New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 1; p. 145
• Main Authors: Batchelor, Jonathan L., Wilson, Todd M., Olsen, Michael J., Ripple, William J.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Complexity; depth; Depth perception; Forest & brush fires; Forest ecosystems; Forest management; forest structure; Forests; Growth patterns; Lidar; Occlusion; openness; Perturbation; Scanners; Signatures; Site selection; Stand structure; Statistical analysis; Strategic management; Terrestrial ecosystems; terrestrial lidar; TLS; Trees; Vegetation; viewshed
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15010145

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.