# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 4 Discussion  
This study has demonstrated the potential for the use of remote sensing to monitor vegetation in irrigation channel networks, which is a major cause of impeded flow rates. GEE was used to process, analyse and classify UAV and satellite images in seconds. UAV and satellite imagery each have their benefits. Data from UAVs are useful for intensive monitoring of small areas of channels with high resolution. They offer a relatively low cost way of mapping stretches of channel, with the typical time taken to set up and capture a stretch of 1 km being about 1 h. Applications for this include intensive experiments to assess the effectiveness of weed control methods (chemical or mechanical), which require a time series of images to analyse change. Purchasing satellite images over small experimental areas and multiple dates would not be cost effective. Use of UAVs also provides very high resolution, which may be needed for deeper classification than was performed in this study, such as classifying between species of weeds. UAVs will also be useful to check for critical stretches of channel at short notice. An example where this is useful is when a farmer complains that the promised water delivery rates are not being met, a UAV could be used to survey the channels leading to the farm to determine if weed is the reason for the constraint, and the water delivery provider could then quickly make a decision on how to rectify the issue. Satellite imagery will be important to monitor the large areas covered by the entire irrigation channel networks. It is clear that the resolution of current free satellite imagery is not sufficient. For example, the Sentinel constellation resolution is 10 m, which is not useful in monitoring channels with widths of much less than 10 m. Thus, images must be purchased, in order to obtain resolutions of 1 m or better. Images should contain at least the red, green, blue and near-infrared bands. Satellite images could be obtained for a few key times in the year. For example, one in the off-season could be used to plan weed control when it is possible to dry down channels and to target intensive work on problem areas. An image immediately preceding the peak irrigation season could be used to ensure the network is ready to deliver high flows to all areas and any last minute rectification of weed issues could be performed. An image in the midst of the peak irrigation season could be used to identify problem areas that are causing impeded flows. As a database of multi-season images are built up, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Water (km\({}^{2}\))** & **Land (km\({}^{2}\))** & **Weed (km\({}^{2}\))** & **Weed (\%)** \\ \hline
2017 & 0.155 & 0.364 & 0.095 & 38.0 \\
2018 & 0.131 & 0.372 & 0.111 & 45.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification areas in km\({}^{2}\), and proportion of weed in the channels, from the WorldView-3 images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a)**} \\ \hline  & \multicolumn{4}{c}{**Predicted**} \\ \cline{3-5}  & & **Water** & **Land** & **Weed** \\ \hline \multirow{3}{*}{Actual} & Water & 988 & 0 & 12 \\  & Land & 1 & 869 & 33 \\  & Weed & 61 & 184 & 771 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classifier validation error matrices for the WorldView-3 images using automated classifier training method. (a) 2017 image, \(\kappa=0.851\); (b) 2018 image, \(\kappa=0.906\). trends in weed growth and problem areas could be identified and the reasons for proliferation of weeds in certain areas investigated and resilience against weed outbreaks improved. Multi-season images will also allow tracking of the proliferation of introduced species, which may be resistant to current weed control strategies. The satellite images used in this study were very high resolution 8-band images, which comes with a high price. The minimum requirements for satellite images for weed area identification should be determined to reduce costs.