Landslide Research Research Articles

Применение теории катастроф для математического моделирования оползневого процесса на вогнутых склонах горных территорий

Introduction. Analysis of existing theoretical and experimental studies has shown that the model approach is the main method of landslide research. The existing mathematical model of landslides does not meet the requirements of the necessary adequacy. Materials and methods. This study uses a mathematical modeling methodology based on catastrophe theory. Results and discussion. To solve this actual problem, in this article authors developed a mathematical model of the landslide process on the concave slopes of mountainous territories. The developed model contains two components. The first of them is a mathematical model of the stress field in the volume of rocks located inside the slope section. This model uses the framework of fractal and multifractal modeling methods developed by the authors. The results of this model research are final expressions for calculating the stress field used rock pressure and bending stress as the external stress field. The superposition of the field induced by these external stresses gives the stress field in the volume of rocks located inside the slope section. Analysis of program implementation of this model showed that there are two areas in the slope section: compressive and tensile stresses adjacent to each other. At the boundary between these areas, there is a discontinuity of the stress field. A displacement surface passes along this boundary, forming a potentially landslide body. Moreover, it was found that a potentially landslide body on a slope is in a state of local and global instabilities. A potentially landslide body tends to occupy the position of the minimum potential energy. Local instability is expressed as the tendency of movement to a stable equilibrium without changing its location in the rock mass. The tendency of landslide body to move down the slope is a demonstration of global instability. The second mathematical model describes the realization of local instability that leads to the formation of a landslide body. Conclusion. According to the model analysis, it was found that the implementation of instability leads to the formation of a landslide body. At the same time, according to this analysis a landslide body can take up three stable equilibrium positions, allowing it to stay on the slope without global instability. Suggestions for practical application and direction of future research. The research results can be used to predict landslides on concave slopes of mountainous territories and to develop new mathematical models allowing to make the analysis of concave slopes of mountainous territories taking into account fracturing.

Recent advancements of landslide hydrology

AbstractOccurrence of rainfall‐induced landslides is increasing worldwide, owing to land use and climate changes. Although the connection between hydrology and rainfall‐induced landslides might seem obvious, hydrological processes have been only marginally considered in landslide research for decades. In 2016, an advanced review paper published in WIREs Water [Bogaard and Greco (2016), WIREs Water, 3(3), 439–459] pointed out several challenging issues for landslide hydrology research: considering large‐scale hydrological processes in the assessment of slope water balance; including antecedent hydrological information in landslide hazard assessment; understanding and quantifying the feedbacks between deformation and infiltration/drainage processes; overcoming the conceptual mismatch of soil mechanics models and hydrological models. While little progress has been made on the latter two issues, a variety of studies have been published, focusing on the role of hydrological processes in landslide initiation and prediction. The importance of the identification of the origin of water to understand the processes leading to landslide activation is largely acknowledged. Techniques and methodologies for the definition of landslide catchments and for the assessment of landslide water balance are progressing fast, often considering the hydraulic effect of vegetation. The use of hydrological information in landslide prediction models has also progressed enormously. Empirical predictive tools, to be implemented in early warning systems for shallow landslides, benefit from the inclusion of antecedent soil moisture, extracted from different sources depending on the scale of the prediction, leading to significant improvement of their predictive skill. However, this kind of information is generally still missing in operational LEWS.This article is categorized under: Science of Water > Hydrological Processes

Developing a more complete understanding of tropical montane forest disturbance ecology through landslide research

Landslides are a central component of tropical montane forest disturbance regimes, including in the tropical Andes biodiversity hotspot, one of the most biodiverse ecosystems in the world. Technological developments in remote sensing have made landscape-scale landslide studies possible, unlocking new avenues for understanding montane biodiversity, ecosystem functioning, and the future effects of climate change. Here, we outline three axes of inquiry for future landslide ecology research in Andean tropical montane forest. We focus exclusively on the Andes due to the vast floral diversity and high endemicity of the tropical Andes biodiversity hotspot, and its importance for global biodiversity and regional ecosystem service provisioning; the broad elevational, latitudinal, and topographic gradients across which landslide dynamics play out; and the existence of long-term plot networks that provide the necessary baseline data on mature forest structure, composition, and functioning to contextualize disturbance impacts. The three lines of study we outline, which draw heavily on remote sensing data and techniques, will deepen scientific understanding of tropical montane forest biodiversity and ecosystem functioning, and the potential impacts of climate change on both. They are: (1) tracking landslide biodiversity dynamics across time and space with high spatial and temporal resolution satellite and unoccupied aerial vehicle imagery; (2) assessing the ecological influence of landslides through the lens of plant functional diversity with imaging spectroscopy; and (3) understanding current and predicting future landslide regimes at scale by building a living landslide inventory spanning the tropical Andes. The research findings from these three axes of inquiry will shed light on the role of landslides and the process of forest recovery from them in both the Andes and worldwide.

Identifying the Groundwater Sources of Huangtupo Landslide in the Three Gorges Reservoir Area of China

Groundwater plays a crucial role in triggering and reactivating deep-seated landslides. However, classical hydrogeological investigations have limitations in their applicability to deep-seated landslides due to anisotropic and heterogeneous media. The Huangtupo landslide in the Three Gorges Reservoir area has garnered significant attention due to its high hazard potential. Of particular interest is the NO.1 Riverside Sliding Mass (HTP-1), which has shown notable deformation and has become the primary focus of landslide research. The study aims to investigate the sources of water in the HTP-1 landslide through hydrochemical analysis. This was achieved by monitoring the major ion content in the groundwater within the landslide for one year. Furthermore, stable isotope investigations were conducted on the groundwater in and around the landslide area, and an analysis of the mineral composition of the landslide soil was also performed. The results indicate that the groundwater in the landslide area (LGW) is a mixture of karst groundwater (KGW) from the adjacent upslope and local precipitation (LP). The karst groundwater is a major contributor to the recharge of the landslide groundwater system, causing a high component of groundwater that can easily exceed the critical level that causes landside failure during heavy rainfall events. Furthermore, prior to the relocation of residents from the Huangtupo landslide, the landslide groundwater was also impacted by human sewage, which not only affected the chemical composition of groundwater, but also had potential implications for slope stability. These findings provide a more scientific basis for the design and implementation of interception and drainage measures for the Huangtupo landslide and other large-scale landslides with similar geological conditions in the Three Gorges Reservoir area.

Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog

Rainfall-triggered landslides can result in devastating loss of life and property damage and are a growing concern from a local to global scale. NASA’s global landslide catalog (GLC) compiles a record of rainfall-triggered landslide events from media reports, academic articles, and existing databases at global scale. The database consists of all types of mass movement events that are triggered by rainfall and represents a minimum number of events occurring between 2007 and 2018. The GLC collection is no longer being compiled, and the dataset will not be updated past 2018. The research presented here evaluates global patterns in landslide reporting from events in the GLC. The evaluation includes an analysis of the spatial and temporal distribution of global landslide events and associated casualties and comparisons with other landslide inventories. This database has been used to estimate landslide hotspots, evaluate geographic patterns in landslides, and train and validate landslide models from local to global scales. The most notable landslide hotspots are in the Pacific Northwest of North America, High Mountain Asia, and the Philippines. Additionally, the relationship between country GDP and income status with landslide occurrence was determined to have a positive correlation between economic status and landslide reporting. The GLC also indicates a reporting bias towards English-speaking countries. The general goal of this research is to assess the decade of global landslide reports from the GLC and show how this database can be used for rainfall-triggered landslide research.

Anatomical growth response of Fagus sylvatica L. to landslide movements

Determining the age of landslide events is crucial for determining landslide risk, triggers, and also for predicting future landslide occurrence. Currently, the most accurate method for dating historical landslide events is dendrogeomorphic analysis. Unfortunately, the standard use of macroscopic growth responses of damaged trees for dating landslide activity suffers from many shortcomings. Thus, the aim of this study is to analyze in detail the growth response of trees to landslide movements at the anatomical level, a completely groundbreaking methodological approach. Ten specimens of European beech (Fagus sylvatica L.) were analyzed at two sampling heights, growing in two morphologically contrasting zones of the landslide area. Detailed anatomical analysis was focused on changes in morphometric parameters of the vessels and in the number of radial rays. The period (2008–2012) with the occurrence of the largest landslide movement (2010) recorded by long-term monitoring was analyzed. The results obtained revealed different anatomical responses in trees growing in different morphological zones of landslide. The tree responses on the ridge corresponded to the manifestations of tension wood formation, which corresponded to the stem tilting due to the landslide block movement. In the case of the trees in the trenches, root damage due to the subsidence of the landslide block blocked the flux of phytohormones, and their accumulation caused a significant reduction in the parameters of vessels and an increase in the number of rays. The study also includes recommendations in the future application of anatomical analyses in landslide research resulting from the obtained results. Thus, the obtained findings will improve the acquisition of chronological data for the purpose of landslide risk assessment.

Multi-Level Data Analyses in the Gajevo Landslide Research, Croatia

The Gajevo landslide is located in a hilly area of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, available landslide data are relatively scarce. Therefore, the Gajevo landslide location was chosen for detailed research and the development of a typical landslide model for this area. During initial research, the geographical and geological settings were reviewed and historical orthophotos were analysed. Due to the complexity and vulnerability of the area, the location required detailed investigations and the integration of multi-level data: remote (based on high-resolution LiDAR data) and field landslide mapping were performed and a map of the landslide area was developed. Precipitation data were reviewed, while shallow boreholes with material sampling and geophysical measurements provided information on material characteristics and 3D (depth) insight. As a result, knowledge was gained about material resistivity and composition along with the depth of sliding surfaces, and an engineering geological map of the Gajevo landslide area with the landslide and directly endangered areas marked was developed to be used by the local community in landslide risk assessment. As it is reasonable to expect that an extreme rainfall event will occur in combination with snowmelt in the coming years, resulting in the reactivation of Gajevo landslide, further research and continuous landslide monitoring are recommended.

Rainfall and hydrogeology

Hydrogeologists know the water cycle well and know that rain and snow are the primary sources of groundwater. We also know that we share the years of popularity with landslide researchers: the rainy years are for the landslide researchers and the dry ones for the hydrogeologists. And how was this 2022 in Italy? It’s been a strange year, as often happens recently.

Emerging Trends in Flood and Landslide Research: Single Vs Multi-Hazard Disaster Analysis Using GIS

Floods and landslides, which cause significant loss of human life and economic loss, are the most reported catastrophic events worldwide. The Geographical Information System (GIS) has been recognized as one of the most effective tools in disaster related analysis. Therefore, this article uses GIS to review the development of landslide and flood research for the past 20 years. The main elements in this review are to scrutinize the trend and scope of studies related to disaster mapping around the globe. Amongst the criteria reviewed are; details of the study area, articles that received many citations, journals with high Impact Factor scores, scope breakdown based on single and multi-hazard analysis and the theme of the study. The methodology used in this Systematic Literature Review is based on the PRISMA guidelines. Results from the review found that studies related to disaster mapping are increasing every year. This trend is influenced by data availability, efforts to produce better disaster management, frequent disaster occurrences due to climate change and evolution of GIS to analyse spatial data. Nevertheless, articles related to multi-hazard analysis are still limited, and this study suggests conducting and publishing more studies related to multi-hazard assessment in the future. This review also shows that GIS has been used widely for various types of application in disaster analysis. Articles on disaster risk assessment have been the most common. This review will help other researchers in the field of disaster management to better understand the current trend of studies related to disaster mapping.

Study on the Risk Assessment Method of Rainfall Landslide

Quantitative risk assessment of landslides has always been the focus and difficulty in the field of landslide research. In this paper, taking Mayang County, Hunan Province as an example, the risk assessment of rainfall-induced landslides was carried out from the regional and individual scales. On the regional scale, the risk factors of geological disasters were analyzed. Based on the slope unit, the risk analysis of slope geological disasters and the vulnerability risk assessment of hazard-bearing bodies were carried out to form the block plan. On an individual scale, based on the analysis of rainfall extreme value, the variation law of landslide seepage field and stability under different rainfall recurrence periods was simulated. Then, the vulnerability of the disaster-bearing body was studied according to the analysis of the impact range and the field investigation. Combined with the evaluation results of landslide hazard and vulnerability of the disaster-bearing body, the life and economic risks under different working conditions were further obtained. Therefore, the research results could provide not only a reference for the risk assessment of rainfall-induced landslides in other regions but also a theoretical basis for the early warning and prediction of landslide disasters.

Superpixel and Supervoxel Segmentation Assessment of Landslides Using UAV-Derived Models

Reality capture technologies such as Structure-from-Motion (SfM) photogrammetry have become a state-of-the-art practice within landslide research workflows in recent years. Such technology has been predominantly utilized to provide detailed digital products in landslide assessment where often, for thorough mapping, significant accessibility restrictions must be overcome. UAV photogrammetry produces a set of multi-dimensional digital models to support landslide management, including orthomosaic, digital surface model (DSM), and 3D point cloud. At the same time, the recognition of objects depicted in images has become increasingly possible with the development of various methodologies. Among those, Geographic Object-Based Image Analysis (GEOBIA) has been established as a new paradigm in the geospatial data domain and has also recently found applications in landslide research. However, most of the landslide-related GEOBIA applications focus on large scales based on satellite imagery. In this work, we examine the potential of different UAV photogrammetry product combinations to be used as inputs to image segmentation techniques for the automated extraction of landslide elements at site-specific scales. Image segmentation is the core process within GEOBIA workflows. The objective of this work is to investigate the incorporation of fully 3D data into GEOBIA workflows for the delineation of landslide elements that are often challenging to be identified within typical rasterized models due to the steepness of the terrain. Here, we apply a common unsupervised image segmentation pipeline to 3D grids based on the superpixel/supervoxel and graph cut algorithms. The products of UAV photogrammetry for two landslide cases in Greece are combined and used as 2D (orthomosaic), 2.5D (orthomosaic + DSM), and 3D (point cloud) terrain representations in this research. We provide a detailed quantitative comparative analysis of the different models based on expert-based annotations of the landscapes and conclude that using fully 3D terrain representations as inputs to segmentation algorithms provides consistently better landslide segments.

Review of the research and evolution of landslides in the hinterland of Koroška Bela settlement (NW Slovenia)

This paper gives an overview of landslide research and the activity of landslides located above the Koroška Bela settlement in Northwest Slovenia. There are several landslides in this area and they pose a direct threat to the settlement below. The settlement is very densely populated (about 2,100 inhabitants) and has well-developed industry and infrastructure. It is built on deposits from past debris flows, indicating that large slope mass movements have occurred in the past. In this regard, the hinterland of Koroška Bela has been investigated since 2006, within the framework of various research, technical and European projects. The most extensive geological and geotechnical investigations were carried out after April 2017, when part of the Čikla landslide collapsed and mobilised into a debris flow. All of the investigations which have been carried out over the years revealed that the hinterland of Koroška Bela is characterised by high landslide activity due to geological, hydrogeological and tectonic conditions. In order to protect people and their property, it is essential to implement a holistic mitigation measure which includes remediation works (drainage works, debris flow breaker, etc.) and non-structural measures (monitoring system, early warning system, risk management, etc.). Regular and continuous monitoring of all landslides is also crucial to observe the landslide dynamics and evaluate the effectiveness of structural mitigation measures.

New Landslide Reporting Tool Uses Social Media and AI

The tool extracts landslide information in real time, which could advance landslide research as well as disaster response.

Specifics of slope movements on slopes with contrasting structural conditions: Evidence from tree-ring records

The geological structure of a slope is one of the key factors that drives the development of landslide types. Various landslide types, although occurring in the same locality, can result in different spatio-temporal dynamics of their movements. Moreover, the presence of shallow creep movements on such slopes can modify the effect of slide movements and possibly cause the false impression of landslide activity. The nonstandard combination of detailed tree-ring-based (dendrogeomorphic) approaches involving the careful assessment of growth disturbance properties (the duration, intensity, mutual ratio of different disturbances), the statistical testing of the spatial pattern of disturbed trees, the analysis of possible extreme precipitation triggers, and geophysical sounding was used in this study in an attempt to distinguish creep and slide movements and assess the general slope movement activity on slopes with contrasting geological structures. Data from 1032 tree-ring series of 516 disturbed individuals of common spruce (Picea abies (L.) Karst.) enabled the reconstruction of 13 slope movements on all three studied sites (3, 7, and 3) for the last approximately 70 years. Although some indices correspond with the general assumption of a specific landslide type to the structure of induced growth disturbances (e.g., an increased ratio of reaction wood on rotational landslides), the advanced dendrogeomorphic approach used suggests that not all dated events are of slide origin and that creep movements significantly modify the effect of original slide movement on tree growth. Some dated events are probably the result of only creep movements themselves. Thus, the present study critically assesses results of the dendrogeomorphic dating of various landslides, presents contrasting behavior of slope movements on slopes with contrasting geological structures, and recommends the use of more advanced tree-ring-based approaches for more effective dendrogeomorphic research of landslides.

Performance evaluation of a real-time high-precision landslide displacement detection algorithm based on GNSS virtual reference station technology

Global navigation satellite system (GNSS) technology is one of the most effective landslide displacement monitoring methods, but most of the current landslide research based on GNSS is focused on long-term landslide monitoring, but short-term landslide monitoring is particularly important for the early warning of landslides. The main purpose of this paper is to extend the existing GNSS positioning model and design a set of different landslide displacement detection algorithms, which can guarantee high-precision displacement detection and give consideration to real-time, mainly for short-term landslide displacement, and can also give consideration to long-term landslide monitoring. In addition, the traditional layout mode of “one reference station corresponds to one landslide” has many problems, such as high cost, low utilization rate of reference station and excessive reliance on single reference station, which restrict the large-scale promotion and application of GNSS in landslide monitoring. Based on the existing VRS processing method and the displacement detection algorithm presented in this paper, the service effect of virtual reference station and physical reference station is compared and analyzed in detail, and the feasibility of virtual reference station replacing physical reference station is demonstrated. Experimental results show that the positioning accuracy of real time kinematic (RTK) is slightly worse than that of physical reference station due to the existence of atmospheric residual errors in the regional reference station network when VRS replaces physical reference station for displacement detection. However, after processing the real-time sliding window model, it significantly suppressed the influence of all kinds of observation noise and gross error, and the solution accuracy of the output sliding window period was not much different from that of the physical reference station, basically at the sub-centimeter level, which could fully meet the needs of centimeter and sub-centimeter level landslide displacement detection. In addition, the “short and long period mutual checking method” proposed in this paper can realize the detection and early warning of the rapid displacement type in a short period by reasonably adjusting the window length of the short and long period. The “ epoch difference method” can realize the detection and early warning of the sudden displacement in a few epoch. In practical application, the combined detection of the two methods can satisfy the real-time detection and early warning of short-term landslide displacement. In addition, by storing the solution positions of each long period sliding window period, the periodic position comparison can be carried out to judge the displacement changes of the monitoring body, which is equivalent to the usual long-term landslide monitoring strategy.

Machine learning and landslide studies: recent advances and applications

Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.

Application of Certainty Factor and Bayesian statistics models for evaluation of landslides and environmental factors at Bao Thang district and Lao Cai city, Lao Cai province

The study area is heavily affected by landslides with increasing frequency and intensity, causing serious damages and affecting the sustainable socio-economic development of the region. The use of mathematical methods in landslide research is increasingly interested due to the quantitative nature of parameters and calculation results. This study aims to apply the Certainty Factor (CF) and Bayesian statistics models for geological hazard evaluation. Landslide distribution is identified from remote sensing images and field surveys. Landslide inventory maps (428 landslides) were compiled by reference to historical reports, Google Earth, and field mapping. All landslides were randomly separated into two data sets: 70% were used to establish the models (training data sets) and the rest for validation (validation data sets). Fifteen environmental factors from geology, topography and hydrological information of the studied area were extracted from the spatial database. Results show that the group of factors of slope angle, Terrain Ruggedness Index, fault/lineament density, stratigraphy, geoengineering characteristics, weathering types, and maximum daily rainfall play the most important role in the formation of landslides in the study area. Validation from Certainty Factor (CF) and Bayesian statistics models show 87% and 92% prediction accuracy between hazard maps and existing landslide locations. These models show reasonably accurate landslide predictions in the study area and can be served as the basis of landslide risk-management studies in the future.

A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides

The effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.

InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China

Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting.

Mechanism of rainfall induced landslides in Yunnan Province using multi-scale spatiotemporal analysis and remote sensing interpretation

In order to explore the disaster mechanism of rainfall induced landslides in Yunnan Province, a new method is developed in the field of landslide research, which provides a strong basis for the study of rainfall induced landslide disaster mechanism in different regions. First, the mechanism of rainfall induced landslide initiation and the mechanism of rainfall impacting slope are introduced, and the temporal and spatial characteristics of landslide disasters in Yunnan Province are analyzed comprehensively. The influence of rainfall on landslide density is analyzed in 8 regions randomly, and a semi supervised feature selection algorithm based on manifold is proposed to optimize the remote sensing image of Zhaojiapo rainfall type landslide area in Yunnan Province. The results show that in the province the monthly rainfall days account for a large proportion from June to September, and the months with the largest number of landslides in the whole year are from June to August. The seasonal change of rainfall belt and the frequent occurrence time of landslide disasters and the spatial characteristics of landslide distribution and rainfall distribution basically belong to corresponding relationship; the greater the rainfall of 8 regions, the greater the probability of landslide occurrence, and the greater the landslide density, which belongs to the proportional relationship; the landform characteristics in the optimized remote sensing image can be clearly displayed, and the classification accuracy of the data is enhanced. The results show that based on multi-scale spatiotemporal analysis combined with optimized remote sensing technology, the cause of rainfall induced landslides in Yunnan Province is the change of landslide density caused by heavy rainfall, which eventually leads to disasters. It is conducive to scientifically understanding the inducement of rainfall induced landslides in Yunnan Province, provides a scientific basis for further disaster control and prevention, and provides a new possible method for landslide real-time prediction and alarm system.