Tree Canopy Change 2018 2024

- This data set, a collaboration between the Global Land Analysis & Discovery (GLAD) () 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, 2024 and 2025. 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 2025 (Version 1.13). 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 2011-2025, 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 1.13 update here:  and access on GEE 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.  Identifies areas of gross tree cover loss

This data set from the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland measures areas of tree cover gain from the year 2000 to 2020 across the globe at 30 × 30 meter resolution, displayed as a 20-year cumulative layer. Tree cover gain was determined using tree height information from the years 2000 and 2020. Tree height was modeled by the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. Gain was identified where pixels had tree height ≥5 m in 2020 and tree height <5 m in 2000. Tree cover gain may indicate a number of potential activities, including natural forest growth, the tree crop rotation cycle, or tree plantation management.  When zoomed out (< zoom level 12), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 12).  Identifies areas of tree cover gain

This 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 gain across all global land (except Antarctica and other Arctic islands) at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30 meter pixels) in the satellite images were assembled and a supervised learning algorithm was then applied to identify per pixel tree cover gain. Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations. When zoomed out (50%. Identifies areas of tree cover gain

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of net tree cover change across all global land (except Antarctica and other Arctic islands) at the administrative area. The data shows how much more or less tree cover a given area (country, state/province, or county/municipality) had in 2020 compared to 2000. This is not an annual data set and is displayed as a 20-year cumulative layer with a baseline year of 2000 and end year of 2020. Tree cover change was determined using tree cover extent for the years 2000 and 2020, which are based on tree height information. Tree height was determined by the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height between 52°N and 52°S globally. The tree cover extent maps for 2000 and 2000 were produced by attributing pixels with ≥5 m height as the tree cover extent. The tree cover gain or loss was derived from a comparison between the year 2000 and 2020 maps, with steps taken to eliminate small changes in height which are “noise” in the data. The net change corresponds to the area of gross gain minus the area of gross loss to show the overall change. The net change percent per administrative region was then derived by calculating tree cover gain or loss as a percentage of the baseline 2000 tree cover area.   **Stable forest** – Area of tree cover that remained at or above 5m in height between 2000 and 2020.   **Gain** – Area of tree cover that was below 5m height in 2000 but at or above 5m height in 2020.   **Loss** – Area of tree cover that was at or above 5m height in 2000 but below 5m height in 2020.   **Disturbed** - Represents areas that experienced both loss and gain between 2000 and 2020.   Identifies net tree cover change

City of Cambridge Zoning By-Law showing the extent of By-law 150-85 and 26-007.

This data is produced by the Global Land Analysis & Discovery (GLAD) lab at the University of Maryland (UMD) and measures areas of tree cover loss due to fire compared to all other drivers across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30-meter resolution. The data were generated using global Landsat-based annual change detection metrics for 2001-2025 as input data to a set of regionally calibrated classification tree ensemble models. The result of the mapping process can be viewed as a set of binary maps (tree cover loss due to fire vs. tree cover loss due to all other drivers).  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 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 due to fire may be caused by natural or human-induced fire activity.  The analysis method for the base tree cover loss map on GNW that is used as input for this dataset has been modified in numerous ways to improve detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations for data covering the 2011-2025 period. Due to these changes, comparing trends across the 2000-2010 and 2011-2025 periods should be performed with caution. You can read more about updates to the tree cover loss modeling process 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 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.  This data is available for download from UMD here and also accessible through Google Earth Engine using the following image IDs (view the download page from UMD for more information):  - Fire certainty: users/sashatyu/2001-2025\_fire\_forest\_loss  - Date of loss: users/sashatyu/2001-2025\_fire\_forest\_loss\_annual  Identifies areas of tree cover loss due to fires compared to all other drivers of tree cover loss