2023-05-04T13:11:58.897434

2025-04-02T17:39:42.796554

30

30 m

Global land area (excluding Antarctica and other Arctic islands).

Annual

Hansen et al., 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.”. Accessed through Global Forest Watch on 21/11/2024[date]. [www.globalforestwatch.org](www.globalforestwatch.org) 

Tree cover loss

annual, 30m, global, UMD GLAD

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” _Science_ 342 (15 November): 850–53. [doi: 10.1126/science.1244693](https://doi.org/10.1126/science.1244693). Data available from: [https://glad.earthengine.app/view/global-forest-change](https://glad.earthengine.app/view/global-forest-change).

[CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)

English

This data set, a collaboration between the [GLAD](https://glad.geog.umd.edu/) (Global Land Analysis & Discovery) lab at the University of Maryland (UMD), Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval, and additional images used for 2023. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time, and is biome-dependent. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced. This data set has been updated five times since its creation, and now includes loss up to 2023 (Version 1.10). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2023, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Since the entire historical timeseries was not reprocessed with the updated methodology, time-series assessments should be performed with caution. Read more about the Version 1.11 update [here](https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html). When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.

Identifies areas of gross tree cover loss

In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation. Due to variation in research methodology and date of content, tree cover, loss, and gain data sets on GFW cannot be compared accurately against each other. Accordingly, “net” loss cannot be calculated by subtracting figures for tree cover gain from tree cover loss, and current (post-2000) tree cover cannot be determined by subtracting figures for annual tree cover loss from year 2000 tree cover. The 2011-2023 data was produced using an [updated methodology](https://storage.googleapis.com/earthenginepartners-hansen/GFC-2022-v1.10/download.html). Comparisons between the original 2001-2010 data and the 2011-2023 update should be performed with caution. In the original publication, the authors evaluated the overall prevalence of false positives (commission errors) in this data at 13%, and the prevalence of false negatives (omission errors) at 12%, though the accuracy varies by biome and thus may be higher or lower in any particular location. The model often misses disturbances in smallholder landscapes, resulting in lower accuracy of the data in sub-Saharan Africa, where this type of disturbance is more common. Largely because of a delay between an actual disturbance and the event being observed by the satellite imagery, the authors are 75% confident that the loss occurred within the stated year, and 97% confident that it occurred within a year before or after. Users of the data can smooth out such uncertainty by examining the average over multiple years. Read our [blog series](http://blog.globalforestwatch.org/data/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1.html) on the accuracy of this data for more information.

CC BY 4.0

Forest Change

Best available global data on forest change

https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html

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