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Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

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