# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 5 Discussion
The weighting in the horizontal plane did not improve the results as assumed, hence was not presented in the tables. The Gaussian function puts more weight on points near the center point, i.e., the mean, and less weight on points near the boundary of the kernel, so that the kernel is less likely to shift if there are not enough points outside of the center area of the kernel. Therefore, weighting in either height or in the XY plane should be further investigated before implementing for each specific data. Crown localization is assessed because crown tops are used to validate the segmentation results. Crown tops can be simply decided by the highest points of the segmented crowns. But the tree locations can be inaccurate, as real trees are normally not perfectly straight upwards. An alternative approach is to fit the segmented crowns with ellipses and take the ellipse centers as crown locations. The results varied in this regard. The results of the simulated data showed clear advantage when simply using the highest points as tree tops, giving better segmentation results and lower RMSEs, whereas the results of the Aberfoyle data showed the contrary, better segmentation results and lower RMSEs when fitting the crowns with ellipses. This is because the first simulated data are perfectly upright trees with almost symmetric crowns, whereas the second is from a plantation forest where most trees are naturally inclined, and have more diverse crown structures. The benchmark data also showed better segmentation results when fitting the crowns, hence crown fitting for tree top detection is recommended for real forests. The localization accuracy itself can be further assessed when accurate ground truth of crown locations are available, which can be difficult by either field measurements or other sensing techniques [50]. The compared marker-controlled watershed method performed well on the simulated data, with a particularly high precision. Similarly, the raster-based region growing method produced the best results for the simulated data. However, both were outperformed by the two point-based methods. For such a simple and single-layered plot, raster-based methods are expected to have a good performance. However, it clearly struggled with a more natural data, especially with the multi-layered benchmark plots. The advantage is that they are extremely fast, thus are still worth trying for less structurally complicated forests. Even though the focus of the paper is on the thorough assessment of the mean shift method itself, the comparisons with other raster- and point-based methods prove the value of such assessment, as mean shift is able to produce competitive results. There are other possible improvements of mean shift segmentation for ITD. For example, the Pollock model crown parameter can be adaptive from prior knowledge, or classification from other data sources. Raster-based methods can be combined with mean shift to approximately estimate the crown size, which can then feed into the kernel. Moreover, a hierarchical approach can be adopted, similar to [28], where the data is segmented by mean shift in two rounds. The first round segments the original data into plausible individual trees. A pre-trained classifier is then used to detect the oversegmentedand undersegmented trees, which are refined by a second round of mean shift segmentation with appropriate parameter settings derived from the classification. As these approaches require extra steps other than the mean shift algorithm, they are considered to be out of the scope of the paper, which is focusing on the assessment of the method itself.