Global Pasture Watch
This repository provides the source code used to produce the follow Global Pasture Watch products (2000–2022+): * GGC-30m: Global grassland class and extent maps at 30m * GLD-1km: Global livestock distributions maps at 1km * GSVH-30m: Global short vegetation height maps at 30m * GGPP-30m: Global gross primary productivity maps at 30m
Zenodo:
The output maps, reference samples, ML models and additional datasets are publicly available in Zenodo:
- Global grassland class and extent maps at 30m
- Grassland reference samples based on visual interpretation of VHR imagery (2000–2022)
- Global machine learning model for prediction of cultivated and natural/semi-natural grassland
- Grassland sampling design derived by Feature Space Coverage Sampling (FSCV) at 1-km spatial resolution
- Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization
- Global livestock distributions maps at 1km
- Annual livestock headcount maps for cattle, goats, sheep and horses at 1-km 2000–2022 (FAOSTAT-adjusted)
- Annual cattle density maps at 1-km for 2000–2022 including prediction interval
- Annual goat density maps at 1-km for 2000–2022 including prediction interval
- Annual sheep density maps at 1-km for 2000–2022 including prediction interval
- Annual horse density maps at 1-km for 2000–2022 including prediction interval
- Livestock reference samples based on multi-source sub-national census data (2000—2022)
- Annual maps of potential land for livestock production at 1-km for 2000–2022
Webinars:
The webinars organized by the initiative are publicly available:
- Global Pasture Watch: Mapping & Monitoring Global Grasslands and Livestock
- Grassland Mapping & Monitoring: A Focus on Latin America
Related softwares / libraries
The softwares and libraries produced / maintained in the context of the initiative are publicly available: * QGIS Fast Grid Inspection * Scikit-Map * Geo-Wiki
Acknowledgements & Funding
This work is conducted by WRI, OpenGeoHub Foundation, LAPIG/UFG, IIASA and iDiv and has received funding from Land and Carbon Lab through the Global Pasture Watch.