Download Esri Land Cover
Amatista Sheeley
Land use/land cover (LULC) maps are an increasingly important tool for decision-makers at local, regional, and national levels around the world. LULC maps can help us to quantify and better understand the impacts of earth processes and human activity on our environment. This information helps inform policy and land management decisions that support sustainable development.
ArcGIS not only provides users with the capabilities necessary to produce and share data and maps such as LULC, it also provides all the capabilities necessary to derive and disseminate insights. Data extraction is foundational. Getting the right information into the hands of land managers and decision makers is invaluable. As we seek to reveal insights from land use/land cover maps, two fundamental aspects come to mind: 1) timeliness of the data and maps, and 2) change analysis. Fortunately, we are able to provide both.
The aforementioned annual updates allow for detecting year-to-year shifts in vegetation and crops, forests, bare surfaces, and urban areas. Sentinel-2 Land Cover Explorer provides a dynamic and intuitive interface for detecting, displaying, and reporting change.
At any time, you can choose to expand the on screen chart. The expanded view provides statistical coverage trends across class and time for your current map extent or for your selected region and sub-region.
If you wish to take the LULC data for offline analysis, you can leverage the Land Cover Download mode within the app. There you can select a single geography and year, or you can download global coverage by year using the annual hyperlinks provided.
Sentinel-2 Land Cover Explorer joins a suite of apps in ArcGIS Living Atlas demonstrating capabilities and patterns of use within the ArcGIS system. If you wish to leverage or expand upon the capabilities demonstrated in Sentinel-2 Land Cover Explorer, feel free to access the code repository on GitHub.
The new map will be updated annually supporting change detection and highlighting planetary land changes, especially related to the effects of human activity. A consistent map of land cover for the entire world based on the most current satellite information, the 2020 Global Land Cover Map can be combined with other data layers for green infrastructure, sustainability projects, and other conservation efforts that require a holistic picture of both the human and natural footprint on the planet. Later this year, Esri and Impact Observatory will make this new land cover model available to support on-demand land cover classification, allowing the GIS community to create maps for project areas as often as every week.
High-resolution, open, accurate, comparable, and timely land cover maps are critical for decision-makers in many industry sectors and developing nations. These maps improve understanding of important topics such as food security, land use planning, hydrology modeling, and resource management planning. In addition, national government resource agencies use land cover as a basis for understanding trends in natural capital, which helps define land planning priorities and is the basis of budget allocations.
Impact Observatory, contracted by Esri, developed a deep learning AI land classification model using a massive training dataset of billions of human-labeled image pixels, and applied this model to the Sentinel-2 2020 scene collection, processing over 400,000 Earth observations to produce the map. The unique machine learning approach used to create this global map will soon be available on demand, supporting land managers who need to monitor change in a specific area of interest, looking at annual change and seasonal differences in land cover.
Esri is releasing this valuable resource under a Creative Commons license to encourage broad adoption and ensure equitable access for planners creating a more sustainable planet. This content will be made available in ArcGIS Online as a map service and be freely available for use by its 10 million users. It will also be available for download and viewing. To explore the new 2020 Global Land Cover Map, visit livingatlas.arcgis.com/landcover.
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Time series of annual global maps of land use and land cover (LULC) was updated to v3 with global 10m land cover from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These datasets produced by Impact Observatory and licensed by Esri were fetched from Impact Observatory
This map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory and you can find it in GEE here. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.
Water Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.
Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.
Built Area Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.
Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.
Rangeland Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.
This dataset was produced by Impact Observatory for Esri. 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:
thank you for your kind response, this has fixed the display issue but the problem is that the classes are gone and turned into 3 bands image again. I need those classes for analysis because they represent land use/cover, is there a way to merge them and keep the classes?
Another option to consider is the Mosaic geoprocessing tool or maybe the Mosaic to New Raster GP tool. Explore the parameters to ensure that the output raster dataset colormaps match the input images.
I used the reclassification tool first to reclassify every raster and then merged them using the merge tool in the raster function. This seems to do the trick! Thank you again, sir. have a wonderful day
Hello all. I recently downloaded a tile from the new 2020 ESRI 10 m land cover data (as a tif), and I notice that when I go to "project" the raster to an Albers Equal area projection, I lose the attribute table with the categorical land cover variables. Once I go to "build attribute table," it fails with an error 000049. Build attribute table will work with the original tif file. I checked and when I project the raster, the bit, band, and unsigned don't change. How do I fix this issue?
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