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Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km2. These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest­–north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group‑occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability.

The 2013 heavy rainfall event (from June to July) in the Tianshui area triggered the most serious rainfall-induced group-occurring landslides since 1984, causing extensive casualties and economic losses. To better understand the characteristics and triggers of these loess landslides, we conducted a detailed analysis of the landslides and relevant influencing factors. Based on the detailed rainfall-induced landslide database obtained using visual interpretation of remote sensing images before and after rainfall, the correlation between the landslide occurrence and different influencing factors such as terrain, geomorphology, geology, and rainfall condition was analyzed. This rainfall event triggered approximately 54,000 landslides with a total area of 67.9 km2, mainly consisting of shallow loess landslides with elongated type, shallow rockslides, collapses, and mudflows. The landslides exhibited a clustered distribution, with the majority concentrated in two specific areas (i.e., Niangniangba and Shetang). The abundance index of landslides was closely associated with the hillslope gradient, total rainfall, and drainage (river) density. The landslide area density (LAD) was positively correlated with these influential factors, characterized by either an exponential or a linear relationship. The Middle Devonian Shujiaba formation (D2S) was identified to be highly susceptible to landslides, and the landslide events therein accounted for 35% of the total landslide occurrences within 22% of the study area. In addition, the E-SE aspect was more prone to landslides, while the W-NW aspect exhibited a low abundance of landslides.

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous area. From 5 to 10 May 2016, Sanming City in Fujian Province, China, experienced a heavy rainfall event that caused massive landslides, leading to significant loss of life and property. Using high-resolution satellite imagery, we created a detailed inventory of landslides triggered by this event, which totaled 2665 across an area of 3700 km2. The majority of landslides were small-scale, shallow and elongated, with a dominant distribution in Xiaqu town. We analyzed the correlations between the landslide abundance and topographic, geological and hydro-meteorological factors. Our results indicated that the landslide abundance index is related to the gradient of the hillslope, distance from a river and total rainfall. The landslide area density, i.e., LAD increases with the increase in these influencing factors and is described by an exponential or linear relationship. Among all lithological types, Sinian mica schist and quartz schist (Sn-s) were found to be the most prone to landslides, with over 35% of landslides occurring in just 10% of the area. Overall, the lithology and rainfall characteristics primarily control the abundance of landslides, followed by topography. To gain a better understanding of the triggering conditions for shallow landslides, we conducted a physically based spatio-temporal susceptibility assessment in the landslide abundance area. Our numerical simulations, using the MAT.TRIGRS tool, show that it can accurately reproduce the temporal evolution of the instability process of landslides triggered by this event. Although rainfall before 8 May may have contributed to decreased slope stability in the study area, the short duration of heavy rainfall on 8 May is believed to be the primary triggering factor for the occurrence of massive landslides.

On June 30th, 2012, at 05:07 local time, an Ms 6.6 earthquake (43.4°N, 84.8°E) struck Hejing County, Xinjiang Province (hereinafter called the Hejing earthquake). This study aims to establish a comprehensive landslide inventory associated with the Hejing earthquake based on pre- and post-quake RapidEye and Ikonos satellite images. According to the detailed landslide interpretations, this earthquake has triggered at least 453 coseismic landslides, with a total area of 0.66 km². The landslides are primarily shallow landslides, rockfalls, and rolling stones, with a few shallow debris flows. The landslides are predominantly distributed along the strike of the southern edge of the Awulale mountain fault (SEAMF), and most of them are concentrated near the epicenter, particularly in regions with PGAs exceeding 0.12 g. Statistically, landslide area density (LAD) increases as the hillslope gradient and relief increase, demonstrating a linear relationship. Notably, landslides are more likely to occur in high-elevation areas with significant reliefs and seismic activity such as aftershocks. Furthermore, over 80% of coseismic landslides occurred on the hanging wall of the SEAMF, and as the distance of the fault increases, the landslide abundance index rapidly decreases. Based on the focal source mechanisms, aftershock data, and landslide distributions, we suggest that the most dominant seismogenic structure of the Ms6.6 Hejing earthquake is the SEAMF.

Coseismic landslides triggered by the 2014 Mw 6.2 Ludian earthquake substantially disturbed vegetation and altered ecosystem productivity in mountainous southwestern China. Using MODIS net primary productivity (NPP) data, a high-resolution landslide inventory, and multi-source environmental datasets, this study assessed post-seismic vegetation responses by integrating landslide area density (LAD), multivariate linear regression, and Geodetector analysis. NPP declined sharply in 2015, particularly in grid cells with LAD > 40%, where productivity decreased from 725 to 670 gC/m² and required nearly five years to recover. Regression analyses indicated that precipitation and temperature were positively but weakly associated with NPP, explaining substantially less variance than LAD. Multivariate regression further revealed that LAD exerted the strongest negative effect on NPP (β = –155.20, p < 0.001), exceeding the contributions of both climatic variables and land-use types. Geodetector results demonstrated that LAD had the highest explanatory power for the spatial heterogeneity of NPP, and its interaction with precipitation further enhanced explanatory strength. Among land-cover types, Cropland and shrubland experienced the largest NPP losses and the slowest recovery, whereas forest and grassland exhibited greater resilience. These findings show that coseismic landslides dominate short- to medium-term ecosystem productivity, with important implications for post-earthquake carbon cycling and restoration.

Choice of appropriate mapping units is important in landslide susceptibility mapping (LSM). There are various possible units for this choice, while it remains unclear which one is better in performance. The purpose of this study is to make a quantitative comparison of two commonly-used units: slope-unit (SU) and raster-unit (RU) based on the landslides triggered by the 2013 Minxian, Gansu, China M w 5.9 earthquake. Ten landslide influencing factors were considered in this analysis. For each type of mapping units, the 70% samples were randomly selected and trained 20 times on the LR model, yielding 20 susceptibility maps, and the remaining 30% samples were tested for the accuracy of the modeling outcome. Different metrics, including the mean probability, model uncertainty, and model prediction skills, were used to evaluate the quality of the susceptibility maps. The results show that the resultant probability maps using two mapping units can largely predict the distribution of actual landslides, on which the high susceptibility area corresponds to the landslide-prone area. The AUC (area under curve) values, ranging from 0.8 to 0.86, show that the prediction ability of two mapping units is roughly the same. While comparing with the RU, the use of SU can lower the model uncertainties caused by the variation of training sets. We converted the RU-based assessment results into SU-based scheme. The results show that two assessment results are well fitted with good linear relationship, which implies that it is feasible to convert the RU-based landslide susceptibility mapping into the SU-based scheme. This analysis indicates that compared with the RU, the SU cannot improve the performance and accuracy of seismic landslide susceptibility mapping.

The position of landslides on a slope plays a crucial role in determining landslide susceptibility and the likelihood of landslide debris interacting with the fluvial system. Most studies primarily focus on shallow landslides in the bedrock weathering zone or large-scale bedrock landslides, but the relevant work about the location and connectivity to channels of loess landslides is limited despite their potential to provide insights into slope stability and material transport in loess regions. In this study, we explored differences in landslide location and connectivity to channels between 2013 Mw5.9 Minxian earthquake-induced (EQ) landslides and 2013 Tianshui rainfall-induced (RF) landslides in the Loess Plateau area, China. The result shows that more than 37% of EQ landslides occur in the vicinity of ridges and ~ 30% are concentrated near river channels. Landslide locations of the Minxian earthquake not only occur in ridge crest areas but also exhibit clustering near the channels. We attribute the former cluster to seismic shaking along the ridge crest, and the latter cluster to dynamic changes in pore pressure within saturated lower hillslopes due to nearly a month of rainfall prior to the Minxian earthquake. Compared to EQ landslides, RF landslides are more evenly distributed across slopes. However, due to heavy rainfall and river erosion, landslides are more concentrated in the middle and lower slope areas, especially near the river channels. Moreover, the connectivity of landslides to channels indicates that RF landslides exhibit stronger connectivity with river channels compared to EQ landslides, which may be related to the concentration of EQ landslides near ridge areas. Furthermore, due to the smaller scale of EQ landslides compared to RF landslides, larger landslides are more likely to be located closer to river channels. This may contribute to the lower observed connectivity index between EQ landslides and river channels.

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model.

On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher.

After a major earthquake, the rapid identification and mapping of co-seismic landslides in the whole affected area is of great significance for emergency rescue and loss assessment of seismic hazards. In recent years, researchers have achieved good results in research on a small scale and single environment characteristics of this issue. However, for the whole earthquake-affected area with large scale and complex environments, the correct rate of extracting co-seismic landslides remains low, and there is no ideal method to solve this problem. In this paper, Planet Satellite images with a spatial resolution of 3 m are used to train a seismic landslide recognition model based on the deep learning method to carry out rapid and automatic extraction of landslides triggered by the 2018 Iburi earthquake, Japan. The study area is about 671.87 km2, of which 60% is used to train the model, and the remaining 40% is used to verify the accuracy of the model. The results show that most of the co-seismic landslides can be identified by this method. In this experiment, the verification precision of the model is 0.7965 and the F1 score is 0.8288. This method can intelligently identify and map landslides triggered by earthquakes from Planet images. It has strong practicability and high accuracy. It can provide assistance for earthquake emergency rescue and rapid disaster assessment.