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Title: Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

Abstract

Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.

Authors:
 [1];  [2];  [2];  [1];  [3]
  1. ORNL
  2. United States Department of Agriculture (USDA), United States Forest Service (USFS)
  3. U.S. Fish and Wildlife Service
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC); Work for Others (WFO)
OSTI Identifier:
1286923
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Data Mining series (ICDM), Atlantic City, NJ, USA, 20151114, 20151117
Country of Publication:
United States
Language:
English
Subject:
LiDAR; Great Smoky Mountains National Park; Vegetation map; Data Mining; Clustering

Citation Formats

Kumar, Jitendra, HargroveJr., William Walter, Norman, Steven P, Hoffman, Forrest M, and Newcomb, Doug. Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR. United States: N. p., 2015. Web.
Kumar, Jitendra, HargroveJr., William Walter, Norman, Steven P, Hoffman, Forrest M, & Newcomb, Doug. Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR. United States.
Kumar, Jitendra, HargroveJr., William Walter, Norman, Steven P, Hoffman, Forrest M, and Newcomb, Doug. Thu . "Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR". United States. doi:. https://www.osti.gov/servlets/purl/1286923.
@article{osti_1286923,
title = {Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR},
author = {Kumar, Jitendra and HargroveJr., William Walter and Norman, Steven P and Hoffman, Forrest M and Newcomb, Doug},
abstractNote = {Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
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