Search Results (45)

Mediterranean subtidal reefs host ecologically significant habitats, including forests of Cystoseira spp., which form complex benthic communities within the photic zone. These habitats are increasingly degraded due to climate change, invasive species, and anthropogenic pressures, particularly in the eastern Mediterranean. In support of habitat monitoring under the EU Natura 2000 directive and the Nature Restoration Regulation, this study investigates the utility of high-resolution satellite remote sensing for mapping subtidal brown algae and associated benthic classes. Using imagery from the SuperDove sensor (Planet Labs, San Francisco, CA, USA), we developed an integrated mapping workflow at the Natura 2000 site GR2420009. Aquatic reflectance was derived using ACOLITE v.20250114.0, and both supervised classification and spectral unmixing were implemented in the EnMAP Toolbox v.3.16.3 within QGIS. A Random Forest classifier (100 fully grown trees) achieved high thematic accuracy across all habitat types (F1 scores: 0.87–1.00), with perfect classification of shallow soft bottoms and strong performance for Cystoseira s.l. (F1 = 0.94) and Seagrass (F1 = 0.93). Spectral unmixing further enabled quantitative estimation of fractional cover, with high predictive accuracy for deep soft bottoms (R² = 0.99; RPD = 18.66), shallow soft bottoms (R² = 0.98; RPD = 8.72), Seagrass (R² = 0.88; RPD = 3.01) and Cystoseira s.l. (R² = 0.82; RPD = 2.37). The lower performance for rocky reefs with other cover (R² = 0.71) reflects spectral heterogeneity and shadowing effects. The results highlight the effectiveness of combining classification and unmixing approaches for benthic habitat mapping using CubeSat constellations, offering scalable tools for large-area monitoring and ecosystem assessment. Despite challenges in field data acquisition, the presented framework provides a robust foundation for remote sensing-based conservation planning in optically shallow marine environments. Full article

Effective management to conserve marine environments requires up-to-date information on the location, distribution, and extent of major benthic habitats. Remote sensing is a key tool for such assessments, enabling consistent, repeated measurements over large areas. There is particular interest in using freely available satellite images such as from the Copernicus Sentinel-2 series for accessible repeat assessments. In this study, an area of 438 km² of the northern Red Sea coastline, adjacent to the NEOM development was mapped using Sentinel-2 imagery. A hierarchical Random Forest classification method was used, where the initial level classified pixels into a geomorphological class, followed by a second level of benthic cover classification. Uncrewed Aerial Vehicle (UAV) surveys were carried out in 12 locations in the NEOM area to collect field data on benthic cover for training and validation. The overall accuracy of the geomorphic and benthic classifications was 84.15% and 72.97%, respectively. Approximately 12% (26.26 km²) of the shallow Red Sea study area was classified as coral or dense algae and 16% (36.12 km²) was classified as rubble. These reef environments offer crucial ecosystem services and are believed to be internationally important as a global warming refugium. Seagrass meadows, covering an estimated 29.17 km² of the study area, play a regionally significant role in carbon sequestration and are estimated to store 200 tonnes of carbon annually, emphasising the importance of their conservation for meeting the environmental goals of the NEOM megaproject. This is the first map of this region generated using Sentinel-2 data and demonstrates the feasibility of using an open source and reproducible methodology for monitoring coastal habitats in the region. The use of training data derived from UAV imagery provides a low-cost and time-efficient alternative to traditional methods of boat or snorkel surveys for covering large areas in remote sites. Full article

Seagrass meadows, including those formed by Zostera noltei, play a crucial role in marine ecosystem health by providing habitat stability and coastal protection. In the Romanian Black Sea, Z. noltei meadows are critically endangered due to pressures from eutrophication, habitat loss, and climate change. This study presents a comprehensive baseline assessment of Z. noltei meadows near Mangalia, Romania, utilizing in situ field methods and UAV mapping conducted in the spring and summer of 2023. Seven meadow sites (Z1–Z7) were identified, with notable variability in density, shoot counts, and coverage across sites. Site Z1 exhibited the highest density (1223 shoots/m⁻²) and Z5 and Z7 the longest leaves (an average of 60 cm), reflecting possible environmental influences. Statistical analyses revealed significant inter-site differences in shoot density and leaf length, with density emerging as a primary differentiator. Ex situ analyses of epiphyte load indicated a median, balanced epiphyte load. This baseline dataset supported the selection of Z1 as a reference donor site for seagrass relocation activities along the Romanian coast in 2023. By providing critical insights into Z. noltei structure and health, this study supports future conservation efforts and evidence-based management of these vulnerable coastal habitats. Full article

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m². The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales. Full article

Sustainable coastal social–ecological systems rely on healthy ecosystems known to provide benefits to both nature and people. A key ecosystem found globally is seagrass, for which maps at a scale relevant to inform conservation and management efforts are often missing. Eelgrass (Zostera marina), a species of seagrass found throughout the northern hemisphere, has been declining in Placentia Bay, an ecologically and biologically significant area of Canada’s east coast subject to an increasing human impact. This research provides baseline information on the distribution of eelgrass meadows and their anthropogenic stressors at seven sites of Placentia Bay and three sites of the adjacent Trinity Bay, on the island of Newfoundland. High-resolution maps of eelgrass meadows were created by combining ground-truth underwater videos with unmanned aerial vehicle imagery classified with an object-based image analysis approach. Visual analyses of the imagery and underwater videos were conducted to characterize sites based on the presence of physical disturbances and the semi-quantitative cover of epiphytes, an indication of nutrient enrichment. A total eelgrass area of ~1 km² was mapped across the 10 sites, with an overall map accuracy of over 80% for 8 of the 10 sites. Results indicated minimum pressures of physical disturbance and eutrophication affecting eelgrass in the region, likely due to the small population size of the communities near the eelgrass meadows. These baseline data will promote the sustainability of potential future coastal development in the region by facilitating the future monitoring and conservation of eelgrass ecosystems. Full article

Seagrasses, rooted aquatic plants growing completely underwater, are extremely important for the coastal ecosystem. They are an important component of the total carbon burial in the ocean, they provide food, shelter, and nursery to many aquatic organisms in coastal ecosystems, and they improve water quality. Due to human activity, seagrass coverage has been rapidly declining, and there is an urgent need to monitor seagrasses consistently. Seagrass coverage has been closely monitored in the Chesapeake Bay since 1970 using air photos and ground samples. These efforts are costly and time-consuming. Many studies have used remote sensing data to identify seagrass bed outlines, but few have mapped seagrass bed density. This study used Sentinel-2 satellite data and machine learning in Google Earth Engine and the Chesapeake Bay Program field data to map seagrass density. We used seagrass density data from the Chincoteague and Sinepuxent Bay to train machine learning algorithms and evaluate their accuracies. Out of the four machine learning models tested (Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF)), the RF model outperformed the other three models with overall accuracies of 0.874 and Kappa coefficients of 0.777. The SVM and CART models performed similarly and NB performed the poorest. We tested two different approaches to assess the models’ accuracy. When we used all the available ground samples to train the models, whereby our analysis showed that model performance was associated with seagrass density class, and that higher seagrass density classes had better consumer accuracy, producer accuracy, and F1 scores. However, the association of model performance with seagrass density class disappeared when using the same training data size for each class. Very sparse and dense seagrass classes had replacedhigherbetter accuracies than the sparse and moderate seagrass density classes. This finding suggests that training data impacts machine learning model performance. The uneven training data size for different classes can result in biased assessment results. Selecting proper training data and machine learning models are equally important when using machine learning and remote sensing data to map seagrass density. In summary, this study demonstrates the potential to map seagrass density using satellite data. Full article

Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha⁻¹ was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems. Full article

Macrophytes and seagrasses play a crucial role in a variety of functions in marine ecosystems and respond in a synchronized manner to a changing climate and the subsequent ecological status. The monitoring of seagrasses is one of the most important issues in the marine environment. One rapidly emerging monitoring technique is the use of acoustics, which has advantages compared to other remote sensing techniques. The acoustic method alone is ambiguous regarding the identities of backscatterers. Therefore, a computer program package was developed to identify and estimate the leaf biometrics (leaf length and biomass) of one of the most common seagrasses, Posidonia oceanica. Some problems in the acoustic data were resolved in order to obtain estimates related to problems with vegetation as well as fisheries and plankton acoustics. One of the problems was the “lost” bottom that occurred during the data collection and postprocessing due to the presence of acoustic noise, reverberation, interferences and intense scatterers, such as fish shoals. Another problem to be eliminated was the occurrence of near-bottom echoes belonging to submerged vegetation, such as seagrasses, followed by spurious echoes during the survey. The last one was the recognition of the seagrass to estimate the leaf length and biomass, the calibration of the sheaths/vertical rhizomes of the seagrass and the establishment of relationships between the acoustic units and biometrics. As a result, an autonomous package of code written in MATLAB was developed to perform all the processes, named “POSIBIOM”, an acronym for POSIdonia BIOMass. This study presents the algorithms, methodology, acoustic–biometric relationship and mapping of biometrics for the first time, and discusses the advantages and disadvantages of the package compared to the software dedicated to the bottom types, habitat and vegetation acoustics. Future studies are recommended to improve the package. Full article

Biological invasions are a human-induced environmental disturbance that can cause major changes in ecosystem structure and functioning. Located in the northeastern Mediterranean basin, the Aegean Sea is a hotspot of biological invasions. Although the presence of alien species in the Aegean has been studied and monitored, no assessment has been conducted on their cumulative impacts on native biodiversity. To address this gap, we applied the CIMPAL index, a framework developed for mapping the cumulative impacts of invasive species, to identify the most affected areas and habitat types and determine the most invasive species in the region. Coastal areas showed stronger impacts than the open sea. The highest CIMPAL scores were four times more frequent in the South than in the North Aegean. Shallow (0–60 m) hard substrates were the most heavily impacted habitat type, followed by shallow soft substrates and seagrass meadows. We identified Caulerpa cylindracea, Lophocladia lallemandii, Siganus luridus, Siganus rivulatus, and Womersleyella setacea as the most impactful species across their range of occurrence in the Aegean but rankings varied depending on the habitat type and impact indicator applied. Our assessment can support marine managers in prioritizing decisions and actions to control biological invasions and mitigate their impacts. Full article

The Great Barrier Reef (GBR) is a marine protected area subject to natural and anthropogenic disturbances. Water quality is critical for the health and protecting resilience of GBR coral ecosystems against the synergistic and cumulative pressures of tropical cyclones, marine heat waves, and outbreaks of crown-of-thorns starfish. The concentration of Total Suspended Solids (TSS) is a key water quality parameter measured at multiple spatio-temporal scales from in situ probes to satellite observations. High TSS concentrations can adversely impact coral and seagrasses on the inshore GBR. We present diurnal TSS derived from Himawari-8 Geostationary satellite observations at 10 min frequency and demonstrate its applicability for improved monitoring of GBR water quality. Diurnal TSS obtained from Himawari-8 observations were compared to TSS computed from in situ bio-optical measurements at the Lucinda Jetty Coastal Observatory (LJCO). The coastal waters at LJCO experience diurnal variability of TSS (~7 mg L⁻¹), where magnitude peaks followed the slack tides, and the largest diurnal changes were associated with freshwater discharge residuals from the wet season. Exceedance maps revealed that TSS is above guideline thresholds in the open coastal and mid-shelf waters for ~60% of the valid monthly observations, including during dry season months. Full article

Seagrass beds provide a range of ecosystem services but are at risk from anthropogenic pressures. While recent progress has been made, the distribution and condition of South Pacific seagrass is relatively poorly known and selecting an appropriate approach for mapping it is challenging. A variety of remote sensing tools are available for this purpose and here we develop a mapping toolbox and associated decision tree tailored to the South Pacific context. The decision tree considers the scale at which data are needed, the reason that monitoring is required, the finances available, technical skills of the monitoring team, data resolution, site safety/accessibility and whether seagrass is predominantly intertidal or subtidal. Satellite mapping is recommended for monitoring at the national and regional scale, with associated ground-reference data where possible but without if time and funds are limiting. At the local scale, satellite, remotely piloted aircraft (RPA), kites, underwater camera systems and in situ surveys are all recommended. In the special cases of community-based initiatives and emergency response monitoring, in situ or satellite/RPA are recommended, respectively. For other types of monitoring the primary driver is funding, with in situ, kite and satellite recommended when finances are limited and satellite, underwater camera, RPA or kites otherwise, dependent on specific circumstances. The tools can be used individually or in combination, though caution is recommended when combining tools due to data comparability. Full article

The onset of a major seagrass initiative in West Africa enabled important seagrass discoveries in several countries, in one of the least documented seagrass regions in the world. Four seagrass species occur in western Africa, Cymodocea nodosa, Halodule wrightii, Ruppia maritima and Zostera noltei. An area of about 62,108 ha of seagrasses was documented in the studied region comprising seven countries: Mauritania, Senegal, The Gambia, Guinea Bissau, Guinea, Sierra Leone and Cabo Verde. Extensive meadows of Zostera noltei were recorded for the first time at Saloum Delta, Senegal, which represents the new southernmost distribution limit of this species. This paper also describes the seagrass morphology for some study areas and explores the main stressors to seagrasses as well as conservation initiatives to protect these newly documented meadows in West Africa. The produced information and maps serve as a starting point for researchers and managers to monitor temporal and spatial changes in the meadows’ extent, health and condition as an efficient management tool. Full article

Estimating the distribution, extent and change of coastal ecosystems is essential for monitoring global change. However, spatial models developed to estimate the distribution of land cover types require accurate and up-to-date reference data to support model development, model training and data validations. Owing to the labor-intensive tasks required to develop reference datasets, often requiring intensive campaigns of image interpretation and/or field work, the availability of sufficiently large quality and well distributed reference datasets has emerged as a major bottleneck hindering advances in the field of continental to global-scale ecosystem mapping. To enhance our ability to model coastal ecosystem distributions globally, we developed a global reference dataset of 193,105 occurrence records of seven coastal ecosystem types—muddy shorelines, mangroves, coral reefs, coastal saltmarshes, seagrass meadows, rocky shoreline, and kelp forests—suitable for supporting current and next-generation remote sensing classification models. coastTrain version 1.0 contains curated occurrence records collected by several global mapping initiatives, including the Allen Coral Atlas, Global Tidal Flats, Global Mangrove Watch and Global Tidal Wetlands Change. To facilitate use and support consistency across studies, coastTrain has been harmonized to the International Union for the Conservation of Nature’s (IUCN) Global Ecosystem Typology. coastTrain is an ongoing collaborative initiative designed to support sharing of reference data for coastal ecosystems, and is expected to support novel global mapping initiatives, promote validations of independently developed data products and to enable improved monitoring of rapidly changing coastal environments worldwide. Full article

Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea. Full article

Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms. Four such algorithms––Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast Fuzzy C-Means (FF), Otsu clustering (OS), and K-means segmentation (KM)––were tested for accuracy for segmentation. Further, YCbCr and the Commission Internationale de l’Éclairage (CIE) LAB color spaces were evaluated to correct variations in image illumination and shadow effects. Benthic habitat field data from a geo-located high-resolution towed camera were used to evaluate proposed algorithms. The Shiraho study area, located off Ishigaki Island, Japan, was used, and six benthic habitats were classified. These categories were corals (Acropora and Porites), blue corals (Heliopora coerulea), brown algae, other algae, sediments, and seagrass (Thalassia hemprichii). Analysis showed that the K-means clustering algorithm yielded the highest overall accuracy. However, the differences between the KM and OS overall accuracies were statistically insignificant at the 5% level. Findings showed the importance of eliminating underwater illumination variations and outperformance of the red difference chrominance values (Cr) in the YCbCr color space for habitat segmentation. The proposed framework enhanced the automation of benthic habitat classification processes. Full article