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XIII World Forestry Congress Buenos Aires, Argentina, 18 23 October 2009

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Beyond forest and non-forest: MODIS & AVHRR in mapping& quantifying the evolution of forest to cropland types in

the seasonal tropics of Bolivia

Danny Redo1

and Andrew C. Millington2

The main objective of this study was to utilize MODIS and AVHRR NDVI, supplemented with Landsat

TM and CBERS-2 imagery and biophysical and phenological data, in order to map/quantify the

conversion of forest to cropland in southeastern Bolivia from 1986 to 2007. Analyses focus specifically

on the Tierras Bajas region, one of the most important agricultural region-deforestation hotspots in

South America and located in some of the most sensitive and poorly understood ecosystem ecosystems in

the world. Here, we seek to answer the following questions: what types of forest are being converted to a

particular cropping regime? What are the potential consequences of change for the regions ecology?

Phenology curves from NDVI and a crop calendar were used as inputs in a decision tree for classifyingcropland in 2007 according to a particular growing season. We classify forests in 1986 according to

several biophysical characteristics such as seasonal variations in precipitation, soil types, and elevation

data in order to overcome problems associated with previous approaches.

During the 21 year study period, cropland cultivated in summer and winter as well as summer only

caused the loss of nearly 8,500 km2 of forest in an area of only 21,800 km2. 52.8% (4,442 km2) were

classified as seasonal while another 25.5% (2,143 km2) were classified as dry/seasonal. Proportionally,

68% (1,760 km2) of all wet/seasonal forest present in 1986 was converted to cropland by 2007 compared

to only 48% seasonal (4,442 km2), 38% dry (66 km2) and 37% dry/seasonal forest (2,143 km2). The

significance of these findings is that farmers appear to be seeking out lands which receive the most

rainfall the fragile transition zone between the Chaco and Chiquitano ecosystems. By overlaying a

DEM, soils map, and mean annual precipitation data, it also evident that these parcels also contain thebest soils and the most level terrain in addition to the highest rainfall totals. Farmers are consciously

aware of the most suitable lands for agriculture and attempting to maximize production by producing two

crops per year soybeans in the summer and sunflower or sorghum in the winter. By assessing between

changes in various types of forest and crop classes, we provide planners and conservationists with more

than simply quality, accurate forest cover and change maps. This methodology provides decision-makers

with more detailed insight as to the proximate causes or driving forces of change.

Keywords: remote sensing, MODIS, AVHRR, land-use and land-cover change, Bolivia

Introduction

A primary goal of remote sensing and land change science is to map land-use and land-cover (LULC) anddetermine how much of it is in a state of expansion, decline, or resistant to change (Hansen et al. 2008a). To

achieve this objective, scientists have turned to Landsat MSS, TM, and ETM+, owing to their near-global

and longer temporal coverage of 30+ years. For this reason, they are the preferred choice the workhorses

for use in mapping the conversion of forest to agriculture. A major drawback, however, is that Landsat is

unable to capture seasonal variations due to infrequent temporal coverage and cloud contamination, two

issues common in the tropics (DeFries and Belward 2000). It is therefore ineffective for use in mapping

types of forest or agriculture classes in many tropical areas. The usual remedy is AVHRR or MODIS with

1Corresponding author: Ph.D. Candidate and Graduate LecturerMailing Address: Department of Geography, Texas A&M University, 810 O&M Building, College Station,TX 77843 USA

2 Corresponding author:Advisor, Director of Environmental Programs in Geosciences, and Professor of GeographyMailing Address: Department of Geography, Texas A&M University, 810 O&M Building,College Station, TX 77843 USA Texas A&M University, USA


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their greater image acquisition frequency and areal coverage, but the comparatively coarser spatial

resolution is also problematic as most change occurs at scales below 0.25 to 1-km resolution (Hansen et al.

2008b). In either case, the end result is that one or more types of LULC classes are often collapsed into a

generic forest and non-forest classification scheme giving rise to pluralistic interpretations and anecdotal

evidence about the changes that are occurring or possibly, concealing them altogether (Robbins 2001).

The degree to which Landsat, the newly launched CBERS-2, AVHRR and MODIS imagery cancomplement one another in overcoming these issues is underexplored. Our aim is to utilize imagery from

these four sensors as well as biophysical and phenological data (soil types, rainfall, elevation, and crop

calendars) in order to map/quantify the types of forest to cropland conversion that have occurred in lowland

Bolivia between 1986 and 2000. Analyses focus specifically on the Tierras Bajas region, one of the most

important agricultural region-deforestation hotspots in South America and located in some of the most

sensitive and poorly understood ecosystem ecosystems in the world (Lepers et al. 2005). Here, we seek to

answer the following questions: what types of forest are being converted to a particular cropping regime?

What are the potential consequences of change for the regions ecology?

Bolivias Tierras Bajas

The Corredor Biocenico is a continental transportation artery used to move agricultural products betweenChile and Brazil. In southeastern Bolivia, the corridors main highway and railroad bisect a 21,800 km2 area

of level terrain overlaid with rich alluvial soils known as the Tierras Bajas (Fig. 1). Mechanized,

commercial production is concentrated in the Tierras Bajas owing to the regions superior soils, higher

rainfall, level terrain, proximity to the city of Santa Cruz, as well as a highway and railroad to transport

produce to the Hidrova Paran-Paraguay and beyond. To meet demand, generally two crops are sown per

year soy, wheat, or maize in the rainy, summer months and soy, sunflower, rice, sugar cane or sorghum in

the drier, winter months (ANAPO 2007).

Most natural vegetation in the region has been converted to cropland, but along the southern and eastern

periphery of the region are the once widespread remnants of Chaco forest. Based on precipitation, soils, and

topography, vegetation is dry/seasonal, receiving less moisture and/or situated on poorer, sandier soils

compared to seasonal or transitional types, which are wet for approximately half the year and dry the other.

Towards the north-central part of the study area is the Chiquitano ecosystem, forming a transition zonebetween the humid forests of the Amazon Basin and the drier Chaco ecosystem (Kennard 2002).

Characterized by its deciduousness, for more than half the year Chiquitano forest is tropical wet (Pennington

et al. 2000). These forests are wet/seasonal or seasonal as they receive more moisture and/or also have

better soils and poorly-drained topography compared.

Previous approaches

Since the mid-1980s, AVHRR has been successfully used to map land cover at the regional and global

scales (e.g. Tucker et al. 1985; Millington and Townshend 1988; DeFries et al. 1998; Hansen et al. 2000;

Loveland et al. 2000). The overarching aim was to assess forest resources for use in climate change studies

or energy balance models resulting in land cover being categorized according to ecosystems (e.g. evergreen

forest) biomass, or simply aggregated into a forest class. Consequently, land use was ignored and

categorized as some variation of agriculture. Another factor was spatial as land use normally occurs below

AVHRRs finest resolution of 1km2. Since 2000, the classification of land-use types has improved with the

use of 250m2 and 500m2 MODIS data, but once again, spatial limitations have restricted use to regions of

large-scale commercial agriculture such as western Brazil (e.g. Wessels et al. 2004; Brown et al. 2007) and

the US Great Plains (e.g. Wardlow 2005, 2006, 2007). Using phenology curves derived from NDVI, these

studies illustrate the efficacy of MODIS for use in classifying detailed types of land-use.

LULC data sets available for South America and the Bolivian lowlands (Table 1) lack the level of detail

found in the U.S. and Brazil, due largely to the exclusive use of Landsat. Vegetation is classified as forest

and in the case of Davies (1993) according to forest succession (e.g. primary forest). The continental

assessments (e.g. Eva et al. 2004; Dinerstein et al. 1995) use an ecosystem scheme to classify natural

vegetation into either Chaco or Chiquitano forest. The problem with this approach is separability of classes

owing to seasonal and annual variations in precipitation, which together create shifting mosaics. It is in thistransition zone that the Tierras Bajas is situated. Land use, on the other hand, was given little attention and


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labeled as either non-forest or agriculture. The final factor to consider in these studies is modernity; most, if

not all, are dated. The two which focus exclusively on the Tierras Bajas (Davies 1993; Steininger et al.

2001a,b) proceed only to the year 1991 and 1998, respectively, at a time when the greatest rates of forest

loss in this region had yet to be attained (Redo et al. 2009). This paper addresses these knowledge gaps by

building and expanding on previous LULC mapping carried out in the U.S., Brazil, and Bolivia through a

protocol outlined below.

Data description and mapping protocol

Mapping Crop Types from 250-m MODIS NDVIMOD13Q1 NDVI (16-day L3 Global 250-m) coverage for two tiles (h11v10 and h12v10) for the entire

calendar year of 2007 (23 scenes per tile) was acquired from NASAs Land Processes Distributed Active

Archive Center (LP DAAC). Characteristics of MODIS and NDVI are well known (e.g. see Huete et al.

2002) and will not be repeated here.

Individual 16-day composites in adjacent tiles were mosaicked to provide coverage of the study area

(Fig. 2). We performed geometric correction of each mosaic using a 2006/07 CBERS-2 (20-m) classified

product (99% accuracy Redo et al. 2009) resulting in a range of RMS errors between 0.15 and 0.31 pereach 16-day composite. All analyses were restricted to a 50-km north-south buffer centered on the main

highway with endpoints in the west at the Andean front range/Rio Pira and the western extent of the

Brazilian Shield/Quimome River in the east (Fig. 1). Masks were created from the 2006/07 CBERS-2 (20-

m) imagery to remove areas not cultivated for agriculture (forest, water bodies, natural bare ground, and

urban regions).

We classified types of cropland using decision trees. Decision trees have been used to classify and detect

change in LULC data since the 1990s, and have proved accurate in a number of studies (e.g. Friedl and

Brodley 1997; Zhan et al. 2002; Wardlow 2006, 2007; Hansen et al. 2008a). For a complete description of

characteristics, see Hansen et al. (1996) and DeFries et al. (1998). Using this method, composites were first

classified into two discrete categories: green (cultivated) or fallow (bare) cropland. Decisions were based on

phenology curves, which were produced through random selection of twenty agricultural fields identified on

both MODIS and CBERS imagery. One 250-m pixel centrally located within a randomly selected

agricultural field was chosen as a representative. A single pixel was used instead of a pixel window orsquare (e.g., 2 x 2 pixels) in an attempt to reduce, and in some larger fields, eliminate the inclusion of mixed

edge pixels (Wardlow 2005, 2006). Analysis of the resulting phenology curves revealed a threshold NDVI

value of 5,500 (original MOD13Q1 NDVI imagery is scaled from 1 to 10,000), which was used to

determine field status for a particular composite.

Further analysis of phenology curves coupled with a local crop calendar also revealed the presence of two

distinct growing seasons in the Tierras Bajas: summer, wet (December 19-March 5) and winter, dry (May

25-July 27). Only 16-day composites which fell within the peak growing season were selected for final

analysis (Fig. 2). For example, the peak winter cropland map included only the following: May 25-June 9;

June 10-25; June 26-July 11; July 12-27. After selecting composites for growing seasons, a singular

classified map of each growing season was created using maximum value compositing (MVC). The MVC

technique selects, pixel-by-pixel, the highest NDVI value for multiple raster images to create a single

composited image (Holben 1986). A pixel was classified as green or fallow in the new winter and summerrecomposites based on the majority rule in each of the final growing season maps. For example, if a pixel

was initially classified as fallow from May 25-June 9, but green in the periods June 10-25, June 26-July 11,

and July 12-27, it was labeled as cultivated during the peak winter growing season. Finally, the winter and

summer maps were composited and majority rule employed once more to create a singular crop map with

four cropland classes: summer and winter cultivation, summer only, winter only, and fallow.

Accuracy assessment was initially scheduled for October, 2008, but the recent outbreaks of violence in

Pando, and continued protests and threats in Santa Cruz over proposed land expropriation have prevented

field work. A future visit is tentatively scheduled for May, 2009 and will consist of interviewing 5 types of

land managers with varying degrees of ownership and modernization. They will be prompted with the maps

generated in this study and asked to recall aspects of forest clearance and types of crops grown.


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Mapping Forest Types from 1-km AVHRR NDVIAVH13C1 NDVI (daily, 1-km NDVI) scenes for the entire calendar year of 1986 were acquired from

NASAs Long-Term Data Record (LTDR). Daily scenes were aggregated into monthly composites using

the MVC technique (see above section for description of technique) in order to reduce data volume,

eliminate cloud contamination, and because monthly intervals can still capture seasonal variations in forests

(Fig. 3). We performed geometric correction of each monthly composite using a 1986/89 Landsat TM (30-m) classified product (Redo et al. 2009) resulting in a range of RMS errors between 0.17 and 0.31 per each

monthly composite. To compare changes between forest and crop types, the spatial extent of the study area

was kept consistent (see above section). Masks were created from the 1986/89 Landsat TM (30-m) imagery

in order to remove non-forest areas (cropland, pasture, bare ground, and water bodies).

Field research (ground and overhead) coupled with biophysical data (a DEM, soils map, mean annual

precipitation data, drainage network coverage, and Landsat TM imagery were used to identify an NDVI

threshold separating dry or moist forest. After several iterations, a decision tree was used to classify all

monthly composites according to this scheme. These were then recomposited into a single yearly

classification using the majority rule and a second decision tree classifier resulting in five forest classes: dry

forest, dry/seasonal forest, seasonal forest, wet/seasonal forest, and inundated forest. Accuracy of classes

was assessed using a variety of techniques. Field visits during the dry seasons of 2006, 2007, and 2008

helped to identify dry, seasonal, and inundated forest classes on-site. We supplemented field notes with

stream network, elevation, and soil data too indentify areas on-screen. Areas of interest which could be

positively identified were denoted on the forest final map and an accuracy assessment was conducted. High

overall accuracy was achieved at 96.43%.

Results

In 2007, 67.5% (7,693 km2) of all cropland in the Tierras Bajas was in both summer and winter production

(Table 2). Distribution was ubiquitous, lacking any discernable pattern as summer and winter cropland

appeared in the older, wetter areas of production north of Santa Cruz as well as in newer, more marginal

agricultural zones such as those south of El Tinto (Fig. 4). 28.5% (3,248 km2) of all cropland was cultivated

only during the summer growing season and associated with the Mennonite communities of Basilio, Tres

Cruces, and Pozo del Tigre. Both areas receive relatively less precipitation compared to the northwest andare located on soils less suitable for year-round cultivation (i.e. vertisols, dark clayey soils that sink during

the dry season). Another possibility is the practice of land lying fallow during the dry season or every four

years in marginal areas per the advice of Mennonite extension agencies based in Santa Cruz. This may also

explain the 4% (455 km2) of cropland which remained under fallow throughout both growing seasons

between Tres Cruces and Pozo del Tigre.

In 1986, the Tierras Bajas was a largely forested landscape with nearly 18,000 km2 remaining east of the

Rio Grande. 52.3% (9,317 km2) was seasonal in nature and well-distributed throughout the central and

southern portions of the study region (Table 2; Fig. 5). Homogenous areas were located in association with

the large, poorly-drained floodplains of the Rio Quimome and Rio Parapeti floodplains as well as near

smaller drainage systems northeast of Pozo del Tigre. Nearly one-third (5,279 km2) was dry/seasonal forest

with much of it confined to one large patch centered on El Tinto. This pattern is the result of comparatively

less precipitation and the presence of well-drained, rocky soils and outcrops of the western-most extent ofthe Brazilian Shield. 14.6% (2,597 km2) was wet/seasonal forest and concentrated in a single large patch in

the north where, in contrast to the extreme east, annual rainfall is hundreds of millimeters higher than areas

further south or east. Finally, 1% (174 km2) of all forest types was classified as dry. Counter intuitively, it

was found in the usually permanent wetlands northeast of Lago Concepcon and likely should have been

classified as inundated forest. One possible explanation for this classification error is that more water was

present than vegetation in 1986 and the absorption of near-infrared energy resulted in consistently low

monthly NDVI values.

Crop and forest classes were intersected to map and quantify the types of forest to cropland conversion

that occurred from 1986 to 2007 (Table 3; Fig. 6). Results show that cropland which was cultivated in both

summer and winter was responsible for 69% (5,820 km2) of all deforestation that took place during the study

period. 35.5% (2,987 km2) was classified as seasonal forest, 18.8% (1,583 km2) dry/seasonal and 14.3%

(1,198 km2) wet/seasonal. The loss of seasonal forest was well distributed throughout the study region, but

dry/seasonal deforestation mainly occurred in close proximity to the highway and marginal areas such as El

Tinto and Tres Cruces. The loss of wet/seasonal forest was confined to its two main areas of previous


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existence: around the north-central and southwest portions of the study region. Cropland cultivated only

during the summer was responsible for the loss of 15% (1,253 km2) seasonal forest as well as 5.7% (476

km2) of forest which was classified as wet/seasonal. Concentrated losses of seasonal forest occurred mainly

around Tres Cruces while loss of wet/seasonal forest to summer cropland was in the same areas as that

which was lost to cropland cultivated in both summer and winter. Less than 1% (59 km2) of dry/seasonal

was converted to this cropping regime and was well-distributed. Fallow cropland was responsible for only4% of all forest loss during the study period, but over approximately two-thirds was forest classified as

seasonal and confined mainly to area north of the highway between Pozo del Tigre and Tres Cruces.

Discussion

During the 21 year study period, establishment of cropland (cultivated in both growing seasons and summer

only) east of the Rio Grande caused the loss of nearly 8,500 km2 of forest in the Tierras Bajas an area of

only 21,800 km2. A reasonable means of contextualization is to compare loss to the size of US states. In

this case, two Rhode Islands worth of forest was lost in the short time of two decades in an area as small as

New Jersey. 52.8% (4,442 km2) were classified as seasonal while another 25.5% (2,143 km2) were

classified as dry/seasonal, but this was mainly due to the fact that these two types covered a combined84.5% of the Tierras Bajas in 1986. In short, absolute values of deforestation can obscure other, more

meaningful indicators of the underlying drivers of change.

One such indicator is the proportion of each forest type cleared. 68% (1,760 km2) of all wet/seasonal

forest present in 1986 was converted to cropland by 2007 compared to only 48% seasonal (4,442 km 2), 38%

dry (66 km2) and 37% dry/seasonal forest (2,143 km2). The significance of these findings is that farmers

appear to be seeking out lands which receive the most rainfall the fragile transition zone between the

Chaco and Chiquitano ecosystems. By overlaying a DEM, soils map, and mean annual precipitation data, it

also evident that these parcels also contain the best soils and the most level terrain in addition to the highest

rainfall totals. Clearly, farmers are consciously aware of the most suitable lands for agriculture and

attempting to maximize production by producing two crops per year soybeans in the summer and

sunflower or sorghum in the winter.

Development, however, has come at high cost. The Chaco and Chiquitania ecosystems, both listed among

the worlds 200 most sensitive (Wassenaar et al. 2007), are currently separated by widening gaps of forestclearance for agriculture. This transition zone, once composed of wet/seasonal forests, was intact as of 1986

but had shrunk to half its size in 2007 as it was the type most preferred by farmers. Species movement and

interaction between ecoregions is greatly hampered causing its functioning as an ecosystem to cease at the

local level.

To the south, an equally alarming trend is occurring. Results from this study also show that in 2007, the

frontier of cultivation in the eastern Tierras Bajas was threatening the northern boundary of South

Americas largest protected area (3.44 million ha), Kaa-Iya National Park and Integrated Management Areas

(IMA), which together cosset the largest area of tropical dry forest under full-protected area status anywhere

in the world (Winer 2003). The park is also a model that puts community-based conservation into practice

through being co-managed by the Bolivian government, the indigenous Izoceo-Guarani and conservation

organizations. As pressure to continue producing lucrative soybeans and sunflower increases, those

displaced from development, might well push settlement into the thinly settled northern IMA or even theparks core.

The current status of southeastern Bolivias remaining forests is also bleak. The Bolivian government is

nearly finished upgrading the highway through paving and the construction of bridge and drainage

infrastructure. When complete in 2010, the new highway will be the only paved, all-weather thoroughfare

in central South America. It will connect the continents agricultural heartlands of Santa Cruz and

neighboring Mato Grosso do Sul, Brazil more directly to international markets through Brazilian and

Chilean ports. Imagery analysis (2005-2008) for emerging agricultural colonies to the east shows that

deforestation rates are accelerating (Redo et al. 2009). If deforestation reaches scales seen in the Tierras

Bajas, it could be ecologically devastating for the regions indigenous inhabitants and the forests they

depend on, as well as several noteworthy national parks protecting sensitive ecosystems such as the Pantanal

wetlands. Analysis of change between 1987 and 2007 for the central and eastern portions of the Corridor

Biocenico are currently underway. However, more time points need to be incorporated in order to further

support this evidence. Future work is aimed at acquired AVHRR NDVI for 1993 and MODIS NDVI for

2000 and 2008 so that trajectories of change can be produced.


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Conclusion

This study shows that it is possible to incorporate several data sources and supplement one sensors

weakness with anothers strength for use in mapping and quantifying changes in detailed land-use and land-cover types. Results of this nature provide better input to spatial models which can tackle issues such as

trajectory, consequences and future of LULC change. By going beyond classic classification schemes such

as forest vs. non-forest or ecosystem approaches and assessing between changes in various types of forest

and crop classes, we provide planners and conservationists with more than simply quality, accurate forest

cover and change maps. A hybrid methodology provides decision-makers with more detailed insight as to

the proximate causes or driving forces of change. This information is imperative for raising both

government and public awareness so that more informed policy responses can be made about landscape

management and conservation (e.g. planning of future protected areas or effectiveness of existing units). In

addition, scientists studying human-environment relationships can better understand the dynamic impact

humans have on the environment. Finally, by focusing on one of the most dynamic regions in the

Neotropics, we have advanced the basic scientific knowledge of LULC change in southern hemisphere

semi-arid wooded ecosystems and provided a better understanding of the nature of human-environmentrelationships in one of the most dynamic, contemporary frontier regions in South America.

AcknowledgmentsThe research described in this paper was funded by the National Science Foundation, SBE DDRI Geography

and Regional Science/Office of International Science and Engineering (Grant: BCS-0802672) and Texas

A&M University. Special thanks to our driver and friend, Don Lucho Ramirez, who expertly navigated

long hours on unpaved, dusty, and pot-hole ridden roads, as well as provided unwavering companionship

during our visits over the last three years.

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Figures

Figure 1

Map of the Tierras Bajas and the main elements of the Corredor Biocenico.

A 50-km buffer north and south of the highway has been used to demarcate the horizontal boundaries of the study area.The western boundary is defined by the Andean foothills and the Pira River to the west while the eastern boundary isdefined by the Quimome River and western ranges of the Brazilian Shield.


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Figure 2

Methodology for transforming 2007 MODIS NDVI data into cropland types.


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Figure 3

Methodology for transforming 1986 AVHRR NDVI data into forest types.


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Figure 4

2007 crop types (classified from MODIS) overlaid onto a 2006-07 CBERS 2 classification

of forest, infrastructure, water bodies and bare ground for the Tierras Bajas study area.

Figure 5

1986 forest classes (classified from AVHRR) overlaid onto a 1986-89 Landsat TM classificationof cultivation, urban, bare ground, and water bodies for the Tierras Bajas study area.


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Figure 6

Evolution of forest classes (classified from AVHRR)

to crop types (classified from MODIS) from 1986 to 2007.


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Tables

TABLE 1

Comparison of past land-use and land-cover change studies which have included some portion ofthe Tierras Bajas.Forest Loss

AuthorTimePeriod

Total Area(km

2)

Sensors Classes AnnualRate

Total(km

2)

Davies 1993 1975-1991

15,659 Landsat MSS/TM PF, SF, F, A,P

1.2-18.2%

2,605

Tucker & Townshend2000

1992-1994

784,759 Landsat TM F, NF NA 28,208

Steininger et al. 2001a 1975-1998

700,000 Landsat MSS/TM F, NF ~16.7% 24,703

Steininger et al. 2001b 1976-1998

19,533 Landsat MSS/TM F, NF, WA,C

~6.0% 9,400

Mertens et al. 2004 1989-1994

364,000 Landsat TM F, NF 0.5-4.1% 5,117

Killeen et al. 2008 1975-

2004

720,915 Landsat

MSS/TM/ETM+

F, S, G, WE 0.8-6.3% *45,411

*Including scrub and savanna (9,042 km2), total vegetation loss would increase to 54,453 km2

Classes:

PF = Primary Forest NF = Non-Forest S = Scrubland WA = WaterSF = Secondary Forest A = Agriculture G = Grassland C = Cloud

F = Forest P = Pasture WE = Wetland NA = Not available

Table 2.2007 cropland types (classified from MODIS) and 1986 forest classes (classified from AVHRR)

2007 Cropland Types Percentage Area (km2) 1986 ForestClasses Percentage Area (km2)

Summer & Wintercropland

67.5 7,693.4 Dry forest1.0 174.7

Summer only cropland 28.5 3,248.7 Dry/Seasonalforest 32.2 5,729.8

Winter only cropland 0.0 0.0 Seasonal forest 52.3 9,317.5Fallow 4.0 455.5 Wet/Seasonal 14.6 2,597.0

-- -- -- Inundated forest 0.0 0.0TOTAL 100.0 11,397.5 TOTAL 100.0 17,818.9

TABLE 3Evolution of forest classes to crop types (% and km

2) from 1987 to 2007

Summer &WinterCropland

Summer OnlyCropland Winter OnlyCropland FallowCropland TOTAL

% km2 % km2 % km2 % km2 % km2

Dry Forest 0.6 50.0 0.2 15.2 0.0 0.0 0.0 0.9 0.8 66.1Dry/SeasonalForest

18.8 1,583.8 0.7 58.6 0.0 0.0 0.7 58.6 25.5 2,143.2

Seasonal Forest 35.5 2,987.6 14.9 1,253.6 0.0 0.0 2.4 201.6 52.8 4,442.8Wet/SeasonalForest

14.3 1,198.8 5.7 475.6 0.0 0.0 1.0 86.2 20.9 1,760.6

Inundated Forest 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

TOTAL 69.2 5,820.2 21.5 1,803 0.0 0.0 4.1 347.3 100.0 8,412.7

beyond forest and non forest fd

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