Machine learning fusion of lidar and optical data for high-precision rice canopy height estimation: A multi-method comparison

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dc.creatorRamirez Gonzalez, Aymara Olin
dc.creatorSelvaraj, Michael Gomez
dc.date2025-11-27
dc.date2026-02-10T12:06:06Z
dc.date2026-02-10T12:06:06Z
dc.date.accessioned2026-06-27T13:24:48Z
dc.descriptionRice canopy height is critical for biomass estimation and crop monitoring. LiDAR provides accurate structure but limited temporal coverage, while multispectral sensors offer frequent monitoring but lack direct height information. Challenge: Can data fusion leverage complementary strengths of both sensors? This study evaluates four fusion approaches combining LiDAR percentiles with optical vegetation indices to generate improved Canopy Height Models (CHM) for rice phenology monitoring.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/181361
dc.identifier.urihttp://hdl.handle.net/123456789/58592
dc.languageen
dc.rightsOpen Access
dc.sourceRamirez Gonzalez, A.O.; Selvaraj, M.G. (2025) Machine learning fusion of lidar and optical data for high-precision rice canopy height estimation: A multi-method comparison. 1 p.
dc.subjectrice
dc.subjectmachine learning
dc.subjectvariable costs
dc.titleMachine learning fusion of lidar and optical data for high-precision rice canopy height estimation: A multi-method comparison
dc.typePoster

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