Search Results (1,573)

Coastal cities are typical regions where economic growth, population agglomeration, and eco-environmental pressures are strongly coupled. Assessing the coordination between urban development and the eco-environment is therefore important for regional sustainability. This study selected seven representative coastal cities in China—Dalian, Qinhuangdao, Qingdao, Shanghai, Fuzhou, Xiamen, and Zhuhai—and integrated multisource remote sensing data with statistical yearbook data to construct a comprehensive evaluation system for urban development level (UDL) and eco-environmental quality (EEQ). An ecologically enhanced indicator system incorporating vegetation condition index (VCI), biological richness index (BRI), normalized difference vegetation index (NDVI), and dynamic habitat index (DHI) was developed. The coupling coordination degree (CCD) model was then used to evaluate urban sustainable development from 2014 to 2023. In addition, an EWM–MLP adaptive weighting strategy was applied to refine entropy-derived weights, and Random Forest was used to identify variables associated with CCD prediction. The results show that CCD values generally increased during the study period, indicating improved coordination between urban development and the eco-environment. However, the evolutionary pathways differed markedly among cities, and UDL and EEQ changes were not fully synchronized. The EWM–MLP strategy introduced adaptive numerical refinements to CCD values while maintaining the overall stability of coordination-level classification. Random Forest analysis showed that CCD prediction was mainly associated with a limited number of high-contribution indicators. For all indicators combined, approximately 7–10 top-ranked variables were generally required to exceed 80% of the total importance, whereas the UDL and EEQ subsystems reached this threshold with fewer indicators. UDL-related variation was mainly associated with land-use structure, population agglomeration, and economic activity, whereas EEQ-related variation was related to ecological conditions, land-cover composition, and environmental pressure. The high-importance indicators exhibited clear inter-city heterogeneity, suggesting the need for differentiated governance strategies. The proposed framework provides methodological support for sustainable development assessment and differentiated governance in coastal cities. Full article

As one of the three major bays in the Chinese Bohai Sea, Bohai Bay is located in a semi-encircled area consisting of three important provinces and cities with rich energy and fishery resources. The bay is not only a maritime gateway and transportation hub but also an important industrial base, energy production base, and port. In this study, we combined Landsat remote sensing and Geographic Information System technologies to extract the coastline of Bohai Bay from 2001 to 2021 and obtained the variation in coastline length by refinement vector processing. Sediment as the natural driver was quantitatively analyzed based on sand transport in the Yellow River and Hai River. Moreover, port construction was qualitatively analyzed as the anthropogenic driver. The results demonstrated that the coastline of Bohai Bay showed an overall growth trend in this period, with a total increase of 881.05 km in shoreline length; the main increase was in the artificial shoreline. The two natural driving factors, sediment and hydrodynamic conditions, were weak, and the anthropogenic driving factor, i.e., various human activities, played a dominant role in the variation in the Bohai Bay shoreline in the past 20 years. The extracted shoreline information is important not only for the rational and effective development and utilization of the various natural resources in the coastal zone of Bohai Bay but also for the plan to develop this important region in the future. Full article

Assessing the ecological and environmental quality of old industrial cities is crucial for understanding the spatial heterogeneity of ecological quality and its associated factors during regional transformation. Taking Tangshan, a typical old industrial city in China, as a case study, this study employed Landsat 8/9 remote sensing imagery and multi-source auxiliary data from 2015 to 2024 to calculate annual Remote Sensing Ecological Index (RSEI) values using a unified multi-year standardization and principal component analysis framework. Global and local Moran’s I analyses were conducted to examine spatial clustering patterns, and the Optimal-Parameter Geographical Detector (OPGD) was used to quantify the spatial correspondence between RSEI and selected natural and anthropogenic explanatory factors. The results indicate the following. (1) The mean RSEI in Tangshan fluctuated between 0.34 and 0.54 from 2015 to 2024, exhibiting significant interannual variability. (2) Higher RSEI values were primarily distributed in the northern mountainous and southern coastal ecological zones, while lower values were concentrated in the central and eastern industrial-mining zones. (3) The global Moran’s I was significantly positive in all years (0.702–0.778, p = 0.001), indicating the persistence of spatial clustering; the proportion of non-significant local spatial units decreased from 72.00% in 2015 to 69.46% in 2024. (4) Land use/land cover (LULC) exhibited the most consistently high explanatory power. Elevation (ELE), nighttime light (NTL), and built-up intensity (BUILT) also formed a leading group of spatially associated factors, although their relative ranking varied between the optimal-parameter results and the robustness analysis. Slope (SLOPE), annual precipitation (Pre), and annual mean temperature (Tmean) generally showed relatively lower explanatory power. Interaction detection showed that pairwise factor combinations generally had higher q values than individual factors, with LULC × ELE showing consistently high explanatory power in representative years. This study provides a scientific reference for ecological and environmental monitoring and differentiated management in old industrial cities. Full article

Monitoring coastal and estuarine dynamics is crucial for understanding coupled physical, biogeochemical, and human impacts on coastal waters. Motivated by the availability of high spatial resolution ocean color data from the proof-of-concept SeaHawk-HawkEye ocean color CubeSat, this study assesses the capabilities and limitations of satellite remote sensing in capturing shallow water (<10 m) coastal dynamics by integrating in situ measurements with satellite imagery. A Sea Sciences Acrobat collected detailed transects at the mouth of Masonboro Inlet (Wilmington, NC, USA), with “tow-yo” style profiles from the surface to 10 m. It measured conductivity, temperature, and depth (CTD), chlorophyll a (Chl a), turbidity, and dissolved oxygen. Satellite data from SeaHawk-HawkEye, Aqua-MODIS, and Sentinel 3A/3B-OLCI provided extensive spatial coverage, revealing surface-level physical/biological interactions, but were only available 48 h after in situ sampling due to cloud cover during field sampling. Tow-yo profiles elucidated a three-dimensional phytoplankton plume, the spatial extent of which we further characterize with satellite imagery, demonstrating the value of integrating in situ and satellite data. A spatial matchup comparison between data from each satellite and the in situ sensor package revealed significant discrepancies across all satellite sensors analyzed, attributed to differences in sensor resolution, atmospheric correction approaches, and proximity to land/benthos. This study emphasizes key challenges with study design and data interpretation in dynamic nearshore environments. In particular, results suggest that meaningful comparisons of satellite vs. in situ observations in such systems require near-synchronous sampling, careful consideration of spatial scale, and improved characterization of optical complexity. Full article

Coastal small-scale fishers in the Asia-Pacific region (APR) face mounting challenges from climate change (CC), with vulnerability shaped by ecological exposure, socio-economic dependence, and limited adaptive capacity. This study reflects on two contrasting cases, Taiwan and Papua New Guinea (PNG), to explore fishers’ perceptions and perspectives on CC and practical adaptation strategies. In PNG, 209 respondents from Momase, the Islands, and Southern regions participated. In Taiwan, 45 respondents from the Yunlin and Chiayi coastal regions participated. Significant correlations in coastal communities’ vulnerabilities and perceptions towards CC were revealed. Small-scale fishers perceive rising sea temperatures, shifting fish stocks, and intensifying typhoons as disruptive shocks to livelihoods and eroding traditional fishing practices. In Taiwan, despite relatively stronger infrastructure, household income, and access to technology, adaptation remains constrained by market pressures, declining youth participation, and regulatory complexities. In PNG, fishers deeply rely on natural resources and coastal ecosystems for subsistence and income, yet face acute risks from sea-level rise, coral bleaching, and unpredictable weather. With limited financial resources, weak institutional support, and geographic isolation, fishers perceive CC as an amplifying factor to existing vulnerabilities, leaving communities dependent on traditional knowledge and communal coping strategies. Fishers’ perceptions of CC are shaped by lived experiences rather than scientific discourse, influencing adaptation choices ranging from livelihood diversification to migration. Perceptions of CC drivers, their distal and proximal impacts on coastal fishing community livelihoods are viewed as siloed; yet, remote sensing data revealed that the impacts are transboundary. The findings underscore the urgent need for context-sensitive policies that integrate local knowledge, science-based data (such as remote sensing CC maps) to strengthen institutional support, and enhance resilience among vulnerable and underserved coastal small-scale fishers. Full article

Monitoring marine outfalls is crucial for mitigating coastal pollution and protecting marine environments. Current methods rely mainly on manual inspection and satellite remote sensing interpretation, which are inefficient, inaccurate, and inadequate for large-scale real-time monitoring. Although UAV visible-light imagery has been introduced for marine outfall detection, challenges remain, including insufficient and diverse target features, small multi-scale target detection difficulties, and complex background interference. To address these limitations, this study systematically benchmarks mainstream object detection models (YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, and RTDETR-light) on a dedicated multi-source remote sensing fusion dataset that we constructed for marine outfalls along Zhanjiang’s southern coast, incorporating NDWIs. Our comparative experiments evaluate the models’ effectiveness in this challenging scenario. Experimental results indicate that YOLOv8n is the most balanced model for marine outfall detection, achieving 84.1% precision, 68.6% recall, 77% mAP50, and an F1 score of 0.75. This benchmark provides empirical evidence and practical model selection criteria for intelligent marine outfall monitoring, thereby offering a reference framework for researchers and engineers in related fields. Full article

The armless snake eel, Dalophis imberbis, is a fossorial rare species. It is considered to be a non-target fishery resource with elusive behavior, and there is a paucity of knowledge regarding its distribution and biology. This study reports three new documented occurrence records of D. imberbis along the northern and southeastern coastal areas of Sicily (central Mediterranean Sea) during 2025. Specimens were collected at depths ranging from 43 m to an unusually shallow depth of 5.4 m, extending the known upper vertical limit of the species, which was previously considered a 20 m depth. Environmental parameters were collected through a multiparametric probe and integrated with products from the Copernicus Marine Service (CMS), providing new insights which highlight the presence of the species in relatively warm (17.6–20.8 °C) and moderately oxygen-undersaturated (6.9–8.5 mg/L) waters. A global distributional analysis was performed by aggregating the field data with literature records and datasets published in the Global Biodiversity Information Facility (GBIF), refining the distribution of the species in the Mediterranean and Atlantic Ocean. Thus, the three new records expand the known distribution of the species in the center of the Mediterranean Sea, providing an updated bathymetric range and the first preliminary insights into the environmental preferences of this data-deficient ophichthid. This work underscores the importance of combining traditional surveys with big-data repositories and remote sensing to monitor rare marine biodiversity. Full article

This study presents the first fine-scale Coastal Vulnerability Index (CVI) assessment for Togo, evaluating coastal vulnerability and risk along the country’s 50 km barrier coastline in the context of accelerating erosion, rising sea level, and growing human exposure. Using remote sensing, GIS, and a CVI framework, shoreline trend rates, beach width, land use, and the role of existing coastal defences were analysed to support risk-informed decision-making. The coastline was segmented into 99 coastal units of 500 m, and shoreline trend rates were computed using the End Point Rate (EPR) method based on multi-temporal satellite-derived shorelines spanning from 1988 to 2024. Results show strong spatial contrasts in vulnerability, with the eastern sector of the Port of Lomé, particularly a 24.5 km stretch, exhibiting high vulnerability due to persistent shoreline retreat and narrow beach widths. In contrast, the western coastline displays lower vulnerability levels. Several erosion hotspots were identified, including Baguida and Dévinkemé, where recent shoreline retreat reaches up to −12.8 m/year. Existing coastal defences locally mitigate erosion impacts, reducing the extent of highly vulnerable shoreline from 23.5 km to 15 km. The integrated risk assessment identifies 6.5 km of coastline, primarily in the eastern port area, as being at high risk due to the combined effects of erosion and dense human settlement. These results provide spatially explicit information to support integrated coastal zone management, land-use planning, and adaptation strategies in Togo. Full article

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

High-latitude coastal regions are highly sensitive to climate change, yet their geomorphology is obscured by sea ice, landfast ice and seasonal snow, restricting the applicability of optical remote sensing for fine coastal classification. To address this limitation, we develop an interpretable coastal classification framework integrating ICESat-2 photon-counting LiDAR and explainable machine learning. Multi-dimensional morphometric features describing cross-shore geometry, vertical relief and local slope variability are extracted from ICESat-2 ATL03 along-track profiles to train a CatBoost classifier, with five-fold cross-validation and sample weighting to mitigate class imbalance. Introducing SHAP-based interpretability into ICESat-2-driven coastal geomorphic classification enables the identification of morphometric controls on coastal-type differentiation. Validated in the Bering Sea with 447 profiles and a 75%/25% stratified split, the framework achieved an overall accuracy of 86.6%, a macro-average recall of 89.4% and a Kappa coefficient of 0.84. SHAP analysis identifies that coastal width is the most influential feature for model-based classification of coastal geomorphic types, while slope and local steepness variability serve as important predictive indicators for distinguishing rocky and sedimentary coasts. This framework links data-driven classification to geomorphic processes and provides a potentially generalisable approach for fine-scale coastal mapping in high-latitude environments. Full article

Coastal aquifers are essential freshwater sources for domestic, agricultural, and industrial use, particularly in regions where surface water is limited. However, these systems face growing stress from saltwater intrusion, climate-driven reductions in recharge, sea level rise, and intensified groundwater extraction. This review synthesizes recent research on coastal aquifer responses to these pressures, highlighting the interplay between natural hydrogeologic conditions and human-induced demand. Across deltaic and sedimentary systems, studies consistently show declining groundwater levels, the landward migration of saline interfaces, and reduced aquifer buffering capacity, especially in areas with high evaporation and limited recharge. The review also evaluates emerging strategies to preserve coastal groundwater security. Integrated hydrological models, managed aquifer recharge (MAR), optimized abstraction schemes, and remote sensing-based monitoring are advancing adaptive management capabilities. In parallel, policy and nature-based interventions—such as aquifer protection zoning, wetland rehabilitation, and dune system restoration—support long-term resilience by enhancing natural recharge and reducing vulnerability. The overall findings reveal the need for climate-informed and locally tailored groundwater management. Future efforts should prioritize coupling high-resolution climate projections with aquifer system models, evaluating MAR viability in saline-prone environments, and strengthening collaborative governance frameworks to ensure sustainable and equitable use of coastal aquifers. Full article

The water–land classification is fundamental for shoreline extraction and coastal habitat mapping, which is the basis of a comprehensive assessment and ecosystem-based coastal zone management. This study aims to separate water and land for coastal zones by taking advantage of both high-resolution satellite imagery and airborne lidar point clouds. Considering physical principles of optical remote sensing and lidar, we developed a prior knowledge-based localization classification approach that eliminates the need for collecting training sets and handling temporal differences across multiple data sources. Our approach first created the initial classification using the WorldView-2 (WV2) Normalized Difference Water Index. Then, the Connected Components Labeling algorithm was used to create a non-overlapping partition of the working area. The third step involved processing the water blocks using prior land cover knowledge. Finally, we used lidar point clouds to refine the initial water blocks and their neighboring areas. This classification approach showed promising results along Matagorda Bay, Texas, an approximately 2449 km² area that is covered by 26 WV2 images and 1568 lidar tiles. Full article

Rapid urbanization in South Asian coastal cities is systematically dismantling natural cooling infrastructure, driving unprecedented urban heat island (UHI) intensification with severe consequences for human health, energy systems, and urban livability. Despite growing research attention, comprehensive frameworks that simultaneously capture temporal UHI dynamics, machine learning-based thermal projections, and community-grounded validation remain scarce, particularly for secondary coastal cities in tropical developing regions. This study addresses these gaps by investigating UHI dynamics in Chattogram City Corporation (CCC), Bangladesh, through three integrated methodological pillars: (1) multi-temporal remote sensing analysis using Landsat 5 and 8 imagery spanning 2005–2025; (2) comparative evaluation of five machine learning algorithms (LightGBM, Random Forest, XGBoost, SVM, and MLP) for land use/land cover (LULC) classification and land surface temperature (LST) regression, with iterative scenario projections for 2029, 2033, and 2037; and (3) a structured public perception survey of 384 residents validated through participatory mapping and focus group discussions. Landsat analysis revealed dramatic LULC transformations: built-up areas expanded 88% (12,649 to 23,719 acres), while waterbodies declined 53.1% and vegetation decreased 21.9%. Mean LST increased by 9.09 °C (from 30.94 °C to 40.03 °C), with mean UHI intensity rising from 19.59 to 33.88 standardized units over two decades. LightGBM achieved optimal LULC classification (F1-weighted: 0.765) while Random Forest best predicted LST (RMSE: 1.51, R²: 0.809). Projections indicate continued thermal escalation, with mean LST reaching 43.64 °C and UHI intensity exceeding 37.41 standardized units by 2037. Persistent thermal hotspots were identified in the southwestern coastal corridor, western industrial belt, and central business district. Community survey data corroborated satellite-derived patterns, with 73.44% of respondents observing environmental degradation, yet only 22% aware of formal heat mitigation policies, and 87% supporting vegetation-based cooling interventions. This integrated framework advances urban thermal monitoring in tropical coastal cities and provides spatially targeted, community-endorsed evidence for climate-responsive urban planning. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article