CHARACTERIZING MOVEMENT PATTERNS OF NOMADIC PASTORALISTS AND THEIR EXPOSURE TO RIFT VALLEY FEVER IN KENYA

WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.

The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.

The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored for land cover changes. The Global Land Monitoring Service of the Copernicus Programme, and in particular the Hot Spot Monitoring activity, developed a satellite-imagery-based workflow to monitor such areas. Here, we present a total of 852 025 km² of areas mapped with up to 32 land cover classes. Thematic land cover and land cover change maps, as well as validation datasets, are presented.

Decision-making for sustainable vegetable oil production requires accurate global oil crop maps. We used high-resolution satellite data to train a deep learning model that accurately classified industrial and smallholder oil palm, the main oil-producing crop. Our results outperformed previous studies and proved the suitability of deep learning for land use mapping. The global oil palm area was 21±0.42 Mha for 2019; however, young and sparse plantations were not included in this estimate.

Larger ecological zones and wildlife corridors in sub-Saharan Africa require monitoring, as social and economic demands put high pressure on them. Copernicus’ Hot-Spot Monitoring service developed a satellite-imagery-based monitoring workflow to map such areas. Here, we present a total of 560 442 km² from which 153 665 km² is mapped with eight land cover classes while 406 776 km² is mapped with up to 32 classes. Besides presenting the thematic products, we also present our validation datasets.