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In July 2015, more than 100 landslides caused by Cyclone Komen resulted in damage to approximately 1000 buildings in the mountainous region of Hakha City, Myanmar. This study aimed to identify potential landslide susceptibility for newly developed resettlement areas in Hakha City before and after urbanization. The study evaluated landslide susceptibility through statistical modeling and compared the level of susceptibility before and after urbanization in the region. The information value model was used to predict landslide susceptibility before and after urbanization, using 10 parameter maps as independent variables and 1 landslide inventory map as the dependent variable. Four landslide types were identified in the study area: shallow earth slide, deep slide, earth slump, and debris flow. Susceptibility analyses were conducted separately for each type to better recognize the different aspects of landslide susceptibility in planned urban areas. By comparing the results of the susceptibility index before and after urbanization, suitable urban areas with lower landslide susceptibility could be identified. The results showed that high-potential landslide susceptibility increased by 10%, 16%, and 5% after urbanization compared with before urbanization in three Town Plans, respectively. Therefore, Town Plan 3 is selected as the most suitable location for the resettlement area in terms of low risk of landslides.

On 8th August 2017, a magnitude Ms 7.0 earthquake struck the County of Jiuzhaigou, in Sichuan Province, China. It was the third Ms ≥ 7.0 earthquake in the Longmenshan area in the last decade, after the 2008 Ms 8.0 Wenchuan earthquake and the 2013 Ms 7.0 Lushan earthquake. The event did not produce any evident surface rupture but triggered significant mass wasting. Based on a large set of pre- and post-earthquake high-resolution satellite images (SPOT-5, Gaofen-1 and Gaofen-2) as well as on 0.2-m-resolution UAV photographs, a polygon-based interpretation of the coseismic landslides was carried out. In total, 1883 landslides were identified, covering an area of 8.11 km2, with an estimated total volume in the order of 25–30 × 106 m3. The total landslide area was lower than that produced by other earthquakes of similar magnitude with strike-slip motion, possibly because of the limited surface rupture. The spatial distribution of the landslides was correlated statistically to a number of seismic, terrain and geological factors, to evaluate the landslide susceptibility at regional scale and to identify the most typical characteristics of the coseismic failures. The landslides, mainly small-scale rockfalls and rock/debris slides, occurred mostly along two NE-SW-oriented valleys near the epicentre. Comparatively, high landslide density was found at locations where the landform evolves from upper, broad valleys to lower, deep-cut gorges. The spatial distribution of the coseismic landslides did not seem correlated to the location of any known active faults. On the contrary, it revealed that a previously-unknown blind fault segment—which is possibly the north-western extension of the Huya fault—is the plausible seismogenic fault. This finding is consistent with what hypothesised on the basis of field observations and ground displacements.

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.

The present work aims to evaluate potential sources of uncertainty associated with rainfall-triggered event-based landslide inventories within the framework of landslide susceptibility assessment. Therefore, this study addresses the following questions: (i) How representative is an event-based landslide inventory map of the total landslide activity and distribution in a study area?; (ii) How reliable is an event-based landslide susceptibility map?; (iii) How appropriate is an event-based landslide inventory map for independently validating a landslide susceptibility map? To address these questions, two independent and contrasting rainfall event-based landslide inventories were used, together with a historical landslide inventory, to assess landslide susceptibility for different types of landslides in a study area located north of Lisbon, Portugal. The results revealed the following findings: (i) contrasting rainfall critical conditions for failure can trigger similar landslide types, although they may vary in size and be spatially constrained by different predisposing conditions, particularly lithology and soil type; (ii) landslide susceptibility models using event-based landslide inventories are not reliable in the study area, regardless of the landslide inventory map used for training and validation; and (iii) complementary sources of uncertainty results from using incomplete historical landslide inventories to assess landslide susceptibility and non-totally independent landslide inventories for modeling validation. The present study enhances the understanding of regional landslide susceptibility patterns based on contrasting rainfall-trigger conditions, providing valuable information to minimize exposure; to design regional landslide early warning systems for specific rainfall-trigger landslide events; and to improve the response and preparedness of civil protection services.

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.

Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions.

The reliability of input data to be used within statistically based landslide susceptibility models usually determines the quality of the resulting maps. For very large territories, landslide susceptibility assessments are commonly built upon spatially incomplete and positionally inaccurate landslide information. The unavailability of flawless input data is contrasted by the need to identify landslide-prone terrain at such spatial scales. Instead of simply ignoring errors in the landslide data, we argue that modellers have to explicitly adopt their modelling design to avoid misleading results. This study examined different modelling strategies to reduce undesirable effects of error-prone landslide inventory data, namely systematic spatial incompleteness and positional inaccuracies. For this purpose, the Austrian territory with its abundant but heterogeneous landslide data was selected as a study site. Conventional modelling practices were compared with alternative modelling designs to elucidate whether an active counterbalancing of flawed landslide information can improve the modelling results. In this context, we compared widely applied logistic regression with an approach that allows minimizing the effects of heterogeneously complete landslide information (i.e. mixed-effects logistic regression). The challenge of positionally inaccurate landslide samples was tackled by elaborating and comparing the models for different terrain representations, namely grid cells, and slope units. The results showed that conventional logistic regression tended to reproduce incompleteness inherent in landslide training data in case the underlying model relied on explanatory variables directly related to the data bias. The adoption of a mixed-effects modelling approach appeared to reduce these undesired effects and led to geomorphologically more coherent spatial predictions. As a consequence of their larger spatial extent, the slope unit–based models were able to better cope with positional inaccuracies of the landslide data compared to their grid-based equals. The presented research demonstrates that in the context of very large area susceptibility modelling (i) ignoring flaws in available landslide data can lead to geomorphically incoherent results despite an apparent high statistical performance and that (ii) landslide data imperfections can actively be diminished by adjusting the research design according to the respective input data imperfections.

An accurate and detailed seismic landslide inventory is essential to better understand the landslide mechanism and susceptibility. The 8th August 2017 Mw 6.5 Jiuzhaigou Earthquake of China initiated a large number of coseismic landslides. The results of the post-seismic survey show the actual landslide number might be underestimated in previous publications. Coupled with field investigation and visual interpretation on high-resolution remote sensing images before and after the main shock, we established a detailed inventory of landslides triggered by the earthquake. Results show that this event caused at least 4 834 individual landslides with a total area of 9.64 km2. They are concentrated in an elliptical area of 434 km2, dominated by medium- and small-scale rock falls and debris slides. Statistics indicate that, except for slope aspect that seems not significantly correlated with the landsliding, these landslides are most common in the places with following features: elevation of 2 800–3 400 m, slope angle greater than 30°, slope positions of upper, middle and flat slopes, and Carboniferous limestone and dolomite. Besides, the landslide area percentage (LAP) and landslide number density (LND) values decrease with the increasing distance to river channels and roads, implying a positive correlation. Instead of centering around the epicenter, most of these coseismic landslides are distributed along the inferred seismogenic fault, which means that the seismogenic structure played a more important role than the location of the epicenter. Remarkable differences in landslide densities along the fault indicate the varied landslide susceptibility which may be attributed to other varied controls along the fault such as the rock mass strength. In sum, this study presents a more detailed inventory of the landslides triggered by the 2017 Mw 6.5 Jiuzhaigou Earthquake, describes their distribution pattern and analyzes its control factors, which would be helpful to understand the genesis of the coseismic landslides and further study their long-term impact on the environment of the affected area.

Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models.

Landslide susceptibility maps are very important tools in the planning and management of landslide prone areas. Qualitative and quantitative methods each have their own advantages and dis-advantages in landslide susceptibility mapping. The aim of this study is to compare three models, i.e., frequency ratio (FR), Shannon’s entropy and analytic hierarchy process (AHP) by implementing them for the preparation of landslide susceptibility maps. Shimla, a district in Himachal Pradesh (H.P.), India was chosen for the study. A landslide inventory containing more than 1500 landslide events was prepared using previous literature, available historical data and a field survey. Out of the total number of landslide events, 30% data was used for training and 70% data was used for testing purpose. The frequency ratio, Shannon’s entropy and AHP models were implemented and three landslide susceptibility maps were prepared for the study area. The final landslide susceptibility maps were validated using a receiver operating characteristic (ROC) curve. The frequency ratio (FR) model yielded the highest accuracy, with 0.925 fitted ROC area, while the accuracy achieved by Shannon’s entropy model was 0.883. Analytic hierarchy process (AHP) yielded the lowest accuracy, with 0.732 fitted ROC area. The results of this study can be used by engineers and planners for better management and mitigation of landslides in the study area.