Search Results (243)

Marine renewable energy could support Costa Rica’s decarbonization pathway, but its offshore resource base and enabling conditions remain poorly characterized in the body of knowledge. This study provides the first integrated assessment of marine energy resources, grid integration opportunities, and governance challenges in Costa Rica. A meta-analysis of 76 technical, legal, and policy sources is combined with qualitative doctrinal analysis, GIS-based multi-criteria evaluation for Ocean Thermal Energy Conversion (OTEC), and satellite and reanalysis data for winds, waves, currents, and sea surface temperature to estimate power densities and extractable energy. Results show a contrast between the Pacific and Caribbean coasts. For instance, on the Northern Pacific coast, there are strong Papagayo winds, and persistent swells yield high offshore wind and wave energy potentials, with technical offshore wind resources of around 14.4 GW and Pacific wave power frequently exceeding 20–25 kW/m with relatively low seasonal variability. Furthermore, twelve OTEC-suitable zones are identified with two priority areas in the southern Pacific that combine steep bathymetry and strong thermal gradients with limited environmental conflicts, but they overlap with sensitive conservation and Indigenous territories. Current energy potential is more localized and modest in the Caribbean coast. The analysis highlights major infrastructural, legal, and social barriers but concludes that marine energy can play a pivotal role in diversifying Costa Rica’s renewable-dominated electricity market. Full article

Equipped with an Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 (Ice, Cloud and land Elevation Satellite-2) is a photon-counting laser altimetry mission with strong potential for nearshore bathymetry. In this study, a novel filtering and bathymetric method termed a segmented adaptive filtering bathymetry has been proposed. Sea-surface photons are identified from peaks in the elevation-density histogram, enabling separation of surface and seafloor photons. The seafloor photons are then partitioned into along-track segments, where seafloor signal photons are extracted using an adaptive elliptical kernel whose parameters and orientation are determined from local density patterns and seafloor slope. The seafloor profile is obtained by polynomial fitting, and nearshore depth is estimated from the elevations of the surface and seafloor signal photons. To ensure and improve the accuracy and reliability of the proposed method, ICESat-2 data from Qilianyu Islands at the South China Sea and West Island at the Florida Keys of the United States were adopted to perform experiments. Furthermore, the bathymetric results obtained by ICESat-2 datasets at different experimental areas were compared with the reference bathymetry obtained by the airborne light detection and ranging (LiDAR) bathymetry (ALB) system. Finally, the bathymetric accuracy validation and assessment were performed. The highest accuracy of root mean square error (RMSE) and coefficient of determination (R²) has reached 0.37 m and 98%, respectively. The accuracy validation of bathymetric results at different study areas demonstrated that the method proposed in this study can automatically and effectively achieve high-precision nearshore bathymetry and topographic surveys. Full article

According to existing procedures for defining the velocity distribution across cross profile sections of watercourses (e.g., Entropy theory and Power Law theory), surface velocity is a key input parameter, together with cross-sectional bathymetry. Field measurements to obtain velocity values and their distributions are often difficult due to limited equipment, unreliable data, missing data, or hazardous conditions such as flooding and inaccessible locations. This creates a strong need for alternative approaches to measuring surface velocities in rivers. The application of unmanned aerial vehicles (UAVs), mobile phones, and traditional field instruments such as the Propeller Current Meter (PCM) can significantly improve measurement efficiency, especially in situations where conventional methods are not feasible. This paper presents an algorithm for comparing these measurement approaches and quantifying their differences. The methodology is demonstrated using a real case study on the Bednja River in Croatia, which flows through alluvial deposits. The results show that video-based surface velocity estimation using UAV and mobile phone imagery is feasible under real river conditions. Still, its accuracy depends strongly on flow conditions and surface characteristics. While UAV recordings provide reliable results in fast and turbulent flows, mobile phone videos yield more stable performance in smoother flow conditions, where additional surface texture is available from natural tracers. Full article

Accurate nearshore bathymetry is essential for various marine applications, including navigation, resource management, and the protection of coastal ecosystems and the services they provide. This study presents an approach to enhance the accuracy of bathymetric estimates derived from high-spatial- and high-temporal-resolution optical satellite imagery. The proposed technique is particularly suited for multispectral sensors that acquire spectral bands sequentially rather than simultaneously. PlanetScope SuperDove imagery was employed and validated against bathymetric data collected using a multibeam echosounder. The study area is the Gulf of Sciacca, located along the southwestern coast of Sicily in the Mediterranean Sea. Here, multibeam data were acquired along transects that are subparallel to the shoreline, covering depths ranging from approximately 7 m to 50 m. Satellite imagery was radiometrically and atmospherically corrected and then processed using a simplified radiative transfer transformation to generate a continuous bathymetric map extending over the entire gulf. The resulting satellite-derived bathymetry achieved reliable accuracy between approximately 5 m and 25 m depth. Beyond these limits, excessive signal attenuation for higher depths and increased water turbidity close to shore introduced significant uncertainties. The innovative aspect of this approach lies in the combined use of spectral averaging among the most water-penetrating bands, temporal averaging across multiple acquisitions, and a liquid-facets noise reduction technique. The integration of these multi-layer inputs led to improved accuracy compared to using single-date or single-band imagery alone. Results show a strong correlation between the satellite-derived bathymetry and multibeam measurements over sandy substrates, with an estimated error of ±6% at a 95% confidence interval. Some discrepancies, however, were observed in the presence of mixed pixels (e.g., submerged vegetation or rocky substrates) or surface artifacts. Full article

Lakes on the Qinghai–Tibet Plateau are important indicators of global climate change, and variations in their water storage strongly influence regional hydrological cycles and ecosystems. However, existing studies have largely focused on relative changes in lake volume, while the precise quantification of absolute water storage remains insufficient, largely due to the lack of long-term, high-accuracy water storage time series. Constrained by harsh natural conditions and limited in situ observations, conventional approaches struggle to achieve the accurate long-term monitoring of lake water storage across the Plateau. To address this challenge, we propose a DEM-based underwater topography extrapolation method. Under the assumption of continuity between surrounding onshore terrain and submerged lakebed morphology, nearshore DEM data are extrapolated to reconstruct lake bathymetry. By integrating multi-source remote sensing observations of lake area and water level, we estimate and reconstruct 30-year absolute water storage time series for 120 Plateau lakes larger than 50 km². This method does not require measured water depth data and is particularly suitable for data-scarce, topographically complex, high-altitude lake regions, effectively overcoming key limitations of conventional methods used for absolute water storage monitoring. Validation shows strong agreement between our estimates and an independent validation dataset, with an overall correlation coefficient of 0.95; the reconstructed time series are highly reliable, with correlation coefficients exceeding 0.6. During the study period, the total lake water storage of the Qinghai–Tibet Plateau exhibited a significant increasing trend, with a cumulative growth of approximately 137.297 billion m³, representing a 20.73% increase, and showing notable spatial heterogeneity. The water storage dataset constructed in this study provides reliable data support for research on water cycles, climate change assessment, and regional water resource management on the Qinghai–Tibet Plateau. Full article

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of $H_s$ = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height ($H_{s100}$ = 4.66 m, $T_{p100}$ = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, $H_{s100}$ = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia. Full article

Coastal bathymetric mapping is essential for marine conservation, navigation, and environmental management. Satellite-derived bathymetry (SDB) is a cost-effective solution to mapping bathymetry over large shallow areas. However, traditional multispectral instruments can produce poor depth estimates for several reasons, including image noise, atmospheric interference, waves and white caps, and where the seafloor-reflected signal is weak, e.g., in areas with deep water or a low-albedo seafloor. This study investigates the potential of PRISMA hyperspectral imagery to improve SDB performance. Through an iterative process, hyperspectral bands were added to a base Random Forest model, and model performance was assessed across different water pixel classes, including bright shallow substrates, seagrass, and deep water. The model’s performance was then compared to that of multispectral Landsat 8 imagery. The results demonstrated that adding hyperspectral bands to the base model improved bathymetric accuracy, particularly in deeper waters (25 m–30 m), where Mean Absolute Error decreased by 2.51 m from a 3-band to a 24-band model. However, the best-performing model was achieved using Landsat 8, resulting in a lower Mean Absolute Error (1.88 m) than the optimized 24-band PRISMA model (2.01 m). Our findings suggest that although additional hyperspectral bands can improve bathymetry estimation, multispectral imagery may still be more effective for general coastal bathymetry mapping despite its lower spectral resolution. Full article

This study evaluates the use of UAV-based photogrammetry to document shallow submerged cultural heritage, focusing on the Tirpitz wreck salvage site near Håkøya, Norway. Using a DJI Phantom 4 Multispectral drone, we acquired RGB and multispectral imagery over structures located at depths of up to 5–10 m. Structure-from-motion (SfM) processing enabled the three-dimensional reconstruction of submerged features, including a 52 × 10 m wharf and adjacent debris piles, with an accuracy of the order of 10 cm. Our data represents the first and only accurate mapping of the site yet carried out, with an absolute position uncertainty estimated to be no greater than 3 m. Volumes of imaged debris could be estimated, using a background subtraction method to allow for variable bathymetry, at around 350 m³. Bathymetric data for the sea floor could be derived effectively from an SfM point cloud, though less effectively applying the Stumpf model to the multispectral data as a result of significant spectral variation in the sea floor reflectance. Our results show that UAV-based through-surface SfM is a viable, low-cost method for reconstructing submerged heritage with high spatial accuracy. These findings support the integration of UAV-based remote sensing into heritage and environmental monitoring frameworks for shallow aquatic environments. Full article

The ICESat-2 satellite collects LiDAR data along linear orbital tracks using a photon-counting green wavelength (532.27 nm) instrument. The utility of combining ICESat-2 data with satellite imagery for training and subsequently applying satellite-derived bathymetry models to provide estimates of shallow water depth is well-established. However, automating and improving the accuracy of the identification of ICESat-2 photon events (PEs) representing bathymetry remains a challenge. This article presents an algorithm for automated extraction of PEs reflected from the ocean floor (rather than the ocean surface or noise in the water column). The algorithm is unique in examining both the density of PEs surrounding a subject PE and their position relative to the subject PE. This is accomplished by establishing three concentric ellipses around the subject PE, dividing them into radial “sectors” in 2D space (along-track vs. PE depth/height), recording the number of neighboring PEs in each sector and using this information to fit a LightGBM model. Agreement with PEs identified by an image interpreter is approximately 98%. Testing suggests that the accuracy of the algorithm is relatively insensitive to the size and shape of the ellipses used to define a PE’s neighborhood and to the number of radial sectors used. The model produced also appears to be robust across different geographic areas and data densities. Full article

Offshore wind farms (OWFs) represent an increasingly important renewable energy source, yet their environmental impacts, particularly underwater noise, require systematic study. Estimating the operational source level (SL) of a single turbine and predicting sound pressure levels (SPLs) at sensitive locations can be challenging. Here, we integrate a turbine SL prediction algorithm with open-source propagation models in a Jupyter Notebook (version 7.4.7) to streamline aggregated SPL estimation for OWFs. Species-specific audiograms and weighting functions are included to assess potential biological impacts. The tool is applied to four planned OWFs, two in the Canary region and two in the Belgian and German North Seas, under conservative assumptions. Results indicate that at 10 m/s wind speed, a single turbine’s SL reaches 143 dB re 1 µPa in the one-third octave band centered at 160 Hz. Sensitivity analyses indicate that variations in wind speed can cause the operational source level at 160 Hz to increase by up to approximately 2 dB re 1 µPa²/Hz from the nominal value used in this study, while differences in sediment type can lead to transmission loss variations ranging from 0 to on the order of 100 dB, depending on bathymetry and range. Maximum SPLs of 112 dB re 1 µPa are predicted within OWFs, decreasing to ~50 dB re 1 µPa at ~100 km. Within OWFs, Low-Frequency (LF) cetaceans and Phocid Carnivores in Water (PCW) would likely perceive the noise; National Marine Fisheries Service (NMFS) marine mammals’ auditory-injury thresholds are not exceeded, but behavioral-harassment thresholds may be crossed. Outside the farms, only LF audiograms are crossed. In high-traffic North Sea regions, OWF noise is largely masked, whereas in lower-noise areas, such as the Canary Islands, it can exceed ambient levels, highlighting the importance of site-specific assessments, accurate ambient noise monitoring and propagation modeling for ecological impact evaluation. Full article

Water depth in shallow marine environments is a fundamental parameter for oceanographic research and coastal engineering applications. High-resolution satellite imagery and long-term medium-resolution imagery offer significant potential for detailed bathymetric mapping and monitoring spatiotemporal variations in bathymetry. However, most of these images contain only three visible bands (blue, green, and red), making bathymetric mapping from such images challenging in practical applications. For the empirical approach, high-quality in situ depth calibration data, which are essential for establishing a reliable empirical bathymetric model, are either unavailable or excessively expensive. For the physics-based approach, images containing only three visible bands can be problematic in accurately deriving depths. To address this limitation, this study proposes a novel semi-analytical-empirical hybrid model for water depth retrieval. The core of the proposed method is the integration of a semi-analytical model with a physics-based dual-band model. This integration quantifies the relative depth relationships among pixels and uses them as a physical constraint. Through this constraint, the method identifies physically reliable depth estimates from the multiple numerical solutions of the semi-analytical model for a subset of shallow-water pixels, which then serve as an in situ–free calibration dataset. This dataset is subsequently used to determine the empirically based optimal retrieval model, which is finally applied to generate the complete bathymetric map. The results from four typical coral reef regions—Buck Island, Yongxing Island, Kaneohe Bay, and Yongle Atoll—demonstrated that the proposed model achieved root-mean-square errors (RMSE) of 0.98–1.62 m, mean absolute errors (MAE) of 0.73–1.13 m, and coefficients of determination (R²) of 0.91–0.95 in comparison to in situ measurements. Compared to both the physics-based dual-band model and the L-S model (i.e., the bathymetry mapping approach combining Log-ratio and Semi-analytical models), the proposed model reduced the RMSE by 9–55%, reduced the MAE by 4–56%, and improved the R² by 0.01–0.29. Additionally, the accuracy of the proposed model surpasses that of both the physics-based dual-band model and the L-S model across all depth intervals, particularly in deeper depth waters (>15 m). This study offers a robust solution for bathymetric mapping in areas lacking in situ depth data and contributes significantly to advancing optical remote sensing techniques for underwater topography detection. Full article

Satellite-derived bathymetry (SDB) enhances monitoring capabilities in the context of global change and provides a cost-effective alternative to traditional in situ methods. However, a significant gap remains in the accuracy of SDB at shallow water depths (0–10 m), particularly in complex coastal settings. In this study, we developed a two-step methodology to improve shallow water depth estimates using empirical models and multi-temporal Sentinel-2 satellite imagery. Ten Sentinel-2 images from a one-year period were analysed using the Lyzenga and Stumpf empirical reference models, followed by the application of an empirical generalised linear model (GLM). Composite images were created by combining pixel values across the temporal dataset and compared with individual image results within the model. The validation results confirmed that the GLM outperformed the reference empirical models. The optimal selection of multi-temporal images demonstrated superior performance compared to single-image regression, achieving a 42% reduction in RMSE and a minimum MAE of 0.34 m. Furthermore, enhanced outlier identification within the multi-temporal analysis reduces local anomalies and enables further improvements in accuracy. These findings underscore the enhanced capability of GLM and multi-temporal images for improving the accuracy of SDB, with a relevant impact on many coastal monitoring applications and potential for scalable implementation in other regions. Full article

This study presents a comprehensive techno-economic assessment of offshore wind projects in the Colombian Caribbean, emphasizing the impact of site-specific parameters on development costs and performance. Wind resource conditions were evaluated in four coastal regions (La Guajira, Magdalena, Atlántico, and Bolívar) using hourly meteorological data from 2015 to 2024, adjusted to 100 m above ground level through logarithmic and power law wind profile models. The analysis included wind speed, bathymetry, distance to shore, distance to substation, foundation type, wind power density (WPD), and capacity factor (C_f). Based on these parameters, annual energy generation was estimated, and both capital expenditures (CAPEX) and operational expenditures (OPEX) were calculated, considering the technical and cost differences between fixed and floating foundations. Results show that La Guajira combines excellent wind conditions (WPD of 796 W/m² and C_f of 61.5%) with favorable construction feasibility (bathymetry of −32 m), resulting in the lowest CAPEX among the studied regions. In contrast, Magdalena and Atlántico, with bathymetries exceeding 200 m, require floating foundations that more than double the investment costs. Bolívar presents an intermediate profile, offering solid wind potential and fixed foundation feasibility at a moderate cost. The findings confirm that offshore wind project viability depends not only on wind resource quality but also on physical site constraints, which directly influence the cost structure and energy yield. This integrated approach supports more accurate project prioritization and contributes to strategic planning for the sustainable deployment of offshore wind energy in Colombia. Full article

This research presents a comprehensive analysis of the spatial extent and temporal change in benthic habitats within the Puck Lagoon in the southern Baltic Sea, utilizing integrated machine learning classification and multi-sourced remote sensing. Object-based image analysis was integrated with Random Forest, Support Vector Machine, and K-Nearest Neighbors algorithms for benthic habitat classification based on airborne bathymetric LiDAR (ALB), multibeam echosounder (MBES), satellite bathymetry, and high-resolution aerial photography. Ground-truth data collected by 2023 field surveys were supplemented with long temporal datasets (2010–2023) for seagrass meadow analysis. Boruta feature selection showed that geomorphometric variables (aspect, slope, and terrain ruggedness index) and optical features (ALB intensity and spectral bands) were the most significant discriminators in each classification case. Binary classification models were more effective (93.3% accuracy in the presence/absence of Zostera marina) compared to advanced multi-class models (43.3% for EUNIS Level 4/5), which identified the inherent equilibrium between ecological complexity and map validity. Change detection between contemporary and 1957 habitat data revealed extensive Zostera marina loss, with 84.1–99.0% cover reduction across modeling frameworks. Seagrass coverage declined from 61.15% of the study area to just 9.70% or 0.63%, depending on the model. Seasonal mismatch may inflate loss estimates by 5–15%, but even adjusted values (70–94%) indicate severe ecosystem degradation. Spatial exchange components exhibited patterns of habitat change, whereas net losses in total were many orders of magnitude larger than any redistribution in space. These findings recorded the most severe seagrass habitat destruction ever described within Baltic Sea ecosystems and emphasize the imperative for conservation action at the landscape level. The methodology framework provides a reproducible model for analogous change detection analysis in shallow nearshore habitats, creating critical baselines to inform restoration planning and biodiversity conservation activities. It also demonstrated both the capabilities and limitations of automatic techniques for habitat monitoring. Full article

This study introduces a hybrid siting approach for Offshore Wind Farms by combining bottom-fixed and floating wind turbines to address seabed variability in the Mediterranean region. Using Heraklion Bay, Crete, as a case study, a multi-step methodology was adopted, integrating GIS tools, micro-siting analysis, and WAsP simulations to estimate the energy output of three layout scenarios. A comprehensive energy and economic assessment was performed, including key metrics such as Net Present Value, Internal Rate of Return, Payback Period and Levelised Cost of Energy. Scenario 2, which featured a mixed deployment of Vestas and Siemens Gamesa turbines, proved to be the most financially attractive option, yielding the highest Net Present Value (€167 million) and shortest Payback Period. Sensitivity analysis under a 20% reduction in wind resources confirmed the robustness of this scenario. Results demonstrate that hybrid configurations offer a flexible and scalable solution, particularly in island regions with varied bathymetry and seasonal energy demands. The findings highlight the potential of hybrid offshore systems to accelerate energy transitions, optimise spatial utilisation, and improve cost-effectiveness in medium-depth seas. Full article