Search Results (58)

This study assesses the comparative performance of two geotechnologies for shoreline monitoring—Unmanned Aerial Vehicle (UAV) surveys and CoastSnap citizen-science imagery—at Guarita Beach, southern Brazil. The analysis was based on twelve paired monitoring dates distributed over a two-year interval. Shorelines were extracted from the wet–dry line, manually digitized from UAV orthomosaics, and automatically detected from CoastSnap images with subsequent quality control. Shoreline change was quantified in the Digital Shoreline Analysis System (DSAS) using the Shoreline Change Envelope (SCE) and the Linear Regression Rate (LRR). The SCE showed the highest equivalence between methods, with a mean difference close to zero (−0.14 m) and no evidence of systematic bias. For LRR, values derived from CoastSnap tended to be lower than those derived from UAVs (mean difference = −2.14 m year⁻¹), although without statistically significant divergence at the adopted significance level. The results demonstrate that the agreement between CoastSnap and UAV data depends directly on the metric analyzed: SCE was more robust for inter-method comparison, whereas LRR was useful for medium-term trend interpretation but more sensitive to uncertainty propagation. Overall, CoastSnap did not replace UAV surveys, but it proved to be a valuable complementary tool for expanding temporal coverage in coastal monitoring programs. Full article

This study presents a comprehensive 35-year (1990–2025) shoreline change assessment along the southeast coast of Ireland, integrating multi-decadal Landsat satellite archives with GIS-based Digital Shoreline Analysis System (DSAS) metrics to quantify both spatial and temporal coastal dynamics. Unlike previous studies that focus on shorter timeframes or localized sectors, this research provides a regional-scale, orientation-specific comparison between the eastern-facing (SE1; County Wexford) and southern-facing (SE2; County Waterford) shorelines. Shoreline evolution was quantified using four complementary DSAS indicators—Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), allowing robust discrimination between short-term variability and multi-decadal trends. The results reveal noticeable spatial variability in shoreline behavior with 57% accretion and 42% erosion across the eastern-facing coast (SE1) in County Wexford and the southern-facing coast (SE2) in County Waterford. SCE values ranging from 2.26 m to 663.83 m indicate considerable short-term shoreline variability, particularly within dynamic barrier and embayed systems. NSM values between −216.65 m and +663.83 m indicate erosional hotspots, particularly along soft-sediment coasts and exposed southern-facing sectors, whereas accretion is limited to embayments, sandy beaches, and zones of effective sediment trapping. Rate-based analyses show EPR values between −14.82 and +20.38 m/yr and LRR values between −5.27 and +20 m/yr, with LRR providing more reliable estimates of multi-decadal trends in highly dynamic environments. The findings highlight the strong influence of coastal orientation, sediment availability, geological controls, and human activities on shoreline change in southeastern Ireland. These findings provide valuable evidence to support coastal management, hazard mitigation, and climate adaptation planning, with the assistance of policymakers, to develop effective strategies that enhance the resilience and quality of life of coastal communities. Full article

Shoreline variations in closed-basin lakes are closely linked to hydrological fluctuations and long-term changes in water balance, making them important indicators of environmental change. This study analyzes historical shoreline dynamics in Lake Van (Türkiye), the world’s largest soda lake, and provides scenario-based shoreline projections for 2032 and 2042 to support hydrological assessment and water-related management. Multi-temporal Landsat satellite images from 1982, 1992, 2002, 2012, and 2022 were processed using the Digital Shoreline Analysis System (DSAS 5.0) to quantify shoreline retreat and accretion, while future shoreline positions were estimated using the Kalman filter model. The results show pronounced spatial variability, with the most significant shoreline retreat observed in the Çelebibağ and Karahan regions, where sediment supplied by major inflowing streams contributes to shoreline instability through reworking and redistribution rather than stable accretion. Net shoreline movement values reached −2580.1 m for erosion and up to 1700 m for accretion. Model projections indicate an increasing trend of shoreline retreat by 2032 and 2042, accompanied by localized accretion zones. These hydrological-driven shoreline changes have potential implications for littoral habitats, water–land interactions, and human use of the shoreline, including fisheries infrastructure. The study demonstrates the value of integrating remote sensing and statistical forecasting for monitoring shoreline dynamics in closed-basin lake systems. Full article

Coastal settlements worldwide face increasing threats from erosion, and the Monrovia coastline in Liberia is no exception. This study investigates shoreline dynamics along a 20.5 km stretch of Monrovia’s coast, which is characterized by low-lying elevations, gentle slopes, and sandy beaches. Using Landsat satellite imagery (1986–2025), supported by Sentinel-2 MSI and qualitative validation drone data, we analyzed historical shoreline change with remote sensing and GIS techniques. Shorelines were extracted using a band-ratio thresholding method and quantified with the Digital Shoreline Analysis System (DSAS 5.0), applying end-point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM). Exploratory projections for 2036 and 2046 were generated using a Kalman Filter model integrated into DSAS. Results show maximum historical erosion rates of up to 3.8 m/yr and accretion rates of up to 5.9 m/yr, with shoreline retreat reaching 150 m and advance up to 194 m. Erosion hotspots are projected for Hotel Africa, Westpoint, New Kru Town, and the JFK–ELWA corridor, while areas near the St. Paul and Mesurado estuaries are expected to accrete. These findings confirm historical trends and suggest that Monrovia will continue to face significant shoreline change, with implications for natural habitats, infrastructure, land loss, and population displacement. Full article

Coastal erosion poses a significant threat to global shorelines, exacerbated by anthropogenic pressures and climate change. The Sea of Azov, a shallow, semi-enclosed basin with coastlines composed of weakly consolidated sediments, represents a highly vulnerable and understudied hotspot for abrasion processes. This study provides a comprehensive, multi-decadal assessment of coastal retreat rates for the Sea of Azov by synergistically integrating long-term field observations with a multi-temporal analysis of satellite imagery from 1971 to 2022. We employed a diverse array of satellite data, including declassified CORONA, SPOT, Sentinel-2, and high-resolution Resurs-P imagery, which were processed and analyzed within a GIS framework using the Digital Shoreline Analysis System (DSAS). Our results quantify extreme coastal abrasion, revealing maximum retreat rates of 1.0–3.5 m/yr along the eastern Sea of Azov coast and specific sectors of Taganrog Bay. The spatiotemporal analysis identified the period of 2013–2014, marked by two major storms, as a peak of erosional activity across all coastal sectors. This study demonstrates that the spatial distribution of erosion is controlled by a convergence of high-energy wind-wave forcing, low geotechnical resistance of Quaternary sedimentary deposits, and unfavorable coastal morphometry. This research underscores the critical value of merging historical field data with modern geospatial technologies to establish baseline rates, identify erosion hotspots, and inform future coastal zone management strategies in vulnerable marine environments. Full article

Pakistan’s coastline encompasses the Indus Delta, a critical ecosystem that sustains biodiversity, fisheries, and local livelihoods, yet it is increasingly threatened by both natural and anthropogenic pressures. This study quantifies multi-decadal shoreline changes in the Indus Delta and examines how changes in climatic factors (precipitation and wind) affect these changes, using the Digital Shoreline Analysis System (DSAS v5.1) and multi-temporal Landsat imagery (TM, ETM+, OLI) to quantify long-term shoreline dynamics from 1990 to 2020 (30-year period). Key metrics, including End Point Rate (EPR), Net Shoreline Movement (NSM), and Linear Regression Rate (LRR), indicated an overall retreat, with a mean NSM of −1810 m and a mean LRR of −173 m·year across the 30-year period. Shoreline change rates exhibited a significant relationship with climatic variables, particularly wind speed and precipitation, with dynamics shifting from erosion-dominated to localized accretion in areas where mangrove rehabilitation programs were implemented after 2005. Seasonal variability further influenced shoreline behavior: low-rainfall years intensified erosion due to reduced sediment availability, while high-rainfall years enhanced accretion through increased sediment input. These findings underscore the urgent need for integrated coastal management strategies, including mangrove conservation, sustainable sediment management, and climate-adaptive planning, to strengthen the resilience of the Indus Delta. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

Understanding the spatiotemporal variations and driving mechanisms of coastlines is crucial for their adequate protection, utilization, and sustainable development. In this study, the changes in various coastline types in Zhuhai from 1987 to 2022 were analyzed by using long-term Landsat and GaoFen satellite imagery. The Index of Coastline Type Diversity (ICTD), Index of Coastline Utilization Degree (ICUD) and the Digital Shoreline Analysis System (DSAS) analysis indicators were employed to investigate coastline change. Both quantitative and qualitative analyses were integrated to comprehensively elucidate the impacts of various driving factors. The results indicate that the total length of Zhuhai coastline increased from 761.50 km in 1987 to 798.91 km in 2022, with natural coastlines decreasing by 89.82 km and artificial coastlines increasing by 153.40 km. The rapid expansion of artificial coastlines since 2007 led to a marked decline in the ICTD indicator, while the ICUD indicator increased from 146.42 in 1987 to 216.37 in 2022, reflecting the intensified and continuous influence of anthropogenic activities. Additionally, the end point rate (EPR) and Weighted Linear Regression Rate (WLR) changed by 8.09 m/yr and 6.62 m/yr, respectively. The Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) exhibited average changes of 331.42 m and 224.32 m, respectively. Gray correlation and regression analyses further revealed that climate factors exhibited the strongest association with natural coastline changes, while economic development indicators showed the strongest correlation with artificial coastline dynamics. The relationship of Number of Berths in Main Ports (Nb) with coastline changes strongly suggests that human activities are the primary driver of these changes. These findings provide a robust scientific basis for coastal zone management in Zhuhai. Full article

Coastal erosion and shoreline change represent major challenges for the sustainable management of coastal environments, with implications for infrastructure, ecosystems, biodiversity, and the socio-economic well-being of coastal communities. This study investigates the shoreline evolution of 79 Mediterranean microtidal beaches located along the southern coast of Sardinia Island (Italy), using the Digital Shoreline Analysis System (DSAS). Shorelines were manually digitised from high-resolution aerial orthophotos made available through the WMS service of the Autonomous Region of Sardinia, covering the period 1954–2022. Shoreline changes were assessed through five statistical indicators: Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), Weighted Linear Regression (WLR), and Linear Regression Rate (LRR). The results highlight marked spatial and temporal variability in shoreline retreat and accretion, revealing patterns that link shoreline dynamics to the degree of anthropisation or naturalness of each beach. In fact, coastal areas characterised by local anthropogenic factors showed higher rates of shoreline retreat and/or accretion, while natural beaches showed greater stability and resilience in the long term. The outcomes of this analysis provide valuable insights into local coastal dynamics and represent a critical knowledge base for developing targeted adaptation strategies, supporting spatial planning, and reducing coastal risks under future climate change scenarios. Full article

Over the past 50 years, coastal erosion has become an increasingly critical issue worldwide, and Colombia’s Caribbean coast is no exception. In urban areas, this challenge is further complicated by hard protection structures, which, while often implemented as immediate solutions, can disrupt sediment transport and trigger unintended long-term consequences. This study examines shoreline changes in Riohacha, the capital of La Guajira Department, over a 35-year period (1987–2022), focusing on the impacts of coastal protection structures—specifically, the construction of seven groins and a seawall between 2006 and 2009—on coastal dynamics. Using twelve images (photographs and satellite) and the Digital Shoreline Analysis System (DSAS), the evolution of both beaches and cliffs has been analyzed. The results reveal a dramatic shift in shoreline behavior: erosion rates of approximately 0.5 m/year prior to the interventions transitioned to accretion rates of up to 11 m/year within the groin field, where rapid infill occurred. However, this sediment retention has exacerbated erosion in downstream cliff areas, with retreat rates reaching 1.8 ± 0.2 m/year. To anticipate future coastal evolution, predictive models were applied through 2045, providing insights into potential risks for infrastructure and urban development. These findings highlight the need for a strategic, long-term approach to coastal management that considers both the benefits and unintended consequences of engineering interventions. Full article

The dynamic change in the shorelines reflects an important sign to the socio-economic development of coastal areas. The Huizhou–Shanwei region of China has experienced rapid socio-economic development over the past 33 years. The study of the dynamic change in the shorelines in this region can provide basic data support for the marine environmental protection and regional development planning in this region. Based on Landsat RS (remote sensing) images from 1990 to 2023, this study obtained the length and structure data of the shorelines in eight periods by manual visual interpretation. DSAS (Digital Shoreline Analysis System) and other methods were also used to calculate indices such as EPR (End Point Rate) and fractal dimension of the shorelines The results show that, during 33 years, the length of the shorelines increased 15.83 km, with an average growth rate of 0.48 km/y; the value of the intensity of change in the shorelines was 0.08%; the average EPR was 3.66 (m/y), and the artificiality index of the shorelines increased from 0.2895 to 0.4295; the greatest intensity of change was in the estuarine shorelines, with an intensity of change of −2.69%. The overall change in the fractal dimension of the shorelines was small, both between 1.0395 and 1.0673; the shorelines became slightly more curved. As far as the influencing factors are concerned, the influence of the natural environment is a long process, and human activities are more capable of changing the length and shape of the shorelines in a short period of time, with factors such as the degree of economic development having a greater impact on the shorelines. Full article

This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years. Full article

The Ranchería River delta, located in Riohacha, Colombia, exemplifies the complex dynamics of coastal systems influenced by environmental and anthropogenic factors. This study analyzes the spatial and temporal evolution of the delta’s shoreline over the past two decades (2003–2023) using Google Earth imagery, the Digital Shoreline Analysis System (DSAS) within a GIS environment, and statistical methods such as ANOVA and Tukey’s test. Satellite images from 2003 to 2023 were processed to evaluate shoreline evolution through metrics like the Net Shoreline Movement (NSM) and Linear Regression Rate (LRR). The results reveal a predominant trend of accretion, with values reaching up to 260 m of NSM, particularly between 2003 and 2018. However, the 2018–2023 period shows a shift toward stabilization and localized erosion (e.g., the NSM ranges from 96 m of erosion to 32 m of accretion), with significant changes in the northeastern area (the delta’s Santa Rita arm) attributed to anthropic and natural factors (e.g., absence of mangroves or ongoing human activities). The comparison of LRR and NSM values reveals consistent linearity in shoreline behavior across the study period, suggesting stable coastal processes during accretion-dominated phases and increased variability during recent erosion. Variability across zones highlights the role of natural barriers like mangroves in mitigating erosion. The findings underscore the importance of integrating long-term data with recent trends for shoreline management and emphasize adaptive strategies to conserve critical ecosystems while addressing the socio-economic needs of local communities. Full article

Coastal erosion is a critical environmental challenge in the Upper Gulf of Thailand, driven by both natural processes and human activities. This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). The results show that the Random Forest algorithm, utilizing spectral bands and indices (NDVI, NDWI, MNDWI, SAVI), achieved the highest classification accuracy (98.17%) and a Kappa coefficient of 0.9432, enabling reliable delineation of land and water boundaries. The extracted annual shorelines were validated with high accuracy, yielding RMSE values of 13.59 m (2018) and 8.90 m (2023). The DSAS analysis identified significant spatial and temporal variations in shoreline erosion and accretion. Between 1988 and 2006, the most intense erosion occurred in regions 4 and 5, influenced by sea-level rise, strong monsoonal currents, and human activities. However, from 2006 to 2018, erosion rates declined significantly, attributed to coastal protection structures and mangrove restoration. The period 2018–2023 exhibited a combination of erosion and accretion, reflecting dynamic sediment transport processes and the impact of coastal management measures. Over time, erosion rates declined due to the implementation of protective structures (e.g., bamboo fences, rock revetments) and the natural expansion of mangrove forests. However, localized erosion remains persistent in low-lying, vulnerable areas, exacerbated by tidal forces, rising sea levels, and seasonal monsoons. Anthropogenic activities, including urban development, mangrove deforestation, and aquaculture expansion, continue to destabilize shorelines. The findings underscore the importance of sustainable coastal management strategies, such as mangrove restoration, soft engineering coastal protection, and integrated land-use planning. This study demonstrates the effectiveness of combining machine learning and geoinformatics for shoreline monitoring and provides valuable insights for coastal erosion mitigation and enhancing coastal resilience in the Upper Gulf of Thailand. Full article

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth^® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth^®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change. Full article