Deciphering the many maps of the Xingu, an assessment of deforestation from land cover classifications at multiple scales

Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. Over the years, diverse Earth Observation platforms have been used to generate land cover maps, each with its unique characteristics. In addition, considerable semantic differences between the definition of land cover classes results in inevitable differences in baseline estimates for each class (e.g. forest). Here we compare forest cover and surface water estimates over four time periods spanning three decades for the Xingu River basin, Brazil, from pre-existing remotely sensed classifications for this area based on both optical and radar data. Because forest health in this area is directly related with the health of the freshwater ecosystem, we illustrate potential impacts of map choice on conservation of fish fauna. Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse and objectively decide on the most appropriate dataset for each conservation or policy making question. Our findings demonstrate the importance of transparency in the generation of remotely sensed datasets and in the importance of users familiarizing themselves with the characteristics and limitations of each data set chosen.