# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results
### Results of Aberfoyle Forest  
The results of tree segmentation of the Aberfoyle forest is presented in Table 4. Note that the ground measured trees do not cover all the trees in the data, only the match rates (recall) can be determined. Therefore, no precision and F1-Score is given. The precision is related to false detections, which can reflect oversegmentation. The best match rates were achieved by both Cylinder and Pollock kernels. The Sphere kernel produced slightly lower match rates, but the undersegmentation rates were also lower. The Cylinder and Pollock kernels both produced similar results under settings where the kernel size was adaptive to the height, regardless of weighting on the height or not, which is different  
Figure 4: Segmentation and tree top detection results of the simulated data from three methods (scale in meters): (**a**) Mean shift with Cylinder kernel; (**b**) Mean shift with Pollock kernel; (**c**) Marker-controlled watershed method. Red points denote true tree tops, and blue ones are detected tops. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Shape** & **Crown** & **Match** & **Precision** & **F1-Score** & **RMSE\_xy** & **RMSE\_h** & **RMSE\_1** & **RMSE\_1’** \\ \hline Cylinder3 & Centre & 0.6 & 0.6977 & 0.6452 & 1.2098 & 0.6116 & 1.0225 & 1.9072 \\ Cylinder3 & Top & 0.7 & 0.814 & 0.7527 & 1.2415 & 0.4287 & 1.2428 & 2.137 \\ Pollock4 & Centre & 0.66 & 0.7857 & 0.7174 & 1.3849 & 0.5995 & **1.0156** & **1.9044** \\ Pollock4 & Top & 0.72 & 0.8571 & 0.7826 & 1.2582 & 0.4336 & 1.1704 & 2.1496 \\ Mk+WS & - & 0.64 & 0.9697 & 0.7711 & 1.2488 & 0.5236 & 1.8598 & - \\ RegGrow & - & **0.8** & **1** & **0.8889** & 1.2256 & **0.4237** & 2.0324 & - \\ Vox+Rule & - & 0.76 & 0.6333 & 0.6909 & **0.9832** & 0.441 & 1.9807 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tree top detection results of the simulated data. The best Cylinder and Pollock kernel settings are tested to extract crown locations, using either ellipse centers (Center) or top points (Top), compared with watershed (Mk+WS), region growing (RegGrow) and voxel-based ruling (Vox+Rule) methods. The best results are highlighted in **bold**. The accuracies of location, height and spreads of detected tree crowns are shown by RMSEs respectively. from the simulated data. So the weight is not very influential for this specific data, which might be due to the less accurate ground measurements. Notice the bandwidth values are also different from what we had with the simulated data, so the best settings for each dataset should be tested. Similarly, the best performing Cylinder and Pollock kernels were used to extract the crown parameters. The match rates of Cylinder and Pollock kernels are quite similar, but the Cylinder kernel (Figure 5a) produced many more trees than the Pollock kernel (Figure 5b). Some of the detected points are too close to each other to be individual trees, caused by widespread branching and scattered points. The watershed method (Figure 5c) yielded less trees; it can be seen that a number of tree tops were not detected. Quantitatively, as shown in Table 5, the highest match rates were achieved by mean shift when the crown was located by the fitted ellipse center for both Cylinder and Pollock kernels, which is contradictory to the simulated data. As explained for the simulated data, where trees are single layered, perfectly straight, and there is less noise, the highest points do better represent the tree tops. For real tree plots, these conditions do not hold, so the crown fitting gives better results. It is worth noting that the ground truth is measured at tree bottoms, but real trees normally have certain degrees of inclination, hence the assessment of crown locations can be affected. The oversegmentation and undersegmentation rates are also lower when the crown is centered. Among the mean shift models, the