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- H34F-07: Deep‑Learning Enhanced UAS–Sap‑Flow Method to Partition Evapotranspiration in Semi‑Arid Footprints
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NOLA CC
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Hernan Moreno, University of Texas at El Paso (First Author, Presenting Author)
Stephanie Marquez, University of Texas at El Paso
Marguerite Mauritz, The University of Texas at El Paso
Habibur Howlider, University of Texas at El Paso
Understanding how much water plants release into the air is important for predicting water use in dry landscapes. In this study, we combined field sensors that measure water movement inside plants with aerial images taken by drones. Using artificial intelligence, we identified and measured the size of different plant species to estimate how much water each one used. We tested this method at a dryland site in New Mexico, focusing on mesquite and creosote bushes. Our results show that these plants release different amounts of water, and that this water use can make up more than half of the total evaporation from the land. This approach offers a new, low-cost way to track plant water use across larger areas and could help improve how we manage water in dry ecosystems.
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