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针对传统洪水演进研究方法计算复杂、实时性弱以及现有遥感监测多聚焦静态分析的问题，以2023年江西中部洪涝为例，融合环境二号、哨兵一号、高分一号等多源遥感影像与水库分布、地形、河网、地物分类、降水等地理空间数据，利用水体指数、随机森林与AI解译方法提取多时序洪水淹没范围，揭示其时空演进与地物响应规律，为理解洪涝过程和精准监测提供重要参考。结果表明：(1)洪水主要沿低洼、河流弯曲密集区扩散，高程20～50 m和坡度2～5°的区域为核心汇水区；(2)消退过程时空分异显著，低洼、缓坡区消退滞后，消退后期4级河网消退面积占比反超3级河网，说明局部河网拓扑调控消退路径，且降水过程与洪水淹没面积呈现一致性；(3)耕地和水体为主要淹没类型，但耕地消退速度快于水体，洪涝导致NDVI显著降低，耕地NDVI降幅与恢复幅度均远大于林地，反映植被响应差异。
Abstract：Addressing limitations in the traditional flood inundation evolution method(high computational complexity, weak real-time monitoring)and static single-source remote sensing analysis, taking the flood in central Jiangxi Province, China in 2023 as an example, we integrated multi-source remote sensing images(HJ-2, Sentinel-1, GF-1) with geospatial data on reservoir distribution, 30 m ASTER DEM-derived terrain and river networks, high-resolution land coverage and precipitation, and used water body index, random forest, and AI interpretation methods to extract multi-temporal flood inundation extents to reveal spatio-temporal evolution patterns and land coverage responses, providing critical insights for understanding flood processes and enhancing monitoring precision. Key findings show that(1)flood expansion exhibits distinct topographic dependency primarily diffusing from low-lying areas with dense sinuous rivers. The areas with an elevation of 20 m to 50 m and a slope of 2° to 5° are the core convergence areas.(2)Recession displays significant spatio-temporal differentiation with notable lag in low-lying areas and gentle slopes. During late recession fourth streams exceed third streams in receding area proportion indicating local river network topology modulates recession pathways. In addition, the precipitation process shows consistency with the flood inundation area.(3)Cultivated land and water bodies dominate inundation types but exhibit divergent responses. Flood significantly reduces NDVI. The reduction and recovery range of NDVI in cultivated land are much greater than those in forest.
[1]Roy P,Pal S C,Chakrabortty R,et al. Threats of Climate and Land Use Change on Future Flood Susceptibility[J].Journal of Cleaner Production,2020,272:122757
[2]IPCC. Climate Change 2021:The Physical Science Basis Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[R].
[3]Allen M R,Ingram W J. Constraints on Future Changes in Climate and the Hydrologic Cycle[J]. Nature,2002,419(6903):224-232
[4]中华人民共和国水利部.中国水旱灾害防御公报2023[M].北京:中国水利水电出版社，2024
[5]尚晓三，王栋.考虑历史洪水不确定性的多维联合洪水频率分析[J].地球科学进展，2022,37(4):407-416
[6]Fatichi S,Vivoni E R,Ogden F L,et al. An Overview of Current Applications,Challenges,and Future Trends in Distributed Process Based Models in Hydrology[J]. Journal of Hydrology,2016,537:4560
[7]周月华，彭涛，史瑞琴.我国暴雨洪涝灾害风险评估研究进展[J].暴雨灾害，2019,38(5):494-501
[8]Hoch J M,Eilander D,Ikeuchi H,et al. Evaluating the Impact of Model Complexity on Flood Wave Propagation and Inundation Extent with a Hydrologic-hydrodynamic Model Coupling Framework[J]. Natural Hazards and Earth System Sciences,2019,19(8):1723-1735
[9]Schumann G,Brakenridge G,Kettner A,et al. Assisting Flood Disaster Response with Earth Observation Data and Products:A Critical Assessment[J]. Remote Sensing,2018,10(8):1230
[10]Huang C,Chen Y,Zhang S,et al. Detecting,Extracting,and Monitoring Surface Water from Space Using Optical Sensors:A Review[J]. Reviews of Geophysics,2018,56(2):305-340
[11]Frazier P S,Page K J. Water Body Detection and Delineation with Landsat TM Data[J]. Photogrammetric Engineering and Remote Sensing,2000,66(12):1461-1467
[12]Berthon J F,Zibordi G. Optically Black Waters in the Northern Baltic Sea[J]. Geophysical Research Letters,2010,37(9):L09605
[13]Gasparovic M,Klobucar D. Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Objectbased Approach[J]. Forests,2021,12(5):602
[14]曾玲方，李霖，万丽华.基于Sentinel-1卫星SAR数据的洪水淹没范围快速提取[J].地理信息世界，2015,22(5):100-103
[15]王磊，连增增.基于Sentinel-1A的2020年鄱阳湖流域洪水灾害遥感监测[J].地理空间信息，2022,20(6):43-46
[16]孙书腾，刘培，王光彦.基于多时相主被动遥感协同处理的洪涝灾害应急监测[J].水利水电技术(中英文)，2022,53(9):25-35
[17]朱璨阳.基于Sentinel-1数据的2020年合肥市洪涝监测与分析[D].合肥:合肥工业大学，2022
[18]宋文龙，马建威，孙亚勇，等.海河“23·7”流域性特大洪水启用蓄滞洪区洪水淹没全过程卫星遥感监测分析[J].中国防汛抗旱，2023,33(10):31-36
[19]王燕婷，杨耘，刘艳，等.多源异构遥感影像的半干旱区洪涝水体识别与变化监测[J].水利水电技术(中英文)，2025,56(2):45-58
[20]郑美霞，刘玲. 2011—2020年江西省自然灾害灾情时空特征分析[J].自然灾害学报，2022,31(2):233-241
[21]谭国良，张桂娇，刘筱琴，等.江西省水文分区研究[J].水文，2006,26(5):86-88
[22]Morbidelli R,Saltalippi C,Flammini A,et al. Role of Slope on Infiltration:A Review[J]. Journal of Hydrology,2018,557:878-886
[23]张欣，季明锋，高强，等.植树造林对北京市山区场次洪水的影响[J].中国防汛抗旱，2025,35(12):93-97
[24]Bogaard T A,Greco R. Soil Hydrology in Landslide-prone Areas:From Monitoring to Early Warning Systems[J].Earth-science Reviews,2018,179:1-21
基本信息:
中图分类号:TV87;P237
引用信息:
[1]宁愿,刘勇,孙洪泉,等.利用多源遥感数据分析洪涝淹没演进过程[J].地理空间信息,2026,24(09):59-65.
基金信息:
国家重点研发计划专项资助项目（2024YFC3013300）; 应急管理部国家自然灾害防治研究院重点项目（2023-JBKY-58、ZDJ2024-29）
2026-09-28
2026-09-28