Tree cover gross loss estimations in the boreal regions using MODIS time-series data sets
Peter
Potapov, Geographic Information Science Center of Excellence, South Dakota State University, peter.potapov@sdstate.edu
(Presenting)
Matthew
C
Hansen, Geographic Information Science Center of Excellence, South Dakota State University, matthew.hansen@sdstate.edu
Stephen
Stehman, State University of New York, College of Environmental Science and Forestry, svstehma@syr.edu
Thomas
R
Loveland, U.S. Geological Survey, Center of Earth Resources Observation and Science (EROS), loveland@usgs.gov
Kyle
Pittman, Geographic Information Science Center of Excellence, South Dakota State University, kyle.pittman@sdstate.edu
Estimation of tree canopy cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used MODIS time-series data to produce annual forest canopy loss hotspot maps. These maps were used to assign all blocks (18.5 km per side) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using Landsat imagery from 2000 and 2005. Biome-wide and regional forest cover and forest cover gross loss were derived through the integrated use of MODIS and Landsat data. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Our results reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Of the total forest cover loss identified, 58.9% is attributable to wildfires. Validation results illustrate the robustness of the proposed monitoring algorithm that could be used as an operational and cost-effective method for producing timely and accurate estimates of biome-wide forest cover loss.
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