Tropical Tree Cover 10 m
Description
This dataset classifies tree extent for the year 2020, with coverage over the tropics at 10 m spatial resolution.
72cfdb7e…Usage notes
Spatial coverage is non-desert land surfaces between 23.44ºN and 23.44ºS. Inconsistent imagery usage and normalisation between adjacent tiles may cause inaccurate predictions or visual artefacts. Data availability is limited in regions with high cloud cover. Model versions may vary between regions due to selective back-processing of affected areas.
Variables
tree_extent_probability
Description
The probability of one or more tree canopies intersecting a pixel centroid. Data is generated using a convolutional neural network trained to predict tree extent in 18,000 plots from Sentinel-1 and Sentinel-2 satellite data.
Usage notes
Values are binned probabilities rounded to the nearest 10% (0 , 10 , 20 , 30 , 40 , 50 , 60 , 70 , 80 , 90 , 100). A tree is defined as any woody vegetation with > 5 m canopy height or 3-5 m canopy height plus > 5 m crown diameter. Includes tree-based plantations (e.g. eucalyptus, avocado) but excludes herbaceous vegetation (e.g. sugarcane, bananas, cacti) and short woody crops (e.g. tea, coffee). Recommended cut-off for converting probabilities to binary tree/no-tree classifications is 35%. Overall accuracy is 94% ± 0.1% for binary classifications. Accuracy for non-forested areas is between 70% and 95%, with lowest accuracy in urban and arid areas.
Pricing
This dataset has no data acquisition cost.
Resources
Provider
WRI
www.wri.orgThe World Resources Institute (WRI) is a global research organisation that provides authoritative data, research, and tools to address critical environmental and development challenges. Through initiatives like Global Forest Watch and Aqueduct, WRI combines research with innovative technology to make environmental data accessible and actionable for governments, businesses, and civil society worldwide.
GFW
www.globalforestwatch.orgGlobal Forest Watch (GFW) is an online platform that provides data and tools for monitoring forests. Established by the World Resources Institute (WRI), GFW harnesses cutting-edge geospatial technology to allow anybody to access near realtime information about where and how forests are changing around the world.
UMD
glad.umd.eduThe Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland (UMD) studies and develops tools for assessing global land surface change using satellite imagery. GLAD also collaborates on major initiatives including Global Forest Watch and the WRI Land and Carbon Lab.