Debris flow hazard assessment method and device coupling InSAR and dynamic process simulation

Abstract

The present invention relates to the technical field of debris flow evaluation, and discloses a debris flow hazard evaluation method and device that couple InSAR and dynamic process simulation. Regarding the method, it includes: determining the spatial positions and areas of landslide and collapse sources, and quantitatively estimating the volume; determining the spatial positions of potential sources and quantitatively estimating the volume; estimating the control effect of terrain on the possible coupling between the source area and the river network of the basin based on the source connectivity index; determining the sediment initiation position; simulating using a debris flow simulation model based on debris flow dynamic parameters to obtain the debris flow flow velocity, deposition depth, and intensity; and obtaining the debris flow hazard zoning result of the study area according to the debris flow hazard grading standard. The technical solution of this application establishes a connection between the source and potential sources through the connectivity index, overcomes the influence of the traditional subjective determination of the sediment initiation position, which leads to more errors in debris flow prediction, and provides new ideas for research on debris flow dynamic processes, monitoring and early warning, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of debris flow evaluation, and specifically relates to a debris flow hazard evaluation method and device that couple InSAR and dynamic process simulation. Background Technique

[0002] (I) Debris Flow Source Identification Method

[0003] Debris flow is one of the most dangerous processes in mountain geomorphological evolution. It can wash away a large amount of source materials and cause damage in a very short time. Debris flow poses a considerable threat to vulnerable human communities and infrastructure. The formation of debris flow is mainly controlled by three factors: steep terrain, the amount and frequency of rainfall, and the reserve of debris flow material sources. In order to accurately evaluate the hazard of the debris flow source area, it is necessary to evaluate the source area of debris flow (Guo and Cui, 2020). Due to various physical processes, the scale of debris flow will increase. The source materials usually come from riverbed sediments (Iverson et al., 2011), nearby landslides (Hungr et al., 2005), and the upwelling and surface erosion of slopes near the river channel (Santi et al., 2008). A large amount of source material reserves produce a large amount of material that can be washed out, and potential debris flows are more dangerous. Bovis and Jakob (1999) believe that the scale and frequency of debris flow are closely related to the supply of source materials within the basin. Therefore, the spatial location and volume of source materials in the entire basin should be evaluated.

[0004] Traditional methods for evaluating debris flow sources mainly include delineating the quantity and spatial distribution characteristics of loose debris sources based on field surveys and optical image interpretation. However, field surveys are labor-intensive and costly, and it is difficult to conduct accurate assessments when the terrain of the basin is complex. In addition, the interpretation method based on optical images (including satellite images and UAV aerial survey images) is highly subjective, resulting in large errors, and optical remote sensing images cannot identify potential surface displacement and erosion information, often underestimating the supply of debris sources to debris flows. With the rapid development of remote sensing technology, the Sentinel-1 SAR satellite can capture large-scale real-time surface information in a short time for continuous monitoring. The Interferometric Synthetic Aperture Radar (InSAR) technology can be used for long-term continuous monitoring of surface deformation. InSAR has been successfully used to map surface deformations related to earthquakes, glacier movements, and landslide warning and monitoring systems; it has also been widely used to identify potential landslides at the regional scale. However, applying InSAR technology to identify debris flow sources at the basin scale still poses certain challenges. This paper combines optical remote sensing images and time-series SBAS-InSAR technology to identify the spatial distribution of debris sources in the basin and estimate the volume of debris sources.

[0005] (2) Debris flow hazard analysis methods

[0006] Debris flow hazard analysis is considered an effective way to reduce debris flow hazards (Liu Fuzhen et al., 2022). Scholars at home and abroad have conducted a certain degree of research on debris flow hazards, which can be mainly classified into two categories: the multi-factor superposition method and the numerical simulation method (Zhou Bin et al., 2022). The former focuses on studying the overall hazard degree of the basin (Zou Qiang et al., 2013), mainly including the minimum entropy method (Chen et al., 2007), the information quantity method (Ruan Shenyong et al., 2001), neural network (Chen Gang et al., 2010), etc. However, they cannot accurately quantify the impact of debris flows on settlements and are difficult to describe the movement characteristics of debris flows. The evaluation method of numerical simulation can simulate the movement process and characteristics under complex terrain (Jakob et al., 2013), and quantitatively depict the hazards of debris flows, mainly including the Massflow model (Horton et al., 2019), the FLO-2D model (d’Agostino et al., 2006), etc. At present, as a two-dimensional dynamic simulation model, FLO-2D has the advantages of strong model adaptability, high calculation efficiency, and rich post-processing functions, and has been widely used in the quantitative assessment of debris flow hazards (Peng et al., 2013; Han et al., 2015; Li Baoxing et al., 2022). When using FLO-2D for debris flow simulation, it is crucial to select a suitable sediment initiation location. Usually, scholars determine the sediment initiation location based on the large-scale collapse and landslide bodies interpreted from remote sensing data and the disaster development characteristics of the debris flow gully surveyed on the spot. However, a large-scale debris flow may not start from only one location. Therefore, this method has certain subjectivity and limitations, which may cause errors in the simulation results. Source connectivity is an important indicator for debris flow hazard assessment. Source connectivity, especially the connectivity between the source and the downstream area, is defined as the sediment flux that controls the entire landscape (Cavalli et al., 2013). This is an important attribute for studying the sediment transport process in mountainous basins. The supply of sediment from the hillslope to the ditch is an important process (Cavalli et al., 2013; Uchida et al., 2018); previous studies have shown that researchers are increasingly concerned about the connection between the hillslope and the river channel, and many have proposed many multidisciplinary methods to evaluate sediment connectivity (Cislaghi and Bischetti, 2018; Germain et al., 2020; Schopper et al., 2018). However, previous studies mostly regarded source connectivity as a qualitative evaluation factor and did not establish a connection with the actual source and the debris flow dynamic process. Summary of the Invention

[0007] The present application provides a debris flow hazard assessment method and device that couple InSAR and dynamic process simulation to solve the above technical problem of large mismeasurement errors in existing debris flow detection technologies.

[0008] According to one aspect of the present application, an embodiment provides a debris flow hazard assessment method that couples InSAR and dynamic process simulation, including:

[0009] Determine the spatial location and area of landslides and collapse sources based on optical remote sensing image technology, and quantitatively estimate the volume.

[0010] Resolve multi-dimensional surface deformation based on SBAS-InSAR and SAR geometric imaging principles, determine the spatial location of potential sources, and quantitatively estimate the volume.

[0011] Estimate the control effect of terrain on the possible coupling between the source area and the river network of the watershed based on the source connectivity index.

[0012] Combine the degree of river network division in the watershed, the distribution of small watersheds, source connectivity, and the volume and spatial distribution of sources to determine the sediment initiation location.

[0013] Simulate based on debris flow dynamic parameters and with the aid of a debris flow simulation model to obtain the debris flow velocity, deposition depth, and intensity.

[0014] Obtain the debris flow hazard zoning results for the study area according to the debris flow hazard grading standard.

[0015] In one embodiment, the optical remote sensing image technology includes:

[0016] Collect optical remote sensing satellite and UAV images from different periods.

[0017] Analyze the images, sampling a method that combines direct interpretation, comparative interpretation, and comprehensive interpretation by human-computer interaction, and consider the spectral, geometric, and texture characteristics of landslide debris in the remote sensing images to obtain the spatial distribution and area of loose sources.

[0018] Among them, the loose sources include landslides and collapses.

[0019] In one embodiment, continuously monitor the terrain undulation and surface erosion occurring on the slope near the gully. Among them, long-term continuous monitoring of a large area is achieved by using SBAS-InSAR technology.

[0020] In one embodiment, the debris flow dynamic parameters include one or more of the following parameters: debris flow peak discharge, debris flow density, sediment volume concentration, debris flow duration, Manning coefficient, amplification coefficient, viscosity coefficient, yield stress coefficient, and laminar flow resistance coefficient.

[0021] In one embodiment, the debris flow simulation model is a two-dimensional dynamic simulation model, preferably the FLO-2D dynamics model.

[0022] In one embodiment, the debris flow hazard grading standard includes one or more of the following situations:

[0023] In the high-hazard area corresponding to the high-level hazard, the debris flow accumulation depth is higher than 2.5 m, or the product of the accumulation depth and the flow velocity is greater than 2.5;

[0024] In the medium-hazard area corresponding to the medium-level hazard, the debris flow mud depth is numerically greater than 0.5 and less than 2.5, or the product of the debris flow mud depth and the flow velocity is numerically greater than 0.5 and less than 2.5;

[0025] In the low-hazard area corresponding to the low-level hazard, the debris flow mud depth is numerically less than 0.5, and the product of the debris flow mud depth and the flow velocity is numerically less than 0.5.

[0026] In one embodiment, the debris flow hazard assessment method further includes:

[0027] Dividing the study area into small watersheds and analyzing the debris flow hazard from a detailed perspective. Among them, the debris flow small watershed is not only the basic unit for the gestation of debris flow but also the basic bearing space for settlements.

[0028] In one embodiment, the dividing the study area into small watersheds and analyzing the debris flow hazard from a detailed perspective includes:

[0029] Depression filling, removing depressions;

[0030] Flow direction analysis;

[0031] Flow rate calculation;

[0032] River network extraction and catchment generation, where the river network of the watershed is generated based on the accumulated flow raster.

[0033] According to one aspect of the present application, one embodiment provides a debris flow hazard assessment device coupling InSAR and dynamic process simulation, including:

[0034] The first processing module is used to determine the spatial position and area of landslide and collapse sources based on optical remote sensing image technology and quantitatively estimate the volume;

[0035] The second processing module is used to solve the multi-dimensional surface deformation based on SBAS-InSAR and SAR geometric imaging principles, determine the spatial position of potential sources and quantitatively estimate the volume;

[0036] An estimation module for estimating the control effect of terrain on the possible coupling between the provenance area and the river network of the basin based on the provenance connectivity index;

[0037] A determination module for determining the sediment entrainment position by combining the division degree of the river network of the basin, the distribution of small basins, the provenance connectivity, and the volume and spatial distribution of the provenance;

[0038] A simulation module for simulating based on the debris flow dynamic parameters and with the aid of a debris flow simulation model to obtain the debris flow flow velocity, deposition depth, and intensity; and

[0039] An evaluation module for obtaining the debris flow disaster risk zoning result of the study area according to the debris flow risk grading standard.

[0040] According to one aspect of the present application, an embodiment provides a readable storage medium, on which computer instructions are stored; wherein, when the computer instructions are executed by a processor, the method described in any one of the above is implemented.

[0041] In the above embodiments of the present application, the provenance and potential provenance are linked through the connectivity index, overcoming the influence of the traditional subjective determination of the sediment entrainment position, which leads to more errors in debris flow prediction, and can provide new ideas for research on debris flow dynamic processes, monitoring and early warning, etc. Brief Description of the Drawings

[0042] Figure 1 is the technical roadmap of a debris flow risk assessment method in an embodiment;

[0043] Figure 2 is the technical flowchart of SBAS-InSAR technology in an embodiment;

[0044] Figure 3 is the provenance interpretation display diagram in an embodiment (left: interpretation based on optical remote sensing images; right: interpretation based on InSAR);

[0045] Figure 4 is the multi-source remote sensing provenance spatial distribution diagram in an embodiment;

[0046] Figure 5 is the small watershed distribution diagram of Qutan Town in an embodiment;

[0047] Figure 6 is the IC zoning map of the provenance connectivity index in an embodiment;

[0048] Figure 7 is the sediment entrainment position distribution diagram in an embodiment;

[0049] Figure 8 is the once-in-a-century debris flow discharge hydrograph in an embodiment;

[0050] Figure 9 is the flow velocity of debris flow in an embodiment;

[0051] Figure 10 is the accumulation depth of debris flow in an embodiment;

[0052] Figure 11 is the zoning map of debris flow disaster risk in an embodiment;

[0053] Figure 12 is the flowchart of the debris flow risk assessment method in an embodiment;

[0054] Figure 13 is the structural schematic diagram of the debris flow risk assessment device coupling InSAR and dynamic process simulation in an embodiment. Detailed implementation manners

[0055] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0057] It should be noted that the terms "first", "second", etc. in the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data may be interchanged under appropriate circumstances for the embodiments of the present application described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these process, method, product, or device.

[0058] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may also be an intermediate element. Moreover, in the present application, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element through a third element.

[0059] Embodiment 1

[0060] Please refer to Figure 12 , an embodiment provides a debris flow hazard assessment method that couples InSAR and dynamic process simulation, including the following steps:

[0061] S1. Determine the spatial positions and areas of landslide and collapse sources based on optical remote sensing image technology, and quantitatively estimate the volume;

[0062] S2. Calculate multi-dimensional surface deformation based on SBAS-InSAR and SAR geometric imaging principles, determine the spatial positions of potential sources, and quantitatively estimate the volume;

[0063] S3. Estimate the control effect of terrain on the possible coupling between the source area and the river network of the basin based on the source connectivity index;

[0064] S4. Combine the degree of river network division in the basin, the distribution of small basins, source connectivity, and the volume and spatial distribution of sources to determine the sediment initiation position;

[0065] S5. Based on debris flow dynamic parameters and with the aid of a debris flow simulation model for simulation, obtain the debris flow flow velocity, deposition depth, and intensity;

[0066] S6. Obtain the debris flow hazard zoning results in the study area according to the debris flow hazard grading standard.

[0067] In one embodiment, the optical remote sensing image technology includes the following steps:

[0068] 1. Collect optical remote sensing satellite and UAV images from different periods;

[0069] 2. Analyze the images, sampling a method that combines direct interpretation, comparative interpretation, and comprehensive interpretation of human-computer interaction, considering the spectral, geometric, and texture features of landslide debris in the remote sensing images, to obtain the spatial distribution and area of loose sources; generally, use ArcGIS software to analyze the images; wherein, the loose sources include landslides and collapses.

[0070] At the same time, estimating the volume of a landslide in an area can be achieved through an empirical formula. The landslide volume is generally obtained by multiplying the area by the average thickness. The average thickness of a landslide can be defined as the slope-normal thickness of the failure slab under uniform conditions.

[0071] In one embodiment, the topographic undulations and surface erosion occurring on the slope near the channel are a continuous process, and it is difficult to obtain key information without continuous monitoring. In small debris flow basins, topographic undulations and surface erosion are widely distributed. On-site surveys are limited by manpower and material resources and cannot monitor erosion or estimate the volume of erosion that occurs; optical remote sensing images cannot identify the amount of slow surface erosion. Therefore, it is necessary to continuously monitor the topographic undulations and surface erosion occurring on the slope near the channel, and among them, long-term sequence monitoring using the SBAS-InSAR technology is used to achieve continuous monitoring of a large area. Solve the potential surface deformation and loose material sources that cannot be identified by optical remote sensing images. Among them, Figure 2 is the flow chart of the SBAS-InSAR technology.

[0072] In one embodiment, the debris flow dynamic parameters include one or more of the following parameters: debris flow peak discharge, debris flow density, sediment volume concentration, debris flow duration, Manning coefficient, amplification coefficient, viscosity coefficient, yield stress coefficient, and laminar resistance coefficient. Among them, the storm peak discharge is calculated according to the empirical formula introduced in the rainstorm flood manual of the province where the study area is located, and the debris flow peak discharge is calculated according to the rainstorm flood method; the Manning coefficient ( ), amplification coefficient ( ), viscosity coefficient ( , ), yield stress coefficient ( , ), and laminar resistance coefficient ( ) are determined based on the empirical values in the FLO-2D technical manual.

[0073] In one embodiment, the debris flow simulation model is a two-dimensional dynamic simulation model, preferably the FLO-2D dynamic model. Among them, the FLO-2D software uses non-Newtonian fluids and central finite differences for numerical simulation. The software controls the flow velocity and deposition depth of debris flow in the two-dimensional coordinate system X and Y through the continuity equation and the motion equation FLO-2D.

[0074] In one embodiment, the damage ability of debris flow to disaster-bearing bodies such as residential buildings, public facilities, and cultivated land is mainly manifested in two factors: the burying ability and the impact ability, which are related to the deposition thickness and impact force of the debris flow respectively. At the same time, the deposition depth and impact force are important factors affecting the magnitude of debris flow hazard. Based on the numerical simulation results of the debris flow dynamic process, the area covered by the debris flow is divided into multiple squares with the same area as the topographic grid, and the fluid in each grid is regarded as an independent moving body. For the fluid in each grid, the impact force F can be calculated.

[0075] In one embodiment, the debris flow hazard grading criteria include one or more of the following situations:

[0076] 1. For the high - risk area corresponding to the high - grade danger of debris - flow gullies in the study area, the accumulation depth of debris - flow is higher than 2.5 m, or the product of the accumulation depth and the flow velocity is greater than 2.5;

[0077] 2. For the medium - risk area corresponding to the medium - grade danger of debris - flow gullies in the study area, the depth of debris - flow mud is numerically greater than 0.5 and less than 2.5, or the product of the depth of debris - flow mud and the flow velocity is numerically greater than 0.5 and less than 2.5;

[0078] 3. For the low - risk area corresponding to the low - grade danger of debris - flow gullies in the study area, the depth of debris - flow mud is numerically less than 0.5, and the product of the depth of debris - flow mud and the flow velocity is numerically less than 0.5.

[0079] In one embodiment, the debris - flow danger assessment method further includes the following steps: dividing the study area into small watersheds and analyzing the danger of debris - flow disasters from a detailed perspective. Among them, the small debris - flow watershed is not only the basic unit for the gestation of debris - flow but also the basic bearing space for settlements. Specifically, the steps of dividing the study area into small watersheds and analyzing the danger of debris - flow disasters from a detailed perspective include the following sub - steps:

[0080] 1. Depression filling, removing depressions; the common method is "filling and leveling the depressions";

[0081] 2. Flow - direction analysis; among them, the discrimination of the flow - direction is the basis for extracting watershed information. To accurately delimit the watershed boundary, it is necessary to first determine the flow - direction of water in each grid cell; the methods for flow - direction judgment are divided into two types: the single - flow method and the multi - flow method;

[0082] 3. Discharge calculation; using the cumulative grid of flow - direction for calculation;

[0083] 4. River - network extraction and catchment generation. Among them, the watershed river - network is generated based on the cumulative grid of confluence. Specifically, set an appropriate minimum catchment - area threshold, define the grid points with a catchment area greater than or equal to this threshold as the starting points of the watercourse, and the grid points with a catchment area less than this threshold are not regarded as watercourses. By extracting all grid points greater than or equal to the minimum catchment - area threshold, the river - network is obtained. Among them, the minimum catchment - area threshold is generally determined according to the DEM data accuracy, watershed topography, geomorphic features and research purposes, or the effects of different values can also be determined through experiments.

[0084] Embodiment Two

[0085] Please refer to Figure 1 、 Figure 2, an embodiment provides a debris flow hazard assessment method that couples InSAR and dynamic process simulation. The technical solution of the present invention will be described below according to the inventor's thinking to facilitate those skilled in the art to become familiar with the idea of the present invention. Among them, for some technical key points, detailed explanations are provided in order that those skilled in the art can fully understand the technical solution of the present invention.

[0086] The implementation steps are as follows:

[0087] (1) Debris flow source identification and volume quantitative estimation based on multi-source remote sensing technology

[0088] 1. Multi-track SBAS-InSAR technology

[0089] Image datasets of the ascending and descending orbits of C-band SLC format dual-polarized Sentinel-1A are used, and the orbital replay period is 12 d. Long-time-series SBAS-InSAR is a new SAR data time series analysis technology that overcomes the limitations of traditional D-InSAR in spatio-temporal decorrelation and atmospheric effects. By integrating the images of the ascending and descending orbits, the problem of partial area unmonitorable due to shadow masking can be reduced. In addition, the 30 m resolution ALOS DEM is used as a terrain reference to remove the influence of terrain effects.

[0090] The topographic undulation and surface erosion occurring on the slope near the gully are a continuous process, and it is difficult to obtain this information without continuous monitoring. In debris flow small basins, topographic undulation and surface erosion are widely distributed. Field surveys are limited by manpower and material resources and cannot monitor erosion and estimate the volume of erosion; optical remote sensing images cannot identify the amount of slow surface erosion. In the early 21st century, Berardino and Lanari first proposed the method of Small Baseline Subset (SBAS) interferometry, which can overcome the problems encountered in traditional interferometry and effectively improve defects such as spatio-temporal incoherence and atmospheric delay, and has been widely used in deformation monitoring in recent years. As an advanced technology in the development of InSAR technology, SBAS technology first needs to set spatio-temporal baseline thresholds to determine the number of interferometric pairs formed by SAR data during the processing, then perform differential interferometry on these interferometric pairs, and finally obtain a high-precision deformation sequence according to the least squares method. Continuous monitoring of a large area can be achieved by using the long-time-series monitoring of SBAS-InSAR technology, and potential surface deformation and loose debris sources that cannot be identified by optical remote sensing images can be solved. Among them, Figure 2 is the flow chart of SBAS-InSAR technology.

[0091] The line-of-sight (LOS) deformations of the ascending and descending orbits in the study area are obtained through the multi-track SBAS-InSAR technology. By combining with the SAR satellite geometric imaging principle (Equation (1)), the vertical deformation rate of the region is obtained. The obtained deformation data can determine the spatial position of potential material sources, and the volume of surface erosion can be estimated through Equation (3).

[0092] (1)

[0093] (2)

[0094] In the formula: are the line-of-sight deformation rates of the ascending and descending orbits respectively, are the incident angles in the ascending and descending directions of the satellite respectively, is the azimuth angle in the ascending and descending directions of the satellite, is the horizontal deformation, is the vertical deformation, is the erosion volume (m 3 ) of the study area, is the pixel area (m 2 ), and T is the time (year).

[0095] 2. Optical remote sensing images

[0096] First, collect optical remote sensing satellite and UAV images from different periods. Use ArcGIS software to analyze the images, and adopt a method combining direct interpretation, comparative interpretation, and comprehensive interpretation with human-computer interaction. Considering the spectral, geometric, and texture characteristics of landslide debris in the remote sensing images, obtain the spatial distribution and area of loose material sources (landslides, collapses).

[0097] The estimation of the landslide volume in an area can be achieved through empirical formulas. The landslide volume is generally obtained by multiplying the area by the average thickness. The average thickness of a landslide is defined as the slope-normal thickness of the failure slab under uniform conditions (Tang Chuan et al., 2012). Due to the disconnection of the material sources in the catchment area, it can be seen that only part of the landslide debris can supply the debris flow. According to statistical analysis, Huang (2011) explained more than 35,000 landslides and collapses triggered by the Wenchuan earthquake. He pointed out that 30% of the landslide or collapse sediments can be transferred into the source materials of the debris flow. In addition, Zhang (2017) found that the material source transportation rate of a debris flow does not exceed 30%. Therefore, 30% of the total landslide debris flow volume is selected as the supply of the debris flow. The formula for estimating the volume of loose material sources in the basin through optical remote sensing images is as follows:

[0098] (3)

[0099] Where, For the landslide volume (m 3 ), For the landslide area (m 2 ).

[0100] (II) Watershed Division and Connectivity Analysis

[0101] 1. Debris Flow Small Watershed Division

[0102] The small watershed of debris flow is not only the basic unit for the gestation of debris flow but also the basic bearing space for settlements (Li Qinwen, 2019). Dividing the study area into small watersheds for subsequent research work can analyze the hazard risk of debris flow disasters from a more detailed perspective. The specific ideas are as follows:

[0103] (1) Depression Filling

[0104] The common method for depression removal is "depression filling treatment". The steps are as follows: First, scan the DEM matrix to determine the depression cells. Then, scan the window centered on the depression cell to mark the depression catchment area. Find the lowest potential outflow point within the depression catchment area and compare its elevation value with that of the depression cell. If the outflow point is higher, the depression is a concave; otherwise, it is flat. Raise the elevation of the cells within the concave catchment area that are lower than the outflow point to the elevation of the outflow point to achieve the filling of the depression. Repeat the above scanning process until all depressions are removed.

[0105] (2) Flow Direction Analysis

[0106] The discrimination of flow direction is the basis for extracting watershed information. To accurately delimit the watershed boundary, it is necessary to first determine the flow direction of water in each grid cell. The methods for flow direction judgment are divided into two types: the single-flow method and the multi-flow method. Among them, the single-flow method mainly includes: D8 algorithm, Rh08 algorithm, DEMON algorithm, etc.

[0107] (3) Flow Calculation (Flow Accumulation Grid Calculation)

[0108] Principle of flow accumulation grid calculation: Assume that 1 unit of water volume falls on each grid in the catchment area and moves according to the flow direction of the grid. The cumulative flow value of the passed grids increases by 1 unit, so as to calculate the upstream flow value of each grid. Starting from each grid cell along the flow direction matrix and tracing to the DEM boundary, after the scanning is completed, a grid distribution map of the watershed confluence ability is obtained. The value of each unit on the confluence grid represents the total number of upstream grid cells (NIP) flowing into this unit within the upstream catchment area. A larger NIP value is regarded as a river valley, and a NIP value of zero may be the watershed divide of the watershed. The confluence accumulation grid distribution map can conveniently extract various characteristic parameters of the watershed.

[0109] (4) River Network Extraction and Catchment Generation

[0110] The river network of the watershed can be generated using the flow accumulation raster. In the DEM, for a grid point to form a water system, there must be an upstream water supply area of a certain scale. Therefore, by setting an appropriate minimum catchment area threshold, the grid points with a catchment area greater than or equal to this threshold are defined as the starting points of the watercourse, while the grid points with a catchment area less than this threshold are not considered watercourses. By extracting all grid points greater than or equal to the minimum catchment area threshold, the river network is obtained. The detail level of the river network is determined by the set minimum catchment area threshold. The smaller the threshold, the finer the extracted river network and the more upstream the starting points extend; the larger the threshold, the sparser the extracted river network. The minimum catchment area threshold is generally determined according to the DEM data accuracy, watershed topography, geomorphic features, and research purposes, and the effects of different values can also be determined through experiments.

[0111] 2. Watershed Connectivity Analysis

[0112] The Index of Connectivity (IC) of the watershed was initially proposed by Borselli et al. (2008) and applied to agricultural watersheds. Cavalli et al. (2013) made important modifications to this method to utilize high-resolution digital terrain models (DTMs) and make it applicable to mountainous environments. IC is a distributed morphometric index that focuses on the impact of topography on sediment connectivity and represents the degree of coupling of different parts of the watershed relative to a selected target (such as the main river network), that is, the probability of debris flow channels propagating to the gully mouth, and can be calculated using ArcGIS modeling tools. The surface roughness can be used as a weight factor for gully connectivity. The calculation formula of IC is as follows:

[0113] (4)

[0114] where IC is the connectivity index, D up represents the uphill component, D dn represents the downhill component, and respectively represent the weight of the uphill contributing area and the average slope, A is the area of the uphill region; d i represents the length of the i-th river extracted according to the steepest downstream downhill flow direction, W i and S i respectively represent the weight value and slope value of the i-th grid point; RI is the surface roughness, calculated by the standard deviation of all grid points within the radius of 3 surrounding grid points for each grid point.

[0115] (III) Debris Flow Dynamic Process Simulation

[0116] 1. Determine the sediment incipient motion location

[0117] The sediment incipient motion location of debris flow usually appears on the landslide accumulation body intersecting with the river network. In these areas, the sediments are chaotic and non-uniform, characterized by a matrix formed by common gravel and clay silt. Using the spatial analysis function of the ArcGIS platform, a 50 m buffer zone of the river network line is obtained, and the IC map is subdivided into 4 categories (low, relatively low, relatively high, high) by the natural break method proposed by Cavalli et al. to facilitate highlighting potential coupled and uncoupled areas. Then, the river network development degree map, the source spatial distribution map and the IC map are overlaid, and corrected in combination with the source volume to determine the sediment incipient motion location. This method can provide a more comprehensive and accurate understanding for the study of river sediment movement, and is of great significance for soil and water conservation, river regulation, etc.

[0118] 2. Calculation of dynamic parameters

[0119] (1) Peak flood discharge

[0120] The peak flood discharge of rainstorm is calculated according to the empirical formula introduced in the rainstorm and flood handbook of the province where the study area is located:

[0121] (5)

[0122] In the formula: is the rainstorm discharge (m 3 / s); is the peak runoff coefficient; is the maximum average rainstorm intensity (mm / h); is the basin area (km 2 ); is the rainstorm intensity (mm / h); is the rainstorm formula index; is the basin catchment time (h).

[0123] The relevant parameters can be obtained by querying the isohyetal map of rainstorm amount mean value and the isohyetal map of variation coefficient in the rainstorm and flood handbook of the province where the study area is located.

[0124] (6)

[0125] (7)

[0126] (8)

[0127] (9)

[0128] (10)

[0129] (11)

[0130] (12)

[0131] (13)

[0132] In the formula: and are the modulus ratio coefficients for different recurrence intervals, which can be obtained from the modulus ratio coefficient table of the Pearson type III curve; is the basin characteristic parameter; L is the river length (km) from the outlet section along the main river channel to the watershed; F is the basin area (km 2 ); m is the concentration parameter; n is the rainstorm parameter; s is the rainstorm intensity (mm / h); are the rainstorm amounts for durations of 6 hours and 24 hours respectively; is the peak runoff coefficient; is when the basin concentration time (h); is the basin concentration time (h); is the runoff generation parameter (mm / h).

[0133] The peak discharge of debris flow is calculated according to the rain - flood method as follows:

[0134] (14)

[0135] In the formula: is the peak discharge of debris flow, is the blockage coefficient, is the sediment correction coefficient of debris flow, which can be obtained by looking up the table and .

[0136] (2)Debris flow dynamic parameters

[0137] A. Debris flow density

[0138] According to the relationship formula (15) between debris flow bulk density and clay content proposed by Chen Ningsheng (Chen Ningsheng et al., 2003), the density is calculated.

[0139] (15)

[0140] In the formula: x is the percentage of clay particle content.

[0141] B. Sediment volume concentration

[0142] The calculation formula for sediment volume concentration is as shown in (16).

[0143] (16)

[0144] In the formula: is the debris flow unit weight; is the clear water unit weight, taking 1.0 t / m 3 ; is the solid material unit weight of debris flow, taking 2.65 t / m 3 .

[0145] C. Debris flow duration

[0146] According to the calculation formula of the debris flow duration T (s) based on the basin area proposed by Zou (Zou et al., 2020), the calculation formula is as follows:

[0147] (17)

[0148] In the formula: is the debris flow flow time (s); F is the basin area (km 2 ).

[0149] D. Other parameters

[0150] Other parameters such as Manning coefficient ( ), amplification coefficient ( ), viscosity coefficient ( , ), yield stress coefficient ( , ), and laminar flow resistance coefficient ( ) are determined according to the empirical values in the FLO-2D technical manual.

[0151] 3. FLO-2D model

[0152] Numerical simulation is carried out using the FLO-2D software based on non-Newtonian fluid and central finite difference. This software controls the flow velocity and deposition depth of debris flow in the X and Y directions of the two-dimensional coordinate system through the continuity equation and the motion equation (Obrien et al., 1993). The FLO-2D model simulates debris flow using the dynamic wave mode and the diffusion wave mode, controls the mass conservation of debris flow through the continuity equation, such as Equation (18), and controls the momentum balance during the movement of debris flow through the motion equation, as shown in Equations (19) - (20).

[0153] (18)

[0154] (19)

[0155] (20)

[0156] In the formula: is the effective rainfall intensity (mm / h); is time (s); is the average flow velocity (m / s) of debris flow in the X-axis direction in the two-dimensional model; is the fluid depth (m); is the average flow velocity (m / s) of debris flow in the Y-axis direction; is the gravitational acceleration (m / s 2 ); and are the frictional forces in the X and Y directions during the movement process, respectively; and is the longitudinal slope of the channel.

[0157] Based on the governing equations (mass and momentum conservation equations) described by Lagrangian, the dynamic process of debris flow fluid can be transformed into a set of partial differential equations (Castelli et al., 2017). The total stress equation includes the following two parts:

[0158] (21)

[0159] In the formula: is the isotropic pressure; is the total stress tensor; , are different coordinate directions; is the viscous stress tensor.

[0160] The FLO-2D model simultaneously has the following assumptions: ① Ensure that the simulated grid points have unique elevation values and roughness; ② The simulated fluid movement process belongs to steady flow; ③ The fluid movement belongs to the shallow water wave mode and the retardation equation of stable fluid; ④ The distribution of pressure in the fluid belongs to hydrostatic pressure. During the simulation process, the stability of data is ensured by quickly calculating within a very small time step. The time step is mainly combined with the depth percentage change parameter (DPCP) and the data stability (CFL / FWPS) criterion. When the minimum time step does not satisfy 3 consecutive time intervals, the time step can be reduced to carry out the simulation of debris flow with a reasonable time to the greatest extent.

[0161] (IV) Debris flow hazard assessment

[0162] The destructive ability of debris flow to disaster-bearing bodies such as residential buildings, public facilities, and cultivated land is mainly manifested in two factors: the burying ability and the impact ability, which are respectively related to the accumulation thickness and impact force of the debris flow. At the same time, the accumulation depth and impact force are important factors affecting the risk level of debris flow. Currently, there are various methods to describe the risk level of debris flow, such as single indicators like mud depth or flow velocity, combinations of mud depth and flow velocity, and combinations of mud depth and momentum. In this paper, referring to Wang Zhongwen's debris flow risk assessment method based on the dynamic process, that is, using the maximum values of the depth and momentum of the debris flow as indicators, where the mud depth can reflect the burying ability of the debris flow, and the maximum value of momentum can reflect the impact ability of the debris flow. By further considering the mud depth and flow velocity of the debris flow for the maximum value of momentum, the debris flow risk assessment is transformed from qualitative to quantitative, and at the same time, the burying and impact abilities of the debris flow can be reflected. Based on the numerical simulation results of the debris flow dynamic process, the area covered by the debris flow is divided into multiple squares with the same area as the terrain grid, and the fluid in each grid is regarded as an independent moving body. For the fluid in each grid, the impact force F can usually be expressed as:

[0163] (22)

[0164] In the formula: is the fluid mass; is the fluid velocity; t is the time when the fluid acts on the disaster-bearing body.

[0165] Assume that the acting time of the fluid in each grid on the disaster-bearing body is equal, then the impact force is only related to the fluid mass and velocity of the unit grid. Thus, the impact force can be expressed by the newly defined momentum formula:

[0166] (23)

[0167] In the formula: represents the area of each square, represents the depth of the fluid in the unit square, represents the density of the debris flow fluid, represents the maximum flow velocity of each unit grid. For a specific debris flow, can be regarded as a constant value, can be obtained through numerical simulation calculation. Therefore, we can take the maximum value of each square during a debris flow movement process as the parameter for momentum assessment.

[0168] The debris flow gully hazard in the study area is divided into three levels: high, medium, and low. In the high-hazard area, the accumulation depth is higher than 2.5 m, or the product of the accumulation depth and the flow velocity is greater than 2.5. The debris flow seriously damages buildings, or causes casualties, destroys farmland and roads, and people basically lose their living places, with prominent disasters. In the medium-hazard area, the mud depth or the product of the mud depth and the flow velocity is numerically greater than 0.5 and less than 2.5. Buildings are partially damaged, with almost no casualties, serious damage to farmland, and certain difficulties in restoring the living and production areas, with obvious disaster situations. In the low-hazard area, both the mud depth and the product of the mud depth and the flow velocity are numerically less than 0.5. Buildings are basically not damaged, farmland is buried, and the living place can be restored, with relatively minor disaster situations. When formulating the logical relationship formula between the indicators in the medium-hazard area, if AND is used, there will be situations where the values do not belong to any of the three hazard levels. Therefore, the logical relationship in the medium-hazard area is modified to OR. The debris flow hazard classification in the study area is shown in Table 1.

[0169] Table 1 Debris Flow Hazard Classification Table Based on the Dynamic Process

[0170]

[0171] Example 3

[0172] Please refer to Figures 3 - 11 , an embodiment of the present application provides a hazard assessment method that couples InSAR and debris flow dynamic process simulation, which is illustrated by taking the Qutan Town Basin in Ledu District, Haidong City, Qinghai Province as an example.

[0173] The case study area is selected as the Qutan Town Basin in Ledu District, Haidong City, Qinghai Province. Through investigation, it is found that the erosion landform in this area is well-developed, with serious soil and water loss and a lot of rainfall in the upper reaches. Therefore, it is necessary to conduct a debris flow hazard assessment. Using 113 scenes of Sentinel-1A ascending orbit and 104 scenes of descending orbit data from January 9, 2019 to December 31, 2022, the SBAS-InSAR technology is used to obtain the time series deformation rates of the ascending and descending orbits in the study area. By reading the Sentinel-1A source data, it can be known that the satellite incidence angle of the ascending orbit is 39.6379° and the azimuth angle is 346.8372°, and the satellite incidence angle of the descending orbit is 41.6120° and the azimuth angle is 193.1623°. Combining with formula (1), the vertical deformation rate and the horizontal deformation rate can be calculated. Through the deformation rate, the spatial distribution and area of surface erosion can be determined, as Figure 5 shown. Using formula (2), the InSAR-based surface erosion is calculated to be 6535.38 m 3 .

[0174] A method combining direct interpretation, comparative interpretation, and comprehensive interpretation based on human-computer interaction, considering the spectral, geometric, and texture features of landslide debris in remote sensing images, obtains the spatial distribution of loose material sources (landslides, collapses) as Figure 5 shown, with a statistical area of 3.39×10 6 km 2 . Using formula (3), the volume of the material source interpreted by optical remote sensing is calculated to be 1.17×10 7 m 3 . Among them Figure 3 is the display map of material source interpretation, Figure 4 is the distribution map of material sources.

[0175] The research is based on the ALOS 12.5 m Digital Elevation Model (DEM) covering the Qutan Temple Basin as the basic data, and uses the hydrological analysis module of the ArcGIS platform to preliminarily extract the small watersheds in the study area. The extraction steps are: depression filling - flow direction analysis - flow calculation - setting flow threshold - river network definition - generating catchment areas. Since the boundaries of the small watersheds extracted by this method are relatively rough, there are dislocation phenomena, and using a unified flow threshold for processing results in unreasonable division of some small watersheds. Therefore, on this basis, the small watersheds are corrected by combining satellite images and DEM data to improve the accuracy of small watershed division, and the distribution of small watersheds in Qutan Town is obtained as Figure 5 shown.

[0176] According to formula (4), the open-source SedInConnect program is used to calculate the material source connectivity index IC. The obtained IC map is classified using the natural break algorithm of the ArcGIS platform, that is, subdivided into 4 categories: low, relatively low, relatively high, and high, as Figure 6 shown.

[0177] According to the research of Bosco et al. (2007), the material sources within 50 m buffer zones of the river channel axes have the greatest impact on debris flow supply. Therefore, a 50 m buffer zone is established for the river network system in the study area, and intersections are taken with the high and relatively high areas of the IC distribution map and the spatial positions of the material sources. Combining the divided small watersheds and the distribution of material source volumes, the sediment initiation positions are determined, as Figure 7 shown.

[0178] Through the soil particle size classification experiment on the soil samples collected on-site, combined with formula (15), the soil density in the study area is obtained as 1.82 t / m 3 , and then the volume concentration is obtained as 0.4970 according to formula (16). According to the recommended values of design frequency rainstorms provided in the "Hydrological Handbook of Qinghai Province", combined with formulas (5) - (13), the peak flood discharge of the study area with a return period of 100 years (P = 1%) is calculated to be 539.74 m 3 / s. The peak discharge of debris flow is calculated to be 1049.80 m 3 / s according to formula (14) of the rain-flood method. The duration of debris flow is determined according to formula (17). The peak discharge hydrograph with a return period of 100 years obtained by the pentagon method is as Figure 8 shown.

[0179] Based on the DEM with a resolution of 12.5 m of the ALOS in the study area as the basic data, it is converted into ASC II code recognizable by FLO-2D in ArcGIS. Taking the characteristics of the study area and computer performance as the reference basis, a calculation grid of 30 m × 30 m is established, and elevation interpolation is performed on the grid.

[0180] When using FLO-2D for debris flow simulation, it is crucial to select a suitable sediment initiation point. In this paper, the simulation is carried out according to the sediment initiation position determined by the method introduced in 3.3.1. The flow velocity and deposition depth of debris flow under the rainfall condition with a return period of 100 years in the study area are obtained by simulating with the FLO-2D model, as Figure 9 and Figure 10 shown.

[0181] According to the hazard zoning method in Table 1, the hazard is divided into three categories: high, medium, and low, and the debris flow hazard zoning map under the rainfall condition with a return period of 100 years in the Qutan Town Basin is obtained, as Figure 11 shown.

[0182] In this case, first, the spatial position and area of landslide and collapse sources are determined by using optical remote sensing images and the volume is estimated; the multi-dimensional surface deformation is calculated by the principles of SBAS-InSAR and SAR geometric imaging to determine the spatial position of potential sources and estimate the volume; secondly, the control effect of terrain on the possible coupling of the source area and the river network of the basin is estimated by calculating the source connectivity index; combining the degree of river network division in the basin, the distribution of small basins, source connectivity, and the volume and spatial distribution of sources, the sediment initiation position is determined. This step can evaluate the debris flow supply, which is crucial for the simulation of the debris flow dynamic process. After that, the debris flow dynamic parameters are calculated and simulated by means of the FLO-2D dynamic model to obtain the flow velocity, deposition depth, and intensity of debris flow. Finally, the debris flow hazard zoning result of the study area is obtained according to the debris flow hazard grading standard.

[0183] Example 4

[0184] Please refer to Figure 13 , based on the same inventive concept, an embodiment of the present application provides a debris flow hazard assessment device coupling InSAR and dynamic process simulation, which adopts the following form:

[0185] (I) The first processing module

[0186] The first processing module 10 is used to determine the spatial location and area of landslide and collapse sources based on optical remote sensing image technology, and quantitatively estimate the volume.

[0187] (2) The second processing module

[0188] The second processing module 20 is used to solve multi-dimensional surface deformation based on SBAS-InSAR and SAR geometric imaging principles, determine the spatial location of potential sources, and quantitatively estimate the volume.

[0189] (3) The estimation module

[0190] The estimation module 30 is used to estimate the control effect of terrain on the possible coupling between the source area and the river network of the watershed based on the source connectivity index.

[0191] (4) The determination module

[0192] The determination module 40 is used to determine the sediment initiation position by combining the degree of watershed river network division, the distribution of small watersheds, source connectivity, and the volume and spatial distribution of sources.

[0193] (5) The simulation module

[0194] The simulation module 50 is used to perform simulations based on debris flow dynamic parameters and with the aid of a debris flow simulation model to obtain the debris flow flow velocity, deposition depth, and intensity.

[0195] (6) The evaluation module

[0196] The evaluation module 60 is used to obtain the debris flow disaster risk zoning results of the study area according to the debris flow hazard grading standard.

[0197] The above-mentioned debris flow hazard assessment device coupling InSAR and dynamic process simulation is used to implement the debris flow hazard assessment method coupling InSAR and dynamic process simulation in the above-mentioned various embodiments. Each module in the device corresponds to each step in the method, and will not be elaborated.

[0198] Embodiment 5

[0199] Based on the same inventive concept, an embodiment of the present application provides an electronic device, including: a memory and a processor; wherein, the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to perform the debris flow hazard assessment method described in any one of the above-mentioned various embodiments.

[0200] Among them, the above-mentioned one or more computer instructions can form a program.

[0201] Embodiment 6

[0202] Based on the same inventive concept, an embodiment of the present application provides a readable storage medium, on which computer instructions are stored; wherein, when the computer instructions are executed by a processor, the debris flow hazard assessment method described in any one of the above embodiments is implemented.

[0203] Wherein, one or more of the above computer instructions can form a program.

[0204] The above program can run in a processor, or can also be stored in a memory (or referred to as a computer-readable medium). The computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0205] These computer programs can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in a process Figure 1 a process or multiple processes and / or blocks Figure 1 steps of the functions specified in a block or multiple blocks. Corresponding to different steps, different modules can be implemented.

[0206] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A debris flow hazard assessment method coupling InSAR and dynamic process simulation, characterized in that: include: Determine the spatial location and area of ​​landslide and collapse sources based on optical remote sensing imaging technology, and quantitatively estimate the volume; Calculate multi-dimensional surface deformation based on SBAS-InSAR and SAR geometric imaging principles, determine the spatial location of potential sources and quantitatively estimate the volume; The control effect of topography on the possible coupling between provenance areas and river networks in the basin is estimated based on the provenance connectivity index; Determine the sediment initiation location based on the river network division degree, small watershed distribution, source connectivity, and source volume and spatial distribution; Based on the dynamic parameters of the debris flow and with the help of a debris flow simulation model, the flow velocity, accumulation depth and intensity of the debris flow are obtained, wherein the debris flow simulation model is a two-dimensional dynamic simulation model; The debris flow hazard zoning results of the study area are obtained according to the debris flow hazard classification standard, wherein the debris flow hazard classification standard includes factors that affect the size of the debris flow hazard, including the accumulation thickness and impact force of the debris flow, the mud depth reflects the burial capacity of the debris flow, and the maximum momentum reflects the impact capacity of the debris flow.

2. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 1 is characterized in that: The optical remote sensing imaging technology includes: Collect optical remote sensing satellite and drone images from different periods; The images are analyzed by combining direct interpretation, comparative interpretation and comprehensive interpretation with human-computer interaction, taking into account the spectral, geometric and texture characteristics of landslide debris in remote sensing images, and obtaining the spatial distribution and area of ​​loose material sources; Among them, the loose material sources include landslides and collapses.

3. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 1 is characterized in that: Continuously monitor the terrain undulation and surface erosion on the slope near the ditch, and use SBAS-InSAR technology for long-term monitoring to achieve continuous monitoring of a large area.

4. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 1 is characterized in that: The debris flow dynamic parameters include one or more of the following parameters: debris flow peak flow, debris flow density, sediment volume concentration, debris flow duration, Manning coefficient, amplification coefficient, viscosity coefficient, yield stress coefficient and laminar resistance coefficient.

5. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 1 is characterized in that: The two-dimensional dynamic simulation model is preferably a FLO-2D dynamic model.

6. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 1 is characterized in that: The debris flow hazard classification standard includes one or more of the following situations: In the high-risk area corresponding to the high-level hazard, the accumulation depth of debris flow is higher than 2.5m, or the product of accumulation depth and flow velocity is greater than 2.5; In the medium-risk area corresponding to the medium-level hazard, the mud depth of the debris flow is greater than 0.5 and less than 2.5, or the product of the mud depth of the debris flow and the flow velocity is greater than 0.5 and less than 2.5; In the low-risk area corresponding to the low-level hazard, the mud depth of the debris flow is less than 0.5 in value, and the product of the mud depth of the debris flow and the flow velocity is less than 0.5 in value.

7. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 1 is characterized in that: The debris flow hazard assessment method further comprises: The study area is divided into small watersheds, and the risk of debris flow disasters is analyzed from a detailed perspective. Among them, the debris flow small watershed is not only the basic unit for the breeding of debris flow, but also the basic carrying space for settlements.

8. The debris flow hazard assessment method of coupling InSAR and dynamic process simulation according to claim 7 is characterized in that: The study area is divided into small watersheds and the debris flow hazard risk is analyzed from a detailed perspective, including: Depression filling, depression removal; Flow analysis; Flow calculation; River network extraction and watershed generation, where the basin river network is generated based on the confluence accumulation raster.

9. A debris flow hazard assessment device coupled with InSAR and dynamic process simulation, characterized in that: include: The first processing module is used to determine the spatial location and area of ​​landslide and collapse sources based on optical remote sensing imaging technology, and to quantitatively estimate the volume; The second processing module is used to calculate multi-dimensional surface deformation based on SBAS-InSAR and SAR geometric imaging principles, determine the spatial location of potential sources and quantitatively estimate the volume; An estimation module is used to estimate the control effect of topography on the possible coupling between provenance areas and river networks in the basin based on the provenance connectivity index; The determination module is used to determine the sediment initiation location by combining the degree of river network division in the basin, the distribution of small watersheds, the connectivity of the source, and the volume and spatial distribution of the source; A simulation module, used for performing simulation based on debris flow dynamic parameters and with the aid of a debris flow simulation model to obtain the flow velocity, accumulation depth and intensity of the debris flow, wherein the debris flow simulation model is a two-dimensional dynamic simulation model; and The evaluation module is used to obtain the debris flow disaster hazard zoning results of the study area according to the debris flow hazard classification standard, wherein the debris flow hazard classification standard includes factors affecting the size of the debris flow hazard, including the accumulation thickness and impact force of the debris flow, the mud depth reflects the burial capacity of the debris flow, and the maximum momentum reflects the impact capacity of the debris flow.

10. A readable storage medium, characterized in that: The readable storage medium stores computer instructions; wherein, when the computer instructions are executed by a processor, the method described in any one of claims 1 to 8 is implemented.

Images