Tropical Tree Cover 70 m
Description
This dataset classifies fractional tree cover for the year 2020, with coverage over the tropics at 70 m (0.5 ha) spatial resolution.
e8fe6f99…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_cover_percent
Description
Percent tree cover. Data is calculated as the average tree extent probability of all 10 m pixels in a 0.5 ha area (see Tropical Tree Cover 10 m dataset).
Usage notes
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). Overall RMSE is 12.23%, with an r-squared of 0.93. For tree cover values > 10%, user accuracy is 92% ± 0.5% and producer accuracy is 93% ± 0.5%. For tree cover values < 10%, user accuracy is 93% ± 0.5% and producer accuracy is 89% ± 0.6%. Lowest accuracy is 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.