Using LiDAR to Study Forest Characteristics in Lusk Creek Wilderness

The main objective of this study was to explore the different applications Light Detection and Ranging (LiDAR) has in forestry, and how that data can be used to study different characteristic of the forest. LiDAR offers multipoint data defining the first points that hit the top of the canopy, and the points that penetrate the canopy and hit the ground. Point Cloud Lidar LiDAR data from Lusk Creek Wilderness in the Shawnee National Forest were used to help explore these applications. The different methods, tree height estimation, Biomass density, and classifying points, were used to build structure models, and creating Triangulated Irregular Network (TIN) models to help show terrain structure. Tree height estimation was done by creating a Digital Elevation Model (DEM) and Digital Surface Model (DSM) from ground and first filter returns. Subtracting these two from each other gave a rough estimate for tree height in the forest. Biomass density is a calculation that measures how dense the vegetation is. This is done by converting ground and vegetation points into point vector format. Then converting those points into raster’s you can run them through several math inputs that allows one to calculate and map the biomass density. Creating A forest model was created by classifying each point into four different classes. These classes are being ground, low vegetation, medium vegetation, and high vegetation. Viewing each class in a 3D viewer like ARC Scene could show forest structure. TIN model were also created to just give a basic overview of the elevation and layout of the terrain. In this study LiDAR was proven to be a very fast and effective method for giving an overview of forest structure and layout