Predicting Forage Availability from Open Source LiDAR Data

Due to the variation in British Columbia’s topography and plant communities, rangeland management faces unique challenges with natural resource operations and rural development. The goal of this study was to create a predictive model of forage availability using light detection and ranging (LiDAR) data from the Open LiDAR Data Portal and biometric ground truthing data from the Vegetation Resource Index.

LiDAR point cloud was filtered to < 2m returns to capture understory vegetation prior to modeling. The predicative model created from single linear regression using the lidar metric pzabovezmean produced an insignificant result (R2 0.11, p-value 0.08). Random forest model with the inclusion of topographic variables derived from digital elevation model could not produce a better result than the single linear regression (R2 0.01, p-value 0.76).