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遥感技术与应用  2022, Vol. 37 Issue (1): 173-185    DOI: 10.11873/j.issn.1004-0323.2022.1.0173
青促会十周年专栏     
基于Sentinel-1 SAR数据巢湖流域洪水时空动态变化监测研究
贾佳佳1,2(),马金戈2,沈明2,齐天赐2,曹志刚2,何艳芬1(),段洪涛1,2
1.西北大学 城市与环境学院,陕西 西安 710127
2.中国科学院南京地理与湖泊研究所 遥感与地理信息科学研究室,江苏 南京 210008
Research on Spatial-temporal Dynamic Changes of Flood in Lake Chaohu Basin based on Sentinel-1 SAR Satellite Image
Jiajia Jia1,2(),Jinge Ma2,Ming Shen2,Tianci Qi2,Zhigang Cao2,Yanfen He1(),Hongtao Duan1,2
1.Northwestern University School of Urban and Environmental Sciences,Xi'an 710127,China
2.Institute of Remote Sensing and Geo-Information Science,Nanjing,China Institute of Geography and Lakes,Chinese Academy of Sciences,Nanjing 21000,China
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摘要:

洪涝灾害危害巨大,对区域人民生命财产和经济发展造成重大威胁,回溯区域洪水事件,甄别洪水风险区,判断洪水时空特征,并给出规划建议非常重要。以巢湖流域为试验区,基于Sentinel-1合成孔径雷达(SAR)影像,构建了谱间关系与阈值分割相结合的洪水淹没识别方法,应用于Google Earth Engine平台上,获得了2015~2020年巢湖流域洪水时空格局,结合土地利用数据,分析了洪水对巢湖流域农田和以建设用地代表的居民点的影响。结果表明:①该方法精度相比单波段阈值和简单指数法提升了3%~7%,能够快速应用遥感数据提取历年流域洪水淹没范围;②2015~2020年间,监测到两次特大洪水和一次小规模洪水,淹没范围集中在杭埠河、裕溪河、兆河等河流区域;③农田占据受淹面积的86.47%~95.35%,建设用地占据4.47%~5.36%,受到洪水影响的居民点主要为基层村庄及乡镇。研究表明了SAR卫星数据在监测巢湖流域洪水有良好的适用性,有助于掌握洪水对农田和乡村居民点的破坏程度,对于未来制定相关规划战略,加强流域乡村洪水控制保障人员与粮食安全十分关键。

关键词: 巢湖流域Sentinel-1洪水灾害GEE    
Abstract:

Flood disaster has great harm and poses a great threat to regional people's lives and property and economic development. Therefore, the continuous high-time resolution remote sensing monitoring will be conducive to more objective and accurate detection of the temporal and spatial variation characteristics of flood risk areas. This research takes Chaohu Lake Basin as the experimental area, based on Google Earth Engine platform, collects Sentinel-1 Synthetic Aperture Radar (SAR) images, uses the flood inundation identification method combining spectral relationship and threshold segmentation to map the flood range of Chaohu Lake Basin from 2015 to 2020, and combines land use data, The impact of flood on farmland and residential areas represented by construction land in Chaohu Lake Basin is analyzed. The results show that: (1) the accuracy of this method is 3%~7% higher than that of single band threshold method and simple index method, and can quickly extract the flood inundation range of watershed over the years by using remote sensing data; (2) From 2015 to 2020, two major floods and one small-scale flood were monitored, and the inundation scope was concentrated in Hangbu River, Yuxi River, Zhaohe River and other river areas; (3) Farmland accounts for 86.47%~95.35% of the flooded area, and construction land accounts for 4.47%~5.36%. The residential areas affected by the flood are mainly grass-roots villages and towns. The research shows that the application of SAR satellite data in flood monitoring can effectively monitor the impact range of flood on farmland and rural residential areas, which is very key to formulate relevant planning strategies in the future, strengthen rural flood control in the basin and ensure personnel and food security.

Key words: Chaohu Lake Basin    Sentinel-1    Flood disaster    GEE
收稿日期: 2021-10-21 出版日期: 2022-04-08
ZTFLH:  TN957.52  
基金资助: 国家水体污染控制与治理科技重大专项(2017ZX07603-001)
通讯作者: 何艳芬     E-mail: jiajiajia@stumail.nwu.edu.cn;yanfen_lily@163.com
作者简介: 贾佳佳(1994-),女,河南驻马店人,硕士研究生,主要从事洪水遥感及规划应用研究。E?mail:jiajiajia@stumail.nwu.edu.cn
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引用本文:

贾佳佳,马金戈,沈明,齐天赐,曹志刚,何艳芬,段洪涛. 基于Sentinel-1 SAR数据巢湖流域洪水时空动态变化监测研究[J]. 遥感技术与应用, 2022, 37(1): 173-185.

Jiajia Jia,Jinge Ma,Ming Shen,Tianci Qi,Zhigang Cao,Yanfen He,Hongtao Duan. Research on Spatial-temporal Dynamic Changes of Flood in Lake Chaohu Basin based on Sentinel-1 SAR Satellite Image. Remote Sensing Technology and Application, 2022, 37(1): 173-185.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0173        http://www.rsta.ac.cn/CN/Y2022/V37/I1/173

图1  巢湖流域地理特征
数据时间范围/年分辨率/m来源使用目的下载地址

哨兵一号

(Sentinel-1)

2015~202010欧洲航天局提取洪水影响区域并为易发洪水的建设用地区域识别https://developers.google.com/earth-engine

高程数据

(SRTM-DEM)

200030美国国家航空航天局掩膜山体阴影https://developers.google.com/earth-engine
全球地表水动态数据集1999~202030马里兰大学全球土地分析和发现实验室验证数据集https://www.glad.umd.edu/dataset/global-surface-water-dynamics
中国水利部水旱公报2006~2018-中国水利部验证洪水地图http://www.mwr.gov.cn/2006-2018
EM-DAT洪水档案1960~2018-国际灾难数据库统计洪灾记录https://www.emdat.be/
土地利用数据集2015-中华人民共和国土地覆被地图集计算受洪水影响的农田与建设用地-
表1  研究所用数据集概要
时间阶段/年201520162017201820192020总计
洪水前5月31099121154
期间6月76810101051
7月96911101055
8月91199171368
总计283335394944228
表2  Sentinel-1所使用的影像统计
图2  方法流程框架图
图3  基于2020年8月Sentinel-1图像的不同方法对比
方法201608202006202008
单波段+OTSU0.810.880.83
SDWI--0.89
VV*VH+OTSU0.850.900.91
表3  不同方法精度结果
图4  精度验证
时间分类非洪水洪水用户精度生产者精度总体精度
201607非洪水19700.735 110.842 2
洪水7118210.719 4Kappa:0.691 8
201608非洪水21820.767 60.990 90.847 5
洪水661600.987 60.707 9Kappa:0.696 2
202006非洪水19830.872 20.985 10.909 9
洪水291250.976 60.811 7Kappa:0.812 8
202008非洪水219110.883 10.952 20.910 1
表 4  精度验证混淆矩阵
图5  2015~2020 Sentinel-1影像洪水提取结果注:(a~d)2015年;(e~h)2016年;(i~l)2017年;(m~p)2018年;(q~t)2019年;(u~x)2020年
图6  研究期间共捕捉到三次较为明显的洪水数据(分别发生在2016年、2017年与2020年,其中2016年与2020年均为特大洪水。2017年主要在8月出现了小范围的洪水;(a)2015~2020雨季降雨量月值与洪水面积统计;(b)2016年8月洪水;(c)2017年8月洪水;(d)2020年8月洪水;(1)~(9)洪水淹没区域放大图)
图7  巢湖流域洪水频率图(2015~2020)(a)双河镇;(b)千人桥镇;(c)红庙镇;(d)十里墩乡和赫店镇
图8  2015~2020年淹没面积统计表
图9  2015~2020年受淹建设用地、农田和受洪水影响的居民点图
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