Estimating Basal Area and Stem Volume for Individual Trees from Lidar Data

Abstract

This study proposes a new metric called canopy geometric volume G, which is derived from small-footprint lidar data, for estimating individual-tree basal area and stem volume. Based on the plant allometry relationship, we found that basal area B is exponentially related to G (B �� 1G 3⁄4 , where � 1 is a constant) and stem volume V is proportional to G (V � � 2G, where � 2 is a constant). The models based on these relationships were compared with a number of models based on tree height and/or crown diameter. The models were tested over individual trees in a deciduous oak woodland in California in the case that individual tree crowns are either correctly or incorrectly segmented. When trees are incorrectly segmented, the theoretical model B � � 1G 3⁄4 has the best performance (adjusted R 2 , � 0.78) and the model V � � 2G has the second to the best performance ( � 0.78). When trees are correctly segmented, the theoretical models are among the top three models for estimating basal area ( � 0.77) and stem volume ( � 0.79). Overall, these theoretical models are the best when considering a number of factors such as the performance, the model parsimony, and the sensitivity to errors in tree crown segmentation. Further research is needed to test these models over sites with multiple species.