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Title: UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

Abstract

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).

Authors:
; ; ;
Publication Date:
Research Org.:
Idaho National Laboratory (INL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1045490
Report Number(s):
INL/CON-12-24971
TRN: US201215%%42
DOE Contract Number:  
DE-AC07-05ID14517
Resource Type:
Conference
Resource Relation:
Conference: Hyperspectral Image and Signal Processing: Evolution in Remote Sensing,Shanghai, China,06/04/2012,06/07/2012
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ALTITUDE; CLASSIFICATION; DISTRIBUTION; MANAGEMENT; MONITORING; PLANTS; PROCESSING; RANUNCULACEAE; REMOTE SENSING; SHRUBS; SPATIAL RESOLUTION; TIME-SERIES ANALYSIS; IDAHO NATIONAL LABORATORY; UAV, hyperspectral, vegetation, classification, dr

Citation Formats

Glenn, Nancy F, Mitchell, Jessica J, Anderson, Matthew O, and Hruska, Ryan C. UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING. United States: N. p., 2012. Web.
Glenn, Nancy F, Mitchell, Jessica J, Anderson, Matthew O, & Hruska, Ryan C. UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING. United States.
Glenn, Nancy F, Mitchell, Jessica J, Anderson, Matthew O, and Hruska, Ryan C. Fri . "UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING". United States. https://www.osti.gov/servlets/purl/1045490.
@article{osti_1045490,
title = {UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING},
author = {Glenn, Nancy F and Mitchell, Jessica J and Anderson, Matthew O and Hruska, Ryan C},
abstractNote = {UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {6}
}

Conference:
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