UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING
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).
- Publication Date:
- OSTI Identifier:
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- Resource Relation:
- Conference: Hyperspectral Image and Signal Processing: Evolution in Remote Sensing,Shanghai, China,06/04/2012,06/07/2012
- Research Org:
- Idaho National Laboratory (INL)
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- Country of Publication:
- United States
- 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
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