How to evaluate laser scanner data easily – with “3D Forest”

In the following step, you import the TLS raw data into the created project by clicking on “Project - Import - Import base cloud” on the menu bar. At the LWF, the original scan data and an export in the form of a text file (PTS) are saved by default. The evaluations are carried out generally on the basis of the PTS-file. During its application to date, it has been observed that 3D Forest often aborts the import of very large files. To make it basically possible to work with very large input files, scientists at the LWF have created an R-function that reduces the size of huge files by taking into account only every “nth” dataset. For the evaluation presented here, the original file of around 2.4 gigabytes was reduced to around 500 megabytes by considering only every fifth dataset. The result of the import is shown in Fig. 3. The three-dimensional data can be viewed from different angles by dragging with the mouse or by holding down the Strg/Crtrl key and dragging with the mouse at the same time. By holding down the shift key, you can move the point cloud generally within the screen section. You can use the rotating middle button on the mouse to zoom in and out.

In use of this technology up to now, it has always been necessary to clean up the terrain point cloud by removing points that do not represent the terrain. The clean-up can be started via “Terrain - Manual adjustment” in the menu bar. Once the “terrain point cloud” (Terrain_octree.pcd) has been selected, it can be moved around within the view using the mouse and keyboard as described in the previous section. By clicking on Shift+X, you can switch to the mode for deleting data points. To clean-up the terrain point cloud we found it works well if you select the side view on to the point cloud. If you then switch to the “deletion mode” (Shift+X), you can remove the “ground noise” by “dragging” the relevant two-dimensional sections. By clicking on “Stop EDIT”, you save the cleaned up terrain point cloud under Terrain_octree.pcd and the deleted ground points under Terrain_rest.pcd. The results of the segmentation of the point cloud for Altdorf forest climate station according to terrain points (cleaned up) and vegetation points are presented in Fig. 4.

In the next step in the process, the “Vegetation point cloud” (Vegetation_octree.pcd) is segmented according to individual trees. It proved useful to trim the area in which the trees to be evaluated are in advance, i.e. remove outlying data points first. This can be done by selecting “Vegetation - Manual tree selection” from the menu list. This is as described in Section (4) Cleaning up the “terrain point cloud” using X + Shift. By clicking on “Stop EDIT”, you stop the process, and the trees that have not been deleted are saved under Tree.pcd. In the case of the Altdorf forest climate station, we removed the points outside the boundary fence, which is clearly visible in the point cloud. The result is a point cloud called "Vegetation_tree.pcd" and a point cloud called "Vegetation-rest.pcd".

The former shows the results of the trimmed point cloud (Fig. 5) before it is segmented further into individual trees. The further segmentation is done by clicking on “Vegetation - Automatic tree segmentation” in the menu bar. It proved useful to adjust the default settings in the “Automatic segmentation” dialogue box and to go to the “Input distance” field and then change the values for “input range in %”. For the Altdorf forest climate station, values of 5 m and 60 % were selected. Once the input is confirmed, individual point clouds are created for each individual segmented tree and transferred into the project. The checking of the results proved extremely time-consuming, as the results of the automated segmentation had to be checked visually. Where necessary, each individual tree had to be removed singly by marking it with the left mouse button and then confirming the removal with the right mouse button. Figure 6 shows the results of the automated segmentation for the area in Altdorf. Fig. 6a includes misclassifications. These are eliminated in a time-consuming and labour-intensive further step by deactivating the tree group and visually inspecting each individual object. Misclassified objects are removed from the overall dataset (by selecting with the left mouse button, removing with the right mouse button, and then confirming with left mouse button).

In the next step in the process, dendrometric parameters are determined at individual tree level for the segmented tree objects. For this purpose, you determine the lowest point of each tree relative to its z-coordinate. This is done via the menu bar by clicking on “Trees - Position lowest point” or “Trees - Position RHT” (tree base position computed by randomised Hough transformation).

Up to now, the best results have been achieved by choosing the first of these options. This step should also be carried out at individual tree level, as it has been shown that the selection of several tree objects at the same time in the “Compute tree position using [...]” dialogue box often leads to the program crashing. This step is made somewhat easier if misclassified objects are removed in advance from the tree list as described above, and the “Input Tree Cloud” list only contains valid tree objects. Once the lowest tree positions have been determined, experience to date has shown that the other parameters (e.g. BHD or height calculation) can be calculated for all tree objects together by using the relevant multiple selection option. Once the calculations at single tree level have been completed, the values can be exported in tabular form as a txt-file by clicking on “Trees - Export Tree Parameters” on the menu bar, and processed further with external software where applicable. Graphic outputs can be generated to show the individual calculated parameters, shown in Figure 7 below using the height measurement values determined as an example.