Global Airborne Laser Scanning Data Providers Database (GlobALS)—A New Tool for Monitoring Ecosystems and Biodiversity

Abstract

Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

“…After a period of intensive research that has shown the irreplaceability of ALS‐derived vegetation structure variables for understanding species–environment relationships, it is now time to make them available to a wider audience which will allow incorporating them as common variables in predictive models. Finding new ways to make ALS data easily accessible is a priority to accelerate ecological research (Assmann et al, 2022; Stereńczak et al, 2020). It would be valuable to have a list of several standardized variables recommended for use in SDM and ecological research, so that scientists or authorities generating ALS‐derived products know which to focus on.…”

Remote sensing approaches to identify trees to species-level in the urban forest: A review

“…Second, LiDAR data is critical for mapping the x, y of trees and delineating the tree top position and tree crowns into polygons from which other spectral analyses can be undertaken. Broadly discrete return airborne LiDAR surveys are continuing to expand across urban areas in the United States (Stoker and Miller, 2022) across the globe (Stereńczak et al, 2020). Although repeat time is infrequent (e.g., 5 to 10 years), initial LiDAR surveys provide baseline data on tree location, crown area, and height along with other more complex metrics that can be used in species identification.…”

Which metrics derived from airborne laser scanning are essential to measure the vertical profile of ecosystems?

“…We therefore recommend that ALS metrics of vertical variability of vegetation should not be restricted to SD and FHD, but also include skewness, kurtosis and CV of vegetation height. Moreover, some authors emphasize that FHD is not designed to describe continuous variables, and hence suggest replacing FHD with Lorenz curves and Gini coefficients (Valbuena et al, 2021). Whether these alternative metrics do indeed provide a better description of the vertical variability of vegetation than FHD needs to be tested with a range of datasets and in different ecosystems, not only in forests.…”

“…horizontal variability) needs to be aggregated at a coarser resolution (Graham et al, 2019). The time for such assessments is ripe because (1) a large number of country‐wide ALS datasets is now openly accessible (see overviews in Kissling et al, 2022; Moudrý et al, 2023; Stereńczak et al, 2020), (2) user friendly, free and open source software has been developed to calculate a large range of ALS metrics (Meijer et al, 2020; Roussel et al, 2020) and (3) high‐throughput (reproducible and open source) workflows are now available to perform the efficient, scalable, distributed and standardized processing of multi‐terabyte LiDAR point clouds into ALS metrics (Kissling et al, 2022). Until such assessments are performed, proposing a list of 10 ALS metrics may seem premature.…”