NEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud

Jianmin Wang; Dennis H Choi; Elizabeth LaRue; Jeff W Atkins; Jane R Foster; Jaclyn H Matthes; Robert T Fahey; Songlin Fei; Brady S Hardiman

doi:10.1038/s41597-024-04018-0

NEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud

Sci Data. 2024 Oct 29;11(1):1174. doi: 10.1038/s41597-024-04018-0.

Authors

Jianmin Wang^#¹, Dennis H Choi^#¹, Elizabeth LaRue², Jeff W Atkins³, Jane R Foster^{4

5}, Jaclyn H Matthes⁶, Robert T Fahey⁷, Songlin Fei¹, Brady S Hardiman^{8

9}

Affiliations

¹ Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA.
² Department of Biological Sciences, The University of Texas at El Paso, El Paso, Texas, USA.
³ USDA Forest Service, Southern Research Station, New Ellenton, South Carolina, USA.
⁴ USDA Forest Service, Southern Research Station, Tennessee, Knoxville, USA.
⁵ Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont, USA.
⁶ Harvard Forest, Harvard University, Petersham, Massachusetts, USA.
⁷ Department of Natural Resources and the Environment and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut, USA.
⁸ Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA. [email protected].
⁹ Department of Environmental and Ecological Engineering, Purdue University, West Lafayette, Indiana, USA. [email protected].

^# Contributed equally.

Abstract

Structural diversity (SD) characterizes the volume and physical arrangement of biotic components in an ecosystem which control critical ecosystem functions and processes. LiDAR data provides detailed 3-D spatial position information of components and has been widely used to calculate SD. However, the intensive computation of SD metrics from extensive LiDAR datasets is time-consuming and challenging for researchers who lack access to high-performance computing resources. Moreover, a lack of understanding of LiDAR data and algorithms could lead to inconsistent SD metrics. Here, we developed a SD product using the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform. This product provides SD metrics detailing height, density, openness, and complexity at a spatial resolution of 30 m, aligned to the Landsat grids, for 211 site-years for 45 Terrestrial NEON sites from 2013 to 2022. To accommodate various ecosystems with different understory heights, it includes three different cut-off heights (0.5 m, 2 m, and 5 m). This structural diversity product can enable various applications such as ecosystem productivity estimation and disturbance monitoring.

Abstract

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