Please wait a minute...
 
Remote Sensing for Land & Resources    2018, Vol. 30 Issue (4) : 187-192     DOI: 10.6046/gtzyyg.2018.04.28
|
Analysis of Ningyuan Estuary coastline transition based on the multi-resource remote sensing image
Kun LUO1, Bo DING2,3(), Genyuan LONG1
1. Marine Geological Survey Institute of Hainan Province, Haikou 570206, China
2. School of Earth Science, China University of Geosciences(Wuhan), Wuhan 430074, China
3. Hainan Comprehensive Design Institute of Geological Survey, Haikou 570206, China
Download: PDF(3646 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

In this paper, by utilizing 4 phases of multi-resource remote sensing images in 1987, 2000, 2010 and 2015, the authors carried out the information extraction of coastline in Ningyuan Estuary in 4 phases from 1987 to 2015,and analyzed spatial and temporal characteristics,influencing factors and development trend of coastline transition by using the density segmentation and binarization processing based on object-oriented information extraction technology. Some conclusions have been reached: The coastline of Ningyuan Estuary overall showed a trend of growth from 1987 to 2015, with the total length increasing up to 8.14 km. The ecological environment of the coastal zone in Ningyuan Estuary has changed greatly with the disappearance of mangrove resources near the estuary largely, the expansion of artificial breeding zones and the continuous deterioration of water quality. The overall feature in the delta of Ningyuan Estuary is siltation, and that in the west of the estuary is erosion. The major influencing factors include coastal erosion and siltation, artificial breeding in the tideland and the construction of the artificial island as well as the building of a dam for sand control. The situation of Ningyuan Estuary is not optimistic in recent years. The erosion of the artificial island’s west coastline tends to become worse and the coastal siltation will be further intensified in the east of the sand-protecting dam.

Keywords Ningyuan Estuary      coastline transition      remote sensing     
:  TP79  
Corresponding Authors: Bo DING     E-mail: ding_boo@126.com
Issue Date: 07 December 2018
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Kun LUO
Bo DING
Genyuan LONG
Cite this article:   
Kun LUO,Bo DING,Genyuan LONG. Analysis of Ningyuan Estuary coastline transition based on the multi-resource remote sensing image[J]. Remote Sensing for Land & Resources, 2018, 30(4): 187-192.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.04.28     OR     https://www.gtzyyg.com/EN/Y2018/V30/I4/187
Fig.1  Study area
数据类型 最高空间
分辨率/m
时相 波段数/个 范围
QuickBird 0.61 20150811 4 120 km2
RapidEYE 5.00 20100813 3 1景
ETM+ 15.00 20000726 8 1景
TM 30.00 19871206 7 1景
Tab.1  Information of remote sensing images in Ningyuan Estuary in different times
Fig.2  Technical flowchart of coastline extraction
影像特征 海岸线类型 海岸线判读标准 提取方法
砂质海岸线 海岸线较为平直,潮上带因大潮潮水搬运,常出现与海岸线平行的脊状砂质沉积,且伴有灰黑色漂浮物附着,滩脊的位置即为海岸线位置 以计算机自动提取为主,辅以人工目视解译修正
红树林海岸线 红树林植被影像特征多为深绿色或墨绿色,与其他植被光谱特征差异较大,多集中在海湾处或淤泥质富集的入海口地势低洼区域,红树林与陆地相连时靠陆一侧作为海岸线;孤立于河口三角区域的红树林,其植被外侧界线作为红树林海岸线 计算机自动提取,边缘做适当圆滑处理
填海造地 与海水相接,如港口、闭合的堤坝等,一般有规则的海陆分界线,取向海一侧为人工海岸线 计算机自动提取
围池堤坝 对于沿海岸线分布的人工养殖区,如高位养虾池、海湾内侧围池筑堤形成的养殖区,其外侧形成的规则状堤坝,大潮时海水不能越过,在遥感影像上往往形成较为平直的线性隆起纹理,灰白色夹杂绿色植被,光谱特征明显,其线性隆起作为围池堤坝类人工海岸线 人工目视解译
  
Fig.3  Coastline of Ningyuan Estuary in different times
年份 自然海岸线 人工海岸线 合计
砂质海
岸线
红树林
海岸线
填海造地 围池堤坝
1987年 22.09 9.65 0 0 31.74
2000年 13.44 0 0.61 14.57 28.62
2010年 12.13 0 0.68 21.82 34.63
2015年 13.47 0 7.85 18.56 39.88
Tab.3  Coastline lengths of Ningyuan Estuary in different times(km)
类型 1987年 2000年 2010年 2015年
红树林 0.45 0 0 0
养殖区 0 1.72 2.54 1.70
Tab.4  Area of mangrove forest and anquiculture of Ningyuan Estuary in different times(km2)
[1] 杨磊, 李加林, 袁麒翔 , 等. 中国南方大陆海岸线时空变迁[J]. 海洋学研究, 2014,32(3):42-49.
doi: 10.3969/j.issn.1001-909X.2014.03.006 url: http://d.wanfangdata.com.cn/Periodical/dhhy201403006
[1] Yang L, Li J L, Yuan Q X , et al. Spatial-temporal changes of continental coastline in southern China[J]. Journal of Marine Sciences, 2014,32(3):42-49.
[2] 赵玉灵 . 广东省海岸线与红树林现状遥感调查与保护建议[J]. 国土资源遥感, 2017,29(s1):114-120.doi: 10.6046/gtzyyg.2017.s1.19.
doi: 10.6046/gtzyyg.2017.s1.19 url: http://d.wanfangdata.com.cn/Periodical_gtzyyg2017z1020.aspx
[2] Zhao Y L . Remote sensing survey and proposal for protection of the shoreline and the mangrove wetland in Guangdong Province[J]. Remote Sensing for Land and Resources, 2017,29(s1):114-120.doi: 10.6046/gtzyyg.2017.s1.19.
[3] 赵玉灵 . 近40年来伶仃洋海岸线与红树林遥感调查与演变分析[J]. 国土资源遥感, 2017,29(1):136— 142.doi: 10.6046/gtzyyg.2017.01.21.
doi: 10.6046/gtzyyg.2017.01.21 url: http://d.wanfangdata.com.cn/Periodical/gtzyyg201701021
[3] Zhao Y L . Remote sensing dynamic monitoring of the shoreline and the mangrove wetland in the Lingdingyang Estuary in the past 40 years[J]. Remote Sensing for Land and Resources, 2017,29(1):136-142.doi: 10.6046/gtzyyg.2017.01.21.
[4] 王集宁, 蒙永辉, 张丽霞 . 近42年黄河口海岸线遥感监测与变迁分析[J]. 国土资源遥感, 2017,28(3):188-193.doi: 10.6046/gtzyyg.2016.03.29.
doi: 10.6046/gtzyyg.2016.03.29 url: http://d.wanfangdata.com.cn/Periodical/gtzyyg201603029
[4] Wang J N, Meng Y H, Zhang L X . Remote sensing monitoring and change analysis of Yellow River Estuary coastline in the past 42 years[J]. Remote Sensing for Land and Resources, 2017,28(3):188-193.doi: 10.6046/gtzyyg.2016.03.29.
[5] 杨长坤, 刘召芹, 王崇倡 , 等. 2001—2013年辽东湾海岸带空间变化分析[J]. 国土资源遥感, 2015,27(4):150-157.doi: 10.6046/gtzyyg.2015.04.23.
[5] Yang C K, Liu Z Q, Wang C C , et al. Spatial change analysis of the coastal zone of Liaodong Bay from 2001 to 2013[J] Remote Sensing for Land and Resources, 2015,27(4):150-157.doi: 10.6046/gtzyyg.2015.04.23.
[6] Solomon S M . Spatial and temporal variability of shoreline change in the Beaufort-Mackenzie region,northwest territories[J]. Geo-Marine Letters, 2005,25(2-3):127-137.
doi: 10.1007/s00367-004-0194-x url: http://link.springer.com/10.1007/s00367-004-0194-x
[7] Sheik M , Chandrasekar.Ashoreline change analysis along the coast between Kanyakumari and Tuticorin,India,using digital shoreline analysis system[J]. Geo-spatial Information Science, 2011,14(4):282-293.
doi: 10.1007/s11806-011-0551-7 url: http://www.tandfonline.com/doi/abs/10.1007/s11806-011-0551-7
[8] 朱俊凤, 王耿明, 张金兰 , 等. 珠江三角洲海岸线遥感调查和近期演变分析[J]. 国土资源遥感, 2013,25(3):130-137.doi: 10.6046/gtzyyg.2013.03.22.
[8] Zhu J F, Wang G M, Zhang J L , et al. Remote sensing investigation and recent evolution analysis of Pearl River Delta coastline[J]. Remote Sensing for Land and Resources, 2013,25(3):130-137.doi: 10.6046/gtzyyg.2013.03.22.
[9] 刘勇, 黄海军, 严立文 . 不同空间尺度下石臼陀岛海岸线提取的遥感应用研究[J]. 遥感技术与应用, 2013,28(1):144-149.
doi: 10.11873/j.issn.1004-0323.2013.1.144 url: http://d.wanfangdata.com.cn/Periodical/ygjsyyy201301021
[9] Liu Y, Huang H J, Yan L W . Remote sensing applications in extraction of Shijiutuo Island coastline based on different spatial scales[J]. Remote Sensing Technology and Application, 2013,28(1):144-149.
[10] Dellepiane S, Laurentiis R D, Giordano F . Coastline extraction from SAR images and a method for the evaluation of the coastline precision[J]. Pattern Recognition Letters, 2004,25(13):1461-1470.
doi: 10.1016/j.patrec.2004.05.022 url: http://linkinghub.elsevier.com/retrieve/pii/S0167865504001291
[11] 严海兵, 李秉柏, 陈敏东 . 遥感技术提取海岸线的研究进展[J]. 地域研究与开发, 2009,28(1):101-105.
doi: 10.3969/j.issn.1003-2363.2009.01.021 url: http://d.wanfangdata.com.cn/Periodical/dyyjykf200901021
[11] Yan H B, Li B B, Chen M D . Progress of researches in coastline extraction based on RS technique[J]. Areal Research and Development, 2009,28(1):101-105.
[12] 杜涛, 张斌 . 用小波技术分析遥感图像确定岸线位置的研究[J]. 海洋科学, 1999,04:19-21.
doi: 10.3969/j.issn.1000-3096.1999.04.009 url: http://www.cqvip.com/qk/90010x/1999004/3581170.html
[12] Du T, Zhang B . A study of mapping coast by progressing remote sensing image with wavelets[J]. Marine Sciences, 1999,04:19-21.
[13] Liu H X, Wang L, Douglas J S , et al. Algorithmic foundation and software tools for extracting shoreline features from remote sensing imagery and LiDAR data[J]. Journal of Geographic Information System, 2011,3(2):99-119.
doi: 10.4236/jgis.2011.32007 url: http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jgis.2011.32007
[14] 冯永玖, 袁佳宇, 宋丽君 , 等. 杭州湾海岸线信息的遥感提取及其变迁分析[J]. 遥感技术与应用, 2015,30(2):345-352.
doi: 10.11873/j.issn.1004-0323.2015.2.0345 url: http://www.cqvip.com/QK/96858A/201502/1005689239.html
[14] Feng Y J, Yuan J Y, Song L J , et al. Coastline mapping and change detection along Hangzhou Bay using remotely sensed imagery[J] . Remote Sensing Technology and Application, 2015,30(2):345-352.
[15] 于杰, 陈国宝, 黄梓荣 , 等. 近10年间广东省3个典型海湾海岸线变迁的遥感分析[J]. 海洋湖沼通报, 2014,3:91-96.
url: http://d.wanfangdata.com.cn/Periodical_hyhztb201403013.aspx
[15] Yu J, Chen G B, Huang Z R , et al. Changes in the coastline of three typical bays in Guangdong during recent 10 years revealed by satellite image[J]. Transaction of Oceanology and Limnology, 2014,3:91-96.
[1] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[5] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[7] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[11] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[12] YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[13] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[14] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech