Search Results (23)

This study investigates the use of the radar vegetation index (RVI) derived from Sentinel-1 synthetic aperture radar (SAR) data for landslide detection. Traditional landslide detection methods often rely on the Normalized Difference Vegetation Index (NDVI) derived from optical imagery, which is susceptible to limitations imposed by weather conditions (clouds, rain) and nighttime. In contrast, SAR data, acquired by Sentinel-1, provides all-weather, day-and-night coverage. To leverage this advantage, we propose a novel approach utilizing RVI, a vegetation index calculated from SAR data, to identify non-vegetated areas, which often indicate potential landslide zones. To enhance the accuracy of non-vegetated area classification, we employ the high-performing EfficientNetV2 deep learning model. We evaluated the classification performance of EfficientNetV2 using RVI derived from Sentinel-1 SAR data with VV and VH polarizations. Experiments were conducted on SAR imagery of the Iburi district in Hokkaido, Japan, severely impacted by an earthquake in 2018. Our findings demonstrate that the classification performance using RVI with both VV and VH polarizations significantly surpasses that of using VV and VH polarizations alone. These results highlight the effectiveness of RVI for identifying non-vegetated areas, particularly in landslide detection scenarios. The proposed RVI-based method has broader applications beyond landslide detection, including other disaster area assessments, agricultural field monitoring, and forest inventory. Full article

A mathematical method of combining several elements has emerged in recent times, providing a more comprehensive approach. Adhering to the foregoing mathematical methodology, we fuse two extremely potent methods, namely graph theory and neutrosophic sets, and present the concept of neutrosophic graphs ($\aleph G$). Next, we outline many ideas, such as union, join, and composition of $\aleph G$s, which facilitate the straightforward manipulation of $\aleph Gs$ in decision-making scenarios. We provide a few scenarios to clarify these activities. The homomorphisms of $\aleph Gs$ are also described. Lastly, understanding neutrosophic graphs and how Japan responds to earthquakes can help develop more resilient and adaptable disaster management plans, which can eventually save lives and lessen the effects of seismic disasters. With the support of using an absolute score function value, Hokkaido (H) and Saitama (SA) were the optimized locations. Because of its location in the Pacific Ring of Fire, Japan is vulnerable to regular earthquakes. As such, it is critical to customize reaction plans to the unique difficulties and features of Japan’s seismic activity. Examining neutrosophic graphs within the framework of earthquake response centers might offer valuable perspectives on tailoring and enhancing response tactics, particularly for Japan’s requirements. Full article

Strong earthquakes can not only trigger many landslides in a short period of time but can also change the stability of slopes in the earthquake area, causing them to be active for a long time after the earthquake. Research on the variation of slow-motion slopes before and after earthquakes can help us to better understand the mechanism of earthquake-affected landslides, which is also crucial for assessing the long-term landslide risk in seismically active areas. Here, L-band ALOS-2 PALSAR-2 images are utilized with the SBAS-InSAR algorithm to monitor and assess the location and activity changes of slow-moving landslides in the Iburi region (Hokkaido, Japan) before and after an earthquake occurred on 6 September 2018. Unlike previous studies, which focused on single typical landslides, we tracked all the landslides within a 33 × 55 km region close to the epicenter. According to the results, the majority of the co-seismic landslides that quickly failed during the earthquake are now stable, and a few of them are still moving. In contrast, due to near-field seismic shaking, certain slopes that did not show substantial surface changes during the earthquake period continued to move and eventually developed into slow-moving landslides. In addition, it can be seen from the spatial distribution of slow-moving landslides after the earthquake that this distribution is not only dependent on strong earthquake seismic vibration or the hanging-wall effect. Far-field weak vibrations can also accelerate landslides. Additionally, we discovered that the earthquake made the unstable slopes move more quickly but also tended to stabilize the slopes that were already in motion before the earthquake. The various response modes of slow-moving landslides to seismic events are related not only to the intensity of seismic vibration but also to the geological conditions of the region and to the size of the landslide itself. These findings are extremely valuable for studying the mechanism of earthquake-affected landslides. Full article

Managing inflammatory bowel disease (IBD) is a major challenge for physicians and patients during the COVID-19 pandemic. To understand the impact of the pandemic on patient behaviors and disruptions in medical care, we used a combination of population-based modeling, system dynamics simulation, and linear optimization. Synthetic IBD populations in Tokyo and Hokkaido were created by localizing an existing US-based synthetic IBD population using data from the Ministry of Health, Labor, and Welfare in Japan. A clinical pathway of IBD-specific disease progression was constructed and calibrated using longitudinal claims data from JMDC Inc for patients with IBD before and during the COVID-19 pandemic. Key points considered for disruptions in patient behavior (demand) and medical care (supply) were diagnosis of new patients, clinic visits for new patients seeking care and diagnosed patients receiving continuous care, number of procedures, and the interval between procedures or biologic prescriptions. COVID-19 had a large initial impact and subsequent smaller impacts on demand and supply despite higher infection rates. Our population model (Behavior Predictor) and patient treatment simulation model (Demand Simulator) represent the dynamics of clinical care demand among patients with IBD in Japan, both in recapitulating historical demand curves and simulating future demand during disruption scenarios, such as pandemic, earthquake, and economic crisis. Full article

Predicting the susceptibility of a specific part of a landslide (SSPL) involves predicting the likelihood that the part of the landslide (e.g., the entire landslide, the source area, or the scarp) will form in a given area. When predicting SSPL, the landslide samples are far less than the non-landslide samples. This class imbalance makes it difficult to predict the SSPL. This paper proposes an advanced artificial intelligence (AI) model based on the dice-cross entropy (DCE) loss function and XGBoost (XGB_DCE) or Light Gradient Boosting Machine (LGB_DCE) to ameliorate the class imbalance in the SSPL prediction. We select the earthquake-induced landslides from the 2018 Hokkaido earthquake as a case study to evaluate our proposed method. First, six different datasets with 24 landslide influencing factors and 10,422 samples of a specific part of the landslides are established using remote sensing and geographic information system technologies. Then, based on each of the six datasets, four landslide susceptibility algorithms (XGB, LGB, random-forest (RF) and linear discriminant analysis (LDA)) and four class balancing methods (non-balance (NB), equal-quantity sampling (EQS), inverse landslide-frequency weighting (ILW), and DCE loss) are applied to predict the SSPL. The results show that the non-balanced method underestimates landslide susceptibility, and the ILW or EQS methods overestimate the landslide susceptibility, while the DCE loss method produces more balanced results. The prediction performance of the XGB_DCE (average area under the receiver operating characteristic curve (0.970) surpasses that of RF (0.956), LGB (0.962), and LDA (0.921). Our proposed methods produce more unbiased and precise results than the existing models, and have a great potential to produce accurate general (e.g., predicting the entire landslide) and detailed (e.g., combining the prediction of the landslide source area with the landslide run-out modeling) landslide susceptibility assessments, which can be further applied to landslide hazard and risk assessments. Full article

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

At 3:08 on 6 September 2018 (UTC +9), massive landslides were triggered by an earthquake of Mw 6.6 that occurred in Hokkaido, Japan. In this paper, a coseismic landslide inventory that covers 388 km² of the earthquake-impacted area and includes 5828 coseismic landslides with a total landslide area of 23.66 km² was compiled by using visual interpretations of various high-resolution satellite images. To analyze the spatial distribution and characteristics of coseismic landslides, five factors were considered: the peak ground acceleration (PGA), elevation, slope gradient, slope aspect, and lithology. Results show more than 87% of the landslides occurred at 100 to 200 m elevations. Slopes in the range of 10~20°are the most susceptible to failure. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.02 g. Compared with the number and scale of coseismic landslides caused by other strong earthquakes and the mobility of the coseismic landslides caused by the Haiyan and Wenchuan earthquakes, it was found that the distribution of coseismic landslides was extremely dense and that the mobility of the Hokkaido earthquake was greater than that of the Wenchuan earthquake and weaker than that of the Haiyuan earthquake, and is described by the following relationship: L = 18.454 ∗ H^0.612. Comparative analysis of coseismic landslides with similar magnitude has important guiding significance for disaster prevention and reduction and reconstruction planning of landslides in affected areas. Full article

In this work, staurozoans of two distinct morphotypes are reported in Kitsunezaki (Ishinomaki City, Miyagi, Japan) in the years following the Great East Japan Earthquake and Tsunami. Staurozoa specimens were collected from Eisenia and Gelidium macroalgal beds at the Kitsunezaki survey site (October 2019–July 2021). Morphological observations indicated that the Kitsunezaki staurozoans represented two species, Haliclystus inabai and Calvadosia nagatensis, but molecular analyses of the genetic markers 16S rRNA and COI suggested that the former actually encompasses two distinct lineages, H. inabai and a cryptic as yet unnamed species. Phylogenetic analysis reveals the two H. inabai lineages are separated by significant divergences for both gene markers. H. inabai lineage 1 includes specimens sampled with molecular sequences from Hokkaido (Japan) and Kitsunezaki (Japan), whereas H. inabai lineage 2 includes sequences from Victoria (Australia), Kitsunezaki, as well as populations that appeared in a lab in Germany and aquariums in Tsuruoka and Kagoshima (Japan) and Québec (Canada). Conversely, C. nagatensis from Kitsunezaki appears to be a species distributed only in the temperate NW Pacific. Observations on early life stages of H. inabai lineage 2 within aquarium tanks permitted confirmation of the presence of “microhydrula” settled larva, frustules, and elongated settled larvae. C. nagatensis was collected from the Kitsunezaki survey site in warm months only, and always exhibited gonads, while H. inabai stauromedusae were collected in most months throughout the year, with gonads usually present irrespective of season. An extensive literature review covering more than 100 years and observations in this study revealed seaweed and seagrass as the primary substrates for these two Staurozoa species. Our findings show C. nagatensis is associated with just two types of algal substrates and seagrass, while H. inabai has a much broader substrate preference, consistent with its wider geographic distribution. These findings have contributed to our understanding of Staurozoa epibiotic associations in exposed bays during the recovery period following a major natural disaster. 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

On 6 September at 03:08 a.m. local time, a 33 km deep earthquake underneath the Iburi mountains triggered more than 7000 co-seismic mass movements within 25 km of the epicenter. Most of the mass movements occurred in complex terrain and became coalescent. However, a total of 59 mass movements occurred as discrete events and stopped on the semi-horizontal valley floor. Using this case study, the authors aimed to define planar and vertical parameters to (1) compare the geometrical parameters with rain-triggered mass movements and (2) to extend existing datasets used for hazards and disaster risk purposes. To reach these objectives, the methodology relies on LiDAR data flown in the aftermath of the earthquake as well as aerial photographs. Using a Geographical Information System (GIS), planform and vertical parameters were extracted from the DEM in order to calculate the relationship between areas and volume, between the Fahrböschung and the volume of the deposits, and to discuss the relationship between the deposit slope surface and the effective stress of the deposit. Results have shown that the relation $S=k{\left[V_d\right]}^{2/3}$ (where S is the surface area of a deposit and V_d the volume, and k a scalar that is function of S) is k = 2.1842ln(S) − 10.167 with a R² of 0.52, with less variability in deposits left by valley-confined processes compared to open-slope processes. The Fahrböschung for events that started as valley-confined mass-movements was Fc = −0.043ln(D) + 0.7082, with a R² of 0.5, while for open-slope mass-movements, the Fo = −0.046ln(D) + 0.7088 with a R² of 0.52. The “T-values”, as defined by Takahashi (2014), are displaying values as high as nine times that of the values for experimental rainfall debris-flow, signifying that the effective stress is higher than in rain-triggered counterparts, which have an increased pore pressure due to the need for further water in the material to be moving. For co-seismic debris-flows and other co-seismic mass movements it is the ground acceleration that “fluidizes” the material. The maxima found in this study are as high as 3.75. 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