Lidar-based Mapping of Forest Volume and Biomass by Taxonomic Group Using Structurally Homogenous Segments

Abstract

This study evaluated the potential of an object-oriented approach to forest type classification as well as volume and biomass estimation using small-footprint, multiple return lidar data. The approach was applied to coniferous, deciduous, and mixed forest stands in the Virginia Piedmont, U.S.A. A multiresolution, hierarchical segmentation algorithm was applied to a canopy height model (CHM) to delineate objects ranging from 0.035 to 5.632 ha/average object. Per-object lidar point (per return height and intensity) and CHM distributional parameters were used as input to a discriminant classification of 2-class (deciduous-coniferous) and 3-class (deciduous-coniferous-mixed) forest definitions. Lidar point-height-based and CHM classifications yielded overall accuracies of 89 percent and 79 percent, respectively. Volume and biomass estimates exhibited differences of no more than 5.5 percent compared to field estimates, while showing distinctly improved precisions (up to 45.5 percent). There were no significant differences between accuracies for varying object sizes, which implies that reducing the lidar point coverage would not affect classification accuracy. These results lead to the conclusion that a lidar-based approach to forest type classification and volume/biomass assessment has the potential to serve as a single-source inventory tool.