<?xml version="1.0"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Land sat satellite images and the random farests classifier</dc:title>
  <dc:creator>Grinand, C.</dc:creator>
  <dc:creator>Rakotomalala, F.</dc:creator>
  <dc:creator>Gond, V.</dc:creator>
  <dc:creator>Vaudry, R.</dc:creator>
  <dc:creator>/Bernoux, Martial</dc:creator>
  <dc:creator>Vieilledent, G.</dc:creator>
  <dc:subject>Deforestation</dc:subject>
  <dc:subject>Change detection</dc:subject>
  <dc:subject>Classification</dc:subject>
  <dc:subject>Land cover</dc:subject>
  <dc:subject>Landsat TM</dc:subject>
  <dc:subject>Machine learning</dc:subject>
  <dc:subject>Madagascar</dc:subject>
  <dc:subject>Random forests</dc:subject>
  <dc:subject>REDD</dc:subject>
  <dc:description>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.</dc:description>
  <dc:date>2013</dc:date>
  <dc:type>text</dc:type>
  <dc:identifier>https://www.documentation.ird.fr/hor/fdi:010061471</dc:identifier>
  <dc:identifier>fdi:010061471</dc:identifier>
  <dc:identifier>Grinand C., Rakotomalala F., Gond V., Vaudry R., Bernoux Martial, Vieilledent G.. Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Land sat satellite images and the random farests classifier. 2013, 139,  68-80</dc:identifier>
  <dc:language>EN</dc:language>
  <dc:coverage>MADAGASCAR</dc:coverage>
</oai_dc:dc>
