10m Annual Land Use Land Cover (9-class) V1

Time series of annual global maps of land use and land cover (LULC). It currently has data 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, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. 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, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. All years are available under a Creative Commons BY-4.0.

__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer 9-class dataset._ Global estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. 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 relevant yearly Sentinel-2 scenes on the Planetary Computer. This dataset is also available on the ArcGIS Living Atlas of the World.

The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. It has 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and was produced and independently validated with an overall accuracy of about 77%.

NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, Oceania, and Africa are available. This dataset includes global land cover and land cover change data for the period 2001 to 2019. Africa is extended through 2021. GLanCE is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule. Known Issues * Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * All GLnanCE granules should contain 7 products except the year 2001, which contain 4, as there are no change products (chgDate, EVI2chg, prevClass) for the initial year of data. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. * High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. * The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data, with 11 land cover classes.

Overview This 30-meter dataset reflects land cover information for 2020 from Mexico and Canada, and 2021 over the conterminous United States and Alaska. Each country developed its own classification method to identify land cover classes and provided an input layer to produce a continental land cover map across North America. The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data across the three countries; image selection and spectral band reduction varied by country according to site-specific requirements. Canada selected most images from 2020, with a few from 2019 and 2021; the conterminous United States and Alaska relied mainly on 2021 imagery; and the Mexico map was based on change detection between 2015 and 2020 Landsat 8 mosaics. National maps were generated for Canada by the Canada Centre for Remote Sensing (CCRS); for Mexico by the National Commission for the Knowledge and Use of Biodiversity (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad), National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), and National Forestry Commission of Mexico (Comisión Nacional Forestal); and for the United States by the United States Geological Survey. Each country used its own approach, ancillary data, and mapping methodology, and the national datasets were then combined by the Commission for Environmental Cooperation (CEC) into a seamless North American product, merging four map sections: Alaska, Canada, the conterminous United States, and Mexico. The current version (version 2) integrates updated land cover information for the conterminous United States, Alaska, and Canada, based on national maps updated in February 2024 by the USGS and CCRS. Unlike version 1, where the conterminous United States represented 2019 conditions, this version corresponds to 2021. Land Cover Classes The raster attribute table provides 19 thematic land cover classes plus a background value, with class names in English, Spanish, and French: 1: Temperate or sub-polar needleleaf forest 2: Sub-polar taiga needleleaf forest 3: Tropical or sub-tropical broadleaf evergreen forest 4: Tropical or sub-tropical broadleaf deciduous forest 5: Temperate or sub-polar broadleaf deciduous forest 6: Mixed forest 7: Tropical or sub-tropical shrubland 8: Temperate or sub-polar shrubland 9: Tropical or sub-tropical grassland 10: Temperate or sub-polar grassland 11: Sub-polar or polar shrubland-lichen-moss 12: Sub-polar or polar grassland-lichen-moss 13: Sub-polar or polar barren-lichen-moss 14: Wetland 15: Cropland 16: Barren lands 17: Urban and built-up 18: Water 19: Snow and ice Geographic Coverage The service covers Canada, the United States, and Mexico, including Alaska. Data Sources and Licensing This dataset is produced by the Commission for Environmental Cooperation (CEC), derived from national land cover maps provided by Natural Resources Canada, the National Commission for the Knowledge and Use of Biodiversity, the National Institute of Statistics and Geography, the National Forestry Commission; and the United States Geological Survey. The compiled dataset is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: Intended for continental-scale reference. Vintage: 2020 Applications (Intended Use) Land cover review and continental- to national-scale mapping. Environmental monitoring and change detection over time. Spatial analysis and integration with other thematic layers in the North American Environmental Atlas. Supporting cross-border environmental cooperation and policy-making among North American nations. Files Download