1. Mapping burned scars in Amazon region using MODIS data Big Bear Lake, California, USA, 2011. André Lima Yosio Edemir Shimabukuro Luiz Eduardo Aragão SCGIS 2011

2. Context It is estimated that 75% of Brazil's emissions of CO 2 from forest fires (MCT, 2004)

3. Context It is estimated that 75% of Brazil's emissions of CO2 comes from forest fires (MCT, 2004); According to Bowman et al. (2009) until 50% of GHG emissions in the world comes from forest fires.

5. Context It is estimated that 75% of Brazil's emissions of CO2 from forest fires (MCT, 2004); According to Bowman et al. (2009) until 50% of GHG emissions comes from burning the globe; There are not systematic regional mapping of burnt scars in the tropics (Giglio et. Al., 2010, Setzer et al., 2011).

6. Justification Need for data on fires in tropical forests to generate estimates of GHG emissions (IPCC, 2008).

7. It is possible to map fire scars using MODIS data (250m) at a level of detail appropriate to the assessments of GHG emissions caused by burning. Hipotese

8. Objective To develop methodology for mapping of burned scars using MODIS data – Surface Reflectance Daily (MOD09);

9. Study Area Amazonia Legal area 5.217.423 km², equivalent 61% Brazilian territory

10. Study Area Location General chateristics Amazon Forest, the largest tropical biome of the world equivalent to 30% of remaining tropical forests; High biodiversity; Agriculture frontier, Deforestation, region called “Deforestation Arc”.

11. Material and Methods  Images Selection per Brazilian Federation Unit Based on hot spot active fire frequency distribution, PROARCO Data (http://sigma.cptec.inpe.br/queimadas/)http://sigma.cptec.inpe.br/queimadas/ Acre Amapa Maranhao Mato Grosso Para RoraimaRondonia Amazonas

12. Material and Methods  Images Selection in Rondonia State Based on hot spot active fire frequency distribution, PROARCO Data (http://sigma.cptec.inpe.br/queimadas/)http://sigma.cptec.inpe.br/queimadas/ January February March April May June July August September October November December Images correspond to months with higher occurrence of fire hotspots

13. Material and Methods  Used images Surface Reflectance daily 250m (Mod09 product); Spectral Bands: 1 (Red), 2 (Near-infrared), 6* (Middle-infrared);  Total images used =105. * Spatial resolution 500 m.  Images selected Table 01. Images used to map burnt scars occurred in 2005 02 03 02 05 03

14.  Spectral Linear Mixing Model (SLMM) –Decomposition (n) spectral bands in three fraction images. Material and Methods Vegetation Fraction Soil Fraction Shade Fraction Shade Fraction – Targets with low reflectance are realced. Water body Burnt Scar

15. Materiais e Métodos  Segmentation –Region algorithm –Threshold Area = 4 (pixels) Similarity = 8 (digital number value variation) Shade Fraction imageSegmentation

16. Classification –Classification non-supervised Algorithm ISOSEG –Threshold 75% (probability) Material Methods Burnt scars boundaryBurnt scar mapped

17. Results Burnt scars in 2005

18. Results Table 02. Burnt scars total area mapped in 2005 per State. *Table 02. Estimates affected by the large cloud cover.

19. Final Considerations  Useful methodology;  Support for a future detection burnt scars program in the Amazon (DETEQ);  Important source of data for emission models of Greenhouse Gases;  Validation in process.

20. Obrigado. André Lima