洪水淹没历时是洪涝灾害损失评估的关键变量，为满足大尺度洪涝灾害遥感应急监测和灾损评估需求，亟需构建一种利用多源密集雷达遥感的流域洪水淹没历时估算方法。以2020年长江中下游鄱阳湖流域性洪水为例，综合利用国内外多源密集时相的雷达遥感数据，对鄱阳湖流域周边受灾地区的洪水淹没范围进行提取，在此基础上构建基于遥感的洪水淹没历时估算模型，并依据乡镇级平均淹没历时和水稻的淹没时长-产量损失关系对鄱阳湖周边地区进行洪涝灾情评估。结果表明：密集时相洪水淹没范围提取结果的Kappa系数高于0.89，流域洪水淹没历时为1~44 d，鄱阳湖流域周边地区受灾极其严重乡镇为新建县南矶乡、鄱阳县昌洲乡和永修县三角乡，重灾区为鄱阳镇、莲湖乡、蛟塘镇等，绝大部分地区为水稻不同程度减产的中度灾区，评估结果与实际灾情基本一致。本文提出的淹没历时计算和灾情评估方法具有大尺度、高频次和准确性等优势，可为流域性洪涝灾害应急监测和灾损评估提供支撑。
张若旭
,
李小涛
,
江威
,
张丽萍
,
邓清海
,
宋小宁
. 多源雷达遥感协同的鄱阳湖流域洪水淹没历时估算[J]. 中国科学院大学学报, 2023
, 40(4)
: 486
-495
.
DOI: 10.7523/j.ucas.2021.0079
To investigate flood disaster damage at large-scale, it is necessary to propose inundation duration estimation method using multi-source radar remote sensing data. Firstly, Poyang Lake basin flood located in middle of the Yangtze River is taken as study area; and then, multi-temporal radar remote sensing data are collected to extract flood inundation areas using random forest model; moreover, inundation duration is calculated at Poyang Lake basin using the estimation model of inundation duration with multi-temporal inundation areas; finally, the flood disaster assessment is conducted using average inundation duration and relationship of inundation duration and the yield loss degree of rice. The results show that:1) the Kappa coefficients of the flood inundation extracted area are higher than 0.89 and the basin flood inundation lasts 1-44 days; 2) extremely severely flood affected at township scale locate at Nanji, Changzhou, Sanjiao and the most severely flood affected areas mainly locate at Poyang, Lianhu, and Jiaotang. The majority of areas are classified as moderately affected and the assessment results are consistent with the actual disaster damage situation. The proposed inundation duration estimation method has the advantages of large scale monitoring with high frequency, which can provide support for basin flood emergency monitoring and rapid disaster damage assessment.
[1] Liang J Y, Liu D S. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159:53-62. DOI:10.1016/j.isprsjprs.2019.10.017.
[2] 史培军, 袁艺. 重特大自然灾害综合评估[J]. 地理科学进展, 2014, 33(9):1145-1151. DOI:10.11820/dlkxjz.2014.09.001.
[3] 侯俊东, 李铭泽. 自然灾害应急管理研究综述与展望[J]. 防灾科技学院学报, 2013, 15(1):48-55. DOI:10.3969/j.issn.1673-8047.2013.01.009.
[4] 中华人民共和国应急管理部救灾和物资保障司. 应急管理部公布2019年全国十大自然灾害[EB/OL]. (2020-01-12)[2021-11-25]. https://www.mem.gov.cn/xw/bndt/202001/t20200112_343410.shtml.
[5] 中华人民共和国应急管理部救灾和物资保障司. 应急管理部公布2020年全国十大自然灾害[EB/OL]. (2021-01-02)[2021-11-25]. https://www.mem.gov.cn/xw/yjglbgzdt/202101/t20210102_376288.shtml.
[6] 黄诗峰, 马建威, 孙亚勇. 我国洪涝灾害遥感监测现状与展望[J]. 中国水利, 2021(15):15-17. DOI:10.3969/j.issn.1000-1123.2021.15.017.
[7] 胡凯龙, 刘明, 刘明博, 等. 高分多模卫星在洪涝灾害监测中的应用[J]. 航天器工程, 2021, 30(3):218-224. DOI:10.3969/j.issn.1673-8748.2021.03.031.
[8] Rahman M R, Thakur P K. Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS:a case study from the Kendrapara District of Orissa State of India[J]. The Egyptian Journal of Remote Sensing and Space Science, 2018, 21:S37-S41. DOI:10.1016/j.ejrs.2017.10.002.
[9] 邵芸. 雷达卫星防灾减灾应用现状与展望[J]. 城市与减灾, 2018(6):25-29. DOI:10.3969/j.issn.1671-0495.2018.06.006.
[10] Tripathi G, Pandey A C, Parida B R, et al. Flood inundation mapping and impact assessment using multi-temporal optical and SAR satellite data:a case study of 2017 flood in darbhanga district, Bihar, India[J]. Water Resources Management, 2020, 34(6):1871-1892. DOI:10.1007/s11269-020-02534-3.
[11] Zoka M, Psomiadis E, Dercas N. The complementary use of optical and SAR data in monitoring flood events and their effects[J]. Proceedings, 2018, 2(11):644. DOI:10.3390/proceedings2110644.
[12] Brivio P A, Colombo R, Maggi M, et al. Integration of remote sensing data and GIS for accurate mapping of flooded areas[J]. International Journal of Remote Sensing, 2002, 23(3):429-441. DOI:10.1080/01431160010014729.
[13] Oberstadler R, Hönsch H, Huth D. Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping:a case study in Germany[J]. Hydrological Processes, 1997, 11(10):1415-1425. DOI:10.1002/(sici)1099-1085(199708)11:10<1415:aid-hyp532>3.0.co;2-2.
[14] Guo H D. Spaceborne and airborne SAR for target detection and flood monitoring[J]. Photogrammetric Engineering & Remote Sensing, 2000, 66(5):611-617. DOI:10.1016/S0031-0182(00)00034-1.
[15] Uddin K, Matin M A, Meyer F J. Operational flood mapping using multi-temporal Sentinel-1 SAR images:a case study from Bangladesh[J]. Remote Sensing, 2019, 11(13):1581. DOI:10.3390/rs11131581.
[16] Gao W, Shen Q, Zhou Y H, et al. Analysis of flood inundation in ungauged basins based on multi-source remote sensing data[J]. Environmental Monitoring and Assessment, 2018, 190(3):129. DOI:10.1007/s10661-018-6499-4.
[17] 李晟铭, 刘吉平, 王铭, 等. 基于Sentinel-1A的2016年长江中下游重灾区洪水遥感监测及灾情评估[J]. 安徽农业科学, 2018, 46(10):68-74, 91. DOI:10.13989/j.cnki.0517-6611.2018.10.020.
[18] 汪权方, 孙佩, 王新生, 等. 基于洪水过程的农业洪灾变化遥感快速评估模型及其应用[J]. 长江流域资源与环境, 2017, 26(11):1831-1842. DOI:10.11870/cjlyzyyhj201711012.
[19] 伍俊斌, 刘雷震, 田丰, 等. 面向重大洪涝灾害应急响应的灾情动态评估方法研究:以2018年8月山东寿光洪涝灾害为例[J]. 北京师范大学学报(自然科学版), 2020, 56(6):846-855. DOI:10.12202/j.0476-0301.2020004.
[20] Cao H, Zhang H, Wang C, et al. Operational flood detection using sentinel-1 SAR data over large areas[J]. Water, 2019, 11(4):786. DOI:10.3390/w11040786.
[21] Cian F, Marconcini M, Ceccato P, et al. Flood depth estimation by means of high-resolution SAR images and lidar data[J]. Natural Hazards and Earth System Sciences, 2018, 18(11):3063-3084. DOI:10.5194/nhess-18-3063-2018.
[22] Scarpino S, Albano R, Cantisani A, et al. Multitemporal SAR data and 2D hydrodynamic model flood scenario dynamics assessment[J]. ISPRS International Journal of Geo-Information, 2018, 7(3):105. DOI:10.3390/ijgi7030105.
[23] 湛南渝. "台风-暴雨"洪涝灾害遥感监测与评估研究[D]. 成都:电子科技大学, 2020. DOI:10.27005/d.cnki.gdzku.2020.001035.
[24] 邵长秀, 潘学标, 李家文, 等. 不同生育阶段洪涝淹没时长对水稻生长发育及产量构成的影响[J]. 农业工程学报, 2019, 35(3):125-133. DOI:10.11975/j.issn.1002-6819.2019.03.016.
[25] 梅少华, 梅金先, 陈兴国, 等. 洪涝灾害对水稻生产的影响评估及抗灾对策研究[J]. 作物杂志, 2011(2):89-93. DOI:10.16035/j.issn.1001-7283.2011.02.023.
