Thursday, August 9, 2007

PS 66-128: Remote sensing and tropical forest damage: Using CLAS-II for the detection of forest disturbances and deforestation in the Peruvian Amazon

Paulo J. C. Oliveira, Gregory P. Asner, David E. Knapp, and Rebecca F. Raybin. Carnegie Institution

Forest damage as a result of human activity has been one of the most prevalent land use and land cover changes in Latin America’s tropical forests in recent decades, has been shown to have large impacts on the ecological, as well as the socio-economic sustainability of these regions, and is difficult to detect and quantify at a large geographic scale. Peru, with its 650,000 km2 of tropical forest, has seen an increasing presence of timber activity in recent years, which has yet to be quantified. We developed and applied the CLAS-II forest damage detection algorithm to calculate the extent and intensity of human induced disturbance and deforestation in approximately 80% of the Peruvian Amazon, from 1999 to 2005, using Landsat 5 TM and Landsat 7 ETM+ satellite data. We have found that new human-induced forest disturbance, which may include selective logging, and new deforestation, were responsible for an average Peruvian Amazon impacted forest area of approximately 630 km2 and 650 km2, respectively, during that period. Our results show moderate levels of forest disturbance and deforestation in the Peruvian Amazon, and can be used to inform conservation, management and resource policy development strategies for the region.

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