Assessment and prediction of shoreline change using multi-temporal satellite images and statistics: Case study of Damietta coast, Egypt

Esmail, Mohammed; Mahmod, Wael Elham; Fath, Hassan E.S.

doi:10.1016/j.apor.2018.11.009

Cited by 87 publications

(37 citation statements)

References 6 publications

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“…Some scholars have studied remote sensing extraction methods for coastlines [13]. Esmail et al [14] compared and analyzed three methods, iso clustering, threshold, and screen digitization and concluded that iso clustering is the best method of coastline extraction, but traditional methods such as visual interpretation and human-computer interactive visual interpretation are still the more common research methods used by experts and scholars to extract coastal information [15]. For remote sensing monitoring of coastal zones, different scholars have defined the scope of a coastal zone in different ways.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018

Zhou

Zhang

et al. 2019

Applied Sciences

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Using remote sensing images of different time phases from 1990 to 2018, the surface coverage information of northern Yantai (coastline, 2 km from coastline to land) was extracted by means of average high tide line and visual interpretation. The end point change rate (EPR) and linear regression rate were used to study the coastline change rate, the fractal dimension of the coastline in the study area was analyzed, and the change of the type of coastal surface cover was analyzed by the transition matrix. The results show that: (1) Form 1990 to 2018, a significant trend of a continuous increase in the total length of coastline was observed with an increase of 181.08 km (43.18%). In the study area, the coastline of Laizhou had the greatest change rate with an EPR value of 33.67 m/a, whereas the coastline of Laishan had the smallest change rate with an EPR value of 0.30 m/a. (2) Over the past 30 years, with the rapid economic development of Yantai and the ensuant urbanization, the total surface area of the coastal zone in the study area has increased by 144.94 km2, mainly in the areas covered by structures and forests/grasses, by 112.96 km2 and 96.08 km2, respectively, while the areas of desert/bare land and water have decreased by 92.26 km2 and 12.32 km2, respectively. (3) The changes among different types in the study area were clear, mainly from desert/bare land, cultivated land, and building areas to forests/grasses cover and structures. The change areas were mainly concentrated in Laizhou, Longkou, Zhifu, and Penglai. Frequent human activities are an important factor affecting the continuous expansion of the coastal areas of Jiaodong Peninsula to the sea. Aquaculture, coastal construction, construction of artificial islands, and expansion of port terminals have seriously affected the sustainability of ecological resources in the coastal areas. At the same time, the changes in the ecological environment in the coastal zone will have a greater impact on the health of the coastal zone.

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Section: Introductionmentioning

confidence: 99%

Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018

Zhou

Zhang

et al. 2019

Applied Sciences

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“…To better determine the accuracy of the shoreline extraction, a method already used in literature [10,46] was used. Ten transects (Figure 8), about one every 100 m and drawn from the land to the sea, were used to calculate the intersection between them and the seven shorelines extracted from WorldView-2, WVWI, J-Net Dynamic on the VHR image, Sentinel-1, Sentinel-2, Canny and J-Net Dynamic on the HR image.…”

Section: Resultsmentioning

confidence: 99%

Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy

Zollini

Alicandro

Cuevas-González

et al. 2019

JMSE

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Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including the WorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images.

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“…In order to facilitate unified standards for monitoring remote sensing change, the default coastline in this article is the instantaneous waterline [54]. Esmail et al [55] have compared and analyzed the three methods of squared-error clustering method, threshold, and screen digitization, and concluded that squared-error clustering was the best method for coastline extraction, but that traditional methods such as the visual interpretation of human-computer interaction were still commonly used by experts to extract coastal information [56]. Therefore, this paper adopted the method of human-computer interactive visual interpretation.…”

Section: Extraction and Classification Of Coastlinementioning

confidence: 99%

Spatial-Temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018

et al. 2020

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As a valuable resource in coastal areas, coastlines are not only vulnerable to natural processes such as erosion, siltation, and disasters, but are also subjected to strong pressures from human processes such as urban growth, resource development, and pollution discharge. This is especially true for reef nations with rich coastline resources and a large population, like Indonesia. The technical joint of remote sensing (RS) and geographic information system (GIS) has significant advantages for monitoring coastline changes on a large scale and for quantitatively analyzing their change mechanisms. Indonesia was taken as an example in this study because of its abundant coastline resources and large population. First, Landsat images from 1990 to 2018 were used to obtain coastline information. Then, the index of coastline utilization degree (ICUD) method, the changes in land and sea patterns method, and the ICUD at different scales method were used to reveal the spatiotemporal change pattern for the coastline. The results found that: (1) Indonesia’s total coastline length has increased by 777.40 km in the past 28 years, of which the natural coastline decreased by 5995.52 km and the artificial coastline increased by 6771.92 km. (2) From the analysis of the island scale, it was known that the island with the largest increase in ICUD was Kalimantan, at the expense of the mangrove coastline. (3) On the provincial scale, the province with the largest change of ICUD was Sumatera Selatan Province, which increased from 100 in 1900 to 266.43 in 2018. (4) The change trend of the land and sea pattern for the Indonesian coastline was mainly expanded to the sea. The part that eroded to the land was relatively small; among which, Riau Province had the most significant expansion of land area, about 177.73 km2, accounting for 23.08% of the increased national land area. The worst seawater erosion was in the Jawa Barat Province. Based on the analysis of population and economic data during the same period, it was found that the main driving mechanism behind Indonesia’s coastline change was population growth, which outweighed the impact of economic development. However, the main constraint on the Indonesian coastline was the topographic factor. The RS and GIS scheme used in this study can not only provide support for coastline resource development and policy formulation in Indonesia, but also provide a valuable reference for the evolution of coastline resources and environments in other regions around the world.

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Assessment and prediction of shoreline change using multi-temporal satellite images and statistics: Case study of Damietta coast, Egypt

Cited by 87 publications

References 6 publications

Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018

Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018

Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy

Spatial-Temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018

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