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27 march 2020 00:00:32

 
Remote Sensing, Vol. 12, Pages 1071: Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar (Remote Sensing)
 


Forest three-dimensional (3-D) structure, in the vertical dimension, consists of at least two components, including overstory and a forest background matrix (i.e., shrubs, grass, and bare earth). Quantitatively characterizing the proportions of forest sunlit (i.e., sunlit overstory and forest background) and shaded (i.e., shaded overstory and forest background) components is a crucial step in simulating the spatial variations of bidirectional reflectance distribution function (BRDF) of a forest canopy. By developing a Voxel-based sorest sunlit and shaded (VFSS) approach driven by aerial laser scanning data (ALS), we investigated the spatial variations of the forest sunlit and shaded components in a heterogeneous urban forest park (Washington Park Arboretum) with abundant tree species and a homogeneous natural forest area (Panther Creek). Meanwhile, we validated the forest canopy directional reflectance at both solar principal and perpendicular planes at the plot level. Moreover, we explored the effects of ALS data characteristics and forest stand conditions on the estimation accuracy of forest sunlit and shaded components. Our results show that (1) ALS data effectively stratify overstory and forest background with the accuracy decreasing from 87% to 65% as forest densities increase; (2) the root mean square errors (RMSEs) between the modeled- and ALS-based proportions of forest sunlit and shaded components range from 5.8% to 11.1% affected by forest densities; and (3) the scan angles and flight directions have apparent effects on the estimation accuracy of forest sunlit and shaded components. This work provides a solid foundation to investigate the spatial variations of directional forest canopy reflectance with a high spatial resolution of 1 m.


 
179 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 1072: The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty (Remote Sensing)
Remote Sensing, Vol. 12, Pages 1070: Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks (Remote Sensing)
 
 
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