Global data and tools for local forest cover loss and REDD+ performance assessment: Accuracy, uncertainty, complementarity and impact - Bos, A.B.; Sy, Veronique de; Duchelle, Amy E.; Herold, M.; Martius, C.; Tsendbazar, N.E. (Elsevier, 2019)
Assessing the performance of efforts to reduce emissions from deforestation and forest degradation (REDD+) requires data on forest cover change. Innovations in remote sensing and forest monitoring provide ever-increasing levels of coverage, spatial and temporal detail, and accuracy. More global products and advanced open-source algorithms are becoming available. Still, these datasets and tools are not always consistent or complementary, and their suitability for local REDD+ performance assessments remains unclear. These assessments should, ideally, be free of any confounding factors, but performance estimates are affected by data uncertainties in unknown ways. Here, we analyse (1) differences in accuracy between datasets of forest cover change; (2) if and how combinations of datasets can increase accuracy; and we demonstrate (3) the effect of (not) doing accuracy assessments for REDD+ performance measurements.