Cause and damage analysis of 2010 flood disaster in district Muzaffar Garh, Pakistan

Mahmood, Shakeel; Sajjad, Asif; Rahman, Atta-ur

doi:10.1007/s11069-021-04652-6

Cited by 16 publications

(1 citation statement)

References 21 publications

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“…Despite the better spatial resolution, Landsat satellite data is constrained and cannot obtain geospatial data on time, which usually reduces its applicability for RFM (Mahmood et al 2019;Sajjad et al 2020). Our study area, however, is located between two close Landsat pathways (150 and 151), allowing for the capture of high-temporal resolution Landsat data (eight days), compared to a 15-day temporal resolution for an area (Shuhua et al 2009;Mahmood et al 2021). As a result of the high temporal resolution, we were able to conduct a thorough investigation of rapid flood mapping.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%