[1]张　会,潘晓宁,贾　浩,等.基于遥感和社交媒体数据的城市洪涝灾害监测[J].郑州大学学报(工学版),2025,46(01):82-89.[doi:10.13705/j.issn.1671-6833.2025.01.011]
　ZHANG Hui,PAN Xiaoning,JIA Hao,et al.Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data[J].Journal of Zhengzhou University (Engineering Science),2025,46(01):82-89.[doi:10.13705/j.issn.1671-6833.2025.01.011]
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基于遥感和社交媒体数据的城市洪涝灾害监测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年01期
页码:
82-89
栏目:
出版日期:
2024-12-23

文章信息/Info

Title:
Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data
文章编号:
1671-6833(2025)01-0082-08
作者:
张　会;  潘晓宁;  贾　浩;  许德合
华北水利水电大学 测绘与地理信息学院,河南 郑州 450046
Author(s):
ZHANG Hui;  PAN Xiaoning;  JIA Hao;  XU Dehe
College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
关键词:
洪涝灾害;  高分三号;  阈值分割;  随机森林;  社交媒体数据
Keywords:
flood disaster;  GF-3;  threshold segmentation;  random forest;  social media data
分类号:
TP79
DOI:
10.13705/j.issn.1671-6833.2025.01.011
文献标志码:
A
摘要:
城市洪涝监测对灾害管理与应急响应至关重要,单一数据源往往存在各自的缺陷,不利于多维度、全方位地分析城市复杂洪涝灾害灾情。结合不受云雨影响且覆盖范围大的GF-3 SAR影像与实时性较强的社交媒体数据,建立“7·20”特大暴雨郑州市中心城区洪涝监测方法,采用阈值分割、随机森林对GF-3 SAR影像进行多方案的灾前灾中水体提取,获取精度最高的水体提取与洪涝监测结果,分析典型区域的水体提取效果;利用Python工具获取城市内涝的社交媒体数据,对其进行处理、可视化与空间分析;结合GF-3 SAR影像与社交媒体数据洪涝监测结果探讨二者优势互补性。结果表明:SAR水体总体提取精度从高到低依次为随机森林、Otsu阈值法、水体指数法,但是在一些典型区域分析中,随机森林的提取效果低于其他方法;基于SAR影像提取的洪涝淹没范围主要集中在三环外的城市边缘地区与大型水体周围,基于社交媒体数据提取的洪涝信息主要集中在城市人口和建筑密集的三环内。
Abstract:
Urban flood monitoring is crucial for disaster management and emergency response. Single data sources often have their own shortcomings, which are not conducive to the multi-dimensional and all-round analysis of complex urban flooding disasters. The GF-3 SAR images were combined, which were not affected by clouds and rain and had a large coverage, with real-time social media data to establish a flood monitoring method for the downtown area of Zhengzhou City during the "7·20" heavy rainstorm. Threshold segmentation and random forest were used to extract water bodies from GF-3 SAR images before and during the flood, the most accurate water body extraction results and flood monitoring results were obtained, and the effect of water body extraction in typical areas was analyzed. Using Python tools,the obtained social media data about urban waterlogging were processed, visualized and spatially analyzed. GF-3 SAR images and social media data flood monitoring results were combined to explore the complementary advantages of the two. The results showed that the overall extraction accuracy of SAR water bodies was in the order of random forest, Otsu threshold method, and water body index method. However, in some typical regional analyses, the extraction effect of random forest was lower than that of the other methods.The flood inundation range extracted based on SAR images was mainly concentrated in the urban fringe areas outside the Third Ring Road and around large water bodies.The flood information extracted based on social media data was mainly concentrated in the Third Ring Road, where the urban population was densely populated with buildings.

参考文献/References:

[1]刘家宏, 裴羽佳, 梅超, 等. 郑州“7·20”特大暴雨内涝成因及灾害防控[J]. 郑州大学学报(工学版), 2023, 44(2): 38-45. 

LIU J H, PEI Y J, MEI C, et al. Waterlogging cause and disaster prevention and control of “7·20” torrential rain in Zhengzhou[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44(2): 38-45. 
[2]SHI J, CUI L L, TIAN Z. Spatial and temporal distribution and trend in flood and drought disasters in East China [J]. Environmental Research, 2020, 185: 109406.
[3]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. 
[4]高寒新, 陈波, 孙洪泉, 等. SAR卫星影像洪水检测研究进展及展望[J]. 地球信息科学学报, 2023, 25 (10): 1933-1953. 
GAO H X, CHEN B, SUN H Q, et al. Research progress and prospect of flood detection based on SAR satellite images[J]. Journal of Geo-information Science, 2023, 25(10): 1933-1953. 
[5]谷鑫志, 曾庆伟, 谌华, 等. 高分三号影像水体信息提取[J]. 遥感学报, 2019, 23(3): 555-565. 
GU X Z, ZENG Q W, SHEN H, et al. Study on water information extraction using domestic GF-3 image[J]. Journal of Remote Sensing, 2019, 23(3): 555-565. 
[6]MARKERT K N, MARKERT A M, MAYER T, et al. Comparing Sentinel-1 surface water mapping algorithms and radiometric terrain correction processing in Southeast Asia utilizing Google Earth Engine[J]. Remote Sensing, 2020, 12(15): 2469. 
[7]LI Y, MARTINIS S, WIELAND M. Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2019,152:178-191. 
[8]TANGUY M, CHOKMANI K, BERNIER M, et al. River flood mapping in urban areas combining RADARSAT-2 data and flood return period data[J]. Remote Sensing of Environment, 2017, 198: 442-459. 
[9]LI J, HE Z, PLAZA J, et al. Social media: new perspectives to improve remote sensing for emergency response[J]. Proceedings of the IEEE, 2017, 105(10): 1900-1912. 
[10]邢子瑶, 董芯蕊, 昝糈莉, 等. 融合VGI和遥感等多源数据的洪涝范围提取与模拟方法[J]. 地球信息科学学报, 2023, 25(9): 1869-1881. 
XING Z Y, DONG X R, ZAN X L, et al. flood inundation mapping and estimation using VGI, remote sensing images and other multi-source data[J]. Journal of GeoInformation Science, 2023, 25(9): 1869-1881. 
[11]国务院灾害调查组. 河南郑州“7·20”特大暴雨灾害调查报告[R/OL].(2021-01-21)[2024-06-04].https:∥ www.mem.gov.cn/gk/sgcc/ tbzdsgdcbg/202201/P02022 0121639049697767.pdf. 
The Disaster Investigation Group of the State Council. Disaster investigation report of Zhengzhou “7·20” heavy rain[R/OL].(2021-01-21)[2024-06-04].https:∥ www.mem.gov.cn/gk/sgcc/ tbzdsgdcbg/202201/P02022 0121639049697767.pdf. 
[12] HUANG W L, DEVRIES B, HUANG C Q, et al. Automated extraction of surface water extent from Sentinel-1 data[J]. Remote Sensing, 2018, 10: 797. 
[13]贾诗超, 薛东剑, 李成绕, 等. 基于Sentinel-1数据的水体信息提取方法研究[J]. 人民长江, 2019, 50 (2): 213-217. 
JIA S C, XUE D J, LI C R, et al. Study on new method for water area information extraction based on Sentinel-1 data[J]. Yangtze River, 2019, 50(2): 213-217. 
[14]方匡南, 吴见彬, 朱建平, 等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011, 26(3): 32-38.
FANG K N, WU J B, ZHU J P, et al. A review of technologies on random forests[J]. Statistics & Information Forum, 2011, 26(3): 32-38. 
[15] TANG H L, LU S L, BAIG M H A, et al. Large-scale surface water mapping based on Landsat and Sentinel-1 images[J]. Water, 2022,14(9): 1454. 
[16]沈兰芝, 谌华, 范凤云, 等. 基于Q-OTSU算法高分三号水体快速提取方法研究: 以河南鹤壁洪涝灾害监测为例[J]. 自然灾害学报, 2022, 31(4): 134-143. 
SHEN L Z, SHEN H, FAN F Y, et al. Research on rapid extraction method of water based on Q-OTSU algorithm using domestic GF-3 image: a case of flood disaster monitoring in Hebi, Henan Province[J]. Journal of Natural Disasters, 2022, 31(4): 134-143. 
[17]WANG Z Y, YE X Y. Social media analytics for natural disaster management[J]. International Journal of Geographical Information Science, 2018, 32(1): 49-72. 
[18]余水. 强降雨对合成孔径雷达风速反演的影响[D]. 杭州: 浙江大学, 2017. 
YU S. Effects of large precipitation to the wind retrieval from synthetic aperture radar[D]. Hangzhou: Zhejiang University, 2017. 
[19]张金萍, 张朝阳, 左其亭. 极端暴雨下城市内涝模拟与应急响应能力评估[J]. 郑州大学学报(工学版), 2023, 44(2): 30-37. 
ZHANG J P, ZHANG Z Y, ZUO Q T. Urban waterlogging simulation and emergency response capacity evaluation with extreme rainstorms[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44 (2): 30-37.

更新日期/Last Update: 2024-12-31