A machine learning framework combining multi-temporal lidar and satellite data mapped post-fire debris flows across seven Oregon watersheds, with multi-temporal ...
UAV-derived vegetation indices, especially GNDVI and SR, predicted wheat grain yield more reliably than a handheld GreenSeeker sensor across 13 ...
A global satellite analysis identified weakening resilience and vegetation reorganization in 27% of terrestrial ecoregions, but the findings are statistical ...
A 2000–2024 satellite analysis of 500 large southeastern U.S. flooded forest swamps found highly variable associations between drought, precipitation and ...
Along the sun-scorched coastlines of Ras Al Khaimah in the United Arab Emirates, one of the most thermally extreme mangrove ...
Researchers showed that simple curve-based rules applied to freely available Sentinel-2 and Sentinel-1 satellite data can accurately retrieve five key ...
A systematic search of more than a thousand candidate vegetation indices found that a simple four-band formula outperforms NDVI for ...
Researchers in China have developed a label-free temporal weighting method that fuses multispectral drone imagery from three wheat growth stages, ...
A multi-year Texas study combining drone imaging, machine learning, and explainable AI shows that wheat yield is best predicted from ...
A new SARIMA-based forecasting framework using two decades of MODIS satellite data projects that India's mangrove vegetation will continue a ...
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