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wiesehahn / lidar-forestry

Personal space to organize information on the topic of lidar (mainly airborne) for forestry
https://wiesehahn.github.io/lidar-forestry/
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Prioritizing commercial thinning: quantification of growth and competition with high-density drone laser scanning #432

Open wiesehahn opened 1 week ago

wiesehahn commented 1 week ago

https://doi.org/10.1093/forestry/cpae030

wiesehahn commented 15 hours ago

Simulation of growing season direct irradiance; 1–2, digital surface models are generated from uppermost canopy returns of the point cloud; 3, solar position is determined for each Monday of the growing season at 11 a.m., 1 p.m., and 3 p.m.; 4, rayshading is conducted for the given solar position at each time point; 5, irradiance is then averaged across growing season timepoints (n = 54); 6, average growing season irradiance is extracted for each segmented tree crown. (B) Characterization of fine-scale topographic wetness across study area; 1, ground classification is conducted on lidar returns; 2, digital terrain model is generated from ground returns; 3, digital terrain model is smoothed with a median filter; 4, slope raster is calculated as the difference between cells; 5, flow accumulation is estimated; 6, topographic wetness index is calculated from constituents and the mean value is extracted for tree crowns.