Automated Processing for Flood Area Detection Using ALOS-2 and Hydrodynamic Simulation Data

Masato Ohki,Kei Yoshimura,Kosuke Yamamoto,Takeo Tadono

doi:10.3390/rs12172709

Masato Ohki, Kei Yoshimura + Show 2 more

Open Access

https://doi.org/10.3390/rs12172709

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Abstract

Rapid and frequent mapping of flood areas are essential for monitoring and mitigating flood disasters. The Advanced Land Observing Satellite-2 (ALOS-2) carries an L-band synthetic aperture radar (SAR) capable of rapid and frequent disaster observations. In this study, we developed a fully automatic, fast computation, and robust method for detecting flood areas using ALOS-2 and hydrodynamic flood simulation data. This study is the first attempt to combine flood simulation data from the Today’s Earth system (TE) with SAR-based disaster mapping. We used Bayesian inference to combine the amplitude/coherence data by ALOS-2 and the flood fraction data by TE. Our experimental results used 12 flood observation sets of data from Japan and had high accuracy and robustness for use under various ALOS-2 observation conditions. Flood simulation contributed to improving the accuracy of flood detection and reducing computation time. Based on these findings, we also assessed the operability of our method and found that the combination of ALOS-2 and TE data with our analysis method was capable of daily flood monitoring.

Highlights

Flooding is the most frequent and widespread natural disaster, which has affected more than 800,000 people and caused USD 300 billion in economic losses over the past decade [1]
Advanced Land Observing Satellite-2 (ALOS-2) is characterized by the unique local solar time (LST) of its observations, which cover hours not monitored by other synthetic aperture radar (SAR) satellites observing at 6:00 and 18:00 LST
To validate the accuracy and robustness of our method, we processed 12 datasets for urban-area flooding in Japan acquired by ALOS-2 under a variety of observation conditions

Summary

Introduction

Flooding is the most frequent and widespread natural disaster, which has affected more than 800,000 people and caused USD 300 billion in economic losses over the past decade [1]. Synthetic aperture radar (SAR), has long been expected to play an important role in rapid flood monitoring because of its ability to make observations in any weather, day or night [4]. The Advanced Land Observing Satellite-2 (ALOS-2) carries an L-band SAR, the Phased Array-type L-band Synthetic Aperture Radar-2 (PALSAR-2), for monitoring disasters and environmental changes [5]. ALOS-2 has the flexibility to change observation off-nadir angles and left- and right-looking for quick access to an affected area. Given these benefits, we need to understand quantitatively the effectiveness of ALOS-2 for rapid flood mapping

Results

Discussion

Conclusion