## 4 Results
### Extraction of Forest Canopy Heights  
Figure 4 shows the results of the extraction of ground surfaces using leaf-off stereo imagery by the IMF at different aspects. The number and percentage of points removed in each iteration is shown in Figure 4a. It is clear that the vegetation points are mostly removed in the first two iterations. The number of removed points decreased gradually after each iteration and was lower than 1% after the fifth iteration. Figure 4b shows the identification of ground points along a profile over flat terrain conditions. The profile is a subset of the point cloud along the X direction (from west to east) with a length of 115 m. It could be seen that the identified points gradually approached the ground surface. The difference between iterations becomes very weak after the third iteration. Figure 4c depicts the vertical distribution of points over a forest stand of 30 m \(\times\) 30 m. The vertical distribution of the remaining points concentrates gradually on the ground surface. Figure 4 showed that the elevation of ground surface can be extracted well by the IMF algorithm in both flat and mountainous areas. Figure 3: Ortho-rectified mosaic images and digital surface model (DSM) of UAV stereo imagery acquired in leaf-on and leaf-off conditions (pixel size 8.6 cm). (**a**) the mosaic image of leaf-on with the flying trajectory showed as yellow lines; (**b**) the mosaic image of leaf-off with ground control points (GCPs) used in the stereoscopic processing showed as yellow dots; (**c**) the digital surface model of stereo imagery acquired on leaf-on season, the dash and solid lines indicates positions of two profiles used in the following sections; (**d**) the digital surface model of stereo imagery acquired on leaf-off season;The MCHM extracted by the synergy of point cloud of leaf-on stereo imagery and the filtered point cloud of leaf-off stereo imagery is shown in Figure 5. Figure 5a,b are the leaf-on UAV MCHM and lidar MCHM, respectively. The two images look nearly identical at a first glance. These results demonstrated that the elevation of ground surface extracted from the point cloud of leaf-off stereo imagery by IMF was enough. In addition, the point cloud of leaf-on stereo imagery and leaf-off stereo imagery has been co-registered accurately enough by the GCPs; otherwise, there would be terrain-related errors remained in Figure 5a caused by their mis-registration. The comparison between the leaf-on UAV MCHM and lidar MCHM is shown in Figure 5c,d along two profiles which are identical to Figure 4d,e respectively. The changing trends of forest heights along profiles of leaf-on UAV MCHM and lidar MCHM were consistent. The pixels where the leaf-on UAV MCHM was higher than the lidar MCHM could be attributed to the bad penetration abilities of stereo imagery over higher tree neighboring those pixels. The pixels where the leaf-on UAV MCHM was lower than lidar MCHM were caused by a failure to catch the canopy tops of trees neighboring those pixels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GCP\#** & **X Error** & **Y Error** & **Z Error** & **GCP\#** & **X Error** & **Y Error** & **Z Error** \\ \hline leaf-off p1 & 0.05 & 0.24 & \(-\)0.98 & leaf-on p1 & \(-\)0.13 & \(-\)0.23 & \(-\)1.11 \\ leaf-off p2 & 0.26 & \(-\)0.90 & 0.90 & leaf-on p2 & 0.20 & \(-\)0.35 & 0.96 \\ leaf-off p3 & 0.08 & \(-\)0.18 & 0.34 & leaf-on p3 & \(-\)0.34 & \(-\)0.13 & 0.48 \\ leaf-off p4 & 0.80 & 0.44 & \(-\)0.08 & leaf-on p4 & 0.67 & 0.22 & 0.37 \\ leaf-off p5 & \(-\)0.01 & 0.38 & \(-\)0.92 & leaf-on p5 & \(-\)0.13 & 0.60 & \(-\)0.54 \\ leaf-off p6 & 0.01 & 0.29 & 1.0 & leaf-on p6 & \(-\)0.02 & \(-\)0.13 & 0.72 \\ leaf-off p7 & \(-\)1.18 & \(-\)0.28 & \(-\)0.27 & leaf-on p7 & \(-\)0.26 & 0.01 & \(-\)0.87 \\ Standard deviation & 0.59 & 0.48 & 0.80 & Standard deviation & 0.34 & 0.32 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of the GCP in the stereoscopic processing of UAV stereo imagery (in meter).