Landslide disaster monitoring and early warning method and system based on multi-modal remote sensing data

Abstract

This invention discloses a landslide disaster monitoring and early warning method and system based on multimodal remote sensing data, applied in the field of intelligent monitoring technology. The method includes: acquiring LiDAR data and multi-temporal UAV aerial images of a target area, and identifying landslide areas in the target area based on the LiDAR data and multi-temporal UAV aerial images; dividing the landslide areas into four regions based on the LiDAR data and multi-temporal UAV aerial images; the regions include a leading-edge collapse zone, a middle bulging deformation zone, an upstream tensile fracture zone, and a downstream tensile fracture zone; acquiring monitoring data for each region in real time, and issuing landslide disaster early warning based on the monitoring data of the four regions. This invention effectively identifies and categorizes ancient landslides through the fusion of multi-source data, and simultaneously provides early warning of the disaster risk of ancient landslides based on the above-mentioned categorized monitoring, effectively improving the accuracy of the early warning.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for geological disasters, specifically to a method and system for monitoring and early warning of landslide disasters based on multimodal remote sensing data. Background Technology

[0002] Landslide disasters, especially the reactivation of large ancient landslides, often cause serious casualties and property losses. Accurate identification of the boundaries of reactivated ancient landslide bodies, reasonable zoning of their interiors, and timely early warning based on zoning monitoring data are urgent needs in the field of geological disaster prevention and control.

[0003] In practical engineering, manual ground surveys are inefficient and lack complete coverage; while single optical remote sensing images are limited by vegetation cover and lighting direction, making it difficult to identify complete boundaries; meanwhile, although airborne lidar can acquire clean terrain without vegetation, it cannot effectively distinguish accurate boundaries when used alone. Furthermore, in terms of monitoring and early warning, existing technologies mostly treat landslides as homogeneous bodies and use fixed threshold methods for early warning, failing to reflect the differences in deformation characteristics of different parts within the landslide.

[0004] In the prior art, Chinese Patent Application No. 202210128242.3 discloses a method and apparatus for risk classification and assessment of ancient landslide reactivation across time scales. The method includes acquiring historical observation data of the ancient landslide area, calculating spatiotemporal probabilities, and classifying long-term susceptibility levels; calculating the surface deformation rate along the radar line of sight; performing a two-dimensional deformation transformation to obtain the surface deformation rate along the slope direction, classifying the surface deformation rate levels, updating the susceptibility levels, and obtaining a risk level; analyzing dynamic triggering factors, updating the risk level according to the dynamic equation of short-term ancient landslide reactivation, and obtaining an ancient landslide reactivation risk classification and assessment model; inputting the data to be analyzed into the ancient landslide reactivation risk classification and assessment model, and outputting the ancient landslide reactivation risk classification and assessment results. However, this assessment relies on InSAR data to monitor the surface displacement of the ancient landslide, neglecting the differences in displacement between different areas of the ancient landslide, resulting in poor monitoring results. Summary of the Invention

[0005] In order to at least overcome the above-mentioned shortcomings in the prior art, the purpose of this application is to provide a method and system for monitoring and early warning of landslide disasters based on multimodal remote sensing data.

[0006] In a first aspect, embodiments of this application provide a landslide disaster monitoring and early warning method based on multimodal remote sensing data, including:

[0007] Acquire LiDAR data and multi-temporal UAV aerial images of the target area, and identify the landslide area of ​​the target area based on the LiDAR data and multi-temporal UAV aerial images;

[0008] Based on LiDAR data and multi-temporal UAV aerial images, the landslide area was divided into four regions: the leading edge collapse zone, the middle bulging deformation zone, the upstream tensile fracture zone, and the downstream tensile fracture zone.

[0009] Real-time monitoring data for each of the four defined regions is acquired, and landslide disaster early warning is issued based on the monitoring data for the four defined regions.

[0010] In one possible implementation, identifying the landslide area of ​​the target region includes:

[0011] The LiDAR data is preprocessed to form deveined DEM data, and the DEM data and multi-temporal UAV aerial images are unified into the same coordinate system.

[0012] Multi-temporal drone aerial images are converted to grayscale, and the minimum pixel value of each pixel in all temporal phases is selected as the fused pixel value of that pixel to form a shadow image;

[0013] In the shadow image, find at least one continuous light-dark boundary line that extends in an arc, and identify the light-dark boundary line in the slope abrupt change zone as the trailing edge boundary using the DEM data;

[0014] In the shadow image, dark stripes are found along both sides of the trailing edge boundary, and the center line of the dark stripes where the slope changes on both sides is identified as the side edge boundary through the DEM data.

[0015] The slope abrupt change line at the toe of the slope is identified by the DEM data, and the slope abrupt change line in the shadow image where the pixel values ​​on both sides are greater than the preset value is selected as the leading edge boundary.

[0016] The landslide area is formed by using the trailing edge, lateral edge, and leading edge as an envelope.

[0017] In one possible implementation, dividing the landslide area into four regions includes:

[0018] Based on the DEM data, at least one slope abrupt change line is searched from the leading edge boundary into the landslide area, and the slope abrupt change line in the shadow image where the difference in pixel values ​​on both sides is greater than a preset value is taken as the landslide boundary; the area between the landslide boundary and the leading edge boundary is taken as the leading edge landslide area;

[0019] In the shadow image, at least one dark stripe is searched from the lateral boundary on the upstream side into the interior of the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the upstream side boundary; the area between the upstream side boundary and the lateral boundary on the upstream side is defined as the upstream tensile fracture zone.

[0020] In the shadow image, at least one dark stripe is searched from the downstream lateral boundary into the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the downstream boundary; the area between the downstream boundary and the downstream lateral boundary is defined as the downstream tensile fracture zone.

[0021] The undivided area within the landslide body is designated as the intermediate bulging deformation zone.

[0022] In one possible implementation, landslide disaster early warning based on the monitoring data from the four divided regions includes:

[0023] When the rainfall in the landslide area or the upstream area of ​​the landslide area exceeds a rainfall threshold, the risk level of the landslide area is determined according to the following deformation rate principle:

[0024] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the decelerated deformation stage, it is determined to be the first risk level.

[0025] When the upstream tensile fracture zone is in the deceleration stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the deceleration deformation stage, it is determined to be the second risk level.

[0026] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the accelerated deformation stage, it is determined to be the third risk level.

[0027] The landslide disaster warning levels corresponding to the first risk level, the second risk level, and the third risk level increase progressively.

[0028] In one possible implementation, the deformation rate is the average value of the displacement change per unit time detected by all monitoring sensors within the defined region.

[0029] Secondly, this application also provides a landslide disaster monitoring and early warning system based on multimodal remote sensing data, including:

[0030] The identification unit is configured to acquire LiDAR data and multi-temporal drone aerial images of the target area, and identify the landslide area of ​​the target area based on the LiDAR data and multi-temporal drone aerial images.

[0031] The division unit is configured to divide the landslide area into four division regions based on LiDAR data and multi-temporal UAV aerial images; the division regions include the leading edge collapse zone, the middle bulging deformation zone, the upstream tensile fracture zone, and the downstream tensile fracture zone.

[0032] The early warning unit is configured to acquire monitoring data for each of the four divided areas in real time and to issue landslide disaster early warnings based on the monitoring data for the four divided areas.

[0033] In one possible implementation, the identification unit is further configured as follows:

[0034] The LiDAR data is preprocessed to form deveined DEM data, and the DEM data and multi-temporal UAV aerial images are unified into the same coordinate system.

[0035] Multi-temporal drone aerial images are converted to grayscale, and the minimum pixel value of each pixel in all temporal phases is selected as the fused pixel value of that pixel to form a shadow image;

[0036] In the shadow image, find at least one continuous light-dark boundary line that extends in an arc, and identify the light-dark boundary line in the slope abrupt change zone as the trailing edge boundary using the DEM data;

[0037] In the shadow image, dark stripes are found along both sides of the trailing edge boundary, and the center line of the dark stripes where the slope changes on both sides is identified as the side edge boundary through the DEM data.

[0038] The slope abrupt change line at the toe of the slope is identified by the DEM data, and the slope abrupt change line in the shadow image where the pixel values ​​on both sides are greater than the preset value is selected as the leading edge boundary.

[0039] The landslide area is formed by using the trailing edge, lateral edge, and leading edge as an envelope.

[0040] In one possible implementation, the partitioning unit is further configured as follows:

[0041] Based on the DEM data, at least one slope abrupt change line is searched from the leading edge boundary into the landslide area, and the slope abrupt change line in the shadow image where the difference in pixel values ​​on both sides is greater than a preset value is taken as the landslide boundary; the area between the landslide boundary and the leading edge boundary is taken as the leading edge landslide area;

[0042] In the shadow image, at least one dark stripe is searched from the lateral boundary on the upstream side into the interior of the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the upstream side boundary; the area between the upstream side boundary and the lateral boundary on the upstream side is defined as the upstream tensile fracture zone.

[0043] In the shadow image, at least one dark stripe is searched from the downstream lateral boundary into the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the downstream boundary; the area between the downstream boundary and the downstream lateral boundary is defined as the downstream tensile fracture zone.

[0044] The undivided area within the landslide body is designated as the intermediate bulging deformation zone.

[0045] In one possible implementation, the early warning unit is further configured as follows:

[0046] When the rainfall in the landslide area or the upstream area of ​​the landslide area exceeds a rainfall threshold, the risk level of the landslide area is determined according to the following deformation rate principle:

[0047] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the decelerated deformation stage, it is determined to be the first risk level.

[0048] When the upstream tensile fracture zone is in the deceleration stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the deceleration deformation stage, it is determined to be the second risk level.

[0049] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the accelerated deformation stage, it is determined to be the third risk level.

[0050] The landslide disaster warning levels corresponding to the first risk level, the second risk level, and the third risk level increase progressively.

[0051] In one possible implementation, the deformation rate is the average value of the displacement change per unit time detected by all monitoring sensors within the defined region.

[0052] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0053] This invention relates to a landslide disaster monitoring and early warning method and system based on multimodal remote sensing data. By fusing multi-source data, ancient landslides are effectively identified and zoned. At the same time, based on the above-mentioned zoning monitoring, the disaster risk of ancient landslides is warned, which effectively improves the accuracy of the early warning. Attached Figure Description

[0054] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0055] Figure 1 This is a schematic diagram of the method steps in an embodiment of this application;

[0056] Figure 2 These are multi-temporal unmanned aerial images from embodiments of this application;

[0057] Figure 3 These are multi-temporal unmanned aerial images from embodiments of this application;

[0058] Figure 4 These are multi-temporal unmanned aerial images from embodiments of this application;

[0059] Figure 5 This is a schematic diagram of a digital elevation model according to an embodiment of this application;

[0060] Figure 6 This is a schematic diagram of a digital elevation model according to an embodiment of this application;

[0061] Figure 7 This is a schematic diagram of a digital elevation model according to an embodiment of this application;

[0062] Figure 8 This is a schematic diagram of the monitoring point layout in an embodiment of this application;

[0063] Figure 9 This is a schematic diagram of rainfall and deformation rate in an embodiment of this application. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0065] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0066] Please refer to the following: Figure 1 This is a flowchart illustrating the landslide disaster monitoring and early warning method based on multimodal remote sensing data provided in this embodiment of the invention. Further, the landslide disaster monitoring and early warning method based on multimodal remote sensing data may specifically include the contents described in steps S1-S3.

[0067] S1: Acquire LiDAR data and multi-temporal drone aerial images of the target area, and identify the landslide area of ​​the target area based on the LiDAR data and multi-temporal drone aerial images;

[0068] S2: Based on LiDAR data and multi-temporal UAV aerial images, the landslide area is divided into four regions; the regions include the leading edge collapse zone, the middle bulging deformation zone, the upstream tensile fracture zone, and the downstream tensile fracture zone.

[0069] S3: Real-time acquisition of monitoring data for each of the four divided areas, and landslide disaster early warning based on the monitoring data of the four divided areas.

[0070] In implementing this embodiment, it is first necessary to acquire LiDAR data and multi-temporal UAV aerial images of the target area. The LiDAR data is point cloud data obtained by scanning the target area with an airborne lidar; the multi-temporal UAV aerial images refer to aerial images of the target area taken by a UAV at different times. Fusion of these two types of data can effectively identify the landslide area within the target area. Unlike existing technologies, this embodiment divides the landslide area into a leading-edge collapse zone, a central bulging deformation zone, an upstream lateral tensile fracture zone, and a downstream lateral tensile fracture zone before monitoring and early warning. This is because when an ancient landslide is impacted by upstream water flow, these four zones may exhibit different deformation characteristics and mechanisms. Therefore, based on this, it is possible to better analyze which stage of deformation the landslide belongs to, thereby determining the landslide's disaster risk and issuing an early warning.

[0071] In one possible implementation, identifying the landslide area of ​​the target region includes:

[0072] The LiDAR data is preprocessed to form deveined DEM data, and the DEM data and multi-temporal UAV aerial images are unified into the same coordinate system.

[0073] Multi-temporal drone aerial images are converted to grayscale, and the minimum pixel value of each pixel in all temporal phases is selected as the fused pixel value of that pixel to form a shadow image;

[0074] In the shadow image, find at least one continuous light-dark boundary line that extends in an arc, and identify the light-dark boundary line in the slope abrupt change zone as the trailing edge boundary using the DEM data;

[0075] In the shadow image, dark stripes are found along both sides of the trailing edge boundary, and the center line of the dark stripes where the slope changes on both sides is identified as the side edge boundary through the DEM data.

[0076] The slope abrupt change line at the toe of the slope is identified by the DEM data, and the slope abrupt change line in the shadow image where the pixel values ​​on both sides are greater than the preset value is selected as the leading edge boundary.

[0077] The landslide area is formed by using the trailing edge, lateral edge, and leading edge as an envelope.

[0078] Please refer to the multi-temporal UAV aerial images obtained during the implementation of this application embodiment. Figures 2-4 This represents UAV images of the target landslide taken on different dates and under different sunlight conditions. For the preprocessed results of the acquired LiDAR data, please refer to [link / reference needed]. Figures 5-7 The LiDAR data, after preprocessing, is presented as DEM data, i.e., elevation data. For processing, the DEM data and multi-temporal UAV aerial images need to be unified into the same coordinate system. This alignment allows for appropriate processing.

[0079] In this embodiment of the application, in order to fuse multi-temporal UAV aerial images to form a shadow image that better reflects shadow changes, it is necessary to first perform grayscale conversion and then find the minimum value of each pixel to form a shadow image. Among them, the perennial shadow area is darker in all temporal phases, and the minimum value still remains dark after synthesis. At the same time, the sun angle is different in different temporal phases. After taking the minimum value, the darkest shadow in each image is superimposed and retained, which can more clearly represent the light and dark boundary line.

[0080] In this embodiment of the application, when identifying the trailing edge boundary, the main sliding wall at the trailing edge of the landslide is a steep slope. When illuminated by a light source, the sunlit side of the slope is bright, while the shadow area below the slope is dark, which forms a distinct light-dark boundary. At the same time, the sliding wall is shaped like a chair on the plane, and the boundary appears as an arc. It should be understood that the trailing edge boundary is generally searched in some specific areas, namely the area initially identified as the trailing edge of the landslide. If the searched light-dark boundary is the trailing edge boundary, it will show a narrow high-slope band in the DEM slope map, with significant differences in slope on both sides. Since the arc-shaped boundary in the shadow image may be caused by vegetation boundaries or lithological differences, the introduction of the DEM slope map can effectively eliminate false boundaries.

[0081] In the embodiments of this application, when identifying the lateral boundary, the lateral edge of the landslide is a shear fault zone, which is a linear gully or fracture zone formed by long-term erosion. Therefore, it appears as a dark stripe due to the depth and shadow effect. At the same time, since there are obvious slope changes on both sides of the gully, the corresponding accurate lateral boundary can be further identified based on DEM data.

[0082] In this embodiment, when identifying the leading edge boundary, the leading edge of a landslide is the leading edge of a shear outlet or a compression heap. Therefore, it is necessary to first identify the slope abrupt change line at the toe of the slope using DEM data, and then further identify it based on pixel data. Since the rock mass on both sides of the leading edge boundary is generally fractured due to sliding compression and river erosion, vegetation is difficult to grow, resulting in bare rock or gravel. This will show high reflectivity in the shadow image, meaning the pixel values ​​are all greater than a preset value. Because the toe of the slope may have multiple slope abrupt change lines caused by river terraces and artificial roads, it does not have the characteristic of exposed bare rock. Therefore, the leading edge boundary can be effectively screened out using the shadow image. Please refer to the final boundary diagram. Figure 4 .

[0083] In one possible implementation, dividing the landslide area into four regions includes:

[0084] Based on the DEM data, at least one slope abrupt change line is searched from the leading edge boundary into the landslide area, and the slope abrupt change line in the shadow image where the difference in pixel values ​​on both sides is greater than a preset value is taken as the landslide boundary; the area between the landslide boundary and the leading edge boundary is taken as the leading edge landslide area;

[0085] In the shadow image, at least one dark stripe is searched from the lateral boundary on the upstream side into the interior of the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the upstream side boundary; the area between the upstream side boundary and the lateral boundary on the upstream side is defined as the upstream tensile fracture zone.

[0086] In the shadow image, at least one dark stripe is searched from the downstream lateral boundary into the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the downstream boundary; the area between the downstream boundary and the downstream lateral boundary is defined as the downstream tensile fracture zone.

[0087] The undivided area within the landslide body is designated as the intermediate bulging deformation zone.

[0088] In the implementation of this application embodiment, when identifying the leading edge landslide area, the leading edge landslide area is identified by advancing a certain distance inward from the leading edge boundary. Its final landslide boundary will first appear as a slope change line in the DEM data. At the same time, since the upper part of the landslide boundary is relatively stable, there will be more vegetation, while the lower part is bare rock. Therefore, the landslide boundary can be identified and the leading edge landslide area can be identified based on this.

[0089] In the implementation of this application embodiment, the identification process of the upstream and downstream side boundaries is essentially the same as that of the side edge boundaries. Generally, the outermost boundary found is taken as the side edge boundary, while the inner boundary is taken as the upstream and downstream side boundaries, thus identifying the upstream and downstream tensile fracture zones. The area excluding the leading edge collapse zone, the upstream tensile fracture zone, and the downstream tensile fracture zone is the intermediate bulging deformation zone. For a more accurate representation in the figure, we will number these four zones as follows: Zone I (leading edge collapse zone), Zone II (intermediate bulging deformation zone), Zone III (upstream tensile fracture zone), and Zone IV (downstream tensile fracture zone). For their specific distribution, please refer to [link to relevant documentation]. Figure 4 .

[0090] In one possible implementation, landslide disaster early warning based on the monitoring data from the four divided regions includes:

[0091] When the rainfall in the landslide area or the upstream area of ​​the landslide area exceeds a rainfall threshold, the risk level of the landslide area is determined according to the following deformation rate principle:

[0092] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the decelerated deformation stage, it is determined to be the first risk level.

[0093] When the upstream tensile fracture zone is in the deceleration stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the deceleration deformation stage, it is determined to be the second risk level.

[0094] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the accelerated deformation stage, it is determined to be the third risk level.

[0095] The landslide disaster warning levels corresponding to the first risk level, the second risk level, and the third risk level increase progressively.

[0096] In implementing this application's embodiments, the monitoring data required mainly includes rainfall and displacement values, where the displacement values ​​can be used to calculate the deformation rate; please refer to... Figure 8 The diagram shows the deployment locations of the GNNS sensors. Since the bare rock in Zone I is too loose, InSAR technology can be used for displacement acquisition, which is a mature existing technology and will not be limited in this application. For details on the deformation rate changes in each zone during rainfall, please refer to [link to relevant documentation]. Figure 9 The pattern is characterized by the deformation rate of zones I, II, and IV exhibiting four stages: initial deformation (deceleration), accelerated deformation, deceleration deformation, and stabilization. Zone III, on the other hand, undergoes five deformation stages: initial deformation (acceleration), deceleration deformation, accelerated deformation, deceleration deformation, and stabilization. This is because zone III is located upstream and directly faces the impact of the flood peak, leading to earlier accelerated deformation. Once this acceleration reaches a certain level, the deformation stabilizes and decelerates until the delayed deformation caused by rainfall accelerates along with the other three zones. Based on this principle, this application's embodiments divide the risk into three levels. In the first risk level, the upstream tensile fracture zone is in the accelerated deformation stage, while the leading-edge landslide zone, downstream tensile fracture zone, and intermediate bulging deformation zone are all in the deceleration deformation stage. At this point, although the flood peak has reached the toe of the landslide, the accumulated rainwater has caused the surface moisture content to rise, but it has not yet reached full saturation. Therefore, the upstream tensile fracture zone experiences acceleration but remains generally stable. In the second risk level, the upstream tensile fracture zone is in a deceleration phase, as are the leading-edge collapse zone, downstream tensile fracture zone, and intermediate bulging deformation zone. Although the acceleration in the upstream tensile fracture zone has even decreased compared to the second risk level, continuous rainfall is causing the deposit to gradually approach the saturation critical point. However, the loading in the characterization has not yet exceeded the critical value, indicating a deterioration in potential geological conditions, but the deformation response remains lagging, thus still decelerating. It should be understood that the judgment of the second risk level is generally related to whether rainfall continues. If there is a prolonged cessation of rainfall, it can be downgraded to the first risk level. In the third risk level, the deposit reaches the saturation critical point, and the shear strength of the sliding surface decreases significantly. The previously accumulated internal stress transfer has also been completed, reaching the critical point and triggering coordinated acceleration across the entire region, representing the most dangerous moment. This application combines the situation of the river at the foot of the slope for risk classification, which is closer to the actual situation.

[0097] In one possible implementation, the deformation rate is the average value of the displacement change per unit time detected by all monitoring sensors within the defined region.

[0098] Secondly, this application also provides a landslide disaster monitoring and early warning system based on multimodal remote sensing data, including:

[0099] The identification unit is configured to acquire LiDAR data and multi-temporal drone aerial images of the target area, and identify the landslide area of ​​the target area based on the LiDAR data and multi-temporal drone aerial images.

[0100] The division unit is configured to divide the landslide area into four division regions based on LiDAR data and multi-temporal UAV aerial images; the division regions include the leading edge collapse zone, the middle bulging deformation zone, the upstream tensile fracture zone, and the downstream tensile fracture zone.

[0101] The early warning unit is configured to acquire monitoring data for each of the four divided areas in real time and to issue landslide disaster early warnings based on the monitoring data for the four divided areas.

[0102] In one possible implementation, the identification unit is further configured as follows:

[0103] The LiDAR data is preprocessed to form deveined DEM data, and the DEM data and multi-temporal UAV aerial images are unified into the same coordinate system.

[0104] Multi-temporal drone aerial images are converted to grayscale, and the minimum pixel value of each pixel in all temporal phases is selected as the fused pixel value of that pixel to form a shadow image;

[0105] In the shadow image, find at least one continuous light-dark boundary line that extends in an arc, and identify the light-dark boundary line in the slope abrupt change zone as the trailing edge boundary using the DEM data;

[0106] In the shadow image, dark stripes are found along both sides of the trailing edge boundary, and the center line of the dark stripes where the slope changes on both sides is identified as the side edge boundary through the DEM data.

[0107] The slope abrupt change line at the toe of the slope is identified by the DEM data, and the slope abrupt change line in the shadow image where the pixel values ​​on both sides are greater than the preset value is selected as the leading edge boundary.

[0108] The landslide area is formed by using the trailing edge, lateral edge, and leading edge as an envelope.

[0109] In one possible implementation, the partitioning unit is further configured as follows:

[0110] Based on the DEM data, at least one slope abrupt change line is searched from the leading edge boundary into the landslide area, and the slope abrupt change line in the shadow image where the difference in pixel values ​​on both sides is greater than a preset value is taken as the landslide boundary; the area between the landslide boundary and the leading edge boundary is taken as the leading edge landslide area;

[0111] In the shadow image, at least one dark stripe is searched from the lateral boundary on the upstream side into the interior of the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the upstream side boundary; the area between the upstream side boundary and the lateral boundary on the upstream side is defined as the upstream tensile fracture zone.

[0112] In the shadow image, at least one dark stripe is searched from the downstream lateral boundary into the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the downstream boundary; the area between the downstream boundary and the downstream lateral boundary is defined as the downstream tensile fracture zone.

[0113] The undivided area within the landslide body is designated as the intermediate bulging deformation zone.

[0114] In one possible implementation, the early warning unit is further configured as follows:

[0115] When the rainfall in the landslide area or the upstream area of ​​the landslide area exceeds a rainfall threshold, the risk level of the landslide area is determined according to the following deformation rate principle:

[0116] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the decelerated deformation stage, it is determined to be the first risk level.

[0117] When the upstream tensile fracture zone is in the deceleration stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the deceleration deformation stage, it is determined to be the second risk level.

[0118] When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the accelerated deformation stage, it is determined to be the third risk level.

[0119] The landslide disaster warning levels corresponding to the first risk level, the second risk level, and the third risk level increase progressively.

[0120] In one possible implementation, the deformation rate is the average value of the displacement change per unit time detected by all monitoring sensors within the defined region.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0123] The units described as separate components may or may not be physically separate. As will be apparent to those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0124] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A landslide disaster monitoring and early warning method based on multimodal remote sensing data, characterized in that, include: Acquire LiDAR data and multi-temporal UAV aerial images of the target area, and identify the landslide area of ​​the target area based on the LiDAR data and multi-temporal UAV aerial images; Based on LiDAR data and multi-temporal UAV aerial images, the landslide area was divided into four regions: the leading edge collapse zone, the middle bulging deformation zone, the upstream tensile fracture zone, and the downstream tensile fracture zone. Real-time monitoring data for each of the four defined regions is acquired, and landslide disaster early warning is issued based on the monitoring data for the four defined regions.

2. The landslide disaster monitoring and early warning method based on multimodal remote sensing data according to claim 1, characterized in that, The landslide area identified in the target area includes: The LiDAR data is preprocessed to form deveined DEM data, and the DEM data and multi-temporal UAV aerial images are unified into the same coordinate system. Multi-temporal drone aerial images are converted to grayscale, and the minimum pixel value of each pixel in all temporal phases is selected as the fused pixel value of that pixel to form a shadow image; In the shadow image, find at least one continuous light-dark boundary line that extends in an arc, and identify the light-dark boundary line in the slope abrupt change zone as the trailing edge boundary using the DEM data; In the shadow image, dark stripes are found along both sides of the trailing edge boundary, and the center line of the dark stripes where the slope changes on both sides is identified as the side edge boundary through the DEM data. The slope abrupt change line at the toe of the slope is identified by the DEM data, and the slope abrupt change line in the shadow image where the pixel values ​​on both sides are greater than the preset value is selected as the leading edge boundary. The landslide area is formed by using the trailing edge, lateral edge, and leading edge as an envelope.

3. The landslide disaster monitoring and early warning method based on multimodal remote sensing data according to claim 2, characterized in that, The landslide area is divided into four regions, including: Based on the DEM data, at least one slope abrupt change line is searched from the leading edge boundary into the landslide area, and the slope abrupt change line in the shadow image where the difference in pixel values ​​on both sides is greater than a preset value is taken as the landslide boundary; the area between the landslide boundary and the leading edge boundary is taken as the leading edge landslide area; In the shadow image, at least one dark stripe is searched from the lateral boundary on the upstream side into the interior of the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the upstream side boundary; the area between the upstream side boundary and the lateral boundary on the upstream side is defined as the upstream tensile fracture zone. In the shadow image, at least one dark stripe is searched from the downstream lateral boundary into the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the downstream boundary; the area between the downstream boundary and the downstream lateral boundary is defined as the downstream tensile fracture zone. The undivided area within the landslide body is designated as the intermediate bulging deformation zone.

4. The landslide disaster monitoring and early warning method based on multimodal remote sensing data according to claim 1, characterized in that, Landslide disaster early warning based on the monitoring data from the four divided regions includes: When the rainfall in the landslide area or the upstream area of ​​the landslide area exceeds a rainfall threshold, the risk level of the landslide area is determined according to the following deformation rate principle: When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the decelerated deformation stage, it is determined to be the first risk level. When the upstream tensile fracture zone is in the deceleration stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the deceleration deformation stage, it is determined to be the second risk level. When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the accelerated deformation stage, it is determined to be the third risk level. The landslide disaster warning levels corresponding to the first risk level, the second risk level, and the third risk level increase progressively.

5. The landslide disaster monitoring and early warning method based on multimodal remote sensing data according to claim 4, characterized in that, The deformation rate is the average value of the displacement change per unit time detected by all monitoring sensors within the defined region.

6. A landslide disaster monitoring and early warning system based on multimodal remote sensing data, characterized in that, include: The identification unit is configured to acquire LiDAR data and multi-temporal drone aerial images of the target area, and identify the landslide area of ​​the target area based on the LiDAR data and multi-temporal drone aerial images. The division unit is configured to divide the landslide area into four division regions based on LiDAR data and multi-temporal UAV aerial images; the division regions include the leading edge collapse zone, the middle bulging deformation zone, the upstream tensile fracture zone, and the downstream tensile fracture zone. The early warning unit is configured to acquire monitoring data for each of the four divided areas in real time and to issue landslide disaster early warnings based on the monitoring data for the four divided areas.

7. The landslide disaster monitoring and early warning system based on multimodal remote sensing data according to claim 6, characterized in that, The identification unit is further configured to: The LiDAR data is preprocessed to form deveined DEM data, and the DEM data and multi-temporal UAV aerial images are unified into the same coordinate system. Multi-temporal drone aerial images are converted to grayscale, and the minimum pixel value of each pixel in all temporal phases is selected as the fused pixel value of that pixel to form a shadow image; In the shadow image, find at least one continuous light-dark boundary line that extends in an arc, and identify the light-dark boundary line in the slope abrupt change zone as the trailing edge boundary using the DEM data; In the shadow image, dark stripes are found along both sides of the trailing edge boundary, and the center line of the dark stripes where the slope changes on both sides is identified as the side edge boundary through the DEM data. The slope abrupt change line at the toe of the slope is identified by the DEM data, and the slope abrupt change line in the shadow image where the pixel values ​​on both sides are greater than the preset value is selected as the leading edge boundary. The landslide area is formed by using the trailing edge, lateral edge, and leading edge as an envelope.

8. The landslide disaster monitoring and early warning system based on multimodal remote sensing data according to claim 7, characterized in that, The partitioning unit is further configured as follows: Based on the DEM data, at least one slope abrupt change line is searched from the leading edge boundary into the landslide area, and the slope abrupt change line in the shadow image where the difference in pixel values ​​on both sides is greater than a preset value is taken as the landslide boundary; the area between the landslide boundary and the leading edge boundary is taken as the leading edge landslide area; In the shadow image, at least one dark stripe is searched from the lateral boundary on the upstream side into the interior of the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the upstream side boundary; the area between the upstream side boundary and the lateral boundary on the upstream side is defined as the upstream tensile fracture zone. In the shadow image, at least one dark stripe is searched from the downstream lateral boundary into the landslide area, and the center line of the dark stripe where the slope aspect changes on both sides is identified by the DEM data as the downstream boundary; the area between the downstream boundary and the downstream lateral boundary is defined as the downstream tensile fracture zone. The undivided area within the landslide body is designated as the intermediate bulging deformation zone.

9. The landslide disaster monitoring and early warning system based on multimodal remote sensing data according to claim 6, characterized in that, The early warning unit is also configured to: When the rainfall in the landslide area or the upstream area of ​​the landslide area exceeds a rainfall threshold, the risk level of the landslide area is determined according to the following deformation rate principle: When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the decelerated deformation stage, it is determined to be the first risk level. When the upstream tensile fracture zone is in the deceleration stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the deceleration deformation stage, it is determined to be the second risk level. When the upstream tensile fracture zone is in the accelerated deformation stage, and the leading edge collapse zone, the downstream tensile fracture zone, and the intermediate bulging deformation zone are all in the accelerated deformation stage, it is determined to be the third risk level. The landslide disaster warning levels corresponding to the first risk level, the second risk level, and the third risk level increase progressively.

10. The landslide disaster monitoring and early warning system based on multimodal remote sensing data according to claim 9, characterized in that, The deformation rate is the average value of the displacement change per unit time detected by all monitoring sensors within the defined region.

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