An efficient method to exploit LiDAR data in animal ecology

Abstract

Light detection and ranging (LiDAR) technology provides ecologists with high‐resolution data on three‐dimensional vegetation structure. Large LiDAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely LiDAR‐based metrics such as canopy height), which are used as predictors in ecological models, potentially with loss of relevant information. We illustrate an efficient alternative approach to reduce the dimensionality of LiDAR data that aims at minimal data filtering with no a priori assumptions on the ecology of the target species. We first fit the ecological model exploiting the full variability in the LiDAR point cloud, then we explain the results using post‐modelling LiDAR‐data classification for ecological interpretation only. This is the classical logic of explorative, hypothesis generating and predictive statistics, rather than testing specific vegetation‐structural hypotheses. First, we reduce the dimensionality of the LiDAR point cloud by principal component analysis (PCA) to fewer predictors. Second, we show that LiDAR‐PCs are capable to outperforming commonly used environmental predictors in ecological modelling, including LiDAR‐based metrics. We exemplify this by modelling red deer (Cervus elaphus) and roe deer (Capreolus capreolus) resource selection in the Bavarian Forest National Park, Germany. After fitting the ecological model, we provide an interpretation of the information included in LiDAR‐PCs, which allows users to draw conclusions whenever using them as predictors. We make use of the PCA rotation matrix and post‐modelling data classification, and document deer selection for understorey vegetation at unprecedented fine scale. Our approach is the first attempt in animal ecology to avoid the use of LiDAR‐based metrics as model predictors, but rather generate principal components able to capture most of the LiDAR point cloud variability. Our study demonstrates that LiDAR‐PCs can boost ecological models. We envision a potential use of LiDAR‐PCs in several applications, particularly species distribution and habitat suitability models. We demonstrate an application of our approach by building suitability maps for both deer species, which can be used by practitioners to visualize model spatial predictions and understand the type of forest structures selected by deer.

Use and categorization of Light Detection and Ranging vegetation metrics in avian diversity and species distribution research

“…However, choosing the right metrics is not always easy and might depend on a priori ecological knowledge of animal habitat use. In cases where such knowledge is lacking, it is also possible to condense the variability in LiDAR point clouds by generating principal components as predictors and using them in exploratory analyses with animal species distribution data to generate hypotheses about animal habitat and space use (Ciuti et al, ).…”

“…LiDAR‐derived vegetation metrics (e.g., mean canopy height) are then calculated to summarize the vegetation structural information from the point cloud within each pixel or voxel. An advantage of such area‐based approaches (i.e., rasterization) and voxel‐based approaches (i.e., voxelization) is that they are computationally efficient, but they also lead to information loss regarding the 3‐D information of the point cloud (Ciuti et al, ). An alternative is to use object‐based approaches (Figure b) where either the point cloud or a derived rasterized map is segmented into objects such as trees, forest stands, reed beds or hedges based on similarities and differences in neighbourhood information around points or grid cells (Höfle, Hollaus, & Hagenauer, ; Koch, Kattenborn, Straub, & Vauhkonen, ).…”

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

“…In the case this resulted in eleven principal components (PC), which were used as new predictor variables in the final statistical analyzes. With this setup, we followed the procedure described in Ciuti et al [21]. To account for possible nonlinear relationships between the predictors and the response we used generalized additive models (GAMs).…”

“…The remote sensing (RS) discipline mainly describes forest structure by summarizing variables that can 2 of 20 be determined from sensor data. These include maximum height, quantiles of height from surface models or point clouds, point densities, or structural complexity indices, tree counts, biomass estimates and many more [3,[13][14][15][16][17][18][19][20][21][22][23][24][25][26]. If the aim is to provide a broader perspective of the forest structure, metrics such as the Stand Structural Complexity Index (SSCI) [4] are for instance applicable.…”

Spatial behavior in rehabilitated orangutans in Sumatra: Where do they go?

“…A sensitivity analysis showed that at least seven random points associated to each orangutan relocation were needed to obtain stable model estimates. Therefore, we opted to draw ten random points per used location to obtain robust parameter estimates [67] (Table 1).…”