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The scan angle information implicitly contained within the 3-D point cloud data (PCD) generated from light detection and ranging strongly affects the retrieval accuracy of the forest canopy structural parameters. Using information generated from overlapping aerial laser scanning (ALS) flight paths with multiple scan angles over a forest canopy can help to remove the occlusion effects between foliage elements, ultimately creating a relative comprehensive PCD. In this paper, we develop a novel physically based scan angle correction algorithm to retrieve the effective leaf area index (LAIe) of a forest canopy using ALS. Furthermore, we investigate the effects of scan angle and the number of ALS overpass lines from adjacent flight paths over a forest canopy on directional gap fraction (DGF) estimates. Our results suggest that ALS-based LAIe estimates capture 71.35% of the variations in LAIe derived from digital hemispherical photography. A forest canopy point cloud created using PCD from multiple overlapping ALS flight paths was sufficient to quantitatively reveal the anisotropy characteristics of DGF variations. These results suggest that scan angle information should not be neglected in retrieving forest canopy structural parameters, especially when using ALS data collected with a wide scan angle (i.e., −30° to 30° in this paper). Finally, this paper provides a solid foundation to characterize the 3-D spatial distribution of a forest radiation regime using ALS-based forest PCD. [ABSTRACT FROM PUBLISHER]

The spatial distribution of the photosynthetic components of a forest canopy plays a key role in ecological related processes such as gas exchange, photosynthesis, and evapotranspiration through affecting radiation regimes of the forest canopy. However, quantitative evaluation of woody materials' contribution to effective leaf area index (LAIe) using 3-D terrestrial laser scanning (TLS) is a challenging work. In this paper, we first identified the differences between directional gap fraction (DGF) and angular gap fraction (AGF) and then developed a local geometric feature-based approach to automatically classify a TLS forest point cloud data (PCD) into three different classes, including nonphotosynthetic canopy components (i.e., stem and branch points), photosynthetic canopy components (i.e., leaf and grass points), and bare ground. In addition, we proposed a new approach named “radial hemispherical point cloud slicing” algorithm to investigate the 3-D spatial distribution of foliage elements and retrieve LAIe from a given forest PCD. Our results showed that nonphotosynthetic canopy components contributed from 19% to 54% to LAIe depending on various forest densities. Moreover, TLS-based LAIe estimates can explain 74.27% variations of digital-hemispherical-photography-based LAIe values with a linear regression statistical model. This paper provides a theoretical foundation for LAI estimation based on the PCD generated using the TLS system and facilitates the application of TLS on retrieving 3-D forest canopy structural biophysical parameters. [ABSTRACT FROM PUBLISHER]

Accurate separation of photosynthetic and nonphotosynthetic components in a forest canopy from 3-D terrestrial laser scanning (TLS) data is a challenging but of key importance to understand the spatial distribution of the radiation regime, photosynthetic processes, and carbon and water exchanges of the forest canopy. The objective of this paper was to improve current methods for separating photosynthetic and nonphotosynthetic components in TLS data of forest canopies by adding two additional filters only based on its geometric information. By comparing the proposed approach with the eigenvalues plus color information-based method, we found that the proposed approach could effectively improve the overall producer's accuracy from 62.12% to 95.45%, and the overall classification producer's accuracy would increase from 84.28% to 97.80% as the forest leaf area index (LAI) decreases from 4.15 to 3.13. In addition, variations in tree species had negligible effects on the final classification accuracy, as shown by the overall producer's accuracy for coniferous (93.09%) and broadleaf (94.96%) trees. To remove quantitatively the effects of the woody materials in a forest canopy for improving TLS-based LAI estimates, we also computed the “woody-to-total area ratio” based on the classified linear class points from an individual tree. Automatic classification of the forest point cloud data set will facilitate the application of TLS on retrieving 3-D forest canopy structural parameters, including LAI and leaf and woody area ratios. [ABSTRACT FROM PUBLISHER]