Extraction of Individual Tree Factor and Tree Height Model Construction of Larix olgensis–Fraxinus mandshurica Mixed Forest Based on TLS Data

Taking the 1 hm² mixed stand of Larix olgensis−Fraxinus mandshurica in Mengjiagang Forest Farm as the research object, the forest trees were divided into three grades: dominant trees, average trees, and pressed trees, using the equal plant diameter step standard wood method. Then, with the measured values as the reference value, the accuracy of extracting the individual tree factors of the three grades of the two species by TLS was analyzed respectively, and finally the tree height model was constructed using the individual tree factors extracted from TLS data. The optimal basic tree height models of the two species were screened out, and the tree height models built with the dummy variable of tree classification were further evaluated and compared. The results showed that: for the mixed stand of standing water and falling leaves selected in this paper, the matching accuracy of the individual tree between the point cloud data and the measured data was 92.79% for Larix olgensis and 92.25% for Fraxinus mandshurica. The extraction accuracy of the diameter at breast height of the two species reached more than 97%, and the extraction accuracy of the diameter at breast height of the dominant trees was higher than that of the average trees and the pressed trees. The extraction accuracy of the tree height of the two species reached more than 95%, and the extraction accuracy of the average trees of Larix olgensis was higher than that of the dominant trees and the pressed trees. The extraction accuracy of the dominant trees of Fraxinus mandshurica was higher than that of the average trees and the pressed trees. In the basic tree height models built using TLS data, the Logistic model (R²=0.7830, RMSE=1.9516) fitted Larix olgensis best, and the Gompertz model (R²=0.7248, RMSE=1.9536) fitted Fraxinus mandshurica best. Therefore, the Logistic model and Gompertz model were the optimal basic models built based on TLS data for the two tree species respectively. Finally, the models built with tree classification as dummy variable for the two tree species had R² values of 0.7907 and 0.7312, respectively. TLS technology has a high individual tree matching rate for mixed stand and a good accuracy in extracting individual tree factors. The model built based on TLS data with tree classification as dummy variable performs better than the basic model in predicting the growth differences of tree height and diameter at breast height, with better prediction accuracy and adaptability, which can provide a theoretical basis for the forestry management of mixed stand in this region.