Please wait a minute...
 
自然资源遥感  2023, Vol. 35 Issue (3): 53-63    DOI: 10.6046/zrzyyg.2022471
  海岸带空间资源及生态健康遥感监测专栏 本期目录 | 过刊浏览 | 高级检索 |
1989—2021年中国大陆海岸带潮滩、海岸线、养殖水体遥感观测
闫柏琨1,2(), 甘甫平1,2, 印萍3, 葛晓立1,2, 郭艺1,2, 白娟1,2
1.中国自然资源航空物探遥感中心,北京 100083
2.自然资源部航空地球物理与遥感地质重点实验室,北京 100083
3.青岛海洋地质研究所,青岛 266237
Remote sensing observations of tidal flats, shorelines, and aquacultural water bodies along coastal zones in China mainland during 1989—2021
YAN Bokun1,2(), GAN Fuping1,2, YIN Ping3, GE Xiaoli1,2, GUO Yi1,2, BAI Juan1,2
1. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
2. Key Laboratory of Airborne Geophysics and Remote Sensing Geology, Ministry of Natural Resources, Beijing 100083, China
3. Qingdao Institute of Marine Geology, Qingdao 266237, China
全文: PDF(10262 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

海岸带是世界上容纳人口最多的区域,其生态系统受到人类活动的强烈影响。潮滩、海岸线、养殖水体变化是海岸带生态系统健康监测的重要要素。由于潮汐作用,海陆水边线处于动态变化状态,是应用遥感技术进行潮滩、海岸线检测的主要难点,故通过综合Landsat4/5/7/8和Sentinel-2A/B卫星遥感数据对1989—2021年间中国大陆海岸带潮滩、海岸线、养殖水体进行了7期监测,发挥了多源卫星观测频次高的优势,通过检测不同潮位的水边线实现潮滩、海岸线、养殖水体的识别。结果表明: 针对不同水色的海水应选用不同的水体指数组合,对于清澈或低浑浊度的海水,应分别采用修正的水体指数(modified normalized difference water index,mNDWI)与归一化差值水体指数(normalized difference water index,NDWI)检测高潮位、低潮位水边线,有效提高潮滩检测的可靠性,检测的潮滩面积比通常仅用mNDWI指数检测的潮滩面积大122%; 对于高浑浊度海水(浙江省、江苏省和上海市),应采用mNDWI进行高潮位、低潮位水边线的检测,以避免NDWI将高浑浊度海水误识别为潮滩,对养殖水体应采用NDWI检测。1989—2021年,中国大陆海岸带发生了巨大变化,潮滩快速消失,养殖水体面积和海岸线长度增加,整个中国大陆海岸带的潮滩减少率、海岸线长度和养殖水体增加率平均分别为46.2%,34.4%和149.3%,潮滩面积减少了7 173.2 km2,海岸线长度增加5 320.5 km,养殖水体面积增加了9 046.5 km2。北方省份或城市遭受的潮滩损失比南方省份或城市更严重。以1989—2021年潮滩平均减少率计算,辽宁省、河北省和天津市、山东省的潮滩将分别在27 a,10 a和22 a内完全消失。潮滩和养殖水体的面积变化高度负相关,表明养殖水体的扩展是潮滩减少的重要驱动因素。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
闫柏琨
甘甫平
印萍
葛晓立
郭艺
白娟
关键词 海岸带潮滩海岸线河口    
Abstract

Coastal zones are the world’s most populated areas, with their ecosystems being strongly influenced by human activities. Tidal flats, shorelines, and aquacultural water bodies are critical elements in monitoring the health of coastal zone ecosystems. However, the dynamic changes in the waterlines between land and sea areas caused by tidal effects make it challenging to detect tidal flats and shorelines using the remote sensing technology. By integrating Landsat4/5/7/8 and Sentinel-2A/B satellite remote sensing images, this study conducted seven phases (1989—2021) of monitoring of tidal flats, shorelines, and aquacultural water bodies along coastal zones in China mainland. By taking advantage of the high frequency of multi-source satellite observations, this study identified tidal flats, shorelines, and aquacultural water bodies by detecting the waterlines at different tidal levels. The results are as follows: ① Seawater of different colors requires different combinations of water body indices. For clear or low-turbidity seawater, this study selected the modified normalized difference water index (mNDWI) and the normalized difference water index (NDWI) to detect the waterlines at high and low tidal levels, respectively. This improved the reliability of tidal flat detection, with the detected tidal flat area being 122% larger than that detected only using the mNDWI. For high-turbidity seawater (in Zhejiang, Jiangsu, and Shanghai), this study selected mNDWI to detect the waterlines at high and low tidal levels, avoiding misidentifying high-turbidity seawater as tidal flats using NDWI. Besides, this study selected NDWI to detect aquacultural water bodies. ② During 1989—2021, coastal zones in China mainland changed significantly, as evidenced by rapidly decreased tidal flats and increased aquacultural water bodies and shorelines. The decreased rate of tidal flats and the increased rates of shorelines and aquacultural water bodies along the coastal zones averaged 46.2%, 34.4%, and 149.3%, respectively. Correspondingly, the tidal flat area decreased by 7 173.2 km2, while the the shoreline length and aquacultural water body area increased by 5 320.5 km and 9 046.5 km2, respectively. Provinces or cities in northern China suffered more tidal flat losses than those in southern China. Based on the average decrease rate of tidal flats during 1989—2021, tidal flats in Liaoning, Hebei and Tianjin, and Shandong will disappear within 27 a, 10 a, and 22 a, respectively. ③ The area changes between tidal flats and aquacultural water bodies are highly negatively correlated, indicating that the expansion of aquacultural water bodies is a critical driving factor for the decrease in tidal flats.

Key wordscoastal zone    tidal flat    shoreline    estuary
收稿日期: 2022-12-05      出版日期: 2023-09-19
ZTFLH:  TP79  
基金资助:国家重点研发计划项目“河口三角洲生态环境地球观测应用研究”(2019YFE0127200)
作者简介: 闫柏琨(1977-),男,博士,教授级高级工程师,主要从事遥感水文研究。Email: 55561161@qq.com
引用本文:   
闫柏琨, 甘甫平, 印萍, 葛晓立, 郭艺, 白娟. 1989—2021年中国大陆海岸带潮滩、海岸线、养殖水体遥感观测[J]. 自然资源遥感, 2023, 35(3): 53-63.
YAN Bokun, GAN Fuping, YIN Ping, GE Xiaoli, GUO Yi, BAI Juan. Remote sensing observations of tidal flats, shorelines, and aquacultural water bodies along coastal zones in China mainland during 1989—2021. Remote Sensing for Natural Resources, 2023, 35(3): 53-63.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022471      或      https://www.gtzyyg.com/CN/Y2023/V35/I3/53
Fig.1  潮滩、海岸线和养殖水体检测流程
Fig.2  不同水色NDWI与mNDWI水体指数对比
Fig.3  使用不同水体指数检测潮滩对比
Fig.4  典型区2021年潮滩、海岸线和养殖水体分布
Fig.5  广西珍珠湾2021年潮滩、海岸线和养殖水体检测结果与高分影像目视解译结果对比
Fig.6  中国大陆海岸带2021年潮滩、海岸线和养殖水体统计
Fig.7  潮滩、海岸线和养殖水体变化统计
Fig.8  潮滩与养殖水体面积相关关系
Fig.9  潮滩面积分省/区/市统计及与其他结果对比
Fig.10  典型区潮滩分布对比
Fig.11  养殖水体面积统计及与其他结果对比
Fig.12  东营潮位年内变化与卫星有效观测时的潮位变化对比
[1] Primavera J H. Overcoming the impacts of aquaculture on the coastal zone[J]. Ocean and Coastal Management, 2006, 49(9-10):531-545.
doi: 10.1016/j.ocecoaman.2006.06.018
[2] Ren C, Wang Z, Zhang Y, et al. Rapid expansion of coastal aquaculture ponds in China from Landsat observations during 1984—2016[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 82:101902.
doi: 10.1016/j.jag.2019.101902
[3] Wu T, Hou X, Xu X. Spatio-temporal characteristics of the mainland coastline utilization degree over the last 70 years in China[J]. Ocean and Coastal Management, 2014, 98:150-157.
doi: 10.1016/j.ocecoaman.2014.06.016
[4] Sun Z, Luo J, Yang J, et al. Nation-scale mapping of coastal aquaculture ponds with Sentinel-1 SAR data using Google Earth Engine[J]. Remote Sensing, 2020, 12(18):3086.
doi: 10.3390/rs12183086
[5] Cao L, Naylor R, Henriksson P, et al. China’s aquaculture and the world’s wild fisheries[J]. Science, 2015, 347(6218):133-135.
doi: 10.1126/science.1260149
[6] Ottinger M, Clauss K, Kuenzer C. Large-scale assessment of coastal aquaculture ponds with Sentinel-1 time series data[J]. Remote Sensing, 2017, 9(5):440.
doi: 10.3390/rs9050440
[7] Murray N J, Phinn S R, Dewitt M, et al. The global distribution and trajectory of tidal flats[J]. Nature, 2019, 565(7738):222-225.
doi: 10.1038/s41586-018-0805-8
[8] Murray N J, Clemens R S, Phinn S R, et al. Tracking the rapid loss of tidal wetlands in the Yellow Sea[J]. Frontiers in Ecology and the Environment, 2014, 12(5):267-272.
doi: 10.1890/130260
[9] Wang X, Xiao X, Zou Z, et al. Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163:312-326.
doi: 10.1016/j.isprsjprs.2020.03.014 pmid: 32405155
[10] Moore L J, Ruggiero P, List J H. Comparing mean high water and high water line shorelines:Should proxy-datum offsets be incorporated into shoreline change analysis?[J]. Journal of Coastal Research, 2006, 224:894-905.
doi: 10.2112/04-0401.1
[11] Dyer K R, Christie M C, Wright E W. The classification of intertidal mudflats[J]. Continental Shelf Research, 2000, 20(10-11):1039-1060.
doi: 10.1016/S0278-4343(00)00011-X
[12] Pajak M J, Leatherman S P. The high water line as shoreline indicator[J]. Journal of Coastal Research, 2002, 18(2):329-337.
[13] García-Rubio G, Huntley D, Russell P. Evaluating shoreline identification using optical satellite images[J]. Marine Geology, 2015, 359:96-105.
doi: 10.1016/j.margeo.2014.11.002
[14] Li W, Gong P. Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery[J]. Remote Sensing of Environment, 2016, 179:196-209.
doi: 10.1016/j.rse.2016.03.031
[15] Yang X, Zhu Z, Qiu S, et al. Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series[J]. Remote Sensing of Environment, 2022, 276:113047.
doi: 10.1016/j.rse.2022.113047
[16] Sagar S, Roberts D, Bala B, et al. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations[J]. Remote Sensing of Environment, 2017, 195:153-169.
doi: 10.1016/j.rse.2017.04.009
[17] Zhao C, Qin C Z, Teng J. Mapping large-area tidal flats without the dependence on tidal elevations:A case study of Southern China[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159:256-270.
doi: 10.1016/j.isprsjprs.2019.11.022
[18] Jia M, Wang Z, Mao D, et al. Rapid,robust,and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine[J]. Remote Sensing of Environment, 2021, 255:112285.
doi: 10.1016/j.rse.2021.112285
[19] Chang M, Li P, Li Z, et al. Mapping tidal flats of the Bohai and Yellow Seas using time series Sentinel-2 images and Google Earth Engine[J]. Remote Sensing, 2022, 14(8):1789.
doi: 10.3390/rs14081789
[20] Wang X, Xiao X, Zou Z, et al. Tracking annual changes of coastal tidal flats in China during 1986—2016 through analyses of Landsat images with Google Earth Engine[J]. Remote Sensing of Environment, 2020, 238:110987.
doi: 10.1016/j.rse.2018.11.030
[21] Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine:Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202:18-27.
doi: 10.1016/j.rse.2017.06.031
[22] Davranche A, Lefebvre G, Poulin B. Wetland monitoring using classification trees and SPOT-5 seasonal time series[J]. Remote Sensing of Environment, 2010, 114(3):552-562.
doi: 10.1016/j.rse.2009.10.009
[23] Feng L, Hu C, Chen X, et al. Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010[J]. Remote Sensing of Environment, 2012, 121:80-92.
doi: 10.1016/j.rse.2012.01.014
[24] Feyisa G L, Meilby H, Fensholt R, et al. Automated water extraction index:A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014, 140:23-35.
doi: 10.1016/j.rse.2013.08.029
[25] Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006, 27(14):3025-3033.
doi: 10.1080/01431160600589179
[26] Zhou Y, Dong J, Xiao X, et al. Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine[J]. Science of the Total Environment, 2019, 689:366-380.
doi: 10.1016/j.scitotenv.2019.06.341
[1] 王静, 王佳, 徐江琪, 黄邵东, 刘东云. 改进遥感生态指数的典型海岸带城市生态环境质量评价——以湛江市为例[J]. 自然资源遥感, 2023, 35(3): 43-52.
[2] 朱琳, 黄玉玲, 杨刚, 孙伟伟, 陈超, 黄可. 基于GEE的杭州湾海岸线遥感提取与时空演变分析[J]. 自然资源遥感, 2023, 35(2): 50-60.
[3] 史姝姝, 窦银银, 陈永强, 匡文慧. 中国海岸带区域城市扩展遥感监测与内部地表覆盖时空分异特征分析[J]. 自然资源遥感, 2022, 34(4): 76-86.
[4] 江娜, 陈超, 韩海丰. 海岸带地类统计模型中DEM空间尺度优选方法[J]. 自然资源遥感, 2022, 34(1): 34-42.
[5] 郑修诚, 周斌, 雷惠, 黄祺宇, 叶浩林. 基于GEE的杭州湾慈溪段潮滩提取及时空变化分析[J]. 自然资源遥感, 2022, 34(1): 18-26.
[6] 吴芳, 金鼎坚, 张宗贵, 冀欣阳, 李天祺, 高宇. 基于CZMIL测深技术的海陆一体地形测量初探[J]. 自然资源遥感, 2021, 33(4): 173-180.
[7] 李阳, 袁琳, 赵志远, 张晋磊, 王宪业, 张利权. 基于无人机低空遥感和现场调查的潮滩地形反演研究[J]. 自然资源遥感, 2021, 33(3): 80-88.
[8] 苗苗, 谢小平. 基于GIS和RS的山东日照海岸带1988—2018年间演化分析[J]. 国土资源遥感, 2021, 33(2): 237-247.
[9] 陈超, 陈慧欣, 陈东, 张自力, 张旭锋, 庄悦, 褚衍丽, 陈建裕, 郑红. 舟山群岛海岸线遥感信息提取及时空演变分析[J]. 国土资源遥感, 2021, 33(2): 141-152.
[10] 李天祺, 王建超, 吴芳, 赵政, 张文凯. 基于多算法水边线提取的潮滩DEM构建[J]. 国土资源遥感, 2021, 33(1): 38-44.
[11] 韩亚超, 李奇, 张永军, 高子弘, 杨达昌, 陈洁. 机载高光谱仪几何检校方法及其在海岸带航空遥感调查中的示范应用[J]. 国土资源遥感, 2020, 32(1): 60-65.
[12] 金鼎坚, 王建超, 吴芳, 高子弘, 韩亚超, 李奇. 航空遥感技术及其在地质调查中的应用[J]. 国土资源遥感, 2019, 31(4): 1-10.
[13] 罗昆, 丁波, 龙根元. 基于多源遥感影像的宁远河口海岸线变迁分析[J]. 国土资源遥感, 2018, 30(4): 187-192.
[14] 蒙永辉, 王集宁, 张丽霞, 罗梅. 1979—2012年莱州湾南岸海水入侵与区域海岸线变动时空耦合分析[J]. 国土资源遥感, 2018, 30(3): 189-195.
[15] 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.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发