### Report to EPA

December 2009



# Land Use Change and Emission

Factors:

Updates since the RFS Proposed Rule

Nancy Harris, Sean Grimland and Sandra Brown1 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



## 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 L AND C OVER C HANGE

................................ ................................ ................................ ........................... 7

3.2 U PDATED E MISSION F ACTOR A NALYSIS

................................ ................................ ................................ . 9

3.3 R EVERSION F ACTOR A NALYSIS

................................ ................................ ................................ ............ 18

4.0 UNCERTAINTY ANAL YSIS

................................ ................................ ................................ .............. 19

4.1 U NCERTAINTY ESTIMATIO N FOR INDIVIDUAL INP UT PARAMETERS

................................ ....................... 20

4.2 C OMBINING U NCERTAINTIES : S IMPLE E RROR P ROPAGATION A PPROACH (IPCC T IER 1)

..................... 23

4.2 C OMBINING U NCERTAINTIES : M ONTE C ARLO A PPROACH

................................ ................................ .... 25

5.0 RESULTS AND DISC USSION

................................ ................................ ................................ ............ 27

5.1 L AND C OVER C HANGE

................................ ................................ ................................ ......................... 27

5.2 E MISSION F ACTORS

................................ ................................ ................................ .............................. 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. 2 009

. Land Use Change and
Emission Factors: Updates since

Proposed RFS Rule. Report submitted

to EPA.2 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



# 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 Renewabl e Fuel Standard (RFS) program. In an initial analysis, W inrock
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 Winro ck‟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 estimate s) 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 („barr en‟ 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 groun d 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 ca ses. 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 wer e 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

industri al 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‟ sta tements, 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 pastur e land is abandoned and reverts to forest3 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

fore st 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 indiv idual 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 k nown 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 resol ution 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 abl e 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 ju st 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 Renewabl e Fuel Standard (RFS) program. In an initial analysis, W inrock estimated the extent of recent land cover change at national and sub

- national scales for all countrie s 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 ana lysis 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 emiss ion 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 la nd 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 detail4 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 = 3.6 7 tons of CO 2

.



# 2.0 RESPONSES TO PEE R 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 produ cts (e.g., ASTER, ALOS/PALSAR) to estimate land cover change. However, land cover products derived from ASTER data are not currently available, 1 and

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

ALOS/PALSA R 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 init ial 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 ev ery 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 t ime 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 th e 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 MO DIS land cover products as forests, not wetlands. The proportion of

1 See https://lpdaac.usgs.gov/lpdaac/products/aster_produc ts_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 interpreta tion 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 tran sition 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 moderate

resolution lan d 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 co ver change and so the methods used to quantify uncertainty are n ot 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 use d 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 s oil carbon stock s was too coarse . One peer reviewer found the ma p 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 Soi l 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.

C arbon 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 pre dict 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,6 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

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 f oregone 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 g ains. 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 f or 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 t he product of total area

and a mean annual biomass growth rate (t C ha - 1 yr - 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 CO 2 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. W e updated forest sequestration values to

reflect recent additional peer

- reviewed literature that also demonstrate s 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 trop ical forests are gaining carbon as slowly as old

growth 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 addre ss 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 th e

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 ve getation 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 su ch as forests . As t he forest

cano py 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 betwee n pasture (i.e. grassland) biomass and vegetation indices and found that the chlorophyll

- based indices such as NDVI had an7 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

extremely poor correlation with biomass (R 2 =0.05) and only slightly better for water content and height
(R 2 =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.

T he database for estimating emissions from peat drainage was too limited

. Winrock‟s initial
emission factor analysis included emissions from peat soils when cleared and draine d for another land

u se. 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 wa s a

literature review of all available data on CO 2 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 (Couwenber g et al. 2009) that extended the Hooijer et al. (2006) analysis and presents a virtually identical relationship between drainage depth and CO 2 emissions. The Couwenberg et al. (2009) study also demonstrates that
methane fluxes in peat soils are insignifica nt compared to CO 2 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 p eat experts such as Dr. Susan Page agree that the CO 2 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 e t al. (2006) and Couwenberg et al. (2009) literature reviews closely match IPCC default emission factors for tropical drained organic soils.

F ire 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 dist inction between fire that
occurs for land conversion vs. fire that occurs due to wildfires, especially for temperate regions.
Therefore, w e considered an approach in which various fire maps could be overlain onto land cover change maps to determine fires t hat occur red on changed pixels (land conversion) vs. fires that

occur red on pixels that remain ed 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, l and cover change was8 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

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 cov er 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. How ever, 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 datase ts 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 ch ange 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 analysi s. 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 thr ough 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 cha n ge 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.9 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



### 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 so ils (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

- CO 2 emissions (CH 4 , N 2 O) resulting from land clearing with fire where ap plicable. 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 CO 2 e ha - 1 yr - 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

Win rock‟s initial emission factor analysis incorporated spatial maps of forest carbon stocks from s everal data sources, each derived from a different methodological approach . This was due to the fact that the only globally consistent carbon stock map availabl e 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 200 8). The region

- specific maps, though not directly comparable across regions, we re 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 Gi bbs (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 belowgr ound forest c arbon 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 refine d 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 region s 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 Saa tchi 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 (a nd 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

stoc k estimates in Asia. The Saatchi et al. (in prep) results repres ent 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 a n 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). Althoug h 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) average d 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, respect ively).

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 t he 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 estimat es using an equation from Cairns et al. (1997) or Mokany et al (2006), both which estimate belowground biomass as approximately 2 5 % of abovegr ound 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 uncer tain

– 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

Figure 4 . Spatially averaged forest carbon stocks in above

- and belowground
biomass, in t CO 2 ha - 1

.



### Cropland Carbon Stocks

In the initial emission factor analysis, all cropland conversion was assumed to be conversion to annual cropland. In the update d 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 assu med to be sugarcane. Carbon stocks in oil palm plantations after one year of growth were estimated as 15 t CO 2 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 - 1 yr - 1 (15 t CO 2 ha - 1 yr - 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 clima te 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 CO 2 ha - 1 . (Carbon stocks for long

- lived tree species such as oil palm accumulate carbo n 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 - 1 or 62 t CO 2 , Amaral et al. 2008) and in belowground biomass (7 t C ha - 1 or 26 t CO 2 ha - 1 , Sm ith et al. 2005),
for a total of 88 t CO 2 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 CO 2 ha - 1

.

All biomass accumulated after Year 1 w ould 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 year s). 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 year s

was assume d 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 categ ories 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 r eport 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 t rees 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, gr assland 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 savann a 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

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 CO 2 ha - 1

.



### Wetland, Barren and Mixed Carbon Stocks

The updated analysis included land cover ch ange 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 we re 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 conver ted to agriculture

– by multiplying the volume of soil in a given hectare (1 ha x 30 cm depth = 3,000 m 3 ) 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 ca rbon stock value per hectare (t C

ha - 1 ), which was then converted to CO 2

. 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

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 CO 2 ha - 1

.



### Harvested Wood Products

In the updated analysis, we addressed the potential significan ce 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 info rmation 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 i ndustrial 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. Win jum et al. (1998) estimated inherited emissions from the retirement of past wood products

. T hese 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.

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

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

Next, we analysed per

- hectare extraction volum es 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 m 3 ha - 1 ). This value was much higher compared to countr ies in Africa and Asia, which weren‟t much higher than about 20 m 3 ha - 1 and often less.

For the 50 m 3 ha - 1 Indonesia 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 m 3 ha - 1 of 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 CO 2 ha - 1 ), or approximately 1

- 2% of the emission factor value.

This result of 11 t CO 2 ha - 1 stored 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 yea rs (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 CO 2 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 produ cts 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

anot her 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 - 1 yr - 1 for 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 mois t deciduous forests (0.6 5 t C ha - 1 yr - 1 ) but lower than for >20 yr old African tropi cal rain forests (1.55 t C ha - 1 yr - 1 ). Baker et al. (2004) also report an annual Amazonian C sequestration rate of 0.61 t C ha - 1 yr - 1 , which is lower than the IPCC default of 1.0 t C ha - 1 yr - 1 for >20 yr old tropical moist de ci duous forests and 1.55 t C ha - 1 yr - 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 - 1 yr - 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 f orests that have high r ates 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

r ates of approximately 3

- 4 t CO 2 ha - 1 yr - 1 on average but extending up to 7

- 8 t CO 2 ha - 1 yr - 1 in 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 an d have more recently been illustrated for old

- growth tropical forests as

well.



### Locations of Fire for Land Clearing

Non

- CO 2 emissions [methane (CH 4 ), nitrous oxide (N 2 O)] 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 mat ter 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 errors18 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 Castr o 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, s everal peer reviewers suggested the use of spatial fire products, su ch as those derived from MODIS and AVHRR sensors . However,

t he 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 consid ered 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 r egions where fire is commonly used when land is cleared for agricul tural 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 occ ur as part of site preparation for

a new land use.



### 3.3 Reversion Factor Analysis

In addition to estimating emission fact ors, reversion factors were developed to estimate the carbon accumulation in biomass and soils that occurs when managed crop land and p asture 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 carb on stocks on abandoned cropland recover s 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 accu mulation 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, t he annual

rate of carbon accumula tion on abandoned croplands that re vert back to forests was conservatively assumed to be equal to the foregone forest sequestration rate ( estimated for the emission f actor analysis)

. I f the forest

biomass carbon stock (estimated for the emission factor ana lysis , 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 1/20 th 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 ANAL YSIS

For the proposed RFS rule, EPA estimated uncertainty around its lifecycle GHG emission estimates by perf orming 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 eve ry estimate was c onsidered 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, wit h 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 i n 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 ari se from interpretation of satellite imagery to derive land cover maps; and (3) Errors in parameters used in estimating emission factor s.

In its current analysis, EPA uses output from economic models to predict the total area of indirect land use change attri butable 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 (MODI S) 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 assess20 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 c over change was also outside the scope of Winrock‟s analysis and is thus not discussed here. (For additional info rmation on uncertainty estimates around land cover change , refer to the report submitted to EPA by Dr. William Pepper).

Assessing unce rtaintie s 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 r esources to sample adequately and thus to arrive at accurate and precise estimates of the C stock s in a given pool. F inal emission factors are derived from a c ombination of

several different input parameters, each with its own uncertainty value, whereas es timating uncertainty in area data deals only with one parameter.

After uncertainty in individual input parameters is estimated, t he 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) Mont e Carlo simulation

.



### 4

. 1 U ncertainty 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 identi fy an uncertainty range, and the upper bound was assumed as the uncertainty value. This produced final emission factor u ncertainty values that are likely overestimate d 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 der ive mean values, u ncertainty around these mean values was estimated using accuracy information reported for a prior forest biomass product
for Latin Am erica 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%. T he new pantropical carbon stock map has now been assessed for accuracy using cross validation with approximately 50% of the ground and l idar biomass data and has an overall accuracy of 76% across the three regions (Latin America: 81%, Afri ca: 86%, Southeast Asia: 69%). Because we used the new pantropical carbon stock map to derive estimates only for Latin America and Africa, the 19% uncert ainty 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 n o formal accuracy assessment was performed as part of their analysis. Therefore, we consu lted
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 extremely21 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 (3 3%; 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

car bon 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 C airns et al. (1997) to add in the uncertainty related to estimating belowground biomass . The relationship in C airns et al. (1997) relates belowground biomass to aboveground biomass, and the equation has an adjusted R 2 value of 0. 8 3. 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 ), 1 7% (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

t herefore better than IPCC Tier 1)

Blackard et al. (2007) 7

– 31% (aboveground )

, 1 7% (belowground) Pixel

- level uncertainty values averaged per state

Houghton et al. (2007) 40% (above

- and belowground combined) Reported in original source

Myneni et al. (20 01) 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



### Cropl and, Grassla nd, 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 Guid elines. 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

T he 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

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

However , neither data source reports inform ation on accuracy or uncertainty. Therefore, there is no guarantee that the accuracy of the information has improv ed 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

- c limate 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 rela te 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 con version were calculated as the difference between initial and final carbon stocks divided by an ass umed transition period of 20 years during which soil emissions take place.



### Foregone forest sequestration

Uncertainty values for foregone forest carbon seques tration w ere 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 wer e calculated in the updated ana lysis 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 p roduct 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 factors23 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 cat egories (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 Un certainty 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, whi ch 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 f or estimating uncertainty is shown in Equation 1.



### (1)

Combining Uncertainties

When calculating a n 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, t he propagation of errors is based on two equations: one for multiplication and one for

addition and subtraction.

The equatio n used in the case of multiplication is:



### (2)

w here:

U 1,2,...,n = percentage uncertainty associated with each of the parameters

U total = the percentage uncertainty in the product of the parameters24 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

Box 1 gives an example of the use of Equation 2 for the calcula tion 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 l and 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 CO 2 e ha - 1 )

is:

(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 CH 4. Therefore, we added the global warming potential term (GWP CH4 ) to the equation above to convert tons of methane to tons of CO 2 e.)

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 (M B ) 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 o f 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, t he last column in the last row represents the total uncertainty around the final estimate of methane emissions, ca lculated using Equation 2

above. T he emission factor from the methane component of biomass burning is 7.7 t CO 2 e ha - 1 ± 94% , or 7.7 ±
7.2 t CO 2 e ha - 1

.

Parameter Mean Value SD Uncertainty (%)

M B 150 19 %

C f 0. 36 0.13 72%

G ef 6.8 2.0 59%

GWP CH4 21 0

Total 7.7 t CO 2 e ±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:



### (3)25 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

Where:

U i = percentage uncertainty associated with each of the parameters

X i = the value of the parameter

U total = the percentage uncertainty in the sum or difference of the parameters

An example of the use of Equation 3 i s shown in Box 2 for the calculation of total uncertainty of an emission factor for, e.g., forest to cropland conversio n.



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

The final emission factor for a given land cover conversion is c alculated as the sum of individual emission components:



### EF LUC 

### = C BM 

### + C soil 

### + C lost_seq 

### + E fire

There are four parameters in the final equation, each with an associated uncertainty value. The table below lists a sample uncertainty value for each paramete r 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 CO 2 e ) Uncertainty (%)

C BM 554 15 %

C soil 5.5 70 %

C lost_seq 1.8 20 %

E fire 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 deriv ed 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 t he final emission

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

615 t CO 2 e ha - 1 ± 15.5 % , or 615 ± 95 t CO 2 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 (C BM )

.



### 4.2 Combining Uncertainties: Monte Carlo Appro ach

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 diffe rent variables

– both the correlation of one variable across space as well as the26 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

. Wi nrock 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

c orrelated. 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 ( C airns 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 correlat ed 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 w ere 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 w ith 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 - 1 and therefore all regions are correlated for this input parameter. Perennial croplands in Indonesia and Mal aysia a re 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 delineate d based on data

source.27 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



# 5.0 RESULTS AND DISC USSION

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 f inal 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 4 A, red values) while the total area of forest

increased over the sam e time period (Table 4 A, 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 lan d 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 conve rted 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 . Altho ugh 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 glob al datasets, especially among classes with a variety of woody and herbaceous vegetation cover. To improve the analysis, a subsample of global l and cover data was compared to higher resolution Landsat imagery for various biome types (see ICF report submitte d to EPA for more information). Landsat

- scale imagery has been used by other authors to derive correction factors for t he area changes observed in coarse scale MODIS imagery. For example, Hansen et al. (2008) detected forest cover loss for the humid tropic s 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 t he 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 chang e was
stre ngthened 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).28 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 4 . Comparison of land cover change matrices in Brazil among different time periods and spatial resolutions.



### (A) 1

- km resolution, 2001 to 2004

(B) 500

- m resolution, 2001 to 2004

(C) 500

- m resolution, 2001 to 2007

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 coun try (and administrative unit for key countries) are given in the supplementary information

.29 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



# 6.0 REFERENCES

Amaral W. A. N., J.P. Marinho, R. Tarasantchi, A. Beber, and E. Giuliani. 2008, Environmental sustainability of sugarcane ethanol in Brazil. In: Zuurbier P.; van de Vooren J. (eds) Sugarcane ethanol: Contribution to climate change mitigation and the environment. Wageningen Academic, Wageningen, pp 121

- 124.

Baker, T.R., O.L. Phillips, Y. Mahli, S. Almeida, L. Arroyo, A. Di Fiore, T. Erwin, N. Higu chi, T.J. Killeen, S.G. 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 of the Royal Society of London: Bi ological Sciences 359: 353

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Cairns, M.A., S. Brown, E.H. Helmer, G.A. Baumgardner. 1997. Root b iomass allocation in the world ‟ s upland forests. Oecologia 111:1

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Couwenberg, J., R. Dommain, and H. Joosten. 2009. Greenhouse gas fluxes from tropical peatlands in south

east Asia. Global Change Biology, doi: 10.1111/j.1365

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Hansen, M.C., S.V. Stehman, P.V. Potapov, T.R . Loveland, J.R.G. Townshend, R.S. DeFries, K.W. Pittman, B. Arunarwati, F. Stolle, M.K. Steininger, M. Carroll, and C. DiMiceli. 2008. Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed d ata. PNAS

105: 9439

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Hooijer, A., M. Silvius, H. W ö sten and S. Page. 2006. PEAT

- CO2, Assessment of CO 2 emissions from drained peatlands in SE Asia. Delft Hydraulics report Q3943 (2006).

Lewis, S.L., G. Lopez

- Gonzalez, B. Sonk é , K. Affum Bafoe, T.R. Baker, L.O. Ojo, O.L. Phillips, J.M. Reitsma, L. White, J.A. Comiskey, et al. 2009. Increasing carbon storage in intact African tropical forests. Nature

457: 1003

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Mokany, K., R.J. Raison, and A.S. Prokushkin. 2006. Critical analysis of root:shoot ratios in terrestrial biomes. Global Change Biology 12: 84

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Monfreda, C., N. Ramankutty and J. Foley. 2008. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types and net primary production in the year 2000. Global B iogeochemical Cycles
22: GB1003.

Myneni, R.B., D. Jong, C.J. Tucker, R.K. Kaufmann, P.E. Kauppi, J. Liski, L. Zhou, V. Alexeyev, and M.K. Hughes. 2001. A large carbon sink in the woody biomass of Northern forests. PNAS 98: 14784

- 14789.

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

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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 Laboratory, Oak Ridge, Tennessee.

Saatchi, S.S., N.L. Harris, S. Brown, M. Lefsky and S. Petrova. New benchmark map of tropical forest carbon stocks. In preparation, Science.

Smith, D.M., N.G. Inman

- Bamber and P.J. Thorburn. 2005. Growth and function of the sugarcane root sys tem. Field Crops Research 92: 169

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Winjum, J.K., S. Brown and B. Schlamadinger. 1998. Forest harvests and wood products: sources and sinks of 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.org31 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