Landslide Inventory Research Articles

An Optimization of Statistical Index Method Based on Gaussian Process Regression and GeoDetector, for Higher Accurate Landslide Susceptibility Modeling

Landslide susceptibility assessment is an effective non-engineering landslide prevention at the regional scale. This study aims to improve the accuracy of landslide susceptibility assessment by using an optimized statistical index (SI) method. A landslide inventory containing 493 historical landslides was established, and 20 initial influencing factors were selected for modeling. First, a combination of GeoDetector and recursive feature elimination was used to eliminate the redundant factors. Then, an optimization method for weights of SI was adopted based on Gaussian process regression (GPR). Finally, the predictive abilities of the original SI model, the SI model with optimized factors (GD-SI), and the SI model with optimized factors and weights (GD-GPR-SI) were compared and evaluated by the area under the receiver operating characteristic curve (AUC) on the testing datasets. The GD-GPR-SI model has the highest AUC value (0.943), and the GD-SI model (0.936) also has a higher value than the SI model (0.931). The results highlight the necessity of factor screening and weight optimization. The factor screening method used in this study can effectively eliminate factors that negatively affect the SI model. Furthermore, by optimizing the SI weights through GPR, more reasonable weights can be obtained for model performance improvement.

Two public inventories of landslides induced by the 10 June 2022 Maerkang Earthquake swarm, China and ancient landslides in the affected area

The June 10, 2022 Ms5.8, Ms6.0 and Ms5.2 earthquake swarm of Maerkang, Sichuan, China has triggered a lot of landslides. The primary purpose of this paper is to prepare a detailed coseismic landslides inventory and ancient landslide inventory in the earthquake swarm-affected area. Based on pre- and post-quake high-resolution optical satellite images (planet), we delineated 650 individual coseismic landslides in the VI-intensity area and above, occupying an area of about 1.2 ​km2. The largest landslide covers about 70217 ​m2 in horizontal projection, while the smallest one is just 81 ​m2. In addition, based on the series of high-precise images on the Google Earth Platform, 759 ancient landslides were also delineated with an area of 117 ​km2. The maximum area reaches about 2 ​km2 and the minimum is 2580 ​m2. The two inventories provide a basis for landslide spatial distribution and hazard assessment, disaster prevention and mitigation of earthquake-triggered landslides in similar regions. We opened these two landslide inventories to facilitate researchers to carry out more in-depth work.

Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan.

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016-2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area.

Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR

The Hunza Valley, in the northwestern Karakoram Mountains, North Pakistan, is a typical region with many towns and villages, and a dense population and is prone to landslides. The present study completed landslide identification, updating a comprehensive landslide inventory and analysis. First, the ground surface deformation was detected in the Hunza Valley by SBAS-InSAR from ascending and descending datasets, respectively. Then, the locations and boundaries were interpreted and delineated, and a comprehensive inventory of 118 landslides, including the 53 most recent InSAR identified active landslides and 65 landslides cited from the literature, was completed. This study firstly named all 118 landslides, considering the demand for globally intensive research and hazard mitigation. Finally, the deformation, spatial–topographic development, and distribution characteristics in the Hunza Valley scale and three large significant landslides were analyzed. Information on 72 reported landslides was used to construct an empirical power law relationship linking landslide area (AL) to volume (VL) (VL = 0.067 × AL1.351), and this formula predicted the volume of 118 landslides in this study. We discovered that the landslides from the literature, which were interpreted from optical images, had lower levels of velocity, area, elevation, and height. The SBAS-InSAR-detected active landslide was characterized by higher velocity, larger area, higher elevation, larger slope gradient, larger NDVI (normalized difference vegetation index), and greater height. The melting glacier water and rainfall infiltration from cracks on the landslide’s upper part may promote the action of a push from gravity on the upper part. Simultaneously, the coupling of actions from river erosion and active tectonics could have an impact on the stability of the slope toe. The up-to-date comprehensive identification and understanding of the characteristics and mechanism of landslide development in this study provide a reference for the next step in landslide disaster prevention and risk assessment.

Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China

Many landslides are triggered by excess precipitation. In the eastern part of China, landslides caused by extreme rainfall from typhoons in the monsoon season are the main geomorphological process with catastrophic impacts on society and the environment. In this study, Wenzhou City in eastern China was taken as the study area, and we compiled a detailed inventory of rainfall-triggered landslides between 1999 and 2016. The developmental characteristics of these landslides were determined with an emphasis on temporo-spatial distribution. The results showed that most of the landslides were located in the mountainous area of the western part of Wenzhou City. Landslides triggered by typhoon rainfall were commonly concentrated in a short period from July to September, when more than 70% of the landslides occurred. The landslides in this region were mainly of the debris-flow type, most of which were on a small scale, but had severe effects because of large elevation differences and long runout distances. Because the precipitation in typhoon events was commonly extreme, the initiation area of most landslides coincided with the region of highest hourly precipitation. Our results can provide reference data and guidelines for developing an early warning system for landslides and risk reduction in the study area.

Seasonal Landslide Activity Lags Annual Precipitation Pattern in the Pacific Northwest

AbstractSeasonal variations in landslide activity remain understudied compared to recent advances in landslide early warning at hourly to daily timescales. Here, we learn the seasonal pattern of monthly landslide activity in the Pacific Northwest from five heterogeneous landslide inventories with differing spatial and temporal coverage and reporting protocols combined in a Bayesian multi‐level model. We find that landslide activity is distinctly seasonal, with credible increases in landslide intensity, inter‐annual variability, and probability marking the onset of the landslide season in November. Peaks in landslide probability in January and intensity in February lag the annual peak in mean monthly precipitation and landslide activity is more variable in winter than in summer, when landslides are rare. For a given monthly rainfall, landslide intensity at the season peak in February is up to 10 times higher than at the onset in November, underlining the importance of antecedent seasonal hillslope conditions.

Estimating global landslide susceptibility and its uncertainty through ensemble modeling

Abstract. This study assesses global landslide susceptibility (LSS) at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of a global LSS map with dynamic satellite-based soil moisture estimates for landslide modeling. Global LSS estimation contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale LSS model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modeling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables describing the landscape. The latter are taken from the Catchment land surface modeling system (including input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), as well as geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS, because of the asymptotic nature of the LSS model. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the receiver operating characteristic between 0.84 and 0.92 for independent regional to continental landslide inventories.

Patagonian Andes Landslides Inventory: The Deep Learning’s Way to Their Automatic Detection

Landslide inventories are crucial to studying the dynamics, associated risks, and effects of these geomorphological processes on the evolution of mountainous landscapes. The production of landslide maps is mainly based on manual visual interpretation methods of aerial and satellite images combined with field surveys. In recent times, advances in machine learning methods have made it possible to explore new semi-automated landslide detection methodologies using remotely detected images. In this sense, developing new artificial intelligence models based on Deep Learning (DL) opens up an excellent opportunity to automate this arduous process. Although the Andes mountain range is one of the most geomorphologically active areas on the planet, the few investigations that use DL mainly focus on mountain ranges in Europe and Asia. One of the main reasons is the low density of landslide data available in the Andean areas, making it difficult to experiment with DL models requiring large data volumes. In this work, we seek to narrow the existing gap in the availability of landslide inventories in the area of the Patagonian Andes. In addition, the feasibility and efficiency of DL techniques are studied to develop landslide detection models in the Andes from the generated datasets. To achieve this goal, we generated in a manual process a datasets of 10,000 landslides for northern Chilean Patagonia (42–45°S), being the largest freely accessible landslide datasets in this region. We implement a machine learning model, through DL, to detect landslides in optical images of the Sentinel-2 constellation using a model based on the DeepLabv3+ architecture, a state-of-the-art deep learning network for semantic segmentation. Our results indicate that the algorithm detects landslides with an accuracy of 0.75 at the object level. For its part, the segmentation reaches a precision of 0.86, a recall of 0.74, and an F1-score of 0.79. The correlation of the segmentation measured through the Matthews correlation coefficient shows a value of 0.59, and the geometric similarity of the correctly detected landslides measured through the Jaccard score reaches 0.70. Although the model shows a good response in the testing area, errors are generated that can be explained by geometric and spectral relationships, which should be solved through new training approaches and data sets.

The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin

The Lower Mekong River Basin in Southeast Asia experiences frequent rainfall-triggered landslides especially during the monsoon season. In this study, the influence of land use and land cover change and other causative factors on landslide susceptibility is evaluated in the Lower Mekong Basin. Frequency ratio analysis is performed to quantify the relationship between LULC change and susceptibility. Detailed landslide inventory maps are used for analysis with yearly LULC maps. The LULC change is used as a contributing variable in a logistic regression-based susceptibility model with other variables including distance to roads, slope, aspect, forest loss, and soil properties. The Receiver Operating Characteristic (ROC) curve and Area Under the Curve are estimated for the model trained by each landslide inventory. The models show good performance, with AUC values ranging from 0.697 to 0.958 and an average AUC equal to 0.820. Both the Frequency Ratio analysis and the Logistic Regression models indicate LULC change from agricultural land to forest has a positive correlation with landslide occurrence. The most significant factors in the models are found to be distance to roads, slope, and aspect. A better understanding of the effects of LULC on landslide susceptibility can be useful for local land and disaster management and for the implementation of LULC as a factor in future susceptibility models. Using datasets that are unique to the Lower Mekong region, this study provides additional insights into the relationship between causative factors and landslide activity to better inform regional and global landslide susceptibility modeling.

Deforestation controls landslide susceptibility in Far-Western Nepal

Landslides are a major problem in the Far-Western region of Nepal, where one of the highest densities of shallow landslides within the country is observed. However, there is still a lack of understanding around the relative impact of deforestation as a predisposing factor. Here, we use remote-sensing techniques and freely accessible satellite data to quantify the effects of deforestation and related agriculture practices in the assessment of landslide susceptibility. We use a new regional landslide inventory of 8,778 events dated between 1993 to 2018 to train and validate bivariate landslide susceptibility models, in which preceding land cover/land use is included as a predisposition factor. Our results show that the use of land-cover /land-use maps enhances the average probability of model success by 7.9%. Besides, we assess land-cover changes for a period of 1 to 10 years prior to each landslide event to analyze the relative incidence of changes related to deforestation and agricultural uses. The analysis suggests that agricultural practices and deforestation that occur 5–7 years preceding the landslide event have a significant influence on the landslide occurrence (+16%). Our results reveal that negligent anthropological land use practices can significantly enhance landslide risk.

Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India)

Abstract. Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality and completeness can depend on the data and the methods with which they were produced. In this work we evaluate the effects of a variable visibility of the territory to map on the spatial distribution of the information collected in different landslide inventories prepared using different approaches in a study area. The method first classifies the territory in areas with different visibility levels from the paths (roads) used to map landslides and then estimates the landslide density reported in the inventories into the different visibility classes. Our results show that (1) the density of the information is strongly related to the visibility in inventories obtained through fieldwork, technical reports and/or newspapers, where landslides are under-sampled in low-visibility areas; and (2) the inventories obtained by photo interpretation of images suffer from a marked under-representation of small landslides close to roads or infrastructures. We maintain that the proposed procedure can be useful to evaluate the quality and completeness of landslide inventories and then properly orient their use.

Assessing rainfall-induced landslide risk in a humanitarian context: The Kutupalong Rohingya Camp in Cox's Bazar, Bangladesh

We have proposed a novel method for assessing landslide risk by applying cutting-edge geospatial technologies and combining hazard, exposure, and vulnerability components. The Kutupalong Rohingya Camp (KRC) in Cox's Bazar, Bangladesh, was taken as a case study area. The district was hosting over one million Rohingya refugees from Myanmar, fleeing genocide and crimes against humanity. Extensive vegetation removal, hill cutting, exceptionally dense camps, fragile shelters, unstable soils, and excess monsoon rainfall were linked to yearly landslides. At first, ten landslide factor maps were produced, and a detailed fieldwork-based landslide inventory was conducted. Next, we applied various statistical and knowledge-based approaches for landslide susceptibility modelling. The most accurate susceptibility map was utilised to produce landslide hazard maps for different rainfall return periods. Next, we used population and shelter density data for exposure mapping and integrated age and gender-based factors for social vulnerability analysis. The hazard, exposure, and vulnerability maps were combined to produce the landslide risk map. Finally, we calculated shelters and refugee populations at risk for different scenarios. Camp 10 had the highest risk value, and historical landslide events validated the results. This study is replicable in a similar context and can be scaled up. It provides authentic information on landslide disaster scenarios that should be adopted in the decision-making process to reduce future impacts of catastrophic hydrometeorological disasters in the KRC and surrounding host community areas.

Landslide susceptibility mapping of the Main Boundary Thrust (MBT) region in Tinau-Mathagadhi Section of Palpa District, Lumbini Province

The study was carried out in Tinau-Mathagadhi section of Palpa District, Lumbini Province. The output of the landslide susceptibility analysis using frequency ratio (FR) is evaluated. Google Earth (CNES/Airbus and Maxar Technologies) Imagery of 50 cm spatial resolution was used to detect landslides. Data training and testing sampling was created using the landslide inventory. Eight causative factors were derived from topographic, geological, and land-use maps. The causative factors and training events were used to determine the FR ratings. The objective of present study is to address the effect of the Main Boundary Thrust (MBT) along with other important parameters in landslide susceptibility mapping. The integration of causative factors that assigned FR scores yielded the landslide susceptibility map. The validation of 79.6% was obtained using the ROC-AUC curve. Among eight causative factors, it was found that distance from the Main Boundary Thrust (MBT), aspect, land-use, and geology are dominating factors in the occurrence of the landslide. The FR method is based on a quantitative relationship between landslide inventory and landslide affecting factors. The method is valid for Tinau-Mathagadhi section and MBT is one of the causes of landslides in this area.

A simple method for landslide risk assessment in the Rivière Aux Vases basin, Quebec, Canada

This research offers a simple approach for landslide risk assessment on a basin-scale in the Quebec Province of Canada, where landslides are common. Landslide inventory and distribution mapping are developed for the Aux Vases Watershed, the most landslide-affected area in the Saguenay region, for a detailed landslide hazard and risk assessment process. The study uses publicly available data from Google Earth images, published literature, Government data sources for topographic maps and landuse information, as well as the 2016 Census Profile of the Saguenay region of Quebec. ArcGIS software is used for spatial data processing and analyzing datasets to develop maps of landslide susceptibility, vulnerability, and lack of coping capacity, and a landslide risk map of the Aux Vases Watershed was developed. Landslide risk assessment is critical for minimizing the loss of life and property from potential landslides in the study area and empowers communities with useful information so they can make informed decisions to mitigate the risk and build coping capacities. In addition, risk reduction policies and strategies, public safety, and future development planning initiatives will benefit from this research.

Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia

The Glacier Bay area in southeastern Alaska and British Columbia, encompassing Glacier Bay National Park and Preserve, has experienced rapid glacier retreat since the end of the Little Ice Age in the mid-1800s. The impact that rapid deglaciation has had on the slope stability of valley walls and on the sedimentation of fans and deltas adjacent to fjords and inlets is an ongoing research topic. Using 3-year (2018–2020) Sentinel-1 datasets, and an automated time-series persistent scatterer interferometric synthetic aperture radar (PSInSAR) processing method, we detected landslides or delta subsidence at 27 sites within a vast 180 × 180 km remote region encompassing Glacier Bay proper. Most of the sites that we identified had not been previously identified. We categorized the hazard source areas that we identified into three general types:1) slow-moving landslides on steep rocky slopes not near (> 2 km away from) present-day glacier termini (e.g., Tidal Inlet landslide), 2) slow-moving landslides directly adjacent to (< 2 km away from), and associated with glacier thinning and retreat, and 3) subsidence of glacial outwash fan deltas. In categories 1 and 2, we observed 22 landslides moving at velocities ranging from 0.5 to 4 cm/yr. In category 3, we detected five fan deltas subsiding at velocities ranging from 0.5 to 6 cm/yr. Within our measurement error, these velocities were consistent during the monitoring period. Because acceleration was not observed, the issuance of warnings of imminent rapid failure is not warranted, however, continued remote monitoring is warranted. Our interferometric synthetic aperture radar (InSAR) results could be combined with other data sets including field observations, subaerial and submarine landslide inventories, bedrock fabric mapping from newly available light detection and ranging (lidar) data, and geologic maps to produce an inherent susceptibility map for landslides in bedrock and fan deltas. This map could be used to forecast susceptibility for both earthquake and climatically induced landslides.

Landslide Hazard Assessment and Distribution Mapping: A Case from Triveni Rural Municipality, Nepal

An application of GIS for landslide hazard assessment using multivariate statistical analysis, mapping, and the evaluation of the hazard maps is crucial for disaster risk reduction. Landslides are the rapid downward movement of a mass of rock, earth or artificial fill to the slope. The study was conducted the Khara of Triveni Rural municipality of Rukum West district and it covers three wards (3, 4 &amp; 5) of rural municipality covering the total area of about 33.52 square kilometres located in the Karnali province of Nepal which is under the pressure of high road construction activities. Data used to construct the landslide distribution map and hazard assessments were obtained from the direct field, and mapping using GIS. More data sources and verifications were made from the rural municipality, published articles and journals, topographical and geological maps, google images and aerial photographs and other digital sources. To determine the factors and classes influencing land sliding, the layers of topographic factors derived from a digital elevation model, geology, and land use/land covers were analyzed and the results were used for landslide distribution mapping and hazard analyses. From the landslide inventory of more than 200 landslides that were occurred during the last five years since 2015, the landslide distribution map, landslide-size distribution map and hazard level of sliding graphics were presented. Hazard map of the study area shows 4.34% area lies in the high hazard level, 53.64% on moderate hazard level and 42.02% in low hazard level in the study area. The results would give insights to the landslide distribution in the area that could support rural municipality for shaping disaster risk reduction policies and strategies.

Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China

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.

Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.

Do standing trees affect landslide mobility on forested hillslopes in Japan?

AbstractLandslide mobility likely changes in response to the effects of standing trees on the landslide runout. However, the properties of standing trees that influence landslide runout are not well understood. In this study, the relationships between the properties of landslides and the heights of standing trees and the runout distances of rainfall‐induced landslides (primarily 101–103 m3 in volume) in the Hiroshima Prefecture, Japan, were examined to investigate whether the heights of the trees around the runout area change the landslide mobility. The results indicate that the landslide runout was constrained not only by the landslide area and volume but also by the tree height around the runout area. Tall but sparse standing trees did not prevent landslide runout, whereas dense standing trees resulted in relatively low landslide mobility, irrespective of whether the trees were broadleaf or coniferous. A comparison between our landslide inventory and previously reported landslides showed that the landslide mobility was lower in areas with standing trees than in areas without standing trees, such as under experimental conditions and for landslides in Martian and submarine environments. Therefore, differences in the heights of the trees around the runout area can result in variations in the extent of landslide‐related hazards and disturbances.

Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding

Abstract. Heavy-rainfall events in mountainous areas trigger destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape. Landslide locations are commonly mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights into the processes behind the triggered landsliding require information on both the spatial extent and the timing of landslides. Here we demonstrate that Sentinel-1 synthetic aperture radar amplitude time series can be used to constrain landslide timing to within a few days and present four techniques to accomplish this based on time series of (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability in amplitude between pixels within the landslide, and (iii) geometric shadows and (iv) geometric bright spots cast within the landslide. We test these techniques on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that a method combining them allows 20 %–30 % of landslides to be timed with an accuracy of 80 %. Application of this method could provide an insight into landslide timings throughout events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis.