LiDAR Reveals the Process of Vision-Mediated Predator–Prey Relationships
"> Figure 1
<p>Study system and experimental design. (<b>a</b>) Study area. The type of land cover in the study area consisted mainly of Taiga Forest. The 241 gray triangles represent the locations of boar nests obtained during our field survey, and the 20 red circles represent the locations of boar nests used to collect point cloud data in this study. (<b>b</b>,<b>c</b>) Sample internal point cloud data display. Point cloud data from the sample of 2021-05-19-09-47-40. (<b>b</b>) Top view of point cloud data. (<b>c</b>) Flat view of point cloud data. (<b>d</b>) The purpose of our study was to find a more general rule, which required that the environmental conditions of 20 samples were different. We used three indicators—canopy height, diameter at breast height, and canopy cover—to represent the environmental conditions. The Kruskal–Wallis test shows that the environmental conditions of the 20 quadrats were significantly different (<span class="html-italic">p</span> < 0.001 ***). The data are z-scaled to be presented in the same dimension.</p> "> Figure 2
<p>Workflow of the research framework. (<b>a</b>) Initial field survey to obtain the location of wild boar nests. (<b>b</b>) Point cloud data collection. The red cube is an artificial fence that represents the location of the wild boar’s nest to accurately identify it when processing the point cloud data. The red line is the trajectory when the point cloud data are collected by backpack laser scanning (BLS).This trajectory can improve the consistency of point cloud data with real things. (<b>c</b>,<b>d</b>) LiDAR 360 was used to preprocess point cloud data. (<b>c</b>) Original point cloud data. The range is larger than the sample range, and the lowest point cloud is not on the same horizontal plane. (<b>d</b>) Pre-processed point cloud data. The sample range is a circular area with the wild boar’s nest as the center and a radius of 25 m. The lowest point cloud is in the same horizontal plane. (<b>e</b>,<b>f</b>) Calculation of the visibility using the minimum enclosing rectangle (<b>e</b>), and positioning of the minimum enclosing rectangle to simulate the tiger near the nest (<b>f</b>), using GvEcology. We set up a minimum enclosing rectangle in 1–360 directions (θ) around the nest and 39 distance gradients (i) in each direction (θ) (<b>f</b>). (<b>g</b>) Use of (<b>e</b>–<b>f</b>) with point cloud data and mapping of an actual scene.</p> "> Figure 3
<p>A sample of the minimum enclosing rectangle used to calculate visibility. Data were obtained from sample 2021-05-18-12-01-28, θ = 4. The blue portion of each minimum enclosing rectangle indicates the area (n<sub>θ,i</sub>) obscured by the projection of the point cloud. The white portion indicates the exposed area, which indicates visibility.</p> "> Figure 4
<p>Pattern of visibility change with distance. Within each sample, the change pattern between distance and visibility conforms to a logarithmic curve (Visibility= a – b × log (Distance), <span class="html-italic">p</span> < 0.05, R<sup>2</sup> > 0.7). The gray line perpendicular to the X axis is 5 m.</p> "> Figure 5
<p>Ambush behaviors can be closer to the wild boar than the standing behaviors. Blue represents the standing behaviors; red represents the ambush behaviors. Gray shade indicates that the two groups of data are not significantly different (<span class="html-italic">p</span> > 0.05). The red line is 5 m, the blue line is 10 m. Within 5 m, ambush behaviors no longer brought tigers closer to the nest.</p> "> Figure 6
<p>Changes in visibility along the simulated animal trajectories. Point cloud data from the sample of 2021-05-19-09-47-40. (<b>a</b>) Simulated animal trajectories in environment point clouds. (<b>b</b>) The relationship (R = −0.646, <span class="html-italic">p</span> < 0.01 **) between the distance from the target and the change of visibility.</p> "> Figure 7
<p>‘Visibility fortress’ hint. Visibility data from sample 2021-05-17-11-08-33. The center of the ‘visibility fortress’ is the location of the boar’s nest. The original visibility ranges from 0 to 1. To clearly show the spatial distribution of the visibility on a 50-m scale, we scaled the visibility to 0–10. We used a triangular irregular network surface to display the visibility surface to completely preserve the distribution details of the visibility in the 3D space. We did not use the model to fit the visibility surface because we were not sure if those details were important in the 3D space.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection
2.2.1. Location Data of Wild Boar Nests
2.2.2. Point Cloud Data of the Environment Surrounding the Wild Boar Nests
2.3. Data Processing
2.3.1. Point Cloud Data Pre-Processing
2.3.2. Calculation of the Visibility Index
2.3.3. Simulated Scene Showing the Tiger Near the Nest
2.4. Statistical Analysis
3. Results
3.1. Visibility Calculation
3.2. 3D Forest Structure Mediated Predation Strategy
3.3. 3D Forest Structure and Predation Mediated Anti-Predation Strategy
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Fu, Y.; Xu, G.; Gao, S.; Feng, L.; Guo, Q.; Yang, H. LiDAR Reveals the Process of Vision-Mediated Predator–Prey Relationships. Remote Sens. 2022, 14, 3730. https://doi.org/10.3390/rs14153730
Fu Y, Xu G, Gao S, Feng L, Guo Q, Yang H. LiDAR Reveals the Process of Vision-Mediated Predator–Prey Relationships. Remote Sensing. 2022; 14(15):3730. https://doi.org/10.3390/rs14153730
Chicago/Turabian StyleFu, Yanwen, Guangcai Xu, Shang Gao, Limin Feng, Qinghua Guo, and Haitao Yang. 2022. "LiDAR Reveals the Process of Vision-Mediated Predator–Prey Relationships" Remote Sensing 14, no. 15: 3730. https://doi.org/10.3390/rs14153730