Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the FORESTR R package
Abstract
Abstract Terrestrial Li DAR (light detection and ranging) technologies have created new means of quantifying forest canopy structure, allowing not only the estimation of biomass, but also descriptions of the position and variability in canopy elements in space. Such measures provide novel structural information broadly useful to ecologists. There is a growing need for both a detailed taxonomy of forest canopy structural complexity ( CSC ) and open, transparent, and flexible tools to quantify complexity in ways that will advance foundational ecological knowledge of structure‐function relationships. The CSC taxonomy we present groups structural descriptors into five categories: leaf area and density, canopy height, canopy arrangement, canopy openness, and canopy variability. This paper also introduces the r package forestr , the first open‐source r package for the calculation of CSC metrics from terrestrial Li DAR data. The r package forestr is an analysis toolbox that works with portable canopy Li DAR ( PCL ) data and other pixelated/voxelized point clouds derived from terrestrial Li DAR scanning ( TLS ) data to calculate CSC metrics of interest to ecologists, modellers, forest managers, and remote sensing scientists.
- Authors:
-
[1];
[5];
- Univ. of Richmond, VA (United States)
- The Ohio State Univ., Columbus, OH (United States)
- Univ. of Connecticut, Storrs, CT (United States)
- State Univ. of New York (SUNY), Syracuse, NY (United States). College of Environmental Science and Forestry; The Ohio State Univ., Columbus, OH (United States); Purdue Univ., West Lafayette, IN (United States)
- Purdue Univ., West Lafayette, IN (United States); Univ. of Virginia, Charlottesville, VA (United States)
- University of San Francisco, San Francisco, CA (United States)
- Virginia Commonwealth Univ., Richmond, VA (United States)
- Publication Date:
- Research Org.:
- The Ohio State University, Columbus, OH (United States); Rutgers Univ., Piscataway, NJ (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1610891
- Alternate Identifier(s):
- OSTI ID: 1464572
- Grant/Contract Number:
- SC0006708; SC0007041; DE‐SC0006708; DE‐SC0007041
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Methods in Ecology and Evolution (Online)
- Additional Journal Information:
- Journal Name: Methods in Ecology and Evolution (Online); Journal Volume: 9; Journal Issue: 10; Journal ID: ISSN 2041-210X
- Publisher:
- British Ecological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Environmental Sciences & Ecology; canopy complexity; forest; LiDAR, R, R package; terrestrial ecology; structure; forestry; 52 ecosystem; structure-function
Citation Formats
Atkins, Jeff W., Bohrer, Gil, Fahey, Robert T., Hardiman, Brady S., Morin, Timothy H., Stovall, Atticus L., Zimmerman, Naupaka, and Gough, Christopher M. Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the FORESTR R package. United States: N. p., 2018.
Web. doi:10.1111/2041-210x.13061.
Atkins, Jeff W., Bohrer, Gil, Fahey, Robert T., Hardiman, Brady S., Morin, Timothy H., Stovall, Atticus L., Zimmerman, Naupaka, & Gough, Christopher M. Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the FORESTR R package. United States. https://doi.org/10.1111/2041-210x.13061
Atkins, Jeff W., Bohrer, Gil, Fahey, Robert T., Hardiman, Brady S., Morin, Timothy H., Stovall, Atticus L., Zimmerman, Naupaka, and Gough, Christopher M. Sat .
"Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the FORESTR R package". United States. https://doi.org/10.1111/2041-210x.13061. https://www.osti.gov/servlets/purl/1610891.
@article{osti_1610891,
title = {Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the FORESTR R package},
author = {Atkins, Jeff W. and Bohrer, Gil and Fahey, Robert T. and Hardiman, Brady S. and Morin, Timothy H. and Stovall, Atticus L. and Zimmerman, Naupaka and Gough, Christopher M.},
abstractNote = {Abstract Terrestrial Li DAR (light detection and ranging) technologies have created new means of quantifying forest canopy structure, allowing not only the estimation of biomass, but also descriptions of the position and variability in canopy elements in space. Such measures provide novel structural information broadly useful to ecologists. There is a growing need for both a detailed taxonomy of forest canopy structural complexity ( CSC ) and open, transparent, and flexible tools to quantify complexity in ways that will advance foundational ecological knowledge of structure‐function relationships. The CSC taxonomy we present groups structural descriptors into five categories: leaf area and density, canopy height, canopy arrangement, canopy openness, and canopy variability. This paper also introduces the r package forestr , the first open‐source r package for the calculation of CSC metrics from terrestrial Li DAR data. The r package forestr is an analysis toolbox that works with portable canopy Li DAR ( PCL ) data and other pixelated/voxelized point clouds derived from terrestrial Li DAR scanning ( TLS ) data to calculate CSC metrics of interest to ecologists, modellers, forest managers, and remote sensing scientists.},
doi = {10.1111/2041-210x.13061},
journal = {Methods in Ecology and Evolution (Online)},
number = 10,
volume = 9,
place = {United States},
year = {Sat Jul 07 00:00:00 EDT 2018},
month = {Sat Jul 07 00:00:00 EDT 2018}
}
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