## 4 Results
### Model Development and Validation of Forest AGB Maps of UAV  
The positions of 24 sampling plots are shown in Figure 6a. The symbols of yellow dots, blue dots, yellow stars, and blue stars represent the sampling plots of type A, type B, type C, and type D, respectively. Three concentric square plots with different sizes as shown in Figure 6b are setup at each position to evaluate the effects of plot size on the accuracy of prediction models based on UAV stereo imagery. The MCHM images of four 45 m \(\times\) 45 m sampling plots representing four stand categories are shown in Figure 7. Figure 7a to Figure 7d are the MCHM of the UAV stereo imagery with a resolution of 0.5 m corresponding to the sampling plots of A1, B1, C1, and D1. Figure 7e to Figure 7h are the vertical distribution of points corresponding to Figure 7a to Figure 7d. Figure 5: Forest spatial structures extracted by the synergy of point cloud of leaf-on stereo imagery and the filtered point cloud of leaf-off stereo imagery. (**a**) leaf-on UAV mean canopy height model (MCHM), i.e., MCHM extracted from point cloud of leaf-on stereo imagery subtracted by the digital terrain model (DTM) produced by IMF; (**b**) lidar MCHM, the MCHM from lidar data; (**c**) profiles of leaf-on UAV MCHM and lidar MCHM along the same line as Figure 4d; (**d**) profiles of leaf-on UAV MCHM and lidar MCHM along the same line as Figure 4e. The prediction models of forest AGB using mean forest heights from UAV stereo imagery take the form of power functions as that of lidar. The model coefficients and accuracy are shown in Table 3. The scattering plot of the forest AGB from lidar against that predicted by the prediction models is given in Figure 8. Both Table 3 and Figure 8 show that the differences of prediction accuracy from the models developed at different plot sizes are not obvious in terms of R\({}^{2}\). The accuracy differences in terms of RMSE are also weak, which is about 1.8 Mg/ha between 15 m \(\times\) 15 m and 30 m \(\times\) 30 m, and 1.4 Mg/ha between 30 m \(\times\) 30 m and 45 m \(\times\) 45 m. Figure 9 shows that the forest AGB of sampling plots scattered evenly among the dynamic range from 0 to 140 Mg/ha. The results show that the dynamic range of forest AGB can be covered by sampling plots selected on MCHM of UAV and ortho-rectified mosaic image. Figure 6: The sampling plots of forest AGB used for model developments. (**a**) the spatial distributions of sampling plots, type A: yellow dots, type B: blue dots, type C: yellow stars, type D: blue stars; the background image was the same as Figure 6a; (**b**) the relative positions of sampling plots with different sizes. Figure 7: The MCHM of 45 m \(\times\) 45 m field sampling plots. (**a–d**) were the MCHM of UAV stereo imagery with a resolution of 0.5 m corresponding to the sampling plots of A1, B1, C1, and D1. (**e–h**) were the vertical distribution of points corresponding to (**a–d**). Three maps of forest AGB from UAV are produced by the three prediction models developed at sampling plot sizes of 15 m \(\times\) 15 m, 30 m \(\times\) 30 m and 45 m \(\times\) 45 m. Figure 9 shows the validation of forest AGB maps by taking those of lidar as a reference. The three forest AGB maps of the UAV stereo images are highly correlated with those of the lidar data with correlation coefficients (R\({}^{2}\)) larger than 0.94 and the root mean square errors (RMSEs) are smaller than 10 Mg/ha. No obvious differences or trends of the estimation accuracy of forest AGB are observed along with the changes of sampling plot sizes. The results indicate that the field sampling plots with a size of 15 m \(\times\) 15 m are large enough, at least over the research area of this study, for the mapping of forest AGB using UAV stereo imagery. Figure 8: Comparisons of forest AGB estimated from lidar and UAV stereo data using models developed at different plot sizes (24 plots for each plot size, i.e., 4 types \(\times\)6 plots/types). RMSE, root mean square error.