Riverine flood mapping and impact assessment using remote sensing technique: a case study of Chenab flood-2014 in Multan district, Punjab, Pakistan

Sajjad, Asif; Lu, Jianzhong; Chen, Xiaoling; Chisenga, Chikondi; Mazhar, Nausheen; Nadeem, Basit

doi:10.1007/s11069-021-05033-9

Cited by 9 publications

(1 citation statement)

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“…In most situations, RFM is based on water indices that use band ratios generated from satellite spectral data and threshold values applied to the ratios to extract flooded areas (Revilla-Romero et al2015;Sajjad et al 2021). RS-based water indices are utilized for the extraction of flooded areas, including the Normalized Difference Water Index (NDWI) (McFeeters 1996), which uses reflected NIR and Green bands for water area assessment, and the Modified NDWI (MNDWI) (Xu 2006), which uses the SWIR band and the Green band to enhance water areas while removing built-up and vegetation area noise.…”

Section: Introductionmentioning

confidence: 99%

Rapid assessment of riverine flood inundation in Chenab floodplain using remote sensing techniques

Sajjad

Jianzhong

Chen

et al. 2023

Geoenviron Disasters

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Introduction After flood occurrences, remote sensing images provide crucial information for mapping flood inundation extent. Optical satellite images can be utilized to generate flooded area maps when the flooded areas are free from clouds. Materials and Methods In this study flooded area was calculated using a variety of water indices and classification algorithms, calculated on Landsat data. Pre-flood, during flood, and post-flood satellite data were collected for in-depth flood investigation. The delineation of inundated areas was done using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Water Ratio Index (WRI). In order to detect and compare flooded areas with water indices, the supervised maximum likelihood algorithm was also used for land use and land cover mapping. Results The results of the investigation allowed for a flooded area and recession. The analysis revealed that the flooded area covered about 68% of the study area, and remained standing for seven weeks. We used the misclassified areas approach, as determined, using the classified results, to improve the results of the flooded areas, generated through the use of each of the 3 water indices. The result showed that the MNDWI images showed better accuracy of above 90%, which reflects the reliability of the results. Conclusion This proposed remote sensing (RS) technique provides a basis for the identification of inundated areas with less misclassified areas, which enable an emergency response to be targeted, for newly flooded areas. Thus, the present study provides a novel rapid flood mapping perspective and provides a considerable contribution to flood monitoring.

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Section: Introductionmentioning

confidence: 99%