Accurate Estimation of Chlorophyll-a Concentration in the Coastal Areas of the Ebro Delta (NW Mediterranean) Using Sentinel-2 and Its Application in the Selection of Areas for Mussel Aquaculture

Multispectral satellite remote sensing imagery, together with appropriate modeling, have been proven to provide chlorophyll-a maps that are useful to evaluate the suitability of coastal areas for carrying out shellfish aquaculture. However, current approaches used for chlorophyll-a estimation in very shallow coastal areas often fail in their accuracy. To overcome this limitation, an algorithm that provides an accurate estimation of chlorophyll-a concentration in the coastal areas of the Ebro delta (North Western Mediterranean) using atmospherically corrected Sentinel 2 (S2) remote sensing reflectances (Rrs) has been calibrated and validated. The derived chlorophyll-a maps created have been used in a dynamic carrying capacity model that covers areas from very rich waters inside the embayment to the more oligotrophic waters in the open sea. The use of carrying capacity models is recommended to evaluate the potential of marine coastal areas for bivalve mollusk aquaculture. In this context, the depletion of chlorophyll-a is an indicator of negative environmental impact and thus a continuous monitoring of chlorophyll-a is key. The proposed methodology allows estimation of chlorophyll-a concentration from Sentinel-2 with an accuracy higher than 70% in most cases. The carrying capacity and the suitability of the external areas of the Ebro delta have been determined. The results show that these areas can hold a significant mussel production. The methodology presented in this study aims to provide a tool to the shellfish aquaculture industry.info:eu-repo/semantics/publishedVersio

Combined flooding and water quality monitoring during short extreme events using Sentinel 2: the case study of Gloria storm in Ebro delta

Short extreme events have significant impact on landscape and ecosystems in low-lying and exposed areas such as deltaic systems. In this context, this paper proposes a combined methodology for the mapping and monitoring of the flooding and water quality dynamics of coastal areas under extreme storms from Sentinel 2 imagery. The proposed methodology has been applied in a coastal bay of the Ebro Delta (Catalonia, NE Spain) to evaluate jointly the impact of Gloria storm (January 2020) in land-flooding and water quality. The experimental results show that the Gloria storm had a strong morphological impact and altered the water quality (chl-a) dynamics. The results show a recovery in terms of water quality after some weeks but in contrast the coastal morphology did not show the same degree of resilience. This paper is the first step of an overall goal that is to set the bases in a long term, for a workflow for rapid response and continuous monitoring of storm effects in coastal areas and/or highly valuable ecosystems such as the Ebro Delta.This research was partially funded by the project New-TechAqua (European Union's Programme H2020, GA 862658). J. Soriano-González held a pre-doctoral grant funded by by Agència de Gestió d’Ajuts Universitaris I de Recerca (2020FI_B2 00148)Peer ReviewedObjectius de Desenvolupament Sostenible::14 - Vida SubmarinaObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaPostprint (published version

First results of phytoplankton spatial dynamics in two NW-Mediterranean bays from Chlorophyll-a estimates using Sentinel 2: potential implications for aquaculture

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.info:eu-repo/semantics/publishedVersio

Sustainable marine ecosystems: deep learning for water quality assessment and forecasting

An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.Postprint (published version

Performance analysis of the IOPES seamless indoor-outdoor positioning approach

Tracking the members of civil protection or emergency teams is still an open issue. Although outdoors tracking is routinely performed using well-seasoned techniques such as GNSS, this same problem must be still solved for indoors situations. There exist several approaches for indoor positioning, but these are not appropriate for tracking emergency staff in real-time: some of these approaches rely on existing infrastructures; others have not been tested in light devices in real-time; none offers a combined solution. The IOPES project seeks to solve or at least alleviate this problem by building a portable, unobtrusive, lightweight device combining GNSS for outdoor positioning and visual-inertial odometry / SLAM for the indoors case. This work, the third of the IOPES series, presents the analysis of the performance results obtained after developing and testing the first IOPES prototype. To do it, the operational aspects of the prototype, the real-life scenarios where the tests took place and the actual results thus obtained are described.This publication has been produced with the support of the European Commission. The contents of this publication are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission. This contribution is part of the results of IOPES project, co-funded by the European Commission, Directorate-General Humanitarian Aid and Civil Protection (ECHO), under the call UCPM-2019-PP-AG.Peer ReviewedPostprint (published version

Monitoring coastal storms’ effects on the Trabucador barrier beach (Ebro Delta) through Sentinel-2 derived shorelines

The vulnerable Trabucador barrier beach has recently suffered significant storm-induced geomorphological changes. This study presents the monitoring of its shoreline during storm events for assessing their effects on beach dynamics. After fine-tuning the CoastSat tool (i.e. optimal NDWI threshold) for shoreline extraction from Sentinel-2 imagery (S2), results were validated with GNSS-RTK reference shorelines (RMSE = 6.8 m). Shorelines were extracted from Dec-2019 to Feb-2021, encompassing 11 storms (Hs > 2m; duration = 24h), including Gloria (Jan-2020). Results showed that S2 imagery provides enough temporal and spatial resolution to capture the storm effects on the site. The shoreline timeseries gave relevant information about the geomorphological processes occurring during storm events (barrier breaching, erosion, washover), allowing the assessment of their cumulative effects. These results might be important for coastal management, in a site suffering from chronic flooding.Peer ReviewedPostprint (author's final draft

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (ZSD). The relationship between in situ ZSD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.info:eu-repo/semantics/publishedVersio

On hybrid positioning using non-semantic image information

(English) Hybrid or multi-sensor-based positioning has been a research topic actively investigated in the last decade. In this context, the possibility of using information extracted from imaging sensors, for positioning, is very appealing to mitigate the problems that GNSS or INS/GNSS-based trajectories have in terms of robustness and accuracy. On the other hand, different processing workflows, sensor positioning quality or system calibration errors, may also produce even in GNSS-friendly conditions, that multiple geospatial datasets are not properly co-registered. This thesis proposes the use of non-semantic information, this is, the use of a set of geometric entities or features, to improve the trajectory estimation in a multi-sensor-based approach. This thesis covers the mathematical modelling of non-semantic information, implements several hybrid-based trajectory estimation approaches that use this kind of information with the appropriate modelling, and also explores the use of non-semantic features to model the trajectory error modelling. The implementation of combined models allowing to use of observations from camera or LiDAR sensors is the first contribution of this thesis. The proposed models have enabled improved trajectory determination in both urban post-processing and airborne environments with good accuracy (cm level). The implemented INS/GNSS trajectory error models are relatively simple but proved to be efficient. The combined models have been tested, in post-processing, using a bundle adjustment approach, with real data from metric cameras and aerial laser mapping systems as well as in Terrestrial Mobile Mapping systems (TMM). The second contribution of this thesis is the characterization of trajectory errors that TMM may have in GNSS urban scenarios. The non-semantic information extracted from the images has allowed, using an integrated sensor orientation approach, to model these errors in an urban environment. This modelling opens the door to the development of new, more advanced trajectory error models that go beyond the deterministic models currently used. The determination of trajectories in real time, in GNSS unfriendly environments, is also explored in this thesis using non-semantic features. An approach has been implemented based on a tightly coupling sequential nonlinear least squares using GNSS positions, image coordinates and raw inertial measurements. The proposed approach exploits a sliding window bundle adjustment technique to use the image coordinates of tie points and the positions and attitudes derived from the last epochs to determine the position and attitude parameters of the most recent epoch. The approach has been evaluated using both real and simulated data from a mobile mapping campaign over an urban area with long GNSS outage periods, with promising results. This thesis also presents an approach to improve the determination of Remotely Piloted Aircraft Systems (RPAS) trajectories using open aerial data obtained in the framework of a national mapping project (PNOA). The development of this methodology is another contribution aiming to ensure the geospatial coherence between the orthophotos, and digital elevation models obtained with an RPAS and the orthophotos and digital models of the PNOA. The results, applied in the context of a multi-temporal and multi-sensor high-resolution archaeological documentation, show that photogrammetric products can be generated with a similar accuracy (cm level accuracy) to the ones generated with more complex approaches. Last but not least, this thesis presents a seamless indoor-outdoor positioning approach with encouraging results (meter-level accuracy) in several scenarios. This contribution opens the door for enhanced tracking of members of civil protection and emergency teams. This is an open field of research with not widely accepted /adopted solution yet.(Català) El posicionament híbrid o multisensorial ha estat un tema de recerca molt estudiat durant la darrera dècada. En aquest context, la possibilitat d'utilitzar informació extreta de sensors d'imatge per al posicionament és molt prometedora per mitigar els problemes que presenten les trajectòries basades en GNSS o INS/GNSS en termes de robustesa i precisió. D'altra banda, els diferents fluxos de treball durant el processament, la qualitat dels sensors utilitzats per al posicionament o els errors de calibratge del sistema, també poden produir, fins i tot en condicions favorables de visibilitat de satèl·lits GNSS, que múltiples conjunts de dades geoespacials no estiguin correctament co-registrats. Aquesta tesi proposa l’ús d’informació no semàntica, és a dir, l’ús d’un conjunt d’entitats o característiques geomètriques, per millorar l’estimació de la trajectòria en un enfocament multisensorial. Aquesta tesi aborda el modelatge matemàtic de la informació no semàntica, implementa diverses aproximacions híbrides per a l'estimació de trajectòria basada en aquest tipus d'informació i també explora l'ús de característiques no semàntiques per modelar l'error d'una trajectòria. La implementació de models combinats que permeten utilitzar observacions de càmeres o sensors LiDAR és la primera contribució d'aquesta tesi. Els models han permès millorar la determinació de trajectòries tant en entorns urbans, com en aerotransportats amb una bona precisió (nivell centimètric). La modelització de l’error de les trajectòries INS/GNSS implementada és relativament senzilla, però ha demostrat ser eficient. Els models combinats s'han provat, en post procés, utilitzant un ajust de xarxes, amb dades reals de càmeres mètriques i de sistemes LiDAR tant aerotransportats com embarcats en sistemes de cartografia mòbil terrestre (TMM). La segona contribució d’aquesta tesi és la caracterització dels errors de les trajectòries TMM en escenaris urbans poc propicis per al GNSS. La informació no semàntica extreta de les imatges ha permès, utilitzant una orientació integrada, modelitzar aquests errors en aquest entorn, i ha obert la porta al desenvolupament de nous models d’error més avançats. La determinació de trajectòries en temps real fent servir característiques no semàntiques, en entorns poc propicis per a GNSS, també s'explora en aquesta tesi. Per a això s'ha implementat una aproximació basada en mínims quadrats seqüencials no lineals acoblats profundament, i que utilitza posicions GNSS, coordenades d'imatge i mesures inercials. L’enfocament proposat es basa en una tècnica d’ajust de xarxes utilitzant les coordenades imatge dels punts d’enllaç i les posicions i actituds derivades de les últimes èpoques, per determinar els paràmetres de posició i actitud de l’època més recent. L’aproximació s’ha avaluat utilitzant dades reals i simulades d’una campanya de cartografia mòbil sobre una zona urbana amb llargs períodes d’interrupció del senyal GNSS, amb resultats prometedors. Aquesta tesi també presenta una aproximació per millorar la determinació de les trajectòries dels vehicles aeris no tripulats (RPAS) utilitzant dades aèries d'accés obert, obtingudes en el marc d'un projecte cartogràfic nacional (PNOA). El desenvolupament d’aquesta metodologia busca garantir la coherència geoespacial entre les ortofotos i els models digitals d’elevació obtinguts amb un RPAS i les ortofotos i els models digitals del PNOA. Els resultats, utilitzats per la documentació arqueològica multitemporal i multisensorial d'alta resolució, mostren que es poden generar productes fotogramètrics amb una precisió similar (precisió a nivell de cm) als generats amb aproximacions més complexes.(Español) El posicionamiento híbrido o multisensorial ha sido un tema de investigación muy estudiado en la última década. En este contexto, la posibilidad de utilizar información extraída de sensores de imagen para el posicionamiento es muy prometedora para mitigar los problemas que presentan las trayectorias basadas en GNSS o INS/GNSS en términos de robustez y precisión. Por otra parte, los distintos flujos de trabajo durante el procesamiento, la calidad de los sensores utilizados o los errores de calibración del sistema, también pueden producir, incluso en condiciones favorables de visibilidad de satélites GNSS, que múltiples conjuntos de datos geoespaciales no estén correctamente co-registrados. Esta tesis propone el uso de información no semántica, es decir, el uso de un conjunto de entidades o características geométricas, para mejorar la estimación de la trayectoria en un enfoque multisensorial. Esta tesis aborda el modelado matemático de este tipo de información, implementa diversas aproximaciones híbridas para la estimación de trayectoria basada en esta información y explora su uso para modelar el error de una trayectoria. La implementación de modelos combinados que permiten utilizar observaciones de cámaras o sensores LiDAR es la primera contribución de esta tesis. Los modelos propuestos han permitido mejorar la determinación de trayectorias tanto en entornos urbanos, como en aerotransportados con buena precisión (nivel centimétrico). La modelización del error de las trayectorias INS/GNSS es relativamente sencilla, pero ha demostrado ser eficiente. Los modelos combinados se han probado, utilizando un ajuste de redes, con datos reales de cámaras métricas y de sistemas LiDAR tanto aerotransportados como en sistemas de cartografía móvil terrestre (TMM). La segunda contribución de esta tesis es la caracterización de los errores de las trayectorias TMM en escenarios urbanos poco propicios para el GNSS. La información no semántica extraída de las imágenes ha permitido, utilizando una orientación integrada, modelizar estos errores, y ha abierto la puerta al desarrollo de nuevos modelos de error más avanzados. La determinación de trayectorias en tiempo real utilizando características no semánticas, en entornos poco propicios para GNSS, también se explora en esta tesis. Para ello se ha implementado una aproximación basada en mínimos cuadrados secuenciales no lineales acoplados profundamente, utilizando posiciones GNSS, coordenadas de imagen y medidas inerciales. El enfoque propuesto se basa en una técnica de ajuste de redes que utiliza las coordenadas imagen de los puntos de enlace y las posiciones y actitudes derivadas de las últimas épocas, para estimar los parámetros de posición y actitud de la época más actual. La aproximación se ha evaluado utilizando datos reales y simulados de una campaña de TMM en una zona urbana con largos períodos de interrupción de la señal GNSS, con resultados prometedores. Esta tesis también presenta una aproximación para mejorar la determinación de las trayectorias de los vehículos aéreos no tripulados (RPAS) utilizando datos aéreos, obtenidos en el marco de un proyecto cartográfico nacional (PNOA). El desarrollo de esta metodología tiene como objetivo garantizar la coherencia geoespacial entre las ortofotos y los modelos digitales de elevación obtenidos con un RPAS y las ortofotos y modelos digitales del PNOA. Los resultados, utilizados por la documentación arqueológica multitemporal y multisensorial de alta resolución, muestran que pueden generarse productos fotogramétricos con una precisión similar (precisión a nivel de cm) a los generados con aproximaciones más complejas. Por último, esta tesis explora un método de posicionamiento continuo tanto en interior como exterior con resultados alentadores (precisión a nivel de metros) en varios escenarios. Esta contribución abre la puerta a la mejora del seguimiento de los miembros de los equipos de emergencias, un campo de investigación abierto todavía.DOCTORAT EN CIÈNCIA I TECNOLOGIA AEROESPACIALS (Pla 2013

An interferometric radar sensor for monitoring the vibrations of structures at short ranges

The Real-Aperture-Radar (RAR) interferometry technique consolidated in the last decade as an operational tool for the monitoring of large civil engineering structures as bridges, towers, and buildings. In literature, experimental campaigns collected through a well-known commercial equipment have been widely documented, while the cases where different types of sensors have been tested are a few. On the bases of some experimental tests, a new sensor working at high frequency, providing some improved performances, is here discussed. The core of the proposed system is an off-the-shelf, linear frequency modulated continuous wave device. The development of this apparatus is aimed at achieving a proof-of-concept, tackling operative aspects related to the development of a low cost and reliable system. The capability to detect the natural frequencies of a lightpole has been verified; comparing the results of the proposed sensor with those ones obtained through a commercial system based on the same technique, a more detailed description of the vibrating structure has been achieved. The results of this investigation confirmed that the development of sensors working at higher frequencies, although deserving deeper studies, is very promising and could open new applications demanding higher spatial resolutions at close ranges

Automatic mapping of seagrass beds in alfacs bay using Sentinel-2 imagery

Seagrass are marine flowering plants that form extensive meadows in shallow coastal waters. They play a critical role in coastal ecosystems by providing food and shelter for animals, recycling nutrients, and stabilizing sediments. Therefore, they are widely used as an ideal biological indicator for assessing the health status and quality of coastal ecosystems. In the Alfacs Bay (Ebro Delta), seagrasses are located in the shores, showing an annual variation with a peak in summer. The decreasing of averaged salinity and increasing of nutrients concentration and turbidity, has led to a notable reduction of the seagrass beds. Thus, a cartography to monitor spatiotemporal changes of meadows and to forecast the evolution of the environmental characteristics of the system, is needed. Nowadays, the standard methodology is a combination of photointerpretation and field prospection with significant workload resources. In contrast, an automatic methodology relying on multispectral moderate resolution Sentinel 2 (S2) satellite imagery is proposed. The methodology consists of: atmospheric correction of Level-1C images, application of Green Normalized Difference Vegetation Index, statistic thresholding to tell apart possible seagrass areas and a supervised learning method to refine this classification and to identify habitats. The methodology has been applied and calibrated using S2 satellite imagery and reference data comprising several patches distributed along the Alfacs Bay. In these patches, seagrass areas were identified (visually and location with GNSS). The results showed that seagrass meadows can be automatically delineated using S2 imagery.This work was supported by the early stage researcher grant ‘2018 FI_B00705’.018 FI_B00705.Peer ReviewedPostprint (published version