# 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. U 8 October  
Many works have considered alternatives of manual monitoring of aquatic vegetation, including echo sounding and remote sensing (optical and radar). Most of these studies focused on lakes and rivers rather than irrigation channels. Echo sounding for vegetation detection in an estuary was investigated in [5]. Sonar detection of vegetation works by sensing reflections, probably caused by the presence of gas in plants, which causes an acoustic impedance discontinuity with the surrounding water. Unvegetated bottom returns the strongest signal, while vegetation returns a weaker echo (but still stronger than ambient water noise). They classify each ping return into unclassified, noise, out of water, too deep, bare bottom and plant. They performed physical sampling (over a quadrat), and visual analysis using video (clear water) to compare against sonar estimates of vegetation coverage. They found reasonable relationships, but suggested optical techniques are better for shallow water or for vegetation appearing on the surface. A comparison of spatial environmental models, echo sounding and remote sensing to predict vegetation distribution and biomass in a lake [6] found remote sensing was the least accurate in this environment due to water turbidity. This raises a difficulty in earthen irrigation channels, as the water is often turbid. Despite the advantages of echo sounding for vegetation mapping, it would be impractical to cover irrigation channel networks using this technique due to time and labour costs. Stationary optical sensors may be useful to assess weed growth in fixed locations. Methods to eliminate reflections from the water surface using Red-Green-Blue (RGB) cameras were discussed in [7]. They trialled a number of solutions including a camera mounted in a tube breaking the water surface with the tube bottom 10 cm below the surface, an underwater camera on a floating frame, and dedicated underwater cameras facing up and down. While fixed sensors are attractive to monitor weed growth in irrigation channels due to the high temporal resolution, weeds often grow in small patches so it is possible that they would not be detected at sensor locations. A large number of sensors would be required to cover a network. A theoretical background for remote sensing in aquatic areas was reviewed in [8].