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Biomass is important in monitoring global carbon storage and the carbon cycle, which quickly and accurately estimates forest biomass. Precision forestry and forest modeling place high requirements on obtaining the individual parameters of various tree species in complex stands, and studies have included both the overall stand and individual trees. Most of the existing literature focuses on calculating the individual tree species’ biomass in a single stand, and there is little research on calculating the individual tree biomass in complex stands. This paper calculates the individual tree biomass of various tree species in complex stands by combining multispectral and light detection and ranging (LIDAR) data. The main research steps are as follows. First, tree species are classified through multispectral data combined with field investigations. Second, multispectral classification data are combined with LIDAR point cloud data to classify point cloud tree species. Finally, the divided point cloud tree species are used to compare the diameter at breast height (DBH) and height of each tree species to calculate the individual tree biomass and classify the overall stand and individual measurements. The results show that under suitable conditions, it is feasible to identify tree species through multispectral classification and calculate the individual tree biomass of each species in conjunction with point-cloud data. The overall accuracy of identifying tree species in multispectral classification is 52%. Comparing the DBH of the classified tree species after terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to give UAV-LS+TLS, the concordance correlation coefficient (CCC) is 0.87 and the root-mean-square error (RMSE) is 10.45. The CCC and RMSE are 0.92 and 1.41 compared with the tree height after UAV-LS and UAV-LS+TLS.
Lian et al. (Wed,) studied this question.