Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon

Abstract

Vegetation fires remain as one of the most important processes governing land use and land cover change in tropical areas. The large area extent of fire prone areas associated with human activities makes satellite remote sensing of active fires a valuable tool to help monitor biomass burning in those regions. However, identification of active fire fronts under optically thick clouds is not possible through passive remote sensing, often resulting in omission errors. Previous analyses of fire activity either ignored the cloud obscuration problem or applied corrections based on the assumption that fire occurrence is not impacted by the presence of clouds. In this study we addressed the cloud obscuration problem in the Brazilian Amazon region using a pixel based probabilistic approach, using information on previous fire occurrence, precipitation and land use. We implemented the methodology using data from the geostationary GOES imager, covering the entire diurnal cycle of fire activity and cloud occurrence. Our assessment of the method indicated that the cloud adjustment reproduced the number of potential fires missed within 1.5% and 5% of the true fire counts on annual and monthly bases respectively. Spatially explicit comparison with high resolution burn scar maps in Acre state showed a reduction of omission error (from 58.3% to 43.7%) and only slight increase of commission error (from 6.4% to 8.8%) compared to uncorrected fire counts. A basin-wide analysis of corrected GOES fire counts during 2005 showed a mean cloud adjustment factor of approximately 11%, ranging from negligible adjustment in the central and western part of the Brazilian Amazon to as high as 50% in parts of Roraima, Para and Mato Grosso.