Article
Water Resources
Josko Troselj, Han Soo Lee
Summary: This study utilized the CDRM model and SCE-UA optimization method to accurately forecast river discharges induced by three Japanese typhoons, highlighting the importance of developing real-time forecasting tools for extreme river discharges during floods.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2021)
Article
Engineering, Geological
Satoru Ohtsuka, Yutaka Sato, Takahiro Yoshikawa, Toshio Sugii, Takeshi Kodaka, Kenichi Maeda
Summary: Typhoon Hagibis in October 2019 caused severe damage to levees and bridges in Nagano Prefecture, Japan, with the main factor of collapse being structural weakness. Simple measures against seepage were found to improve levee tensile strength.
SOILS AND FOUNDATIONS
(2021)
Article
Environmental Sciences
Ryo Omagari, Yuichi Miyabara, Shunji Hashimoto, Takashi Miyawaki, Masashi Toyota, Kiwao Kadokami, Daisuke Nakajima
Summary: The novel assessment system combines bioassay and chemical analysis to evaluate the human health risk posed by toxic chemicals discharged due to natural disasters quickly. Through experimental verification, the method has been proven to be applicable in environmental samples, providing rapid risk assessment results.
ENVIRONMENT INTERNATIONAL
(2022)
Article
Environmental Sciences
Ziming Wang, Ce Zhang, Peter M. Atkinson
Summary: Synthetic Aperture Radar (SAR) is essential for mapping and monitoring flood hazards. A novel method using SAR imagery and land use-land cover (LULC) products for rapid urban flood mapping is proposed. The method achieved high accuracy in two use cases and provides strong capability for rapid flood mapping in urban settings.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Environmental Sciences
Nguyen Xuan Tinh, Hitoshi Tanaka, Gen Abe, Yuka Okamoto, Kwanchai Pakoksung
Summary: Typhoon Hagibis in early October 2019 caused devastating flooding in Marumori Town, Miyagi Prefecture, Japan. The study found that most levee breaches were due to water overflow at narrow areas such as tributaries' junctions and intersections of the river embankment. Numerical simulations showed that upstream water flow led to levee breaches downstream, and a new criterion for levee breaches was proposed.
Article
Environmental Sciences
Wen Liu, Kiho Fujii, Yoshihisa Maruyama, Fumio Yamazaki
Summary: This study proposed a quick analysis procedure using multi-temporal Sentinel-1 SAR intensity images to estimate inundations due to Typhoon Hagibis in Japan. By detecting and extracting the flooded state-managed rivers in Ibaraki Prefecture, it successfully identified 74% of the inundated areas.
Article
Geosciences, Multidisciplinary
Jiafeng Wang, Yongjiu Feng, Rong Wang, Xiaohua Tong, Shurui Chen, Zhenkun Lei, Pengshuo Li, Mengrong Xi
Summary: In this study, we used multispectral, panchromatic, and synthetic aperture radar (SAR) images for land use and land cover (LULC) classification and flood event mapping. The results showed that the MultiSenCNN algorithm, which fused the multispectral and SAR images, achieved high accuracy in LULC classification. The flood mapping also demonstrated high accuracy and highlighted significant damage to cropland. SAR images proved to be effective in monitoring flood events and providing crucial information for rescuers and governments to make timely decisions.
GEOMATICS NATURAL HAZARDS & RISK
(2022)
Article
Geochemistry & Geophysics
Wei Zhan, Kosuke Heki, Syachrul Arief, Mizuki Yoshida
Summary: A study was conducted on the impact of the super typhoon Hagibis in October 11 and 12, 2019, which caused significant subsidence and spatial distribution of water vapor in eastern Honshu, Japan. The research revealed the influence of selective deployment of GNSS stations on ground subsidence, as well as the distribution of surface water in the region.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Environmental Sciences
Kristy F. Tiampo, Lingcao Huang, Conor Simmons, Clay Woods, Margaret T. Glasscoe
Summary: The increasing number of flood events and coastal urbanization have caused significant economic losses. This study compares several methods for characterizing flood inundation using SAR remote sensing data and machine learning techniques, and provides some effective results.
Article
Geochemistry & Geophysics
Oliver L. Stephenson, Tobias Kohne, Eric Zhan, Brent E. Cahill, Sang-Ho Yun, Zachary E. Ross, Mark Simons
Summary: Satellite remote sensing, especially synthetic aperture radar (SAR), plays a key role in rapid damage mapping after natural disasters. However, current SAR damage mapping methods face challenges in distinguishing damage from other surface changes. This study proposes a novel approach that combines deep learning with the full time history of SAR observations to detect anomalous variations in surface properties due to natural disasters. By using recurrent neural networks (RNN) as probabilistic anomaly detectors on coherence time series, the method shows good agreement with observed damage and quantitative improvement compared to traditional methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Pei Zhan, Wenquan Zhu, Nan Li
Summary: The researchers proposed an automated rice mapping method called ARM-SARFS based on Sentinel-1A data, which has high accuracy and is not sensitive to thresholds. Validation results show that ARM-SARFS exhibits high classification accuracy under different rice cropping systems and geographical-climatic conditions, with significant improvements compared to previous methods.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Chemistry, Multidisciplinary
Indra Riyanto, Mia Rizkinia, Rahmat Arief, Dodi Sudiana
Summary: This research proposes a method using machine learning and satellite image data to identify flood-prone areas. By observing backscatter differences before and after floods using Synthetic Aperture Radar (SAR) sensors, combined with the 3D CNN method, the method achieved good results in experiments covering Jakarta City and the coastal area of Bekasi Regency.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Natsumi Kitajima, Rie Seto, Dai Yamazaki, Xudong Zhou, Wenchao Ma, Shinjiro Kanae
Summary: This study utilizes a constellation of small satellites equipped with synthetic aperture radar to conduct flood monitoring experiments, demonstrating that the technology can independently observe flood extents in short time intervals effectively. However, different flood events exhibit varying characteristics and satellite observation systems, requiring individual assessments for accurate monitoring.
Article
Environmental Sciences
David C. Mason, John Bevington, Sarah L. Dance, Beatriz Revilla-Romero, Richard Smith, Sanita Vetra-Carvalho, Hannah L. Cloke
Summary: This study presents a method for detecting urban flooding by merging near real-time SAR flood extents with model-derived flood hazard maps. The method improves the accuracy of urban flood detection and provides favorable conditions for future flood forecasting.
Article
Geochemistry & Geophysics
Ramona Pelich, Marco Chini, Renaud Hostache, Patrick Matgen, Luca Pulvirenti, Nazzareno Pierdicca
Summary: This study utilizes both co- and cross-polarization images to enhance the mapping of urban floodwater by detecting significant decreases in multitemporal InSAR coherence. It is suggested that both double-bounce scattering and multiple-bounce may occur in urban areas, impacting the effectiveness and accuracy of the approach. The proposed methodology demonstrates an increase in the accuracy of urban flood maps by utilizing dual polarization information, particularly with the use of Sentinel-1 mission data.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Rishav Mallick, Judith A. Hubbard, Eric O. Lindsey, Kyle E. Bradley, James D. P. Moore, Aktarul Ahsan, A. K. M. Khorshed Alam, Emma M. Hill
EARTH AND PLANETARY SCIENCE LETTERS
(2020)
Article
Geochemistry & Geophysics
Kathryn Materna, Lujia Feng, Eric O. Lindsey, Emma M. Hill, Aktarul Ahsan, A. K. M. Khorshed Alam, Kyaw Moe Oo, Oo Than, Thura Aung, Saw Ngwe Khaing, Roland Buergmann
Summary: The study uses GRACE temporal gravity products and GNSS observations to compare and find that elastic loading derived from the GRACE gravity model can explain a significant portion of vertical oscillations in South and Southeast Asia, with GRACE-based corrections reducing RMS scatter of GNSS data. However, the approach does not capture all seasonal deformation, indicating the need for further research on the effects of hydrological processes and groundwater on observations.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geosciences, Multidisciplinary
Alok Bhardwaj, Robert J. Wasson, Winston T. L. Chow, Alan D. Ziegler
Summary: This study examines the trends in high-intensity monsoon rainfall events in the Indian Himalaya from 1901 to 2013, finding statistically significant positive trends in intensity and frequency. The majority of trends are located in the Higher Himalayan region, with extreme rainfall trends specifically in the upstream section of the Mandakini Catchment. The study also reveals a potential relationship between the Arctic Oscillation (AO) and the frequency of extreme monsoon events, suggesting that AO may influence these events when in a negative phase.
Article
Geochemistry & Geophysics
Fabio Manta, Giovanni Occhipinti, Emma M. Hill, Anna Perttu, Jelle Assink, Benoit Taisne
Summary: This study explores the use of GNSS to measure ionospheric total electron content (TEC) perturbations to complement traditional volcano monitoring systems. A new metric, the Ionospheric Volcanic Power Index (IVPI), is introduced to quantify the energy transferred to the ionosphere by volcanic explosions. Results indicate that IVPI correlates well with the Volcanic Explosivity Index (VEI) and could potentially improve continuous volcano monitoring and warning systems through remote sensing.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Geosciences, Multidisciplinary
Rishav Mallick, Aron J. Meltzner, Louisa L. H. Tsang, Eric O. Lindsey, Lujia Feng, Emma M. Hill
Summary: A 32-year-long slow-slip event occurred on a shallow part of the Sunda megathrust, perhaps because of stress accumulation after fluid expulsion, according to an analysis of the deformation history of the area and physics-based simulations. This study highlights the potential for missing or mis-modelling these transient phenomena globally and provides a method for detecting slow-slip events that could substantially revise earthquake and tsunami hazard and risk assessments for populations living near fault lines.
Article
Geosciences, Multidisciplinary
Eric O. Lindsey, Rishav Mallick, Judith A. Hubbard, Kyle E. Bradley, Rafael V. Almeida, James D. P. Moore, Roland Burgmann, Emma M. Hill
Summary: The study introduces a new method to infer the slip rate deficit of offshore megathrusts, providing better understanding of seismic slip behavior. It reveals that the shallow fault generally has a slip rate deficit between 80% and 100% of the plate convergence rate when locked patches are present. This finding suggests a potentially higher global tsunami hazard than currently recognized.
Article
Geochemistry & Geophysics
Oliver L. Stephenson, Tobias Kohne, Eric Zhan, Brent E. Cahill, Sang-Ho Yun, Zachary E. Ross, Mark Simons
Summary: Satellite remote sensing, especially synthetic aperture radar (SAR), plays a key role in rapid damage mapping after natural disasters. However, current SAR damage mapping methods face challenges in distinguishing damage from other surface changes. This study proposes a novel approach that combines deep learning with the full time history of SAR observations to detect anomalous variations in surface properties due to natural disasters. By using recurrent neural networks (RNN) as probabilistic anomaly detectors on coherence time series, the method shows good agreement with observed damage and quantitative improvement compared to traditional methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Dongju Peng, Lujia Feng, Kristine M. Larson, Emma M. Hill
Summary: This study examines the potential of using GNSS-IR technology to measure coastal absolute sea-level changes and integrate on-land and offshore observations. Results show that GNSS-IR measurements have high accuracy in monitoring daily mean sea levels and trends, with good agreement with tide-gauge and satellite altimetry data. The study suggests that GNSS-IR has the potential to monitor coastal absolute sea-level changes and provide valuable information for coastal sea-level and climate studies.
Article
Environmental Sciences
Noel Ivan Ulloa, Sang-Ho Yun, Shou-Hao Chiang, Ryoichi Furuta
Summary: This study utilizes synthetic aperture radar (SAR) imagery and deep learning methods for flood mapping, incorporating historical SAR images and spatial features. By differentiating the synthetic image from the post-image, the accuracy of flood-induced change detection can be improved. Experimental results demonstrate that the Convolutional Long Short-Term Memory (ConvLSTM) method achieves higher classification accuracy in flood mapping.
Article
Geosciences, Multidisciplinary
Matthew Bonnema, Cedric H. David, Renato Prata de Moraes Frasson, Catalina Oaida, Sang-Ho Yun
Summary: This study presents the global variation of lake and reservoir surface areas using radar remote sensing. The global aggregate area variations were only 2% of total surface area, but the variations of shoreline regions equaled 20% of total surface area. Smaller water bodies contributed more to these variations, and reservoirs tended to be more variable than lakes of similar size. The large surface area variations, especially in small water bodies, could have a previously underappreciated impact on the Earth System.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Green & Sustainable Science & Technology
Cheryl Tay, Eric O. Lindsey, Shi Tong Chin, Jamie W. McCaughey, David Bekaert, Michele Nguyen, Hook Hua, Gerald Manipon, Mohammed Karim, Benjamin P. Horton, Tanghua Li, Emma M. Hill
Summary: Coastal cities are facing a dual threat from rising sea levels and land subsidence, but it is challenging to quantify the variability of local land subsidence rates. Remote interferometric radar observations can provide high-resolution estimations of local land subsidence, improving our understanding of future prospects for major coastal cities. Using this method, we found that the fastest rates of local land subsidence are concentrated in Asia. The variability of local land subsidence across the 48 cities studied is greater than the estimations of vertical land motion by the Intergovernmental Panel on Climate Change. Our standardized method allows for the identification of relative vulnerabilities to local land subsidence and facilitates comparisons of the effects of sea-level rise that account for local land subsidence.
NATURE SUSTAINABILITY
(2022)
Article
Geosciences, Multidisciplinary
Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, Sang-Ho Yun
Summary: This article proposes a framework that combines remote-sensing data, supplementary datasets, and machine-learning algorithms to assess building damage caused by earthquakes in a semi-automated manner. The framework integrates building inventory data, earthquake ground shaking intensity maps, and InSAR images to classify the damage state of buildings using ensemble models. Case studies of four recent earthquakes demonstrate the successful identification of damaged buildings using both multi-class and binary classification approaches.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2023)
Article
Geochemistry & Geophysics
Zhang-Feng Ma, Sheng-Ji Wei, Xing Li, Yosuke Aoki, Ji-Hong Liu, Xiao-Jie Liu, Wen-Fei Mao, Shun Yang, Nan-Xin Wang, Qi-Huan Huang, Teng Huang, Sang-Ho Yun
Summary: Obtaining millimeter-scale along-track deformations using phase measurements is a challenging task for the InSAR community. In this study, a new time series burst overlap interferometry algorithm is proposed to improve measurement accuracy and achieve high precision along-track deformation measurements.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Meteorology & Atmospheric Sciences
Lujia Feng, Tengfei Zhang, Tieh-Yong Koh, Emma M. Hill
Summary: This study investigates the summer intra-seasonal variability of precipitable water vapor over Sumatra using data from the Sumatran GPS Array. Results show that the South Asian Summer Monsoon and dry-air intrusions associated with Rossby waves in the Southern Hemisphere midlatitudes are key mechanisms influencing the dryness over Sumatra during the northern summer. Additionally, there is an intra-seasonal connection between the South Asian and western North Pacific Summer Monsoons, and a tropical-extratropical teleconnection modulating PWV over the southern Maritime Continent.
JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN
(2021)
