Towards an Integrated System for Vegetation Fire Monitoring in the Amazon Basin
Wilfrid
Schroeder, University of Maryland, schroeder@hermes.geog.umd.edu
(Presenting)
Biomass burning is a major environmental problem in Amazonia. The main objective of this study was to develop an integrated fire product capable of improved monitoring and characterization of fire activity in Brazilian Amazonia. Two major active fire detection algorithms based on MODIS and GOES data are used to meet the users demand for fire information. Large differences involving the performance of the MODIS and GOES fire products required the quantification of omission and commissions error in order to allow appropriate treatment of individual detections produced by each data set. Relatively small omission errors due to cloud obscuration were estimated for Brazilian Amazonia. Regional climate conditions favor the occurrence of clear sky conditions in regions of high fire activity during the peak of the dry season, therefore attenuating the effects of cloud coverage on fire detection omission errors. Clear sky omission and commission errors were largely dependent on the vegetation and background conditions. Relatively large commission errors occurring in high percentage tree cover areas suggested that fire detection algorithms must either be regionalized or incorporate additional tests to provide more consistent fire information across a broader range of surface conditions. Integration of MODIS and GOES fire products using physical parameters describing fire properties (e.g., fire radiative power) was proven difficult due to limitations involving sensor characteristics and the types of fires that occur in Amazonia. Alternatively, a new integrated product was generated based on binary fire detection information derived from MODIS and GOES data incorporating adjustments to reduce commission and omission errors and optimizing the complementarities among individual detections. The material originated from this research made a significant contribution for the fire monitoring science in Amazonia and should play an important role in the future developments of fire detection algorithms for tropical regions.
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