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The retreat rates of Arctic coasts have increased in recent decades at many sites, and an essential part of coasts considered accumulative before have turned erosional due to global climate changes and construction in the coastal zone. In this paper, we study a 7 km long coastal section of the western Gydan Peninsula in a new construction area. Based on the interpretation of multi-temporal satellite imagery, we assessed coastal dynamics in distinct periods from 1972 to 2020. We analyzed the geological structure of the coast as well as changes in hydrometeorological parameters with time, and considering the human impact, we proposed the main drivers of spatial and temporal variations of coastal dynamics. The studied low-lying sandy accumulative marine terrace was more or less stable in the period before construction (1972–2014). However, with the area's development, the coast dynamics changed drastically: in 2014–2017, three-quarters of the studied area experienced retreat, and the average retreat rate amounted to 5.8 m/yr, up to 28.5 m/yr near the construction sites. We relate this coastal erosion intensification to human impact combined with the growth of hydrometeorological forcing. Although coastal erosion slowed down after 2017, the retreat trend remained. In the coming years, with Arctic climate warming, erosion of the studied coast will continue. [ABSTRACT FROM AUTHOR]

Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-use/land-cover (LULC) change should be considered. This paper presents a remotely sensed shoreline monitoring in Sandbanks Provincial Park, Ontario, Canada, from 1984 to 2021. The CoastSat toolkit for Python and a multilayer perceptron (MLP) neural network classifier were used for shoreline detection, and an unsupervised change detection framework followed by a postclassification change detection method was implemented for LULC classification and change detection. The study assessed the recent coastal erosion and accretion trends in the region in association with spatiotemporal changes in the total area of the West and East Lakes, the transition between LULC classes, extreme climate events, population growth, and future climate projection scenarios. The results of the study illustrate that the accretion trend apparently can be seen in most parts of the study area since 1984 and is affected by several factors, including lake water-level changes, total annual precipitations, sand movements, and other hydrologic/climatic parameters. Furthermore, the observed LULC changes could be in line with climate change-driven forces and population growth to accelerate the detected accretion trend in the East and West Lakes. In total, the synergistic interaction of the investigated parameters would result in a greater accretion trend along with a lower groundwater table amid even a low carbon scenario. The discussed findings could be beneficial to regional/provincial authorities, policymakers, and environmental advocates for the sustainable development of coastal communities. [ABSTRACT FROM AUTHOR]

The world has a long record of shoreline and related erosion problems due to the impacts of climate change/variability in sea level rise. This has made coastal systems and large inland water environments vulnerable, thereby activating research concern globally. This study is a bibliometric analysis of the global scientific production of data sources and tools for shoreline change analysis and detection. The bibliometric mapping method (bibliometric R and VOSviewer package) was utilized to analyze 1578 scientific documents (1968–2022) retrieved from Scopus and Web of Science databases. There is a chance that in the selection process one or more important scientific papers might be omitted due to the selection criteria. Thus, there could be a bias in the present results due to the search criteria here employed. The results revealed that the U.S.A. is the country with the most scientific production (16.9%) on the subject. Again, more country collaborations exist among the developed countries compared with the developing countries. The results further revealed that tools for shoreline change analysis have changed from a simple beach transect (0.1%) to the utilization of geospatial tools such as DSAS (14.6%), ArcGIS/ArcMap (13.8%), and, currently, machine learning (5.1%). Considering the benefits of these geospatial tools, and machine learning in particular, more utilization is essential to the continuous growth of the field. Found research gaps were mostly addressed by the researchers themselves or addressed in other studies, while others have still not been addressed, especially the ones emerged from the recent work. For instance, the one on insights for reef restoration projects focused on erosion mitigation and designing artificial reefs in microtidal sandy beaches. [ABSTRACT FROM AUTHOR]

The amount of Earth observation images available to the public has been the main source of information, helping governments and decision-makers tackling the current world's most pressing global challenge. However, a number of highly skilled and qualified personnel are still needed to fill the gap and help turn these data into intelligence. In addition, the accuracy of this intelligence relies on the quality of these images in times of temporal, spatial, and spectral resolution. For the purpose of contributing to the global effort aiming at monitoring natural and anthropic processes affecting coastal areas, we proposed a framework for image processing to extract the shoreline and the shallow water depth on GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV) on the coast of San Vito Lo Capo, with image preprocessing steps involving orthorectification, atmospheric correction, pan sharpening, and binary imaging for water and non-water pixels analysis. Binary imaging analysis step was followed by automatic instantaneous shoreline extraction on a digital image and satellite-derived bathymetry (SDB) mapping on GeoEye-1 water pixels. The extraction of instantaneous shoreline was conducted automatically in ENVI software using a raster to vector (R2V) algorithm, whereas the SDB was computed in ArcGIS software using a log-band ratio method applied on the satellite image and available field data for calibration and vertical referencing. The results obtained from these very high spatial resolution images demonstrated the ability of remote sensing techniques in providing information where techniques using traditional methods present some limitations, especially due to their inability to map hard-to-reach areas and very dynamic near shoreline waters. We noticed that for the period of 5 years, the shoreline of San Vito Lo Capo sand beach migrated about 15 m inland, indicating the high dynamism of this coastal area. The bathymetric information obtained on the GeoEye-1 satellite image provided water depth until 10 m deep with R2 = 0.753. In this paper, we presented cost-effective and practical methods for automatic shoreline extraction and bathymetric mapping of shallow water, which can be adopted for the management and the monitoring of coastal areas. [ABSTRACT FROM AUTHOR]

Coastal zones are considered to be highly vulnerable to the effects of climate change, such as erosion, flooding, and storms, including sea level rise (SLR). The effects of rising sea levels endanger several nations, including Indonesia, and it potentially affects the coastal population and natural environment. Quantification is needed to determine the degree of vulnerability experienced by a coast since measuring vulnerability is a fundamental phase towards effective risk reduction. Therefore, the main objective of this research is to identify how vulnerable the coastal zone of Bali Province by develop a Coastal Vulnerability Index (CVI) of areas exposed to the sea-level rise on regional scales using remote sensing and Geographic Information System (GIS) approaches. This study was conducted in Bali Province, Indonesia, which has a beach length of ~640 km, and six parameters were considered in the creation to measure the degree of coastal vulnerability by CVI: geomorphology, shoreline change rate, coastal elevation, sea-level change rate, tidal range, and significant wave height. The different vulnerability parameters were assigned ranks ranging from 1 to 5, with 1 indicating the lowest and 5 indicating the highest vulnerabilities. The study revealed that about 138 km (22%) of the mapped shoreline is classified as being at very high vulnerability and 164 km (26%) of shoreline is at high vulnerability. Of remaining shoreline, 168 km (26%) and 169 km (26%) are at moderate and low risk of coastal vulnerability, respectively. This study outcomes can provide an updated vulnerability map and valuable information for the Bali Province coast, aimed at increasing awareness among decision-makers and related stakeholders for development in mitigation and adaptation strategies. Additionally, the result may be utilized as basic data to build and implement appropriate coastal zone management. [ABSTRACT FROM AUTHOR]

Arctic coasts composed of frozen deposits are extremely sensitive to climate change and human impact. They retreat with average rates of 1–2 m per year, depending on climatic and permafrost conditions. In recent decades, retreat rates have shown a tendency to increase. In this paper, we studied the coastal dynamics of two key sites (Ural and Yamal coasts) of Baydaratskaya Bay, Kara Sea, where a gas pipeline had been constructed. Based on multi-temporal aerial and satellite imagery, we identified coastal erosion rates at several time lapses, in natural conditions and under human impact, and discussed their temporal variability. In addition to planimetric (m/yr), we calculated volumetric (m3/m/yr) retreat rates of erosional coasts using ArcticDEM. We also estimated the influence of geomorphology, lithology, and permafrost structure of the coasts on spatial variations of their dynamics. Erosional coasts of the Ural key site retreat with higher mean rates (1.2 m/yr and 8.7 m3/m/yr) as compared to the Yamal key site (0.3 m/yr and 3.7 m3/m/yr) due to their exposure to higher open sea waves, more complex lithology, higher ice content and lower coastal bluffs. Since the 1960s, coastal retreat rates have been growing on both coasts of Baydaratskaya Bay; we relate this effect with Arctic climate warming. From the 1960s to 2005, such growth was moderate, while in 2005–2016 it became rapid, which may be explained by the enhanced wave and thermal action or by the onset of industrial development. The adjacent coastal segments, originally accumulative, remained relatively stable from the 1960s to 2005. After 2005, a considerable part of them began to retreat as a result of changing weather conditions and/or increasing human impact. [ABSTRACT FROM AUTHOR]

Coastal erosion endangers millions living near-shore and puts coastal infrastructure at risk, particularly in low-lying deltaic coasts of developing nations. This study focuses on morphological changes along the ~320-km-long Sindh coastline of Pakistan over past three decades. In this study, the Landsat images from 1989 to 2018 at an interval of 10 years are used to analyze the state of coastline erosion. For this purpose, well-known statistical approaches such as end point rate (EPR), least median of squares (LMS), and linear regression rate (LRR) are used to calculate the rates of coastline change. We analyze the erosion trend along with the underlying controlling variables of coastal change. Results show that most areas along the coastline have experienced noteworthy erosion during the study period. It is found that Karachi coastline experienced 2.43 ± 0.45 m/yr of erosion and 8.34 ± 0.45 m/yr of accretion, while erosion on the western and eastern sides of Indus River reached 12.5 ± 0.55 and 19.96 ± 0.65 m/yr on average, respectively. Coastal erosion is widespread along the entire coastline. However, the rate of erosion varies across the study area with a general trend of erosion increasing from west to east in the Indus Delta region (IDR), and the highest average erosion rate is 27.46 m/yr. The interdecadal change during 1989–1999, 1999–2009 and 2009–2018 periods depicted an increasing linear trend (R2 = 0.78) from Karachi to Indus River (IR) East zone. The spatial trend from west to east is positively correlated with mean sea level rise, which has increased from 1.1 to 1.9 mm/year, and negatively correlated with topographic slope, which is found to be decreasing eastward along the coastline. The findings necessitate appropriate actions and have important implications to better manage coastal areas in Pakistan in the wake of global climate change. [ABSTRACT FROM AUTHOR]