Advances in LiDAR technology in the field of animal ecology

Abstract

LiDAR (light detection and ranging), a fairly new active remote sensing technology, is being widely used in the field of animal ecology by more and more scholars due to the recent development where forest parameters can be extracted and inverted from LiDAR. In this paper, we review the advances in forest parameter extraction from LiDAR and its many applications in studying wildlife habitat. We also analyze current research on forest parameter inversion algorithms based on LiDAR, mainly in forestry research, though we lack quantitative parameters related to the ecological significance of animals. Because few studies have applied LiDAR technology to animal ecology research in China, we consider foreign research in this field in three categories: (1) The relationship between species habitat selection and three-dimensional forest structure; (2) Three-dimensional habitat mapping; (3) Biodiversity assessment and species distribution model prediction. Compared with traditional methods, the high-precision three-dimensional structure information provided by LiDAR can significantly improve the efficacy of monitoring animal habitat quality and biodiversity and the modelling accuracy of species distribution models. These advancements contribute to deeper understanding of species habitat selection and the clustering process mechanism. However, the studies that utilize LiDAR to date have mainly focused on previously known ecological relationships, especially the •综述• © 1022 生 物 多 样 性 Biodiversity Science 第 27 卷 综述 relationship between canopy structure and species diversity. These studies fail to account for either forest understory habitat quality or biodiversity monitoring and evaluation. In short, the relationship between wildlife and its three-dimensional habitat needs to be further explored through analysis of LiDAR data. Future studies should focus on extracting three-dimensional structures of forest understories to improve the efficacy of monitoring habitat quality and biodiversity in the understory, and to provide standard quantitative indicators for the evaluation of animal ecology.

Transboundary Cooperation in the Tumen River Basin Is the Key to Amur Leopard (Panthera pardus) Population Recovery in the Korean Peninsula

“…In the vicinity of the camera point, a 20 m × 20 m area was selected and scanned using lidar equipment (PARACOSM PX-80 HANDHELD MOBILE LiDAR, PX-80, Topsfield, MA, USA). Using the LIDAR360 software, we classified and extracted information on tree species, shrub coverage, tree height, and leaf area index [ 26 ]. Similarly, a manual vegetation survey was conducted within a 20 m × 20 m area near the camera point to record transparency, concealment, and the diversity and quantity of herbs, shrubs, and trees.…”

Three‐dimensional forest foodscape in large herbivores' habitat based on UAV with LiDAR detection

“…More importantly, three‐dimensional forest structure could directly and indirectly indicate the food resources and the foodscapes of large herbivores (Li et al. 2019). Although the resolution of the LiDAR scan at this stage is still not enough to precisely quantify shrubs and herbs, with the continuous updating of technology and the deepening of research, it will be possible to quantify the foodscape through LiDAR.…”

“…With the rapid development of UAV and ground-based LiDAR monitoring technology, new opportunities have been provided for the study of the scale-dependent relationship between wildlife and habitat (Bergen et al 2009;Davies & Asner 2014;Simonson et al 2014). Li-DAR monitoring could quickly and accurately provide the three-dimensional forest structure of the habitat where wildlife lives and in-depth studies on habitat selection and utilization in wildlife ecology have been promoted (Li et al 2019). The latest research, through the comprehensive applications of LiDAR to detect three-dimensional forest structure and automatic camera monitoring data, has shown that mammals are most sensitive to the covariates of forest vertical and horizontal characteristics and prefer to choose environments with complex structures, emphasizing that the identification of mammalian priority restoration areas through three-dimensional forest structure is superior to simple forest classification (Deere et al 2020).…”