Search

India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India's landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions. [ABSTRACT FROM AUTHOR]

This work aims to characterize the earthquake-triggered landslides (EQTLs) during the 2017 Jiuzhaigou earthquake and to describe the geological and geomorphological control of landslide hazards. The Jiuzhaigou-EQTLs were studied via field-based investigation and remote sensing techniques during few weeks immediately after the mainshock occurred. The Jiuzhaigou earthquake produced an 11.4-km2 landslide area with 5633 landslides, dominated by shallow disrupted landslides, rock falls, and rockslides/rock avalanche. Statistical analyses show that the Jiuzhaigou-EQTLs had low mobility with a modal apparent friction angle of 40–44°. The Jiuzhaigou-EQTLs mostly occurred at elevations of 2700–3500 m and below 900 m relative relief to the main riverbed, where small-scale landslides were situated at the lower to middle parts of hillslope and large-scale landslides were situated close to the ridge crests. Jiuzhaigou-EQTLs clustered on river valley-side at the upstream of Baihe River (Jiuzhai Paradise and Ganhaizi village), Rize gully, and Danzhu gully on the tectonically active region at the southwest side of seismogenic fault F2. Landslides are prone to failure on the carbonate rocks with anaclinal hillslopes, following the cataclinal and cata-plagioclinal hillslopes. Most Jiuzhaigou-EQTLs preferentially occur on the below low-level break line (L2) in V-shaped valley-side and between first-level (L2) and high-level break line (L1) in U-shaped valley-side formed from long-term paleo-glacier and river incision. We found that landslide frequency is determined by a combination of the earthquake source with ground deformation, slope distribution, and rock strength due to weathering and dissolution along the fractures. Ground surface deformation, rock type (strength), geological structures, and valley-side slope breaks dictated the Jiuzhaigou-EQTL distribution pattern and hazards. This study elucidates the roles that local conditions play in influencing the occurrence, distribution, and frequency of slope failures and provides a better understanding of EQTL phenomenon during the Jiuzhaigou earthquake. [ABSTRACT FROM AUTHOR]

Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression—one of the most widely used susceptibility models—to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change. [ABSTRACT FROM AUTHOR]