Search Results (12)

Risk of landslide hazards strongly depends on how far landslide sediment travels, known as landslide mobility. Previous studies mentioned enhanced mobility of earthquake-induced landslides in volcanic deposits compared to those from other geologic/soil settings. A flume apparatus constructed at a 1:300 scale was used to examine the mobility of landslides with pumice. Four pumice samples were collected from landslides induced by the 2018 Eastern Iburi earthquake, Hokkaido, Japan. Laboratory tests confirmed the unique low specific gravity of the pumice (1.29–1.33), indicating numerous voids within pumice particles. These voids allowed pumice to absorb a substantial amount of water (95–143%), about 9–15 times higher than other coarse-grained soils. Our flume experiments using various saturation levels (0–1) confirmed the influence of this inner-particle water absorption on pumice mobility. Because a low value of specific gravity indicates a low strength of soil, grain crushing may occur on the pumice layer, causing water from the internal voids to discharge and fluidize the transported landslide mass. Our findings indicate that such earthquake-induced landslides can be as mobile as those induced by rainfall, depending on the initial water content of the pumice layers. These conditions might be associated with water accumulation from previous rainfall events and the water-holding capability on pumice layers. Full article

Efficient detection of earthquake−triggered landslides is crucial for emergency response and risk assessment. With the development of multi−source remote sensing images, artificial intelligence has gradually become a powerful landslide detection method for similar tasks, aiming to mitigate time−consuming problems and meet emergency requirements. In this study, a relatively new deep learning (DL) network, called U−Net++, was designed to detect landslides for regions affected by the Iburi, Japan Mw = 6.6 earthquake, with only small training samples. For feature extraction, ResNet50 was selected as the feature extraction layer, and transfer learning was adopted to introduce the pre−trained weights for accelerating the model convergence. To prove the feasibility and validity of the proposed model, the random forest algorithm (RF) was selected as the benchmark, and the F1−score, Kappa coefficient, and IoU (Intersection of Union) were chosen to quantitatively evaluate the model’s performance. In addition, the proposed model was trained with different sample sizes (256,512) and network depths (3,4,5), respectively, to analyze their impacts on performance. The results showed that both models detected the majority of landslides, while the proposed model obtained the highest metric value (F1−score = 0.7580, Kappa = 0.7441, and IoU = 0.6104) and was capable of resisting the noise. In addition, the proposed model trained with sample size 256 possessed optimal performance, proving that the size is a non−negligible parameter in U−Net++, and it was found that the U−Net++ trained with shallower layer 3 yielded better results than that with the standard layer 5. Finally, the outstanding performance of the proposed model on a public landslide dataset demonstrated the generalization of U−Net++. Full article

Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7 operativity. To address the issue, we explore the usage of SAR data over the eastern Iburi sub-prefecture of Hokkaido, Japan. In the area, about 8000 co-seismic landslides were triggered by an Mw 6.6 earthquake on 6 September 2018, at 03.08 local time (JST). In the following study, we modify a Deep Learning (DL) convolutional neural network (CNN) architecture suited for pixel-based classification purposes, the so-called Attention U-Net (Attn-U-Net) and we employ it to evaluate the potential of bi- and tri-temporal SAR amplitude data from the Sentinel-1 satellite and slope angle to map landslides even under thick cloud cover. Four different datasets, composed of two different band combinations per two satellite orbits (ascending and descending) are analyzed. Moreover, the impact of augmentations is evaluated independently for each dataset. The models’ predictions are compared against an accurate landslide inventory obtained by manual mapping on pre-and post-event PlanetScope imagery through F1-score and other common metrics. The best result was yielded by the augmented ascending tri-temporal SAR composite image (61% F1-score). Augmentations have a positive impact on the ascending Sentinel-1 orbit, while metrics decrease when augmentations are applied on descending path. Our findings demonstrate that combining SAR data with other data sources may help to map landslides quickly, even during storms and under deep cloud cover. However, further investigations and improvements are still needed, this being one of the first attempts in which the combination of SAR data and DL algorithms are employed for landslide mapping purposes. Full article

The 2018 Hokkaido Eastern Iburi earthquake and its landslides threaten the safety and stability of the Atsuma River basin. This study investigates land use and land cover (LULC) change by analyzing the 2015 and 2020 LULC maps of the basin, and its impact on runoff and sediment transport in the basin by using the soil and water assessment tool (SWAT) model to accurately simulate the runoff and sediment transport process. This study finds that the earthquake and landslide transformed nearly 10% of the forest into bare land in the basin. The simulation results showed that the runoff, which was simulated based on the 2020 LULC data, was slightly higher than that based on the 2015 LULC data, and the sediment transport after the earthquake is significantly higher than before. The rate of sediment transportation after the earthquake, adjusted according to the runoff, was about 3.42 times more than before. This shows that as the forest land decreased, the bare land increased. Conversely, the runoff increased slightly, whereas the sediment transport rate increased significantly in the Atsuma River basin after the earthquake. In future, active governance activities performed by humans can reduce the amount of sediment transport in the basin. Full article

Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping. Full article

Landslides, debris flows, and other secondary disasters caused by earthquakes threaten the safety and stability of river basins. Earthquakes occur frequently in Japan. Therefore, it is necessary to study the impact of earthquakes on sediment transport in river basins. In this study, considering the influence of reservoirs, the Soil and Water Assessment Tool-calibration and uncertainty program (SWAT-CUP) was employed to analyze the runoff parameter sensitivity and to optimize the parameters. We manually corrected the sediment transport parameters after earthquake, using the Soil and Water Assessment Tool (SWAT) model to assess the process of runoff and sediment transport in the Atsuma River basin before and after the 2018 Hokkaido Eastern Iburi Earthquake. The applicability of the SWAT model to runoff simulation in the Atsuma River basin and the changes of sediment transport process after the earthquake were studied. The research results show that the SWAT model can accurately simulate the runoff process in the Atsuma River basin, the Nash–Sutcliffe efficiency coefficient (NSE) is 0.61 in the calibration period, and is 0.74 in the verification period. The sediment transport increased greatly after the earthquake and it is roughly estimated that the amount of sediment transport per unit rainfall increased from 3.5 tons/mm/year before the earthquake to 6.2 tons/mm/year after the earthquake. Full article

The 2018 Eastern Iburi Hokkaido earthquake in Japan caused infrastructural damage and tourism disruption within a natural-hazard-prone country. This research advances the theoretical foundation and development of natural disaster management through a series of in-depth interviews with the local tourism stakeholders on the investigation of how the role of tourism across the pre-to-post earthquake period is considered by the stakeholders. These local tourism stakeholders have performed or expected a range of actions related to the disaster-management process and contributed to destination resilience. The qualitative analysis discovers, firstly, the multi-functionality of tourism resources, spaces, and industries for disaster preparation; secondly, the evacuation and emergency arrangements during the prodromal and emergency phases; and moreover, more possibilities of restoring the affected destination to a state of long-term (re)development during the post-disaster phases. Information and communication barriers are the major difficulties to be tackled for disaster preparedness. Product creation, image improvement, local knowledge enrichment, and, more importantly, people-to-people and people-to-place connections all contribute to the result of sustainable tourism development. From the destination resilience perspective, collaboration is the key determinant of an improved Hokkaido region. This factor could integrate stakeholders through shared local values, experiences, and memories of disaster risk communication and strategies for preparedness. Full article

A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick response, no contact and broad coverage. This study aimed to conduct a comprehensive exploration on the intensity and coherence information of three Advanced Land Observing Satellite-2 (ALOS-2) SAR images, for rapid massive landslide mapping in a pixel level, in order to provide a reference for future applications. Applied data were two pre-event and one post-event high-resolution ALOS-2 products. Studied area was in the east of Iburi, Hokkaido, Japan, where massive shallow landslides were triggered in the 2018 Hokkaido Eastern Iburi Earthquake. Potential parameters, including intensity difference (d), co-event correlation coefficient (r), correlation coefficient difference ( $∆r$ ), co-event coherence ( $\mathsf{\gamma }$ ), and coherence difference ( $∆\mathsf{\gamma }$ ), were first selected and calculated based on a radar reflection mechanism, to facilitate rapid detection. Qualitative observation was then performed by overlapping ground truth landslides to calculated parameter images. Based on qualitative observation, an absolute value of d ( $d_{abs1}$ ) was applied to facility analyses, and a new parameter ( $d_{abs2}$ ) was proposed to avoid information loss in the calculation. After that, quantitative analyses of the six parameters ( $d_{abs1}$ , $d_{abs2}$ , r, $∆r$ , $\mathsf{\gamma }$ and $∆\mathsf{\gamma }$ ) were performed by receiver operating characteristic. $d_{abs2}$ and $∆r$ were found to be favorable parameters, which had the highest AUC values of 0.82 and 0.75, and correctly classified 69.36% and 64.57% landslide and non-landslide pixels by appropriate thresholds. Finally, a discriminant function was developed, combining three relatively favorable parameters ( $d_{abs2}$ , $∆r$ , and $∆\mathsf{\gamma }$ ) with one in each type, and achieved an overall accuracy of 74.31% for landslide mapping. Full article

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (M_w 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively. Full article

In this study, Sentinel-1 and Advanced Land Observation Satellite-2 (ALOS-2) interferometric synthetic aperture radar (InSAR) and global positioning system (GPS) data were used to jointly determine the source parameters and fault slip distribution of the Mw 6.6 Hokkaido eastern Iburi, Japan, earthquake that occurred on 5 September 2018. The coseismic deformation map obtained from the ascending and descending Sentinel-1 and ALOS-2 InSAR data and GPS data is consistent with a thrust faulting event. A comparison between the InSAR-observed and GPS-projected line-of-sight (LOS) deformation suggests that descending Sentinel-1 track T046D, descending ALOS-2 track P018D, and ascending ALOS-2 track P112A and GPS data can be used to invert for the source parameters. The results of a nonlinear inversion show that the seismogenic fault is a blind NNW-trending (strike angle ~347.2°), east-dipping (dip angle ~79.6°) thrust fault. On the basis of the optimal fault geometry model, the fault slip distribution jointly inverted from the three datasets reveals that a significant slip area extends 30 km along the strike and 25 km in the downdip direction, and the peak slip magnitude can approach 0.53 m at a depth of 15.5 km. The estimated geodetic moment magnitude released by the distributed slip model is $6.16\times {10}^{18}\mathrm{N}·\mathrm{m}$ , equivalent to an event magnitude of Mw 6.50, which is slightly smaller than the estimates of focal mechanism solutions. According to the Coulomb stress change at the surrounding faults, more attention should be paid to potential earthquake disasters in this region in the near future. In consideration of the possibility of multi-fault rupture and complexity of regional geologic framework, the refined distributed slip and seismogenic mechanism of this deep reverse faulting should be investigated with multi-disciplinary (e.g., geodetic, seismic, and geological) data in further studies. Full article

Self- and mutual-help by citizens are important as well as social-help from the local governments, for disaster prevention and mitigation. Then, town watching and disaster prevention map-making workshops are held to review the town and promote self- and mutual-help by citizens. On the other hand, the use of social media for information sharing during and after disasters has been gaining attention. To facilitate information sharing in disasters, we developed a web system, Disaster Information Tweeting and Mapping System (DITS/DIMS). From the above background, we organized a town-watching workshop using DITS/DIMS in October 2018 in Minami Ward, Sapporo City, Hokkaido, Japan; affected area of the Hokkaido Eastern Iburi Earthquake in September 2018. In this paper, we explain the workshop procedure, outcome, questionnaire survey results, and post-meeting. The questionnaire survey result shows that the workshop educated the participants about posting useful information on social media during a disaster. In addition, at the post-meeting, the participants recognized that they had reviewed the town only from the perspective of “daily life” convenience before the earthquake, and they had not evaluated the “emergency viewpoint.” Therefore, the workshop was a meaningful opportunity for the participants to review the town in terms of disaster prevention and mitigation. Full article

We performed interferometric synthetic aperture radar (InSAR) analyses to observe ground displacements and assess damage after the M 6.6 Hokkaido Eastern Iburi earthquake in northern Japan on 6 September 2018. A multitemporal SAR coherence map is extracted from 3-m resolution ascending (track 116) and descending (track 18) ALOS-2 Stripmap datasets to cover the entire affected area. To distinguish damaged buildings associated with liquefaction, three influential parameters from the space-based InSAR results, ground-based LiquickMap (from seismic intensities in Japanese networks) and topographic slope of the study area are considered together in a weighted overlay (WO) analysis, according to prior knowledge of the study area. The WO analysis results in liquefaction potential values that agree with our field survey results. To investigate further, we conducted microtremor measurements at 14 points in Hobetsu, in which the predominant frequency showed a negative correlation with the WO values, especially when drastic coherence decay occurred. Full article