Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data

The two main approaches to deriving forest variables from laser-scanning data are the statistical area-based approach (ABA) and individual tree detection (ITD). With ITD it is feasible to acquire single tree information, as in field measurements. Here, ITD was used for measuring training data for the ABA. In addition to automatic ITD (ITDauto), we tested a combination of ITDauto and visual interpretation (ITDvisual). ITDvisual had two stages: in the first, ITDauto was carried out and in the second, the results of the ITDauto were visually corrected by interpreting three-dimensional laser point clouds. The field data comprised 509 circular plots (r = 10 m) that were divided equally for testing and training. ITD-derived forest variables were used for training the ABA and the accuracies of the k-most similar neighbor (k-MSN) imputations were evaluated and compared with the ABA trained with traditional measurements. The root-mean-squared error (RMSE) in the mean volume was 24.8%, 25.9%, and 27.2% with the ABA trained with field measurements, ITDauto, and ITDvisual, respectively. When ITD methods were applied in acquiring training data, the mean volume, basal area, and basal area-weighted mean diameter were underestimated in the ABA by 2.7−9.2%. This project constituted a pilot study for using ITD measurements as training data for the ABA. Further studies are needed to reduce the bias and to determine the accuracy obtained in imputation of species-specific variables. The method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized.