Search Results (48)

Under the combined impacts of climate change and intensified human activities in coastal zones, mangrove ecosystems are increasingly exposed to degradation, fragmentation, and declines in ecological functions. It is therefore important to systematically examine the progress and evolution of the research hotspots in remote sensing monitoring of mangroves. In this study, 942 publications on mangrove remote sensing monitoring from 2000 to 2025 were retrieved from the China National Knowledge Infrastructure (CNKI) and the Web of Science Core Collection, comprising 485 CNKI records and 457 Web of Science records. CiteSpace 6.4.R2 was used to conduct bibliometric and knowledge-mapping analyses of publication trends, geographic distribution, author collaboration networks, keyword co-occurrence, keyword cluster timelines, and burst keywords. The results show that research on mangrove remote sensing monitoring generally increased over time, with marked growth after 2015. Research topics gradually shifted from early studies on mangrove distribution mapping, land-use change, and image classification to multi-source remote sensing applications, change detection, biomass estimation, blue carbon assessment, and machine learning- and deep learning-based methods. Author collaboration networks provide a descriptive overview of collaboration patterns and suggest that cross-team and cross-regional collaboration still needs to be strengthened. Keyword co-occurrence, cluster timeline, and burst keyword results indicate that remote sensing monitoring, machine learning, deep learning, random forest, support vector machine, object-based image analysis, ALOS PALSAR, ALOS-2 PALSAR-2, blue carbon, carbon stock, aboveground biomass, ecosystem services, and forest degradation are important themes in this field. Future research should further strengthen multi-source remote sensing data integration, cross-regional validation of intelligent algorithms, degradation monitoring indicator systems, uncertainty assessment, and long-term time-series analysis. These efforts will improve the accuracy, comparability, and management applicability of mangrove remote sensing monitoring and provide scientific support for coastal ecological conservation, mangrove restoration, and blue carbon governance. Full article

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields. Full article

For addressing difficult detail extraction and low operating efficiency in monitoring sea ice in a large area with wide-field-of-view images from the Chinese Gaofen-1 satellite, a lightweight, high-precision sea ice segmentation network adaptive multistatistic fusion attention (AMFA) module using DeepLabV3+ as the base architecture (AMFA-DeepLab) is proposed. First, the module replaces the backbone network with a lightweight MobileNetV2 to ensure feature extraction capability and greatly reduce model computational complexity using inverted residuals and depthwise separable convolution. Second, to solve the problems of fragmented ice texture blurring and speckle noise interference in optical images, an AMFA is designed and introduced into the decoder side. This module innovatively integrates the global median pooling branch and adapts the recalibrated feature weight through a dynamic channel mixing mechanism, effectively enhancing the model’s capability of capturing fine sea ice edge features and its antinoise robustness in complex backgrounds. Experimental results based on the dataset from Liaodong Bay in the Bohai Sea of China show that the intersection over union of AMFA-DeepLab reaches 92.15% and the F1-score reaches 95.91%, increases of 3.06%, and 1.68%, respectively, compared with those of the baseline model. In addition, only 5.85 million model parameters are needed, the training time is shortened to 4.42 h, and the inference speed is 281.76 frames per second. Visualized analysis and generalization test further demonstrates that this model can accurately eliminate clutter interference from coastal land and seawater and extract the fine filamentous structure of drift ice in the scene of complex melting ice. This research overcomes the precision bottleneck while achieving an ultimate lightweight model, providing efficient technical support for operational dynamic monitoring of sea ice disasters based on Chinese GaoFen-1 satellites. Full article

Climate change is impacting atmospheric patterns and therefore wave conditions, with ports being among the most affected infrastructures, making it crucial to ensure their operability under changing climatic conditions. Most scientific studies on climate change focus on coastal erosion and flooding, whereas research on its impact on port operability remains relatively scarce. This challenge could be tackled with the emergence of Artificial Intelligence (AI), where alternative modeling approaches can be developed. Thus, a novel AI-based model specifically designed for studying port agitation is introduced herein. By integrating a hybrid deep learning approach, combining Feedforward Neural Networks (FFNNs) to model wave climate and Convolutional Neural Networks (CNNs) for port image analysis, port agitation has been successfully predicted compared to linear wave propagation models. This marks the first instance of utilizing image processing tools to analyze port agitation, resulting in a model with a remarkably low error rate, while offering a significant reduction in computational time compared to traditional wave propagation models, reducing computational time by a factor of four to ten. The accuracy of the proposed model has been investigated and validated for the Port of Valencia, located in the Spanish section of the Mediterranean Sea. Full article

Unmanned aerial vehicle (UAV) self-localization in complex environments is critical when global navigation satellite systems (GNSSs) are unreliable. Existing datasets, often limited to low-altitude urban scenes, hinder generalization. This study introduces Multi-UAV, a novel dataset with 17.4 k high-resolution UAV–satellite image pairs from diverse terrains (urban, rural, mountainous, farmland, coastal) and altitudes across China, enhancing cross-view geolocalization research. We propose a lightweight value reduction pyramid transformer (VRPT) for efficient feature extraction and a residual feature pyramid network (RFPN) for multi-scale feature fusion. Using meter-level accuracy (MA@K) and relative distance score (RDS), VRPT achieves robust, high-precision localization across varied terrains, offering significant potential for resource-constrained UAV deployment. Full article

In recent years, the global commercial aerospace industry has flourished, witnessing a rapid surge in customized satellite services. Deep learning has emerged as a pivotal tool for accurately identifying wetland vegetation. However, hyperspectral remote sensing images are often plagued by varying degrees of noise during acquisition, leading to subtle differences in spectral responses. Currently, vegetation classification models are tailored specifically for each hyperspectral sensor, making it challenging to generalize a model designed for one sensor to others. Furthermore, discrepancies in data distribution between training and test sets result in a notable decline in model performance, impeding model sharing across satellite hyperspectral sensors and hindering the interpretation of wetland scenes. Domain adaptation methods leveraging Generative Adversarial Networks (GANs) have been extensively researched and applied in the realm of cross-sensor land feature classification. Nevertheless, these data-level cross-domain classification strategies typically focus on band selection or alignment using relatively similar data to address image differences, without addressing spectral variability or incorporating pseudo-labels to enhance classification accuracy. Noise changes aggravate the distribution characteristics and model differences of vegetation in classification tasks. This has a negative impact on subsequent classification accuracy. To alleviate these problems, we have designed a linear unbiased stochastic network classification framework based on adversarial learning. The framework employs a style randomization algorithm to simulate spectral drift. It generates simulated images to enhance the model’s generalization ability. Supervised contrastive learning is utilized to prevent redundant learning of the same training images. Domain discrimination and domain-invariant characteristics are considered. We optimize the generator and discriminator using inter-class and intra-class contrast loss functions. The dual regularization training method is adopted, and non-redundant expansion is realized. It achieves similarity and addresses offsets. This method minimizes computational cost. Cross-sensor classification experiments were conducted, with comparative tests performed on a self-made wetland dataset. This method demonstrates significant advantages in wetland vegetation classification. According to the visualization results, our classification strategy can be used for cross-domain vegetation classification in coastal wetlands. It can also be applied to other small-satellite hyperspectral images and cross-satellite multispectral data, reducing on-site sampling costs and proving cost-effective. Full article

The rapid development of factory aquaculture not only brings economic benefits to coastal areas but also poses numerous ecological and environmental challenges. Therefore, understanding the distribution of coastal factory aquaculture is of great significance for ecological protection. To tackle the issue of the complex spectral and spatial characteristics in remote-sensing images of different factory aquaculture plants in coastal areas, a multiscale residual attention network (MRAN) model for extracting factory aquaculture information is proposed in this study. MRAN is a modification of the U-Net model. By introducing a residual structure, an attention module, and a multiscale connection MRAN can solve the problem of inadequately detailed information extraction from a complex background. In addition, the coastal areas of Huludao City and Dalian City in Liaoning Province were selected as the research areas, and experiments were conducted using the domestic Gaofen-1 remote-sensing image data. The results indicate that the pixel accuracy (PA), mean PA, and mean intersection over union of the proposed model are 98.31%, 97.85%, and 92.46%, respectively, which are superior to those of other comparison models. Moreover, the proposed model can effectively reduce misidentification and missing identification phenomena caused by complex backgrounds and multiple scales. Full article

HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. Full article

Wetlands are dubbed the “kidneys of the earth” and are involved in climate regulation, carbon sequestration, ecological balance preservation, and reducing the surface water pollution. Ongoing economic development has introduced pressing challenges to wetland environments. In this context, extracting coastal wetland information and monitoring the dynamic changes are essential. Using long-term sequence Sentinel-2 satellite remote sensing images and field observations, this research proposed a Dynamic Bayesian Network classification model framework based on conjugate gradient updates. We compared the wetland feature extraction effects of the Fletcher–Reeves and the Polak–Ribière–Polyak algorithms of the conjugate gradient. Then, remote sensing combined with the FRDBN classification model was used to extract the information pertinent to wetland feature types and changes in wetland areas and analyze alterations in the distribution characteristics of land cover types. The results showed that the FRDBN model achieved high accuracy (above 96%), and kappa coefficients exceeded 0.96. Long-term monitoring revealed that the area of wetlands increased by 0.85 × 104 hm² from 2016 to 2021. Non-aquatic land cover types exhibited pronounced dynamic changes, with the area of change representing 58–69% of the monitored total. Specifically, the transition between salt marsh vegetation and artificial wetlands was relatively obvious. The FRDBN model provides a new method for extracting wetland feature information. Wetland protection, dynamic monitoring, and carbon sink research can provide robust technology support, facilitating investigations into coastal salt marsh carbon sinks and technological advances in carbon sink assessment. Full article

Accurate observation of nearshore waves is crucial for coastal safety. In this study, the feasibility of extracting wave information from wave video images captured by shore-based cameras using deep learning methods was explored, focusing on inverting nearshore significant wave height (SWH) from instantaneous wave video images. The accuracy of deep learning models in classifying wind wave and swell wave images was investigated, providing reliable classification results for SWH inversion research. A classification network named ResNet-SW for wave types with improved ResNet was proposed. On this basis, the impact of instantaneous wave images, meteorological factors, and oceanographic factors on SWH inversion was evaluated, and an inversion network named Inversion-Net for SWH that integrates multiple factors was proposed. The inversion performance was significantly enhanced by the specialized models for wind wave and swell. Additionally, the inversion accuracy and stability were further enhanced by improving the loss function of Inversion-Net. Ultimately, time series inversion results were synthesized from the outputs of multiple models; the final inversion results yielded a mean absolute error of 0.04 m and a mean absolute percentage error of 8.52%. Despite certain limitations, this method can still serve as a useful alternative for wave observation. Full article

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel. Full article

Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia’s national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping projects, the full spatial extent and outlines of many Australian beaches remain unmapped. This study leverages deep learning (DL), specifically convolutional neural networks, for binary image segmentation to map beach outlines along the coast of Southeastern Australia. It focuses on Victoria and New South Wales coasts, each approximately 2000 to 2500 km in length. Our methodology includes training and evaluating the model using state-specific datasets, followed by applying the trained model to predict the beach outlines, size, shape, and morphology in both regions. The results demonstrate the model’s ability to generate accurate segmentation and rapid predictions, although it faces challenges such as misclassifying cliffs and sensitivity to fine details. Overall, this research presents a significant advancement in integrating DL with coastal science, providing a scalable solution of citizen science mapping efforts for comprehensive beach mapping to support sustainable coastal management and conservation efforts across Australia. Open access datasets and models are provided to further support beach mapping efforts around Australia. Full article

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model’s performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. Full article

River silt deposited by water in coastal areas is unsuitable for engineering construction. Thus, the in situ stabilization treatment of river silt as the bearing layer has been an important research area in geotechnical engineering. The strength degradation behavior and mechanism of stabilized river silt reinforced with cement and alginate fibers (AFCS) in different engineering environments are crucial for engineering applications. Therefore, freeze–thaw (F–T) cycle tests, wetting-drying (W–D) cycle tests, water immersion tests and seawater erosion tests were conducted to explore the strength attenuation of stabilized river silt reinforced with the same cement content (9% by wet weight) and different fiber contents (0%, 0.3%, 0.6% and 0.9% by weight of wet soil) and fiber lengths (3 mm, 6 mm and 9 mm). The reinforcement and damage mechanism of AFCS was analyzed by scanning electron microscopy (SEM) imaging. The results indicate that the strength of AFCS was improved from 84% to 180% at 15 F–T cycle tests, and the strength of AFCS was improved by 26% and 40% at 30 W–D cycles, which showed better stability and excellent characteristics owing to the hygroscopic characteristics of alginate fiber arousing the release of calcium and magnesium ions within the alginate. Also, the strength attenuation of AFCS was reduced with the increase in the length and content of alginate fibers. Further, the strength of specimens in the freshwater environment was higher than that in the seawater environment at the same fiber content, and the softening coefficient of AFCS in the freshwater environment was above 0.85, indicating that the AFCS had good water stability. The optimal fiber content was found to be 0.6% based on the unconfined compressive strength (UCS) reduction in specimens cured in seawater and a freshwater environment. And the strength of AFCS was improved by about 10% compared with that of cement-stabilized soil (CS) in a seawater environment. A stable spatial network structure inside the soil was formed, in which the reinforcing effect of fibers was affected by mechanical connection, friction and interfacial bonding. However, noticeable cracks developed in the immersed and F–T specimens. These microscopic characteristics contributed to decreased mechanical properties for AFCS. The results of this research provide a reference for the engineering application of AFCS. 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