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Remote sensing prediction method of coastline based on self-adaptive profile morphology
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Hongjie Sha1, Dong Zhang2, 3, *, Dandan Cui4, Lin Lü4, Peng Ni2
Haiyang Xuebao | 2019, 41(9) : 170 - 180
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Haiyang Xuebao | 2019, 41(9): 170-180
Marine Information Science
Remote sensing prediction method of coastline based on self-adaptive profile morphology
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Hongjie Sha1, Dong Zhang2, 3, *, Dandan Cui4, Lin Lü4, Peng Ni2
Affiliations
  • 1 Department of Geography, Nanjing Normal University, Nanjing 210023, China
  • 2 College of Marine Science and Engineering, Nanjing Normal University, Nanjing 210023, China
  • 3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • 4 Sea Area Use Dynamic Surveillant and Monitoring Center of Jiangsu Province, Nanjing 210003, China
Published: 2019-09-25 doi: 10.3969/j.issn.0253-4193.2019.09.016
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The muddy coast has a large change in scouring and silting, and the beach profile is diverse. Firstly, according to the tidal range relationship between muti-temporal remote sensing watelines, the shape of the shoreline is automatically judged, and then the different functions are used to fit the profile. A new method of coastline remote sensing prediction based on self-adaptive profile morphology is constructed. The central muddy coast in Jiangsu has been empirically applied. The research shows that the concave-shaped erosion shore section, the slope-shaped gentle bank section and the upper convex-shaped siltation section use a three-exponential decay function, a linear function and a second-order polynomial function respectively to have a good profile fitting effect, using three waterlines. The absolute slope error of the profile obtained by data fitting is 0.20‰, –0.17‰, and 0.13‰, respectively, which is less than an order of magnitude than the measured average slope. When using the five waterlines data fitting to calculate the coastline, the error of the coastline plane position of the erosion shore section and gentle shore section are 6.5 m and –91.96 m, respectively, and the error is reduced by about 82.4% compared with the average slope method. Further consideration of seasonal changes in the beach, using the waterline data of the winter to calculate the coastline, has little effect on the erosion of the shore and the long section of the silt, but for the slope-shaped smooth section, the error is reduced by about 63.65%, so the use of winter waterline data has a higher shoreline projection accuracy than the season without distinction.

profile morphology  /  self-adaption  /  coastline  /  remote sensing  /  seasonal variation
Hongjie Sha, Dong Zhang, Dandan Cui, Lin Lü, Peng Ni. Remote sensing prediction method of coastline based on self-adaptive profile morphology[J]. Haiyang Xuebao, 2019 , 41 (9) : 170 -180 . DOI: 10.3969/j.issn.0253-4193.2019.09.016
Year 2019 volume 41 Issue 9
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Article Info
doi: 10.3969/j.issn.0253-4193.2019.09.016
  • Receive Date:2018-09-06
  • Online Date:2026-04-03
  • Published:2019-09-25
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History
  • Received:2018-09-06
  • Revised:2018-12-06
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Affiliations
    1 Department of Geography, Nanjing Normal University, Nanjing 210023, China
    2 College of Marine Science and Engineering, Nanjing Normal University, Nanjing 210023, China
    3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    4 Sea Area Use Dynamic Surveillant and Monitoring Center of Jiangsu Province, Nanjing 210003, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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