Comparing Urban Flood Dynamics Using SAR Imagery and Google Earth Engine: Case Studies In Dhaka And Houston
Abstract
Flooding in urban areas pose significant economic and social impact on the population across the globe. Urban flooding adversely affects the economic well-being of millions of people through billions of dollars of property damages, health impacts, loss of income, and loss of life. Addressing this challenge requires delineating flood extent at a high spatial temporal resolution. Efforts to fully quantify urban flood distribution utilizing the potential of Synthetic Aperture Radar (SAR) imageries in a cloud-based platform, have been limited. Due to the limitations in spatio-temporal resolution and complex back scatter mechanisms in urban settings, flood detection has been a challenging task in urban areas. However, in the age of big-data and cloud-computing, data acquisition, satellite image processing and predictive analysis are rapidly becoming more accessible. Building on recent advancements, in this study, we perform a comparative analysis to explore dynamics of flood signature in Houston, TX and Dhaka, Bangladesh, two geographically dissimilar cities. We developed a pipeline to prepare flood maps based on the change detection of SAR image statistics and range of pixel intensity for urban flood water in Google Earth Engine (GEE). We explored the variation of pixel attributes resulted from the backscatters of normal flood water in open areas as well as adjacent to the urban features. The proposed framework enables flood extents extraction within urban areas, subject to complex backscatter mechanism by leveraging the high computational efficiency of GEE. This opens the door for image parameters, related to pixel intensities, defining flood extent to be transferred to other urban areas for flood detection.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H42F1363M