Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
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 aeri...
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Veröffentlicht in: | Water (Basel) 2018-10, Vol.10 (11), p.1497 |
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description | 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. |
doi_str_mv | 10.3390/w10111497 |
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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.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w10111497</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Aquatic plants ; Aquatic weeds ; Atmospheric correction ; Biomass ; Cameras ; Channels ; Chemical control ; Classification ; Clustering ; Discrimination ; Flow rates ; Flow velocity ; Image classification ; Irrigation ; Network reliability ; Remote monitoring ; Remote sensing ; Rivers ; Satellite imagery ; Satellites ; Sensors ; Statistical analysis ; Unmanned aerial vehicles ; Vegetation ; Vegetation mapping ; Water delivery ; Weed control ; Weeds</subject><ispartof>Water (Basel), 2018-10, Vol.10 (11), p.1497</ispartof><rights>2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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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. 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Hornbuckle, John ; Barton, Jan L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c292t-f17841aa83539abf62742ff30556f152b1946a7b1a620f0bc7877a6a0fd8dda53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Aquatic plants</topic><topic>Aquatic weeds</topic><topic>Atmospheric correction</topic><topic>Biomass</topic><topic>Cameras</topic><topic>Channels</topic><topic>Chemical control</topic><topic>Classification</topic><topic>Clustering</topic><topic>Discrimination</topic><topic>Flow rates</topic><topic>Flow velocity</topic><topic>Image classification</topic><topic>Irrigation</topic><topic>Network reliability</topic><topic>Remote monitoring</topic><topic>Remote sensing</topic><topic>Rivers</topic><topic>Satellite imagery</topic><topic>Satellites</topic><topic>Sensors</topic><topic>Statistical analysis</topic><topic>Unmanned aerial vehicles</topic><topic>Vegetation</topic><topic>Vegetation mapping</topic><topic>Water delivery</topic><topic>Weed control</topic><topic>Weeds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brinkhoff, James</creatorcontrib><creatorcontrib>Hornbuckle, John</creatorcontrib><creatorcontrib>Barton, Jan L.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Brinkhoff, James</au><au>Hornbuckle, John</au><au>Barton, Jan L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery</atitle><jtitle>Water (Basel)</jtitle><date>2018-10-23</date><risdate>2018</risdate><volume>10</volume><issue>11</issue><spage>1497</spage><pages>1497-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w10111497</doi><orcidid>https://orcid.org/0000-0002-0721-2458</orcidid><orcidid>https://orcid.org/0000-0003-0714-6646</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algorithms Aquatic plants Aquatic weeds Atmospheric correction Biomass Cameras Channels Chemical control Classification Clustering Discrimination Flow rates Flow velocity Image classification Irrigation Network reliability Remote monitoring Remote sensing Rivers Satellite imagery Satellites Sensors Statistical analysis Unmanned aerial vehicles Vegetation Vegetation mapping Water delivery Weed control Weeds |
title | Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery |
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