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Enhancing Vegetation Mapping and Evolution in Rondonia, Brazil

This study explores vegetation continuous fields evolution in Rondonia, Brazil, using various spatial and temporal resolutions and innovative algorithms. It discusses the importance of using different products for accurate mapping to assess forest disturbance and vegetation cover changes over time.

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Enhancing Vegetation Mapping and Evolution in Rondonia, Brazil

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  1. Vegetation Continuous Fields Evolution through multiple spatial and temporal resolutions John R. Townshend1, Charlene M. DiMiceli1, Robert Sohlberg1, Mark Carroll2, Do-Hyung Kim1 1 University of Maryland, Department of Geographical Sciences, 2 Sigma Space, NASA Goddard Space Flight Center

  2. AVHRR VCF Rondonia , Brazil 1 km resolution * Mixture model algorithm

  3. MODIS VCF C3 Rondonia , Brazil 500 m resolution * Regression tree algorithm Increased training

  4. MODIS VCF C5 Rondonia , Brazil 250 m resolution * Bagged regression tree algorithm Increased training * Annual * Fully automated

  5. WaterMask MODIS C5 * 250 m resolution

  6. Landsat VCF Rondonia , Brazil 30 m resolution * Regression tree algorithm Training—MODIS VCF and high resolution data

  7. MEaSURES VCF 1982 - 2010 Rondonia, Brazil 30 year time series * Bagged regression tree algorithm Expanded training * 1/20 degree resolution 1982 1990 1998 2010

  8. Vegetation Continuous Fields Products -- History * * ** * http://www.landcover.org , ** http://earthenginepartners.appspot.com/science-2013-global-forest

  9. Forest DisturbanceXiaopeng Song, 2014 Disturbance Year

  10. Why do we need both VCF and LAI products?

  11. LAI and VCF VCF Non-tree Vegetation MOD44B--2010 Maximum Annual LAI MOD15A2--2010 VCF Tree Cover MOD44B--2010

  12. VCF vs LAI 1 km USDA Farm Service Agency, Northern Michigan

  13. VCF vs LAI 1 km USDA Farm Service Agency, Northern Michigan

  14. VCF vs LAI 1 km USDA Farm Service Agency, Northern Michigan

  15. Future Plans • Further additions to training to improve difficult regions • Improved data mining models—Random Forest, Gradient Boosting • Testing on C6 data • Analysis of improvements with BRDF-corrected data • VIIRS • Updated error and uncertainty analysis

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