{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang J"],"funding":["United States Department of Agriculture | National Institute of Food and Agriculture (NIFA)","National Science Foundation (NSF)","United States Department of Agriculture | National Institute of Food and Agriculture","National Science Foundation"],"pagination":["1174"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11522374"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_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 resolu"],"journal":["Scientific data"],"pubmed_title":["NEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud."],"pmcid":["PMC11522374"],"funding_grant_id":["1926538","1926454","1926442","2023-68012-38992"],"pubmed_authors":["Choi DH","Matthes JH","Atkins JW","Fei S","Hardiman BS","Foster JR","LaRue E","Fahey RT","Wang J"],"additional_accession":[]},"is_claimable":false,"name":"NEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud.","description":"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 resolu","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-07-15T17:45:40.812Z","creation":"2025-04-04T03:01:02.983Z"},"accession":"S-EPMC11522374","cross_references":{"pubmed":["39472447"],"doi":["10.1038/s41597-024-04018-0"]}}