Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images

Abstract

Typhoon Hagibis passed through Japan on October 12, 2019, bringing heavy rainfall over half of Japan. Twelve banks of seven state-managed rivers collapsed, flooding a wide area. Quick and accurate damage proximity maps are helpful for emergency responses and relief activities after such disasters. In this study, we propose a quick analysis procedure to estimate inundations due to Typhoon Hagibis using multi-temporal Sentinel-1 SAR intensity images. The study area was Ibaraki Prefecture, Japan, including two flooded state-managed rivers, Naka and Kuji. First, the completely flooded areas were detected by two traditional methods, the change detection and the thresholding methods. By comparing the results in a part of the affected area with our field survey, the change detection was adopted due to its higher recall accuracy. Then, a new index combining the average value and the standard deviation of the differences was proposed for extracting partially flooded built-up areas. Finally, inundation maps were created by merging the completely and partially flooded areas. The final inundation map was evaluated via comparison with the flooding boundary produced by the Geospatial Information Authority (GSI) and the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. As a result, 74% of the inundated areas were able to be identified successfully using the proposed quick procedure.

A Temporal Deep Convolutional Neural Network Model on Sentinel-1 Image Time Series for Pixel-Wise Flood Classification

“…Training epochs were set to 60 with a batch size of 32. The experiments were ran on a CPU machine using Tensorflow 4 and Keras 5 .…”

“…Core flood detection approaches regardless of number of images are thresholding, change detection [5], change detection and thresholding [6], supervised [7,8], semi-supervised [9,10] and unsupervised [2] image classification based on classical machine and deep learning. Multi-temporal pixelwise time series approaches such as [11,12], despite being lightweight, do not exploit the temporal autocorrelation, as they treat the time series using statistical analysis without any learning procedure.…”

Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques

“…Thresholding of the significantly changed area (v) was defined by subtracting twice the standard deviation from the average value of the difference for the whole scene. This thresholding method has been used to extract flooding areas where positive results were obtained in our previous studies [47,48].…”

Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

“…Typhoons are tropical cyclones that occur in the Western Pacific and adjacent waters and are common climate phenomena. Given that typhoons have significant destructive power and often imperil the coastal areas where they make landfall, the nature of these typhoons has long been an important research topic [1][2][3].…”