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

Mapping burned scars in Amazon region using MODIS data

Big Bear Lake, California, USA, 2011.

André LimaYosio Edemir ShimabukuroLuiz Eduardo Aragão

SCGIS 2011

Context

It is estimated that 75% of Brazil's emissions of CO2 from forest fires (MCT, 2004)

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.

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).

Justification

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

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

Objective

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

Study Area

Amazonia Legal area 5.217.423 km², equivalent 61% Brazilian territory

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”.

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/) Acre

Amapa Maranhao

Mato Grosso Para

RoraimaRondonia

Amazonas

Material and Methods

Images Selection in Rondonia State Based on hot spot active fire frequency distribution,

PROARCO Data (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

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

0202

02

02

03020503

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

Materiais e Métodos

Segmentation– Region algorithm– Threshold

Area = 4 (pixels)Similarity = 8 (digital number value variation)

Shade Fraction image Segmentation

• Classification– Classification non-supervised

• Algorithm ISOSEG– Threshold 75% (probability)

Material Methods

Burnt scars boundary Burnt scar mapped

Results

Burnt scars in 2005

Results

Table 02. Burnt scars total area mapped in 2005 per State.

*Table 02. Estimates affected by the large cloud cover.

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.

Obrigado.André Lima