README.md

FACT Land Use Land Cover

Global annual (2017-2021) land use land cover classification in ten classes at 10 meters scale.

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Use

The docker container can be started with the following code:

podman run -t -i --name fact_lulc -p 5004:5000 -d registry.git.nilu.no/fact/data/fact_lulc:0.3

The service could be queried passing the JSON polygon object as in the following, but please note that the entrypoint requires the year and the ones available are 2017-2021:

curl -X POST http://localhost:5004/band_statistics/2017 \
-H "Content-Type: application/json" \
-d '{
    "polygon_wkt": "POLYGON (())"
}'

The polygon definition is made of several points coordinates, as in the case of polygon objects from shapefiles stored in databases. Polygon and COG rasters must share the same coordinate system, hence the code transforms the polygon coordinates to match the COGs CRS.

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Specifications

The API is based on the Flask based web service provided by the FACT Utils polygonqueryonraster package from the the FACT Polygon Query on Raster project.

The data is from the global land cover released by Impact Observatory (IO) and Esri, using Sentinel-2 imagery to classify into ten unique land use/land cover (LULC) classes using a deep learning model in partnership with Microsoft AI for Earth.

Karra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.

Value	Name	Description
1	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.
2	Trees	Any significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).
3	Grass	Removed and transformed into 11
4	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.
5	Crops	Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.
7	Built Area	Human made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.
8	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.
9	Snow/Ice	Large homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.
10	Clouds	No land cover information due to persistent cloud cover.
11	Rangeland	Open areas covered in homogenous 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.

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Notes

Since this service is based on a continuous raster, results for polygon elevation will include:

• 'count' of pixels in the polygon,
• 'min' elevation,
• 'max' elevation,
• 'mean' elevation,
• 'std' standard deviation of elevation,
• 'median' elevation,
• 'entropy', which is the Shannon entropy measure of elevation data.

The data is at 10 meter scale and as descrtibed in https://www.gislounge.com/2020-global-land-cover-data/ and in https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d.

It considers ten categories of land use cover:

1. Water (areas that are predominately water such as rivers ponds, lakes, and ocean)
2. Trees (clusters that are at least 10 meters high)
3. Grasslands such as open savannas, parks, and golf courses
4. Flooded vegetation such as wetlands, rice paddies, and
5. Crops
6. Scrubland
7. Built areas such as urban/suburban, highways, railways, and paved areas.
8. Bare ground in areas with little or no vegetation such as exposed rock/soil and sparsely vegetated deserts.
9. Permanent snow and ice areas
10. Cloud cover areas where the persistent cloud cover prevents an analysis of the underlying land cover. ​

• Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022
• Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84
• Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)
• Extent: Global
• Source imagery: Sentinel-2 L2A
• Cell Size: 10-meters
• Type: Thematic
• Attribution: Esri, Impact Observatory, and Microsoft

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Authors

Riccardo Boero - ribo@nilu.no

License