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Satellite
MTBS: Monitoring Trends in Burn Severity

Monitoring Trends in Burn Severity (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.

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Satellite
USGS 3DEP Lidar Point Source

This collection is derived from the USGS 3DEP COPC collection. It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.

This values are based on the PointSourceId PDAL dimension and uses pdal.filters.outlier and pdal.filters.range to remove outliers and noise.

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Satellite
USGS 3DEP Lidar Intensity

This collection is derived from the USGS 3DEP COPC collection. It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.

The values are based on the Intensity PDAL dimension and uses pdal.filters.outlier and pdal.filters.range to remove outliers and noise.

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Satellite
Sentinel-2 Level-2A

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

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Satellite
Esri 10-Meter Land Cover (10-class)

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.

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