Recognition of specified errors of Individual Tree Detection methods based on Canopy Height Model

In the last two decades, many individual tree detection (ITD) methods have been developed based on Airborne Laser Scanner (ALS) data, but their application still has some shortcomings. In complex and dense forest stands, the number of tree segments extracted from Canopy Height Model (CHM) are frequently over- and under-estimated, reducing the ability to predict stand structure. This study presents the possibility of distinguishing correct from erroneous segments resulting from the ITD methods based on CHM. For this purpose, three machine learning methods were tested: Random Forest (RF), Support Vector Machine and k-Nearest Neighbor among which the RF algorithm gave the best results. Groups of predictors based on segment geometry as well as structural and intensity metrics from the ALS point cloud were used. From the whole set of predictors, the ratio of segment perimeter to its area proved to be the most important. Using RF classifier, it was possible to identify under-segmentation and over-segmentation errors, as well as correct segments, with high accuracies for training (OA = 87.0% and κ = 0.794) and test data (OA = 85.3 and κ = 0.641). Recognition of specific segmentation errors is important in that it can be used to determine which conditions favour the occurrence of errors and which tree species are more likely to be incorrectly segmented. Therefore, the study verified the error susceptibility of individual tree species. This can help improve calibration and development of segmentation algorithms according to the tree species analysed.