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Article(id=1246840128262529443, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1246840121887187126, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-4193.2019.09.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1536163200000, receivedDateStr=2018-09-06, revisedDate=1544025600000, revisedDateStr=2018-12-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1775199451746, onlineDateStr=2026-04-03, pubDate=1569340800000, pubDateStr=2019-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1775199451746, onlineIssueDateStr=2026-04-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1775199451746, creator=13701087609, updateTime=1775199451746, updator=13701087609, issue=Issue{id=1246840121887187126, tenantId=1146029695717560320, journalId=1149651085930835976, year='2019', volume='41', issue='9', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1775199450227, creator=13701087609, updateTime=1775200621889, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1246845036290265802, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1246840121887187126, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1246845036290265803, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1246840121887187126, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=170, endPage=180, ext={EN=ArticleExt(id=1246840128749068732, articleId=1246840128262529443, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Remote sensing prediction method of coastline based on self-adaptive profile morphology, columnId=1243867133881466948, journalTitle=Haiyang Xuebao, columnName=Marine Information Science, runingTitle=null, highlight=null, articleAbstract=

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.

, correspAuthors=Dong Zhang, authorNote=null, correspAuthorsNote=null, copyrightStatement=Haiyang Xuebao, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Hongjie Sha, Dong Zhang, Dandan Cui, Lin Lü, Peng Ni), CN=ArticleExt(id=1246840130816860729, articleId=1246840128262529443, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=剖面形态自适应的海岸线遥感推算方法, columnId=1243867134770659409, journalTitle=海洋学报, columnName=海洋信息科学, runingTitle=null, highlight=null, articleAbstract=

淤泥质海岸冲淤变化大，岸滩剖面形态多样。本文首先根据多时相遥感水边线之间的潮差关系自动判断岸滩剖面形态，进而分别采用不同的函数进行剖面拟合，构建了一种剖面形态自适应的海岸线遥感推算新方法，并在江苏中部淤泥质海岸进行了实证应用。研究表明：下凹形侵蚀岸段、斜坡形平缓岸段和上凸形淤长岸段分别采用三指数衰减函数、线性函数和二阶多项式函数具有良好的剖面拟合效果，利用3条水边线数据拟合所得剖面平均坡度绝对误差分别为0.20‰、–0.17‰和0.13‰，小于剖面实测平均坡度一个数量级。利用5条水边线数据拟合进行海岸线推算时，侵蚀岸段、平缓岸段的海岸线平面位置误差分别为6.5 m和–91.96 m，与平均坡度法相比，误差减小约82.4%。进一步考虑岸滩季节性变化时，使用冬季的水边线数据推算海岸线，对侵蚀岸段和淤长岸段影响不大，但对斜坡形平缓岸段，误差减小约63.65%，因此使用冬季的水边线数据比不区分季节具有更高的海岸线推算精度。

, correspAuthors=张东, authorNote=null, correspAuthorsNote=
*张东（1975—），男，江苏省南通市人，博士，副教授，研究方向为海洋信息技术与海岸带资源开发管理。E-mail:
, copyrightStatement=版权所有©《海洋学报》编辑部 2023
沙宏杰，张东，崔丹丹，等. 剖面形态自适应的海岸线遥感推算方法[J]. 海洋学报，2019，41(9)：170–180，doi:10.3969/j.issn.0253−4193.2019.09.016Sha Hongjie，Zhang Dong，Cui Dandan, et al. 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
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沙宏杰（1993—），男，江苏省苏州市人，主要从事海岸带资源遥感研究。E-mail:

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沙宏杰（1993—），男，江苏省苏州市人，主要从事海岸带资源遥感研究。E-mail:

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沙宏杰（1993—），男，江苏省苏州市人，主要从事海岸带资源遥感研究。E-mail:

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Oceanologia et Limnologia Sinica, 1985, 16(5): 392−399., articleTitle=null, refAbstract=null)], funds=[Fund(id=1254506257939886349, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, awardId=null, language=CN, fundingSource=国家自然科学基金项目（41771447）；江苏省海洋科技创新专项项目（HY2018-3）。, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1254506236729290783, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, xref=1, ext=[AuthorCompanyExt(id=1254506236737679392, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, companyId=1254506236729290783, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 Department of Geography, Nanjing Normal University, Nanjing 210023, China), AuthorCompanyExt(id=1254506236750262305, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, 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figureFileBig=x1D52Q25HqvFw0H0YURBOg==, tableContent=null), ArticleFig(id=1254506250583077062, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图1, caption=研究区示意图, figureFileSmall=hdIhKQfln2eer2MS2jqcyA==, figureFileBig=x1D52Q25HqvFw0H0YURBOg==, tableContent=null), ArticleFig(id=1254506252659257547, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Fig. 2, caption=Time distribution of remote sensing images, figureFileSmall=xXWh/dqILNc8drg1XQAejg==, figureFileBig=tcOQQlakGAX+9VRt3QNtZw==, tableContent=null), ArticleFig(id=1254506252818641102, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图2, caption=遥感影像时间分布, figureFileSmall=xXWh/dqILNc8drg1XQAejg==, figureFileBig=tcOQQlakGAX+9VRt3QNtZw==, tableContent=null), ArticleFig(id=1254506253078687953, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Fig. 3, caption=Technology road of remote sensing coastline prediction, figureFileSmall=rVPTMDvDMYewXx2649gl/g==, figureFileBig=Sl0rrnS5HIkzA1XAiDao1g==, tableContent=null), ArticleFig(id=1254506253275820244, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图3, caption=遥感海岸线推算技术路线, figureFileSmall=rVPTMDvDMYewXx2649gl/g==, figureFileBig=Sl0rrnS5HIkzA1XAiDao1g==, tableContent=null), ArticleFig(id=1254506253565227224, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Fig. 4, caption=Sketch map of profile morphology discrimination, figureFileSmall=1EfrDpjRI0lh0oW3/3iNMQ==, figureFileBig=wvmChD42/57bNxMqJlnzsg==, tableContent=null), ArticleFig(id=1254506253774942428, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图4, caption=剖面形态判别示意图, figureFileSmall=1EfrDpjRI0lh0oW3/3iNMQ==, figureFileBig=wvmChD42/57bNxMqJlnzsg==, tableContent=null), ArticleFig(id=1254506253988851936, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Fig. 5, caption=Fitting effect of typical profiles, figureFileSmall=jvZlb1rctft8C0r1lDU6Kw==, figureFileBig=9IiR/86959J9qh1hgRNd6A==, tableContent=null), ArticleFig(id=1254506254278258915, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图5, caption=典型剖面拟合效果, figureFileSmall=jvZlb1rctft8C0r1lDU6Kw==, figureFileBig=9IiR/86959J9qh1hgRNd6A==, tableContent=null), ArticleFig(id=1254506254609608932, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Fig. 6, caption=Waterline distribution of typical profiles, figureFileSmall=3N/B4zl+CFneA/Cp9lNZ/A==, figureFileBig=BRNqGEx7gfM0u9Jq9u8Egg==, tableContent=null), ArticleFig(id=1254506254869655783, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图6, caption=典型断面水边线分布, figureFileSmall=3N/B4zl+CFneA/Cp9lNZ/A==, figureFileBig=BRNqGEx7gfM0u9Jq9u8Egg==, tableContent=null), ArticleFig(id=1254506254982901994, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Fig. 7, caption=Fitting effect of different number of waterlines for three typical profiles

a–c are Profile Ⅰ; d–f are Profile Ⅳ; g–i are Profile Ⅵ; a, d, g are fitting effect of three waterlines; b, e, h are fitting effect of five waterlines; c, f, i are fitting effect of seven waterlines

, figureFileSmall=YAM2J2C+/WNbruQvBoT2fQ==, figureFileBig=m+1BsovBFg2O41Nvt9CstA==, tableContent=null), ArticleFig(id=1254506255100342509, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=图7, caption=典型剖面不同数量水边线拟合效果

a–c为断面Ⅰ; d–f为断面Ⅳ; g–i为断面Ⅵ; a, d, g为3条水边线拟合效果; b, e, h为5条水边线拟合效果; c, f, i为7条水边线拟合效果

, figureFileSmall=YAM2J2C+/WNbruQvBoT2fQ==, figureFileBig=m+1BsovBFg2O41Nvt9CstA==, tableContent=null), ArticleFig(id=1254506255213588720, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Table 1, caption=

Tidal sites information

, figureFileSmall=null, figureFileBig=null, tableContent=
站点名称 站点纬度 站点经度 基面 平均海面/cm
射阳河口 33°48′N 120°20′E 废黄河零点 65.1
大丰港 33°16′N 120°45′E 废黄河零点 52.2
梁垛河口 32°52′N 120°54′E 废黄河零点 153.9
), ArticleFig(id=1254506255372972276, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=表1, caption=

潮位站点表

, figureFileSmall=null, figureFileBig=null, tableContent=
站点名称 站点纬度 站点经度 基面 平均海面/cm
射阳河口 33°48′N 120°20′E 废黄河零点 65.1
大丰港 33°16′N 120°45′E 废黄河零点 52.2
梁垛河口 32°52′N 120°54′E 废黄河零点 153.9
), ArticleFig(id=1254506256991973623, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Table 2, caption=

Position error of coastline in typical profiles

, figureFileSmall=null, figureFileBig=null, tableContent=
断面Ⅰ
推算方法 水边线数量/条 预测点X/m 预测点Y/m 实测点X/m 实测点Y/m 距离绝对误差/m
剖面形态自适应法 3 271 375.53 3 738 707.61 271 359.28 3 738 701.70 17.28
5 271 365.39 3 738 703.93 6.5
7 271 366.96 3 738 704.50 8.17
40 271 334.91 3 738 692.84 –25.94
平均坡度法 2 271 318.20 3 738 686.76 –43.72
断面Ⅳ
推算方法 水边线数量/条 预测点X/m 预测点Y/m 实测点X/m 实测点Y/m 距离绝对误差/m
剖面形态自适应法 3 287 555.09 3 697 660.81 287 954.25 3 697 795.01 –418.62
5 287 864.72 3 697 764.91 –91.96
7 287 844.05 3 697 757.96 –113.77
40 287 825.75 3 697 751.81 –133.07
平均坡度法 2 287 523.36 3 697 650.14 –452.09
断面Ⅵ
推算方法 水边线数量/条 预测点X/m 预测点Y/m 实测点X/m 实测点Y/m 距离绝对误差/m
剖面形态自适应法 3 298 925.10 3 665 710.04 299 733.11 3 666 008.54 0（–861.38）
5 298 808.12 3 665 666.83 0（–986.09）
7 298 852.76 3 665 683.32 0（–938.5）
40 298 875.69 3 665 691.79 0（–914.05）
平均坡度法 2 299 008.41 3 665 740.82 0（–772.57）
), ArticleFig(id=1254506257138774266, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=表2, caption=

典型断面海岸线位置误差

, figureFileSmall=null, figureFileBig=null, tableContent=
断面Ⅰ
推算方法 水边线数量/条 预测点X/m 预测点Y/m 实测点X/m 实测点Y/m 距离绝对误差/m
剖面形态自适应法 3 271 375.53 3 738 707.61 271 359.28 3 738 701.70 17.28
5 271 365.39 3 738 703.93 6.5
7 271 366.96 3 738 704.50 8.17
40 271 334.91 3 738 692.84 –25.94
平均坡度法 2 271 318.20 3 738 686.76 –43.72
断面Ⅳ
推算方法 水边线数量/条 预测点X/m 预测点Y/m 实测点X/m 实测点Y/m 距离绝对误差/m
剖面形态自适应法 3 287 555.09 3 697 660.81 287 954.25 3 697 795.01 –418.62
5 287 864.72 3 697 764.91 –91.96
7 287 844.05 3 697 757.96 –113.77
40 287 825.75 3 697 751.81 –133.07
平均坡度法 2 287 523.36 3 697 650.14 –452.09
断面Ⅵ
推算方法 水边线数量/条 预测点X/m 预测点Y/m 实测点X/m 实测点Y/m 距离绝对误差/m
剖面形态自适应法 3 298 925.10 3 665 710.04 299 733.11 3 666 008.54 0（–861.38）
5 298 808.12 3 665 666.83 0（–986.09）
7 298 852.76 3 665 683.32 0（–938.5）
40 298 875.69 3 665 691.79 0（–914.05）
平均坡度法 2 299 008.41 3 665 740.82 0（–772.57）
), ArticleFig(id=1254506257277186302, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Table 3, caption=

Coastline error results of typical profiles by waterline data in different seasons

, figureFileSmall=null, figureFileBig=null, tableContent=
断面 实测点X/m 实测点Y/m 夏季 冬季
预测点X/m 预测点Y/m 距离绝对误差/m 预测点X/m 预测点Y/m 距离绝对误差/m
Ⅰ 271 359.3 3 738 701.7 271 365.8 3 738 704.1 6.89 271 351.2 3 738 698.8 –8.65
Ⅳ 287 954.3 3 697 795.0 288 176.6 3 697 869.8 234.58 287 836.8 3 697 755.5 –85.28
Ⅵ 299 733.1 3 666 008.5 298 771.1 3 665 653.2 0（–1 025.53） 299 092.8 3 665 772.0 0（–682.61）
), ArticleFig(id=1254506257344295170, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=表3, caption=

不同季节水边线数据对典型断面的海岸线推算误差

, figureFileSmall=null, figureFileBig=null, tableContent=
断面 实测点X/m 实测点Y/m 夏季 冬季
预测点X/m 预测点Y/m 距离绝对误差/m 预测点X/m 预测点Y/m 距离绝对误差/m
Ⅰ 271 359.3 3 738 701.7 271 365.8 3 738 704.1 6.89 271 351.2 3 738 698.8 –8.65
Ⅳ 287 954.3 3 697 795.0 288 176.6 3 697 869.8 234.58 287 836.8 3 697 755.5 –85.28
Ⅵ 299 733.1 3 666 008.5 298 771.1 3 665 653.2 0（–1 025.53） 299 092.8 3 665 772.0 0（–682.61）
), ArticleFig(id=1254506257524650246, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=EN, label=Table 4, caption=

Mean slope error of typical profiles

, figureFileSmall=null, figureFileBig=null, tableContent=
断面Ⅰ
推算方法 水边线数量/条 实测坡度/‰ 推算坡度/‰ 坡度绝对误差/‰
剖面形态自适应法 3 1.92 2.12 0.20
5 2.13 0.21
7 2.13 0.21
40 1.82 –0.10
平均坡度法 2 2.65 0.73
断面Ⅳ
推算方法 水边线数量/条 实测坡度/‰ 推算坡度/‰ 坡度绝对误差/‰
剖面形态自适应法 3 1.42 1.25 –0.17
5 1.59 0.17
7 1.36 –0.06
40 1.35 –0.07
平均坡度法 2 1.25 –0.17
断面Ⅵ
推算方法 水边线数量/条 实测坡度/‰ 推算坡度/‰ 坡度绝对误差/‰
剖面形态自适应法 3 1.22 1.35 0.13
5 1.2 –0.02
7 1.25 0.03
40 1.28 0.06
平均坡度法 2 1.36 0.14
), ArticleFig(id=1254506257646285063, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1246840128262529443, language=CN, label=表4, caption=

典型断面平均坡度误差

, figureFileSmall=null, figureFileBig=null, tableContent=
断面Ⅰ
推算方法 水边线数量/条 实测坡度/‰ 推算坡度/‰ 坡度绝对误差/‰
剖面形态自适应法 3 1.92 2.12 0.20
5 2.13 0.21
7 2.13 0.21
40 1.82 –0.10
平均坡度法 2 2.65 0.73
断面Ⅳ
推算方法 水边线数量/条 实测坡度/‰ 推算坡度/‰ 坡度绝对误差/‰
剖面形态自适应法 3 1.42 1.25 –0.17
5 1.59 0.17
7 1.36 –0.06
40 1.35 –0.07
平均坡度法 2 1.25 –0.17
断面Ⅵ
推算方法 水边线数量/条 实测坡度/‰ 推算坡度/‰ 坡度绝对误差/‰
剖面形态自适应法 3 1.22 1.35 0.13
5 1.2 –0.02
7 1.25 0.03
40 1.28 0.06
平均坡度法 2 1.36 0.14
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剖面形态自适应的海岸线遥感推算方法
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沙宏杰 1 , 张东 2, 3, * , 崔丹丹 4 , 吕林 4 , 倪鹏 2
海洋学报 | 海洋信息科学 2019,41(9): 170-180
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海洋学报 | 海洋信息科学 2019, 41(9): 170-180
剖面形态自适应的海岸线遥感推算方法
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沙宏杰1 , 张东2, 3, * , 崔丹丹4, 吕林4, 倪鹏2
作者信息
  • 1 南京师范大学 地理科学学院，江苏 南京 210023
  • 2 南京师范大学 海洋科学与工程学院，江苏 南京 210023
  • 3 江苏省地理信息资源开发与利用协同创新中心，江苏 南京 210023
  • 4 江苏省海域使用动态监视监测中心，江苏 南京 210003

    • 沙宏杰（1993—），男，江苏省苏州市人，主要从事海岸带资源遥感研究。E-mail:

通讯作者:

*张东（1975—），男，江苏省南通市人，博士，副教授，研究方向为海洋信息技术与海岸带资源开发管理。E-mail:
Remote sensing prediction method of coastline based on self-adaptive profile morphology
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
出版时间: 2019-09-25 doi: 10.3969/j.issn.0253-4193.2019.09.016
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摘要
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淤泥质海岸冲淤变化大，岸滩剖面形态多样。本文首先根据多时相遥感水边线之间的潮差关系自动判断岸滩剖面形态，进而分别采用不同的函数进行剖面拟合，构建了一种剖面形态自适应的海岸线遥感推算新方法，并在江苏中部淤泥质海岸进行了实证应用。研究表明：下凹形侵蚀岸段、斜坡形平缓岸段和上凸形淤长岸段分别采用三指数衰减函数、线性函数和二阶多项式函数具有良好的剖面拟合效果，利用3条水边线数据拟合所得剖面平均坡度绝对误差分别为0.20‰、–0.17‰和0.13‰，小于剖面实测平均坡度一个数量级。利用5条水边线数据拟合进行海岸线推算时，侵蚀岸段、平缓岸段的海岸线平面位置误差分别为6.5 m和–91.96 m，与平均坡度法相比，误差减小约82.4%。进一步考虑岸滩季节性变化时，使用冬季的水边线数据推算海岸线，对侵蚀岸段和淤长岸段影响不大，但对斜坡形平缓岸段，误差减小约63.65%，因此使用冬季的水边线数据比不区分季节具有更高的海岸线推算精度。

剖面形态  /  自适应  /  海岸线  /  遥感  /  季节性变化

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
沙宏杰, 张东, 崔丹丹, 吕林, 倪鹏. 剖面形态自适应的海岸线遥感推算方法. 海洋学报, 2019 , 41 (9) : 170 -180 . DOI: 10.3969/j.issn.0253-4193.2019.09.016
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
正文
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1 引言
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海岸线是海洋与陆地的分界线，海岸线的准确确定对于海岸带资源的使用与管理具有重要意义[1]。江苏中部为宽广的粉砂淤泥质平原海岸，受水动力、沉积动力变化以及人类围垦开发利用的影响，海岸冲淤变化大，海岸线位置、形态变化较快。传统的海岸线实地测量虽然精度高，但是费时费力且时效性差，不利于开展大范围海岸动态变化研究。遥感技术的迅速发展，给海岸线动态变化监测提供了新的手段。现有的海岸线遥感识别方法主要有2种：一种是“一般高潮线法”，选用高潮时刻的遥感影像，提取其瞬时水边线作为海岸线[2-3]；另一种是“平均坡度法”，通过提取不同平面位置的两条水边线，分别赋予影像成像时刻的潮位值，然后根据潮差和平距求得岸滩平均坡度，推算得到平均大潮高潮时刻对应的海岸线位置[4-6]。但是受影像时间分辨率和影像成像质量的影响，恰好在平均大潮高潮时刻成像的研究区遥感影像往往很难获得；而运用两条水边线来推算海岸线的方法理论上只适用于地形平缓、坡度单一的地区。根据已有调查可知，实际上淤泥质潮滩的剖面形态变化较大，以江苏中部淤泥质海岸为例，主要有下凹形、斜坡形、斜坡+上凸形组合、上凸形4种[7]，分别指示了岸滩的冲刷、稳定、蚀淤转化以及淤积状态，仅依靠2条水边线计算平均坡度来推算海岸线，不能表现不同的剖面形态差异，得到的海岸线与实际海岸线之间存在较大误差。因此在淤泥质海岸这种剖面形态变化较大的区域，依靠平均坡度方法推算海岸线不具有普适性。
基于此，本文旨在综合考虑潮滩剖面的不同形态特征，通过运用和比较多时相遥感水边线数据之间的潮差关系，研究并提出一种岸滩剖面形态自适应拟合的海岸线遥感推算新方法，实现淤泥质海岸线的准确提取，为海岸带的开发和管理提供依据。
2 研究区域与数据来源
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2.1 研究区概况
本文以江苏中部粉沙淤泥质海岸为研究对象，选择射阳河口至梁垛河口为研究区，开展海岸线遥感推算研究，位置如图1a所示。研究区位于辐射沙洲北翼，岸外辐射沙洲和废黄河口为研究区潮滩提供了丰富的物源。在东海前进潮波和南黄海旋转潮波的控制下，涨潮流向海岸辐聚，落潮流向岸外辐散，对潮滩冲淤演变产生重要影响[8]。近年来的监测结果显示，研究区由北至南剖面形态大致呈现轻微侵蚀–平缓–淤积的格局：射阳河口至斗龙港北部岸段有轻微侵蚀现象[9-10]，剖面以下凹形为主；斗龙港至梁垛河口岸段处于淤长状态，剖面以上凸形为主[11]，且当泥沙供给充足时，会进一步发展为双凸形剖面[12-17]。
2.2 数据采集与预处理
2.2.1 遥感影像数据
综合考虑遥感影像成像质量以及数据一致性等因素，选取2015年、2016年、2017年涵盖不同季节的30 m空间分辨率Landsat影像作为遥感数据源，用于水边线遥感提取。遥感影像资料均从美国地质调查局（United States Geological Survey，USGS）和地理空间数据云下载获得，共搜集得到40幅遥感影像数据，其季节分布如图2所示，影像的水位变化范围基本能够覆盖研究区的整个潮间带区域。对影像数据进行了波段合成、几何精校正、图像增强等预处理，几何校正误差小于0.2个像元。
2.2.2 潮位数据
潮位资料来自于潮位站点实测数据和潮汐表数据，站点情况如表1所示。
2.2.3 实测断面高程数据
实测断面高程数据可用于计算岸滩剖面平均坡度，描述剖面形态变化，也可用于验证遥感推算坡度的精度。共收集了7条实测断面数据，断面实测点以50 m或100 m为间隔，沿垂直于海岸线方向测量，采用RTK测定的经精化似大地水准面拟合的高程，高程精度为±0.1 m。断面分布如图1a所示。7条断面中，断面Ⅰ、Ⅱ和Ⅲ呈下凹形，位于射阳河口至新洋港之间，为典型的侵蚀剖面；断面Ⅳ和Ⅴ呈斜坡形，位于斗龙港至大丰港岸段，剖面形态相对平缓；断面Ⅵ和Ⅶ呈上凸形，位于竹港至梁垛河口之间，为淤长剖面。图1b、图1c、图1d分别表示典型断面Ⅰ、Ⅳ、Ⅵ的位置，图1e、图1f、图1g显示了对应的实测剖面形态。
2.2.4 实测海岸线数据
收集了江苏省2016年实测海岸线（图1a），用于遥感推算海岸线的精度验证。实测海岸线采用基于JSCORS网络的RTK进行测量，UTM投影，WGS-84坐标系，海岸线实测点相对于邻近控制点的点位中误差不大于±0.1 m。实测海岸线包括自然岸线和人工岸线两部分，自然岸线为平均大潮高潮时的痕迹线，属于地理意义上的海岸线；人工岸线以人工海堤为主，潮水在此主要表现为垂直方向的涨落，没有水平方向的进退，因此该部分海岸线不作为遥感推算海岸线的精度验证数据。此外还收集了部分GF1号和GF2号高精遥感影像，通过提取影像上的陆生植物的外边缘线以及平均大潮高潮时的漂浮物上界，作为自然岸线的补充，用于遥感海岸线的精度验证[18]。
3 研究方法
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基于本文提出的剖面形态自适应法的遥感海岸线推算技术路线见图3。剖面形态自适应的遥感海岸线推算方法如下：（1）利用多时相Landsat卫星遥感影像数据，采用归一化差值水体指数[19]图像增强和阈值分割处理，结合形态学方法[20]提取出瞬时水边线；（2）在向海一侧作近似平行于多时相水边线的分割基线，以500 m为间隔，生成垂直于分割基线的分割线簇（图1），利用分割线将水边线分割离散化，得到水边线离散点；（3）使用经典潮汐调和计算方法推算影像成像时刻各控制站点潮位，用距离加权线性插值法完成离散点的潮位赋值，潮位模拟平均误差小于15 cm；（4）提取分割线陆侧端点作为原点，然后根据同一剖面上各水边线离散点之间的潮位高低特征，进行剖面形态判别；（5）采用剖面形态自适应法选择合适的剖面拟合方程，推算平均大潮高潮潮位所在位置，将高潮线与人工岸线进行空间拓扑分析，得到遥感推算的海岸线。
3.1 剖面形态判别与拟合方法
在进行剖面拟合前，先对研究区内各断面进行形态特征判别。判别原理如图4所示，不同的剖面形态主要通过以下方法确定。
在一条断面上，尽量取均匀分布在潮滩上的3个水边线离散点，以离散点到原点间的水平距离L作为横坐标，以离散点的潮位H作为纵坐标，建立直角坐标系。将陆侧离散点(L 1，H 1)和海侧离散点(L 2，H 2)代入公式（1），求得直线方程。再将中间离散点(L 0，H 0)到原点的水平距离L 0代入直线方程，求出该离散点的潮位H *。根据潮位推算的平均误差Δ，当|H *–H 0|≤Δ/2时，即认为剖面平缓，呈斜坡形；当H *–H 0>Δ/2时，剖面呈下凹形；当H *–H 0<–Δ/2时，剖面呈上凸形。
$H = aL + b,$
式中，H表示潮位；L表示离散点至原点间的水平距离；a为直线方程的斜率，即剖面平均坡度；b为截距。
研究区内共有7条实测断面，因此选择与实测断面最近的分割线预测断面进行对比研究，将对应的实测断面与预测断面分为一组，由北向南依次排序，共分7组。对下凹形剖面、斜坡形剖面和上凸形剖面分别在SPSS中进行剖面离散点潮位–距离数据函数拟合，断面Ⅰ、断面Ⅳ和断面Ⅵ的40条水边线拟合效果见图5。根据拟合结果，最终下凹形剖面选择三指数衰减函数（公式（2）），拟合得到的R 2为0.67；斜坡形剖面选择线性拟合函数（同公式（1）），拟合得到的R 2为0.86；上凸形剖面选择二阶多项式函数（公式（3）），拟合得到的R 2为0.86。3种类型函数均具有较高的拟合程度。
$H = {h_0} + \mathop \sum \limits_{i = 0}^2 {A_i}{{\rm e}^{ - L/{t_i}}},$
$H = a{L^2} + bL + c,$
式中，h 0、Ai 、ti 、a、b和c为拟合函数的系数，其余同上。可以看到，平均坡度法相当于本方案中的线性拟合函数推算，下文平均坡度法计算所用数据为海侧和陆侧两个水边线离散点。
3.2 海岸线推算
对于要推算的剖面，首先根据剖面形态判别结果选择相应的拟合函数；然后将该剖面处的多年平均大潮高潮值代入拟合函数，计算出高潮点距离原点的水平距离LH ；最后根据原点平面坐标(x 0，y 0)和该断面上任意一离散点坐标(x 1，y 1)，求得方向角a，解算得到高潮点对应的平面坐标(x 2，y 2)。计算公式如下：
$\alpha = {\rm {arctan}}\left( {{y_0} - {y_1}} \right)/\left( {{x_0} - {x_1}} \right),$
${x_2} = {x_0} + {L_H}\cos\alpha ,$
${y_2} = {y_0} + {L_H}\sin\alpha .$
分别推得每条断面上高潮点位置后，按顺序连接成线即为平均大潮高潮线。如果在该位置存在人工岸线，当推算所得高潮线在人工岸线的向海一侧时，取高潮线为海岸线（自然岸线）；推算的高潮线在人工岸线向陆一侧，取人工岸线为海岸线。合并自然岸线和人工岸线，即为该区域遥感推算的海岸线。
4 结果与分析
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4.1 水边线分布特征
根据收集到的Landsat卫星遥感影像，提取了40条不同时刻的水边线。从图6中可以发现，相邻的不同时相水边线之间会有略微的交叉，产生这种现象主要有3个原因：一是受遥感影像分辨率的限制，提取水边线时会存在一定的误差；二是潮滩地形会有周期性变化，导致不同季节的水边线产生交叉；三是受潮沟发育、摆动和消亡的影响。
第Ⅰ组断面（图6a）属于轻微侵蚀岸段，靠近陆地的区域潮滩下切，坡度变陡，而潮间带下部坡度相对比较平缓。可以看到，有28条水边线集中分布在靠近陆地的0.1 km左右区域范围内，剩下的12条水边线较为均匀的分布在潮间带靠海侧1 km左右范围内。从整体上看，40条水边线的位置分布符合该岸段的侵蚀断面特征。第Ⅳ组断面（图6b）属于地形相对平缓岸段，岸滩宽平，坡度较小，40条水边线能够均衡的分布在整个潮间带地区。第Ⅵ组断面（图6c）属于典型的淤长岸段，呈上凸形，从陆到海剖面地形由平缓逐渐变为陡峭。第Ⅵ组断面所在位置潮滩宽度约为1.8 km，其中21条水边线分布在靠近陆地的1.2 km左右的区域内，剩下19条水边线较为均匀地分布在靠海侧0.6 km的区域范围内。
4.2 海岸线遥感推算结果
4.2.1 岸滩剖面形态拟合结果
在进行大区域范围和长时间尺度的海岸线推算时，对大量水边线进行提取和潮位模拟会带来巨大的工作量。因此需要对以下两个问题进行深入的研究分析：（1）海岸线推算时合理的水边线分布位置；（2）满足海岸线推算精度的水边线数量。
首先选取出断面上最陆侧和最海侧的两个水边线离散点，将它们视为两个端点。计算得到这两点间的距离后，分别进行2n（n=1，2，3 ···）等分；将最靠近等分点的水边线离散点作为所选择的点，就可分别使用2n+1条水边线数据进行剖面拟合。针对下凹形断面Ⅰ、斜坡形断面Ⅳ和上凸形断面Ⅵ，分别采用三指数衰减函数、线性拟合函数和二阶多项式函数，拟合效如图7所示。
4.2.2 海岸线位置误差对比
表2显示了剖面形态自适应法和平均坡度法推算得到的海岸线误差结果。
第Ⅰ组断面处的实测海岸线为自然岸线，该地原属围海养殖区，受到侵蚀作用影响，养殖塘被冲毁，退养还湿恢复成自然岸线。采用剖面形态自适应法推算得到的海岸线距离绝对误差范围为–25.94～17.28 m，而平均坡度法推算得到的海岸线距离绝对误差为–43.72 m。相较而言，剖面形态自适应法具有较高的推算精度。这主要是因为第Ⅰ组断面属于下凹形的侵蚀岸段，靠近陆侧剖面较陡，三指数衰减函数所拟合的曲线更贴合实际剖面形态，因此该函数在推算下凹形岸段海岸线位置时具有良好的推算效果。
第Ⅳ组断面的实测海岸线为人工岸线，由不同水边线数量拟合推算的高潮线位置在人工岸线的向海一侧外侧，属于自然岸线，因此结合高精遥感影像和实地考察结果，提取平均大潮高潮痕迹线作为参考海岸线，对断面Ⅳ处所推海岸线位置做精度验证。结果表明，用剖面形态自适应法所推海岸线距痕迹线距离在–418.62～–91.96 m之间，相较于平均坡度法的–452.09 m，更靠近痕迹线。
第Ⅵ组断面的实测海岸线也为人工岸线，由不同水边线数量拟合推算的高潮线位置在人工岸线向陆一侧，因此该断面以人工岸线作为海岸线，实测海岸线不作为精度验证数据，只用来判别所推高潮线与实测岸线的位置关系。断面Ⅵ处人工岸线外侧均为光滩，见图1d和图1g。结合现场验证结果可知，涨潮时，海水能够淹没至人工岸线，说明实际高潮线应该在人工岸线内侧，该地区以人工岸线作为海岸线，与本文所推算结果一致，海岸线距离绝对误差均为0 m，表明两种推算方法均能得到正确的海岸线推算结果。表2括号中数据为平均大潮高潮线距人工岸线的距离，使用剖面形态自适应法推算得到距人工岸线距离在–986.09～–861.38 m之间，相较于平均坡度法的–772.57 m在更内侧，因此当遭遇特大风暴潮或堤岸被冲毁时，海水可以淹没距离更远，范围更大，使用剖面自适应法的推算结果有助于海岸带管理部门制定防范措施，尽量将人工岸线内侧的围海养殖区可能遭受的损失降到最低。
综合3条典型断面验证结果可知，剖面形态自适应法相较于传统的平均坡度法，在侵蚀岸段以及自然岸段的海岸线推算精度能提高约82.4%。根据不同数量水边线剖面形态自适应法推算结果可以看出，推算精度并不随着水边线数量的增加而提高，在运用5条水边线时，精度达到最佳，因此，我们认为选用5条能够尽量均衡分布在潮间带上的水边线具有满意的推算精度。
4.3 潮滩剖面季节性变化对海岸线推算的影响
受沿海水动力条件变化影响，潮滩地形会发生季节性的冲淤变化。冬季，在季风的持续作用下，苏北沿岸未固结泥沙被风浪掀起，在苏北沿岸流的共同作用下，海水中悬浮泥沙含量增加，并且向南部外海运移。夏季，东南风盛行，黄海沿岸流受到顶托，流速减小，流域变窄，掀沙能力也变小，悬浮泥沙浓度降低。一般来说江苏近岸海域悬浮泥沙含量分布变化的规律是冬季悬沙浓度大于夏季，但夏季台风期间风浪具有强大的掀沙能力，同时对岸滩也有着强大的破坏力，在台风过境时海水中悬浮泥沙含量也会出现短暂的高值[21-22]。因此在水边线选择时，尽可能要考虑典型断面的季节性形态变化影响。根据上文的研究结果，分别采用夏季和冬季的5条水边线数据，对比研究不同季节的水边线推算海岸线的误差情况，结果如表3所示。
可以看到，断面Ⅰ利用夏季和冬季的水边线推算得到的海岸线误差都较小，这是因为下凹形剖面靠近陆侧坡度较陡，近岸悬浮泥沙被掀起或堆积所引起的坡度变化较小，因此多年平均大潮高潮线的水平位置随季节变化影响不大。断面Ⅳ处相较于夏季数据，使用冬季数据推算的海岸线精度提高了约63.65%，该剖面属于相对稳定的斜坡形岸段，夏季时台风和风暴潮频发，对潮滩地形强大的破坏作用，因此使用冬季数据具有更高的推算精度。断面Ⅵ处夏季数据和冬季数据推算得到的高潮线均在人工岸线内侧，以人工岸线作为海岸线，距离绝对误差均为0 m。结合表2、表3可以看出，不区分季节时，3条断面的海岸线推算距离绝对误差分别为6.5 m，–91.96 m和0 m（–986.09 m），使用冬季数据推算得到的距离绝对误差分别为–8.65 m，–85.28 m，和0 m（–682.61 m）。综合不同类型断面推算结果可以看出，使用冬季数据推算海岸线时，在下凹形剖面和上凸形剖面均有较高精度，在推算斜坡形剖面时，误差精度有一定提高，因此建议采用冬季的水边线进行海岸线遥感推算。
4.4 断面平均坡度推算结果
平均坡度可以反映潮滩地形起伏变化的情况。通过实测断面数据，计算预测断面和实测断面的平均坡度之间的相对误差。误差计算结果见表4。由于实测断面测量点之间的间隔距离为50 m，因此将拟合的预测剖面以50 m等间隔划分，计算其平均坡度。
根据不同水边线数量推算得到的平均坡度误差对比，采用剖面形态自适应法推算得到的断面平均坡度，第Ⅰ组断面绝对误差变化范围在–0.1‰～0.21‰之间，第Ⅳ组断面绝对误差变化范围在–0.17‰～0.17‰之间，第Ⅵ组断面绝对误差变化范围在–0.02‰～0.13‰之间，各断面平均坡度的绝对误差均低于实测坡度值一个数量级，所得的结果与真实情况基本符合。
而采用传统的平均坡度法推算断面平均坡度时，第Ⅰ组断面的绝对误差高达0.73‰，相对误差约为38.02%。这主要是因为断面I为下凹形剖面，断面形态变化较大，用两点推算该区域的断面坡度与实际坡度相差较大，不具有代表性。第Ⅳ组断面和第Ⅵ组断面的绝对误差分别为–0.17‰和0.14‰，均达到或超过剖面形态自适应法推算得到的坡度绝对误差的最大值，可见斜坡形和上凸形剖面本身坡度较小，虽然采用平均坡度法也能获得较高的精度，但剖面形态自适应法能够进一步提高平均坡度的推算精度。总体而言，运用剖面形态自适应法推算岸滩平均坡度时，精度能提高约 26.6%。
水边线数量的多少对平均坡度计算结果带来的影响是由两方面原因造成：一是水边线潮位模拟误差；二是所选取水边线位置。但水边线数量带来的平均坡度变化远小于不同形态剖面间的平均坡度变化。综合考虑数据量和结果误差等因素，在研究潮滩坡度时，采用剖面形态自适应法并选择能够尽量均衡分布在潮间带上的3条水边线基本能把绝对误差控制在±0.2‰范围内，实现对潮滩坡度的准确推算。
5 结论
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本文提出了剖面形态自适应法，首先以多时相遥感影像数据为基础提取水边线，然后采用潮汐调和计算方法推算潮位信息，赋予水边线高程，最终通过依据研究区岸滩不同的剖面形态特征，选择不同的函数进行剖面拟合，推算得到多年平均大潮高潮线。研究结果表明：
（1）下凹形剖面采用三指数衰减函数、斜坡形剖面采用线性函数、上凸形剖面采用二阶多项式函数具有良好的拟合效果，并且采用剖面形态自适应法拟合所得曲线与实际剖面形态基本一致。
（2）选用3条水边线计算潮滩平均坡度时，侵蚀、平缓、淤长岸段的平均坡度绝对误差分别为0.20‰、–0.17‰和0.13‰，误差均小于实测平均坡度一个数量级。相较于平均坡度法，剖面形态自适应法推算的岸滩平均坡度精度提高约26.6%。
（3）选用5条水边线推算海岸线位置时，侵蚀、平缓、淤长岸段的距离绝对误差分别为6.5 m、–91.96 m、0 m（–986.09 m），与平均坡度法相比，误差减小约82.4%。
（4）考虑季节性变化时，侵蚀岸段和淤长岸段无论使用夏季数据还是冬季数据都具有较高的推算精度，而平缓岸段使用冬季数据的推算结果明显优于夏季数据，误差减小约 63.65%，因此建议使用冬季的水边线数据来进行海岸线遥感推算。
6 展望
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本文在水边线选择时，统一按水边线均匀分布在潮滩上的原则进行筛选，没有考虑不同断面形态与所选取水边线位置的关系。实际上，下凹形剖面近陆一段坡度较陡，而后逐渐变缓，因此选择水边线时，在保证陆侧和海侧水边线距离尽量大的同时，其余水边线可以适当偏向陆侧；上凸形和斜坡形剖面依旧选择尽量均匀分布的水边线即可。
对于上凸形的淤长岸段，由于靠近陆侧的坡度十分平缓，利用二阶多项式函数所拟合曲线的最大值有时不能达到多年平均大潮高潮的潮位值。因此在推算此类型岸段海岸线时，可以选用适当的插值方法来推算海岸线的位置。
此外，由于拟合函数本身的限制，拟合所得剖面形态与实际情况还是有所差别，可以选用更复杂的表达关系如逻辑斯蒂曲线，或者采用合适的插值方法，来提高剖面拟合效果，这将是剖面形态自适应法下一步的研究方向。
基金
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  • 国家自然科学基金项目（41771447）；江苏省海洋科技创新专项项目（HY2018-3）。
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2019年第41卷第9期
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doi: 10.3969/j.issn.0253-4193.2019.09.016
  • 接收时间：2018-09-06
  • 首发时间：2026-04-03
  • 出版时间：2019-09-25
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  • 收稿日期：2018-09-06
  • 修回日期：2018-12-06
基金
国家自然科学基金项目（41771447）；江苏省海洋科技创新专项项目（HY2018-3）。
作者信息
    1 南京师范大学 地理科学学院，江苏 南京 210023
    2 南京师范大学 海洋科学与工程学院，江苏 南京 210023
    3 江苏省地理信息资源开发与利用协同创新中心，江苏 南京 210023
    4 江苏省海域使用动态监视监测中心，江苏 南京 210003

通讯作者:

*张东（1975—），男，江苏省南通市人，博士，副教授，研究方向为海洋信息技术与海岸带资源开发管理。E-mail:
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