4.3 Article

Shoreline Change Detection and Forecast along the Yancheng Coast Using a Digital Shoreline Analysis System

Journal

WETLANDS
Volume 41, Issue 4, Pages -

Publisher

SPRINGER
DOI: 10.1007/s13157-021-01444-3

Keywords

Object-oriented method; Tasseled cap transformation; Shoreline; DSAS

Funding

  1. National Natural Science Foundation of China [41871097, 41471078]
  2. Jiangsu Agricultural Science and Technology Innovation Fund [CX (18) 2026]
  3. Jiangsu 333 Talent Program, Research Foundation for Advanced Talents of Nanjing Forestry University

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This paper utilized a new approach to extract shoreline along the coast of Yancheng from 1983 to 2020 and analyzed temporal and spatial variations using the Digital Shoreline Analysis System (DSAS). The results showed a better extraction of shoreline results using the new methodology and revealed that 89% of the shoreline change was accretion, 3% was stable coast, and 8% was erosion. The study also forecasted shoreline positions for the years 2019 and 2020 based on historical data and Kalman filter.
Shorelines are the dynamic interfaces of both terrestrial and marine environments and are sensitive to climate and anthropogenic influences. The change in the shoreline plays a critical role in intertidal mudflat resources and coastal ecological environment. This paper extracts shoreline along the coast of Yancheng from 1983 to 2020 using a new procedure. The proposed procedure is based on a combination of object-oriented technology and thresholding the wetness components of the tasseled cap transformation (TCT). Then, we studied the temporal and spatial variation in the shoreline and predicted shore movement using the Digital Shoreline Analysis System (DSAS). The results indicated that we yielded a better extraction of shoreline results using the new approach. The shoreline change analysis for the coast of Yancheng over 40 years indicated that 89% was accretion, 3% was under stable coast and erosion was 8%. The coast showed a significant trend of expanding to the sea. Finally, we forecasted shoreline position for the years 2019 and 2020 based on the historical shoreline position data from 1983 to 2009 and 1983-2010 by using the Kalman filter. The results of this study may help coastal researchers and decision makers to guide the use, management, and planning of coastal zones in further study.

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