Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery

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.

Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South

“…The advent of drone technologies has seen the utility of sensors, such as Nikon (NIKKOR AF-S 24-85 mm f/3.5-4.5G ED VR) and the Nikon D800 [47], GoPro Hero 4 Black Edition [48], Feiyu Mini 3D Pro [48], Sony [44], and CMOS [49] to the multispectral sensors such as the MicaSense, Parrot Sequoia [28,[50][51][52][53][54][55] Sentera [38], MicaSense RedEdge multispectral [29,56], and the hyperspectral sensors such as Headwall Photonics Inc (207 bands), Ocean Optics STS-VIS (640 bands) [27], AvaSpec-dual Gaia (640 bands) [35,57], Sky-mini Nano-Hyperspec [30], Canon EOS 5DS R, and Headwall Nano-Hyperspec (640 bands) for local-scale water remote sensing applications (Table 2). However, as the spectral resolution of drone sensors increases, the associated costs also increase linearly.…”

Application of UAV Imagery to Detect and Quantify Submerged Filamentous Algae and Rooted Macrophytes in a Non-Wadeable River

“…Random Forest algorithms have proven successful in previous studies in complex, heterogeneous landscapes [19,85,86], demonstrating that this method can effectively reduce speckle and noise within imagery. To build on current work, future image analysis techniques that can be applied to benthic surveys in heterogeneous environments include aquatic vegetation indices, OBIA, structure from motion photogrammetry, and hyperspectral image capture [20,40,41,[86][87][88][89].…”

“…In marine environments, researchers such as Slocum et al [43] leverage RGB sensors on fixed-wing and rotary-wing UAVs to create 3D point clouds of submerged aquatic vegetation or coral reefs from structure from motion photogrammetry. Tait et al [42] flew at 50 m, Taddia et al [40] flew at 70 m, and Brinkhoff et al [41] flew at 75 m altitude. Several researchers used supervised classification techniques, such as Tait et al [42] with support vector machines and Taddia et al [40] with maximum likelihood classification, and others, such as Brooks et al [20] and Brinkhoff et al [41], used vegetation indices to identify submerged algae or macrophytes.…”

“…Several researchers used supervised classification techniques, such as Tait et al [42] with support vector machines and Taddia et al [40] with maximum likelihood classification, and others, such as Brooks et al [20] and Brinkhoff et al [41], used vegetation indices to identify submerged algae or macrophytes. Notable vegetation indices that have been applied to submerged aquatic vegetation detection in recent years include the Normalized Difference Aquatic Vegetation Index (NDAVI) and the Water Adjusted Vegetation Index (WAVI), both of which combine the near-infrared and blue bands [20,41]. Furthermore, the red edge band has been incorporated into submerged aquatic vegetation detection, as shown by studies from Taddia et al [40] and Brinkhoff et al [41].…”

“…Notable vegetation indices that have been applied to submerged aquatic vegetation detection in recent years include the Normalized Difference Aquatic Vegetation Index (NDAVI) and the Water Adjusted Vegetation Index (WAVI), both of which combine the near-infrared and blue bands [20,41]. Furthermore, the red edge band has been incorporated into submerged aquatic vegetation detection, as shown by studies from Taddia et al [40] and Brinkhoff et al [41]. Researchers, such as Slocum et al [43] and Kwon et al [37], are continuing to address issues of solar glint by flying slightly off-nadir and flying during times when solar angles are between 35 to 40 • .…”