Articles | Volume 18, issue 7
https://doi.org/10.5194/nhess-18-1905-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-1905-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
10 Jul 2018
Review article |
| 10 Jul 2018
| 10 Jul 2018
Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery
Wen Liu
CORRESPONDING AUTHOR
Department of Urban Environment Systems, Chiba University, Chiba,
263-8522, Japan
Fumio Yamazaki
Department of Urban Environment Systems, Chiba University, Chiba,
263-8522, Japan
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Cited
23 citations as recorded by crossref.
- HIGH-RESOLUTION SURFACE WATER MAP OVER JAPAN AND ESTIMATION OF INUNDATION AREA CAUSED BY TYPHOON HAGIBIS S. SETO 10.2208/jscejhe.76.2_I_613
- Multitemporal UAV surveys of geomorphological changes caused by postfire heavy rain in Kamaishi city, northeast Japan Y. Touge et al. 10.1016/j.catena.2022.106702
- Automated Processing for Flood Area Detection Using ALOS-2 and Hydrodynamic Simulation Data M. Ohki et al. 10.3390/rs12172709
- The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework G. Okada et al. 10.3390/rs13071401
- Synergistic Approach of Remote Sensing and GIS Techniques for Flash-Flood Monitoring and Damage Assessment in Thessaly Plain Area, Greece E. Psomiadis et al. 10.3390/w11030448
- Extraction of Inundation Areas Due to the July 2018 Western Japan Torrential Rain Event Using Multi-Temporal ALOS-2 Images W. Liu et al. 10.20965/jdr.2019.p0445
- ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru N. Alvan Romero et al. 10.3390/app10186598
- Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion Y. Li et al. 10.3390/rs11192231
- Delimitation of flood areas based on a calibrated a DEM and geoprocessing: case study on the Uruguay River, Itaqui, southern Brazil P. Araújo et al. 10.5194/nhess-19-237-2019
- Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A review of characteristics, approaches, and datasets J. Zhao et al. 10.1109/MGRS.2024.3496075
- Flood Extent Mapping During Hurricane Florence With Repeat‐Pass L‐Band UAVSAR Images C. Wang et al. 10.1029/2021WR030606
- Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon L. Moya et al. 10.3390/rs12142244
- Factors related to evacuation intention when a Level 4 evacuation order was issued among people with mental health illnesses using group homes in Japan: A cross-sectional study H. Nakai et al. 10.1097/MD.0000000000039428
- Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data H. Gokon et al. 10.3390/rs15020532
- Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques W. Liu et al. 10.3390/rs13173508
- Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images W. Liu et al. 10.3390/rs13040639
- Flood Detection in Built-Up Areas Using Interferometric Phase Statistics of PALSAR-2 Data M. Ohki et al. 10.1109/LGRS.2019.2960045
- Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan M. Ohki et al. 10.1109/JSTARS.2019.2911596
- Sparse Representation-Based Inundation Depth Estimation Using SAR Data and Digital Elevation Model L. Moya et al. 10.1109/JSTARS.2022.3215719
- Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019 R. Natsuaki & H. Nagai 10.3390/rs12060903
- EXTRACTION OF INUNDATED AREA DUE TO 2011 THAILAND FLOOD USING U-NET TO COSMO-SKYMED IMAGES T. KONISHI et al. 10.2208/jscejcei.77.1_59
- Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods L. Moya et al. 10.3390/rs11192320
- Analysis of the Actions and Motivations of a Community during the 2017 Torrential Rains in Northern Kyushu, Japan A. Nonomura et al. 10.3390/ijerph17072424
23 citations as recorded by crossref.
- HIGH-RESOLUTION SURFACE WATER MAP OVER JAPAN AND ESTIMATION OF INUNDATION AREA CAUSED BY TYPHOON HAGIBIS S. SETO 10.2208/jscejhe.76.2_I_613
- Multitemporal UAV surveys of geomorphological changes caused by postfire heavy rain in Kamaishi city, northeast Japan Y. Touge et al. 10.1016/j.catena.2022.106702
- Automated Processing for Flood Area Detection Using ALOS-2 and Hydrodynamic Simulation Data M. Ohki et al. 10.3390/rs12172709
- The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework G. Okada et al. 10.3390/rs13071401
- Synergistic Approach of Remote Sensing and GIS Techniques for Flash-Flood Monitoring and Damage Assessment in Thessaly Plain Area, Greece E. Psomiadis et al. 10.3390/w11030448
- Extraction of Inundation Areas Due to the July 2018 Western Japan Torrential Rain Event Using Multi-Temporal ALOS-2 Images W. Liu et al. 10.20965/jdr.2019.p0445
- ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru N. Alvan Romero et al. 10.3390/app10186598
- Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion Y. Li et al. 10.3390/rs11192231
- Delimitation of flood areas based on a calibrated a DEM and geoprocessing: case study on the Uruguay River, Itaqui, southern Brazil P. Araújo et al. 10.5194/nhess-19-237-2019
- Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A review of characteristics, approaches, and datasets J. Zhao et al. 10.1109/MGRS.2024.3496075
- Flood Extent Mapping During Hurricane Florence With Repeat‐Pass L‐Band UAVSAR Images C. Wang et al. 10.1029/2021WR030606
- Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon L. Moya et al. 10.3390/rs12142244
- Factors related to evacuation intention when a Level 4 evacuation order was issued among people with mental health illnesses using group homes in Japan: A cross-sectional study H. Nakai et al. 10.1097/MD.0000000000039428
- Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data H. Gokon et al. 10.3390/rs15020532
- Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques W. Liu et al. 10.3390/rs13173508
- Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images W. Liu et al. 10.3390/rs13040639
- Flood Detection in Built-Up Areas Using Interferometric Phase Statistics of PALSAR-2 Data M. Ohki et al. 10.1109/LGRS.2019.2960045
- Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan M. Ohki et al. 10.1109/JSTARS.2019.2911596
- Sparse Representation-Based Inundation Depth Estimation Using SAR Data and Digital Elevation Model L. Moya et al. 10.1109/JSTARS.2022.3215719
- Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019 R. Natsuaki & H. Nagai 10.3390/rs12060903
- EXTRACTION OF INUNDATED AREA DUE TO 2011 THAILAND FLOOD USING U-NET TO COSMO-SKYMED IMAGES T. KONISHI et al. 10.2208/jscejcei.77.1_59
- Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods L. Moya et al. 10.3390/rs11192320
- Analysis of the Actions and Motivations of a Community during the 2017 Torrential Rains in Northern Kyushu, Japan A. Nonomura et al. 10.3390/ijerph17072424
Latest update: 23 Apr 2025
Short summary
Five synthetic aperture radar images are employed to monitor the changes in the inundation areas caused by torrential rain from 9 to 11 September 2015, in the city of Joso, Ibaraki Prefecture, Japan. The inundation areas were then extracted by setting threshold values for backscattering intensity. The results were modified by considering the land cover and a digital elevation model. Compared with the results from visual inspections, 85 % of the inundation area could be extracted successfully.
Five synthetic aperture radar images are employed to monitor the changes in the inundation areas...
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