Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments

Abstract

Macroalgae blooms (MABs) are a global natural hazard that are likely to increase in occurrence with climate change and increased agricultural runoff. MABs can cause major issues for indigenous species, fish farms, nuclear power stations, and tourism activities. This project focuses on the impacts of MABs on the operations of a British nuclear power station. However, the outputs and findings are also of relevance to other coastal operators with similar problems. Through the provision of an early-warning detection system for MABs, it should be possible to minimize the damaging effects and possibly avoid them altogether. Current methods based on satellite imagery cannot be used to detect low-density mobile vegetation at various water depths. This work is the first step towards providing a system that can warn a coastal operator 6–8 h prior to a marine ingress event. A fundamental component of such a warning system is the spectral reflectance properties of the problematic macroalgae species. This is necessary to optimize the detection capability for the problematic macroalgae in the marine environment. We measured the reflectance signatures of eight species of macroalgae that we sampled in the vicinity of the power station. Only wavelengths below 900 nm (700 nm for similarity percentage (SIMPER)) were analyzed, building on current methodologies. We then derived 1st derivative spectra of these eight sampled species. A multifaceted univariate and multivariate approach was used to visualize the spectral reflectance, and an analysis of similarities (ANOSIM) provided a species-level discrimination rate of 85% for all possible pairwise comparisons. A SIMPER analysis was used to detect wavebands that consistently contributed to the simultaneous discrimination of all eight sampled macroalgae species to both a group level (535–570 nm), and to a species level (570–590 nm). Sampling locations were confirmed using a fixed-wing unmanned aerial vehicle (UAV), with the collected imagery being used to produce a single orthographic image via standard photogrammetric processes. The waveband found to contribute consistently to group-level discrimination has previously been found to be associated with photosynthetic pigmentation, whereas the species-level discriminatory waveband did not share this association. This suggests that the photosynthetic pigments were not spectrally diverse enough to successfully distinguish all eight species. We suggest that future work should investigate a Charge-Coupled Device (CCD)-based sensor using the wavebands highlighted above. This should facilitate the development of a regional-scale early-warning MAB detection system using UAVs, and help inform optimum sensor filter selection.

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

“…As the signals recorded in SAV research are often weak in comparison to terrestrial applications, discrimination and analysis rely on subtle features detectable using HSI but potentially not with MSI (Silva et al 2008). Some hyperspectral imagers, such as the Compact Airborne Spectrographic Imager (CASI), allow the user to define the band placement and width according to the expected spectral features (Fyfe 2003;McIlwaine et al 2019). This flexibility makes them especially well-suited to macrophyte studies compared to other fixed-band sensors whose bands may be too wide or inappropriately placed.…”

“…UAV-mounted sensors are preferred in situations when very high spatial resolution optical data over a small physical area is needed (Anderson and Gaston 2013;Visser et al 2013). These systems can be deployed quickly with varying sensor configurations, thus making them well suited to environmental monitoring missions (McIlwaine et al 2019;Rhee et al 2018). Their proximity to the target lessens the atmospheric contribution in the registered signal.…”

“…Some biological and ecological researchers have successfully used statistical methods such as ANOVA or SIMPER to perform band selection (e.g. Fyfe 2003, McIlwaine et al 2019, however these methods are not recommended for high dimension data as they do not consider band redundancy or classification accuracy.…”

“…Determining if a group of species are distinct from one another is often the first step in studies aiming to map vegetation at the species level. McIlwaine et al (2019) extensively examined the spectral separability of eight macroalgal species. These analyses identified which bands were most valuable in discriminating species and higher-level groups.…”

Use of Hyperspectral Remote Sensing to Estimate Water Quality

“…Algal concentrations in water through hyperspectral remote sensing images have been undertaken in the estimation of chlorophyll that is then used as an estimate for monitoring algal content and hence water quality. This approach has been adopted because wavelengths corresponding with the peak reflectance of blue-green and green algae are close together; it is harder to differentiate between them [19,41,42]. Hakvoorth et al [43], however, demonstrate that hyperspectral imagers permit for improved detection of chlorophyll and hereafter algae, as a result of acquired narrow spectral bands between 450 nm and 600 nm [20,44].…”