Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds

Forest biomass is often difficult to quantify because field measurements are time consuming and require destructive sampling. This study explores the retrieval of stem biomass of individual trees by terrestrial laser scanning (TLS). Destructive sampling was done to collect biomass data from sample trees and used as a dependent variable in a regression analysis. Two biomass estimation models were investigated: one based on diameter at breast height (DBH) and another based on the sum of the stem section volume. Both the DBH and the stem section volume were determined from automatic reconstruction of the stem curves. Two tree species (Scots pine and Norway spruce) were considered together. The quality of the performance of the models was evaluated via a leave-one-out cross-validation strategy using accurate field measurements for 30 trees. The correlation coefficient (r) and root mean square errors (RMSEs) between the predicted and measured stem biomass were used as measures of goodness of model fitting. The model with DBH as the predictor produced an r-value of 0.93 and an RMSE of 21.5%. For the model using the reconstructed stem and correspondingly derived stem volume as the predictor, an r-value of 0.98 and an RMSE of 12.5% were achieved. The results indicated that TLS measurements are capable of assessing stem biomass with high automation and accuracy by reconstructing the stem from TLS point clouds.