Estimating Subcanopy Solar Radiation Using Point Clouds and GIS-Based Solar Radiation Models
2025
Daniela Buchalová | Jaroslav Hofierka | Jozef Šupinský | Ján Kaňuk
This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches&mdash:r.sun and the Point Cloud Solar Radiation Tool (PCSRT)&mdash:in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments.
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