lidarderived

LiDARderived encompasses datasets, maps, and metrics created from Light Detection and Ranging (LiDAR) point clouds through processing and analysis. These products translate irregular 3D point data into regular representations such as digital elevation models, surface models, and derivative measurements used for interpretation and decision making. The term can refer to both gridded rasters and vector layers that represent terrain, vegetation, structures, and other features derived from LiDAR returns.

Common LiDAR-derived products include digital elevation models (DEM or DTM for ground surface), digital surface models (DSM for top surfaces), and canopy height models (CHM) that measure vegetation height. Additional derivatives include slope, aspect, curvature, roughness, intensity statistics, and LiDAR-derived vegetation metrics or biomass estimates. Data may be stored as LAS/LAZ point clouds, raster formats (GeoTIFF), or vector layers, with coordinate reference systems and metadata documenting accuracy and provenance.

Production involves data collection (airborne, terrestrial, or mobile LiDAR), preprocessing (georeferencing, noise removal), classification (ground, vegetation, buildings), and surface generation (gridding or TIN-based interpolation). QA/QC and accuracy assessment compare derived surfaces to ground truth. These processes yield products usable for hydrology, urban planning, forestry, archaeology, coastal management, and hazard assessment.

LiDAR-derived products offer high vertical accuracy, fine spatial resolution, and the ability to capture features beneath vegetation in some cases, but they require substantial processing, skilled interpretation, and access to suitable data. Limitations include data gaps due to occlusion, nonuniform point density, cost, and licensing constraints. Ongoing advances integrate LiDAR with multispectral imagery, time-series analysis, and machine learning to expand the range of derivable metrics.