Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry

Liu, Jibo; Liu, Xiaoyu; Lv, Xieyu; Wang, Bo; Lian, Xugang

doi:10.3390/app12189374

Cited by 9 publications

(4 citation statements)

References 21 publications

(21 reference statements)

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“…In soil surface measurement, Foldager et al [75] used a rail-mounted 3D LiDAR to achieve a narrow measurement of 40 mm and monitor changes over time. For mining and earthwork, Carabassa et al [76] monitored soil erosion using a drone equipped with a LiDAR sensor, and Liu et al [77] applied an aerial-borne LiDAR approach to monitor mining. In the context of stockpile monitoring and estimation, LiDAR sensors can be applied in various ways, such as being attached to a drone, rail-mounted, tripod-mounted, or handheld by an operator (Figure 6).…”

Section: Lidar Surveyingmentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

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Section: Lidar Surveyingmentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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“…Comparing digital surface models of lidar and structure from motion (SfM) for three land cover types, UAV-SfM DSMs have an average elevation of 0.4 m lower than lidar DSMs (Liao et al, 2021). In a study by Liu et al (2022), the evaluation of UAV-SfM time-series data using airborne lidar data revealed that the standard deviation of the M3C2 distance ranged from 0.14 to 0.19 m, with a difference in distance between the two DoDs exceeding 0.3 m. However, there is a lack of systematic studies that compare the two techniques in the specific context of mining-induced surface subsidence.…”

Section: Introductionmentioning

confidence: 99%

“…In a study by Liu et al. (2022), the evaluation of UAV‐SfM time‐series data using airborne lidar data revealed that the standard deviation of the M3C2 distance ranged from 0.14 to 0.19 m, with a difference in distance between the two DoDs exceeding 0.3 m. However, there is a lack of systematic studies that compare the two techniques in the specific context of mining‐induced surface subsidence.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of surface deformation monitoring in a mining area based on UAV‐lidar and UAV photogrammetry

Zhan,

Zhang,

Wang

et al. 2024

The Photogrammetric Record

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Unmanned aerial vehicle light detection and ranging (UAV‐lidar) and unmanned aerial vehicle (UAV) photogrammetry are currently commonly used surface monitoring technologies. Previous studies have used the two technologies interchangeably and ignored their correlation, or only compared them on a single product. However, there are few quantitative assessments of the differences between these two techniques in monitoring surface deformation and prediction of their application prospects. Therefore, the paper compared the differences between the digital elevation model (DEM) and subsidence basins obtained by the two techniques using Gaussian analysis. The results indicate that the surface DEMs obtained by both the techniques exhibit a high degree of similarity. The statistical analysis of the difference values in the z direction between the two DEMs follows a Gaussian distribution with a standard deviation of less than 0.36 m. When comparing the surface subsidence values monitored by the two techniques, it was found that UAV‐lidar was more sensitive to small‐scale deformation, with a difference range of 0.23–0.44 m compared to photogrammetry. The conclusion provides valuable information regarding the utilisation of multisource monitoring data.

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“…Mining subsidence was traditionally monitored using geodetic methods, including leveling and Global Positioning System (GPS), and occasionally differential analysis using Digital Elevation Model (DEM) derived from Light Detection and Ranging (LiDAR) (Zhu et al, 2020;Liu et al, 2022). Spaceborne Differential Synthetic Aperture Radar Interferometry (DInSAR) has been effective to monitor ground surface displacement with the advantages of high accuracy, wide coverage, and independence of weather.…”

Section: Introductionmentioning

confidence: 99%

Ground subsidence associated with mining activity in the Ningdong coal base area, northwestern China revealed by InSAR time series analysis

Tang

Wang

et al. 2023

Front. Earth Sci.

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Ningdong coal base area located in northwestern China is one of the largest coal-producing bases in China. The aim of this work is to investigate a regional-scale mining subsidence over the Ningdong coal base area, by using both conventional and advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methods. Fifteen L-band SAR images from ALOS-2 satellite and 102 C-band images from Sentinel-1A satellite spanning between November 2014 and July 2019 were used for the analysis. To increase the spatial extent of the displacement signal because of decorrelated effects, we modified the traditional Small Baseline Subset (SBAS) method to incorporate the coherence into the inverse problem, hereafter we call it coherence-based SBAS method. Instead of excluding decorrelated pixels present in the interferograms, we keep all the pixels in the time series analysis and down-weighted the decorrelated pixels with coherence. We performed the coherence-based SBAS method to both the two SAR datasets to obtain the subsidence rate maps and displacement time-series over the mining areas, and compared the results with that from the traditional stacking InSAR method. We evaluated the effectiveness of L-band and C-band DInSAR for monitoring mining subsidence by comparing differential interferograms and displacements derived from SBAS method between ALOS-2 and Sentinel-1A data. Compared to C-band, L-band SAR are less affected by phase aliasing due to large displacement gradients. The most significant subsidence was found at Maliantai mine with −264 mm/year detected by SBAS method from Sentinel-1 data. We validated the InSAR displacement accuracy by comparing both ALOS-2 and Sentinel-1 results with 18 GPS stations above five active mining regions. The average RMSE between InSAR and GPS measurements is 28.4 mm for Sentinel-1 data and 21 mm for ALOS-2 data. Our results demonstrate that the combined exploitation of L-band and C-band SAR data through both conventional and advanced DInSAR methods could be crucial to monitor ground subsidence in mining areas, which provides insights into subsidence dynamics and determine the characteristic surface response to longwall advance.

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Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry

Cited by 9 publications

References 21 publications

Stockpile Volume Estimation in Open and Confined Environments: A Review

Stockpile Volume Estimation in Open and Confined Environments: A Review

Comparative analysis of surface deformation monitoring in a mining area based on UAV‐lidar and UAV photogrammetry

Ground subsidence associated with mining activity in the Ningdong coal base area, northwestern China revealed by InSAR time series analysis

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