Supplementary online material:
Methods
1. Maps
1.1. Biome map
In order to fit the biome map at the scale of 1:5,000,000 (IBGE 2004) to the scale of our
analysis (scale 1:1,000,000), we used thematic maps obtained from the 1:250,000-scale GISdataset built within the ‘Socioeconomic-Ecological Zoning’ project for Mato Grosso State
(SEPLAN 2001). Topological inconsistencies between biomes and vegetation units across the
borders of neighboring map sheets were corrected on the basis of the original vegetation
map (RADAMBRASIL 1982) and on ancillary vegetation data (IBGE 1992). Additionally, the
limits of the Amazonia, Cerrado and Pantanal biomes were adjusted on the basis of climate,
relief, lithology, and drainage attributes. The Amazonia biome in Mato Grosso can be
conceived as two well discernible regions separated by a large strip of the Cerrado which
crosses it in east-western direction (Fig 1): The northern Amazon (NA) and southern Amazon
(SA) sub-sections. While in the smaller SA sub-section (6.2 million ha) the remnants of the
native vegetation were fewer and more isolated in 2007, in the larger NA sub-section (48
million ha), they were larger and more interconnected, including large protected areas,
specially Conservation Units and Indian Lands (Figures S1 and S2). These differences can be
related to the duration of land occupation process, which started earlier in the southern
sub-section than in the northern. In fact, the agricultural frontier was already established in
1992 in southern Amazonia (Browder et al. 2008), while in the northern Amazonia large
portions of the biome remained unoccupied at that time. For these reasons we preferred to
analyze the Amazonia biome in those two separated sub-sections.
1.2. Natural Vegetation Cover (NVC) Map
For the NVC of Mato Grosso State we used eight contiguous 1:1,000,000-scale map sheets
from 1975 to 1983 of the RADAMBRASIL project which were vectorized in shape files by the
Mato Grosso State Secretary for Environment (SEMA-MT). Since most of the territory was
not occupied at that time, the original configuration of the native vegetation could be
represented on these maps by substituting the few deforested patches (<4% of the
territory) by the prevailing native class found in its neighborhood. The official classification
of the Brazilian Vegetation (IBGE 1992) follows a hierarchical model at eight levels, from
continental scale (1:10,000,000) to very detailed scale (> 1:25,000). At this level there are a
maximum of 82 vegetation classes in Mato Grosso. We considered the fourth level of the
vegetation class (1:100,000) as the most adjustable to the scale of our analysis (biome). At
this level, the classification attributes are related to the vegetation phenology and
influences of edaphic conditions, resulting in 10 types of NVC (Table S1).
1.3. Protected area map
According to the Brazilian legislation and literature, the term Conservation Units (CU) (in
Portuguese: “unidades de conservação”) includes only national, state and municipal nature
reserves but not Indian Lands (ILs), Legal Reserves (LRs) or Permanent Protected Areas
(PPAs). In order to streamline the nomenclature of our study to both Brazilian and
international publications, we use the term “protected area” (PA) to refer to the dataset
formed by ILs, CUs and LRs. Permanent Protected Areas (PPAs) were excluded from our
study because most of them are 30m wide riparian strips and/or part of rocky outcrops
situated on the top of steep hills which in both cases are smaller than the 200 x 200m pixel
size of the categorical maps analyzed in this study. The IL and CU maps were provided by
SEPLAN (2001) while the maps for the LRs was supplied by SEMA, through the Rural
Properties Environmental Management System (Fearnside 2003). Our dataset covers the
period from 1999 to 2007. The registration process of the LRs is not yet completed, and it is
biased towards larger farms, whose owners can afford the high cost of the registration
process.
1.4. Maps of native vegetation clearings
We used the series of native vegetation clearings maps for the years 1992, 1995, 1997,
1999, 2001, 2002, 2003, 2004, 2005, 2006 and 2007, provided by SEMA. The cleared patches
were visually identified and delineated on previously corrected (radiometric, geometric and
atmospheric) digital Landsat-5 and Landsat-7 satellite imagery for each of mentioned years.
These patches were enhanced on computer screen by using false color 3(R)4(G)5(B)
composite bands and vectorized using Geomedia 5.2 software (Intergraph 2000), which also
automatically provided the vectors with topological consistency. Only deforested patches
were delineated on each annual image dataset, i.e. NVC regeneration (secondary), or any
other land use categories were not considered. As a result, incremental deforestation maps
were produced. Finally, we converted the deforestation maps to raster format using pixel
size of 200 x 200m.
2. Accuracy assessment
The accuracy of the clearings maps was assessed by comparing the accumulated map
containing all cleared and non-cleared areas at the end of the studied period (year 2007) to
a mosaic of SPOT 5 images with a 2.5 m spatial resolution, provided by SEPLAN. This mosaic
(SPOTMap) was created from SPOT-5 color satellite images from 2007 and 2008 by using
individual scenes, which were put together to produce an ortho-rectified seamless mosaic
that covered the entire Mato Grosso State. The original 10 x 10 m pixels of the spectral
bands (green, red and near infrared) were resampled to 2.5 x 2.5 m pixel size by fusing
spectral bands with the image acquired simultaneously by the two panchromatic sensors on
the satellite board (see Carroll & Maughan 2010 for methodological details). By using the
utility ‘add random points’ of ERDAS Imagine 8.4TM (ERDAS 1999), we generated 75 random
points over the accumulated deforested map for each cleared and non-cleared class
(stratified sampling) within each biome as surrogate for ground truth data (Congalton 1991;
Congalton 2001; Landis and Koch 1977). These points were then plotted on the SPOTMap,
which has a spatial resolution 80 times higher than the pixel size of our study, in order to
check the accuracy of the deforestation map. A confusion matrix was built to calculate the
overall map accuracy, the omission and commission errors for each class (cleared, noncleared), the kappa coefficient (Congalton 1991; Landis and Koch 1977; Story and Congalton
1986), and the standard error for each accuracy statistic (Carle 2011) (Table S2). Estimates
showed an overall accuracy ranging from 83.3% in the northern Amazonia to 93.3% in the
Pantanal biome. Kappa coefficients varied from 0.67 (substantial agreement) to 0.87 (almost
perfect agreement) for both biomes, respectively.