A mine subsidence monitoring method based on unmanned aerial photogrammetry and LiDAR technology

Abstract

The application discloses a kind of mine subsidence monitoring methods based on unmanned aerial vehicle photogrammetry and LiDAR technology, utilize unmanned aerial vehicle to carry out flight shooting to mining area, obtain mining area surface image data;Image data obtained is handled by motion recovery structure algorithm SfM, generates digital surface model DSM, orthophoto map DOM and digital elevation model DEM;Utilize airborne LiDAR system to scan mining area, obtain the three-dimensional point cloud data of mining area surface;Differential GNSS technology is used to correct and precision improvement to LiDAR point cloud data;The digital elevation model DEM generated by unmanned aerial vehicle photogrammetry is fused with the registration of corrected LiDAR point cloud data in unified coordinate system;Fused data is handled and analyzed, constructs mining area subsidence model, realizes the dynamic monitoring of mining area surface subsidence condition.The application effectively solves the problems existing in the traditional mining area subsidence monitoring method, provides strong technical support for mine geological disaster prevention, ecological environment protection and mining area sustainable development.

Description

Technical Field

[0001] This invention relates to a method for monitoring subsidence in mining areas based on unmanned aerial vehicle (UAV) photogrammetry and LiDAR technology. Background Technology

[0002] With the continuous exploitation of coal resources, the problem of surface subsidence in mining areas has become increasingly serious, severely impacting the ecological environment, infrastructure, and lives of surrounding residents. Accurate monitoring of surface subsidence in mining areas is crucial for ensuring safe mine production, rationally planning land reclamation, and reducing environmental damage.

[0003] Traditional methods for monitoring subsidence in mining areas have many drawbacks:

[0004] (1) Low measurement efficiency: It requires a lot of manpower and material resources, the measurement process is complicated, and it is difficult to quickly obtain information on large-area surface subsidence.

[0005] (2) Limited monitoring accuracy: Due to limitations in measuring instruments and methods, it is difficult to accurately reflect the minute changes and complex morphology of surface subsidence.

[0006] (3) Complex data processing: The amount of data acquired is large and scattered, making it difficult to process and analyze, and difficult to provide accurate subsidence monitoring results in a timely manner.

[0007] In recent years, UAV photogrammetry and LiDAR technology have developed rapidly in the surveying and mapping field, demonstrating enormous potential. UAV photogrammetry technology has advantages such as low cost, flexible operation, and the ability to quickly acquire high-resolution image data, effectively compensating for the shortcomings of traditional monitoring methods. LiDAR technology can actively emit laser signals and receive reflected signals to directly acquire three-dimensional spatial information of the earth's surface. It features high precision and strong anti-interference capabilities, significantly improving the accuracy and reliability of subsidence monitoring.

[0008] However, single technological approaches still have limitations. For example, UAV photogrammetry is easily affected by complex weather conditions and is insufficient in obtaining accurate surface elevation information; LiDAR technology, on the other hand, is costly and its data processing and interpretation are relatively complex. Combining UAV photogrammetry with LiDAR technology can fully leverage the advantages of both, achieve data complementarity, improve the efficiency and accuracy of mine subsidence monitoring, and provide stronger technical support for mine geological disaster prevention and ecological environment restoration. Therefore, researching a mine subsidence monitoring method based on UAV photogrammetry and LiDAR technology has significant practical implications. Summary of the Invention

[0009] This invention provides a method for monitoring mine subsidence based on UAV photogrammetry and LiDAR technology to address the problems existing in the prior art. This invention effectively solves the problems of traditional mine subsidence monitoring methods, providing strong technical support for mine geological disaster prevention, ecological environmental protection, and sustainable development of mining areas.

[0010] The technical solutions adopted in this invention are as follows:

[0011] A method for monitoring mining subsidence based on UAV photogrammetry and LiDAR technology includes the following steps:

[0012] S1. Use drones to take aerial photos of the mining area and obtain surface image data of the mining area;

[0013] S2. The acquired image data is processed using the Structure for Motion Restoration (SfM) algorithm to generate a Digital Surface Model (DSM), an Orthophoto Map (DOM), and a Digital Elevation Model (DEM).

[0014] S3. Use an airborne LiDAR system to scan the mining area and obtain three-dimensional point cloud data of the surface of the mining area;

[0015] S4. Differential GNSS technology is used to correct and improve the accuracy of LiDAR point cloud data;

[0016] S5. Register and fuse the digital elevation model (DEM) generated by UAV photogrammetry with the calibrated LiDAR point cloud data in a unified coordinate system;

[0017] S6. Process and analyze the fused data to construct a mining area subsidence model and realize dynamic monitoring of surface subsidence in the mining area.

[0018] Furthermore, in step 1, the drone conducts aerial photography of the mining area according to a preset route and parameters.

[0019] The preset flight path and parameters include flight altitude, lateral overlap and directional overlap, with the flight altitude being 255m, lateral overlap being 60% and directional overlap being 80%.

[0020] Furthermore, in step 3, the steps for acquiring 3D point cloud data are as follows:

[0021] (1) The laser rangefinder on the UAV emits laser pulses and receives reflected lasers;

[0022] (2) Using the pulse ranging formula Calculate the distance d between the laser rangefinder and the target, where c represents the speed of light and t represents the total time taken for the laser pulse to travel to and from the target.

[0023] (3) Taking the center of the laser scanner on the UAV as the starting point, the azimuth angle (α,β,γ) of the starting point is measured by the attitude measurement unit (IMU) on the UAV, and the distance vector r between the starting point and the target point is obtained by combining differential GNSS, and the coordinates of the target point are calculated.

[0024] (4) Repeat the above steps to collect a large amount of target point coordinate data;

[0025] (5) The collected target point coordinate data is preprocessed to generate a standard LAS point cloud data file, thereby obtaining the three-dimensional point cloud data of the mining area surface.

[0026] Furthermore, in step 4, when using differential GNSS technology to correct and improve the accuracy of LiDAR point cloud data, two acquisition areas of the same size are selected within the airborne LiDAR acquisition area according to preset rules; within each acquisition area, multiple GNSS point data are acquired using differential GNSS technology, and the number of multiple GNSS point data is not less than a preset number threshold. The monitoring points corresponding to the GNSS point data will be used as control points for subsequent correction and accuracy improvement of LiDAR point cloud data.

[0027] Furthermore, before using differential GNSS technology to correct and improve the accuracy of LiDAR point cloud data, the acquired data needs to be preprocessed. The data preprocessing is as follows:

[0028] Flight preparation phase:

[0029] Conduct route planning and determine parameters such as flight altitude, lateral overlap, and forward overlap;

[0030] Data collection phase:

[0031] Aerial photography flights are used to acquire laser ranging data, IMU data, airborne GPS data, and downloaded base station data. Among them, airborne GPS data and IMU data are used for GPS / IMU joint calculation to obtain trajectory data, base station data are used for differential GPS processing, and laser calibration files also need to be downloaded.

[0032] Data processing stage:

[0033] During trajectory calculation, ensure that the position accuracy in each direction is less than 0.02m and the attitude accuracy is less than 3°; use trajectory data to calculate point cloud and perform data quality checks; if layered, perform feature extraction and flight strip adjustment in sequence; if not layered, perform point cloud coloring, coordinate transformation, and point cloud accuracy checks on the laser ranging data, and finally generate a standard point cloud LAS format file.

[0034] Furthermore, step 5 specifically includes:

[0035] (1) Acquire UAV imagery and LiDAR point cloud data;

[0036] (2) Perform feature extraction on the image and point cloud, including extracting image feature contours and extracting point cloud feature contours;

[0037] (3) Based on a unified coordinate system, feature-level registration is performed on the image and point cloud to obtain the image and point cloud data registration results;

[0038] (4) The texture information of the UAV image is fused into the LiDAR point cloud to obtain point cloud data with true color;

[0039] (5) Generate three-dimensional and two-dimensional display forms of true color point cloud data to provide multi-dimensional data support for mining area subsidence monitoring;

[0040] (6) The digital elevation model (DEM) generated by UAV photogrammetry is registered and fused with the corrected LiDAR point cloud data in a unified coordinate system to obtain fused data.

[0041] Furthermore, step 6 specifically includes:

[0042] (1) Extract features from the fused data to identify the subsidence areas and degree of subsidence on the surface of the mining area;

[0043] (2) Based on the fused data and feature analysis results, a subsidence model of the mining area is constructed;

[0044] (3) Using the constructed mining area subsidence model, the surface subsidence of the mining area is dynamically monitored, including the monitoring of subsidence range, subsidence depth and subsidence rate;

[0045] (4) Evaluate the monitoring results, generate a subsidence monitoring report, and issue an early warning when the subsidence exceeds the preset threshold.

[0046] The present invention has the following beneficial effects:

[0047] Unmanned aerial vehicle (UAV) photogrammetry combined with LiDAR technology can acquire high-resolution surface image data and precise 3D point cloud data, generating high-precision digital surface models (DSM), orthophoto maps (DOM), and digital elevation models (DEM). This enables high-precision monitoring of surface subsidence in mining areas, accurately reflecting the extent, depth, and degree of subsidence. Differential GNSS technology is used to correct and enhance the accuracy of the LiDAR point cloud data, further improving the accuracy of the monitoring data. Compared to traditional manual measurement methods, this invention can acquire abundant data in a short time, achieving rapid monitoring of mining area subsidence.

[0048] By registering and fusing the DEM generated by UAV photogrammetry with the calibrated LiDAR point cloud data in a unified coordinate system, the textural information of the UAV imagery and the three-dimensional spatial coordinate information of the LiDAR point cloud are fully utilized, achieving complementary advantages of the data. The fused data can provide richer and more comprehensive surface information of the mining area, providing a solid data foundation for constructing mining area subsidence models and conducting subsequent analysis and evaluation.

[0049] The mining area subsidence model, constructed based on the fused data, enables dynamic monitoring of surface subsidence in mining areas, timely capture of subsidence trends, and provides timely and accurate information support for mine safety production, geological disaster early warning, and ecological restoration of mining areas. It allows for long-term, continuous monitoring of mining area subsidence, helping to promptly identify problems and take corresponding measures to reduce losses caused by subsidence. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the positioning principle of an airborne LiDAR system.

[0051] Figure 2 This is a map showing the area where GNSS point data was collected.

[0052] Figure 3 This is a diagram illustrating the data preprocessing steps.

[0053] Figure 4 A diagram illustrating the registration and fusion steps between LiDAR point clouds and UAV imagery.

[0054] Figure 5 This is a schematic diagram of GNSS points with different densities.

[0055] Figure 6 DEM error results generated for GNSS points of different densities.

[0056] Figure 7 The graph shows the reduction amount and rate of MAE and RMSE in region 2.

[0057] Figure 8 This is a graph comparing the errors of different technologies in the Z direction.

[0058] Figure 9 The images show the subsidence monitoring results for each technology.

[0059] Figure 10 MAE and RMSE graphs for different technologies.

[0060] Figure 11 A scatter density map to integrate technical monitoring results with data from field observation stations. Detailed Implementation

[0061] The invention will now be further described with reference to the accompanying drawings.

[0062] This invention discloses a method for monitoring subsidence in mining areas based on UAV photogrammetry and LiDAR technology, comprising the following steps:

[0063] S1. Use drones to take aerial photos of the mining area and obtain surface image data of the mining area;

[0064] S2. The acquired image data is processed using the Structure for Motion Restoration (SfM) algorithm to generate a Digital Surface Model (DSM), an Orthophoto Map (DOM), and a Digital Elevation Model (DEM).

[0065] S3. Use an airborne LiDAR system to scan the mining area and obtain three-dimensional point cloud data of the surface of the mining area;

[0066] S4. Differential GNSS technology is used to correct and improve the accuracy of LiDAR point cloud data;

[0067] S5. Register and fuse the digital elevation model (DEM) generated by UAV photogrammetry with the calibrated LiDAR point cloud data in a unified coordinate system;

[0068] S6. Process and analyze the fused data to construct a mining area subsidence model and realize dynamic monitoring of surface subsidence in the mining area.

[0069] In the data acquisition planning phase, the flight path of the UAV is first designed, followed by the design of an appropriate ground sampling distance based on the expected measurement accuracy. Based on the flight path, ground sampling distance, and other information, the time required to complete the mission and the number of flights needed are estimated. Before the actual flight, control points are measured. After preparation, the UAV acquires image data. In the data processing phase, the Structure from Motion (SfM) algorithm is used. The first step of this algorithm is to extract keypoint features with certain invariance from the image and create a keypoint feature database. The second step is to match keypoint features in the image. The third step is to reconstruct the positions of the keypoint features and camera parameters, outputting a 3D sparse point cloud of keypoint features in any coordinate system and performing georegistration. The georegistered sparse point cloud is then used as input to construct a dense point cloud. Finally, the dense point cloud is filtered to remove outliers and other noise data, resulting in a ground point cloud. Finally, a Digital Surface Model (DSM) was generated by direct interpolation of the dense point cloud, and an orthophoto map (DOM) was generated through digital differential correction. Simultaneously, a Digital Elevation Model (DEM) was generated by interpolating the ground point cloud. Through UAV imagery surveying technology, high-precision topographic data with geographic reference was formed, providing a spatiotemporal benchmark for monitoring subsidence in mining areas.

[0070] Based on UAV photogrammetry technology, high-precision terrain data was acquired. However, obtaining effective data is difficult under adverse weather conditions and in monitoring tasks involving deeper, subtle deformations. To address this issue, this invention introduces LiDAR technology. Laser pulses are used to scan targets to accurately measure the three-dimensional coordinates of the ground and object surfaces. Combined with UAV photogrammetry, LiDAR technology focuses on acquiring high-precision three-dimensional spatial coordinate information, while UAV photogrammetry provides rich texture and visual information. In the scenario of monitoring subsidence in mining areas, when focusing on the spatial relationship between a target point and the starting point of the laser emitted by the airborne LiDAR system, if the coordinates of the starting point are known... The orientation of the starting point can be measured using an attitude measurement unit (IMU). Then, by using differential GNSS to obtain the distance between the starting point and the target point, the coordinates of the target point can be calculated.

[0071] The positioning principle of the airborne LiDAR system is described in [link to documentation]. Figure 1 . Figure 1 It includes a ground coordinate system and a laser scanner center coordinate system, the former having coordinate axes of... , , The coordinate axes of the laser scanner's central coordinate system are: , , Laser Scanner Center This is the location where the airborne LiDAR system emits its laser, i.e., the starting point of the measurement. (Ground point) This is the target point reached by the laser pulse. (Vector) Indicates from the center of the laser scanner to ground point The distance vector, whose direction is .vector Indicates the origin of the ground coordinate system Pointing to the center of the laser scanner The vector.

[0072] The airborne LiDAR system mainly consists of five parts: a digital camera, GNSS, an IMU, a central control unit, and a laser rangefinder. In this system, the digital camera acquires high-resolution images to obtain the spectral characteristics of the target object. Simultaneously, the camera fuses pixel information with laser point cloud data to obtain information with coordinate attributes. GNSS primarily transmits correction values ​​through the control station to calibrate the receiver in real time. When determining the UAV's coordinates, GNSS is first used for precise positioning, then the data is corrected, and its three-dimensional information is obtained.

[0073] The IMU (Integrated Measurement Unit) incorporates three accelerometers and a gyroscope, primarily used to measure the multi-axis angular velocity and acceleration of an object in three-dimensional space, thereby obtaining its attitude angle. The central control unit manages all the hardware. The laser rangefinder measures the target distance by adjusting laser parameters. The ranging method used in this study is pulse-based, and its expression is as follows:

[0074] ,

[0075] In the formula, This indicates the distance between the laser rangefinder and the target. Represents the speed of light. This indicates the total time taken.

[0076] In airborne LiDAR data acquisition, complex terrain conditions and human factors in mining areas can easily lead to point cloud gaps in multiple areas. To address this issue, this invention selects areas of equal size within the airborne LiDAR acquisition area according to preset rules for extensive GNSS point data collection. GNSS dynamic measurement technology is used to acquire high-precision point data within the selected areas. These GNSS monitoring points will serve as control points for subsequent calibration and accuracy improvement of the airborne LiDAR data. To avoid the influence of random factors, two acquisition areas of equal size are set, such as... Figure 2As shown.

[0077] The preset rules include the following:

[0078] 1) The selected collection area should cover different features of the mining area surface, including flat areas, areas with varying slopes, areas with vegetation cover, and areas with exposed rock, to ensure that the collected GNSS point data can fully reflect the actual situation of the mining area surface.

[0079] 2) The selection of the collection area should be based on the impact range of mining activities in the mining area. Priority should be given to areas that are more affected by mining activities and have a higher possibility of subsidence, such as areas near mining boundaries, fault zones, and directly above and near underground goaf areas.

[0080] 3) The two acquisition areas should be evenly distributed throughout the entire LiDAR acquisition area, avoiding concentration in a certain local area, so as to ensure that the monitoring data of the entire mining area has a better correction and accuracy improvement effect;

[0081] 4) The size of the data collection area should be appropriate, and the specific area should be reasonably determined based on the scale of the mining area, the complexity of the terrain, and the requirements for monitoring accuracy;

[0082] 5) The selected data collection area should facilitate on-site measurement operations, ensure the safety of measurement personnel and equipment, and have good accessibility to facilitate the transportation and installation of measurement equipment.

[0083] After data acquisition is complete, data preprocessing is required to generate standard LAS point cloud data files. Specific steps are as follows: Figure 3 As shown. By Figure 3 As can be seen, data preprocessing is divided into three stages: flight preparation, data acquisition, and data processing.

[0084] Flight preparation phase:

[0085] First, flight route planning is carried out to determine parameters such as flight altitude and lateral overlap.

[0086] Data collection phase:

[0087] Next, aerial surveying is conducted to acquire laser ranging data, IMU data, airborne GPS data, and downloaded base station data. The airborne GPS and IMU data are used for joint GPS / IMU calculations to obtain trajectory data, while the base station data is used for differential GPS processing. Additionally, laser calibration files need to be downloaded.

[0088] Data processing stage:

[0089] During trajectory calculation, it is necessary to ensure that the position accuracy in each direction is less than 0.02m and the attitude accuracy is less than 3°. Subsequently, point cloud calculation is performed using the trajectory data, followed by data quality checks. If layered, feature extraction and flight strip adjustment are performed sequentially. If not layered, point cloud coloring, coordinate transformation, and point cloud accuracy checks are performed on the laser ranging data, ultimately generating a standard point cloud LAS format file.

[0090] In monitoring subsidence in mining areas, it is necessary to register and fuse LiDAR point cloud data with UAV imagery data in the same coordinate system. Specific steps are as follows: Figure 4 As shown. By Figure 4 The process begins with acquiring UAV imagery and LiDAR point cloud data. Next, features are extracted from both the imagery and the point cloud, and they are matched using a unified coordinate system. After registration, the texture information from the UAV imagery is fused into the LiDAR point cloud to obtain true-color point cloud data, which is then displayed in three dimensions. Simultaneously, a two-dimensional display of the true-color point cloud data is generated, providing multi-dimensional data support for monitoring subsidence in mining areas.

[0091] Experimental verification and analysis

[0092] In the monitoring and analysis of subsidence in mining areas, the study mainly adopted the GNSS dynamic measurement method to collect ground points. To find the optimal settings for this method, three areas were selected in a mining area in Shanxi Province for the study. Ground points were collected along the east-west and north-south directions at uniform intervals, and the collected data were imported into ArcMap software. Subsequently, the collected GNSS points were classified according to density, generating GNSS point sets with five different densities: 18m, 36m, 54m, 72m, and 90m. The diagrams of the different GNSS point densities are shown below. Figure 5 As shown.

[0093] DEM error results generated from GNSS points of different densities are as follows: Figure 6 Figure (a) and as shown in Figure (a) Figure 6 As shown in Figure (b) of the document. Figure 6 As shown in Figure (a), the Mean Absolute Error (MAE) of all three regions increases with the increase of GNSS point spacing. When the point spacing is 18m, the MAE for region 1 is only 0.032m, for region 2 it is only 0.028m, and for region 3 it is 0.025m. When the point spacing increases to 90m, the MAE for region 1 increases to 0.065m, and for regions 2 and 3 it increases to 0.054m and 0.049m respectively. Figure 6As shown in Figure (b), the root mean square error (RMSE) is lowest in all regions when the point spacing is 18m, at 0.056m, 0.048m, and 0.043m respectively. It can also be seen that the MAE and RMSE values ​​are highest for regions without GNSS points, indicating that a higher density of GNSS points can effectively improve the accuracy of DEM generation, facilitating subsequent monitoring of mining area subsidence.

[0094] Continuing with the analysis of MAE and RMSE values ​​in region 2, we will examine the amount and rate of error reduction. Figure 7 As shown in Figure (a), the reduction in MAE error and its rate of reduction fluctuate under different point spacing reduction amounts. When the reduction amount is 90-72m, the reduction in MAE error and its rate of reduction are as high as 0.0081m and 22.13%, respectively. When the reduction amount is 72-54m, the reduction in error and its rate of reduction decrease to 0.0022m and 5.96%, respectively. When the reduction amount is 54-36m, the reduction in error and its rate of reduction increase again to 0.0086m and 24.01%, respectively. This indicates that the impact of different point spacing reduction amounts on MAE fluctuates significantly. Figure 7 As shown in Figure (b), when the reduction amount is 90-72m, the reduction in RMSE error and the rate of reduction are relatively small, at 0.0075m and 20.24%, respectively. When the reduction amount is 72-54m, the reduction in error and the rate of reduction are as high as 0.0082m and 21.04%, respectively. This indicates that the reduction in RMSE error and the rate of reduction fluctuate less compared to MAE, and maintain a good error reduction effect in most stages of the point spacing reduction.

[0095] To further verify the accuracy of the mining area subsidence model constructed using the fusion technology, 20 measuring points evenly distributed throughout the modeling area were selected in a mining area in Shanxi Province. The errors in the X / Y / Z directions between the model's measured 3D coordinates and the actual measured coordinates were compared. RMSE and MAE were used as error test indicators. The test results are shown in Table 1. Table 1 shows that in the X / Y direction, the MAE of the model's measured coordinates and the actual measured coordinates are only 11.2 mm and 7.8 mm, respectively, and the RMSE is only 13.5 mm and 9.2 mm, respectively. In the Z direction, the MAE is only 5.6 mm, and the RMSE is only 6.8 mm. This indicates that the fusion technology performs excellently in the measurement accuracy of 3D spatial coordinates, meeting the millimeter-level dynamic monitoring requirements in the mining surface subsidence monitoring specifications.

[0096] Table 2. Errors between model-measured 3D coordinates and field-measured coordinates

[0097]

[0098] To visually demonstrate the accuracy advantages of data fusion, error comparison curves in the Z-direction are plotted for single UAV photogrammetry, single LiDAR technology, and fusion technology, as shown below. Figure 8 As shown. By Figure 8 The results show that the fusion technique used in this study has an error value in the Z direction ranging from a minimum of 3.2 mm to a maximum of 8.4 mm, with an average of 6.8 mm. In contrast, the average error value of a single UAV photogrammetry technique is as high as 20.3 mm, and that of a single LiDAR technique is as high as 16.8 mm, both inferior to the fusion technique proposed in this study. This indicates that the modeling accuracy of fusing UAV photogrammetry and LiDAR techniques is higher.

[0099] To verify the accuracy improvement effect of combining UAV photogrammetry and LiDAR technology in mine subsidence monitoring, a comparative experiment was conducted. The study compared UAV photogrammetry alone with LiDAR technology alone. Subsidence monitoring results for each mine area were obtained through a unified data processing workflow. In the field verification phase, control points were established along the strike and dip directions at the working face of a mine in Shanxi Province, and periodic subsidence observations were conducted using leveling techniques as reference values. A similar method was used to establish monitoring stations in a mine in Xinjiang Province, and high-precision subsidence monitoring was conducted using a total station, taking into account the terrain conditions, as reference values.

[0100] The subsidence monitoring results for each technology are shown in Figures 9(a) and (b). Figure 9 Figure (a) in the middle and Figure 9 In Figure (b), the green area represents the region with a settlement of less than 50 mm, and the settlement measurement results from different technologies are not significantly different. Figure 9 As shown in Figure (a), in a mining area in Shanxi, the subsidence monitoring results of UAV photogrammetry + LiDAR technology and leveling measurement show a similar trend, with relatively small differences in subsidence amounts. However, the subsidence amounts obtained by UAV photogrammetry and LiDAR technology alone differ significantly from the leveling measurement results outside the green area. At the 18th monitoring point, the subsidence amount obtained by UAV photogrammetry was -92.14 mm, while the leveling measurement result was -156.38 mm, a difference of 64.24 mm. Meanwhile, the LiDAR measurement result was -75.34 mm, a difference of 81.04 mm from the leveling measurement result. The subsidence amount obtained by UAV photogrammetry + LiDAR technology was -157.93 mm, a difference of only 1.55 mm from the leveling measurement result. Figure 9 As shown in Figure (b), in a mining area in Xinjiang, the subsidence monitoring results of UAV photogrammetry combined with LiDAR technology are not significantly different from the total station measurement results, and the trends are basically consistent. However, the monitoring results of UAV photogrammetry combined with LiDAR technology alone differ significantly from the total station measurement values. This indicates that the technology combining UAV photogrammetry and LiDAR has higher accuracy in subsidence monitoring.

[0101] To investigate the differences in detection accuracy among different technologies, the subsidence detection data from each technology were subtracted from the data from field observation stations to obtain the error results. Figure 10 As shown in Figure (a), in a mining area in Shanxi, the MAE value of the UAV photogrammetry + LiDAR technology compared to the measured values ​​is only 7.95 mm, and the RMSE value is only 4.96 mm. In contrast, the MAE and RMSE values ​​of the UAV photogrammetry technology are as high as 30.25 mm and 20.84 mm, respectively. The MAE and RMSE values ​​of the LiDAR technology are as high as 33.21 mm and 23.14 mm, respectively. Figure 10 As shown in Figure (b), in a mining area in Xinjiang, the MAE and RMSE values ​​of the UAV photogrammetry + LiDAR technology were only 3.52 mm and 1.84 mm, respectively, significantly lower than those of the individual monitoring technologies. This indicates that the fusion monitoring technology is more effective in monitoring subsidence in mining areas. Due to the large area of ​​the subsidence zone, limitations in manpower, equipment, and complex terrain prevent on-site measurements at every location. The DEM model constructed in this study covers the entire subsidence zone, and the above verification shows that its accuracy in calculating subsidence values ​​at known measurement points has been fully validated. Therefore, for areas within the subsidence zone that have not been measured in person, the surface subsidence value at any point can be obtained directly by measuring on the model without additional on-site measurements. This method not only overcomes the limitations of traditional single-point monitoring and achieves large-scale dynamic monitoring, but also significantly improves monitoring efficiency.

[0102] Finally, a scatter density plot was plotted, combining the monitoring results from UAV photogrammetry and LiDAR technology with data from field observation stations. The results are as follows: Figure 11 Figure (a) and Figure 11 As shown in Figure (b), the Pearson correlation coefficients (r) for the two mining areas are as high as 0.9652 and 0.9124, respectively. This indicates that the monitoring values ​​obtained by fusing UAV photogrammetry and LiDAR technology in both mining areas show a high degree of agreement with the data from the field observation stations. Furthermore, it can be visually observed that most of the scattered points are closely distributed near the fitted line, indicating that the monitoring results obtained by the fusion technology are numerically close to the data from the field observation stations. Although a small number of scattered points deviate from the fitted line, this does not affect the overall high correlation between the two.

[0103] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring subsidence in mining areas based on UAV photogrammetry and LiDAR technology, characterized in that: Includes the following steps: S1. Use drones to take aerial photos of the mining area and obtain surface image data of the mining area; S2. The acquired image data is processed using the Structure for Motion Restoration (SfM) algorithm to generate a Digital Surface Model (DSM), an Orthophoto Map (DOM), and a Digital Elevation Model (DEM). S3. Use an airborne LiDAR system to scan the mining area and obtain three-dimensional point cloud data of the surface of the mining area; S4. Differential GNSS technology is used to correct and improve the accuracy of LiDAR point cloud data; S5. Register and fuse the digital elevation model (DEM) generated by UAV photogrammetry with the calibrated LiDAR point cloud data in a unified coordinate system; S6. Process and analyze the fused data to construct a mining area subsidence model and realize dynamic monitoring of surface subsidence in the mining area; In step 4, when using differential GNSS technology to correct and improve the accuracy of LiDAR point cloud data, two acquisition areas of the same size are selected within the airborne LiDAR acquisition area according to preset rules. In each acquisition area, differential GNSS technology is used to acquire multiple GNSS point data. The number of multiple GNSS point data is no less than a preset threshold. The monitoring points corresponding to the GNSS point data will be used as control points for subsequent correction and accuracy improvement of LiDAR point cloud data. Step 5 specifically includes: (1) Acquire UAV imagery and LiDAR point cloud data; (2) Perform feature extraction on the image and point cloud, including extracting image feature contours and extracting point cloud feature contours; (3) Based on a unified coordinate system, feature-level registration is performed on the image and point cloud to obtain the image and point cloud data registration results; (4) The texture information of the UAV image is fused into the LiDAR point cloud to obtain point cloud data with true color; (5) Generate three-dimensional and two-dimensional display forms of true color point cloud data to provide multi-dimensional data support for mining area subsidence monitoring; (6) The digital elevation model (DEM) generated by UAV photogrammetry is registered and fused with the corrected LiDAR point cloud data in a unified coordinate system to obtain fused data.

2. The method for monitoring mine subsidence based on UAV photogrammetry and LiDAR technology as described in claim 1, characterized in that: In step 1, the drone flies and takes pictures of the mining area according to the preset route and parameters. The preset flight path and parameters include flight altitude, lateral overlap and directional overlap, with the flight altitude being 255m, lateral overlap being 60% and directional overlap being 80%.

3. The method for monitoring mine subsidence based on UAV photogrammetry and LiDAR technology as described in claim 1, characterized in that: In step 3, the steps for acquiring 3D point cloud data are as follows: (1) The laser rangefinder on the UAV emits laser pulses and receives reflected lasers; (2) Using the pulse ranging formula Calculate the distance d between the laser rangefinder and the target, where c represents the speed of light and t represents the total time taken for the laser pulse to travel to and from the target. (3) Taking the center of the laser scanner on the UAV as the starting point, the azimuth angle (α,β,γ) of the starting point is measured by the attitude measurement unit (IMU) on the UAV, and the distance vector r between the starting point and the target point is obtained by combining differential GNSS, and the coordinates of the target point are calculated. (4) Repeat the above steps to collect a large amount of target point coordinate data; (5) The collected target point coordinate data is preprocessed to generate a standard LAS point cloud data file, thereby obtaining the three-dimensional point cloud data of the mining area surface.

4. The method for monitoring mine subsidence based on UAV photogrammetry and LiDAR technology as described in claim 1, characterized in that: Before using differential GNSS technology to correct and improve the accuracy of LiDAR point cloud data, the acquired data needs to be preprocessed. The data preprocessing is as follows: Flight preparation phase: Conduct route planning and determine parameters such as flight altitude, lateral overlap, and forward overlap; Data collection phase: Aerial photography flights are used to acquire laser ranging data, IMU data, airborne GPS data, and downloaded base station data. Among them, airborne GPS data and IMU data are used for GPS / IMU joint calculation to obtain trajectory data, base station data are used for differential GPS processing, and laser calibration files also need to be downloaded. Data processing stage: During trajectory calculation, ensure that the position accuracy in each direction is less than 0.02m and the attitude accuracy is less than 3°; use trajectory data to calculate point cloud and perform data quality checks; if layered, perform feature extraction and flight strip adjustment in sequence; if not layered, perform point cloud coloring, coordinate transformation, and point cloud accuracy checks on the laser ranging data, and finally generate a standard point cloud LAS format file.

5. The method for monitoring mine subsidence based on UAV photogrammetry and LiDAR technology as described in claim 1, characterized in that: Step 6 specifically involves: (1) Extract features from the fused data to identify the subsidence areas and degree of subsidence on the surface of the mining area; (2) Based on the fused data and feature analysis results, a subsidence model of the mining area is constructed; (3) Using the constructed mining area subsidence model, the surface subsidence of the mining area is dynamically monitored, including the monitoring of subsidence range, subsidence depth and subsidence rate; (4) Evaluate the monitoring results, generate a subsidence monitoring report, and issue an early warning when the subsidence exceeds the preset threshold.

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