CN116913046A - Rainfall landslide risk early warning and monitoring method - Google Patents
Rainfall landslide risk early warning and monitoring method Download PDFInfo
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Abstract
The invention relates to the technical field of landslide monitoring, in particular to a rainfall type landslide risk early warning monitoring method, which comprises the steps of performing large-scale general investigation on a landslide body in a remote sensing mode, locking a suspicious region, performing detailed investigation, checking the suspicious or potential landslide body in the suspicious region, and monitoring the suspicious region. And collecting the elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity of the monitoring area as indexes of landslide development evaluation, defining a dissipation system structure of a landslide body and landslide 'entropy' of a research area, energizing each index, and calculating the landslide 'entropy' of each index subsystem and the landslide total 'entropy' of the system. And combining historical landslide data of the research area, obtaining landslide 'entropy' of a landslide occurrence critical value through calculation and analysis, finally obtaining a threshold value of the landslide occurrence of the research area according to an analysis result, establishing a risk evaluation system of the landslide of the research area, and sending out early warning information so as to realize risk early warning and prevention of a landslide body.
Description
Technical Field
The invention relates to the technical field of landslide monitoring, in particular to a rainfall landslide risk early warning and monitoring method.
Background
Landslide refers to the phenomenon that in terrains such as hills and high slopes, the surface and rock layers are broken and slide due to the effects of gravity, moisture, earthquake, artificial activities and other factors. Landslide is a ubiquitous natural disaster in China, and causes serious threat to lives, properties, society and economy of people. Landslide mainly occurs in summer and rainy season in China, especially during heavy rain and strong rainfall, and rainfall type landslide disasters frequently occur and are seriously damaged. Index data such as elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity are important factors influencing landslide disasters in China. These factors interact to determine the formation and evolution of landslide. In China, the change of elevation plays an important role in landslide hazard. On hills, abrupt changes in elevation may cause instability of the formation, thereby inducing landslides. Both a large slope and an unreasonable slope direction may lead to the formation of a landslide, and an excessively steep slope may subject the formation to a large gravitational force, increasing the risk of landslide. The deformation quantity is an important factor of landslide formation and evolution, and the occurrence and evolution process of landslide can be influenced by changing the mechanical property and stability of a rock-soil body. Soil humidity has a direct effect on the shear strength of the soil and also can have an effect on the hydraulic action in the soil, thereby affecting landslide.
Most of current landslide monitoring technologies and methods select a small amount of landslide development factors from displacement, rainfall, soil humidity, lithology and other landslide development factors for research, and neglect the influence of other landslide development factors. Due to the complex geographical environment for landslide inoculation, landslide disaster prediction and early warning still face certain difficulties and challenges. For the existing landslide research method and technology, the landslide body is rarely regarded as an integral system, and the main factors influencing the development of the landslide are systematically and comprehensively analyzed from the perspective of the landslide body system. Therefore, inaccurate prediction occurs in the monitoring and early warning of landslide, and the service of a scientific, reasonable and comprehensive analysis and early warning landslide system cannot be provided.
Disclosure of Invention
The invention aims to provide a rainfall type landslide risk early warning and monitoring method, aiming at a rainfall type landslide, a dissipation structure system of a landslide body is established based on a dissipation structure theory, landslide evaluation and analysis are carried out by researching the change of the 'entropy' of the landslide, the occurrence threshold of the landslide is determined, the landslide monitoring and prediction accuracy is improved, and the landslide disaster prevention and handling capacity is enhanced.
In order to achieve the above purpose, the invention provides a rainfall landslide risk early warning and monitoring method, which comprises the following steps:
performing large-scale general investigation on the landslide body in a remote sensing mode, and locking a suspicious region;
the suspicious region is checked in detail, and suspicious or potential landslide bodies are checked;
acquiring index data of a monitoring area affecting landslide development, and taking the index data as a basis for landslide development evaluation;
defining a dissipation structure system of the landslide body, enabling all indexes in the dissipation structure system, carrying out normalization processing, and calculating the total entropy value of the dissipation structure system;
and determining a threshold value of landslide occurrence according to the analysis result, and sending landslide early warning information.
Optionally, in the process of performing large-scale general investigation on the landslide body by using a remote sensing mode, performing image interpretation on the landslide body by using a remote sensing technology, and identifying a landslide risk area by combining continuous SAR data with an InSAR technology.
Optionally, the index data of landslide development comprises elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity data.
Optionally, the process of collecting the index data of the monitored area affecting landslide development as the basis of landslide development evaluation includes the following steps:
the method comprises the steps of measuring geographical position and elevation information by receiving satellite signals through a GNSS technology, and acquiring elevation data of a research area;
accurate lithology data of a research area is obtained through in-situ geological survey and geological survey data;
acquiring coordinate information of ground points by adopting a GPS technology, acquiring digital elevation model data by adopting the method, and calculating elevation difference and horizontal distance between two adjacent points to acquire gradient and slope data;
the deformation data can be obtained by processing SAR images of landslide areas by utilizing a time sequence InSAR technology;
rainfall data is obtained through rainfall information provided by a weather station;
and acquiring soil humidity data through soil humidity remote sensing estimation.
Optionally, in the process of processing the SAR image of the landslide region by using the time sequence InSAR technology to obtain deformation data, firstly, acquiring the SAR remote sensing image data of the research region, performing image preprocessing by using collected SAR image data, acquiring a displacement field at each moment by using the time sequence InSAR technology, generating a deformation time sequence diagram, and performing deformation monitoring and analysis to obtain the landslide deformation data.
Optionally, in the process of acquiring soil humidity data through soil humidity remote sensing estimation, firstly, preprocessing a Landsat remote sensing image, namely, preprocessing the image, carrying out terrain correction and atmosphere correction to ensure the data quality, then converting the remote sensing image data into the soil humidity data by adopting a soil humidity index model, and finally, acquiring the soil humidity data through carrying out data processing and analysis on the soil humidity data obtained through inversion.
Optionally, the dissipation structure system of the landslide body is established according to 4 basic conditions met by the dissipation structure, and the basic conditions include:
the landslide body is in an open system state, and continuously exchanges material energy with the outside;
the landslide body is in an unstable state which does not reach balance along with the change of the landslide development factor;
a plurality of nonlinear effects exist in the landslide body system;
there are a variety of disturbances to landslide mass systems.
Optionally, calculating the total entropy of the dissipation structure system, specifically by analyzing various landslide related factors, and determining the magnitude of the entropy by quantifying the differences among different indexes by adopting an entropy method.
The invention provides a rainfall landslide risk early warning and monitoring method, which comprises the steps of performing large-scale general investigation on a landslide body in a remote sensing mode, locking a suspicious region, performing detailed investigation, checking the suspicious or potential landslide body in the suspicious region, and monitoring the suspicious region. And collecting the elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity of the monitoring area as indexes for landslide development evaluation, defining a dissipation system structure of a landslide body and landslide 'entropy' of a research area based on the seven main factors, energizing each index, and calculating the landslide 'entropy' of each index subsystem and the landslide total 'entropy' of the system. And combining historical landslide data of the research area, obtaining landslide 'entropy' of a landslide occurrence critical value through calculation and analysis, finally obtaining a threshold value of the landslide occurrence of the research area according to an analysis result, establishing a risk evaluation system of the landslide of the research area, and sending out early warning information, so that risk early warning and prevention and control of a landslide body are realized, meanwhile, the accuracy of landslide monitoring is improved, and the monitoring cost is reduced.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of steps of a rainfall landslide risk early warning and monitoring method.
Fig. 2 is a schematic illustration of the formation and evolution process of the landslide body dissipation structure of the present invention.
Fig. 3 is a schematic flow chart of a landslide monitoring and early warning device according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1, the invention provides a rainfall landslide risk early warning and monitoring method, which comprises the following steps:
s1: performing large-scale general investigation on the landslide body in a remote sensing mode, and locking a suspicious region;
s2: the suspicious region is checked in detail, and suspicious or potential landslide bodies are checked;
s3: acquiring index data of a monitoring area affecting landslide development, and taking the index data as a basis for landslide development evaluation;
s4: defining a dissipation structure system of the landslide body, enabling all indexes in the dissipation structure system, carrying out normalization processing, and calculating the total entropy value of the dissipation structure system;
s5: and determining a threshold value of landslide occurrence according to the analysis result, and sending landslide early warning information.
The invention is described in detail below in connection with the implementation steps:
in step S1, a landslide body is subjected to large-scale general investigation in a remote sensing mode, and then a suspicious region is locked.
In the process of carrying out large-scale general investigation on a landslide body in a remote sensing mode, carrying out image interpretation on the landslide body by utilizing a remote sensing technology, and identifying a landslide risk area by adopting continuous SAR data in time in combination with an InSAR technology.
The InSAR (interferometric synthetic aperture radar) technology is a technology for monitoring and measuring the surface deformation by using the Synthetic Aperture Radar (SAR). The deformation condition of the earth surface is measured by a method of coherence analysis through acquiring SAR images twice or more.
The basic principle of the InSAR technology is that electromagnetic waves emitted by radar beams are reflected on the ground and returned to a receiver, and the deformation condition of the ground surface is deduced by measuring the phase difference of the beams. By comparing the multiple SAR images, the displacement and deformation of the ground surface in the vertical direction can be accurately measured.
The process of step S1 specifically includes: and (3) performing large-scale general investigation on the landslide body by utilizing high-resolution optical images, visual interpretation of unmanned aerial vehicle aviation images, inSAR+unmanned aerial vehicle remote sensing technology and automatic monitoring equipment, so as to identify potential suspicious landslide areas and determine the direction of the landslide body.
And S2, detail checking the suspicious region and checking suspicious or potential landslide bodies.
Specifically, for the suspicious landslide region identified in the above step S1, morphological characteristics, structural parameters and monitoring data of the landslide body can be obtained through visual interpretation, remote sensing technology and comprehensive application of monitoring equipment data. The method comprises the following steps: visual interpretation was performed using high resolution optical images and unmanned aerial vehicle aerial images. The characteristics of the landslide body such as morphology, texture and color are carefully observed, the range and the boundary of the landslide body are judged by an expert, and the landslide body is used as the basis of subsequent analysis. By analyzing the phase difference of the radar signals by using the InSAR technology, the deformation information of the earth surface, including the displacement and deformation condition of the landslide body, can be obtained. The unmanned aerial vehicle remote sensing technology is combined with the high-resolution image, so that more detailed surface characteristics can be provided, and parameters such as texture, gradient, height difference, length-width ratio and the like of a landslide body can be included. Analysis of these remote sensing data can help determine the disaster potential and risk level of the landslide mass. Finally, through arranging various automatic monitoring devices such as a Beidou/GNSS system, a borehole inclinometer, a rain gauge and the like, the observation values such as relative and absolute displacement variation and rainfall and the like in the ground surface and the landslide body are continuously obtained, and the monitoring areas are classified according to the observation values, so that important monitoring areas are defined, scientific basis is provided for evaluation and prevention of the landslide body, and targeted enhanced monitoring of the important areas is realized.
In step S3, the key area of the landslide is monitored, and the index data of the monitored area, such as elevation, lithology, gradient, slope direction, deformation, rainfall, soil humidity, and the like, which influence the development of the landslide are collected and used as the basis for analysis and evaluation of the development of the landslide.
The process for collecting index data of the monitored area affecting landslide development comprises the following steps:
s3.1, altitude data are obtained by measuring geographic position and analyzing altitude information through satellite signals received by using a GNSS technology;
s3.2, accurately acquiring lithology data through in-situ geological investigation and analysis by combining geological investigation data;
s3.3, acquiring coordinate information of ground points by adopting a GPS technology, acquiring Digital Elevation Model (DEM) data by adopting the data, and calculating elevation difference and horizontal distance between two adjacent points to acquire the data;
s3.4, the deformation data can be obtained by processing SAR images of the landslide region by utilizing a time sequence InSAR technology;
s3.5, rainfall data are acquired through rainfall information provided by a weather station;
s3.6, acquiring soil humidity data with high precision through soil humidity remote sensing estimation;
the Digital Elevation Model (DEM) data described in step S3.3 is 30m resolution Digital Elevation Model (DEM) data downloaded at the national aerospace agency (NASA) service. And through SARscape remote sensing processing software of ENVI, the DEM data of the research area are combined and spliced, so that the integral DEM data of the landslide research area can be generated, and data preparation is provided for subsequent research.
In the process of processing the SAR image of the landslide region to obtain the deformation data by using the time sequence InSAR technique in step S3.4, specifically: firstly, SAR remote sensing image data of a research area are acquired, image preprocessing is carried out by collecting SAR image data, a displacement field at each moment is acquired by utilizing a time sequence InSAR technology, a deformation time sequence diagram is generated, and high-precision landslide deformation data can be acquired by deformation monitoring and analysis. Wherein:
(a) And preprocessing the acquired SAR original data. This includes correcting the data to eliminate noise and systematic errors, and making geometric corrections to correspond the data to the surface features. Then, the preprocessed data is subjected to image analysis. Extracting image information related to landslide from the first data of the sentinel by utilizing the technologies of filtering, edge detection, feature extraction and the like;
(b) In the data processing and analyzing stage, the image is further processed and analyzed according to the landslide monitoring requirement. Image registration is performed, and data at different times are compared to detect displacement changes of the earth surface. Classification and object detection are then performed to identify boundaries and features of the landslide region. After the processing and analysis are completed, the data are visualized, and time sequence deformation displacement images are generated so as to intuitively display the change and distribution condition of landslide.
In the process of obtaining high-precision soil humidity data by using soil humidity remote sensing estimation in step S3.6, specifically:
(a) And acquiring Landsat remote sensing image data containing the landslide monitoring area from a related data providing mechanism or platform. And preprocessing the acquired remote sensing image, including image preprocessing, terrain correction and atmosphere correction. The method aims to eliminate noise and artifacts in the image and ensure the quality and accuracy of the image.
(b) And converting the preprocessed remote sensing image data into soil humidity data by adopting a soil humidity index model. The model utilizes the relation between the remote sensing data and the ground measurement data to estimate the spatial distribution of the soil humidity by calculating the reflectivity or the brightness temperature in the remote sensing image.
(c) And processing and analyzing the soil humidity data obtained by inversion. This includes methods such as data verification, spatial interpolation, and statistical analysis. The data verification is used for verifying the accuracy and reliability of soil humidity estimation. The spatial interpolation method interpolates limited soil humidity observation points to the whole landslide monitoring area to obtain more comprehensive soil humidity distribution. The statistical analysis method can be used for carrying out space and time change analysis on soil humidity data and obtaining information about landslide occurrence potential factors.
In step S4, the dissipative structure system of the landslide body is established with 4 basic conditions satisfied by the following dissipative structures, specifically as follows:
1. the landslide body is an open system, and continuously exchanges substances and energy with the outside to generate a negative entropy flow, so that the entropy of the landslide system is reduced to form an ordered structure;
2. the factors influencing the development of the landslide are in a continuously changing process, and the landslide body is in an unstable macroscopic state, namely the landslide body is far from reaching an equilibrium state;
3. the nonlinear interaction exists in the landslide body system, namely the interaction of each landslide factor of the landslide body is nonlinear in time and space, namely the complex nonlinear process exists in the landslide body inoculation process, and the basic condition of nonlinear interaction of a dissipation structure is met.
4. In the equilibrium state and near-equilibrium state, fluctuation is an interference that destroys stable order, but in the far-from-equilibrium state, nonlinear effects amplify the fluctuation to a new order. Because of a plurality of complex nonlinear effects in the landslide body system, the landslide body continuously exchanges material energy with the outside, and when the landslide body is far away from an equilibrium state, the landslide body reaches a new ordered structure through energy dissipation, so that a dissipation structure can be formed.
The formation and evolution process of the landslide body dissipation structure is shown in fig. 2.
The execution process of step S4 specifically includes: and (3) carrying out standardized processing on the data such as the elevation, lithology, gradient, slope direction, deformation, rainfall, soil humidity and the like of the monitored area by utilizing the 7 items of landslide factor data obtained in the step (S3), and obtaining the entropy of each index and the total system entropy of the landslide mass dissipation structure through calculation. And combining the dissipation structure theory and the historical landslide data, and obtaining a landslide occurrence threshold value through calculation and analysis, wherein the method mainly comprises the following steps:
s4.1, performing visual interpretation by using a high-resolution optical image and an unmanned aerial vehicle aerial image in the step S2, judging by an expert to obtain the range and the boundary of a landslide body of a research area, defining the range of a single landslide body system by taking the range and the boundary of the judged research area as the boundary, selecting 7 indexes such as elevation, lithology, gradient, slope direction, deformation, rainfall, soil humidity and the like of the research area, defining a dissipation structure system of the landslide of the research area by the 7 indexes, and providing theoretical preparation for the research of a subsequent single landslide body system.
S4.2, carrying out normalization treatment on the 7 indexes, namely homogenizing heterogeneous indexes.
Because the measurement units of the indexes are not uniform, before the comprehensive indexes are calculated by the measurement units, the measurement units are subjected to standardized treatment, namely the absolute values of the indexes are converted into relative values, so that the homogenization problem of the different index values is solved. Further, since the positive index and the negative index have different meanings (the higher the positive index value is, the better the negative index value is, the lower the negative index value is), the data normalization processing is performed by using different algorithms for the high and low indexes. The specific method comprises the following steps:
from equations (1) and (2) it is possible to obtain:
forward index:
negative index:
wherein: x's' ij The value of the j index of the i-th landslide body (i=1, 2 …, n; j=1, 2, …, m). For convenience, the normalized data is still noted as x ij ；
S4.3, calculating the proportion p of the ith landslide body to the index under the jth index ij The formula is as follows:
s4.4, calculating the entropy value e of the jth index j The formula is as follows:
wherein: k (k)>0, and satisfy e j ≥0。
Through the calculation, the change of the entropy of the landslide subsystem and the total entropy of the landslide system along with time can be obtained, and then through combining historical landslide data, the magnitude of the entropy value of the dissipation structure system can be obtained through calculation, and the threshold value of landslide occurrence of the research area is defined according to the entropy value. And 3, landslide risk early warning prediction is completed, and nonlinear complex occurrence processes such as landslide and the like are scientifically explained and predicted from the perspective of a dissipation structure system.
The landslide subsystem entropy and the total system entropy in the step S4.4 are specifically as follows:
(a) The landslide subsystem entropy is specifically: according to 7 indexes such as elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity of a research area, the dissipation structure of the landslide body system can be subdivided into 7 subsystems, namely the elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity. The entropy of each subsystem is an important index and a reference quantity for evaluating and reflecting the landslide body state.
(b) The total entropy of the landslide system is specifically as follows: in step S4.1, the range of a single landslide system is defined, and 7 indexes such as elevation, lithology, gradient, slope direction, deformation, rainfall, soil humidity and the like of the research area are comprehensively considered. The entropy value of the landslide body dissipation structure system can be calculated by carrying out normalization processing on the indexes and according to the calculation steps in S4.1-S4.4. By calculation and analysis, the total entropy of the dissipation structure of the whole landslide system, namely the total entropy of the landslide system, can be obtained.
In step S5, a threshold value for landslide occurrence is determined according to the analysis result, and landslide early warning information is sent out. The analysis method specifically comprises the following steps:
s5.1, calculating entropy values of 7 landslide main development factors such as elevation, lithology, gradient, slope direction, deformation, rainfall, soil humidity and the like by the step S4, and then obtaining the total entropy of a landslide system, wherein the formula is as follows:
e sum ＝e 1 +e 2 +e 3 +e 4 +e 5 +e 6 +e 7 (6)
wherein: e, e sum The total entropy of the landslide system is recorded as 'landslide system total entropy', e 1 Recorded as elevation system entropy, e 2 Marked as lithology system entropy, e 3 Marking as gradient system entropy, e 4 Marked as slope system entropy, e 5 Recorded as the entropy of the deformation system, e 6 Recorded as the entropy of the rainfall system, e 7 The entropy is recorded as the system entropy of soil humidity.
S5.2, the continuous change of landslide development factors is a key factor influencing landslide generation. As the "landslide subsystem entropy" progresses toward an increasing entropy, the externalization over time sequence appears as: with the continuous increase of time, the landslide body system gradually develops towards disorder and weakening of stability. When the total entropy of the landslide system is increased to the critical value of landslide occurrence, the landslide system can develop in a new and more orderly direction according to the theory of a dissipation structure by combining historical landslide data, namely, the time is recorded as T at the moment 1 ) Entropy value of landslide "entropy" (T 1 The total entropy value of the landslide system at the moment is recorded as e I ) Landslide occurs and a dissipative structure is formed. At the moment, the total entropy e of the landslide system when the landslide critical occurs can be obtained through analysis I Data preparation is provided for analysis and research of subsequent landslide threshold values.
S5.3, obtaining a landslide system total entropy value e when landslide critical occurs from S5.2 I And critical occurrence time T of landslide 1 . By combining historical landslide data and the changes of 7 landslide development factors such as elevation, lithology, gradient, slope direction, deformation, rainfall, soil humidity and the like in time sequence, the entropy value e I And time T 1 The entropy values of 7 'landslide subsystem entropy' are respectively calculated and determined and respectively marked as e a 、e b 、e c 、e d 、e e 、e f 、e g . Thus, the critical entropy value of each subsystem for landslide occurrence can be derived.
The method for judging the occurrence of landslide comprises the following steps:
1. the entropy value of the landslide total system of the landslide body system is a key reference index for judging the critical occurrence of landslide. When the entropy value e of the landslide total system sum Near e I Or when e sum ＝e I And when the landslide body enters a critical landslide state, the landslide early warning information is sent out to a software terminal through the Beidou short message, so that real-time landslide early warning is realized.
2. Entropy values of seven landslide subsystems of the landslide body system are important reference indexes for judging landslide critical occurrence. The specific judgment method is shown in table 1:
table 1 entropy value judging table of seven landslide subsystems of landslide system
The risk evaluation system of the landslide is obtained by analyzing various related factors of the landslide, determining the entropy value by quantifying the difference among different indexes by adopting an entropy method, calculating the entropy values of a total landslide system and a landslide subsystem, and comprehensively evaluating by combining historical landslide data. The method comprises the following steps:
1. when the primary response quantity of the landslide subsystem early warning information is 2, starting three-level landslide early warning information response;
2. when the primary response quantity of the landslide subsystem early warning information is 5, a secondary landslide early warning information response is started;
3. when the primary response quantity of the landslide subsystem early warning information is 7 and the entropy value of the landslide total system is equal, starting a primary early warning landslide early warning information response;
further, the present invention also provides a corresponding landslide monitoring device (as shown in fig. 3), which comprises 15 modules, namely an elevation data sensor module 1, a lithology data sensor module 2, a gradient data sensor module 3, a slope data sensor module 4, a deformation data sensor module 5, a rainfall data sensor module 6, a soil humidity data sensor module 7, a data acquisition module 8, a data transmission module 9, a data storage module 10, a data processing module 11, an early warning module 12, a central processing unit 13, a monitoring data transmission module 14 and a monitoring module 15. Specifically:
s6.1, the elevation data sensor module 1 is used for transmitting elevation data of a landslide body in a research area, and has the main functions of transmitting and receiving satellite signals by using a GNSS technology to measure geographic position and elevation information of the landslide body area, and specifically comprises the following steps:
1. the received satellite signal data is subjected to positioning calculation, namely, longitude, latitude and elevation information of the position of the receiver are calculated through a differential positioning or precise positioning algorithm;
2. correcting and converting the measured elevation according to the ground level data of the region to obtain accurate landslide elevation information relative to the earth surface;
3. finally, transmitting the processed accurate landslide height data to a data acquisition module 8 through a Gao Chengchuan sensor module 1;
and S6.2, the lithology data sensor module 2 is used for transmitting lithology data of the landslide body of the research area, and the main function of the lithology data sensor module is to transmit lithology data of the landslide body of the research area with high precision. The method comprises the following steps: the high-resolution remote sensing data and the multispectral data are analyzed, the high-precision landslide mass lithology data of the landslide mass of the research area are obtained by combining with the field geological survey and the geological survey data, and then the data are transmitted to the data acquisition module 8 through the lithology sensor module 2;
s6.3, the gradient data sensor module 3 is used for transmitting gradient data of a landslide body of a research area, and has the main functions of acquiring coordinate information of ground points by utilizing a GPS technology, acquiring Digital Elevation Model (DEM) data by adopting the method, calculating elevation difference and horizontal distance between two adjacent points to acquire accurate gradient data, and transmitting the data to the data acquisition module 8 through the gradient data sensor module 3;
s6.4, the slope data sensor module 4 is used for transmitting slope data of a landslide body of a research area, and has the main functions of acquiring coordinate information of ground points by utilizing a GPS technology, acquiring Digital Elevation Model (DEM) data by adopting the method, calculating elevation difference and horizontal distance between two adjacent points to acquire accurate slope data, and transmitting the data to the data acquisition module 8 through the slope data sensor module 4;
s6.5, the deformation data sensor module 5 is used for transmitting deformation data of the landslide body of the research area, and the main function of the deformation data sensor module is to process high-precision landslide deformation data obtained by the SAR image data of the landslide body of the research area by using a time sequence InSAR technology in the step S3.4 and transmit the data to the data acquisition module 8 through the deformation data sensor module 5;
s6.6, the rainfall data sensor module 6 is used for transmitting rainfall data of the landslide body, and the main function of the rainfall data sensor module is that rainfall data of 24 hours or even the first two hours in the future in the area range of the landslide body can be obtained by retrieving rainfall information provided by a weather station and transmitted to the data acquisition module 8 through the rainfall data sensor module 6;
s6.7, the soil humidity data sensor module 7 is used for transmitting soil humidity data of a landslide body, and the main function of the soil humidity data sensor module is that the soil humidity data with high precision is obtained through a soil humidity remote sensing estimation method in the step S3.6 and is transmitted to the data acquisition module 8 through the soil humidity data sensor module 7;
s6.8, the data acquisition module 8 is used for receiving the data transmitted by the sensor module in the monitoring device and realizing the function of data acquisition;
s6.9, the data transmission module 9 is used for realizing a data transmission function in the monitoring device;
s6.10, the data storage module 10 has the function of storing the acquired landslide development factor data into a landslide development factor database, and when the landslide development factor data are acquired, the data storage module can store the data into a specially-established landslide development factor database for subsequent analysis and processing;
s6.11, the data processing module 11 is used for enabling all indexes in the landslide body system of the research area in the step 4, carrying out normalization processing, and calculating the entropy of the landslide body dissipation structure system;
s6.12, the early warning module 12 is used for comprehensively analyzing and obtaining landslide monitoring data by utilizing the data processing module 11 in the step S5, comprehensively analyzing and judging the corresponding landslide disaster response grade for the index exceeding the designated landslide 'entropy' threshold value, and sending out a corresponding landslide early warning disaster grade alarm signal;
s6.13, the monitoring data transmission module 14 is used for transmitting video monitoring data covering the area range of the landslide body, and is responsible for transmitting the video data acquired from the monitoring cameras of the landslide area, so that the video monitoring data can be transmitted to a designated target position or receiving equipment in real time;
and S6.14, the monitoring module 15 is used for monitoring videos in the range of the landslide body area in real time, is provided with special monitoring equipment and cameras, corresponding monitoring points are arranged in the landslide body area, and the monitoring module 15 can acquire video signals of the landslide body area in real time through the monitoring equipment and display the video signals in a monitoring system.
S6.15, the elevation data sensor module 1, the lithology data sensor module 2, the gradient data sensor module 3, the slope data sensor module 4, the deformation data sensor module 5, the rainfall data sensor module 6 and the soil humidity data sensor module 7 are respectively connected with the data transmission module 9 through the data acquisition module 8, and then are connected with the data storage module 10 through the data transmission module 9;
s6.16, the data storage module 10 is connected with the central processing unit 13;
s6.17, the data processing module 11 and the early warning module 12 are respectively connected with the central processing unit 13;
and S6.18, the monitoring module 14 is connected with the central processing unit 13 through the monitoring data transmission module 15.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (8)
1. The rainfall landslide risk early warning and monitoring method is characterized by comprising the following steps of:
performing large-scale general investigation on the landslide body in a remote sensing mode, and locking a suspicious region;
the suspicious region is checked in detail, and suspicious or potential landslide bodies are checked;
acquiring index data of a monitoring area affecting landslide development, and taking the index data as a basis for landslide development evaluation;
defining a dissipation structure system of the landslide body, enabling all indexes in the dissipation structure system, carrying out normalization processing, and calculating the total entropy value of the dissipation structure system;
and determining a threshold value of landslide occurrence according to the analysis result, and sending landslide early warning information.
2. The rainfall landslide risk early warning and monitoring method as recited in claim 1, wherein,
in the process of carrying out large-scale general investigation on a landslide body in a remote sensing mode, carrying out image interpretation on the landslide body by utilizing a remote sensing technology, and identifying a landslide risk area by adopting continuous SAR data in time in combination with an InSAR technology.
3. The rainfall landslide risk early warning and monitoring method according to claim 2, characterized in that,
the index data of landslide development comprise elevation, lithology, gradient, slope direction, deformation, rainfall and soil humidity data.
4. A rainfall landslide risk early warning and monitoring method as recited in claim 3, characterized in that,
the process for collecting index data of the monitored area affecting landslide development as the basis of landslide development evaluation comprises the following steps:
the method comprises the steps of measuring geographical position and elevation information by receiving satellite signals through a GNSS technology, and acquiring elevation data of a research area;
accurate lithology data of a research area is obtained through in-situ geological survey and geological survey data;
acquiring coordinate information of ground points by adopting a GPS technology, acquiring digital elevation model data by adopting the method, and calculating elevation difference and horizontal distance between two adjacent points to acquire gradient and slope data;
the deformation data can be obtained by processing SAR images of landslide areas by utilizing a time sequence InSAR technology;
rainfall data is obtained through rainfall information provided by a weather station;
and acquiring soil humidity data through soil humidity remote sensing estimation.
5. The rainfall landslide risk early warning and monitoring method as recited in claim 4, wherein,
in the process that deformation data can be obtained by processing SAR images of landslide areas by utilizing a time sequence InSAR technology, SAR remote sensing image data of a research area are firstly obtained, image preprocessing is carried out by using collected SAR image data, displacement fields at each moment are obtained by utilizing the time sequence InSAR technology, a deformation time sequence diagram is generated, and deformation monitoring and analysis are carried out to obtain the landslide deformation data.
6. The rainfall landslide risk early warning and monitoring method according to claim 5, characterized in that,
in the process of acquiring soil humidity data through soil humidity remote sensing estimation, firstly, preprocessing is carried out on Landsat remote sensing images, namely, image preprocessing, terrain correction and atmosphere correction are carried out to ensure data quality, then, a soil humidity index model is adopted to convert remote sensing image data into soil humidity data, and finally, data processing and analysis are carried out on the soil humidity data obtained through inversion to acquire the soil humidity data.
7. The rainfall landslide risk early warning and monitoring method according to claim 6, characterized in that,
the dissipation structure system of the landslide body is established according to 4 basic conditions met by the dissipation structure, wherein the basic conditions comprise:
the landslide body is in an open system state, and continuously exchanges material energy with the outside;
the landslide body is in an unstable state which does not reach balance along with the change of the landslide development factor;
a plurality of nonlinear effects exist in the landslide body system;
there are a variety of disturbances to landslide mass systems.
8. The rainfall landslide risk early warning and monitoring method according to claim 7, characterized in that,
and calculating the total entropy of the dissipation structure system, specifically, analyzing various landslide related factors, and determining the magnitude of the entropy by quantifying the difference between different indexes by adopting an entropy method.
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