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  • Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
  • Wang, Jianmin; Postdoctoral Research Associate; Purdue University
    Choi, Dennis H.; Postdoctoral Research Associate; Purdue University
    La Rue , Elizabeth; Assistant Professor; The University of Texas at El Paso
    Atkins, Jeff W.; Research Biologist; USDA Forest Service Southern Research Station
    Foster, Jane R.; Assistant Research Professor; University of Vermont
    Matthes, Jaclyn H.; Senior Scientist; Harvard University
    Fahey, Robert T.; Assistant Professor; University of Connecticut
    Fei, Songlin; Professor; Purdue University
    Hardiman, Brady S.; Associate Professor; Purdue University
  • 2023-10-18
  • Wang, J., D.H. Choi, E. La Rue, J.W. Atkins, J.R. Foster, J.H. Matthes, R.T. Fahey, S. Fei, and B.S. Hardiman. 2023. Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022 ver 2. Environmental Data Initiative. https://doi.org/DOI_PLACE_HOLDER (Accessed 2024-12-28).
  • Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.

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  • This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you.
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EDI is a collaboration between the University of New Mexico and the University of Wisconsin – Madison, Center for Limnology:

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