Characterizing Forest Succession in Central Ontario using Lidar-derived Indices

Abstract

This study investigates the potential of discrete return light detection and ranging (lidar) data to characterize forest succession in a mixed mature forest in central Ontario using indices applied to the lidar point cloud. Derived indices include statistical indices, predicted Lorey's height (R 2 = 0.86; RSME = 2.36 m) and quadratic mean diameterat-breast-height (R 2 = 0.68; RMSE = 1.21 cm), canopy density indices and an information theory based complexity index. To assess how well these indices are able to capture the vertical structure of forest stands, they are compared to Oliver and Larson's (1996) four stages of forest stand development. Best subsets regressions indicated that no single index is able to separate all four stages adequately. However, the predicted Lorey's height index is optimal for separating early from mid succession stages (p <.0001) and a combination of height and complexity indices performed best to discriminate between mid- and late-succession stages (p <.0001).