The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing 2008 
Covarage Type 
1 
2 
3 
4 
Primary Forest 
-7,254 
-7.7 ldB 
-9.71 
-8.3 to 7.1 
Recent 
deforestation 
Oid 
-4,992 
— 
-5.75 
— 
deforestation 
(may be crop or 
pasture) 
11,020 
-1.11 dB 
- 14.45 
SO 
o 
o 
Table 1 - Comparison between the sigma values obtained from different 
studies: (1) present study, (2) Sgrenzaroli & al., 2004, (3) Saatchi & al., 
1997 and (4) Luckman & al., 1998. 
Almeida-Filho & al. (2005) notice the importance of high 
quality georegistration on the several databases in order to 
implement an operational monitoring system. In this study the 
georegistration was a very limiting factor and was solved by 
using the recent implementation of the geocoded methodology. 
5. CONCLUSIONS 
The executed methodology, using a threshold to classify new 
deforested areas, has a good potential to be the base of a 
semiautomatic detection system for operational purposes, using 
ScanSAR images. This system has potential to produce data 
that could complement the information already available from 
optical sensor satellites (CBERS-CCD, Landsat-TM and Terra- 
MODIS images). The resulted monitoring system, combining 
optical and SAR data, would decrease the average age of the 
deforested areas. As a result, the response time related to law 
enforcement activities to combat illegal logging would decrease. 
Two points need to be stressed here. One is the new detections 
of ALOS images which were not detected by any other optical 
systems. These detections are probably related to very recent 
deforestations that may have occurred some days before ALOS 
image acquisition. The second point is the number of ALOS 
detection coincident with PRODES 2007, these detections can 
be used to the enforcement law agents, because these polygons 
where not detected by DETER until the end of the year when 
the mask were changed to the PRODES 2007 database. 
REFERENCES 
Almeida-Filho, R.; Rosenqvist, A.; Shimabukuro Y.E.; Santos J. 
R.; 2005. Evaluation and Perspectives of Using Multi temporal 
L-Band SAR Data to Monitor Deforestation in the Brazilian 
Amazonia. IEEE Geoscience and Remote Sensing Letters, 
2(4):409-412. 
Neeffa,T.; Dutra, L.V.; Santos J.R.; Freitas, C.C.; Araujo, L.S.; 
2003. Tropical forest stand table modelling from SAR data. 
Forest Ecology and Management, 186:159-170. 
Saatchi, S.S.; Soares, J.V.; Alves, D.S., 1997. Mapping Amazon 
Deforestation and Land Use in Amazon Rainforest by Using 
SIR-C Imagery. Remote Sensing of Environment. 59:191-202. 
Sgrenzaroli, M.; Baraldi, A.; De Grandi, G.D.; Eva, H.; Achard 
F.; 2004. A Novel Approach to the Classification of Regional- 
Scale Radar Mosaics for Tropical Vegetation Mapping. IEEE 
Transections on Geoscience and Remote Sensing, 42(11):2654- 
2669 
Siqueira, P.; Chapman, B:.; McGarragh, G.; 2003. The 
coregistration, calibration, and interpretation of multiseason 
JERS-1 SAR data over South America. Remote Sensing of 
Environment 87:389-403. 
ACKNOWLEDGEMENTS 
We would like to acknowledge the opportunity given by 
Japanese Aerospace Exploration Agency (JAXA) to be part of 
the ALOS Kyoto and Carbon Initiative science team, as well as 
the ScanSAR data provided. Thanks to the Brazilian National 
Institute for Space Research (INPE) to be a partner and to 
provide CBERS-2 (Chinese Brazilian Earth Resources Satellite) 
and Landsat-TM images, DETER and PRODES data; and to 
National Aeronautics and Space Agency (NASA) for making 
Terra-MODIS images available through the Earth Observation 
Distribution System (EODIS). 
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