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Download the associated GLAD Confidence values here. This data set, created by the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland and supported by Global Forest Watch, is the first Landsat-based alert system for tree cover loss. While most existing loss alert products use 250-meter resolution MODIS imagery, these alerts have a 30-meter resolution and thus can detect loss at a much finer spatial scale. The alerts are currently operational for Brazil, Peru, the Republic of Congo, and Kalimantan in Indonesia, and will eventually be expanded to the rest of the humid tropics.New Landsat 7 and 8 images are downloaded as they  are posted online at USGS EROS, assessed for cloud cover or poor data quality, and compared to the three previous years of Landsat-derived metrics (including ranks, means, and regressions of red, infrared and shortwave bands, and ranks of NDVI, NBR, and NDWI). The metrics and the latest Landsat image are run through seven decision trees to calculate a median probability of forest disturbance. Pixels with probability >50% are reported as tree cover loss alerts. For more information on methodology, see the paper in Environmental Research Letters. Alerts remain unconfirmed until two or more out of four consecutive observations are labelled as tree cover loss. Alerts that remain unconfirmed after subsequent observations are removed from the data set. You can choose to view only confirmed alerts in the menu,  though keep in mind that using only confirmed alerts misses the newest detections of tree cover loss.

Dataset Attributes

  • objectid
    Number
  • data_2015
    Text
    {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/peru_day2015.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/brazil_date2015.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/SEA_day_2015n.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/Africa_day_2015n.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/FE_day2015m.tif", "count"=>1} ()
  • data_2016
    Text
    {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/brazil_day2016.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/FE_day2016m.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/Africa_day_2016n.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/SEA_day_2016n.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/peru_day2016.tif", "count"=>1} ()
  • data_2017
    Text
    {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/brazil_day2017.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/Africa_day_2017n.tif", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/SEA_day_2017n.tif", "count"=>1} (), {"value"=>" ", "count"=>1} (), {"value"=>"http://umd-landsat-alerts.s3.amazonaws.com/peru_day2017.tif", "count"=>1} ()
  • shape
    Number
  • st_area(shape)
    Number
  • st_length(shape)
    Number

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  • By on January 26, 2017
  • Updated about 1 month ago

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