Utilities to Support Lidar Applications at the Landscape, Forest, and Tree Scale
Colorize a LAS object using multiple methods
Compute PCV (Portion de Ciel Visible) for point cloud
Compute SSAO (Screen Space Ambient Occlusion) for point cloud
Create animated GIF of rotating 3D point cloud
Point cloud cylinder fitting as per de Conto et al. 2017 as implemente...
Download NAIP Imagery for LiDAR Extent
Calculates eigen decomposition metrics for fixed neighborhood point cl...
Obtain tree information by rasterizing point cloud values of relative ...
Convert LAS object to XYZ matrix
Merge RGB colors from two colorized LAS objects
Plot a raster by its name
Process rasters based on suitability, gap, and spur parameters
Obtain tree information by processing point cloud data
Segment a terrestrial point cloud using graph theory.
Spanner color palette
Sum rasters by suitability level
Implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) <doi:10.3390/rs13122297>, and a hierarchical patch delineation algorithm 'PatchMorph' (Girvetz & Greco, 2007) <doi:10.1007/s10980-007-9104-8>. Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) <doi:10.1016/j.isprsjprs.2015.08.007> with cylinder fitting methods from de Conto et al. (2017) <doi:10.1016/j.compag.2017.07.019>. PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on 'lidR' (Roussel et al., 2020) <doi:10.1016/j.rse.2020.112061>.