Understanding Spatial Variability and Point Classification Implications on Methods for Retrieval of Leaf Orientation for Effective Leaf Area Index from Terrestrial Laser Scanning.

Abstract:

Tree leaf orientation, including the distribution of the inclinational and azimuthal angles in the canopy, is an important attribute of forest canopy architecture and is critical in determining the within and below canopy solar radiation regimes. We demonstrate techniques for indirectly and nondestructively retrieves foliage elements’ orientation and distribution from point cloud data (PCD) obtained using a terrestrial laser scanning (TLS) approach.

An equation with a single parameter for characterizing the leaf angular distribution of crowns was developed. The TLS-based algorithm captures 97.4% (RMSE =1 .094 degrees, p<0.001) variation of the leaf inclination angle compared to manual measurements for an artificial tree. When applied to a live tree seedling and a mature tree crown, the TLS-based algorithm predicts 78.51% (RMSE =1 .225 degrees, p<0.001) and 57.28% (RMSE =4 .412 degrees, p<0.001) of the angular variability, respectively. Furthermore we demonstrate our approach for retrieve of biophysical characteristics of the forest canopy including extinction coefficient, gap fraction, overlapping effect, and effective leaf area Index (ELAI). Out ELAI model captures 88.7% (rmse =0 .007, p<0.001, andn = 30) variation of the destructive-sample-based leaf area measurement results and 89.1% (rmse =0 .01; p<0.001) of the variation in results from digital hemispherical photographs. Finally we demonstrate how scanner setup which includes lateral scans can reduce effects of occlusion in terrestrial laser data collection.