Novel approach to improve the assessment of biodiversity of phytoplankton communities based on hyperspectral data analysis

Abstract

Sustainable management of marine ecosystems requires a better knowledge about the space-time distribution and dynamics of ecological parameters such as phytoplankton communities, including critical bloom-forming algal groups. Better understanding of phytoplankton biodiversity and dynamics is essential in evaluating the role of each algal group in the global marine ecosystem and biogeochemical cycles. In attempting to address this question, in situ and remotely-sensed spectrometric optical observations have demonstrated to provide previously unavailable information regarding several optically active constituents in seawater at local and global scales, in particular, regarding phytoplankton community structure. In this sense, the advent of high spectral resolution (hyperspectral) optical sensors have raised new expectations about the possibilities of discriminating phytoplankton community composition in the ocean, beyond the estimation of only the primary pigment in phytoplankton, chlorophyll-a, a proxy for the phytoplankton biomass and primary production since it is common to all taxonomic groups. This PhD thesis has been carried out with the aim of improving our ability to extract information regarding phytoplankton community structure in the ocean by developing and evaluating a novel approach based on hyperspectral data analysis. In particular, a dissimilarity-based cluster technique, which accounts for complete spectral behaviour of hyperspectral data of each seawater sample, has been applied in combination with derivative spectroscopy, which exploits the spectral shape features of each analyzed spectrum. As a novelty, a validating tool has been proposed and proven useful to illustrate the effectiveness of the optical-based classication for discriminating different phytoplankton assemblages. This novel validation approach is based on the pigment composition analyzed in conjunction with concurrently obtained optical data, information which has been commonly used by the scientic community as a proxy for the phytoplankton composition. The feasibility of this methodology has initially been demonstrated using a simulation-based approach, i. e. using a radiative transfer modeling framework for open ocean and shallow coastal environments. In addition, different real open ocean environments corresponding to several stations in the eastern Atlantic Ocean have successfully been classied by applying the cluster analysis to different hyperspectral data sets including absorption and remote-sensing reectance spectra and their second derivative spectra. This classication has served to identify a potential application of the proposed methodology: the establishment of different bio-optical provinces from the analysis of hyperspectral oceanographic observations, leading to examination of its biogeographical relevance by comparison to ecological provinces previously proposed in the literature. This thesis concludes by conrming the main hypothesis that discrimination of phytoplankton community structure and dynamics in the ocean can be enhanced while using hyperspectral oceanographic observations. It is noteworthy that the proposed approach is generally applicable to different data sets, besides in-situ pigment or optical data data also to remotely-sensed, biogeochemical or hydrographic data sets