Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
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The Amazon basin remains a major hotspot of tropical deforestation, presenting a clear need for timely, accurate and consistent data on forest cover change. We assessed the utility of a hybrid classification technique, iterative guided spectral class rejection (IGSCR), for accurately mapping Amazonian deforestation using annual imagery from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) from 1992 to 2002. The mean overall accuracy of the 11 annual classifications was 95% with a standard deviation of 1.4%, and z-score analysis revealed that all classifications were significant at the 0.05 level. The IGSCR thus seems inherently suitable for monitoring forest cover in the Amazon. The resulting classifications were sufficiently accurate to assess preliminarily the magnitude and causes of discrepancies between farmer-reported and satellite-based estimates of deforestation at the household level using a sample of 220 farms in Rôndonia mapped in the field in 1992 and 2002. The field-and satellite-derived estimates were significantly different only at the 0.10 level for the 220 farms studied, with the satellite-derived deforestation estimates 8.9% higher than estimates derived from in situ survey methods. Some of this difference was due to a tendency of farmers to overestimate the amount of forest within their property in our survey. Given the objectivity and reduced expense of satellite-based deforestation monitoring, we recommend that it be an integral part of household-level analysis of the causes, patterns and processes of deforestation.