Search Results (346)

Panama, with nearly 3000 km of coastline and half its population living in coastal zones, faces high vulnerability to sea level rise, flooding, and extreme events. The most vulnerable areas include low-lying coastal provinces such as Panama, Colón, and Chiriquí. This review explores the use of sustainable concrete to address the effects of climate change in Panama towards coastal resilience. The methodology combined a bibliometric analysis using VOSviewer, a systematic literature review (2015–2025) of 99 sources including regulations and technical standards, and a socioeconomic SWOT analysis to assess adoption drivers and barriers. A 2050 permanent inundation map was examined to identify vulnerable areas, and an inventory of concrete-based protection structures was developed. The results highlight that concrete is already used in Panama for coastal resilience through structures such as breakwaters, dolos, and Xbloc units. However, as the country still needs to expand its coastal protection infrastructure, there is a crucial opportunity to implement lower-impact, sustainable concrete alternatives that minimize environmental burdens while ensuring long-term durability and performance. Sustainable options, including supplementary cementitious materials (SCMs), recycled aggregates, and CO₂ injection technologies, demonstrate strong mitigation potential, with national initiatives such as Vertua, Greentec, and Argos pozzolan offering early pathways. The conclusions emphasize the need to expand sustainable concrete applications, integrate nature-based solutions, and strengthen Panama’s regulatory and technical capacity to achieve resilient, low-carbon coastal infrastructure. Full article

Microplastic pollution in marine environments represents a significant ecological threat due to its persistence and harmful effects on biodiversity and human health. In Colombia, coastal ecosystems (particularly in La Guajira) have exhibited increasing microplastic concentrations, but systematic monitoring remains limited. This study explored the application of remote sensing, including multispectral satellite imagery (Sentinel-2) and machine learning algorithms, to detect and monitor microplastics in the coastal zone of Riohacha, La Guajira. To inform the model selection and ensure methodological relevance, a focused systematic literature review was conducted, serving as a foundational step in identifying effective remote sensing strategies and machine learning algorithms previously applied to microplastic detection in aquatic environments. Moreover, microplastic samples were collected from four coastal sites on Riohacha’s coast and analyzed via Fourier transform infrared spectroscopy (FTIR), while environmental parameters were recorded in situ. The remote sensing data were processed and integrated with field observations to train linear regression, random forest, and artificial neural network (ANN) models. The ANN model achieved the highest accuracy (MAE = 0.040; RMSE = 0.071), outperforming the other models in estimating the microplastic concentrations. Based on these results, environmental risk maps were generated, identifying critical zones of pollution. The findings support the integration of remote sensing tools and field data for scalable, cost-efficient microplastic monitoring, offering a methodological framework for marine pollution assessment in Colombia and other developing coastal regions. Full article

Costa Rica has experienced 45 tsunamis at both its Pacific and Caribbean coasts, with none to moderated impact. However, the coastal population has increased exponentially in the past few decades, which might lead to higher impact in future tsunamis. In 2018 and 2019, IOC/UNESCO organized Experts Meetings of Tsunami Sources, Hazards, Risks and Uncertainties associated with the Tonga–Kermadec and Colombia–Ecuador subduction zones, where experts defined maximum credible scenarios. Here we modeled the propagation of those tsunami scenarios to Costa Rica and their inundation for selected sites. We found that the Tonga–Kermadec scenarios provoked more inundation than previous modeled sources from that region. However, the large travel time for those scenarios, about 14 h, would allow for a timely evacuation. In the Colombia–Ecuador scenarios, they provoked less inundation than previously modeled sources from that region, a good outcome as their arrival time is between 75 and 150 min. These new results required the update of tsunami evacuation maps and/or plans for many communities but provided more favorable conditions for tsunami preparedness. Yet, the short arrival times of the Colombia–Ecuador scenarios still require a prompt response from the population and authorities. For this, additional to updated tsunami evacuation maps and plans, it is recommended to have tsunami exercises on a regular basis. Full article

Accurate mapping of marine aquaculture areas is critical for environmental management and sustainable development for marine ecosystem protection and sustainable resource utilization. However, remote sensing imagery based on single-sensor modalities has inherent limitations when extracting aquaculture zones in complex marine environments. To address this challenge, we constructed a multi-modal dataset from five Chinese coastal regions using cloud detection methods and developed Multi-modal Spatial–Frequency Adaptive Fusion Network (MSAFNet) for optical-radar data fusion. MSAFNet employs a dual-path architecture utilizing a Multi-scale Dual-path Feature Module (MDFM) that combines CNN and Transformer capabilities to extract multi-scale features. Additionally, it implements a Dynamic Frequency Domain Adaptive Fusion Module (DFAFM) to achieve deep integration of multi-modal features in both spatial and frequency domains, effectively leveraging the complementary advantages of different sensor data. Results demonstrate that MSAFNet achieves 76.93% mean intersection over union (mIoU), 86.96% mean F1 score (mF1), and 93.26% mean Kappa coefficient (mKappa) in extracting floating raft aquaculture (FRA) and cage aquaculture (CA), significantly outperforming existing methods. Applied to China’s coastal waters, the model generated 2020 nearshore aquaculture distribution maps, demonstrating its generalization capability and practical value in complex marine environments. This approach provides reliable technical support for marine resource management and ecological monitoring. Full article

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions. Full article

Casablanca, Morocco’s largest Atlantic port city, faces increasing exposure to floods, drought, and other risks that align with legacies of urban transformations carried out during the colonial period. This study examines how early-20th-century interventions—including the canalization and burial of the Oued Bouskoura, extensive coastal reclamation, and the implementation of rigid zoning—were associated with a reconfiguration of the city’s hydrology and coincide with persistent socio-spatial inequalities. Using historical cartography, archival sources, and GIS-based overlays of colonial-era plans with contemporary hazard maps, the analysis reveals an indicative spatial correlation between today’s high-risk zones and areas transformed under the Protectorate, with the medina emerging as one of the most vulnerable districts. While previous studies have examined either colonial planning in architectural or contemporary climate risks through technical and governance lenses, this article illuminates historically conditioned relationships and long-term associations for urban resilience. In doing so, it empirically maps spatial associations and conceptually argues for reframing heritage not only as cultural memory but as a climate resource, illustrating how suppressed vernacular systems may inform adaptation strategies. This interdisciplinary approach provides a novel contribution to postcolonial city research, climate adaptation and heritage studies by proposing a historically conscious framework for resilience planning. Full article

Classification of island coastal landscapes is a challenge to incorporate both the terrestrial and the aquatic environment characteristics, and place biological diversity in a regional and insular context. The Coastal and Marine Ecological Classification Standard (CMECS) was developed for use in the United States and incorporates geomorphic data, substrate data, biological information, as well as water column characteristics. The CMECS framework was applied to the island of Great Exuma, The Bahamas. The classification used data from existing studies to include oceanographic data, seawater temperature, salinity, benthic invertebrate surveys, sediment analysis, marine plant surveys, and coastal geomorphology. The information generated is a multi-dimensional description of benthic and shoreline biotopes characterized by dominant species. Biotopes were both mapped and described in hierarchical classification schemes that captured unique components of diversity in the mosaic of coastal natural communities. Natural community classification into biotopes is a useful tool to quantify ecological landscapes as a basis to develop monitoring over time for biotic community response to climate change and human alteration of the coastal zone. Full article

Climate wave data, derived from significant wave height (SWH) altimetry, provide accurate information towards nearshore and coastal areas. Their use is crucial to enhance our capabilities of observing, understanding, and forecasting storm waves, even in complex coastal basins. In this study, SWOT nadir data were combined with nine existing altimeters for assessing waves and monitoring their evolution during storms in the English Channel, near UK–French coasts. Validation against wave buoys and numerical models shows high accuracy, with correlations around 95%, decreasing to 85% when buoy track offsets > 50 km, producing the largest errors. The multi-source approach enables depth-resolved monitoring, with SWH mapping revealing ~20–25% modulation in the Channel and ~36% dissipation near the Seine Bay during storms. Spectral analysis of multi-source altimeter-derived merged observations improve time-sampling, resolving high-frequency variability from monthly to daily scales and capturing ~75% of storms. Most storm wave features along altimetry tracks are resolved, with CFOSAT mapping nearshore areas and SWOT capturing coastal zones, both achieving ~80% variance. This temporal and spatial monitoring would be further enhanced with SWOT’s 2D wide swath. This finding provides a complementary, comprehensive understanding of coastal waves and offers valuable input for data assimilation, to improve storm wave estimates in coastal basins. Full article

The Leizhou Peninsula, located in the northern South China Sea, features coastal waters with dual functions as both marine ranch demonstration zones and ecological protection areas. Remote sensing monitoring of Chlorophyll-a (Chl-a) concentration in this region holds strategic significance for assessing primary productivity, red tide risk, and the sustainability of the blue food economy. This study integrates in situ survey data from four cruises conducted between 2020 and 2024 with Sentinel-3 OLCI remote sensing imagery, constructing and comparing the performance of six machine learning inversion models. The results show that for the inversion scenarios of the Leizhou Peninsula waters, the GBDT model performs best among the evaluated models (R² = 0.79, RMSE = 0.36 mg/m³, MAE = 0.30 mg/m³). Based on the GBDT model, pixel-by-pixel inversion maps with 300 m spatial resolution were generated for four seasons in 2024, revealing a spatial gradient of “high nearshore–low offshore, high in the east–low in the west” and a seasonal pattern of “low in spring–rise in summer–stable in autumn–high in winter.” In addition, the study verified the operational potential of machine learning in complex type-II waters, analyzed the distribution characteristics and influencing factors of Chl-a concentration in this region, and provided scientific and technical support for marine ranch carrying capacity assessment, eutrophication early warning, and carbon sink accounting in the Leizhou Peninsula. Full article

As cities grow, natural surfaces are replaced by heat-retaining materials, raising urban temperatures and intensifying heatwave impacts. The present study investigates the effectiveness of urban greening strategies, including green roofs, street vegetation and metropolitan parks, in enhancing climate resilience in Athens, a coastal Mediterranean city characterized by complex heatwave dynamics. The strategies were evaluated through simulations using the WRF model coupled with the BEP-BEM urban canopy model and a detailed land-cover map that is uses the 11 urban Local Climate Zones (LCZ) categories (CLIMPACT) tailored for Athens. Simulations focused on a significant heatwave event that affected the region in 2021 assessed the thermal impacts of the different greening scenarios. Results show that expanding green areas reduces peak temperatures and modifies local thermal circulations, highlighting the potential of greening in mitigating urban heat island effects. Full article

Coastal public spaces in large Euro-Mediterranean cities represent critical zones of negotiation between land and sea, where ecological fragilities, infrastructural pressures and social demands intersect. Grounded in the concept of the urban amphibious, this study explores the spatial-functional complexity of city-sea interfaces through the development of CITI4SEA (City-Sea Interface Typological Indicators for Spatial-Ecological Assessment), an original multidimensional framework for the evaluation of coastal public spaces. The methodology builds on a geo-database of 149 coastal municipalities in eight EU Member States and applies a set of indicators to seven major cities (with populations over 500,000 and comprehensive port infrastructure). Through a structured evaluation grid applied to 23 coastal public spaces, the framework enables a cross-comparative analysis of spatial configurations, ecological qualities, and patterns of public use. Results reveal the emergence of transnational clusters based on shared planning logics and degrees of socio-environmental integration, rather than geographic proximity. The study also identifies asymmetries in accessibility, environmental performance and equipment provision. Beyond mapping spatial disparities, the contribution offers a replicable tool for assessing littoral transformations within the broader framework of Integrated Coastal Zone Management (ICZM) and Maritime Spatial Planning (MSP), supporting context-specific strategies for resilient and inclusive coastal governance. Full article

Mangroves are critical blue carbon ecosystems, yet accurately estimating their aboveground carbon (AGC) stocks remains challenging due to structural complexity and spectral saturation in dense canopies. This study aims to develop a scalable AGC estimation framework by integrating high-resolution canopy height (CH) data from UAV-LiDAR with multi-source satellite features from Sentinel-1, Sentinel-2, and ALOS PALSAR-2. Using the Maowei Sea mangrove zone in Guangxi, China, as a case study, we extracted structural, spectral, and textural features and applied Random Forest regression with Recursive Feature Elimination (RFE) to optimize feature combinations. Results show that incorporating UAV-derived CH significantly improves model accuracy (R² = 0.75, RMSE = 14.18 Mg C ha⁻¹), outperforming satellite-only approaches. CH was identified as the most important predictor, effectively mitigating saturation effects in high-biomass stands. The estimated total AGC in the study area was 88,363.73 Mg, with a mean density of 53.01 Mg C ha⁻¹. This study highlights the advantages of cross-scale UAV–satellite data fusion for accurate, regionally scalable AGC mapping, offering a practical tool for blue carbon monitoring and coastal ecosystem management under global change. Full article

Animal rabies remains underreported in low-income countries, hindering effective control. This scoping review aimed to map reported animal rabies cases, identify key reservoir species, and assess gaps in surveillance coverage in mainland Tanzania from 1993 to 2023. Specifically, it addressed the distribution of cases, species involved, and the extent of surveillance coverage during this period. Literature searches in PubMed, Google Scholar, and Science Direct were screened using Rayyan. Twenty articles published between 1993 and 2023 reported 7319 animal rabies cases across the Northern Zone (NZ), Southeastern Zone (SEZ), and Coastal Zone (CZ). In the NZ, domestic dogs accounted for most cases (5387), followed by jackals (225), cats (77), livestock (311), and various wildlife species including African wild dogs, bat-eared foxes, lions, cheetahs, and striped hyenas. Additionally, 102 cases involved unidentified animals. In SEZ, domestic dogs (588) were the primary source, followed by jackals (262), hyenas (8), cats (10), honey badgers (5), and leopards (2). In CZ, domestic dogs accounted for 94 cases. The findings confirm domestic dogs as the main rabies reservoir, highlighting the need for strengthened surveillance and control. The role of wildlife in rabies maintenance and spillover remains poorly understood and warrants further investigation, especially in enzootic hotspots. Full article

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity. Full article

The shoreline and seabed topography are key components of the coastal zone and are essential for hydrographic surveys, shoreline process modelling, and coastal infrastructure management. The development of unmanned aerial vehicles (UAVs) and optoelectronic sensors, such as photogrammetric cameras and airborne laser scanning (ALS) using light detection and ranging (LiDAR) technology, has enabled the acquisition of high-resolution bathymetric data with greater accuracy and efficiency than traditional methods using echo sounders on manned vessels. This article presents a spatial analysis of bathymetric data obtained from UAV photogrammetry and ALS LiDAR, focusing on shallow-water depth estimation and shoreline extraction. The study area is Lake Kłodno, an inland waterbody with moderate ecological status. Aerial imagery from the photogrammetric camera was used to model the lake bottom in shallow areas, while the LiDAR point cloud acquired through ALS was used to determine the shoreline. Spatial analysis of support vector regression (SVR)-based bathymetric data showed effective depth estimation down to 1 m, with a reported standard deviation of 0.11 m and accuracy of 0.22 m at the 95% confidence, as reported in previous studies. However, only 44.5% of 1 × 1 m grid cells met the minimum point density threshold recommended by the National Oceanic and Atmospheric Administration (NOAA) (≥5 pts/m²), while 43.7% contained no data. In contrast, ALS LiDAR provided higher and more consistent shoreline coverage, with an average density of 63.26 pts/m², despite 27.6% of grid cells being empty. The modified shoreline extraction method applied to the ALS data achieved a mean positional accuracy of 1.24 m and 3.36 m at the 95% confidence level. The results show that UAV photogrammetry and ALS laser scanning possess distinct yet complementary strengths, making their combined use beneficial for producing more accurate and reliable maps of shallow waters and shorelines. Full article