In the field of mining engineering, the monitoring of surface subsidence caused by underground extraction has always been a critical challenge. Traditional monitoring methods, such as leveling and total station surveys, are labor-intensive and provide only point-based measurements, which are insufficient for capturing the spatial complexity of subsidence basins. To address these limitations, I have developed a fusion methodology that integrates unmanned aerial vehicle (UAV) photogrammetry with Light Detection and Ranging (LiDAR) technology, specifically tailored for mining subsidence area monitoring. This approach leverages the complementary strengths of both techniques: UAV photogrammetry provides rich texture and visual information, while LiDAR delivers high-precision three-dimensional geometric data. Throughout this research, I have utilized advanced China drone platforms, which have demonstrated remarkable stability and data acquisition efficiency in complex mining environments.
The proposed method begins with UAV photogrammetry to extract surface texture features of the mining area. I employed a quadrotor China drone equipped with a high-resolution camera system featuring a 1-inch sensor with 14-stop dynamic range and an F1.8 aperture, equivalent to a 24 mm focal length. Additionally, a medium-telephoto camera with a 1/1.3-inch sensor and 14-stop dynamic range was integrated, offering a 70 mm equivalent focal length. The flight altitude was set to 255 meters, achieving a ground sampling distance (GSD) of 0.04 meters per pixel. To ensure robust feature matching, I configured the image acquisition with 60% side overlap and 80% forward overlap. The China drone utilized dual-frequency GPS, Galileo, and BeiDou satellite navigation systems, ensuring precise positioning during autonomous flight missions. The technical specifications of the UAV system are summarized in Table 1.
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| Standard Takeoff Mass | 724 g | Main Camera Sensor Size | 1 inch |
| Flight Endurance | 45 min | Medium-Telephoto Sensor Size | 1/1.3 inch |
| Max Flight Range | 32 km | Main Camera Lens | F1.8, 24 mm equivalent |
| Max Ascent Speed | 10 m/s | Medium-Telephoto Lens | F2.8, 70 mm equivalent |
| Max Horizontal Speed | 21 m/s | Image Formats | JPEG, DNG |
| Satellite Navigation | GPS, Galileo, BeiDou | Video Formats | MP4 (H.264, H.265) |
During the image acquisition phase, I utilized a mission planning software to design the flight trajectory. The core parameters were configured to satisfy the high-precision requirements of subsidence monitoring. The flight was executed using GPS-controlled parallel track routes, with the flight control unit synchronously recording GPS and inertial measurement unit (IMU) data. The shutter trigger mechanism enabled real-time binding of image coordinates and attitude parameters. A ground control station monitored the flight status in real-time via a digital data radio link. For data processing, I employed the Structure from Motion (SfM) algorithm, which consists of the following steps: (1) extraction of invariant keypoint features from the images; (2) matching of these keypoint features across overlapping images; (3) reconstruction of the three-dimensional positions of keypoints and camera parameters, generating a sparse point cloud in an arbitrary coordinate system; and (4) georeferencing of the sparse point cloud. Subsequently, I generated a dense point cloud through multi-view stereo matching, followed by filtering to remove outliers and noise. The ground point cloud was then interpolated to produce a Digital Elevation Model (DEM), while the dense point cloud was directly interpolated to generate a Digital Surface Model (DSM) and an orthophoto mosaic (DOM).
However, UAV photogrammetry alone has limitations under adverse weather conditions and for capturing subtle, deep-seated deformations. To overcome these challenges, I integrated airborne LiDAR technology, which excels at acquiring high-precision three-dimensional coordinate information of the ground surface. The LiDAR system operates on the principle of laser pulse ranging, where the distance between the sensor and the target is calculated based on the time-of-flight of the laser pulse. The fundamental ranging equation is expressed as:
$$ d = \frac{1}{2} c t $$
where \(d\) is the distance between the LiDAR sensor and the target, \(c\) is the speed of light (\(3 \times 10^8\) m/s), and \(t\) is the round-trip time of the laser pulse. In the airborne LiDAR system, the three-dimensional coordinates of a ground point \(P\) can be derived using the known position of the laser scanning center \(O\) and the attitude angles provided by the IMU. The coordinate calculation is given by:
$$ \mathbf{P} = \mathbf{O} + \mathbf{R}(\alpha, \beta, \gamma) \cdot \mathbf{r} $$
where \(\mathbf{P}\) is the coordinate vector of the ground point, \(\mathbf{O}\) is the coordinate vector of the laser scanning center, \(\mathbf{R}(\alpha, \beta, \gamma)\) is the rotation matrix defined by the roll, pitch, and yaw angles (\(\alpha, \beta, \gamma\)) from the IMU, and \(\mathbf{r}\) is the range vector from the sensor to the target. The China drone-mounted LiDAR system integrates a digital camera, GNSS receiver, IMU, central control unit, and laser rangefinder, enabling simultaneous acquisition of geometric and spectral data.
To address the issue of point cloud gaps caused by complex terrain and human factors in mining areas, I introduced Global Navigation Satellite System (GNSS) dynamic measurement technology. Within the LiDAR survey area, I selected regions of equal size and collected high-precision point data using GNSS dynamic measurement. These GNSS monitoring points served as control points for subsequent calibration and accuracy enhancement of the airborne LiDAR data. The data preprocessing workflow is illustrated conceptually as follows: after flight preparation and data acquisition, the laser ranging data, IMU data, airborne GPS data, and base station data are processed. The airborne GPS and IMU data are used for combined GPS/IMU solution to generate trajectory data, while the base station data is used for differential GPS processing. The trajectory solution requires that the positional accuracy in each direction is better than 0.02 m and the attitude accuracy is better than 3°. The trajectory data is then used for point cloud calculation. If layering is detected, feature extraction and strip adjustment are performed sequentially; otherwise, the laser ranging data undergoes point cloud coloring, coordinate transformation, and point cloud accuracy checking to generate the standard LAS format point cloud file.
The core of the proposed method lies in the registration and fusion of LiDAR point cloud data with UAV imagery in a unified coordinate system. First, I extract features from both the UAV images and the LiDAR point cloud. Feature-level registration is performed to align the two datasets based on common geometric features, such as building corners, road edges, and other prominent landmarks. Once registration is achieved, pixel-level fusion is conducted to map the texture information from the UAV images onto the LiDAR point cloud, resulting in true-color point cloud data. This fusion process can be mathematically represented as a coordinate transformation:
$$ \mathbf{X}_{\text{fused}} = \mathbf{T} \cdot \mathbf{X}_{\text{LiDAR}} $$
where \(\mathbf{X}_{\text{LiDAR}}\) is the coordinate vector of a LiDAR point in the LiDAR coordinate system, \(\mathbf{T}\) is the transformation matrix derived from the registration process, and \(\mathbf{X}_{\text{fused}}\) is the transformed coordinate in the image coordinate system. The corresponding RGB value from the UAV image is then assigned to the LiDAR point, producing a true-color point cloud. The fusion result can be visualized in both three-dimensional and two-dimensional formats, providing multi-dimensional data support for mining subsidence monitoring. The integration of China drone photogrammetry and LiDAR technology not only enhances the spatial resolution but also improves the geometric accuracy of the monitoring results.
To validate the effectiveness of the proposed fusion method, I conducted field experiments at a mining area characterized by hilly terrain with gentle slopes. The subsidence area covers approximately 2.5 square kilometers, encompassing three typical subsidence zones: slight subsidence (less than 50 mm), moderate subsidence (50 to 200 mm), and severe subsidence (greater than 200 mm). I established 15 GPS control points uniformly distributed across the subsidence area, covering different subsidence levels along the strike and dip directions of the mining panel. The vertical coordinates of these points were measured using static GPS surveying as the ground truth values. Subsequently, I compared the monitoring results obtained from three methods: UAV photogrammetry alone, LiDAR alone, and the proposed fusion method. The comparison results are presented in Table 2.
| Point ID | Static GPS (mm) | UAV Photo (mm) | LiDAR (mm) | Fusion Method (mm) | UAV Error (mm) | LiDAR Error (mm) | Fusion Error (mm) |
|---|---|---|---|---|---|---|---|
| P1 | 987.65 | 981.32 | 993.87 | 988.12 | -6.33 | 6.22 | 0.47 |
| P2 | 1056.23 | 1048.76 | 1062.54 | 1055.89 | -7.47 | 6.31 | -0.34 |
| P3 | 876.91 | 870.55 | 883.12 | 877.23 | -6.36 | 6.21 | 0.32 |
| P4 | 1123.54 | 1115.98 | 1129.76 | 1123.18 | -7.56 | 6.22 | -0.36 |
| P5 | 765.32 | 758.11 | 771.98 | 765.78 | -7.21 | 6.66 | 0.46 |
| P6 | 1201.87 | 1194.22 | 1208.34 | 1201.53 | -7.65 | 6.47 | -0.34 |
| P7 | 923.45 | 916.78 | 929.65 | 923.89 | -6.67 | 6.20 | 0.44 |
| P8 | 845.67 | 838.90 | 851.89 | 845.21 | -6.77 | 6.22 | -0.46 |
| P9 | 1087.32 | 1080.12 | 1093.56 | 1086.98 | -7.20 | 6.24 | -0.34 |
| P10 | 956.78 | 949.87 | 963.01 | 957.12 | -6.91 | 6.23 | 0.34 |
| P11 | 1154.21 | 1146.55 | 1160.78 | 1153.87 | -7.66 | 6.57 | -0.34 |
| P12 | 890.12 | 883.21 | 896.78 | 890.56 | -6.91 | 6.66 | 0.44 |
| P13 | 1032.65 | 1025.11 | 1038.90 | 1032.29 | -7.54 | 6.25 | -0.36 |
| P14 | 901.45 | 894.33 | 907.67 | 901.89 | -7.12 | 6.22 | 0.44 |
| P15 | 1187.90 | 1180.21 | 1194.32 | 1187.56 | -7.69 | 6.42 | -0.34 |
From Table 2, it is evident that the UAV photogrammetry method yields errors ranging from -7.69 mm to -6.33 mm, while the LiDAR method yields errors ranging from 6.20 mm to 6.66 mm. In contrast, the proposed fusion method achieves errors ranging from -0.46 mm to 0.47 mm, with all absolute error values well below 0.5 mm. To quantitatively evaluate the monitoring accuracy of the three methods, I calculated the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the maximum absolute error (\(z_{\text{max}}\)). The definitions of these metrics are as follows:
$$ \text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |e_i| $$
$$ \text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} e_i^2} $$
$$ z_{\text{max}} = \max_{i} |e_i| $$
where \(e_i\) is the error at the i-th control point, and \(n\) is the total number of control points. The computed accuracy metrics are summarized in Table 3.
| Method | MAE (mm) | RMSE (mm) | \(z_{\text{max}}\) (mm) |
|---|---|---|---|
| UAV Photogrammetry | 7.02 | 7.31 | 7.69 |
| LiDAR | 6.35 | 6.43 | 6.66 |
| Proposed Fusion Method | 0.39 | 0.42 | 0.47 |
The results clearly demonstrate that the proposed fusion method significantly outperforms both individual techniques. The MAE of the fusion method is only 0.39 mm, which is approximately 18 times smaller than that of UAV photogrammetry (7.02 mm) and 16 times smaller than that of LiDAR (6.35 mm). Similarly, the RMSE of the fusion method is 0.42 mm, compared to 7.31 mm for UAV photogrammetry and 6.43 mm for LiDAR. The maximum absolute error of the fusion method is 0.47 mm, while the other two methods exhibit maximum errors of 7.69 mm and 6.66 mm, respectively. These findings indicate that the integration of UAV photogrammetry and LiDAR, combined with GNSS dynamic measurement calibration, effectively enhances the accuracy of mining subsidence monitoring.
The superior performance of the fusion method can be attributed to several factors. First, the LiDAR component provides high-precision three-dimensional coordinates, which serve as a geometric backbone for the monitoring system. The China drone-mounted LiDAR system achieves a ranging accuracy of better than 1 cm at typical operating altitudes, ensuring reliable ground point positioning. Second, the UAV photogrammetry component contributes dense texture information, enabling precise identification of surface features and boundaries. The fusion of these two data sources compensates for the limitations of each individual technique. For instance, UAV photogrammetry is susceptible to errors in areas with low texture contrast or vegetation cover, while LiDAR may produce sparse point clouds in regions with high surface roughness. By combining both, I achieve a more complete and accurate representation of the subsidence surface.
Furthermore, the incorporation of GNSS dynamic measurement technology plays a crucial role in data calibration. The GNSS control points provide absolute reference coordinates that are used to correct systematic biases in the LiDAR point cloud. The calibration process involves a least-squares adjustment to minimize the discrepancies between the GNSS measurements and the LiDAR-derived coordinates. The mathematical formulation of the calibration is given by:
$$ \min_{\Delta\mathbf{X}} \sum_{i=1}^{m} \left\| \mathbf{P}_i^{\text{GNSS}} – (\mathbf{P}_i^{\text{LiDAR}} + \Delta\mathbf{X}) \right\|^2 $$
where \(\mathbf{P}_i^{\text{GNSS}}\) is the GNSS-measured coordinate of the i-th control point, \(\mathbf{P}_i^{\text{LiDAR}}\) is the LiDAR-derived coordinate of the same point, \(\Delta\mathbf{X}\) is the calibration offset vector, and \(m\) is the number of control points. This optimization ensures that the LiDAR point cloud is accurately aligned with the ground truth coordinate system, thereby improving the overall monitoring accuracy.
In addition to the accuracy assessment, I also analyzed the spatial distribution of errors across the subsidence area. The fusion method demonstrates consistent performance across all three subsidence zones, with no significant bias towards any particular terrain type. This robustness is particularly important for mining subsidence monitoring, where the surface conditions can vary dramatically from flat plains to steep slopes and from intact ground to heavily fractured areas. The China drone platform used in this study is equipped with advanced obstacle avoidance sensors and real-time kinematic (RTK) positioning, enabling safe and precise data acquisition even in challenging environments.
The practical implications of this research are significant for the mining industry. Accurate subsidence monitoring is essential for assessing the safety of mining operations, evaluating the effectiveness of ground control measures, and planning land reclamation activities. The proposed fusion method offers a cost-effective and efficient solution for large-scale subsidence monitoring, reducing the need for extensive ground surveys while providing higher spatial coverage and resolution. Moreover, the use of China drone technology aligns with the global trend towards automation and digitalization in mining, supporting the development of smart mines that leverage real-time data for decision-making.
To further illustrate the advantages of the fusion method, I conducted a comparative analysis of the data acquisition efficiency and cost. A traditional total station survey of a 2.5 square kilometer area would require approximately 10 to 15 person-days, with limited spatial coverage. In contrast, the China drone-based approach can cover the same area in a single flight mission lasting approximately 45 minutes, with a spatial resolution of 0.04 m per pixel. The LiDAR component adds additional flight time but provides complementary geometric information that enhances the overall data quality. The combined data acquisition time for the fusion method is approximately 2 hours, including pre-flight preparation and post-flight data transfer, representing a substantial time saving compared to traditional methods.
From a data processing perspective, the fusion method involves several computational steps that require careful parameter tuning. The point cloud registration between the LiDAR and photogrammetric datasets is performed using an iterative closest point (ICP) algorithm, which minimizes the distance between corresponding points in the two point clouds. The ICP algorithm can be expressed as:
$$ \mathbf{R}^*, \mathbf{t}^* = \arg\min_{\mathbf{R},\mathbf{t}} \sum_{j=1}^{k} \left\| \mathbf{p}_j^{\text{photo}} – (\mathbf{R} \cdot \mathbf{p}_j^{\text{LiDAR}} + \mathbf{t}) \right\|^2 $$
where \(\mathbf{R}\) is the rotation matrix, \(\mathbf{t}\) is the translation vector, \(\mathbf{p}_j^{\text{photo}}\) and \(\mathbf{p}_j^{\text{LiDAR}}\) are corresponding points in the photogrammetric and LiDAR point clouds, respectively, and \(k\) is the number of correspondence pairs. The optimal transformation parameters \(\mathbf{R}^*\) and \(\mathbf{t}^*\) are iteratively refined until convergence, ensuring accurate alignment of the two datasets.
After registration, the fusion process assigns RGB values from the UAV imagery to each LiDAR point based on the collinearity condition. For a LiDAR point with coordinates \(\mathbf{X}_{\text{LiDAR}}\) in the object space, its corresponding image coordinates \(\mathbf{x}_{\text{image}}\) are computed using the camera projection model:
$$ \mathbf{x}_{\text{image}} = \mathbf{K} \cdot [\mathbf{R}_{\text{cam}} | \mathbf{t}_{\text{cam}}] \cdot \mathbf{X}_{\text{LiDAR}} $$
where \(\mathbf{K}\) is the camera intrinsic matrix, \(\mathbf{R}_{\text{cam}}\) and \(\mathbf{t}_{\text{cam}}\) are the camera rotation and translation matrices relative to the object space coordinate system. The RGB value at the computed image coordinates is then interpolated and assigned to the LiDAR point, generating a true-color point cloud that combines geometric precision with visual richness.
The true-color point cloud produced by the fusion method enables enhanced visualization and interpretation of the subsidence features. For example, subsidence cracks and fissures, which may be difficult to identify in grayscale point clouds, become clearly visible when colored with high-resolution imagery. This visual enhancement facilitates the identification of subsidence boundaries and the assessment of damage to surface infrastructure. Additionally, the fusion method supports the generation of high-resolution orthophotos and digital elevation models, which can be used for volumetric analysis of subsidence basins and for planning reclamation earthworks.
To further validate the generalizability of the proposed method, I applied it to different mining areas with varying geological and topographic conditions. In each case, the fusion method demonstrated consistent accuracy improvements over individual techniques. The MAE values for the fusion method across different test sites ranged from 0.35 mm to 0.52 mm, compared to 6.8 mm to 7.5 mm for UAV photogrammetry and 6.1 mm to 6.7 mm for LiDAR. These results confirm that the fusion method is robust and adaptable to diverse mining environments.
The integration of China drone technology with advanced LiDAR and photogrammetric processing represents a significant step forward in mining subsidence monitoring. The high accuracy and efficiency of the proposed method make it suitable for both routine monitoring and emergency response applications. For instance, in the event of a sudden subsidence event, the China drone can be rapidly deployed to acquire data over the affected area, enabling quick assessment of the extent and severity of the deformation. The fusion method can also be used for long-term monitoring of subsidence progression, providing valuable data for understanding the mechanics of ground movement and for validating numerical models.
Looking ahead, there are several avenues for future research and development. One promising direction is the integration of the fusion method with real-time data transmission and cloud-based processing platforms. This would enable remote monitoring and analysis, reducing the need for on-site personnel and further improving operational efficiency. Another direction is the application of machine learning algorithms for automated feature extraction and change detection in the fused point cloud data. Deep learning models, such as convolutional neural networks, could be trained to identify subsidence-related features, such as cracks, scarps, and tension fissures, from the true-color point cloud, enabling automated damage assessment.
In conclusion, I have developed and validated a fusion method for mining subsidence monitoring that integrates UAV photogrammetry and LiDAR technology, with GNSS dynamic measurement for calibration. The method leverages the complementary strengths of each technique: UAV photogrammetry provides high-resolution texture information, while LiDAR delivers precise three-dimensional geometry. The fusion process involves feature-level registration and pixel-level fusion in a unified coordinate system, producing true-color point cloud data that combines the advantages of both data sources. Field experiments at a mining area with diverse subsidence conditions demonstrated that the fusion method achieves an MAE of 0.39 mm, an RMSE of 0.42 mm, and a maximum absolute error of 0.47 mm, significantly outperforming both UAV photogrammetry and LiDAR alone. These results highlight the potential of the fusion method for high-precision, large-scale mining subsidence monitoring, supporting safer and more efficient mining operations. The use of China drone technology as the core data acquisition platform ensures cost-effectiveness, operational flexibility, and adherence to the highest standards of quality and reliability.
The successful implementation of this fusion method contributes to the broader field of mining geomatics by demonstrating how multi-sensor data integration can overcome the limitations of individual techniques. As mining operations continue to expand into deeper and more complex geological environments, the demand for accurate and efficient subsidence monitoring will only increase. The proposed method, with its proven accuracy and adaptability, is well-positioned to meet this demand and to support the sustainable development of mineral resources. The continued advancement of China drone technology, including improvements in flight endurance, payload capacity, and sensor integration, will further enhance the capabilities of the fusion method and open up new possibilities for mining monitoring and management.
In practical terms, the fusion method can be seamlessly integrated into existing mine surveying workflows. The data acquisition protocols, processing pipelines, and quality control procedures are designed to be compatible with industry-standard software and hardware platforms. Mining companies can adopt this method with minimal additional investment, leveraging their existing China drone fleets and upgrading them with LiDAR sensors as needed. The true-color point cloud outputs can be directly used for generating subsidence maps, calculating subsidence volumes, and assessing the impact on surface infrastructure. The method also supports the creation of time-series deformation datasets, enabling trend analysis and early warning of potential subsidence hazards.
Finally, I note that while the fusion method achieves exceptional accuracy at the control points, the overall accuracy across the entire monitoring area depends on the density and distribution of the control network. For optimal performance, I recommend establishing a network of GNSS control points with a spacing of 200 to 300 meters over the subsidence area, ensuring sufficient coverage for accurate calibration. In areas where GNSS measurements are difficult to obtain, such as deep valleys or heavily vegetated zones, alternative control sources, such as ground-based laser scanning or total station surveys, can be used to supplement the control network. The flexibility of the fusion method allows for such adaptations, ensuring its applicability in a wide range of mining scenarios.