Monitoring Of Coastal Areas Research Articles

Three-dimensional deformation monitoring of San Francisco Bay based on GNSS-InSAR data

As a cutting-edge technology in the field of remote sensing, Synthetic Aperture Radar Interferometry (InSAR) plays a significant role in monitoring urban surface deformation. The traditional multi-temporal InSAR technique can only obtain surface deformation in the line-of-sight (LOS) direction, and there are fewer studies on three-dimensional(3D) deformation monitoring in coastal areas. At the same time, there is a lack of research on integrating GNSS-InSAR data to obtain the three-dimensional deformation field in the San Francisco Bay area. Using ascending and descending orbit InSAR images and GNSS data to monitor ground subsidence in the San Francisco Bay area, various schemes of integrating GNSS and InSAR data to invert the three-dimensional deformation field were analyzed and compared. An improved Robust-Helmert estimation method was proposed to calculate the 3D ground deformation velocity. The results showed that the Robust-Helmert estimation method performed best, with Root Mean Square Error (RMSE) values of 3.96 mm, 6.51 mm, and 2.75 mm in the East-West (E), North-South (N), and Up-Down (U) directions, respectively. It also exhibited more stable solution accuracy and good universality.

UAV-Assisted IoT Applications, QoS Requirements and Challenges with Future Research Directions

Unmanned Aerial Vehicle (UAV)-assisted Internet of Things application communication is an emerging concept that effectuates the foreknowledge of innovative technologies. With the accelerated advancements in IoT applications, the importance of this technology became more impactful and persistent. Moreover, this technology has demonstrated useful contributions across various domains, ranging from general to specific applications. Examples include wildfire monitoring, coastal area monitoring, deforestation monitoring, and sensitive military operations, where human access is limited or not feasible. These examples underscore the technology’s importance in scenarios where direct human involvement is challenging or impossible. Although this technology offers numerous benefits, it is essential to note that it also faces several challenges. Among these, Quality of Service (QoS) is a key concern, which limits its useability in various applications. Unfortunately, most researchers in the present literature have overlooked this important factor without giving it considerable attention. To fill this gap, we are presenting a systematic review of the present literature associated with the QoS metrics of this emerging technology from 2015 to 2023 to highlight their contributions and limitations. Based on the systematic review, we highlight the open challenges of this technology to set a roadmap for futuristic research. Finally, we compared each portion of this work with the previously published review articles to confirm the essence of this work, along with an explanation of why this survey is needed and in-time.

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

Mercury monitoring in the coastal areas of the Republic of Korea using the black-tailed gull egg as an indicator

In the southeast and east coasts of the Republic of Korea, it is essential to monitor mercury accumulation in coastal organisms in view of the higher mercury distribution in sediments and human samples. However, mercury pollution monitoring in organisms, especially higher trophic-level organisms that can exhibit high mercury accumulation, is limited. Here, we examined the applicability of the eggs of the black-tailed gull (Larus crassirostris), which belongs to a high trophic level, for mercury monitoring in coastal areas. Breeding sites were selected in West, Southeast, and East Seas with different mercury concentrations in other matrices (sediment and biological samples of residents). The 5-year mean total mercury concentration in eggs collected during the breeding seasons from 2016 to 2020 was lower in Baengnyeongdo (705 ± 81 ng/g dry weight (dry), West Sea) than in Hongdo (1,207 ± 214 ng/g dry, Southeast Sea) and Ulleungdo (1,095 ± 95 ng/g dry, East Sea). The different patterns of mercury concentration in gull eggs among the breeding sites was consistent with those in the other matrices among the coastal areas. These results support the applicability of the black-tailed gull egg as an indicator for establishing a monitoring framework in the coastal areas of the Republic of Korea.

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.

Modeling Coastal Area Change Analysis of Coastal Urban Areas at Semarang City, Indonesia

A coastal area is defined as the boundary between land and sea. Coastal urban areas are susceptible to various hazards that are becoming more severe, such as flooding, erosion, and subsidence due to a mix of man-made and natural factors, including urbanization and climate change. Regardless of the high importance of coastal area monitoring, conducting field surveys is expensive, time-consuming, and geographically limited to non-remote regions. Semarang City is one of the cities in Indonesia that is at risk of changes in its coastline and causes various natural problems. This research aims to estimate changes in the coastal land area in Semarang City. In observing the phenomenon of changes in area in coastal areas in Semarang City, remote sensing technology with Sentinel-2 satellite imagery was used. This research implements and compares the Random Forest (RF) and Support Vector Machine (SVM) machine learning methods in building classification models. From the results of land area in 2019, 2021, and 2023 with the best classification model, namely SVM, information was obtained on an increase in coastal area of 387.94 ha in 2021, then a change in area decrease of 417.32 ha in 2023.

Estimation of Water Quality Parameters in Oligotrophic Coastal Waters Using Uncrewed-Aerial-Vehicle-Obtained Hyperspectral Data

Water quality monitoring in coastal areas and estuaries poses significant challenges due to the intricate interplay of hydrodynamic, chemical, and biological processes, regardless of the chosen monitoring methods. In this study, we analyzed the applicability of different monitoring sources using in situ data, uncrewed-aerial-vehicle (UAV)-mounted hyperspectral sensing, and Sentinel-2-based multispectral imagery. In the first part of the study, we evaluated the applicability of existing empirical algorithms for water quality (WQ) parameter retrieval using hyperspectral, simulated multispectral, and satellite multispectral datasets and in situ measurements. In particular, we focused on three optically active WQ parameters: chlorophyll a (Chl,a), turbidity (TUR), and colored dissolved organic matter (CDOM) in oligotrophic coastal waters. We observed that most existing algorithms performed poorly when applied to different reflectance datasets, similar to previous findings in small and optically complex water bodies. Hence, we proposed a novel set of locally based empirical algorithms tailored for determining water quality parameters, which constituted the second part of our study. The newly developed regression-based algorithms utilized all possible combinations of spectral bands derived from UAV-generated hyperspectral data and exhibited coefficients of determination exceeding 0.9 for the three considered WQ parameters. The presented two-part approach was demonstrated in the semi-enclosed area of Kaštela Bay and the Jadro River estuary in the Central Eastern Adriatic Sea. This study introduces a promising and efficient screening method for UAV-based water quality monitoring in coastal areas worldwide. Such an approach may support decision-making processes related to coastal management and ultimately contribute to the conservation of coastal water ecosystems.

MULTI-PLATFORM MONITORING OF COASTAL EROSION AT A POCKET BEACH

As in many other regions of the world, coastal recession in West and Central Africa is the result of natural factors, anthropogenic factors, or both (Anthony et al., 2016; Luijendijk et al., 2018), and when it occurs, it may continue unless it is firmly addressed. Erosion along Ghana's coastline is endemic as in most of the Gulf of Guinea countries in West Africa. The current challenge is how to document and understand the dimensions of erosion despite limited human and logistical capacity. Regular monitoring of coastal areas is a prerequisite to evade any imminent erosive disaster within the coastal cities (Angnuureng et al., 2022). Data-driven decisions have remained more uncertain because most monitoring platforms are unable to capture events of a certain frequency. The main goal of this work is to test the feasibility of using multiple sources of remote sensing platforms to assess the dynamics of a pocket beach at Elmina in Ghana, as a showcase for local governance.

Employing molecular, chemical and physiological techniques using Crassostrea virginica to assess ecosystem health along coastal South Carolina and North Carolina, United States

Natural and anthropogenic environmental impacts can introduce contaminants into sensitive habitats, threatening ecosystems and human health. Consistent monitoring of coastal areas provides critical environmental assessment data. Sediments and Eastern Oyster (Crassostrea virginica) tissues were collected at fourteen South Carolina (SC) and four North Carolina (NC) sites as part of the National Oceanic and Atmospheric Administration's Mussel Watch environmental monitoring program. Cellular and molecular techniques were employed to measure C. virginica stress response, specifically, Lipid Peroxidation (LPx), Glutathione (GSH), and qPCR techniques. Gene specific primers targeted for detecting oxidative stress and cellular death were developed in C. virginica to gauge response to current environmental conditions using gill and hepatopancreas (HP) tissue. In order to validate gene specific markers as additional assessment tools, a 96 h zinc (Zn) laboratory exposure was performed. Cellular biomarker data revealed tissue specific responses. Hepatopancreas data showed C. virginica exhibited stress through the lipid peroxidation assay amongst sampling sites, however, response was managed through glutathione detoxification. Gill tissue data had significantly lower levels of cellular biomarker response compared to hepatopancreas. Molecular biomarkers targeting these cellular stress pathways through qPCR analysis show upregulation of Metallothionein in hepatopancreas and gill tissue with a concurrent > 2-fold upregulation in the detoxification marker Superoxide Dismutase (SOD) at three NC sites. SC sites displayed higher stress levels through LPx assays and down-regulation in GPx gene activity. Laboratory zinc exposure revealed no significance in cellular biomarker results, however, molecular data showed gills responding to zinc treatment through upregulation of Metallothionein, SOD and Cathepsin L, indicating an acute response in gills. Collectively, chemical, cellular and molecular methods clarify sentinel stress response of biological impacts and aid in evaluating environmental health in coastal ecosystems. This combined methodological approach provides a detailed analysis of environmental conditions and improves land-use management decisions.

Land Use/Land Cover Optimized SAR Coherence Analysis for Rapid Coastal Disaster Monitoring: The Impact of the Emma Storm in Southern Spain

The high exposure of coastal areas worldwide to natural and anthropogenic disasters emphasizes the relevance of disaster management processes that ensure a prompt damage detection and identification of affected areas. This paper aimed to develop a novel approach for disaster monitoring in coastal areas using SAR data. The method was based on an interferometric coherence difference analysis of Sentinel 1 data. To calibrate and validate the method, the Emma Storm, a severe coastal storm that affected the southwest coast of the Iberian Peninsula in 2018, was chosen as a case study. A coastal land use/land cover method optimization by optical and UAV field data resulted in an overall improvement of about 20% in the identification of disaster-affected areas by reducing false alarms by up to 33%. Finally, the method achieved hit and false alarm rates of about 80% and 20%, respectively, leading to the identification of approximately 30% (7000 ha) of the study area as being affected by the storm. Marshes and vegetated dunes were the most significantly impacted covers. In addition, SAR data enabled the impact assessment with a time lag of 2 days, contrasting the 25-day delay of optical data. The proposed method stands out as a valuable tool for regional-scale coastal disaster monitoring. In addition, it can be automated and operated at a low cost, making it a valuable tool for decision-making support.

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance.

Monitoring of Coastal Areas by Remote Sensing and Engineering Approaches

Monitoring coastal regions is very important to protect the marine environment as a whole. Coastal erosion, sea level rise, coastal changes, flooding, and harmful algal blooms, are examples of challenges in coastal areas resulting from climate change. Furthermore, pollution from oil spills and agricultural activities that release large quantities of agrochemicals, organic matter, drug residues, sediments and saline drainage into coastal waters are further examples of coastal challenges and criticalities. All of these challenges require a high level of attention. Coastal monitoring through remote sensing approaches and possible models capable of predicting future developments can represent valid tools available to policymakers to develop interventions. Hard and soft engineering interventions can counteract the effects of coastal erosion, wave action, shoreline and flooding, thus restoring coasts and all associated activities. This mini-review describes remote sensing approaches for coastal monitoring, such as the use of unmanned aircraft, and possible hard and soft engineering interventions capable of mitigating coastal damage according to sustainability rules.

Resource allocation optimization for mitigating multi-jammer in underwater sensor network

<p class="16">Wireless underwater sensor networks (UWSNs) are used for coastal area monitoring and military monitoring applications, such as tsunami prevention and target tracking. In UWSNs, jamming is considered to be a serious problem where the intruder affects the lifetime of sensor motes and impacts the performance of the packet transmission. This paper considers that the jammer device is capable of reducing battery life and preventing the trustworthy UWSN mote from communication. Considering the presence of multiple jammers, the existing resource utilization model is not effective. This work presents an efficient resource allocation design to mitigate multiple jammers in UWSNs to overcome research problems. The resource allocation (ERA) model adopts a cross-layer design and can interact in a cooperative manner using direct and hop-based communication to maximize the quality of resource use. Compared to existing resource allocation methodologies, considering the presence of multiple jammer motes, the ERA achieves a much better detection rate, resource utilization, packet drop, and energy efficiency performance.</p>

Determination of Bayesian Cramér–Rao Bounds for Estimating Uncertainties in the Bio-Optical Properties of the Water Column, the Seabed Depth and Composition in a Coastal Environment

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds.

Ground infrastructure monitoring in coastal areas using time-series inSAR technology: the case study of Pudong International Airport, Shanghai

ABSTRACT Shanghai Pudong International Airport (PDIA), with its east side built along the coast with weak geological conditions, is prone to uneven foundation settlement due to the consolidation and compression of soil and erosion of coastal tides, affecting the safe operation of the airport. Therefore, it is crucial to conduct dynamic subsidence monitoring within the airport, especially in the runway area. 29 scenes of ascending track Sentinel-1A radar images from August 2016 to June 2018 are selected to perform surface deformation inversion based on PS-InSAR and improved SBAS-InSAR for PDIA and its around coastal area. Through cross-validation, the reliability of the time-series InSAR technique for dynamic monitoring of surface deformation of coastal zone infrastructures is confirmed. The results show severely uneven settlement. By combining the monitoring results with the local geological and hydrological dataset, the driving factors of differential deformation of the infrastructures are analyzed, including stratigraphic geological conditions, ground loadings, foundation treatment methods, water erosion, and groundwater level changes. Finally, the time-series deformation characteristics and the causes of PDIA's runway are emphasized based on the PS deformation results. This case provides a reference for the safety management of critical infrastructure in coastal areas using advanced InSAR technique.

African coastal camera network efforts at monitoring ocean, climate, and human impacts

Regular and long-term monitoring of coastal areas is a prerequisite to avoiding or mitigating the impacts of climate and human-driven hazards. In Africa, where populations and infrastructures are particularly exposed to risk, there is an urgent need to establish coastal monitoring, as observations are generally scarce. Measurement campaigns and very high-resolution satellite imagery are costly, while freely available satellite observations have temporal and spatial resolutions that are not suited to capture the event scale. To address the gap, a network of low-cost, multi-variable, shore-based video camera systems has been installed along the African coasts. Here, we present this network and its principle of sharing data, methods, and results obtained, building toward the implementation of a common integrated coastal management policy between countries. Further, we list new contributions to the understanding of still poorly documented African beaches’ evolution, waves, and sea level impacts. This network is a solid platform for the development of inter-disciplinary observations for resources and ecology (such as fisheries, and sargassum landing), erosion and flooding, early warning systems during extreme events, and science-based coastal infrastructure management for sustainable future coasts.

Mapping intertidal macrophytes in fjords in Southwest Greenland using Sentinel-2 imagery

Changes in the distribution of coastal macrophytes in Greenland, and elsewhere in the Arctic are difficult to quantify as the region remains challenging to access and monitor. Satellite imagery, in particular Sentinel-2 (S2), may enable large-scale monitoring of coastal areas in Greenland but its use is impacted by the optically complex environments and the scarcity of supporting data in the region. Additionally, the canopies of the dominant macrophyte species in Greenland do not extend to the sea surface, limiting the use of indices that exploit the reflection of near-infrared radiation by vegetation due to its absorption by seawater. Three hypotheses are tested: I) 10-m S2 imagery and commonly used detection methods can identify intertidal macrophytes that are exposed at low tide in an optically complex fjord system in Greenland impacted by marine and land terminating glaciers; II) detached and floating macrophytes accumulate in patches that are sufficiently large to be detected by 10-m S2 images; III) iceberg scour and/or turbid meltwater runoff shape the spatial distribution of intertidal macroalgae in fjord systems with marine-terminating glaciers. The NDVI produced the best results in optically complex fjord systems in Greenland. 12 km2 of exposed intertidal macrophytes were identified in the study area at low tide. Floating mats of macrophytes ranged in area from 400 m2 to 326,800 m2 and were most common at the mouth of the fjord. Icebergs and turbidity appear to play a role in structuring the distribution of intertidal macrophytes and the retreat of marine terminating glaciers could allow macrophytes cover to expand. The challenges and solutions presented here apply to most fjords in Greenland and, therefore, the methodology may be extended to produce a Greenland-wide estimate of intertidal macrophytes.

Monitoring Coastal Erosion Using Remote Images: Comparison between Physically and Remotely Acquired Data on a Limestone Coast

Boulder-sized clasts on rocky coasts are considered as erosional signatures of extreme wave events and boulder attributes are often used in numerical models to estimate wave characteristics. The use of unmanned aerial vehicle (UAV) technology and related software has facilitated the monitoring of coastal areas, by generating models from which 2D and 3D measurements can be derived. However, the reliability and preciseness of such measurements is still to be determined. This study seeks to analyse the accuracy of boulder measurements by comparing the dimension data obtained through in-situ measurements with ex situ data generated from digital models, based on UAV images. The study area is a bouldered sloping coast located on the southeast coast of Malta (Central Mediterranean) that has developed into multiple limestone dipped strata with a fractured and heavily jointed morphology. The dimensions of c. 200 boulders in different morphological settings, such as clusters or ridges, have been statistically compared. The results show a very strong correlation between the two datasets, both in 2D and 3D; however some notable differences were observed at the individual boulder level. For the majority of boulders analysed, the A and B axes dimensions varied by ±10% to 20%. The C axis proved to be harder to measure accurately and showed a wider range of difference. Boulder volume results in the majority of cases varied from 0% to ±40%. Some tested methods of volume calculation may be more accurate and realistic than others depending on the boulder position in relation to other clasts and shore morphology. An automated digital analysis of the terrain surface to identify the boulder extents may offer possibilities for a more accurate estimation of boulder attributes.

Advancing real-time pH sensing capabilities to monitor coastal acidification as measured in a productive and dynamic estuary (Ría de Arousa, NW Spain)

Ocean acidification has critical impacts on marine ecosystems, but presents knowledge gaps on the ecological impacts requiring large-scale monitoring of physicochemical conditions to predict biological responses to ocean pH projections. The threat is especially significant in coastal regions like upwelling areas which are more sensitive and appear to respond more rapidly to anthropogenic perturbations. These ecosystems, such as the northwest coast of the Iberian Peninsula are characterized by complex physical and biogeochemical interactions, supporting enormous biological productivity and productive fisheries. The distribution of pH in upwelling systems has high variability on short temporal and spatial scales preventing a complete picture of acidification, which exhibit long-term pH rates markedly different from the measured in open waters. This motivation to significantly expand the coverage of pH monitoring in coastal areas has driven us to develop an autonomous pH monitoring instrument (from now on SURCOM) based on the Honeywell Durafet® pH electrode. A relevant feature is that SURCOM transmits near real-time pH and temperature measurements every 10.5 min through SIGFOX®, a low-power, low-bandwidth network for data transmission. This very careful design allows us to achieve a very low power consumption for the complete system resulting in 3 years of full autonomy with no other need than external cleaning and calibration. In this paper we describe the setup and the data set obtained by a SURCOM instrument over 240 days in a highly productive and dynamic coastal ecosystem, the Ría de Arousa embayment, providing valuable information on the performance of these low-cost and highly stable sensors, with potential for improving the pH variability description in nearshore systems and for reinforcing the monitoring-modeling of coastal acidification.

Concentration of fecal coliforms in marine waters using satellite images in the vicinity of Pucusana. Bay, Peru.

Water quality monitoring in coastal areas is challenging due to cost and time constraints. Identifying and selecting sampling sites accurately and effectively is crucial for efficient monitoring. The need for efficient monitoring of marine waters has led to exploring the use of remote sensing as one helpful alternative. Remote sensing is practical in several applications based on pattern recognition and information processing of large terrestrial and aquatic surface areas. Collected information is processed with various image processing techniques to identify objects such as microorganisms. Fecal coliforms are microorganisms that are indicators of sanitary quality and are present in human and animal wastes discharged into water bodies reaching coastal regions. The present study estimated the presence of fecal coliforms as an indicator of contamination in coastal marine waters. Satellite data from two sensors, Landsat 7 ETM+ and Landsat 8 OLI, were used to evaluate the reflectance of fecal coliforms in marine waters. Then, statistical analysis and four regression models were tested to establish a functional correlation between the spectral bands and historical in situ fecal coliform measurement. In this research, satellite imagery in the vicinity of Pucusana Bay helped estimate the concentration of fecal coliforms in marine waters. As a result, a significant relationship was found between the shortwave infrared band splitting (SWIR 2) with the blue band and fecal coliforms presence. The relationship was used to estimate coliform concentration from the reflectance of the aquatic surface in Pucusana Bay. Finally, spatial distribution maps of fecal coliform concentrations were generated to compare the increase of these microorganisms over different years in the area. The methodology and results can be calibrated to other water body locations where fecal coliform is a concern.