Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion

Yu Li,Marc Wieland,Ryo Natsuaki,Stefan Schlaffer,Sandro Martinis

doi:10.3390/rs11192231

Abstract

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

Highlights

Flooding is a widespread and dramatic natural disaster that affects lives, infrastructures, economics and local ecosystems in the world
We introduce a method for flood detection in urban environments with synergistic use of Synthetic Aperture Radar (SAR) intensity and coherence based on Bayesian network fusion
We introduced a method for flood mapping in urban environments based on SAR intensity and interferometric coherence under the Bayesian network fusion framework

Summary

Introduction

Flooding is a widespread and dramatic natural disaster that affects lives, infrastructures, economics and local ecosystems in the world. Remote sensing data can offer a synoptic view over large areas systematically and provides useful information about the extent and dynamics of floods. Several international initiatives such as the International Charter “Space and Major Disasters” and the European Copernicus Emergency Management Service – Mapping have leveraged Earth Observation (EO) data to provide products and services for crisis response in the context of disaster management. The growing number of Synthetic Aperture Radar (SAR) satellite missions in orbits such as the Constellation of small Satellites for Mediterranean basin Observation (COSMO-SkyMed) [4], TerraSAR-X [5], Sentinel-1 [6], RADARSAT-2 [7], and the Phased-Array L-band SAR-2 (PALSAR-2) aboard the Advanced Land Observation Satellite-2 (ALOS-2) [8] have shortened the revisit periods (e.g., 6 days with the Sentinel-1A/B constellation, and 1 day with the full COSMO-SkyMed constellation) and facilitated rapid flood mapping within the context of emergency response

Methods

Results

Conclusion