Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

Pradhan, Biswajeet; Rizeei, Hossein Mojaddadi; Abdulle, Abdinur

doi:10.3390/rs10111705

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…Among the filters computed with SNAP to reduce the speckle, the one which maximizes the contrast is the IDAN, but it presents the problem of excessively smoothing the edges. For this reason, the Lee filter is used, because it is a good compromise between keeping the spatial resolution and preserving the edges [41][42][43].…”

Section: Shoreline Extraction With Sentinel-1 Imagementioning

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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Section: Shoreline Extraction With Sentinel-1 Imagementioning

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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“…Because of their high coverage and low cost, remote sensing (RS) image-based methods have now become popular for monitoring coastline changes [19][20][21][22][23][24][25]. Many scientists have documented changes to the coastline, and the causes of these changes have been investigated and recognized [26].…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Change Detection Analysis of Coastline Data in Qingdao, East China

Yasir

Song

Zheng

et al. 2021

Scientific Programming

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This study focuses on the coastal features, environments, and dynamics to accurately describe and regularly monitor the Qingdao shoreline in eastern China. It collects categorical ETM+ and OLI data from 2000, 2010, and 2019 on the mainland coastline and explores the characteristics and spatiotemporal differences across the past 19 years by using remote sensing and geographic information system (GIS) technologies. The results show that the length of the Qingdao coastline has increased continuously over the last two decades, for a total increase of 18.14 km. There are different natural and artificial coastlines that have undergone major changes. The human-induced deterioration of coastlines has gradually and substantially risen from 53.63% in 2000 to 68.40% in 2019, while the length of the natural coastlines has decreased dramatically. Jiaozhou Bay focuses on areas with significantly changing coastlines, and major changes have occurred in the west and east of the Qingdao coast. The coastline has largely expanded seaward because of the comprehensive impact of natural and anthropogenic factors. The leading factor in coastal evolution is coastal engineering constructions. In addition, the top three other construction activities are the restoration of the aquaculture pond, salt field, and harbor edifices. The driving force that triggered the shift in the coastline reveals significant temporal heterogeneity.

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“…At present, remote sensing (RS) image-based methods have become common for monitoring coastline changes due to their large coverage and low cost [16][17][18][19][20][21][22]. Many scholars have researched coastline changes, and the potential causes of these changes have been analyzed and recognized [23].…”

Section: Introductionmentioning

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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Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

Cited by 17 publications

References 33 publications

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

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

A Spatiotemporal Change Detection Analysis of Coastline Data in Qingdao, East China

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

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