Plant pigment types, distributions, and influences on shallow water submerged aquatic vegetation mapping

Abstract

Development of robust protocols for use in mapping shallow water habitats using hyperspectral imagery requires knowledge of absorbing and scattering features present in the environment. These include, but are not limited to, water quality parameters, phytoplankton concentrations and species, submerged aquatic vegetation (SAV) species and densities, epiphytic growth on SAV, benthic microalgae and substrate reflectance characteristics. In the Indian River Lagoon, Fl. USA we conceptualize the system as having three possible basic layers, water column and SAV bed above the bottom. Each layer is occupied by plants with their associated light absorbing pigments that occur in varying proportions and concentrations. Phytoplankton communities are composed primarily of diatoms, dinoflagellates, and picoplanktonic cyanobacteria. SAV beds, including flowering plants and green, red, and brown macro-algae exist along density gradients ranging in coverage from 0-100%. SAV beds may be monotypic, or more typically, mixtures of the several species that may or may not be covered in epiphytes. Shallow water benthic substrates are colonized by periphyton communities that include diatoms, dinoflagellates, chlorophytes and cyanobacteria. Inflection spectra created form ASIA hyperspectral data display a combination of features related to water and select plant pigment absorption peaks.

Image spectroscopy and stable isotopes elucidate functional dissimilarity between native and nonnative plant species in the aquatic environment

“…, 2008), despite interactions with the water column itself (Holden & LeDrew, 2001; Han, 2002; Bostater et al. , 2003; Hall et al. , 2004), which obscures the characteristic SAP spectral patterns.…”

“…Imaging spectroscopy (also called hyperspectral remote sensing) measures hundreds of contiguous narrow bands spanning the solar reflective spectrum from 400 to 2500 nm. The result is a nearly continuous spectrum measured in each pixel that provide the information needed to detect SAPs in the water column (Zhang et al, 1997;Underwood et al, 2006;Hestir et al, 2008), despite interactions with the water column itself (Holden & LeDrew, 2001;Han, 2002;Bostater et al, 2003;Hall et al, 2004), which obscures the characteristic SAP spectral patterns. When plant species are spectrally distinct (Fyfe, 2003), imaging spectroscopy is a likely method for mapping SAPs down to species level .…”

Implementation of a ground truth process for development of a submerged aquatic vegetation (SAV) mapping protocol using hyperspectral imagery

“…6,8 A basic principle in remote sensing is that features of interest reflect or emit light in uniquely different ways and these differences can be detected and recorded by the remote sensing system. 9,10,11 Our experience has shown that one of the major issues hindering the scientific utility and application of hyperspectral data to resource mapping is the uncertainty or error that exists when attempting to link ground truth data with spectra extracted from pixels in the image cube. Hyperspectral images collected from aircraft using line scanning systems such as PHILLS or AISA often contain significant spatial error as a result of aircraft pitch, roll and yaw making direct comparisons between ground truth data and individual pixels difficult or even impossible.…”

Toxic benthic freshwater cyanobacterial proliferations: Challenges and solutions for enhancing knowledge and improving monitoring and mitigation

“…Hyper‐spectral imaging involves a relatively high‐resolution spectrum (wavelengths of 2–10 nm) being collected for each pixel in the image. Hyper‐spectral imaging allows for the estimation of the content of chlorophyll‐ a and other cyanobacterial pigments in water bodies (Hall, Bostater, & Virnstein, 2004; Hunter, Matthews, Kutser, & Tyler, 2017; Hunter, Tyler, Carvalho, Codd, & Maberly, 2010). It is also possible, with appropriate algorithms, to separate different cyanobacterial genera using this technology (Kudela et al., 2015).…”