NEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
29 Oct 2024
29 Oct 2024
Historique:
received:
18
07
2024
accepted:
18
10
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39472447
doi: 10.1038/s41597-024-04018-0
pii: 10.1038/s41597-024-04018-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1174Subventions
Organisme : National Science Foundation (NSF)
ID : 1926538
Organisme : National Science Foundation (NSF)
ID : 1926454
Organisme : National Science Foundation (NSF)
ID : 1926442
Organisme : National Science Foundation (NSF)
ID : 1926538
Organisme : United States Department of Agriculture | National Institute of Food and Agriculture (NIFA)
ID : 2023-68012-38992
Organisme : United States Department of Agriculture | National Institute of Food and Agriculture (NIFA)
ID : 2023-68012-38992
Informations de copyright
© 2024. The Author(s).
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