Rapid flood inundation mapping and impact assessment using Sentinel-1 SAR data over Ghaggar River basin of Punjab, India

被引:6
作者
Arora, Mohit [1 ]
Sahoo, Sashikanta [1 ,2 ]
Bhatt, Chandra Mohan [3 ]
Litoria, Pradeep Kumar [1 ]
Pateriya, Brijendra [1 ]
机构
[1] Punjab Remote Sensing Ctr, PAU Campus, Ludhiana, India
[2] IIT Roorkee, Ctr Excellence Disaster Mitigat & Management, Roorkee, India
[3] ISRO, Indian Inst Remote Sensing, Dehra Dun, India
基金
美国国家航空航天局;
关键词
Flood; SAR; GPM; backscattering coefficient; Ghaggar sub-basin; WATER; BRAHMAPUTRA; MANAGEMENT; ERROR;
D O I
10.1007/s12040-023-02199-7
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The present study focuses primarily on the rapid mapping of flood-inundated areas using high-resolution Synthetic Aperture Radar (SAR) data and its impact assessment in the Ghaggar basin during the 2019 floods. Further, a comprehensive analysis has been carried out by Global Precipitation Mission (GPM) data to investigate the intensity and frequency of precipitation over the basin. Multi-temporal, dual-polarised (VH & VV) Sentinel-1 (SAR) data have been used for mapping the flood inundated area. SAR data is very useful and most preferred for detecting flood-inundated areas in all weather and day/night time. In this study, binarisation technique was applied to separate satellite image pixel values into flooded and non-flooded groups. Thresholds for backscattering coefficient (sigma(0)) between -22 to -30 and -10 to -20 dB have been applied for both the polarised band (VH & VV) to extract the maximum flood pixels. It was also observed that flood extent was at its peak during July 20-23, 2019, and on July 26-27, 2019, and impacted a large part of agricultural and urban patches during this time. The zonal statistics are calculated to find the inundated area over the study region. The results have been validated against Radarsat-1 images provided by NRSC-ISRO during the flood period and found an accuracy of 82% in both polarisations. VH polarisation has been found to have higher accuracy than VV polarisation. The finding reveals that similar to 9600 hectares and similar to 2700 hectares of land have been affected in Patiala and Sangrur districts. The results revealed that paddies were mostly inundated by flooding with low elevations and low slopes. From the proposed approach, it is evident that the SAR images, along with GIS data, can be used efficiently for mapping flood-inundated areas.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Rapid flood inundation mapping and impact assessment using Sentinel-1 SAR data over Ghaggar River basin of Punjab, India
    Mohit Arora
    Sashikanta Sahoo
    Chandra Mohan Bhatt
    Pradeep Kumar Litoria
    Brijendra Pateriya
    Journal of Earth System Science, 132
  • [2] Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data
    Borah, Suranjana B.
    Sivasankar, Thota
    Ramya, M. N. S.
    Raju, P. L. N.
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2018, 190 (09)
  • [3] Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data
    Suranjana B. Borah
    Thota Sivasankar
    M. N. S. Ramya
    P. L. N. Raju
    Environmental Monitoring and Assessment, 2018, 190
  • [4] Global Flood Mapper: a novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR
    Tripathy, Pratyush
    Malladi, Teja
    NATURAL HAZARDS, 2022, 114 (02) : 1341 - 1363
  • [5] Global Flood Mapper: a novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR
    Pratyush Tripathy
    Teja Malladi
    Natural Hazards, 2022, 114 : 1341 - 1363
  • [6] Mapping flood inundation areas over the lower part of the Con River basin using Sentinel 1A imagery
    Nguyen Thien Phuong Thao
    Tran Tuan Linh
    Nguyen Thi Thu Ha
    Pham Quang Vinh
    Nguyen Thuy Linh
    VIETNAM JOURNAL OF EARTH SCIENCES, 2020, 42 (03): : 288 - 297
  • [7] Flood susceptibility and flood frequency modeling for lower Kosi Basin, India using AHP and Sentinel-1 SAR data in geospatial environment
    Shivhare, Vikash
    Kumar, Alok
    Kumar, Reetesh
    Shashtri, Satyanarayan
    Mallick, Javed
    Singh, Chander Kumar
    NATURAL HAZARDS, 2024, 120 (13) : 11579 - 11610
  • [8] Performance of Random Forest Classifier for Flood Mapping Using Sentinel-1 SAR Images
    Chu, Yongjae
    Lee, Hoonyol
    KOREAN JOURNAL OF REMOTE SENSING, 2022, 38 (04) : 375 - 386
  • [9] Optimum flood inundation mapping in mountainous regions using Sentinel-1 data and a GIS-based multi-criteria approach: a case study of Tlawng river basin, Mizoram, India
    Debbarma, Sagar
    Mandal, Sameer
    Borgohain, Ankur
    Ori, Bomken
    Syad, Shonlang
    Sangtam, Lemtsase
    Bandyopadhyay, Arnab
    Bhadra, Aditi
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2024, 196 (12)
  • [10] Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning
    Konapala, Goutam
    Kumar, Sujay, V
    Ahmad, Shahryar Khalique
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 180 : 163 - 173