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Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
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Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
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Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model

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Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
Journal Article

Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model

2025
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Overview
Accurately quantifying fine-scale forest canopy-absorbed photosynthetically active radiation (APAR) is essential for monitoring forest growth and understanding ecological processes. The development of 3D radiative transfer models (3D RTMs) enables the precise simulation of canopy–light interactions, facilitating better quantification of forest canopy radiation dynamics. However, the complex parameters of 3D RTMs, particularly detailed 3D scene structures, pose challenges to the simulation of radiative information. While high-resolution LiDAR offers precise 3D structural data, the effectiveness of different tree crown reconstruction methods for APAR quantification using airborne laser scanning (ALS) data has not been fully investigated. In this study, we employed three ALS-based tree crown reconstruction methods: alphashape, ellipsoid, and voxel-based combined with the 3D RTM LESS to assess their effectiveness in simulating and quantifying 3D APAR distribution. Specifically, we used two distinct 3D forest scenes from the RAMI-V dataset to simulate ALS data, reconstruct virtual forest scenes, and compare their simulated 3D APAR distributions with the benchmark reference scenes using the 3D RTM LESS. Furthermore, we simulated branchless scenes to evaluate the impact of branches on APAR distribution across different reconstruction methods. Our findings indicate that the alphashape-based tree crown reconstruction method depicts 3D APAR distributions that closely align with those of the benchmark scenes. Specifically, in scenarios with sparse (HET09) and dense (HET51) canopy distributions, the APAR values from scenes reconstructed using this method exhibit the smallest discrepancies when compared to the benchmark scenes. For HET09, the branched scenario yields RMSE, MAE, and MAPE values of 33.58 kW, 33.18 kW, and 40.19%, respectively, while for HET51, these metrics are 12.74 kW, 12.97 kW, and 10.27%. In the branchless scenario, HET09′s metrics are 10.65 kW, 10.22 kW, and 9.79%, and for HET51, they are 2.99 kW, 2.65 kW, and 2.11%. However, differences remain between the branched and branchless scenarios, with the extent of these differences being dependent on the canopy structure. Our conclusion demonstrated that among the three tree crown reconstruction methods tested, the alphashape-based method has the potential for simulating and quantifying fine-scale APAR at a regional scale. It provides a convenient technical support for obtaining fine-scale 3D APAR distributions in complex forest environments at a regional scale. However, the impact of branches in quantifying APAR using ALS-reconstructed scenes also needs to be further considered.