Abstract

Forest Inventory Parameters and Carbon Mapping from Airborne LiDAR
Track: Forestry
Authors: Vinod Kumar

Light detection and ranging (LiDAR), a relatively recent active remote sensing technology, can provide accurate appraisal of vertical forest canopy structure. In the present research, a novel method to precisely detect individual trees from high density airborne LiDAR point cloud data has been tested. Tree Canopies are delineated using object based image analysis and a new approach of Thiessen polygons in ArcGIS. Further an array of important tree parameters such as tree height, canopy projection area (CPA), canopy base height, canopy volume, canopy density, local canopy gaps, local tree density and canopy inclination have been extracted from the LiDAR point cloud data to prepare geospatial forest inventory. The research also deals with tree species classification based on query method on structural tree parameters in geospatial inventory database. Lastly, the sequestered forest carbon in the study area has been assessed by developing regression models from the extracted tree parameters.