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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
amount of information from the MODIS image time series 
(Freitas et al., 2011). 
Historical monthly precipitation data from the Tropical Rainfall 
Measuring Mission (TRMM) and information from the Google 
Maps API digital elevation model over two concentric circles 
with adjustable radii around the pixel are also available for each 
MODIS pixel of the time series (Freitas et al., 2011). To locate 
the selected polygon of interest in the Google Maps Virtual 
Globe it is sufficient to indicate the coordinates (latitude and 
longitude). 
3.1 Classification of the EVI2 temporal profile 
The LUC classification using the EVI2 profiles is conducted 
based on the previous knowledge of the temporal patterns of the 
main targets within the deforested polygons. Figure 2 presents 
some typical EVI2 profiles for the region, which allows 
identifying some of the LUC patterns and transitions. 
  
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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 
  
Figure 2. EVI2 temporal profile for two deforested polygons: 
(a) Forest from 2000 to 2006, transition from forest to pasture 
in 2007 and 2008, and pasture after 2009; and (b) Forest from 
2000 to 2007, transition from forest to bare soil in 2008 and 
2009 followed by agriculture use in 2010 and soy in 2011. 
Abbreviations: for. (forest); degrad. (degradation); clear. (total 
clearing of the area); past. (pasture); agric. (agriculture). 
Previous works such as Galford et al. (2008), Freitas et al. 
(2011), and Adami et al. (2012) presented some examples of 
typical temporal behavior of MODIS vegetation indices for 
pasture and agriculture land. A common characteristic of these 
targets is the pronounced seasonality. The amplitude is more 
evident for annual crops, while the duration of the cycle is 
longer for pasture. These characteristics allow the 
differentiation of forest, which has low seasonal variation 
throughout the year (Figure 2). 
In the off-season the dominant spectral response for agriculture 
is bare soil/dry straw with low EVI2 values. During the crop 
growth period a rapid increase of green vegetation causes the 
EVD to peak reaching values as high as 0.9 at maximum 
canopy development, especially on soy crops . Furthermore, the 
agricultural areas tend to have a rapid increase in EVI2 values 
during the crop development period, followed by a strong drop, 
creating a more narrow profile, aiding in its identification. In 
this work, areas with high seasonal variation, narrow profiles 
and EVI2 peaks lower than 0.7 were classified as agriculture. 
This class tends to include rice (predominantly), corn (more 
common in Parä), and in some instances less developed soy 
fields. Profiles with EVI2 20.7 will be classified as soy. The 
threshold of 0.7 was established after a detailed evaluation of 
the 194 polygons with soy in crop year 2010/11. Several of the 
aerial surveyed polygons presented corn and rice fields, which 
were used to define the threshold. 
For pasture land, there is less increase in EVI2 values because 
the canopy tends to be less homogeneous and the soil cover, in 
the majority of cases, is not complete. This is further enhanced 
by the low investments in pasture renewal, typical for extensive 
cattle production zones. The presence of cattle herd, which 
consumes green biomass before the maximum pasture 
vegetative peak, also contributes to the heterogeneity of the 
spectral response. In addition, the predominance of grass with 
erect leaf geometry lowers the EVI2 values. Therefore, 
agriculture and pasture land are easily identified in the temporal 
EVI2 profiles, as can be seen in Figure 2. 
In some isolated cases of rice cultivation amid piles of trunks 
and above ground roots, during one to three crop years after the 
clearing of a former forested land, the temporal profile of the 
EVI2 can be similar to pasture. 
The degraded forest pattern is observed under selective logging 
and/or fire occurrence. This pattern is observed during the 
deforestation process before the area has been totally cleared. 
When the clearing of the area is not completed an intensive 
vegetation regrowth can be observed in the rainy season (Lima 
et al, 2012). In this work, these areas will be classified as 
regrowth. Although they are less common, some areas classified 
by PRODES as deforested may present regrowth after the total 
clearing of the area. 
Eventually, the LUC trajectories will be traced and the most 
frequent patterns of transition after the deforestation process 
will be indicated for the polygons with and without soy 
plantations. For the soy polygons the average time between 
deforestation and soy plantation will also be evaluated to 
characterizing the most common trajectories of the soy 
polygons that are not in agreement with the Soy Moratorium. 
4. RESULTS AND DISCUSSION 
Areas without soy in crop year 2010/11 
The 50 selected polygons without soy in crop year 2010/11 
were classified according to the year of deforestation detected 
by PRODES. Twelve polygons (24%) were from deforestation 
detected in 2007; 32 polygons (64%) from 2008; 4 polygons 
(8%) from 2009; and 2 polygons (4%) from 2010. Most of the 
polygons (68%) were from Mato Grosso, followed by Pará 
(26%) and Rondönia (6%). Forty polygons (80%) presented 
significant degradation prior to the deforestation process. This 
finding agrees with Uhl et al. (1991), Nepstad et al. (1999) and 
Sorrensen (2004), which report that the forest degradation in the 
Amazon region is a common practice prior to the deforestation 
process, either by selective logging or fire. 
Figure 3 shows that 38 polygons (76%) presented indications of 
regrowth in 2011. For 19 of these polygons the information 
from the temporal profiles was not enough to distinguish 
regrowth from pasture. Despite the evidence of regrowth, the 
identification of small seasonal cycles could be associated with 
fire occurrence in the dry season, a common practice in the 
region for pasture renewal (Lima et al., 2012). Nevertheless, if