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Title: Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and LiDAR airborne data

Abstract

This package is part of the Watershed Function SFA project and contains a remote sensing dataset acquired at the East River, Colorado. The remote sensing dataset is composed of vegetation maps computed from hyperspectral and LiDAR airborne data acquired by the NEON team in June 2018. The maps show the spatial distribution of plant species among trees, shrubs, and meadows at 1-meter resolution, covering four main catchments located in the Upper Colorado River Basin: the East River (67.5 km2), Washington Gulch (93.0 km2), Oh-be-Joyful Creek-Slate River (86.9 km2), and Coal Creek (53.2 km2). The maps were obtained through a supervised classification approach based on the support vector machine learning algorithm. The data input to the algorithm is the hyperspectral and LiDAR dataset. As pre-processing, an NDVI-based threshold was applied to mask bare soil, man-made structures, water, and shadows. The classification algorithm was applied following a hierarchical strategy. In the first step, the tree species were estimated. The algorithm was then applied to the remaining areas for the identification of shrubs and meadow plants. The various estimations were then merged to provide the final vegetation map.Some of the files are geotiffs, which require GIS software to visualize. jpeg files are extractedmore » geotiffs.« less

Creator(s)/Author(s):
ORCiD logo ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
DOE Contract Number:  
DEAC0205CH11231
Product Type:
Dataset
Research Org.:
Environmental System Science Data Infrastructure for a Virtual Ecosystem; Watershed Function SFA
Sponsoring Org.:
U.S. DOE > Office of Science > Biological and Environmental Research (BER)
Subject:
54 ENVIRONMENTAL SCIENCES
Keywords:
Species Mapping; Remote Sensing; Hyperspectral imaging; Airborne remote sensing ; watershed; Modelling results
OSTI Identifier:
1602034
DOI:
10.15485/1602034

Citation Formats

Falco, Nicola, Balde, Abdoulaye, Breckheimer, Ian, Brodie, Eoin, G. Brodrick, Philip, Chadwick, K. Dana, Chen, Jiancong, Dafflon, Baptiste, Henderson, Amanda, Lamb, Jack, Maher, Kate, Kueppers, Lara, Steltzer, Heidi, Wainwright, Haruko, Williams, Ken, and S. Hubbard, Susan. Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and LiDAR airborne data. United States: N. p., 2020. Web. doi:10.15485/1602034.
Falco, Nicola, Balde, Abdoulaye, Breckheimer, Ian, Brodie, Eoin, G. Brodrick, Philip, Chadwick, K. Dana, Chen, Jiancong, Dafflon, Baptiste, Henderson, Amanda, Lamb, Jack, Maher, Kate, Kueppers, Lara, Steltzer, Heidi, Wainwright, Haruko, Williams, Ken, & S. Hubbard, Susan. Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and LiDAR airborne data. United States. doi:10.15485/1602034.
Falco, Nicola, Balde, Abdoulaye, Breckheimer, Ian, Brodie, Eoin, G. Brodrick, Philip, Chadwick, K. Dana, Chen, Jiancong, Dafflon, Baptiste, Henderson, Amanda, Lamb, Jack, Maher, Kate, Kueppers, Lara, Steltzer, Heidi, Wainwright, Haruko, Williams, Ken, and S. Hubbard, Susan. 2020. "Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and LiDAR airborne data". United States. doi:10.15485/1602034. https://www.osti.gov/servlets/purl/1602034. Pub date:Wed Jan 01 00:00:00 EST 2020
@article{osti_1602034,
title = {Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and LiDAR airborne data},
author = {Falco, Nicola and Balde, Abdoulaye and Breckheimer, Ian and Brodie, Eoin and G. Brodrick, Philip and Chadwick, K. Dana and Chen, Jiancong and Dafflon, Baptiste and Henderson, Amanda and Lamb, Jack and Maher, Kate and Kueppers, Lara and Steltzer, Heidi and Wainwright, Haruko and Williams, Ken and S. Hubbard, Susan},
abstractNote = {This package is part of the Watershed Function SFA project and contains a remote sensing dataset acquired at the East River, Colorado. The remote sensing dataset is composed of vegetation maps computed from hyperspectral and LiDAR airborne data acquired by the NEON team in June 2018. The maps show the spatial distribution of plant species among trees, shrubs, and meadows at 1-meter resolution, covering four main catchments located in the Upper Colorado River Basin: the East River (67.5 km2), Washington Gulch (93.0 km2), Oh-be-Joyful Creek-Slate River (86.9 km2), and Coal Creek (53.2 km2). The maps were obtained through a supervised classification approach based on the support vector machine learning algorithm. The data input to the algorithm is the hyperspectral and LiDAR dataset. As pre-processing, an NDVI-based threshold was applied to mask bare soil, man-made structures, water, and shadows. The classification algorithm was applied following a hierarchical strategy. In the first step, the tree species were estimated. The algorithm was then applied to the remaining areas for the identification of shrubs and meadow plants. The various estimations were then merged to provide the final vegetation map.Some of the files are geotiffs, which require GIS software to visualize. jpeg files are extracted geotiffs.},
doi = {10.15485/1602034},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {1}
}

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