Near real time detection of deforestation in the Brazilian Amazon using MODIS imagery

  • Yosio Shimabukuro Instituto Nacional de Pesquisas Espaciais (INPE), Brasil
  • Valdete Duarte INPE
  • Liana Anderson
  • Dalton Valeriano INPE
  • Egídio Arai INPE
  • Ramon Freitas INPE
  • Bernardo Friedrich Rudorff INPE
  • Maurício Moreira INPE
Keywords: Deforestation, Brazilian Amazon, change detection, MODIS, spectral mixing model, fraction images


The objective of this paper is to provide near real time information about deforestation detection (DETER) in the entire Brazilian Amazon using MODIS high temporal resolution images. It is part of the operational deforestation monitoring project to estimate the annual deforestation rate in the Brazilian Amazon (PRODES). A rapid deforestation detection method was designed to support land use policies in this region. In order to evaluate the proposed method a test site was selected covering a Landsat ETM+ scene (227/68) located in Mato Grosso State. For this purpose a multitemporal series of MODIS surface reflectance images (MOD09) and the corresponding ETM+ images from June to October 2002 were analyzed. It was found that small deforested areas (lower than 15 ha) were detected by MODIS images with lower accuracy when compared with ETM+ images. As the deforested areas increase MODIS and ETM+ results tend to converge. This procedure showed to be adequate to operationally detect and monitor deforested areas and has been used since 2004 as part of a government plan to control the Amazon deforestation.

Author Biography

Yosio Shimabukuro, Instituto Nacional de Pesquisas Espaciais (INPE), Brasil
Possui graduação em Engenharia Florestal pela Universidade Federal Rural do Rio de Janeiro (1972), mestrado em Sensoriamento Remoto pelo Instituto Nacional de Pesquisas Espaciais (1977), doutorado em Ciências Florestais / Sensoriamento Remoto pelo Colorado State University (1987) e pós-doutorado pela Nasa Goddard Space Flight Center (1993). Atualmente é Pesquisador Titular do Instituto Nacional de Pesquisas Espaciais (INPE). Tem experiência na área de Recursos Florestais e Engenharia Florestal. Atuando principalmente nos seguintes temas: Linear Mixing Model, Landsat MSS TM, Shade Fraction Image, Reforested Areas, Mathematical Modelling. É Pesquisador 1B do CNPq.