Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classes—Pinus densiflora
, Chamaecyparis obtusa
, Larix kaempferi
, and broadleaved trees—using a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blue—RGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications.
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