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Assessment of forest degradation in the Amazon using multi-sensors techniques: the case of Paragominas (Brazil). O-2215-01

Gond V., Bourgoin C., Blanc L., Baghdadi N., Oszwald J., Sist P.. 2015. In : Our Common Future under Climate Change. International scientific conference Abstract Book 7-10 July 2015. Paris, France. Paris : CFCC15, p. 261-261. Our Common Future under Climate Change, 2015-07-07/2015-07-10, Paris (France).

The Amazonian pioneer front region is a mosaic of different forests types and agricultural landscapes resulting from the colonization of the region through forest conversion into pasture and agricultural lands. Fearnside and Guimaraes (1996) showed that 47% of the deforested area is rapidly abandoned. It also appears that logged forests surface is equivalent to deforested areas (Asner et al., 2005). Consequently a degradation gradient exists from low impacted logged forests (depending of the logging intensity) to young secondary (regrowth) forests. To obtain more accurate estimation of carbon stocks, it is important today to take into account the degraded forest gradient including all degraded forest stages between mature intact forests and non-forest areas. The first main challenge is to identify and to characterize the various stages. The identification of forest degradation is still a complex and expansive problem even if it has been focused until now only on logged tropical rainforest (Asner, 2009; Gond and Guitet, 2009; Desclées et al., 2006; Asner et al., 2005; Souza et al., 2003). In parallel estimation of biomass loss in the degraded forest is little-studied. Within temperate and boreal forests some estimation are made by Solberg et al., (2013). The combination of optical remotely sensed data (Landsat-8), radar (Terra-Sar-X) and Lidar (IceSat) have to be studied to analyze the potential of the multisensors techniques to characterize the tropical rainforest degradation (Betbeder et al., 2014). The study presents the first results obtained during the field work at Paragominas (Pará, Brazil) on different forest degradation intensities (Bérenguer et al., 2014). This field database is then compared with multi-sensors remote sensing to better understand multiple interactions and to establish a forest degradation typology. (Texte intégral)

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