2022 Global Land Cover Dataset (Adapted from GLC_FCS30D - v1) at 30m resolution. This dataset builds off the global, fine-scale land cover dynamic monitoringproduct (GLC_FCS30D) with a temporal coverage of 1985-2022, to provide global layers as Cloud Optimized geoTIFFs. Data was downloaded from the sourceand processed by NatCap members as the University of Minnesota. For moreinformation on the processing steps, source data, and land use classifications,please see the 'Lineage' section of the accompanying metadata YAML file.

2021 Global Land Cover Dataset (Adapted from GLC_FCS30D - v1) at 30m resolution. This dataset builds off the global, fine-scale land cover dynamic monitoring product (GLC_FCS30D) with a temporal coverage of 1985-2022, to provide global layers as Cloud Optimized geoTIFFs. Data was downloaded from the sourceand processed by NatCap members as the University of Minnesota. For more information on the processing steps, source data, and land use classifications, please see the 'Lineage' section of the accompanying metadata YAML file.

Global Land Cover Datasets from 2011-2020 (Adapted from GLC_FCS30D - v1) at 30m resolution. This dataset builds off the global, fine-scale land cover dynamic monitoring product (GLC_FCS30D) with a temporal coverage of 1985-2022, to provide global layers as Cloud Optimized geoTIFFs. Data was downloaded from the source and processed by NatCap members as the University of Minnesota. For more information on the processing steps, source data, and land use classifications, please see the 'Lineage' section of the accompanying metadata YAML file.

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20200101_20201231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20150101_20151231 in lc_glc.fcs30d

Reference point samples used in the production of the global maps of annual grassland class and extent for 2000—2022 within the scope of the Global Pasture Wath initiative.

The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises 2.3M points visually classified (using Very High Resolution imagery) in:

1. Cultivated grassland,
2. Natural/semi-natural grassland
3. Other land cover

The file gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20221231_go_epsg.4326_v1.gpkg aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:

• cluster_id: Cluster id defined by k-means (FSCS),
• cluster_distance: Distance from the sample tile to center of the cluster (FSCS),
• cluster_size: Size of cluster (strata) defined by the FSCS,
• priority: Priority used by the visual interpretation,
• tile_id: Sample tile id,
• imagery: VHR reference images used by the visual interpretation,
• min_year: Minimum of year covered by the reference samples,
• max_year: Maximum of year covered by the reference samples,
• n_years: Number of years covered by the reference samples,
• n_samples_c1: Number of reference samples for "Seeded grass" (1),
• n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),
• n_samples_c3: Number of reference samples for "Not grass" (3),
• n_samples_all: Total number of reference samples,

The file gpw_grassland_fscs.vi.vhr_point.samples_20000101_20221231_go_epsg.4326_v1.gpkg provides individual points (with 60-m spatial support) and include the follow collumns:

• sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,
• x: Longitude in WGS84 (EPSG:4326),
• y: Latitude in WGS84 (EPSG:4326),
• vi_tile_id: 1-km tile id,
• imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),
• ref_date: Reference date of VHR image,
• year: Reference year of VHR image,
• class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Other land cover) ,
• class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) ,
• esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,
• glad_glcluc_2000: Land cover class labels extracted from UMD GLAD GLCLUC 2000,
• glad_glcluc_2005: Land cover class labels extracted from UMD GLAD GLCLUC 2005,
• glad_glcluc_2010: Land cover class labels extracted from UMD GLAD GLCLUC 2010,
• glad_glcluc_2015: Land cover class labels extracted from UMD GLAD GLCLUC 2015,
• glad_glcluc_2020: Land cover class labels extracted from UMD GLAD GLCLUC 2020,
• glc_fcs30d_2000: Land cover class labels extracted from GLC_FCS30D 2000,
• glc_fcs30d_2005: Land cover class labels extracted from GLC_FCS30D 2005,
• glc_fcs30d_2010: Land cover class labels extracted from GLC_FCS30D 2010,
• glc_fcs30d_2015: Land cover class labels extracted from GLC_FCS30D 2015,
• glc_fcs30d_2020: Land cover class labels extracted from GLC_FCS30D 2020,
• ml_cv_group: spatial block CV group (based on vi_tile_id),
• ml_type: expecify if the sample was used for (1) training or (2) calibration.

The file gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20221231_go_epsg.4326_v1.gpkg provides the grid samples (with 10-m spatial support) and include the follow collumns:

• tile_id: 1-km tile id,
• bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,
• bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,
• bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,
• google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,
• google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,
• google_image_end_date: End date of the Google Maps Images used in the visual interpretation,
• missing_image_date: No images available,
• same_image_bing_google: Images from the same date available in Google and Bing Maps.

The dataset was produced through the QGIS plugin Fast Grid Inspection.

Related resources

• Maps of dominant grassland:
  2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
• Probability maps of cultivated grassland:
  2000-2022 (All URLs)
• Probability maps of natural/semi-natural grassland:
  2000-2022 (All URLs)
• Grassland reference samples based on VHR imagery (2000–2022):
  GeoPackage files
• Global machine learning models (Random Forest):
  Parquet and joblib python files
• Reference sampling design derived by FSCV:
  GeoPackage and raster files
• Harmonized reference samples based on existing LULC dataset:
  GeoPackage and raster files
• Source code for reproducibility:
  GitHub release
  
• Mapping feedback tool:
  GeoWiki
• Data catalogues:
  OpenLandMap STAC Google Earth Engine

Support

For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watch

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20100101_20101231 in lc_glc.fcs30d

The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset.

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20070101_20071231 in lc_glc.fcs30d

This study integrated ground-based plot surveys and multi-source remote sensing data to develop a canopy height and aboveground biomass estimation model for Hainan Tropical Rainforest National Park from 2003 to 2023. To support scientific transparency and model reproducibility, a portion of the research data has been organized and made publicly available. The dataset includes the following three categories:1、Survey data of tropical rainforest plots：Biomass data of 140 sample plots: Includes 140 sample plots (10 m × 10 m), with information on plot ID, geographic coordinates, forest type, and biomass of tree and understory layers.Biomass data of 64 historical plots: Contains biomass data from 64 historical plots (25.8 m × 25.8 m), including plot ID, location, and aboveground biomass.Data of individual trees from 140 sample plots: Covers detailed measurements of 4,732 individual trees, including species name, diameter at breast height (DBH), tree height, crown volume, and estimated biomass per tree.2、Forest type distribution data：Based on the natural forest classification dataset, GLC_FCS30D land cover data, and ALOS DEM elevation data, this shapefile represents forest type distribution in Hainan Tropical Rainforest National Park from 2003 to 2023 at a 30-meter spatial resolution (WGS 1984 coordinate system).3、Remote sensing estimation results of biomass and canopy height of tropical rainforests in Hainan from 2003 to 2023：Includes annual maps of forest aboveground biomass density and canopy height distribution in Hainan Tropical Rainforest National Park from 2003 to 2023. All data are provided in GeoTIFF format with a spatial resolution of 30 meters, suitable for GIS-based analysis and visualization.

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20210101_20211231 in lc_glc.fcs30d

Area of Habitat (AOH) maps show species distribution patterns, and they are vital for predicting species' survival, assessing species habitat loss or restoration, and developing biodiversity conservation strategies. AOH maps are produced by extracting suitable elevation ranges and habitat types from species’ geographic range maps. AOH reduces errors in geographic range maps. National Key Protected Wildlife are species protected by Chinese law, which are important for maintaining China's biodiversity and ecological security. We produced AOH maps for 720 terrestrial National Key Protected Wildlife, covering the years 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2022. The maps have a resolution of 30 m. We independently validated the AOH maps. On average, AOH maps for all validated species show a mean point and model prevalence of 0.84 ± 0.20 SD and 0.49 ± 0.27 SD, with 97.03% of the species' AOH maps perform better than random. Additionally, all species' AOH maps are spatially overlaid to generate a species richness map, representing the number of species at each grid of 30×30m.All maps are stored in a file geodatabase (.gdb) format, which is accessible and operable using ArcGIS or ArcGIS Pro. The map values are set to 1 for the AOH area and Null for the background. Each species' AOH map is named using its scientific name followed by the year (e.g., Ailuropoda_melanoleuca_2020). The nine AOH maps for the same species, corresponding to nine time points, are stored in a single file geodatabase named using the species' scientific name and Chinese name. The values in the species richness maps represent the number of species at each grid, and the maps are named using the format “Richness_All/Class1/Class2_year”. There are also three information tables in the database. The Species AOH information table records the geographic range, habitat, and elevation preference information and data sources for each species. The Translation table records the mapping of each terrestrial habitat type of IUCN habitat classification scheme to the land cover type of GLC_FCS30D.The AOH Validation table records the validation results (Point prevalence and Model prevalence) for each AOH maps.In this version we have made some new revisions and refinements。We have removed the AOH maps of strictly marine species from the database. And we have strictly distinguishing between water body habitat types and wetland habitat types for all relevant species.

We provide main scripts for the manuscript: Decoding cropland mask effects on the explanatory power of remote sensing and reanalyzed climate data on yield anomalies in Africa.No new data are generated in this study. Yield and harvest area of maize, millet, and sorghum can be accessed through the FAO (2024); MIRCA2000 data is available through Portmann et al. (2010); SPAM data is available through Yu et al. (2020); GLC_FCS30D data is available through Zhang et al. (2024); African administration boundary is from the U.S. Department of State (2017); MODIS LST data is obtained from the NASA (2024a); MODIS ET data is obtained from the NASA (2024b); GPM v6 data is obtained from the NASA (2024c); ERA5-Land data is obtained from the ECMWF (2024); MODIS, GPM v6, and ERA5-Land are pre-processed on the Google Earth Engine Platform (Gorelick et al., 2017). Python 3.10 and R 4.3.3 used for data processing, regression modelling, and figure visualization.

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20160101_20161231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20220101_20221231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20130101_20131231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20170101_20171231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20190101_20191231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20110101_20111231 in lc_glc.fcs30d

SpatioTemporal Asset Catalog (STAC) Item - lc_glc.fcs30d_20180101_20181231 in lc_glc.fcs30d