Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS
Unmanned Aerial Systems (UAS)-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Boise Center Aerospace Lab were tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The motivation for this study was to better understand the challenges associated with UAS-based hyperspectral data for distinguishing native grasses such as Sandberg bluegrass (Poa secunda) from invasives such as burr buttercup (Ranunculus testiculatus) in a shrubland environment. 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. However, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and feathering in areas of flightline overlap. Future UAS flight missions that optimize flight planning; minimize illumination differences between flightlines; and leverage ground reference data and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass from burr buttercup.
- Research Organization:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC07-05ID14517
- OSTI ID:
- 1469647
- Report Number(s):
- INL/JOU--16-38849-Rev000; {"","Journal ID: ISSN 2164-7682"}
- Journal Information:
- Environmental Management and Sustainable Development, Journal Name: Environmental Management and Sustainable Development Journal Issue: 2 Vol. 5; ISSN 2164-7682
- Publisher:
- Macrothink InstituteCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach
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journal | September 2019 |
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