09/12/2025 | Press release | Distributed by Public on 09/12/2025 13:33
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Newly released, first-of-its-kind agriculture maps of the state of Hawaiʻi are expected to help policymakers, land managers and researchers better monitor crop diversity, evaluate land-use change and design programs that support food security and sustainable agriculture. Experts said the new resource can also assist with post-fire disaster assessments, and in the near future, could potentially help better evaluate wildfire risks before a major disaster occurs, such as the 2023 Maui wildfires.
The University of Hawaiʻi at Mānoa, led by project director Qi Chen in the Department of Geography and Environment, in collaboration with the National Agricultural Statistics Service (NASS) in the U.S. Department of Agriculture (USDA), released the high-resolution, crop-specific dataset called Hawaiʻi Cropland Data Layer (HCDL). The maps are publicly available via NASS's geospatial portals: CroplandCROS and AgriWatch.
Filling in the map 'desert'
NASS collaborated with UH Mānoa to develop HCDL by using Google Earth Engine and Google's DeepMind AI-powered data. Hawaiʻi was previously a "desert" for annual agricultural maps, said Chen in the College of Social Sciences. He said the lack of maps hinders efforts to track crops, assess land use and support food security in Hawaiʻi.
"This gap in knowledge became especially evident during the 2023 Maui wildfires, when USDA and state agencies had only limited capacity to assess the agricultural impact," Chen said. "Without up-to-date, field-scale crop maps, agencies were unable to quickly quantify the extent of cropland loss, identify which crops were most affected or prioritize recovery resources. Instead, assessments had to be pieced together from outdated maps, secondary sources and on-the-ground reports, delaying an accurate picture of the disaster's effect on Hawaiʻi's agricultural sector."
Maps are currently available for 2024 and 2023. NASS plans to release HCDL for the 2025 crop year in February 2026. This project was supported by a $268,472 grant from the U.S. Department of Agriculture.
Importance of agriculture maps
Chen said developing such maps for Hawaiʻi is particularly critical because of the state's unique agricultural landscape. Unlike large-scale monocultures (the practice of growing a single species of plant over a large area), which are common on the U.S. continent, Hawaiʻi's farms are often small, fragmented and characterized by diverse crops cultivated side by side.
"While it makes mapping more difficult, it also means that accurate, high-resolution crop maps can provide transformative insights into resource allocation, irrigation planning, invasive species management and resilience to environmental change," said Zhe Li, project co-director and geographer in the USDA.
Li added that since annual crop maps for Hawaiʻi are now available, they can be integrated with real-time satellite data on weather, drought and wildfire risk to safeguard agricultural production.
"Consider a situation similar to the 2023 Maui wildfires: If high-resolution crop maps had been in place, emergency managers could have quickly overlaid fire perimeters with known crop locations to estimate economic losses and identify which producers needed the most help and immediate support," Chen said. "Beyond disaster response, the same maps could also be used proactively-by identifying cropland areas most vulnerable to drought or invasive species, with agencies directing irrigation resources, extension services or pest management programs to the farmers who need them most."
In addition to Chen and Li, members of the research team include: Noa Lincoln, researcher in the Department of Tropical Plant and Soil Sciences in UH Mānoa's College of Tropical Agriculture and Human Resilience; Zhengwei Yang, geographer with USDA; Haonan Chen, associate professor of electrical and computer engineering at Colorado State University; and Changyong Cao, chief of NOAA's Satellite Calibration and Data Assimilation Branch in Satellite Meteorology and Climatology Division.