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

Abstract

Aim Vegetation structure is a key determinant of animal diversity and species distributions. The introduction of Light Detection and Ranging (LiDAR) has enabled the collection of massive amounts of point cloud data for quantifying habitat structure at fine resolution. Here, we review the current use of LiDAR‐derived vegetation metrics in diversity and distribution research of birds, a key group for understanding animal–habitat relationships. Location Global. Methods We review 50 relevant papers and quantify where, in which habitats, at which spatial scales and with what kind of LiDAR data current studies make use of LiDAR metrics. We also harmonize and categorize LiDAR metrics and quantify their current use and effectiveness. Results Most studies have been conducted at local extents in temperate forests of North America and Europe. Rasterization is currently the main method to derive LiDAR metrics, usually from airborne laser scanning data with low point densities (<10 points/m2) and small footprints (<1 m diameter). Our metric harmonization suggests that 40% of the currently used metric names are redundant. A categorization scheme allowed to group all metric names into 18 out of 24 theoretically possible classes, defined by vegetation part (total vegetation, single trees, canopy, understorey, and other single layers as well as multi‐layer) and structural type (cover, height, horizontal variability and vertical variability). Metrics related to canopy cover, canopy height and canopy vertical variability are currently most often used, but not always effective. Main conclusions Light Detection and Ranging metrics play an important role in understanding animal space use. Our review and the developed categorization scheme may facilitate future studies in the selection, prioritization and ecological interpretation of LiDAR metrics. The increasing availability of airborne and spaceborne LiDAR data and the development of voxel‐based and object‐based approaches will further allow novel ecological applications, also for open habitats and other vertebrate and invertebrate taxa.

Niche separation of wetland birds revealed from airborne laser scanning

“…In order to derive ecologically relevant information, the obtained 3D point cloud needs to be further processed, e.g. into metrics which statistically aggregate the 3D point cloud information within raster cells (Davies and Asner 2014, Bakx et al 2019). LiDAR metrics can then be used to map animal habitats (Lucas et al 2019, Koma et al 2020) or to model the geographical distribution of animals such as birds, mammals and invertebrates (Zellweger et al 2013, 2014, Bakx et al 2019).…”

Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain

“…Although airborne and terrestrial LiDAR can accurately quantify ground topography in forested terrain, these methods remain largely impractical at large spatial scales due to high data acquisition costs [8,[12][13][14]. Spaceborne LiDAR is unique since it comes with low acquisition costs and provides a synoptic perspective of certain plot-level details from orbit [15]. The Ice, Cloud, and land Elevation satellite-1 (ICESat-1) [16], the Global Ecosystem Dynamics Investigation (GEDI) [17], and the Ice, Cloud, and land Elevation satellite-2 (ICESat-2) [18] are typical spaceborne LiDAR systems.…”

Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

“…The CA-Markov model was implemented and subjected to visual image interpretation, cognition of patterns, and colors which shows great efficiency in simulating the year 2030 forest cover map of the study area (Figure 9). Bakx et al (2019) and Bank (1991) posits that one of the easy and accurate ways of extracting information from remotely sensed data such as Landsat satellite images is by cognition of patterns and colors. The overall classification accuracy for the years 2010, 2020, and 2030 of the forest cover map was 80.00%, 81.47%, and 89.77%.…”