need to be interpreted (i.e., from what to what), and this can be problematic even for a small 

geographic region. Because true change detection would involve significantly more time and resources 

than were available for this analysis if done at a global scale, the updated analysis retained the 

differencing approach for estimating land cover change. Hansen et al. (2008) performed change 

detection for the humid tropics, but their analysis covered only the transition between forest and 

nonforest rather than covering changes among multiple land cover categories as would be necessary 

for the EPA analysis. 

Uncertainty analysis should be conducted for land cover change. Validation of global moderateresolution land products is challenging. Field data are critical, and gathering high quality, globally 

representative and temporally explicit reference data is both time consuming and resource intensive.

Winrock was not involved in the uncertainty assessment for land cover change and so the methods 

used to quantify uncertainty are not addressed further here but are described in detail in the report 

submitted to EPA by Dr. William Pepper at IAI. Briefly, the global confusion matrix provided in Friedl et 

al. (2010) was used to “correct” the systematic bias in the published MODIS data. Then, producer 

accuracy standard errors were used to assess the uncertainty in the “corrected” MODIS data. 

The estimation of soil carbon stocks was too coarse. One peer reviewer found the map of soil 

carbon distribution used in the initial analysis (FAO-UNESCO Soil Map of the World) to be inadequate 

for characterizing the variation in soil carbon throughout the tropics. In the updated analysis, Winrock 

used a new dataset (Harmonized World Soil Database, released March 2009) to estimate soil carbon 

stocks. This dataset still relies on the FAO-UNESCO Soil Map of the World for some regions, but 

incorporates improved datasets where they exist (e.g., China, South America). The new soil dataset 

was produced at 1-km resolution, which is a significant improvement over the FAO-UNESCO map. 

Carbon stocks should be differentiated by crop type. Some peer reviewers suggested the use of 

crop yield maps such as those produced by Monfreda et al. (2008) to predict the variation in crop 

biomass across broad climate regions. However, we concluded that the variation in crop carbon stocks 

was likely to be minor compared to the carbon dynamics from initial land clearing, and therefore the 

carbon stocks of different annual crop types were not estimated for the updated analysis. However, 
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separate emission factors were estimated for conversion of other land cover types to annual vs. 

perennial cropland to acknowledge the fact that carbon stocks in perennial cropland can be significant 

(e.g., sugarcane, oil palm).

IPCC foregone forest sequestration rates should be supplemented with peer-reviewed data. Peer 

reviewers suggested augmenting the default forest sequestration rates given in the IPCC Guideliness 

with more recent, peer-reviewed literature that has become available. Therefore, we updated foregone 

sequestration rates using literature values from long-term monitoring plots in tropical and temperate 

forests.

Forest carbon losses should be estimated along with forest gains. There are two IPCC 

approaches to calculating the change in biomass carbon stocks, the gain-loss method and the stock 

change method. We applied the stock change method for calculating initial emissions in Yr 0. In 

subsequent years, we accounted only for carbon gains (annual forest C sequestration) without 

accounting for forest losses (from wood removals, fuelwood collection and disturbance) in the initial 

analysis. We did this for two reasons. First, IPCC calculates carbon gains as the product of total area 

and a mean annual biomass growth rate (t C ha-1yr-1), while losses are calculated as a total annual 

carbon loss per year (t C yr-1) without the use of an explicit area term in the calculation. As emission 

factors for the EPA analysis were estimated on a per hectare basis (t CO2 ha-1), it was straightforward to 

disassociate the area from the carbon gain but more difficult to disassociate the area from the carbon 

loss. Second, carbon gain values were meant to reflect the long-term carbon sink capacity of forests 

rather than reflect a carbon loss or gain in a particular year. We updated forest sequestration values to 

reflect recent additional peer-reviewed literature that also demonstrates the long-term carbon sink 

function of both tropical and temperate forests. These estimates are based on long-term records 

(monitoring stations in old-growth tropical forests and multi-decadal inventory comparisons for the 

temperate regions) and reflect all losses/gains over time. Most notably, the old-growth forests in the 

tropics that many once assumed to be in “steady-state” (i.e., gains = losses) are in fact still gaining 

carbon (Lewis et al. 2009). The sequestration value for old growth forests across the tropics is 

extremely conservative, as it assumes that all tropical forests are gaining carbon as slowly as oldgrowth forests, when we know that younger forests accumulate carbon more rapidly. This switch from 

using IPCC default biomass accumulation rates to the approach summarized above should be sufficient 

to address reviewer concerns about not accounting for carbon losses. All available data to date shows 

that across large regions, forests act as long-term carbon sinks.

NDVI data should be used to estimate variation in carbon stocks. One peer reviewer suggested the 

use of 1-km resolution NDVI (Normalized Difference Vegetation Index) data to estimate the spatial 

variation in carbon stocks, thinking that NDVI was a good proxy for green vegetation biomass. Indeed, 

NDVI is an estimation of canopy “greenness”. However, the references cited by the peer reviewer for 

the NDVI approach were 15 years old, and it is now understood that NDVI alone is not a good predictor 

of biomass for many vegetation types, especially those with high biomass such as forests. As the forest 

canopy closes far before maximum biomass is reached, NDVI saturates and therefore NDVI does not 

capture the high biomass classes well. NDVI was also shown to be a poor predictor of grassland 

biomass; Numata et al. (2007) examined relationships in Brazil between pasture (i.e. grassland) 

biomass and vegetation indices and found that the chlorophyll-based indices such as NDVI had an 
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extremely poor correlation with biomass (R2=0.05) and only slightly better for water content and height 

(R2=0.10-0.12). Therefore the use of NDVI as a proxy for biomass, even for herbaceous vegetation 

classes such as grasslands, does not seem like a promising approach. Furthermore, even if this 

approach were used, extensive ground data would be needed for calibration, and the collection of such

ground data across the globe was beyond the scope of the EPA analysis.

The database for estimating emissions from peat drainage was too limited. Winrock‟s initial 

emission factor analysis included emissions from peat soils when cleared and drained for another land 

use. One peer reviewer expressed concern over the use of only one reference (that of Hooijer et al. 

2006) to derive the peat emission estimates. However, the reference used in the initial analysis was a 

literature review of all available data on CO2 emissions from peat drainage; it does not present results 

from one specific study. Furthermore, another review and meta-analysis of available literature on GHG 

fluxes from tropical peat soils in Southeast Asia was published recently (Couwenberg et al. 2009) that 

extended the Hooijer et al. (2006) analysis and presents a virtually identical relationship between 

drainage depth and CO2 emissions. The Couwenberg et al. (2009) study also demonstrates that 

methane fluxes in peat soils are insignificant compared to CO2 fluxes. Combining a peat subsidence 

rate with bulk density is another approach to calculating peat emissions, however there is considerably 

less recent data available on peat subsidence rates for multiple locations. Tropical peat experts such as 

Dr. Susan Page agree that the CO2 vs. drainage depth relationship as used in this analysis is the best 

and most conservative approach that is available in the public domain (personal communication). 

Furthermore, the equation derived from Hooijer et al. (2006) and Couwenberg et al. (2009) literature 

reviews closely match IPCC default emission factors for tropical drained organic soils. 

Fire products derived from remote sensing imagery should be used to determine where fire 

occurs for land clearing. Several peer reviewers suggested the use of fire products, such as those 

derived from MODIS and AVHRR sensors, to determine which regions typically use fire to clear land for 

another land use. The use of these products in isolation would not allow a distinction between fire that 

occurs for land conversion vs. fire that occurs due to wildfires, especially for temperate regions. 

Therefore, we considered an approach in which various fire maps could be overlain onto land cover 

change maps to determine fires that occurred on changed pixels (land conversion) vs. fires that 

occurred on pixels that remained in the same land cover category (e.g., forest fire, annual burning of 

cropland residues, etc.). However, the time needed to do this analysis exceeded the time available. 

Therefore, we maintained the approach used in the initial analysis, whereby expert judgment was used 

to determine the regions where fire is commonly used when land is cleared for agricultural production

(see Section 3 for additional details).

3.0 METHODS USED FOR UPDATED ANALYSIS

3.1 Land Cover Change

The main issues raised about Winrock‟s initial analysis of land cover change were the short time period 

analyzed and the coarse spatial resolution of the datasets used. In the initial analysis, land cover change was 
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estimated at the national scale (and sub-national scale for key countries) using 1-km resolution MODIS products 

(MODIS/Terra Land Cover Type Yearly L3 Global 1km SIN Grid (MOD12Q1) version 4) for the years 2001 and 

2004. MODIS land cover products were chosen originally due to their global, multi-year coverage, low cost and 

homogenous classification scheme. 

Some alternative datasets with higher resolution were suggested by the peer reviewers to replace or 

supplement the MODIS data. However, MODIS products remain the only classified land cover maps with global 

coverage spanning multiple years, which were the necessary criteria to complete the land cover change 

analysis. Calibrating the MODIS land cover maps using higher resolution datasets and/or performing true 

change detection on MODIS imagery was beyond the time and resources available for this work.

Although we retained the use of MODIS land cover products for the updated analysis, the two main criticisms of 

the initial land cover change analysis were addressed by using the latest version (version 5) of the MODIS land 

cover maps. The initial analysis covered only three years, as these were the only years for which MODIS land 

cover maps were available at the time of the initial analysis. However, this three-year time period was thought to 

be too short to capture the often gradual or sequential cropland expansion that often occurs in the tropics. The 

MODIS version 5 land cover dataset (MOD12Q1 version 5) covers additional years (2005 through 2008) and is 

more spatially refined (500-m pixels vs. 1-km in version 4). MODIS land cover products were not available for 

years prior to 2001, and the most recent land cover map for 2008 was not released in time for inclusion in the 

updated analysis. Therefore, we re-analyzed land cover maps and produced land cover change matrices for 

each country for the six-year period 2001 to 2007. 

Land cover categories were also expanded in the updated analysis (Table 1) in response to peer reviewer 

suggestions. Categories that were excluded from the proposed rule but included in the updated analysis were

barren lands and wetlands. 

Table 1. IGBP reclassification scheme for MODIS land cover maps in proposed and 

final rule.

IGBP land cover classes

Modified land cover class 

(proposed rule)

Modified land cover class 

(final rule)

Evergreen Needleleaf Forest Forest

Evergreen Broadleaf Forest Forest

Deciduous Needleleaf Forest Forest

Deciduous Broadleaf Forest Forest

Mixed Forest Forest Forest

Closed Shrublands Shrubland Shrubland

Open Shrublands Shrubland Shrubland

Woody Savanna Savanna Savanna

Savanna Savanna Savanna

Grassland Grassland Grassland

Wetland Excluded Wetland

Cropland Cropland Cropland

Urban & Built-up Excluded Excluded

Crp-Vegn Mosaic Mixed Mixed

Snow - Ice Excluded Excluded

Barren Excluded Barren

Water Excluded Excluded

Unclassified Excluded Excluded
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3.2 Updated Emission Factor Analysis

Emission factors were calculated in the updated analysis using the equation summarized in the initial report, or 

the sum of changes in above- and belowground biomass carbon stocks, annual changes in soil carbon stocks 

on mineral soils (assuming that soil carbon emissions occur over 20 years), annual emissions from peat 

drainage on peat soils cleared for agriculture, annual foregone forest sequestration, and non-CO2 emissions 

(CH4, N2O) resulting from land clearing with fire where applicable. Methane emissions from rice cultivation were 

excluded from the updated emission factors, as these emissions are accounted for elsewhere in EPA‟s lifecycle 

analysis. 

Unlike in the initial analysis where immediate, 20-year and 20-80 year emission factors were estimated, here we 

report one emission factor for a 30-year time period per country and administrative unit (for key countries). 

Emission factors (EF) were estimated on an annual basis (t CO2e ha-1yr-1) for Year 0, Years 1-19 and Years 20-

80 as before, but final 30-year emission factors were calculated for the final rule as follows: (Year 0 EF) + (19 x 

Year 1-19 EF) + (10 x Year 20-80 EF). Updates to various components of the final emission factor are 

described below.

Forest Carbon Stocks

Winrock‟s initial emission factor analysis incorporated spatial maps of forest carbon stocks from several data 

sources, each derived from a different methodological approach. This was due to the fact that the only globally 

consistent carbon stock map available at the time of the initial analysis was derived using adjusted biome-level 

Tier 1 default values from IPCC rather than from country specific data sources (Ruesch and Gibbs 2008). The 

region-specific maps, though not directly comparable across regions, were preferred in the initial analysis over 

the global map due to the use of country-specific data (i.e., IPCC Tier 2 vs. Tier 1). As in the initial analysis, the 

updated analysis used regional and/or country-level maps where available, and the Ruesch and Gibbs (2008) 

product was used only to fill in gaps where no other information on forest carbon stocks was available.

Winrock is currently working with Dr. Sassan Saatchi from NASA‟s Jet Propulsion Laboratory to create a 

pantropical map of above- and belowground forest carbon stocks for the year 2000 at 1-km resolution. The 

methodology uses about 4,000 ground inventory plots of forest biomass, 150,000 biomass values estimated 

from heights measured by spaceborne lidar, and a suite of satellite imagery products to derive a spatially refined 

map of aboveground forest carbon at a 1-km grid cell resolution. Belowground carbon is added to aboveground 

carbon using an updated equation from Mokany et al. (2006). The estimates are directly comparable across 

countries and regions due to the consistency in the methodological approach (Figure 1). 
10 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

Figure 1. Preliminary map of forest carbon stocks in above- and belowground 

biomass for the tropics. From Saatchi et al. (in prep)2

Preliminary results of Saatchi et al. (in prep) for forest carbon stocks in Latin America and Africa were 

incorporated into the updated EPA analysis by clipping the carbon map to MODIS forest cover in 2001 and 

calculating the area-weighted average forest carbon stock per country (and per administrative unit in key 

countries). Preliminary results for Asia are now complete, but were not included in the updated EPA analysis 

due to timing considerations. Therefore, the original Brown et al. (2001) map was retained for forest carbon 

stock estimates in Asia. The Saatchi et al. (in prep) results represent a significant improvement over previous 

estimates; the maps were evaluated for accuracy using cross validation with approximately 50% of the ground 

and lidar biomass data and resulted in an overall accuracy of 76% across the three regions (Latin America: 

81%, Africa: 86%, Southeast Asia: 69%). 

Myneni et al. (2001) also produced a spatially-explicit map of woody biomass for Northern (i.e., boreal and 

temperate) forests (Figure 2). Although we used carbon stock values from other higher resolution data sources 

for the United States, Russia and many countries of the European Union (Blackard et al. 2007, Houghton et al. 

2007, Nabuurs et al. 2003, see Figure 3), the Myneni et al. (2001) dataset filled in the data gap for Canada and 

many Eastern European countries. Although we requested the original spatial data from the authors (presented 

as Figure 2b in the original Myneni et al. 2001 paper, reproduced as Figure 2 here) to enable computation of

area-weighted average forest carbon stocks for individual countries, these spatial data were not made available 

and therefore the country- and region-level estimates in Table 1 of the original paper were used instead.

Although the spatial maps would have provided higher resolution estimates of forest carbon, the use of country-

(or region) averaged values was appropriate because carbon stocks in northern forests are much less variable 

than in tropical forests (as seen in the comparison of Figure 1 vs. Figure 2, respectively).

 

2 Also see Tollefson, J. 2009. Satellites beam in biomass estimates. Nature 462: 834-835.
11 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

Figure 2. Aboveground biomass carbon stocks in Northern forests. Reproduced from 

Figure 2 of Myneni et al. (2001).

Apart from the new data sources described above, the data sources used to estimate forest carbon stocks in 

other regions remained unchanged in the updated analysis. A summary of data sources used in the updated 

analysis is shown in Figure 3. In the case where belowground biomass was not estimated in the original data 

source (Blackard et al. 2007 for the U.S., FAO (2006) for EU countries), belowground biomass was added to 

aboveground biomass estimates using an equation from Cairns et al. (1997) or Mokany et al (2006), both which 

estimate belowground biomass as approximately 25% of aboveground biomass. Forest carbon stock values per 

country or administrative unit (for key countries) are shown in Figure 4. As for the initial analysis, different data 

sets were incorporated into the updated analysis because the use of more accurate regional products was 

thought to be preferable over the use of a globally consistent – but much more uncertain – biomass map. 

Figure 3. Data sources used for estimating forest carbon stocks in updated 

emission factor analysis. Note: Nabuurs et al. and Nabuurs et al. 2003 are the 

same data source.
12 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

< 50

51 - 100

101 - 150

151 - 300

301 - 500

501 - 750

> 750

Figure 4. Spatially averaged forest carbon stocks in above- and belowground 

biomass, in t CO2 ha-1.

Cropland Carbon Stocks

In the initial emission factor analysis, all cropland conversion was assumed to be conversion to annual cropland. 

In the updated analysis, emission factors were estimated separately for conversion to annual cropland and 

conversion to perennial cropland. Perennial cropland in Indonesia and Malaysia was assumed to be oil palm, 

while perennial cropland in all other countries was assumed to be sugarcane. Carbon stocks in oil palm 

plantations after one year of growth were estimated as 15 t CO2 ha-1. Table 5.3 of the 2006 IPCC Guidelines for 

AFOLU gives biomass stocks on oil palm plantation as 136 t ha-1(68 t C ha-1), and if this value is divided by an 

assumed 15-year growth period, a linear growth rate of 4 t C ha-1yr-1(15 t CO2 ha-1yr-1) was assumed. This 

value is also nearly identical to the average carbon stock in biomass after one year of growth averaged across 

all tropical climate regions and all perennial crop types, as given in Table 5.9 of the 2006 IPCC Guidelines. 

Carbon stocks in sugarcane after one year of growth were assumed to be 44 t CO2 ha-1. (Carbon stocks for 

long-lived tree species such as oil palm accumulate carbon more slowly in the early phases of growth.) The 

value for sugarcane was derived from estimates of carbon stocks in sugarcane in aboveground biomass (17 t C 

ha-1or 62 t CO2, Amaral et al. 2008) and in belowground biomass (7 t C ha-1or 26 t CO2 ha-1, Smith et al. 2005), 

for a total of 88 t CO2 ha-1. We assumed a growth period of two years to achieve full carbon stocks, therefore 

the carbon stock in sugarcane after one year of growth was assumed to be 44 t CO2 ha-1.

All biomass accumulated after Year 1 would have been harvested over the course of 30 years in the case of 

both sugarcane and oil palm, leading to little net sequestration during the time period for which emission factors 

were estimated (30 years). Over the long term (e.g., 100 years), oil palm plantations may have a long-term 

average carbon stock higher than that at Year 1, but the land use after 30 years is highly uncertain and there is 

no guarantee of future rotations. Therefore, the average carbon sequestration at any given time over 30 years 

was assumed to be the carbon stock in vegetation after one year of growth (Figure 5).
13 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

Figure 5. Carbon stocks over various rotation lengths for perennial crops. Because 

30-year emission factors were estimated, carbon stocks on perennial cropland were 

assumed to be equal to stocks present after one year of growth with no net 

sequestration.

Grassland, Savanna and Shrubland Carbon Stocks

The approach for estimating carbon stocks in grassland, savanna and shrubland land cover categories was 

unchanged in the updated analysis, as there were no significant comments from peer reviewers. Above- and 

belowground carbon stocks of grassland, savanna and shrublands in Brazil were estimated using values from 

de Castro and Kauffman (1998) who report biomass along a vegetation gradient from campo limpo (pure 

grassland), campo sujo (a savanna with a sparse presence of shrubs), campo cerrado (a dominance of shrubs 

with scattered trees and a grass understory), cerrado sensu stricto (a dominance of trees with scattered shrubs 

and a grass understory) and cerradão (a closed canopy forest) (Coutinho 1978, Eiten 1972, Goodland & Pollard 

1973). Shrubland carbon stocks in Brazil were estimated as the average of biomass values reported for cerrado 

aberto and cerrado denso. Savanna carbon stocks in Brazil were estimated as the average biomass value 

reported for campo sujo and grassland carbon stocks in Brazil were estimated as the average value reported for 

campo limpo.

To maintain a consistent approach, for all countries except Brazil (explained in the paragraph above), carbon 

stocks in grasslands were estimated based on default biomass values given in Table 6.4 of the IPCC AFOLU 

Guidelines. These default values are presented by ecological zone. Therefore, grassland C stocks within each 

country reflect the area-weighted value based on the proportions of each ecological zone present within each 

country. Carbon stocks of savanna and shrubland land cover types in all countries except Brazil were estimated 

using a proportional approach based on the Brazil dataset, which indicates an increasing trend in carbon stocks 

from grassland  savanna  shrubland in a ratio of 1 to 1.8 to 3.4. These ratios were applied to other countries 

for estimating carbon stocks in savanna and shrubland based on the estimated carbon stocks of grassland 

within each country. 

Grassland carbon stock estimates for each country and administrative unit (for key countries) are shown in 

Figure 6.
14 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

< 5

6 - 10

11 - 15

16 - 20

21 - 25

26 - 30

> 30

Figure 6. Grassland carbon stock estimates for each country and administrative 

unit. Multiply by 1.8 and 3.4 for carbon stocks in savanna and shrubland, 

respectively. Units are in t CO2 ha-1.

Wetland, Barren and Mixed Carbon Stocks

The updated analysis included land cover change to/from the wetland and barren land cover categories, and 

therefore emission factors were estimated for these conversions. According to the IGBP land cover description, 

the permanent wetlands category can consist of herbaceous and/or woody vegetation. However, after 

confirming that Indonesian peat swamp forests (a type of permanent forested wetland) are classified as forest 

and not wetland in the MODIS land cover maps, the carbon stocks of permanent wetlands in a given country or 

administrative unit were calculated as the average of carbon stocks in shrubland and grassland land cover 

categories. Carbon stocks on barren lands were assumed to be zero. In accordance with the IGBP land cover 

definitions, mixed carbon stocks were calculated as the average of forest, shrubland, grassland and cropland 

carbon stocks.

Soil Carbon Stocks

For the initial analysis, soil carbon stocks were estimated using the FAO/UNESCO Soil Map of the World. In 

March 2009, a new soil database was released (Harmonized Soil Map of the World v.1.1) with 1-km resolution 

grid cells and therefore this improved dataset was used for the updated analysis. Attribute values of the 

database did not include average pixel-level soil carbon stocks, but values were included instead for bulk 

density (g cm-3) and carbon content (%C) in both the top 30 cm and top meter of soil in each grid cell. Therefore, 

we calculated average soil carbon stocks in the top 30 cm of soil – assumed to be the depth to which soil 

carbon stocks would be affected when converted to agriculture – by multiplying the volume of soil in a given 

hectare (1 ha x 30 cm depth = 3,000 m3) by the bulk density to calculate the mass of soil in a given hectare, 

then multiplied the soil mass by the carbon content to derive an average soil carbon stock value per hectare (t C 

ha-1), which was then converted to CO2. Soil carbon stocks estimated per country (and per administrative unit in 

key countries) are shown in Figure 7.
15 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

< 50

51 - 100

101 - 150

151 - 242

243 - 500

501 - 750

> 750

Figure 7. Soil carbon stocks in the top 30 cm of soil for each country and 

administrative unit (for key countries). Estimates were derived from the 

Harmonized World Soil Database v. 1.1. Units are in t CO2 ha-1.

Harvested Wood Products

In the updated analysis, we addressed the potential significance of the harvested wood product pool and 

concluded that the amount of carbon stored in wood products long-term is probably immaterial for most regions 

of the world, especially if considering a timeframe of 30 years (as proposed by EPA‟s lifecycle analysis). 

Therefore, carbon storage in harvested wood products was not incorporated into updated emission factors.

We reached this conclusion as follows: the proportion of extracted timber that ends up in long-lived (>5 yr) wood 

products was estimated using information presented in Winjum et al. (1998), who related harvesting and use of 

wood products to carbon impacts (Table 2). The proportion of timber volume extracted ending up in long-lived 

wood products was calculated by dividing carbon in net production of industrial roundwood by the total carbon in 

commodity uses >5 yr. We did this for the developing and developed world and calculated percentages of 53% 

and 60%, respectively. The country-level percentages were generally lower than the aggregated values. Also, 

as new wood products are produced, others are retired from use and so the “inherited emissions” from these 

retired wood products should also be accounted for. Winjum et al. (1998) estimated inherited emissions from the 

retirement of past wood products. These values are reproduced in Table 2 below.
16 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

Table 2. Calculation of the proportion of extracted timber that goes to long-lived 

wood products and the proportion of extracted timber as inherited emissions. All 

units are in Tg C. From Winjum et al. (1998). SWD=sawnwood, WBP=woodbase panels, 

OIR=other industrial roundwood and P&P=paper and paperboard.

SWD WBP OIR P&P Total

Developing

 Brazil 23 4 1 1 1 7 4 30 17

 India 9 4 0.1 1 1 6 3 67 33

 Indonesia 12 2 0.1 1 0.42 3 1 25 8

 Ivory Coast 1 0.05 0.03 0.2 0.01 0.3 0.2 30 20

Developed

 Canada 39 3 1 1 2 7 1 18 3

 Finland 9 0.6 0.2 0.1 0.4 1.2 0.3 13 3

 New Zealand 2.7 0.3 0.1 0.1 0.2 0.6 0.3 22 11

 U.S.A. 102 23 8 3 23 57 17 56 17

Worldwide

 Developing 128 26 6 22 14 68 42 53 33

 Developed 308 70 27 29 58 184 71 60 23

Total 436 96 33 51 72 252 113 58 26

Commodity use ≥ 5 yr#

Industrial 

Roundwood 

Production*

Category / 

Country % HWP

Inherited 

emissions

% 

Inherited 

Emissions

* From Table 4 in Winjum et al. (1998).

#

From Table 5 in Winjum et al. (1998).

Next, we analysed per-hectare extraction volumes from 111 developing countries using data reported to FAO 

for the 2005 Forest Resources Assessment. Of the countries analysed, the country with the highest reported 

extraction rate was Indonesia (50 m3ha-1). This value was much higher compared to countries in Africa and 

Asia, which weren‟t much higher than about 20 m3ha-1and often less. 

For the 50 m3ha-1Indonesia case, we converted volume to biomass using an average conversion factor of 0.55 

(Table 1 in Winjum et al. 1998, tropical aggregate), then converted biomass to carbon using a conversion factor 

of 0.5. Therefore, 50 m3ha-1of extracted timber translates into 14 t C ha-1. Assuming that 25% of this carbon 

ends up in long-lived (>5 yrs) wood products (i.e., the value calculated in Table 2 above for Indonesia), the 

emission factor estimated for forest conversion after taking into account carbon storage in wood products would 

be reduced only by 3 t C ha-1(11 t CO2 ha-1), or approximately 1-2% of the emission factor value.

This result of 11 t CO2 ha-1stored in wood products from Indonesian harvests longer than five years is an 

overestimate. The calculation assumes that the carbon that ends up in these wood products is stored forever. 

After taking into account the inherited emissions that emanate from the oxidation (i.e., burning and decay) of 

wood products that were produced from harvests during previous years (retirement rate, see Table 2), the 

Indonesia value of carbon stored in wood products decreases even further to 2 t C ha-1, or 7 t CO2 ha-1.
17 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

Finally, Winjum et al. (1998) states that for the oxidation fractions of 0.04, 0.08 and 0.10 (representing rates for 

woodbase panels, other industrial roundwood and paper/paperboard), the time period of oxidation would extend 

back 25, 12 and 10 yr from the base year, respectively, for tropical regions. Therefore, much of the timber 

harvested today and stored as wood products will be completely oxidized 25 years from now. Considering EPA 

is estimating 30-year emission factors, carbon storage in wood products is likely insignificant, even for 

temperate and boreal regions where oxidation rates are slower. The analysis outlined above is also 

representative of productive forestlands only, and it is unlikely that every hectare of forest that is cleared for 

another land use is stocked for timber production. 

Foregone Sequestration

Forest sequestration rates were estimated in the initial analysis using IPCC Tier 1 default values for native 

forests. These values are listed by ecological zone, so final rates in the initial analysis were calculated by 

weighting the ecological zone-based sequestration rates by the proportion of forest area in each ecological zone 

within a country or administrative unit (for key countries). Peer reviewers pointed out a number of recent papers 

that summarize long-term monitoring plots in old growth tropical forests across the tropics and suggested the 

use of these more recent datasets for estimating annual rates of carbon sequestration in tropical forests. 

Lewis et al. (2009) published long-term aboveground carbon sequestration rates of 0.63 t C ha-1yr-1for African 

“closed canopy mature forests” (assumed moist or rain forest) based on long-term monitoring plots. This is 

similar to IPCC default rate for >20 yr old African tropical moist deciduous forests (0.65 t C ha-1yr-1) but lower 

than for >20 yr old African tropical rain forests (1.55 t C ha-1yr-1). Baker et al. (2004) also report an annual 

Amazonian C sequestration rate of 0.61 t C ha-1yr-1, which is lower than the IPCC default of 1.0 t C ha-1yr-1for 

>20 yr old tropical moist deciduous forests and 1.55 t C ha-1yr-1 for >20 yr old tropical rain forests of South 

America. After combining all standardized inventory data from Africa, tropical America and Asia together, Lewis 

et al. (2009) estimate carbon sequestration across all tropical intact old growth forests as 0.49 t C ha-1yr-1. We 

have used this estimate for foregone sequestration across the tropics in our updated analysis, but this is a 

definite underestimate of the carbon sink that tropical forests provide. The rate of carbon sequestration used in 

the updated analysis assumes that all tropical forests are old-growth forests and disregards the fact that many 

tropical forests are recovering secondary forests that have high rates of carbon sequestration. 

Myneni et al. (2001) and Nabuurs et al. (pers. comm.) also estimated the carbon sink of temperate and boreal 

forests in various countries, and these values were generally higher than sequestration in tropical forests, with 

rates of approximately 3-4 t CO2 ha-1yr-1on average but extending up to 7-8 t CO2 ha-1yr-1in Norway and 

Switzerland. These data reflect the long-term carbon sink capacity of forests, which have long been understood 

to be the case in temperate forests and have more recently been illustrated for old-growth tropical forests as 

well.

Locations of Fire for Land Clearing

Non-CO2 emissions [methane (CH4), nitrous oxide (N2O)] were calculated in the emission factor analysis for 

regions where fire was assumed to be used as a means of site preparation for cropland conversion. Fire 

emissions were calculated following IPCC Guidelines (Equation 2.27) as the product of initial biomass stocks 

available for combustion, a combustion factor (a measure of the proportion of fuel that is actually combusted; 

estimated as a function of the size and architecture of the fuel load, the moisture content of the fuel and the type 

of fire) and a GHG emission factor (the amount of a particular greenhouse gas emitted per unit of dry matter 

combusted; varies as a function of the carbon content of the biomass and the completeness of combustion). 

Combustion and emission factors were estimated per land cover type using information on standard errors 
18 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

reported on combustion factors in de Castro and Kaufmann (1998) as well as default values presented in Tables 

2.5 and 2.6 in the IPCC Guidelines.

To determine which regions typically use fire to clear land for another land use, several peer reviewers 

suggested the use of spatial fire products, such as those derived from MODIS and AVHRR sensors. However, 

the use of these products in isolation would not allow the distinction between fire that occurs for land conversion 

vs. fire that occurs due to wildfires, especially in temperate regions. We considered an approach in which 

various spatial fire products could be overlain onto land cover change maps to separate the fires that occurred 

on changed pixels (land conversion) vs. fires that occurred on pixels that remained in the same land cover 

category (e.g., forest fire, annual burning of cropland residues, etc.). However, the time needed to do this 

analysis exceeded the time available. Therefore, we maintained the approach used in the initial analysis, 

whereby expert judgment was used to determine the regions where fire is commonly used when land is cleared 

for agricultural production. The countries where fire was assumed are shown in Figure 8 (mainly along the 

tropical belt).

Figure 8. Locations where fire is assumed to occur as part of site preparation for 

a new land use.

3.3 Reversion Factor Analysis

In addition to estimating emission factors, reversion factors were developed to estimate the carbon 

accumulation in biomass and soils that occurs when managed cropland and pasture land is abandoned. All 

reversion factors (except reversion to forest) were estimated as the reverse of emission factors, whereby all 

increases in biomass carbon stocks occur in Year 1 (analogous to the stock change approach used to estimate 

emission factors) while changes in soil carbon stocks on abandoned cropland recovers to pre-land use change 

levels in 20 years (analogous to the soil emission factors, which were assumed to emit over 20 years). The only 

reversion factor to include soil carbon accumulation was reversion from abandoned cropland.
19 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

While most reversion factors assumed that all biomass carbon stock changes occurred in Year 1 (i.e., IPCC 

stock change approach), forest reversion factors assumed that biomass accumulates every year over the entire 

30-year time period. This was done to reflect forests‟ slow but continual carbon sink capacity. Despite the fact 

that young (<20 year old) forests accumulate biomass more quickly than older (>20 year old) forests, the annual 

rate of carbon accumulation on abandoned croplands that revert back to forests was conservatively assumed to 

be equal to the foregone forest sequestration rate (estimated for the emission factor analysis). If the forest 

biomass carbon stock (estimated for the emission factor analysis, see Figure 4 above) was less than 20 times 

the assumed annual foregone carbon sequestration value, then the annual carbon sequestration rate for 

reversion factors was assumed to be 1/20th of the initial forest carbon stock. Both of these assumptions provide 

a very conservative estimation of the carbon accumulation that occurs on abandoned land when it reverts to 

forest.

4.0 UNCERTAINTY ANALYSIS 

For the proposed RFS rule, EPA estimated uncertainty around its lifecycle GHG emission estimates by 

performing a type of sensitivity analysis by which, for example, the upper bound of the emissions from indirect 

land use change was estimated by assuming that all crop expansion came from forest and the lower bound was 

estimated by assuming that all expansion came from idle grassland (also by assuming that no pasture 

replacement is necessary). For its updated analysis, EPA took a more rigorous approach towards estimating

uncertainty. 

In our updated analysis, the quality and quantity of the data underlying every estimate was considered and 

uncertainties were combined using two approaches - following IPCC Tier 1 guidelines and using a Monte Carlo 

simulation approach. 

Uncertainty can be expressed as a percentage confidence interval relative to a mean value, with the confidence 

interval defined as a range that encloses the true value of an unknown parameter with a specified probability. 

The 95% confidence interval, which is the value typically used in the context of estimating GHG emissions and 

removals under the United Nations Framework Convention on Climate Change (UNFCCC), has a 95 percent 

probability of enclosing the true but unknown value of a given parameter.

The first step in any uncertainty analysis is to identify the potential sources of uncertainty. In the context of the 

EPA indirect land use change analysis, at the broadest level these sources include: 

(1) Modeling errors due to the inability of the economic models used to fully describe and quantify 

indirect land use change;

(2) Classification errors that arise from interpretation of satellite imagery to derive land cover maps; and

(3) Errors in parameters used in estimating emission factors.

In its current analysis, EPA uses output from economic models to predict the total area of indirect land use 

change attributable to biofuel expansion. There is considerable uncertainty associated with this component, but 

this type of uncertainty is outside the scope of Winrock‟s analysis and thus it is not discussed here.

Land cover maps derived from satellite imagery (MODIS) were used by Winrock and EPA to estimate the 

proportions of total crop expansion coming from each land cover type. Reporting only the proportions fails to 

recognize that maps derived from remote sensing contain classification errors. A suitable approach is to assess 
20 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

the accuracy of the maps and use the results of the accuracy assessment to adjust the estimates. An accuracy 

assessment should lead to a quantitative description of the uncertainty of the area for land categories and the 

associated change in area observed. This may entail category-specific thematic accuracy measures, confidence 

intervals for the area estimates, or an adjustment of the initial area statistics. Estimating the uncertainty in the 

land cover maps used to quantify patterns of land cover change was also outside the scope of Winrock‟s 

analysis and is thus not discussed here. (For additional information on uncertainty estimates around land cover 

change, refer to the report submitted to EPA by Dr. William Pepper).

Assessing uncertainties in the emission factor estimates involves assessing uncertainties in the various carbon

stock change components incorporated into the final estimate. This can be even more challenging than 

estimating uncertainties around land cover change, especially for land transitions characterized by a high 

degree of spatial variability that require sufficient resources to sample adequately and thus to arrive at accurate 

and precise estimates of the C stocks in a given pool. Final emission factors are derived from a combination of 

several different input parameters, each with its own uncertainty value, whereas estimating uncertainty in area

data deals only with one parameter.

After uncertainty in individual input parameters is estimated, the individual uncertainties can be combined to 

derive an uncertainty estimate around final emission factors using either (1) simple error propagation (IPCC Tier 

1) or (2) Monte Carlo simulation.

4.1 Uncertainty estimation for individual input parameters

The estimation of uncertainty was difficult for some parameters due to the absence of quantitative error 

analyses in the source data. Therefore, where no uncertainty information was available for a given parameter, 

expert judgement was used to identify an uncertainty range, and the upper bound was assumed as the 

uncertainty value. This produced final emission factor uncertainty values that are likely overestimated and thus 

conservative.

Forest carbon stocks

Forest carbon stocks for countries in Latin America and Africa were estimated using the new pantropical carbon 

stock map of Saatchi et al. (in prep.). However, an accuracy assessment for this new map was not completed 

in time for our updated analysis. Therefore, although we used the new map to derive mean values, uncertainty

around these mean values was estimated using accuracy information reported for a prior forest biomass product 

for Latin America derived using a similar methodological approach (Saatchi et al. 2007). The accuracy of the 

Saatchi et al. (2007) product was 81%. Therefore, we estimated the uncertainty in our forest carbon stock 

estimates to be 19%. The new pantropical carbon stock map has now been assessed for accuracy using cross 

validation with approximately 50% of the ground and lidar biomass data and has an overall accuracy of 76% 

across the three regions (Latin America: 81%, Africa: 86%, Southeast Asia: 69%). Because we used the new 

pantropical carbon stock map to derive estimates only for Latin America and Africa, the 19% uncertainty is an 

overestimate for Africa.

Forest biomass carbon stocks for countries in South Asia were estimated using a map developed by Brown et 

al. (2001), but no formal accuracy assessment was performed as part of their analysis. Therefore, we consulted 

Table 4.7 in the IPCC 2006 Guidelines for AFOLU to see what the reported uncertainty was around Tier 1 

aboveground biomass estimates. Biomass ranges are reported by continent and ecological zone, with extremely 
21 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

wide ranges in many cases (e.g., mean biomass of subtropical humid forests in Asia is 180 t ha-1, with a range 

of 10 to 560 t ha-1). Because the Brown et al. (2001) map was derived using some Tier 2 (country-level) 

information, we assumed the uncertainty of the Brown et al. (2001) product to be lower than uncertainty of Tier 1

IPCC values and therefore assigned Asia biomass carbon stocks an uncertainty value of 50%. This was done to 

reflect the somewhat better prediction of biomass over IPCC Tier 1 default values.

Some data sources provided accuracy information in the original documentation, and therefore this information 

was used to assign uncertainty values to the country-level estimates derived for our analysis. Uncertainty was 

estimated for biomass carbon estimates in Eastern Europe and Canada (33%; Myneni et al. 2001), China (36%, 

Piao et al. 2005), Russia (40%, Houghton et al. 2007), various EU countries (11%, Nabuurs et al. 2003), and the 

United States (7-31% depending on state, Blackard et al. 2007). For other EU countries in which biomass 

carbon stocks were estimated using FAO data, an uncertainty value of 50% was assumed. We assigned an 

uncertainty value of 80% to the global carbon stock map developed by Reusch and Gibbs (2008), as this was 

developed primarily from IPCC Tier 1 information. A summary of uncertainty values used for forest biomass 

carbon stocks is presented in Table 3.

In cases where data sources reported only aboveground biomass (or aboveground carbon) stocks only, we use

regression equation information presented in Cairns et al. (1997) to add in the uncertainty related to estimating 

belowground biomass. The relationship in Cairns et al. (1997) relates belowground biomass to aboveground 

biomass, and the equation has an adjusted R

2

value of 0.83. Therefore, we assumed the uncertainty (the

percent of variation in belowground biomass NOT explained by aboveground biomass) to be 7%.

Table 3. Uncertainty values used for forest carbon stock estimates.

Data Source Uncertainty Value Justification

Saatchi et al. (2007) 19% (aboveground), 17% (belowground) Accuracy assessment based on 

past product (Saatchi et al. 2007)

Brown et al. (2001) 50% (above- and belowground combined) No formal accuracy assessment 

given; therefore expert opinion as 

50% (some country-level data used 

therefore better than IPCC Tier 1)

Blackard et al. (2007) 7 – 31% (aboveground), 17% 

(belowground)

Pixel-level uncertainty values 

averaged per state

Houghton et al. (2007) 40% (above- and belowground combined) Reported in original source

Myneni et al. (2001) 27% (aboveground), 33% (belowground) Reported in original source

Nabuurs et al. (2003) 11% (above- and belowground combined) Reported in original source

Piao et al. (2005) 36% (above- and belowground combined) Reported in original source

Reusch and Gibbs (2008) 80% (above- and belowground combined) IPCC Tier 1 

FAO (2006) 50% (above- and belowground combined) No formal accuracy assessment 

given; therefore expert opinion as 

50% (some country-level data used 

therefore better than IPCC Tier 1)
22 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

Cropland, Grassland, Savanna, Shrubland and Wetland Carbon Stocks

Uncertainty around carbon stocks in annual croplands and grasslands was assumed to be 75%, based on 

default error margins reported in IPCC Table 5.9 and 6.4, respectively. In the absence of any uncertainty 

information for savanna, shrubland, perennial cropland and wetlands, uncertainty in carbon stock estimates for 

these other land cover categories was also estimated as 75% in keeping with the default uncertainty values 

presented in the IPCC Guidelines. The one exception to the 75% uncertainty assumption was Brazil, for which 

more precise information was available on the carbon stocks along a continuum of grasslands, savanna and 

shrublands. The uncertainty for these land cover types was estimated as 0.6%, 0.9% and 16%, respectively, 

derived from the standard errors reported in the original data source (de Castro and Kaufmann 1998). The 

higher uncertainty in shrubland carbon stocks is likely related to the comparatively large variation in tree cover

(and therefore carbon stocks) in shrublands compared to grasslands and savannas. Uncertainty in the mixed 

land cover category was estimated as the average of the uncertainty in forest, crop, shrub and grass categories

in keeping with the IGBP description of this land cover class.

Soil emissions

The data source used to estimate initial (reference) soil carbon stocks was changed from FAO/UNESCO‟s Soil 

Map of the World to the newly released World Harmonized Soil Database for the updated analysis. This was 

done because the spatial resolution of the new data product is much improved compared to the FAO map. 

However, neither data source reports information on accuracy or uncertainty. Therefore, there is no guarantee

that the accuracy of the information has improved along with the spatial resolution. In the absence of any 

reliable information about the uncertainty of the estimates, we assumed an uncertainty value of 90%, which is 

the default error estimate for Tier 1 default soil organic carbon stocks in all soil-climate types (derived from 

Table 2.3 of the IPCC 2006 Guidelines). 

Carbon stocks after land use conversion to cropland were calculated using IPCC Equation 2.25 as the initial soil 

carbon stock modified by land use, management, and input factors that relate to how the soil is managed. 

Default soil factors presented in Table 5.5 of the 2006 IPCC Guidelines were used, assuming conversion to 

long-term cultivated annual cropland under full tillage and medium inputs. The error margin for the land use 

factor was estimated using default values presented in Table 5.5 of the 2006 IPCC Guidelines and ranged from 

9 to 61% depending on temperature and moisture regime. After carbon stocks after land use conversion were 

estimated, changes in carbon stocks due to land conversion were calculated as the difference between initial 

and final carbon stocks divided by an assumed transition period of 20 years during which soil emissions take 

place.

Foregone forest sequestration

Uncertainty values for foregone forest carbon sequestration were derived from standard errors reported in the 

original data sources. Uncertainty in carbon sequestration rates ranged from 20% to 50%, with higher 

uncertainty in tropical regions.

Fire emissions

Fire emissions were calculated in the updated analysis when land is cleared for cropland in a region where fire 

is assumed to be used as a means of site preparation for the new land use. Fire emissions were calculated 

following IPCC Guidelines as the product of initial carbon stocks, a combustion factor (define) and a GHG 

emission factor (define). Combustion factors were estimated per land cover type using information on standard 

errors reported on combustion factors in de Castro and Kaufmann (1998). Uncertainties in combustion factors 
23 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

ranged from 42% to 69%. IPCC defaults were used to estimate uncertainty in the CH4 and N2O emission 

factors and ranged from 59% to 78% depending on land cover type burned.

4.2 Combining Uncertainties: Simple Error Propagation Approach (IPCC Tier 1)

The Tier 1 method is based on simple error propagation, and cannot therefore handle all kinds of uncertainty 

estimates. The key assumptions of the Tier 1 method are:

Estimation of emissions is based on addition, subtraction and/or multiplication

There are no correlations across categories (or if there are, the categories are aggregated in a manner 

that the correlations become unimportant)

None of the parameters has an uncertainty higher than about +/- 60%

Uncertainties are symmetric and follow a normal distribution

Several of these assumptions are violated in the emission factor uncertainty analysis. However, even in the 

case that not all of the conditions are fulfilled, the method can be used to obtain approximate results. Given the 

available time and resources, a Tier 1 uncertainty assessment was our chosen approach for the EPA analysis

but a Monte Carlo simulation of uncertainty was also done in collaboration with Dr. William Pepper at IAI. 

Detailed methods for the Monte Carlo approach are given in the IAI report.

Calculating an Uncertainty Value for a Given Parameter

The uncertainty value of any parameter used in deriving total emissions per hectare resulting from a given land 

cover conversion can be calculated using either the standard deviation or the 95% confidence interval, which is 

derived from the standard deviation. In a normally distributed curve, 95% of the area is within 1.96 standard 

deviations of the mean, therefore the 95% confidence interval width can be approximated as 4 standard 

deviations of the mean. The equation for estimating uncertainty is shown in Equation 1.

100

(4 )

2

1

100

(95% int )

2

1

% int

confidence ervalwidth

uncerta y

(1)

Combining Uncertainties

When calculating an emission factor for a given land cover conversion, various components of the emission 

factor are calculated, then added together to produce a final emission factor value. In the IPCC Tier 1 Error 

Propagation approach, the propagation of errors is based on two equations: one for multiplication and one for 

addition and subtraction.

The equation used in the case of multiplication is:

2 2

2

2

1

... Utotal U U Un

(2)

where:

U1,2,...,n = percentage uncertainty associated with each of the parameters

Utotal = the percentage uncertainty in the product of the parameters
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Box 1 gives an example of the use of Equation 2 for the calculation of uncertainty associated with methane 

emissions resulting from fire used to clear forest to cropland in primary tropical forest, which is one component 

of the total emission factor for conversion to cropland in countries where fire is used to clear land for agriculture

Box 1: Calculating Uncertainty in methane emissions resulting from clearing land with 

fire for cropland expansion

The IPCC equation (Equation 2.27 in 2006 Guidelines) for calculating methane emissions from fire (t CO2e ha-1) 

is:

4

3

Lfire,CH 4 M B Cf Gef 10 GWPCH

(Clarification: Equation 2.27 in the 2006 Guidelines provides a calculation of the amount of greenhouse gas 

emissions from fire in tons of each GHG - in this case, tons of CH4. Therefore, we added the global warming 

potential term (GWPCH4 ) to the equation above to convert tons of methane to tons of CO2e.) 

Four parameters are incorporated into the equation, each with an associated uncertainty value. The table below 

lists the mean and uncertainty values for each parameter. For example, the uncertainty in the first parameter 

(MB) was estimated using accuracy information presented in the original data source for biomass estimation 

(e.g., see Table 3 above). The default combustion factor for primary tropical forest is 0.36 from Table 2.6 of the 

IPCC Guidelines, with a standard deviation of 0.13. Applying Equation 1 above, the uncertainty around the 

combustion factor becomes [(1⁄2 x 4 x 0.13) / 0.36] x 100 or 72%. Finally, the last column in the last row 

represents the total uncertainty around the final estimate of methane emissions, calculated using Equation 2 

above. The emission factor from the methane component of biomass burning is 7.7 t CO2e ha-1± 94%, or 7.7 ± 

7.2 t CO2e ha-1. 

Parameter Mean Value SD Uncertainty (%)

MB 150 19%

Cf 0.36 0.13 72%

Gef 6.8 2.0 59%

GWPCH4 21 0

Total 7.7 t CO2e ±94%

In the case of addition and subtraction, for example when carbon stocks or individual emission components are 

summed, the following equation is applied for uncertainty:

n

n n

total

x x x

U x U x U x

U

...

( ) ( * ) ... ( * )

1 2

2 2

2

2

1 1 2

(3)
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Where:

Ui = percentage uncertainty associated with each of the parameters

Xi = the value of the parameter

Utotal = the percentage uncertainty in the sum or difference of the parameters

An example of the use of Equation 3 is shown in Box 2 for the calculation of total uncertainty of an emission 

factor for, e.g., forest to cropland conversion.

Box 2: Calculating uncertainty in the final emission factor for forest to cropland 

The final emission factor for a given land cover conversion is calculated as the sum of individual emission 

components:

EFLUC = CBM + Csoil + Clost_seq + Efire

There are four parameters in the final equation, each with an associated uncertainty value. The table below lists 

a sample uncertainty value for each parameter in a given country or administrative unit. Because the final 

emission factor estimate is the sum of individual components, the total uncertainty around the final emission 

factor is calculated using Equation 3 above.

Parameter Estimated Emissions (t CO2e) Uncertainty (%)

CBM 554 15%

Csoil 5.5 70%

Clost_seq 1.8 20%

Efire 53 86%

Total 615 15.5%

Uncertainty values for most of the emission factor parameters above are calculated as the sum or product of 

several terms in an equation, and were thus derived using Equation 1 or 3 above for multiplication or 

addition/subtraction (e.g., see Box 1 for calculation of uncertainty around methane emissions from fire). After 

uncertainty values for all emission factor components were calculated, the uncertainty in the final emission 

factor was calculated by using Equation 3 (addition/subtraction) to add all components together. The result is 

615 t CO2e ha-1± 15.5%, or 615 ± 95 t CO2 ha-1. It is illustrated in this example that although uncertainty in two 

of the four components is high, the final uncertainty value is governed by the low uncertainty around the largest 

contributing component of the emissions, namely, the emissions from the initial biomass clearing (CBM). 

4.2 Combining Uncertainties: Monte Carlo Approach

The uncertainty analysis using a Monte Carlo approach is described in detail in the report of Dr. William Pepper. 

However, some of the information required to run a Monte Carlo simulation involves an estimation of the degree 

of correlation among different variables – both the correlation of one variable across space as well as the 
26 R E P O R T T O E P A U P D A T E S S I N C E P R O P O S E D R U L E

correlation of one variable to any others used in the analysis. This is in contrast to the IPCC error propagation 

approach, which assumes no correlation across categories. Winrock provided IAI with information on 

correlations among different data inputs that were then fed into the Monte Carlo simulation of uncertainty. 

Forest carbon stocks were derived from various sources, and therefore estimates for certain countries are 

correlated. The correlation groups for forest carbon stocks are shown in Figure 3 above. All countries use the 

same equation that relates belowground biomass to aboveground biomass (Cairns et al. 1997), therefore 

belowground biomass is perfectly correlated to aboveground biomass. 

Although the World Harmonized Soil Database was used to estimate soil carbon stocks in the updated analysis, 

the dataset was developed by compiling different data sources together, and therefore soil carbon stocks are 

not correlated across all regions. The regional distribution of data sources is shown in Figure 9 below.

Figure 9. Correlation groups for soil carbon stocks; different colors represent 

different correlation groups.

Grassland carbon stocks were estimated using IPCC default carbon stock values and weighting the carbon 

stock based on the proportion of each country falling within each ecological zone. Correlation groups for these 

land cover categories were determined by assuming that countries with the same dominant ecological zone 

were correlated (i.e., all use the same carbon stock value). In addition, carbon stocks of grassland, shrubland 

and savanna were all assumed to be perfectly correlated because we used a simple proportional approach to 

estimate the carbon stocks of savanna and shrubland (based on the grassland value).

All annual croplands have an assumed carbon stock of 5 t C ha-1and therefore all regions are correlated for this 

input parameter. Perennial croplands in Indonesia and Malaysia are assumed to be oil palm, and therefore 

these regions are correlated, while perennial croplands in the rest of the world are assumed to be in a different 

correlation group (sugarcane). Correlation groups for lost forest sequestration were delineated based on data 

source. 
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5.0 RESULTS AND DISCUSSION

5.1 Land Cover Change

The new MODIS products at a resolution of 500 meters are likely to capture land transitions more accurately 

than the 1-km products that were used in the initial analysis. The change from 1-km to 500-m resolution 

essentially means that for every 1-km pixel classified in the version 4 imagery, version 5 classifies the same 

area as four pixels, which allows smaller land cover changes to be detected and represents an improvement in 

the final change matrices. 

For example, in the MODIS version 4 (1-km) land cover data used for the proposed rule, the total area of 

cropland in Brazil decreased between 2001 and 2004 (Table 4A, red values) while the total area of forest 

increased over the same time period (Table 4A, green values). This trend is not supported by other national 

datasets or higher resolution imagery; the trend of increasing deforestation in Brazil is well known. When 

analyzing the same time period using 500-m resolution MODIS land cover maps, forest area decreased by 11.5 

million ha between 2001 and 2004 (Table 4B, green values). This trend is supported by other, country-level 

datasets that show forest cover loss and demonstrates the importance of spatial scale in detecting small land 

cover changes. However, not all forest area loss appeared as a cropland area gain during the 2001 to 2004 

period; the total area of cropland between 2001 and 2004 using the new 500-m dataset stayed virtually constant 

while much of the forest was converted to the „mixed‟ land cover category. When the time period was extended 

from 2001 to 2007 using 500-m land cover maps, total cropland area in Brazil increased (Table 4C, red values), 

suggesting that the initial 3-year time period analyzed (2001 to 2004) was not sufficient to capture recent trends 

in cropland and savanna (pasture) expansion due to the gradual process of forest cover loss.

Updated land cover change matrices for the time period 2001 to 2007 are provided in the supplementary 

material. Although the 500-m resolution MODIS land cover maps represent a significant improvement over the 

1-km product that was used for the draft rule, there are still significant problems with deriving accurate land 

cover changes among land cover categories using global datasets, especially among classes with a variety of 

woody and herbaceous vegetation cover. To improve the analysis, a subsample of global land cover data was 

compared to higher resolution Landsat imagery for various biome types (see ICF report submitted to EPA for 

more information). Landsat-scale imagery has been used by other authors to derive correction factors for the 

area changes observed in coarse scale MODIS imagery. For example, Hansen et al. (2008) detected forest 

cover loss for the humid tropics using this approach and similar analyses for other biome types are now 

underway by the remote sensing community. However, the focus to date has been on changes in forest cover, 

and less work has been done on delineating non-forest land cover classes at the global scale. This is due to the 

fact that small changes in canopy cover are difficult to distinguish in coarse scale imagery; higher resolution 

Landsat (or similar) data combined with ground validation plots would be better suited for detecting changes

between cropland, grassland and savanna land cover types, for example. 

However, the analysis of global land cover datasets is suitable for capturing relative variation in land cover 

change across regions (vs. absolute areas) – such as in EPA‟s analysis that uses the change matrices to 

quantify the relative proportions of various land cover types converted to cropland and grassland rather than 

absolute estimates of change in any particular region. Furthermore, EPA‟s analysis of land cover change was 

strengthened by using recent and substantial MODIS data validation/ground-truthing efforts to statistically 

correct for systematic errors/biases in the remote sensing change matrices (see the report by Dr. William 

Pepper for more information).
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Table 4. Comparison of land cover change matrices in Brazil among different time 

periods and spatial resolutions.

(A) 1-km resolution, 2001 to 2004

2001 Background 2004 Cropland 2004 Forest 2004 Grassland 2004 Mixed 2004 Savanna 2004 Shrub 2004 Grand Total

Background 15,818,506 20,958 777,842 126,463 23,067 343,724 160,629 17,271,188

Cropland 13,301 9,472,992 248,705 927,137 4,357,659 8,528,129 116,505 23,664,428

Forest 2,186,167 338,822 379,643,981 1,389,103 1,486,724 15,879,360 1,111,260 402,035,417

Grassland 185,802 1,475,505 1,433,883 9,150,009 2,573,951 22,320,200 1,449,632 38,588,983

Mixed 22,487 4,937,479 1,805,628 2,081,576 21,473,787 25,974,736 210,197 56,505,889

Savanna 786,864 6,005,531 18,521,800 12,274,078 21,625,505 214,803,199 4,694,060 278,711,036

Shrub 310,928 299,826 1,276,218 2,178,927 441,017 16,740,056 7,250,443 28,497,415

Grand Total 19,324,053 22,551,114 403,708,057 28,127,293 51,981,709 304,589,404 14,992,726 845,274,356

(B) 500-m resolution, 2001 to 2004

2001 Background 2004 Cropland 2004 Forest 2004 Grassland 2004 Mixed 2004 Savanna 2004 Shrub 2004 Grand Total

Background 18,231,004 48,126 4,250,413 77,449 180,850 222,665 60,061 23,070,568

Cropland 127,357 11,821,661 192,828 1,372,613 4,796,355 9,192,307 56,369 27,559,490

Forest 3,674,420 511,145 366,292,525 490,302 11,585,601 11,197,222 71,095 393,822,310

Grassland 263,730 3,137,666 464,178 11,443,239 4,061,428 25,217,864 334,567 44,922,672

Mixed 369,900 5,397,378 6,117,987 1,872,124 37,301,302 33,646,437 131,457 84,836,585

Savanna 1,112,404 6,769,240 4,942,881 7,570,603 20,873,624 229,365,365 1,409,985 272,044,102

Shrub 305,373 193,343 69,249 587,478 94,514 3,485,327 579,621 5,314,905

Grand Total 24,084,188 27,878,559 382,330,061 23,413,808 78,893,674 312,327,187 2,643,155 851,570,632

(C) 500-m resolution, 2001 to 2007

2001 Background 2007 Cropland 2007 Forest 2007 Grassland 2007 Mixed 2007 Savanna 2007 Shrub 2007 Grand Total

Background 16,901,751 31,060 4,495,803 36,710 134,106 288,754 73,251 21,961,435

Cropland 186,675 12,829,366 223,091 748,438 5,506,220 8,345,175 35,785 27,874,750

Forest 4,128,571 1,217,596 356,173,856 423,922 13,637,426 17,642,770 73,567 393,297,707

Grassland 447,511 4,928,192 480,988 9,305,734 4,994,904 25,102,792 172,566 45,432,686

Mixed 659,192 6,593,064 6,936,747 1,335,851 35,302,854 34,828,219 57,616 85,713,542

Savanna 1,757,715 8,884,278 4,458,221 6,471,101 22,999,614 227,232,878 415,283 272,219,090

Shrubland 398,913 238,957 65,824 729,829 86,305 3,511,326 346,652 5,377,807

Grand Total 24,480,327 34,722,513 372,834,529 19,051,585 82,661,428 316,951,915 1,174,721 851,877,018

5.2 Emission Factors

Unlike in the initial analysis where immediate, 20-year and 20-80 year emission factors were estimated, here we 

report one emission factor for a 30-year time period per country and administrative unit (for key countries). 

Emission factors, as in the initial analysis, were highest for forest conversion to cropland. In some cases, the 

carbon stocks of perennial cropland exceeded the carbon stocks on the native land cover type, leading to net 

carbon sequestration rather than net emissions. Emission factors for each country (and administrative unit for 

key countries) are given in the supplementary information.
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6.0 REFERENCES

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Laurance, W.F. Laurance, S.L. Lewis, A. Monteagudo, D.A. Neill, P. Nuñez Vargas, N.C.A. Pitman, 

J.N.M. Silva, R.V. Martínez. Increasing biomass in Amazonian forest plots. Philosophical Transactions 

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Cairns, M.A., S. Brown, E.H. Helmer, G.A. Baumgardner. 1997. Root biomass allocation in the world‟s upland 

forests. Oecologia 111:1-11.

Couwenberg, J., R. Dommain, and H. Joosten. 2009. Greenhouse gas fluxes from tropical peatlands in southeast Asia. Global Change Biology, doi: 10.1111/j.1365-2486.2009.0216.x

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Arunarwati, F. Stolle, M.K. Steininger, M. Carroll, and C. DiMiceli. 2008. Humid tropical forest clearing 

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Numata, I., D.A. Roberts, O.A. Chadwick, J. Schimel, F.R. Sampaio, F.C. Leonidas and J.V. Soares. 2007. 

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sensed data. Remote Sensing of Environment 109: 314-327.
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Ruesch, A. and H.K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available 

online from the Carbon Dioxide Information Analysis Center [http://cdiac.ornl.gov], Oak Ridge National 

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stocks. In preparation, Science.

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atmospheric carbon dioxide. Forest Science 44: 272-284.

For questions or comments contact:

Nancy Harris

Winrock International

2121 Crystal Drive, Suite 500

Arlington VA 22202

Phone: 703-302-6519

Email: nharris@winrock.org
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