This study highlights the coastline position changes of Qingdao coastal area from 2000 to 2019, using GIS and remote sensing technologies through Digital Shoreline Analysis System and LANDSAT images. Understanding the coastline movement by suitable method is an important challenge for this extremely dynamic coast. The shoreline changes were statistically measured using three techniques, namely; Linear Regression Rate, End Point Rate and Net Shoreline Movement. For the automatic coastline extraction, different methods were applied, but among them most suitable techniques is the canny edge algorithm technique, which gives the accurate result. The result show maximum accretion reached was 266.07m/yr, 2391.85m,124.47m/yr for End point rate, net shoreline movement and linear regression rate, respectively. While, the maximum erosion was -142.55m/yr, -1234.59m, -63.22m/yr for End point rate, net shoreline movement and linear regression rate, respectively. This paper hence presents the monitoring processes of coast and analyzing the coastline change by the use of geospatial techniques that would be helpful for the coastal planning and management of the Qingdao coast. The applicability of the proposed model is tested with other generic edge detection algorithms that include; Sobel, Prewitt, and Robert edge detection techniques and it was concluded that our model outperforming in accurately detecting the coastline.

This in this study, presents the shoreline position changes of Yanbu coastal zone from 1965 to 2109, using an attempt was made to elucidate the accreting and eroding sites from multitemporal and high-resolution satellite data and of the Yanbu coastline (1965–2019) using geospatial techniques coupled with the Digital Shoreline Analysis System (DSAS) application. The rates of shoreline changes rates were calculated based on End Point Rate (EPR) Linear Regression Rate (LRR) and Weighted Linear Regression (WLR) and Net Shoreline Movement (NSM) statistical methods to assess the understand short and long term trends. The trend between successive shoreline pairs and between the oldest and the most recent shoreline. The total distance between the oldest and youngest shorelines are reported as Net Shoreline Movement (NSM The results reveal that over a period of 54 years, the overall average rates indicate an accretional trend of the shoreline. The maximum accretion reached was 1655.9 m (30.66, 32.32 and 36.9 m/year based on EPR, LRR and WLR methods respectively) (and is located in the King Fahd Industrial Port zone while the maximum erosion is recorded was in the Yanbu Waterfront area with about −1484.8 m (−37.9 m/year, −32.7 m/year and −33.5 m/year based on EPR, LRR and WLR methods respectively). The EPR (short-term changes) indicate changed trends on the Yanbu commercial port and various parts of the Yanbu Al Sinaiyah coast. An area of about 20 sq. km of sea and islets has been backfilled or dug in for various facilities. Thus, major changes in the configuration of the coastline are linked to human activities. This study provides a synoptic outlook of the degree of potential threat to the coastal system and their potential consequences, with a view to prioritizing actions and developing suitable adaptation.

The study area has three sand accumulations: Two in Wadi Fatmah and one in Wadi Ash Shumaysi, midwest of Saudi Arabia. The spatial extents of these sand accumulations have significantly increased over the last few decades. Multi-source satellite imagery, such as CORONA (1967, 1972), SPOT 5 (2013), LandSat TM (1986), and LandSat 8 OLI (2013), enabled monitoring and analysis of the interplay between the changes in the anthropogenic activities and spatial expansion of the areas of sand accumulation. The main driving force of the spatial expansion could be strongly linked to extensive changes in the anthropogenic regimes in the middle zone of Wadi Fatmah and its surrounding landforms and mountain masses. In this context, the once dominant agricultural lands of the middle zone of Wadi Fatmah have been transformed into abandoned agricultural areas. Extensive off-road driving has resulted in soil degradation. Excavation and mining activities for urban spatial expansion are widespread over the valley floor, the adjacent bajada, and the mountain blocks. These anthropogenic activities have remarkably induced strong wind erosion of the soil in severe arid conditions in the middle zone of Wadi Fatmah and Wadi Ash Shumaysi. Wind erosion has eventually produced a sufficient sand budget to be transported into the areas of sand accumulation. The primary consequence of the excess sand budget has been an increase in the spatial extents and dune migration rates of sand accumulations in the study area. However, this increase varies from one sand accumulation to another. In this study, we used multi-source remote sensing imagery and the state-of-the-art COSI-Corr technology to characterize sand accumulations in the study area and to determine the spatio-temporal changes in both the spatial extents and the dune migration rates. The mean annual migration rates of sand dunes in the three sand accumulations ranged from 5.5 and 7.2 to 8.6 m/yr. Analysis of the spatial extent and migration rates of sand accumulations indicates that the study area may have experienced desertification in response to changes in the anthropogenic regimes through the last few decades.

We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

Coastal cities are often vulnerable and subject to the risks associated with floods, hence the need to sensitize decision-makers on the threats posed by climate hazards and uncontrolled urbanization. This study is part of this logic and aims to identify and map the flood zones of the city of Jeddah in order to reduce their vulnerability and to integrate them into the strategies of prevention and fight against the risks of flooding. The recent floods in 2009 and 2011 have caused heavy human and material losses that will permanently mark the collective memory of the inhabitants of the city. The multisource and diachronic data used as well as the methodology adopted made it possible to perform a multi-criteria spatial analysis by combining optical satellite imagery and radar DEM, topographic and geological maps, rainfall records, and available statistical data. Thus, risk factors have been identified and combined to understand and appreciate the gravity of recent disasters and provide planners and decision-makers with tools to assist in the effective and adequate management of the ever-changing urban space, in a context of climate change and increased anthropogenic pressure on coastal cities.