Exercise 3: Visualizing the seedling structure and assessing seedling growth using point cloud data
- The question that is addressing in this study
Characterization of the forest canopy and projection of forest inventory from Light detection and ranging (lidar) data has been widely used over the last two decades. Active remote sensing approaches (i.e., lidar) can use to construct three dimensional (3D) structures of tree canopies and underlying terrains (Pearse et al., 2019). However, due to some drawbacks associated with lidar data such as the low density, expensiveness of acquisition of data, and difficulties for utilizing for small areas, the practical use of lidar data becomes limited (Tao et al., 2011). However, the emergence of unmanned areal vehicles (UAVs) photogrammetric point cloud technology could able to address most of the drawbacks associated with lidar data. Especially, lightweight UAVs provide a better platform to acquire digital photos with lower operational costs (Ota et al., 2017). Therefore dense 3D point clouds generated from Structure-from-Motion (SFM) photogrammetry can use as ample source to substitute the lidar point cloud data and utilize to construct 3D structures to estimate forest biophysical properties.
Consequently, voxel-based matrics approaches are commonly used in forestry applications, especially for characterizing the forest canopy and other forest attributes in 3D space. With these approaches, point clouds are split “along both the vertical and horizontal axes to form volumetric pixels (volume pixel or voxel)” (Pearse et al., 2019). The main advantage of the voxel-based prediction is that we can extract the information within different layers of the forest canopy. However, most of the voxel-based matrices have been utilized with lidar point cloud data and terrestrial laser scanning data (Pearse et al., 2019). Since the acquisition of both lidar point cloud data and terrestrial laser scanning data expansive, UAVs based point cloud data use as an alternative solution. Therefore, in this exercise, I am interested in characterizing the forest attributes (in 3D space) using a voxel-based matrics approach by utilizing UAVs photogrammetric point cloud data. Specifically, identify “how the tree crown completeness varies as a function of the number of points that arranged in the 3D point cloud/ voxel resolution”.
2. Name of the tool or approach used, and steps followed.
Step 1: Pre-processing point cloud data
Selecting Area of Interest
I used R software packages to develop point cloud data from UAV images.
- Packages used: lidR, sp, raster, rgdal, and EBImage
As the initial step, point cloud data were extracted using AgiSoft software (Fig.1(a)). Then the lasground function in the lidR package was used to classify the points as ground or not ground by using the “cloth simulation filter” (Fig.1 (b)). In the next step, I used the lasnormalize function in the lidR package to remove the topography from the point cloud created (Fig.1 (c)). Next, the normalized point clouds were cropped with the area of interest (AOI) shapefile (Fig. 2 (d)) using lasclip function.
Step 2: Voxelizing the point cloud data
For better visualization and to increase the efficiency of computer performance, I selected a subset of AOI to illustrate the voxel models(Fig. 1(d)). First, the “lasfilterduplicates” function (in lidR package) was used to remove the duplicate point from point cloud data. After removing the duplicate points, voxelization of point cloud data performed using the “lasvoxelize” function in the lidR package. This function allows two different options (cubic and non-cubic) to reduce the number of points by voxelizing the point cloud. Figure 2 represents the cubic voxel approach, and relative x, y, and z dimensions indicate the resolution of the voxel. Figure 3 represents the non-cubic approach, and relative dimensions for the voxel represent in each diagram. Especially in this approach, z-axis dimensions are different from x and y dimensions.
Step 3: Visualization approaches
The viewshed3d package uses 3D point cloud data for visualizing the different environmental settings, especially for construction 3D environments for ecological research. Due to the potential usefulness of this package, I used it for visualization of the study area and canopy structure of the seedlings in 3D space. At the initial stage, duplicated points were removed from the point cloud. Then the noise associated with point cloud data removed, and the reconstruct_ground() function was used to reconstruct the ground layer of the study area. Finally, an appropriate voxel model (described in section 3) used to visualize the seedlings in 3D space.
Step 4: Geographically weighted regression(continued from exercise 2)
Image classification tools available in ArcMap 10.7:
- Constructing a digital elevation model
- Spatial Analyst Tool: Tools available in Zonal
- Geographically weighted regression
Assessing the effect of geography
To identify the influence of geography for seedling growth, I constructed a digital elevation model for the study area. Relative elevation values for each seedling location were extracted by using the Zonal tool available in ArcMap 10.7. As the next step, a geographically weighted regression performed by considering the tree height as the dependent variable and relative elevation of the seedling locations as the independent variable. The observed coefficients for the geographically weighted regression illustrated in figure 5.
- Voxelization of point cloud data
Results obtain by voxelization of point cloud data indicate that reducing of voxel volume can provide better outputs in terms of visualization of seedlings in 3D space. For example, 1 x1 x 1 m3 cubic voxel cloud not able to visualize the structure of the seedlings in sub-AIO, while a volume of 0.1 x0.1x 0.1 m3 voxel could able to demonstrate the seedling architecture in an appropriate manner. After certain trials, a volume of 0.01 x 0.01 x0.01 m3 showed relatively the best voxel model for visualizing the seedling architecture. Even the voxel volume reduces (beyond 0.01 x 0.01 x0.01 m3), the visualization of the seeling article does not improve drastically. Further reducing the voxel volume requires more time and computer power to process the seedling article. Therefore, I selected 0.01 x 0.01 x0.01 m3 voxel volume (model) as the best cubic voxel model to identify the seedling architecture for this study.
Additionally, I performed voxelization of point cloud data with a non-cubic approach, and the observed results are similar to the cubic voxel models. The only difference for the non-cubic approach is the z-axis change (change of vertical resolution). We can change the resolution of the voxel image by changing the z-axis dimensions based on our interest. For example, we can characterize/observe the canopy structure by changing the resolution of the z-axis, as shown in figure 6. Non-cubic approach appropriate if we are interested in studying the variation of canopy structure relative vertically.
Based on the results obtained by visualization of the study area (step2), provide a better platform to understand the actual canopy structures of each seedling. Mainly, this helps to compare the results we obtained in the 2D environment (in exercise 2) and assess the results with the help of 3D representations. By looking at the canopy structures of the seedlings, we can confirm that seedlings growing in the northwest side of the AOI has relatively larger and healthy seedlings while the southeast side of the AOI has smaller and unhealthy seedlings. This observation agrees with the results obtained by geographically weighted regression (exercise 2 and 3). Both cubic and non-cubic approaches are applicable and can be utilized based on our desired output. The identified optimal voxel resolution (0.01 x 0.01 x0.01 m3) can be used as a reference for further voxel-based analysis, and it will help to save data processing time and with less computer power.
Results obtained by geographically weighted regression (height as the dependent variable and relative elevation of seedling locations as the independent variable) showed a decreasing trend of coefficient values with decreasing the elevation profile (Fig.4 (a)). Furthermore, obtained trends indicate that seedlings located in the northwest part of the study area have higher coeffects values, while seedlings located in the southeast part have relatively lower coefficient values (Fig.4(a)). Generally, the elevation of seedling locations may indicate essential parameters about the geography and water table of the study area. We can assume that lower elevation areas are more prone to accumulate water, and the groundwater table may close to the surface compare to the higher elevations. As described in the previous exercise, this might be the indirect cause for the presence of bare ground in lower elevated areas (i.e., stagnant water may damage the root systems of grass and may die due to rotten roots and will expose the bare ground). Similarly, the presence of excess water may hinder the growth of seedlings as well. As a side effect of the presence of groundwater with woody debris may tend to increase excess moisture conditions in subsurface soil layer and will act as a negative factor for seedling growth, especially for their root system.
Overall, the visualization of seedlings using the voxel approached showed promising results, especially identifying the optimal voxel size/volume that can be utilized for developing seedling architecture in future studies. However, to obtain better visualization outputs, we need to create high-density point clouds (described in step 1), and that requires more time and computer power.
If we are interested in a relative lager area, visualization of objects may need to be enhanced. Therefore, after selecting the appropriate voxel model (i.e., 0.001 x0.001 x 0.001 m3), plotting tools available in lidR package can be used to create better visualization structures, as illustrated in Figure A1.
Additionally, the detection of branches in early-stage seedlings may be difficult due to its unique shape and small branches. However, the developed approach can be useful for identifying the number of branches of a mature trees.
Ota, T., Ogawa, M., Mizoue, N., Fukumoto, K., Yoshida, S., 2017. Forest structure estimation from a UAV-Based photogrammetric point cloud in managed temperate coniferous forests. Forests 8, 343.
Pearse, G.D., Watt, M.S., Dash, J.P., Stone, C., Caccamo, G., 2019. Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities. Int. J. Appl. Earth Obs. Geoinformation 78, 341–351. https://doi.org/10.1016/j.jag.2018.10.008.
Tao, W., Lei, Y., Mooney, P., 2011. Dense point cloud extraction from UAV captured images in forest area, in: Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. Presented at the Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, pp. 389–392. https://doi.org/10.1109/ICSDM.2011.5969071.