@Article{MortonDeShAnEsHaCa:2005:RaAsAn,
               author = "Morton, Douglas C. and DeFries, Ruth S. and Shimabukuro, Yosio 
                         Edemir and Anderson, Liana O. and Esp{\'{\i}}rito-Santo, 
                         Fernando Del Bon and Hansen, Matthew and Carroll, Mark",
          affiliation = "{University of Maryland} and {University of Maryland} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {South Dakota State University} 
                         and {University of Maryland}",
                title = "Rapid assessment of annual deforestation in the Brazilian Amazon 
                         using MODIS data",
              journal = "Earth Interactions",
                 year = "2005",
               volume = "9",
               number = "8",
                pages = "1--22",
             keywords = "deforestation, Amazon, MODIS, remote sensing, Brazil.",
             abstract = "The Brazilian government annually assesses the extent of 
                         deforestation in the Legal Amazon for a variety of scientific and 
                         policy applications. Currently, the assessment requires the 
                         processing and storing of large volumes of Landsat satellite data. 
                         The potential for efficient, accurate, and less data-intensive 
                         assessment of annual deforestation using data from NASAs Moderate 
                         Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution 
                         is evaluated. Landsat-derived deforestation estimates are compared 
                         to MODIS-derived estimates for six Landsat scenes with five 
                         change-detection algorithms and a variety of input dataSurface 
                         Reflectance (MOD09), Vegetation Indices (MOD13), fraction images 
                         derived from a linear mixing model, Vegetation Cover Conversion 
                         (MOD44A), and percent tree cover from the Vegetation Continuous 
                         Fields (MOD44B) product. Several algorithms generated consistently 
                         low commission errors (positive predictive value near 90%) and 
                         identified more than 80% of deforestation polygons larger than 3 
                         ha. All methods accurately identified polygons larger than 20 ha. 
                         However, no method consistently detected a high percent of 
                         Landsat-derived deforestation area across all six scenes. Field 
                         validation in central Mato Grosso confirmed that all MODIS-derived 
                         deforestation clusters larger than three 250-m pixels were true 
                         deforestation. Application of this field-validated method to the 
                         state of Mato Grosso for 200104 highlighted a change in 
                         deforestation dynamics; the number of large clusters (>10 MODIS 
                         pixels) that were detected doubled, from 750 between August 2001 
                         and August 2002 to over 1500 between August 2003 and August 2004. 
                         These analyses demonstrate that MODIS data are appropriate for 
                         rapid identification of the location of deforestation areas and 
                         trends in deforestation dynamics with greatly reduced storage and 
                         processing requirements compared to Landsat-derived assessments. 
                         However, the MODIS-based analyses evaluated in this study are not 
                         a replacement for high-resolution analyses that estimate the total 
                         area of deforestation and identify small clearings.",
                 issn = "1087-3562",
             language = "en",
           targetfile = "18417721.pdf",
        urlaccessdate = "02 maio 2024"
}