Estimating the Underwater Diffuse Attenuation Coefficient with a Low-Cost Instrument: The KdUINO DIY Buoy

A critical parameter to assess the environmental status of water bodies is the transparency of the water, as it is strongly affected by different water quality related components (such as the presence of phytoplankton, organic matter and sediment concentrations). One parameter to assess the water transparency is the diffuse attenuation coefficient. However, the number of subsurface irradiance measurements obtained with conventional instrumentation is relatively low, due to instrument costs and the logistic requirements to provide regular and autonomous observations. In recent years, the citizen science concept has increased the number of environmental observations, both in time and space. The recent technological advances in embedded systems and sensors also enable volunteers (citizens) to create their own devices (known as Do-It-Yourself or DIY technologies). In this paper, a DIY instrument to measure irradiance at different depths and automatically calculate the diffuse attenuation Kd coefficient is presented. The instrument, named KdUINO, is based on an encapsulated low-cost photonic sensor and Arduino (an open-hardware platform for the data acquisition). The whole instrument has been successfully operated and the data validated comparing the KdUINO measurements with the commercial instruments. Workshops have been organized with high school students to validate its feasibility

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 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.Existe una falta de conocimiento sobre las dinámicas de los océanos, lo que lleva a los oceanógrafos a la necesidad de adquirir datos fiables para estudiar las características de los océanos. Los datos oceanográficos son difíciles y costosos de adquirir, pero esenciales para estudiar de manera efectiva los sistemas oceánicos i atmosféricos. A causa de los rápidos avances para muestrear este medio tan hostil, es necesario que diversas disciplinas trabajen juntas para solucionar el gran número de problemáticas que se pueden encontrar. Además, los procesos que se tienen que estudiar pueden perdurar hasta diez órdenes de magnitud, y por culpa de las limitaciones tecnológicas existen importantes faltas en los métodos que se llevan utilizando en las últimas décadas. Por eso, la principal limitación para entender estas dinámicas es la imposibilidad de medir procesos correctamente como consecuencia de la baja frecuencia de muestreo. Por suerte, los recientes avances en plataformas oceánicas y sistemas de muestreo autónomos, junto con datos de satélite, están mejorando estas frecuencias de adquisición, i en consecuencia aumentando la cobertura temporal y espacial de estos procesos. Actualmente hay disciplinas como la computación, nanotecnología, robótica, biología molecular, etc. que están protagonizando unos avances tecnológicos sin precedentes. La intención es aprovechar este esfuerzo y aplicarlo en oceanografía. Vehículos autónomos bajo el agua, sistemas automáticos de muestreo, sensores ópticos o químicos de alta resolución son algunas de las tecnologías que se empiezan a utilizar, pero que por culpa de su coste todavía no están extendidas y se espera que lo puedan estar en los próximos años. Gracias a algunas de estas tecnologías, como por ejemplo la utilización de modelos de asimilación de datos conjuntamente con plataformas autónomas de muestreo, se puede incrementas la capacidad de muestreo, tanto temporal como espacial. Un ejemplo claro de aplicación es el estudio de las dinámicas del fitoplancton, así como el transporte de organismos dentro de la columna de agua. No obstante, no todos los aspectos son positivos, otros retos surgen al aumentar la variedad y cantidad de datos oceanográficos. El número de datos queda limitado por el coste de los instrumentos y las campañas. Además, es necesario estudiar nuevos sistemas para procesar y extraer información útil de estos datos, puesto que los métodos conocidos hasta el momento quizá no son los más adecuados. La detección y discriminación de diferentes especies de fitoplancton en el mar es muy importante en ciertos estudios científicos. Algunos de estos estudios se basan en extraer información de sus propiedades ópticas, ya que es un método no invasivo ni destructivo. Espectroscopia a partir de la respuesta de fluorescencia del fitoplancton se utiliza en muchos experimentos y se ha demostrado que es una técnica con gran potencial, aunque el estudio de los espectros de absorción u otras técnicas basadas en métodos pasivos también se pueden utilizar. En el caso de la fluorescencia, la mayoría de los estudios se han centrado en discriminar grupos de fitoplancton a partir de los espectros de excitación, porque los espectros de emisión contienen menos información. La desventaja es que el tiempo necesario para adquirir una muestra puede estar entorno al segundo, porque se necesita estimular la muestra a diferentes longitudes de onda secuencialmente. En el caso de los espectros de emisión, con los avances actuales en sensores ópticos, las respuestas espectrales pueden ser adquiridas casi instantáneamente. Por este motivo, el objetivo principal de esta tesis es explorar nuevas tecnologías de procesado capaces de discriminar diferentes grupos de fitoplancton a partir de sus espectros de emisión de fluorescencia. Este documento presenta importantes resultados que demuestran la capacidad de discriminación de este tipo de información en combinación con las técnicas de procesado adecuadas

Analysis of Discrimination Techniques for Low-Cost Narrow-Band Spectrofluorometers

24 pages, 12 figures, 12 tablesThe need for covering large areas in oceanographic measurement campaigns and the general interest in reducing the observational costs open the necessity to develop new strategies towards this objective, fundamental to deal with current and future research projects. In this respect, the development of low-cost instruments becomes a key factor, but optimal signal-processing techniques must be used to balance their measurements with those obtained from accurate but expensive instruments. In this paper, a complete signal-processing chain to process the fluorescence spectra of marine organisms for taxonomic discrimination is proposed. It has been designed to deal with noisy, narrow-band and low-resolution data obtained from low-cost sensors or instruments and to optimize its computational cost, and it consists of four separated blocks that denoise, normalize, transform and classify the samples. For each block, several techniques are tested and compared to find the best combination that optimizes the classification of the samples. The signal processing has been focused on the Chlorophyll-a fluorescence peak, since it presents the highest emission levels and it can be measured with sensors presenting poor sensitivity and signal-to-noise ratios. The whole methodology has been successfully validated by means of the fluorescence spectra emitted by five different cultures. © 2014 by the authors; licensee MDPI, Basel, SwitzerlandThis work was supported by the Spanish National Research Council (CSIC) under the project ANERIS (PIF-015-1), by the Ministerio de Ciencia e Innovación under Mestral Project CTM2011-30489-C02-01, and by the European Commission under Citclops Project FP7-ENV-308469. Ismael F. Aymerich is currently funded by the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC) and under the project TACTICA. Sergio Pérez was involved in the SICUE program and funded by the Séneca fellowship, given by the Ministerio de Educación, during the development of this projectPeer Reviewe