Search Results (46)

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. Full article

Seagrass constitutes a vital component of coastal ecosystems, providing a wide array of ecosystem services. The accurate measurement of the seagrass frontal area is crucial for assessing its capacity to inhibit water flow and reduce wave energy; however, few effective indirect methods exist. To address this limitation, we developed an indirect method that combines the Mask R-CNN model with a dual-reference approach for detecting seagrass and estimating its frontal area. A laboratory-scale underwater camera experiment generated an experimental dataset, which was partitioned into training, validation, and test sets. Following training, evaluation metrics—including IoU, accuracy, precision, recall, and F1-score—approached their upper limits and remained within acceptable ranges. Validation on real seagrass images confirmed satisfactory performance, albeit with slightly lower metrics than those observed in the experimental dataset. Furthermore, the method estimated seagrass frontal areas with errors below 10% (maximum 7.68% and minimum –0.43%), thereby demonstrating high accuracy by accounting for seagrass bending under flowing water conditions. Additionally, we showed that the indirect measurement significantly influences estimations of the seagrass bending height and wave height reduction capacity, mitigating the overestimation associated with traditional direct methods. Thus, this indirect approach offers a promising, environmentally friendly alternative that overcomes the limitations of conventional techniques. Full article

Over the past two decades, extensive coastal development in China has led to numerous small-scale enclosed coastal water bodies. Due to complex shoreline geometries, these areas suffer from disturbed hydrodynamic conditions, weak water exchange, which quickly leads to sediment accumulation, and difficulty maintaining ecological water levels, posing serious environmental threats. Enhancing seawater exchange capacity and achieving coordinated optimization of exchange efficiency and ecological water level are critical prerequisites for the environmental restoration of eutrophic enclosed coastal areas. This study takes the Ligao Block in Tianjin as a case study and proposes a real-time sluice gate regulation scheme. By incorporating hydrodynamic conditions, engineering layout, and present characteristics of the benthic substrate environment, the number, width, location, and operation modes of sluice gates are optimized to maximize water exchange efficiency while maintaining natural flow patterns. The result of the numerical simulation of hydrodynamic exchange and intelligent optimization analysis reveals that the optimal sluice gate operation strategy should be tailored to regional tidal flow characteristics and substrate conditions. Through intelligent scheduling of exchange sluice gates, systematic gate parameter optimization, and active control of gate opening, this approach achieves intelligent seawater exchange, optimized flow dynamics, active exchange, and sustained ecological water levels in enclosed coastal water bodies. Full article

Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promotes a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and detecting mangrove area. In addition, we also present an improved model, AttCloudNet+, to identify the distribution of mangrove species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with SegNet, UNet, and DeepUNet, etc. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), and others. The comparison demonstrates that the mangrove species identification results obtained using AttCloudNet+ exhibit the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reaching 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provide an efficient solution based on deep learning with a dual attention mechanism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery. Full article

Mangrove forests are vital for flood reduction, yet their failure mechanisms during storms are poorly known, hampering their integration into engineered coastal protection. In this paper, we aimed to unravel the relationship between the resistance of mangrove trees to overturning and root distribution and the properties of the soil, while avoiding damage to natural mangrove forests. We therefore (i) tested the stability of 3D-printed tree mimics that imitate typical shallow mangrove root systems, mimicking both damaged and intact root systems, in sediments representing the soil properties of contrasting mangrove sites, and subsequently (ii) tested if the existing stability models for terrestrial trees are applicable for mangrove tree species, which have unique shallow root systems to survive waterlogged soils. Root systems of different complexities were modeled after Avicennia alba, Avicennia germinans, and Rhizophora stylosa, and printed at a 1:100 scale using material densities matching those of natural tree roots, to ensure the geometric scaling of overturning moments. The mimic stability increased with the soil shear strength and root plate surface area. The optimal root configuration for mimic stability depended on the sediment properties: spreading root systems performed better in softer sediments, while concentrating root biomass near the trunk improved stability in stronger sediments. An adapted terrestrial tree resistance model reproduced our measurements well, suggesting that such models could be adapted to predict the stability of shallow-rooted mangroves living in waterlogged soils. Field tree-pulling experiments are needed to further confirm our conclusions with real-world data, examine complicating factors like root intertwining, and consider mangrove tree properties like aerial roots. Overall, this work establishes a foundation for incorporating mangrove storm damage into hybrid coastal protection systems. Full article

As one of the countries with the most severe extreme climate disasters in the world, it is of great significance for China to scientifically understand the characteristics of extreme precipitation. The artificial neural network near-real-time dynamic infrared rainfall rate satellite precipitation data (PDIR-Now) is a global, long-term resource with diverse spatial resolutions, rich temporal scales, and broad spatiotemporal coverage, providing an important data source for the study of extreme precipitation. But its applicability and accuracy still need to be evaluated in specific applications. Based on the observation data of 824 surface meteorological stations in China, the correlation coefficient (R), relative deviation (RB), root mean square error (RMSE), and relative root mean square error (RRMSE) of quantitative statistical indicators were used to evaluate the annual maximum daily precipitation of PDIR-Now from 2000 to 2016 in this study, in order to explore the ability of PDIR-Now satellite precipitation products to monitor extreme precipitation in Chinese mainland. The results show that from the perspective of long-term series, the annual maximum daily precipitation of PDIR-Now has a good ability to monitor extreme precipitation across the country, and the R exceeds 0.6 in 65% of the years. The RMSE of different years is generally distributed between 40 and 60 mm, and in terms of time characteristics, the error of each year is relatively stable and does not fluctuate greatly with dry precipitation or abundant years. From the perspective of spatial characteristics, the distribution of RMSE is very regional, with the RMSE in the Qinghai–Tibet Plateau and Northwest China basically in the range of 0~20 mm, the Yunnan–Guizhou Plateau, the Sichuan Basin, Northeast China, and the central part of the study area in the range of 20~50 mm, and the RMSE in a few stations in the southeast coast greater than 80 mm. The RRMSE distribution of most sites is between 0 and 0.6, and the RRMSE distribution of a few sites is between 0.6 and 1.5. Generally, higher RRMSE values and larger errors are observed in the northwest and southeast coastal regions. Overall, PDIR-Now captures the regional characteristics of extreme precipitation in the study area, but it is underestimated in the wet season in humid and semi-humid regions and overestimated in the dry season in arid and semi-arid regions. Full article

Coastal saltmarsh wetlands are vital “blue carbon” ecosystems. Fractional vegetation cover (FVC) is a key indicator revealing the spatial distribution and growth status of vegetation. Remote sensing has proven a vital tool for FVC estimation at regional or landscape scales. Establishing a species-level FVC estimation model usually requires sufficient field measurements as training/validation samples. However, field-based sample collection in wetlands is challenging because of the harsh environment. In this study, we proposed a Fractional Vegetation Cover Wasserstein Generative Adversarial Network (FVC-WGAN) model for FVC sample expansion. We chose the Yellow River Delta as the study area and utilized the time series Sentinel-2 imagery and random forest regression model for species-level FVC estimation with the assistance of FVC-WGAN-generated samples. To assess the efficacy of FVC-WGAN, we designed 13 experimental schemes using different combinations of real and generated samples. Our results show that the FVC-WGAN-generated samples had similar feature values to the real samples. Supplementing 500 real samples with generated samples can achieve good accuracy with an average RMSE < 0.1. As the number of real samples increased, the accuracies of FVC estimation improved. When the number of the generated samples was balanced with the real samples, the accuracy improved in terms of both R², RMSE and the spatial consistency. Full article

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations. Full article

Accurately extracting large-scale offshore floating raft aquaculture (FRA) areas is crucial for supporting scientific planning and precise aquaculture management. While remote sensing technology offers advantages such as wide coverage, rapid imaging, and multispectral capabilities for FRA monitoring, the current methods face challenges in terms of establishing spatial–spectral correlations and extracting multiscale features, thereby limiting their accuracy. To address these issues, we propose an innovative multiscale spatial–spectral fusion network (MSSFNet) designed specifically for extracting offshore FRA areas from multispectral remote sensing imagery. MSSFNet effectively integrates spectral and spatial information through a spatial–spectral feature extraction block (SSFEB), significantly enhancing the accuracy of FRA area identification. Additionally, a multiscale spatial attention block (MSAB) captures contextual information across different scales, improving the ability to detect FRA areas of varying sizes and shapes while minimizing edge artifacts. We created the CHN-YE7-FRA dataset using Sentinel-2 multispectral remote sensing imagery and conducted extensive evaluations. The results showed that MSSFNet achieved impressive metrics: an F1 score of 90.76%, an intersection over union (IoU) of 83.08%, and a kappa coefficient of 89.75%, surpassing those of state-of-the-art methods. The ablation results confirmed that the SSFEB and MSAB modules effectively enhanced the FRA extraction accuracy. Furthermore, the successful practical applications of MSSFNet validated its generalizability and robustness across diverse marine environments. These findings highlight the performance of MSSFNet in both experimental and real-world scenarios, providing reliable, precise FRA area monitoring. This capability provides crucial data for scientific planning and environmental protection purposes in coastal aquaculture zones. Full article

The segmentation of floating algae is a hot topic in the field of marine environmental research. Given the vastness of coastal areas and complex environments, algae detection models must have both higher performance and lower deployment costs. However, relying solely on a single Convolutional Neural Network (CNN) or transformer structure fails to achieve this objective. In this paper, a novel real-time floating algae segmentation method using a distillation network (ADNet) is proposed, based on the RGB images. ADNet can effectively transfer the performance of the transformer-based teacher network to the CNN-based student model while preserving its lightweight design. Faced with complex marine environments, we introduce a novel Channel Purification Module (CPM) to simultaneously strengthen algae features and purify interference responses. Importantly, the CPM achieves this operation without increasing any learnable parameters. Moreover, considering the huge scale differences among algae targets in surveillance RGB images, we propose a lightweight multi-scale feature fusion network (L-MsFFN) to improve the student’s modeling ability across various scales. Additionally, to mitigate interference from low-level noises on higher-level semantics, a novel position purification module (PPM) is proposed. The PPM can achieve more accurate weight attention calculation between different pyramid levels, thereby enhancing the effectiveness of fusion. Compared to CNNs and transformers, our ADNet strikes an optimal balance between performance and speed. Extensive experimental results demonstrate that our ADNet achieves higher application performance in the field of floating algae monitoring tasks. Full article

Addressing the challenge of large-scale uneven deformation and the complexities of monitoring road conditions, this study focuses on a segment of the G15 Coastal Highway in Jiangsu Province. It employs PS-InSAR, SBAS-InSAR, and DS-InSAR techniques to comprehensively observe deformation. Analysis of 73 image datasets spanning 2018 to 2021 enables separate derivation of deformation data using distinct InSAR methodologies. Results are then interpreted alongside geological and geomorphological features. Findings indicate widespread deformation along the G15 Coastal Highway, notably significant settlement near Guanyun North Hub and uplift near Guhe Bridge. Maximum deformation rates exceeding 10 mm/year are observed in adjacent areas by all three techniques. To assess data consistency across techniques, identical observation points are identified, and correlation and difference analyses are conducted using statistical software. Results reveal a high correlation between the monitoring outcomes of the three techniques, with an average observation difference of less than 2 mm/year. This underscores the feasibility of employing a combination of these InSAR techniques for road deformation monitoring, offering a reliable approach for establishing real-time monitoring systems and serving as a foundation for ongoing road health assessments. Full article

In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world’s inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined the GOCI’s strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by Geostationary Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs. Full article

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data. Full article

The overarching goal of this paper is to explore innovative ways to adapt existing urban infrastructure to achieve a greener and more resilient city, specifically on synergies between the power grid, the wastewater treatment system, and community development in low-lying coastal areas. This study addresses the technical feasibility, benefits, and barriers of using wastewater resource recovery facilities (WRRFs) as community-scale microgrids. These microgrids will act as central resilience and community development hubs, enabling the adoption of renewable energy and the provision of ongoing services under emergency conditions. Load flow modeling and analysis were carried out using real network data for a case study in New York City (NYC). The results validate the hypothesis that distributed energy resources (DERs) at WRRFs can play a role in improving grid operation and resiliency. Full article

High-resolution coastline detection and monitoring are challenging on a global scale, especially in flat areas where natural events, sea level rise, and anthropic activities constantly modify the coastal environment. While the coastline related to the 0-level contour line can be extracted from accurate Digital Terrain Models (DTMs), the detection of the real-time, instantaneous coastline, especially at low tide, is a challenge that warrants further study and evaluation. In order to investigate an efficient combination of methods that allows to contribute to the knowledge in this field, this work uses topographic total station measurements, Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) technique, and the Structure from Motion (SfM) approach (using a low-cost drone equipped with optical and thermal cameras). All the data were acquired at the beginning of 2022 and refer to the areas of Boccasette and Barricata, in the Po River Delta (Northeastern of Italy). The real-time coastline obtained from the GNSS data was validated using the topographic total station measurements; the correspondent polylines obtained from the photogrammetric data (using both automatic extraction and manual restitutions by visual inspection of orhophotos) were compared with the GNSS data to evaluate the performances of the different techniques. The results provided good agreement between the real-time coastlines obtained from different approaches. However, using the optical images, the accuracy was strictly connected with the radiometric changes in the photos and using thermal images, both manual and automatic polylines provided differences in the order of 1–2 m. Multi-temporal comparison of the 0-level coastline with those obtained from a LiDAR survey performed in 2018 provided the detection of the erosion and accretion areas in the period 2018–2022. The investigation on the two case studies showed a better accuracy of the GNSS RTK method in the real-time coastline detection. It can be considered as reliable ground-truth reference for the evaluation of the photogrammetric coastlines. While GNSS RTK proved to be more productive and efficient, optical and thermal SfM provided better results in terms of morphological completeness of the data. Full article