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

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

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

Towards seamless indoor-outdoor positioning: the IOPES project approach

The management of emergencies require the use of multiple resources that must be coordinated to achieve the best possible results. For a good decision-making process, the availability of timely and reliable information about the variables on which such process rely is crucial. Among these variables, the ability to track the position in the field - either outdoors or indoors - of the members of the emergency teams it is of special importance. The IOPES project targets at improving an existing, already operational emergency management system where the tracking of operative staff is integrated. This paper concentrates only in the positioning aspect of IOPES - which encompasses other subsystems, such as portable communications or fast mapping - and describes the approach adopted by the project to perform such integration. This includes the concept itself, the hardware selected and well as the algorithms used to implement a portable, lightweight positioning device able to provide seamless indoor / outdoor positioning that will make possible the real-time tracking of personnel in the field. Promising preliminary results for mixed indoor-outdoor trajectories are as well presented.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-AGPeer ReviewedPostprint (published version

Rice farming and macrophyte dynamics monitoring through Sentinel-2 MSI as a proxy of disturbance of agricultural practices over an enclosed bay

Coastal regions are highly dynamic and productive ecosystems with high ecological and economical value. Given the co-existence and interaction of different human activities, both in land and on sea, it is a priority an Integrated Coastal Management (ICM) ensuring their sustainability. In this sense, in the biosphere reserve of the Ebro Delta (NW Mediterranean, Spain), natural ecosystems co-exist with human economic activities. Rice farming is the main activity on the area and is likely to have environmental impacts on coastal areas such as bays, where paddies irrigation channels discharge. Therefore, understanding the interaction between rice farming and the coastal ecosystem is essential for developing an ICM. With this aim, we monitored rice paddies, by using remote sensing data, and macrophytes (seagrass meadows and macroalgae mats) in the Delta-Bay system (Alfacs Bay), as disturbance indicator. Using Sentinel-2 MSI imagery, rice growing dynamics and crop phenology were characterized through the Normalized Vegetation Index (NDVI) and Normalized Water Index (NDWI) over a two-year period. Agricultural management practices such as fertilization were obtained from farmers. For aquatic vegetation, after atmospheric correction for ocean colour remote sensing, spectral band combination of Sentinel-2 MSI together with field observations were used in a supervised classification method to assess the spatial coverage of seagrass and macroalgae communities. The combination of NDVI and NDWI proved to be suitable to identify hydroperiod and crop phenology of rice paddies. The supervised classification of the bay’s vegetation showed spatiotemporal dynamics related with previous results in scientific literature. Aquatic vegetation presented a different temporal pattern in the northern than in the southern margin of the Alfacs Bay, which can be related to rice crop growing cycle. In the northern margin, where rice irrigation channels flow out (i.e. freshwater, nutrients, etc.), overgrowing macroalgae episodes occurred. However, in the southern margin, without the direct impact of the irrigation network, overgrowing-macroalgae was not reported. These results highlight the need for a global management strategy to ensure the sustainability of both human economic activities and natural systems and prove the suitability of Sentinel-2 as a support tool for future policy decision making.This work has been funded by AGAUR (Generalitat de Catalunya) through a grant for the recruitment of early-stage research staff (Ref: 2018 FI_B 00705).Peer ReviewedPostprint (published version

Monitoring rice crop and yield estimation with Sentinel-2 data

The future success of rice farming will lie in developing productive, sustainable, and resilient farming systems in relation to coexistent ecosystems. Thus, accurate information on agricultural practices and grain yield at optimum temporal and spatial scales is crucial. This study evaluates the potential application of Sentinel-2 (S2) to monitor the dynamics of rice fields in two consecutive seasons (2018 and 2019) in the Ebro Delta growing area. For this purpose, time series of four different spectral indices (NDVI, NDWIMF, NDWIGAO, and BSI), derived from smoothed S2 data at 20 m spatial resolution, were generated. Then, a combination of the first and second derivative analysis on the temporal profiles of spectral indices was used to automatically identify key phenology and management features from regional to field scale; and for estimating crop yield at fields. Features extracted from NDVI and NDWIGAO were used for identifying significant phenological stage dates (i.e. Tillering, Heading Date, and Maturity), and field status (i.e. hydroperiod), although the performance of the proposed method at field-scale was limited by S2 data gaps. The absolute minimum of NDWIMF showed great potential for estimating rice yield, including different cultivars (r = - 0.8), and less sensibility to the number of valid images. Sentinel-2 alone cannot assure a consistent phenology monitoring at all fields but demonstrated strong capabilities for studying the performance of rice fields, thus must be considered in the development of new strategies for the management of rice-growing areas.Peer ReviewedAward-winningPostprint (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 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 EbroPeer ReviewedPostprint (published version