Small-scale Landslides Research Articles

Detecting small-scale landslides along electrical lines using robust satellite-based techniques

Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

A Small-Scale Landslide in 2023, Leshan, China: Basic Characteristics, Kinematic Process and Cause Analysis

Sudden mountain landslides can pose substantial threats to human lives and property. On 4 June 2023, a landslide occurred in Jinkouhe District, Leshan City, Sichuan Province, resulting in 19 deaths and 5 injuries. This study, drawing on field investigations, geological data, and historical imagery, elucidates the characteristics and causes of the landslide and conducts a reverse analysis of the landslide movement process using Massflow V2.8 numerical simulation software. The results indicate that rainfall and human engineering activities are key factors that triggered this landslide. Numerical simulation shows that the landslide stopped after 60 s of sliding, with a movement distance of approximately 286 m, a maximum sliding speed of 17 m/s, and a maximum accumulation thickness of 7 m, eventually forming a loose landslide debris accumulation of approximately 5.25 × 103 m3. The findings of this study provide significant reference value for research on landslide movement characteristics and disaster prevention and mitigation in mountainous areas.

Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks

Landslides are a kind of geological hazard with great destructive potential. When a landslide event occurs, a reliable landslide segmentation method is important for assessing the extent of the disaster and preventing secondary disasters. Although deep learning methods have been applied to improve the efficiency of landslide segmentation, there are still some problems that need to be solved, such as the poor segmentation due to the similarity between old landslide areas and the background features and missed detections of small-scale landslides. To tackle these challenges, a proposed high-resolution semantic segmentation algorithm for landslide scenes enhances the accuracy of landslide segmentation and addresses the challenge of missed detections in small-scale landslides. The network is based on the high-resolution network (HR-Net), which effectively integrates the efficient channel attention mechanism (efficient channel attention, ECA) into the network to enhance the representation quality of the feature maps. Moreover, the primary backbone of the high-resolution network is further enhanced to extract more profound semantic information. To improve the network’s ability to perceive small-scale landslides, atrous spatial pyramid pooling (ASPP) with ECA modules is introduced. Furthermore, to address the issues arising from inadequate training and reduced accuracy due to the unequal distribution of positive and negative samples, the network employs a combined loss function. This combined loss function effectively supervises the training of the network. Finally, the paper enhances the Loess Plateau landslide dataset using a fractional-order-based image enhancement approach and conducts experimental comparisons on this enriched dataset to evaluate the enhanced network’s performance. The experimental findings show that the proposed methodology achieves higher accuracy in segmentation performance compared to other networks.

Research on an Object-Oriented Intelligent Extraction Method For Landslide

Landslides are the most important type of geological disaster development in China. Heavy rainfall and earthquakes often cause mass landslides. Rapid extraction of postdisaster landslide information is an important method for realizing scientific and efficient emergency investigations and can provide a key basis for disaster assessment and emergency response decision-making. Rapid extraction of postdisaster landslide information is an important method for realizing scientific and efficient emergency investigations and can provide a key basis for disaster assessment and emergency response decision-making. Using Fujian as an example, based on basic topography, geology and other data, the multiscale segmentation method is used for image segmentation, and different features are selected. The applicability of the rule-based extraction method and support vector machine, random forest and other machine learning methods. The applicability of the rule-based extraction method and support vector machine, random forest and other machine learning methods in small-scale landslide extraction in the study area is compared. The results showed that the extraction accuracy of the rule-based extraction method was 90.86%, the extraction accuracy of the support vector machine was 65.95%, and the extraction accuracy of the random forest was 93.43%. A comparison of the three methods revealed that the random forest method was most suitable for landslide extraction in the study area.

Submarine morphology of Pantelleria volcano: The interplay between volcanic and erosive-depositional processes modulated by sea-level fluctuations.

High-resolution multibeam data integrated with seismic reflection profiles are used to identify and characterize the main primary volcanic and erosive-depositional features along the submarine part (about the 80%) of the active Pantelleria volcano located in the Sicily Channel. Volcanic features include lava flows, cones and elongated ridges. Lava flows are mainly recognized over the insular shelf, while volcanic cones and ridges are mostly concentrated along the steep submarine flanks, especially along the wider SE and NW ones. A strong volcano-tectonic interaction is envisaged for their formation, as indicated by their preferential elongation or alignment along the (main) SE-NW and (secondary) SW-NE directions that have controlled the evolution of the whole volcanic edifice. Erosive-depositional features mainly include small-scale landslide scars and narrow gullies affecting the edge of the insular shelf and overlying submarine depositional terraces. Gullies sometimes merge downslope in larger channels, whose formation is primarily controlled by the distribution of volcanic features and/or shelf sectors characterized by different age or lithologies. Based on the marked morphological differences between the different flanks of the Pantelleria volcano, we infer an overall migration of the volcanic activity from SE to NW over time. This migration is apparently in contrast with the presence of a much wider but shallower NW insular shelf with respect to the SE one. This anomaly can be explained through a two-stage model, with the formation, in the NW sector, of a polygenic shelf rejuvenated by volcanic progradation during the last eustatic hemicycle. The different depths of the insular shelf edge around the island also provide insights on vertical deformations that affected the Pantelleria volcano during the Late-Quaternary.

Experimental study on the failure mechanisms of non-cohesive soil landslide dams with different scales

The blocking of rivers by landslides is common in mountainous areas with deep and narrow valleys. Landslide dams may pose a severe threat to the safety of life and property downstream in the event of a sudden dam failure. The similarities and differences in failure characteristics between the large-scale and small-scale landslide dams are insufficiently understood. Relatively large-scale physical models to study the failure processes are expensive and time-consuming. Relatively small-scale experiments give a better opportunity to explore the failure mechanisms, but the scale effect needs to be considered. In this study, a large-scale test and three small-scale flume tests were conducted to study the failure characteristics of landslide dams with a scale ratio of 10:1. Experimental results showed that the large-scale and small-scale dam tests followed almost the same failure processes, which can be divided into three stages: seepage on the downstream surface, slope failure, and overtopping and erosion. But they differed in some quantitative outcomes such as the failure duration and pore water pressure value. The small-scale dam tests can be used to study the failure processes and mechanisms of landslide dams, but can not be used to predict the specific parameters due to the scale effect.

Formation and Hazard Analysis of Landslide Damming Based on Multi-Source Remote Sensing Data

Remote sensing plays an increasingly important role in the investigation of natural hazards, not only by obtaining specific data related to hazards, but also by realizing targeted research by combining with other data and/or technologies. Small-scale landslide hazard chain events occur frequently in mountainous areas with fragile geological environments and have strong destructive effects, yet have been somewhat understudied. This paper analyzes the Zhoujiaba (ZJB) landslide hazard chain that occurred in Longnan City on 18 August 2020. On the basis of the comprehensive application of multi-source remote sensing data, combined with time-series InSAR technology, electrical resistivity tomography (ERT), and numerical simulations, we studied the formation mechanism, damming characteristics, and potential outburst scenarios of this event. Our research suggests that geological structure and strong natural weathering are the preconditions for landslide development, which is eventually induced by extreme rainfall. Specific topographic conditions determine the rapid sliding and accumulation of landslide materials, and ultimately result in river damming. Our simulation results showed that a flood, rather than a debris flow, will be the result of dam outburst. When the simulated upstream inflow is 1.5 times that when the landslide occurred, 68% of the downstream village area will be flooded. The artificial spillway can effectively reduce the scale of the potential outburst flood, but there remains a risk of dam failure owing to the shallow depth. Our study of the hazard chain of a small-scale landslide using a combination of methods will provide a valuable reference for the analysis and treatment of similar hazard chains.

Method of strain analysis of pipelines under landslides: An improved semi-analytical method for softening bending moment-curvature

The accurate description of the elastic‒plastic stress‒strain of a pipeline is used to evaluate the risk that the pipeline will undergo failure during a landslide. This paper proposes a complete set of refined semi-analytical methods (SAMs) for pipeline elastic-plastic strain to accurately evaluate the safety of pipelines. First, a concise model for the calculation of landslide thrust is established based on the pipe-soil interaction mechanism as load boundaries of the SAMs. Secondly, the plastic softening bending moment-curvature relationship (BMCR) is derived to establish a difference equation for the deformation of the pipeline during the plastic stage based on the principle of internal force balance of the pipe section, and then the bending plastic strain is calculated accurately. Finally, the axial strain is fully calculated considering the interaction between axial force and bending of the pipeline. The comparison of the results of small-scale landslide tests, large-scale landslide tests, and SAM calculations shows that the improved SAM has good applicability, with 20% better accuracy than traditional methods. In conclusion, the SAM proposed in this paper can effectively guide the design and evaluation of the strain-based design pipeline.

Methods of Cluster Analysis for Detection of Uniform Displacement Zones of Landslides and Anti-Landslide Structures

The primary goal of landslide monitoring is the development and implementation of appropriate prediction models. Such models will allow forecasting of the anticipated landslide movements and failures. The deployment of these models is only possible by the results of geospatial monitoring. However, the measured displacements of the monitoring targets mostly have different values that may deviate a couple of times for different parts of the observed landslide. Therefore, the correct prediction model can be developed for the points with similar displacements, or in other words, for the points with the same displacement velocities. The grouping of points with similar values is known as clustering or zoning task. Having the groups of similar displacements, it is possible to work out the proper prediction model for each group of displacements and detect the probable blunders in the measurements. The paper outlines the results of geospatial monitoring for landslide and anti-landslide structures carried out for small-scale landslide and a system of retaining walls in Kyiv, Ukraine. The efficiency of cluster analysis for uniform displacement zone identification has been studied by the results of geospatial monitoring. The basic principles and ideas of cluster analysis and clustering methods have been given. The different clustering methods have been examined. Each clustering method's efficiency has been estimated by distance determination methods and similarity measures. The quantitative analysis of the considered clustering methods was checked by evaluation analysis. The most reliable results in a line of the study have demonstrated centroid clustering and furthest neighbor clustering. The determined similarity measures for those two methods were almost the same.

Simulate landslide activities during flood season 2020 - 2021 in Phong Nha - Ke Bang National Park

Research highlights in study area, there are various small-scale landslide points occurred and located surround from major landslide point with already 42 field-investigated points. The core zone of study area has the gravity displacement coefficient (Kg) by 0.0006, the buffer zone has Kg by 0.0033, all of them are weak and relative stable. The core zone of the National Park has more than 60 landslide points with total area of 81.19 hectares, and total landslide soil volume is over 2.6 mil m3. The largest landslide volume in study area is about 2.4 mil m3, with added 14 large-scale lanslide volume from 1,000 to 65,000 m3. In particular, a number of slopes occurred consecutively during flood seasons in years of 2020 - 2021. Landslide activities strongly affecting the integrity and aesthetic value of Phong Nha – Ke Bang (PNKB) National Park with calculation results, for the largest landside point during rainny season in October, 2020, show that the sliding soil blocks have displaced more than 130 m, with low factor of safety (FS) by 0.32 and the total sliding soil volume is 2.1 million m3, also consistent with field monitoring data and analysis of remote sensing images (2.4 mil m3).

How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m2 could not be detected from a Landsat 8 satellite image.

Qualitative fault tree for the analysis of slope stability loss in road infrastructure

Landslides are natural hazards all over the world, being the loss of slope stability in transport infrastructures considered small-scale landslides causing economic and human losses. Although there are many examples in the literature for the general analysis of landslides susceptibility, little information is available to analyze slopes at a local scale. The objective of this work is to identify all the slopes in road infrastructure, select those located in landslide susceptibility areas and apply a qualitative fault tree, also defined in this work, to determine the factors which can cause the slope stability loss. The results obtained are useful for infrastructure managers to have an inventory of all the slopes and to have a tool (failure tree) to identify the factors that influence each critical slope.

Preliminary Analysis of Coseismic Landslides Induced by the 1 June 2022 Ms 6.1 Lushan Earthquake, China

At 17:00 (UTC+8) on 1 June 2022, an Ms 6.1 reverse earthquake struck Lushan County, Ya’an City, Sichuan Province. This earthquake event had a focal depth of 10 km and the epicenter was located at 30.37° N and 102.94° E. The purpose of this study is to document a comprehensive coseismic landslide inventory for this event and analyze the distribution pattern and factors controlling the landslides. After careful visual interpretations, this quake event was determined to have in total triggered about 2352 landslides in an area of 3900 km2, including both shallow disrupted landslides and collapses, for which the spatial distribution was statistically related to regional topography, geology, and seismicity. Notably, a vast majority of the landslides were located on the NW plate of the seismogenic fault, and were distributed in the area with a seismic intensity of VII. In addition, coseismic landslides were more likely to appear in areas with high altitude, relief, and large slope. The landslide area density (LAD) increased with an increase in the above factors and is explained by an exponential relationship, indicating that the occurrence of coseismic landslides in this area was more easily affected by topographic factors than seismic factors. Most small-scale landslides were clustered in the ridge area, which shows the seismic amplification effects of mountain slopes. Due to the impact of seismic wave propagation direction, hillslopes facing northeast-east (NE-E) were more prone to collapse than southwest-facing ones. Based on the distribution pattern of the landslides, we suggest that the seismogenic fault of this event was NW dipping. These findings indicate that it is effective to identify the dipping of seismogenic faults using the spatial distribution pattern of coseismic landslides.

Cyclic Behaviour of Uniform Sand in Drained and Undrained Conditions at Low Confining Stress in Small-Scale Landslide Model

Earthquakes are one of the main landslide-triggering factors. Earthquakes induce cyclic shear stresses and strains in soil and raise pore water pressure, degrading soil strength towards slope failure. Slopes on which shallow landslides can form due to geological conditions are most susceptible to such failures, mainly because of low confining stress. Effective confining stress has a significant role on soil response during static and cyclic loading. Under low confining stress, soil exhibits dilatant behaviour and can degrade rapidly under cyclic loading. This paper presents the results of undrained and drained cyclic triaxial tests on uniform sandy soil material used for modelling small-scale shallow landslides under 1g conditions. Based on the scaling laws for a model in 1g conditions, the frequencies of straining amplitudes for strain-controlled cyclic triaxial tests were defined. Undrained and drained cyclic triaxial tests on the type of sand used in small-scale landslide models were performed. The sandy samples had been previously consolidated at low confining stress corresponding to the values present in the shallow landslides used as the prototype. The influences of loading frequency and low confining stress on sand behaviour were investigated, and simple models for pore pressure build-up and volumetric strain on sand behaviour were proposed. These simple models can be used to estimate increasing pore water pressure and volumetric strain due to cyclic loading at low confining stress for frequencies lower than 1 Hz. The proposed models show good correlation with the data obtained in the laboratory test.

The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

The initial phase of the 2021 Cumbre Vieja ridge eruption (Canary Islands): Products and dynamics controlling edifice growth and collapse

Tajogaite cone in the Cumbre Vieja ridge (La Palma, Canary Islands) erupted between 19 September and 13 December 2021. The tephra and lava sourced from the newly formed fissure rapidly built a pyroclastic cone. During the early days of eruption and after several small-scale landslides, the west flank of the edifice partially collapsed on 25 September, breaching the cone and emplacing a prominent raft-bearing lava flow. Our research combines direct observations, digital elevation models, thermal and visible imaging, and textural and compositional investigation of the explosive products to describe and characterize the edifice growth and collapse. The cone built over a steep slope (26°) and its failure occurred after an intense phase of lava fountaining (up to 30 m3 s−1) that produced rapid pyroclastic accumulation. We suggest that an increased magma supply, to an ascent rate of 0.30 m s−1, led to the rapid growth of the cone (at 2.4 × 106 m3 day−1). Simultaneously, the SW lava flow reactivated and formed a lava ‘seep’ that undercut the flank of the cone, triggering a lateral collapse via rotational rockslide that moved at minimum speeds of 34–70 m h−1. The lateral collapse formed a ~ 200 m wide scar, involving 5.5 × 106 m3 of material, and covered 1.17 km2 with decametric edifice portions and raft-bearing lava. The collapse produced a modest change in the vent geometry, but did not affect eruptive activity long term. A short pause in the eruption after the collapse may have been favored by rapid emptying of the shallower magma system, reducing ascent rates and increasing crystallization times. These results reveal the complex chain of events related to the growth and destruction of newly formed volcanic cones and highlight hazards when situated close to inhabited areas.

Preliminary analysis of a LiDAR-based landslide inventory in the area of Samobor, Croatia

The paper presents an analysis of the LiDAR-based landslide inventory for the area near Samobor, in northwestern Croatia with two main objectives: i) to define the geological units (obtained from Basic Geological Map of Croatia) most susceptible to landslides, and ii) to analyse the limitations of the Basic Geological Map and its applicability in landslide susceptibility map design. Within the study area of 63.8 km2, 874 landslide polygons were manually outlined, covering an area of 2.15 km2. The landslide outline confidence level, landslide index and the relief energy map were used to analyse the landslide susceptibility of a particular geological unit. By that, units in the same state of stress, i.e., in the same relief energy group were compared. This preliminary analysis has shown that the geological units Pl,Q, M3 1,2, and 1M3 1 are the most susceptible to landslides and that older geological units, Pc and K1,2, are also prone to landslides. Still, landslides within those older units can be considered as old and inactive. As for the limitations of the Basic Geological Map of Croatia, three things emerged, namely scale, the geological unit defining approach, and the neglect of regolith. Despite the limitations presented, the usability of the Basic Geological Map of Croatia in the development of small-scale landslide susceptibility maps is emphasized. However, instructions that should attribute engineering geological features to the geological units outlined in the Basic Geological Map should be prepared in the near future.

Radar İnterferometri Teknikleri Kullanarak Derin Kompleks Heyelanların İzlenmesi: Karaoğlan Havzası (Mersin-Türkiye)

Landslide inventory mapping studies around Mersin and Erdemli regions of southern Turkey revealed that numerous old large-scale and recent small-scale landslides were presumably aroused in different time intervals. While the recent active landslides have been mostly triggered by excessive rainfall, old landslides are considered to be occurred by geomorphologic valley incision processes besides the other preparatory environmental conditions. The evaluation of the spatial distribution of old landslides present retrogressive, deep-seated with complex and rotational slides in the ophiolites and ophiolitic melange units comprising also overlying reefal limestones. In this study, surface deformations caused by landslides were evaluated using radar interferometry techniques for a specified period in Karaoglan catchment. Landslide related deformations were detected over radar images of L-band ALOS-PALSAR sensor for the years between 2007 and 2011. Landslides which are active are investigated in detail cross-sections. The mean displacement from differential interferogram cross-sections was measured 3.5 cm in LOS direction. Differential SAR interferometry for studied period depicts that the average rate of movement for the entire area is extremely slow with a rate of 10 mm/yr.

Analysis of Failure Models and Deformation Evolution Process of Geological Hazards in Ganzhou City, China

In Ganzhou City, China, a complex bedrock lithology and structure, diverse topography, frequent engineering works, and abundant rainfall generate frequent, sudden, small-scale landslides that are difficult to prevent and control. This study integrates evidence data from a field investigation of landslides with geological-engineering analogues to document the distribution and development of these geohazards in Ganzhou City. Based on the distribution of landslides across different types of bedrock and soil, we identify five lithological groups prone to slope failure: granite, metamorphics (slate and phyllite), red sedimentary layers, clastic sedimentary rocks with weak interlayers, and loose Quaternary deposits. Granite and metamorphic bedrock are the two lithologies most prone to landslides. Our analysis of the genesis and mode of slope failure suggests that most landslides in Ganzhou City originated from four modes of slope failure: scouring erosion collapse, steep slope collapse, rock sliding along a rock stratum, and wedge-shaped block sliding and caving. An in-situ model test and numerical simulations were used to explore the evolution of slope deformation and failure on the most landslide-prone lithological groups, and the accumulation of debris post-failure. This work provides a reference for the assessment of the risk from, and the management of, landslide geohazards in Ganzhou City and geologically similar regions.

Assessment and Analysis of a Rainfall–Time-Lagging Water-Related Disaster in Mountainous Areas

Both global climate change and human activities are continuously impacting the abruptness and frequency of water-related natural disasters such as flash floods, debris flows, and landslides in mountainous areas, greatly threatening the safety of lives and properties. A recent rainfall-induced debris flow event happened on July 6, 2020 in the Chenghuangmiao Gully, in Sichuan Province, China, resulting in severe damage to buildings at the outlet. An integrated analysis of the consequence and triggering mechanism of this debris flow event was conducted with hydrologic information, topographic details, vegetation regimes, and drone aerial imagery. The result shows that the entire runout of the debris flow differs from that of common ones (debris flow and rainfall were highly related and synchronized), which happened 4 h after the stop of the rainfall. The hysteretic feature increases the difficulty of the prediction and warning of the debris flow due to lack of a responsible triggering mechanism. The hillslope surface is well covered by vegetation, hindering regular observation and cleaning up of long-term deposited wood and sediment debris. This effect increases the crypticity and abruptness of potential debris flows. With field evidence and analysis, it is speculated that long-term accumulative processes of dead wood sand sediment deposition formed a small-scale debris dam, and the continuous water release from the watershed led to dam breaching, subsequently triggering the initiation of the debris flow. Multiple steps distributing along the gully of an average slope of 15.65° contributed to the amplification of the debris flow once the breach of the upstream wood and sediment dam occurred. Along the gully, small-scale landslide scars can be observed, possibly amplifying the scale of the debris flow and disaster impact. This debris event gives a lesson of necessary demands of predicting and managing the risks of a low-frequency debris flow non-synchronized with rainfall events.