OverviewThis data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, 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 2011-2020 interval. 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,” or the complete removal of tree cover canopy at the Landsat pixel scale. 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 2020 (Version 1.8). 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 2011-2020, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.8 should be performed with caution. Read more about the Version 1.8 update here. 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.Frequency of updates: AnnualDate of content: 2001-2020Resolution: 30x30m

How to download this data:Click on "View Map" button at the top (the Download button allows you to download the footprint of tiles but not the actual alerts)Click on the tile where your area of interest is locatedCopy the whole URL from the pop-up and paste it into your internet browser. Download will begin automaticallyAdditional DetailsThis data set, assembled by Global Forest Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated. The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30m GLAD Landsat-based alerts are up-sampled to match the 10m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels. Alerts are classified as high confidence when detected twice by a single alert system. Alerts detected by multiple alert systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that it was not a false positive and may not need to wait for additional satellite imagery to increase confidence in detected loss. *This data product utilizes a special encoding*Each pixel (alert) encodes the date of disturbance and confidence level in one integer value. The leading integer of the decimal representation is 2 for a low-confidence alert, 3 for a high-confidence alert, and 4 for an alert detected by multiple alert systems, followed by the number of days since December 31, 2014. 0 is the no-data value. For example:20001 is a low confidence alert on January 1st, 201530055 is a high confidence alert on February 24, 201521847 is a low confidence alert on January 21, 202041847 is a highest confidence alert (detected by multiple alert systems) on January 21, 2020. Alert date represents the earliest detection0 represents no alertResolution: 10 x 10mGeographic Coverage: 30°N to 30°SFrequency of Updates: DailyDate of Content: January 1st, 2015 – presentCautionsConfidence level may change retroactively as source data is updated GLAD-L: Available for entire tropics (30°N to 30°S) from January 1, 2018 to the present, and from 2015 to the present for select countries in the Amazon, Congo Basin, and insular Southeast Asia GLAD-S2: Available for the primary humid tropical forest areas of South America from January 2019 to the present RADD: Available for the primary humid tropical forest areas of South America, sub-Saharan Africa and insular Southeast Asia at a 10m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South America and Southeast Asia In order to integrate the three alerting systems on a common grid, GLAD-L is resampled from 30m resolution to 10m resolution to match GLAD-S2 and RADD. As a result, pixels in the integrated layer may not exactly align with pixels in the individual GLAD-L layer. Each pixel in the integrated layer preserves the earliest date of detection from any alerting system, even if multiple systems have reported an alert in that pixel. In some situations, this may lead to inconsistent visualizations when switching from the integrated layer to individual alerting system layers. It is advisable to use in the integrated layer when you are interested in the earliest date of detection by any alerting system. However, it is better to use the individual alerting system layers if you are interested in a specific alert type. Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests (more likely to happen in the GLAD-L system), alerts may indicate timber harvesting operations, without a conversion to a non-forest land use. The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this. LicenseCC by 4.0SourcesGLAD Alerts:Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3). GLAD-S2 Alerts:Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. RADD Alerts:Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. https://doi.org/10.1088/1748-9326/abd0a8

OverviewThis data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions.CautionsThis analysis is limited by various data and attribution issues and methodological assumptions, including the following:Commodity data sets have limited coverage and quality. Only oil palm has recent, detailed maps of extent at a global level. The analysis also uses detailed data on South American soy. Outside of these regions and commodities, the analysis relies on global 10-kilometer resolution data on crop and pasture extent. These data are from 2010 (2000 for pasture), so the amount of forest replaced by a specific commodity is assumed to be proportional to its area during that year and may be misrepresented if significant expansion or contraction of that commodity has occurred since then. While Goldman et al. (2020) presents results using detailed pasture data for Brazil, this data set includes pasture results for the coarse method only.The data cannot capture complex land-use change transitions. The analysis does not consider other possible land uses between the deforestation event and the establishment of the commodity. The analysis also does not consider any forms of indirect land-use change (e.g., the target commodity displacing other activities that may, in turn, expand into forested areas).The data measure tree cover loss rather than deforestation directly. All tree cover loss in an area later used for one of the target commodities is assumed to be deforestation because forest replaced with a crop or pasture represents a permanent land-use change. Historical data from Indonesia and Malaysia were used to filter out older oil palm plantations from the analysis to avoid counting old, unproductive oil palm trees being felled as tree cover loss.The data may miss some forms of tree cover loss. The Hansen et al. (2013) tree cover loss data may not detect all changes related to commodity production. Much of the production of cocoa and coffee occurs on very small farms (less than one hectare) that may not be captured by the tree cover loss data. The analysis may also underestimate the conversion of dry forest and woody savanna areas, which are not well represented in the tree cover loss data. For the detailed soy analysis, we define tree cover as any woody vegetation with a minimum of 10 percent canopy cover (analyses for other commodities use 30 percent) to minimize underestimations in South American biomes such as the Cerrado and the Chaco.Further discussion about the methods, assumptions, and limitations of this analysis is available in Goldman et al. (2020).CitationGoldman, E., M.J. Weisse, N. Harris, and M. Schneider. 2020. “Estimating the Role of Seven Commodities in Agriculture-Linked Deforestation: Oil Palm, Soy, Cattle, Wood Fiber, Cocoa, Coffee, and Rubber.” Technical Note. Washington, DC: World Resources Institute. Available online at: wri.org/publication/estimating-the-role-of-sevencommodities-in- agriculture-linked-deforestationLicenseCreative Commons Attribution 4.0 International License (CC-BY 4.0)

This data set includes tree cover extent, aboveground live biomass stocks and densities, annual tree cover loss, annual forest GHG emissions, and average annual forest CO2 removals (sequestration) and annual net GHG flux at the country and first (state, province) sub-national levels. Tree cover loss and emissions are available as annual data for 2001-2020. Emissions, removals and net flux are available as annual averages for 2001-2020. Tree cover is available for 2000 and 2010. Aboveground biomass stocks and densities are available for 2000. The tree cover data was produced by the University of Maryland's GLAD laboratory in partnership with Google. Carbon densities, emissions, removals, and net flux (megagrams CO2e/yr) are from Harris et al. 2021. The emissions data quantifies the amount of carbon dioxide emissions to the atmosphere where forest disturbances have occurred, and includes CO2, CH4, and N2O and multiple carbon pools. (This replaces the emissions data previously on GFW.) Removals includes the average annual carbon captured by aboveground and belowground woody biomass in forests. Net flux is the difference between average annual emissions and average annual removals; negative values are net sinks and positive values are net sources. All values besides emissions, removals, and net flux are presented for percent canopy cover levels >=10%, 15%, 20%, 25%, 30%, 50% and 75%, while emissions, removals, and net flux are presented only for canopy >=30%, 50%, and 75% and areas with tree cover gain. We recommend that you select your desired percent canopy cover level and use it consistently throughout any analysis. The Global Forest Watch website uses a >=30% canopy cover threshold as a default for all statistics.

Citations
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. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.

Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. (2021). https://doi.org/10.1038/s41558-020-00976-6
Global Administrative Areas Database, version 3.6. Available at http://gadm.org/

For further questions regarding this data set, please contact Mikaela Weisse at the World Resources Institute (mikaela.weisse@wri.org).