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Assessment of aquatic weed in irrigation channels using UAV and satellite imagery

Brinkhoff, James, Hornbuckle, John and Barton, Jan 2018, Assessment of aquatic weed in irrigation channels using UAV and satellite imagery, Water, vol. 10, no. 11, doi: 10.3390/w10111497.

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Title Assessment of aquatic weed in irrigation channels using UAV and satellite imagery
Author(s) Brinkhoff, JamesORCID iD for Brinkhoff, James orcid.org/0000-0002-0721-2458
Hornbuckle, JohnORCID iD for Hornbuckle, John orcid.org/0000-0003-0714-6646
Barton, JanORCID iD for Barton, Jan orcid.org/0000-0002-8216-3756
Journal name Water
Volume number 10
Issue number 11
Article ID 1497
Total pages 20
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2018-10-23
ISSN 2073-4441
Keyword(s) UAV; satellite; remote sensing; irrigation infrastructure; aquatic weeds; macrophytes
Summary Irrigated agriculture requires high reliability from water delivery networks and high flows to satisfy demand at seasonal peak times. Aquatic vegetation in irrigation channels are a major impediment to this, constraining flow rates. This work investigates the use of remote sensing from unmanned aerial vehicles (UAVs) and satellite platforms to monitor and classify vegetation, with a view to using this data to implement targeted weed control strategies and assessing the effectiveness of these control strategies. The images are processed in Google Earth Engine (GEE), including co-registration, atmospheric correction, band statistic calculation, clustering and classification. A combination of unsupervised and supervised classification methods is used to allow semi-automatic training of a new classifier for each new image, improving robustness and efficiency. The accuracy of classification algorithms with various band combinations and spatial resolutions is investigated. With three classes (water, land and weed), good accuracy (typical validation kappa >0.9) was achieved with classification and regression tree (CART) classifier; red, green, blue and near-infrared (RGBN) bands; and resolutions better than 1 m. A demonstration of using a time-series of UAV images over a number of irrigation channel stretches to monitor weed areas after application of mechanical and chemical control is given. The classification method is also applied to high-resolution satellite images, demonstrating scalability of developed techniques to detect weed areas across very large irrigation networks.
Language eng
DOI 10.3390/w10111497
Field of Research MD Multidisciplinary
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, the Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30114539

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.