Horizon 1 1 17 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES Remote Sensing of Environment 68-80 Deforestation Change detection Classification Land cover Landsat TM Machine learning Madagascar Random forests REDD MADAGASCAR 2013 fdi:010061471 ENG Remote Sensing of Environment 0034-4257 ISI:000329417700007 10.1016/j.rse.2013.07.008 https://www.documentation.ird.fr/hor/fdi:010061471 https://www.documentation.ird.fr/intranet/publi/2014/02/010061471.pdf 139 Horizon (IRD) High resolution and low uncertainty deforestation maps covering large spatial areas in tropical countries are needed to plan efficient forest conservation and management programs such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Using an open-source free software (R, GRASS and QGis) and an original statistical approach combining multi-date land cover observations based on Landsat satellite images and the random forests classifier, we obtained up-to-date deforestation maps for the periods 2000-2005 and 2005-2010 with a minimum mapping unit of 036 ha for 7.7 M hectares, i.e. 40.3% of the tropical humid forest and 20.6% of the tropical dry forest in Madagascar. Uncertainty in deforestation on the maps was calculated by comparing the results of the classification to more than 30,000 visual interpretation points on a regular grid. We assessed accuracy on a per-pixel basis (confusion matrix) and by measuring the relative surface difference between wall-to-wall approach and point sampling. At the pixel level, user accuracy was 84.7% for stable land cover and 60.7% for land cover change. On average for the whole study area, we obtained a relative difference of 2% for stable land cover categories and 21.1% land cover change categories respectively between the wall-to-wall and the point sampling approach. Depending on the study area, our conservative assessment of annual deforestation rates ranged from 0.93 to 233%.yr(-1) for the humid forest and from 0.46 to 1.17%.yr(-1) for the dry forest. Here we describe an approach to obtain deforestation maps with reliable uncertainty estimates that can be transposed to other regions in the tropical world. 082 ; 126 UR210 Madagascar