Version 1
: Received: 21 December 2021 / Approved: 23 December 2021 / Online: 23 December 2021 (11:59:27 CET)
How to cite:
La Rue, E.; Fahey, R.; Fuson, T.; Foster, J.; Hatala Matthes, J.; Hardiman, B. Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density. Preprints2021, 2021120390. https://doi.org/10.20944/preprints202112.0390.v1
La Rue, E.; Fahey, R.; Fuson, T.; Foster, J.; Hatala Matthes, J.; Hardiman, B. Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density. Preprints 2021, 2021120390. https://doi.org/10.20944/preprints202112.0390.v1
La Rue, E.; Fahey, R.; Fuson, T.; Foster, J.; Hatala Matthes, J.; Hardiman, B. Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density. Preprints2021, 2021120390. https://doi.org/10.20944/preprints202112.0390.v1
APA Style
La Rue, E., Fahey, R., Fuson, T., Foster, J., Hatala Matthes, J., & Hardiman, B. (2021). Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density. Preprints. https://doi.org/10.20944/preprints202112.0390.v1
Chicago/Turabian Style
La Rue, E., Jaclyn Hatala Matthes and Brady Hardiman. 2021 "Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density" Preprints. https://doi.org/10.20944/preprints202112.0390.v1
Abstract
Recent expansion in data sharing has created unprecedented opportunities to explore structure-function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data’s potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30-year time frame, including discrete return Light Detection and Range (LiDAR) from an airborne observatory platform. LiDAR is a well-established and increasingly available remote sensing technology for measuring three-dimensional (3D) characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 – 25+ points/m2 depending on instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the USA and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve out of sixteen structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7-8 pt/m2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and users of these open-source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.
Keywords
Aerial laser scanning; Canopy structural complexity; Forest structure; National Ecological Observatory Network (NEON); Pulse density
Subject
Biology and Life Sciences, Ecology, Evolution, Behavior and Systematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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