# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
###### Abstract
They note that reflectance of electromagnetic signals differ depending on whether plants are submerged, floating or emergent, due to the significant and variable effect water has on different parts of the spectrum. Synthetic aperture radar uses the microwave part of the spectrum. Though it doesn't penetrate water, it can be used to detect emergent vegetation, and the use of interferometry could be used to determine vegetation height. Use of Light Detection and Ranging (LiDAR) is challenging due to water absorption of the signal. The near infra-red portion of the spectrum is absorbed strongly by water, and thus optical reflectance is more useful for characterising submerged vegetation. They note the reasonable accuracy that has been achieved using even coarse resolution satellite imagery to predict vegetation cover and biomass. New vegetation indices were proposed and tested in [9] that aim to enhance separability between land and aquatic vegetation. An algorithm to correct the absorption of light by a water column above submerged aquatic vegetation (SAV) was developed in [10]. Optical images were able to determine the depth of SAV in clear water [11]. A manually-piloted radio-controlled aircraft with an RGB camera was used to detect and spray weeds including alligator weed and salvinia in an aquatic habitat in [12]. They used the Support Vector Machine (SVM) algorithm to classify images, with a weed expert providing the classification training set. An unmanned aerial vehicle (UAV) with red-green-blue (RGB) and near-infrared cameras was used to map the invasive water soldier weed in [13], using object-based image analysis and the random forest algorithm. They selected training samples throughout the area based on visual interpretation with four classes (water soldier emergent and submerged, native vegetation and other) and achieved a classification kappa of 61%. The relationship between remotely sensed aquatic vegetation areas and biomass in a lake was investigated in [14]. The best correlation was from the near infra-red band, with \(R=0.86\). These works indicate the promise of applying images captured by UAV and satellite to irrigation channel weed detection. It is useful to be able to not only identify areas of aquatic vegetation in irrigation channels, but also to distinguish between species. This would allow analysis of a change of population of certain species,as well as targeted control for the dominant species in sections of channels. Species discrimination using field work is time consuming and only practical in small areas. The authors of [15] focus on discriminating wetland vegetation species and estimating their biophysical properties, using both multi- and hyper-spectral data. Challenges include the difficulty of identifying boundaries between aquatic vegetation (as they often overlap), and the reflectance characteristics of vegetation are often very similar and are combined with the reflectance characteristic of the surrounding water. In [16], lake and river sites were surveyed with with 5-cm resolution. They used visual inspection of the images to classify a large variety of weed species, and compared this with the accuracy of object-based image analysis and classification. Accuracy was better than 90% for water versus vegetation, and better than 50% for taxonomy. The possibility of plant species discrimination in estuaries using hyperspectral data was investigated in [17], considering variation in each species' signature with space and time. Submerged plants can be discriminated only in visible wavelengths, as longer wavelengths (near infra-red) are absorbed by the water. The paper investigated three species, eelgrass _Zostera capricorni_, strapweed _Posidonia australis_, and paddleweed _Halophila ovalis_ over two years. The green wavelengths were most significant in discriminating between species, followed by red. They give suggested wavelengths and bandwidths to optimally classify these species.