New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans

Abstract

We developed new measures of structural complexity using single point terrestrial laser scanning (TLS) point clouds. These metrics are depth, openness, and isovist. Depth is a three-dimensional, radial measure of the visible distance in all directions from plot center. Openness is the percent of scan pulses in the near-omnidirectional view without a return. Isovists are a measurement of the area visible from the scan location, a quantified measurement of the viewshed within the forest canopy. 243 scans were acquired in 27 forested stands in the Pacific Northwest region of the United States, in different ecoregions representing a broad gradient in structural complexity. All stands were designated natural areas with little to no human perturbations. We created “structural signatures” from depth and openness metrics that can be used to qualitatively visualize differences in forest structures and quantitively distinguish the structural composition of a forest at differing height strata. In most cases, the structural signatures of stands were effective at providing statistically significant metrics differentiating forests from various ecoregions and growth patterns. Isovists were less effective at differentiating between forested stands across multiple ecoregions, but they still quantify the ecological important metric of occlusion. These new metrics appear to capture the structural complexity of forests with a high level of precision and low observer bias and have great potential for quantifying structural change to forest ecosystems, quantifying effects of forest management activities, and describing habitat for organisms. Our measures of structure can be used to ground truth data obtained from aerial lidar to develop models estimating forest structure.

Terrestrial 3D Laser Scanning for Ecosystem and Fire Effects Monitoring

“…Other notable references are related to single or multi-scan analyses using other TLS instruments. Batchelor and others (2023) illustrate the use of single scans of the FARO® Focus 3D S120 terrestrial laser scanner (FARO Technologies Inc., Lake Mary, FL, USA) for estimating new structural complexity metrics in Pacific Northwest forests. Many studies have used multiple merged scans to predict grass, shrub, tree, or fuel attributes at various scales and using a variety of metrics of interest (e.g., Alonso-Rego and others 2020, Cooper and others 2017, Loudermilk and others 2009, Olsoy and others 2014, Wallace and others 2022).…”

Terrestrial and Airborne Lidar to Quantify Shrub Cover for Canada Lynx (Lynx canadensis) Habitat Using Machine Learning

“…TLS is well-suited to determine understory vegetation characteristics such as the density of foliage and amount of open area [42][43][44]. The depth of view and openness of a location can be quantified by looking at how far each pulse travels, and if a pulse is not returned at all [45,46]. TLS can replicate the use of a cover board at fixed locations in a non-subjective manner, as well as return a robust estimate of the total depth and openness of a plot.…”

“…The DCB method is the process of determining, at a defined height increment of the plot, how far each pulse traveled before coming into contact with a surface (depth), as well as determining if a pulse interacted with a surface at all (openness) [45]. A 2D depth map of TLS scans can be created by generating a raster where each pixel represents a location where a laser pulse was sent.…”

Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem

“…Currently, over 100 metrics or structural characteristics can be calculated from each point cloud, which represent the vegetation's spatial distribution, density, proportion or identification of various parts of the forest (e.g., tree boles), as well as the structure of openings or space within the forest, and differences between true empty space and space created by occlusion. These metrics have been successful in predicting fire severity [48], forest structure [49], and understory species richness [41], but have yet to predict surface vegetation mass or consumption. The difficulty lies in the laboratory processing, which requires time and resources to sort, dry, and weigh shrubs, grasses, leaf litter and woody debris collected in situ, and the analytical complexity involved in relating 3D structure to mass.…”