Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations. This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries–Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column. Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from VHR image data. The best performing feature ‘length/width ratio of sub-objects’ was obtained through expert knowledge. All of the shape and texture based features performed better at species differentiation than the spectrally based features. In conclusion this study has shown that there is considerable potential for the combination of VHR data and OBIA to map SAV in shallow stream environments, which can benefit species monitoring and management.