A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

被引:5
|
作者
Pedzisai, Ezra [1 ]
Mutanga, Onisimo [1 ]
Odindi, John [1 ]
Bangira, Tsitsi [1 ]
机构
[1] Sch Agr Earth & Environm Sci, Discipline Geog, Private Bag X01, ZA-3201 Pietermaritzburg, South Africa
基金
新加坡国家研究基金会;
关键词
Change detection and thresholding; Flood extent map; SAR; Scenarios ensemble; Sentinel-1; RIVER; INUNDATION;
D O I
10.1016/j.heliyon.2023.e13332
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The usecase calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning
    Konapala, Goutam
    Kumar, Sujay, V
    Ahmad, Shahryar Khalique
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 180 : 163 - 173
  • [42] A PRELIMINARY COMPARISON OF TWO EXCLUSION MAPS FOR LARGE-SCALE FLOOD MAPPING USING SENTINEL-1 DATA
    Zhao, Jie
    Roth, Florian
    Bauer-Marschallinger, Bernhard
    Wagner, Wolfgang
    Chini, Marco
    Zhu, Xiao Xiang
    [J]. GEOSPATIAL WEEK 2023, VOL. 10-1, 2023, : 911 - 918
  • [43] Limitations in the use of Sentinel-1 data for morphological change detection in rivers
    Marchetti, Giulia
    Manconi, Andrea
    Comiti, Francesco
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2023, 44 (21) : 6642 - 6669
  • [44] Change Detection in Dual Polarization Sentinel-1 Data With Wilks' Lambda
    Nielsen, Allan Aasbjerg
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [45] Automatic flood detection using sentinel-1 images on the google earth engine
    Moharrami, Meysam
    Javanbakht, Mohammad
    Attarchi, Sara
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2021, 193 (05)
  • [46] Automatic flood detection using sentinel-1 images on the google earth engine
    Meysam Moharrami
    Mohammad Javanbakht
    Sara Attarchi
    [J]. Environmental Monitoring and Assessment, 2021, 193
  • [47] LARGE-SCALE MAPPING OF FLOOD USING SENTINEL-1 RADAR REMOTE SENSING
    Haghighi, M. H.
    [J]. XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 43-B3 : 1097 - 1102
  • [48] Performance of Random Forest Classifier for Flood Mapping Using Sentinel-1 SAR Images
    Chu, Yongjae
    Lee, Hoonyol
    [J]. KOREAN JOURNAL OF REMOTE SENSING, 2022, 38 (04) : 375 - 386
  • [49] LARGE-SCALE MAPPING OF FLOOD USING SENTINEL-1 RADAR REMOTE SENSING
    Haghighi, M.H.
    [J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2022, 43 (B3-2022): : 1097 - 1102
  • [50] Development of a method for flood detection based on Sentinel-1 images and classifier algorithms
    Sharifi, Alireza
    [J]. WATER AND ENVIRONMENT JOURNAL, 2021, 35 (03) : 924 - 929