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Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and
Muhammad Nasar Ahmad1, Zhenfeng Shao2, Akib Javed2
1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 43007, Hubei, China. mnasarahmad@whu.edu.cn.
Abstract:
Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.
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