Effects of drought on deforestation estimates from different classification methodologies: Implications for REDD+ and other payments for environmental services programs

Payments for ecosystem services (PES) programs depend on consistent environmental monitoring methodologies for measuring, reporting and verification. The case of carbon PES programs is instructive: land cover estimates are crucial for environmental monitoring efforts, but they require a consistent estimation method. Such consistency is however likely to be affected by climatic variability such as that seen during severe droughts. This raises questions as to whether distinct methodologies for land cover monitoring yield the same estimates of land- cover change over time in the presence of climatic variability. This study compares deforestation estimates from four methodologies during a normal year (2008) with those during a period of extreme drought (2010) in the Madre de Dios region of Peru in the Southwestern Amazon. The four methodologies compared are the automated classification with CLASlite 2.2, Tasseled Cap classification with ERDAS Imagine 9.2, Bhattacharya classification with SPRING 5.1, and Spectral Angle Mapper classification with ENVI 4.7. The results show differences in the forest and non-forest estimates derived from using ERDAS and CLASlite compared with those from using the SPRING and ENVI. These findings have implications for forest monitoring efforts for PES programs such as Reduced Emissions from Deforestation and Forest Degradation (REDD+) in the context of climate change.