Advanced optical technologies for phytoplankton discrimination : application in adaptive ocean sampling networks
There is a lack on ocean dynamics understanding, and that lead oceanographers to the need of acquiring more reliable data to study ocean characteristics. Oceanographic measurements are difficult and expensive but essential for effective study oceanic and atmospheric systems. Despite rapid advances i...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/384327 |
| Acceso en línea: | http://hdl.handle.net/10803/384327 https://dx.doi.org/10.5821/dissertation-2117-96146 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
| Sumario: | There is a lack on ocean dynamics understanding, and that lead oceanographers to the need of acquiring more reliable data to study ocean characteristics. Oceanographic measurements are difficult and expensive but essential for effective study oceanic and atmospheric systems. Despite rapid advances in ocean sampling capabilities, the number of disciplinary variables that are necessary to solve oceanographic problems are large. In addition, the time scales of important processes span over ten orders of magnitude, and due to technology limitations, there are important spectral gaps in the sampling methods obtained in the last decades. Thus, the main limitation to understand these dynamics is an inaccurate measurement of the process due to undersampling. But fortunately, recent advances in ocean platforms and in situ autonomous sampling systems and satellite sensors are enabling unprecedented rates of data acquisition as well as the expansion of temporal and spatial coverage. Many advances in technologies involving different areas such as computing, nanotechnology, robotics, molecular biology, etc. are being developed. There exist the effort that these advantages could be applied to ocean sciences and will prove to extremely beneficial for oceanographers in the next few decades. Autonomous underwater vehicles, in situ automatic sampling devices, high spectral resolution optical and chemical sensors are some of the new advances that are being utilized by a limited number of oceanographers, and in a few years are expected to be widely used. Thanks to new technologies and, for instance, utilization of data assimilation models coupled with autonomous sampling platforms can increase temporal and spatial sampling capabilities. For instance, studies of phytoplankton dynamics in the water column, or the transportation and aggregation of organisms need a high rate of sampling because of their rapid evolution, that is why new strategies and technologies to increase sampling rate and coverage would be really useful. However, other challenges come up when increasing the variety and quantity of ocean measurements. For instance, number of measurements are limited by costs of instruments and their deployment, as well as data processing and production of useful data products and visualizations. In some studies, there exists the necessity to discriminate and detect different phytoplankton species present in sea water, and even track their evolution. The use of their optical properties is one of the approximations used by some of them. Acquiring optical properties is a non-invasive and non-destructive method to study phytoplankton communities. Phytoplankton species are then organized thanks to presenting similar optical characteristics. Fluorescence spectroscopy has been used and found as a really potential technique for this goal, although passive optical techniques such as the study of the absorption can be also useful, or even their combination can be studied. Specifically speaking about fluorescence, the majority of the studies have centered their effort in discriminating phytoplankton groups using their excitation spectra because the emission spectra contains less information. The inconvenient of using this kind of information, is that the acquisition is not instantaneous and it is necessary to spend some time (over a second) exciting the sample at different wavelengths sequentially. In contrast, the whole emission spectra can be acquired instantaneously. Therefore, the aim of this thesis is to explore new and powerful signal processing techniques able to discriminate between different phytoplankton groups from their emission fluorescence spectra. This document presents important results that demonstrate the capabilities of these methods. |
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