Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry
Addressing the problem that traditional methods cannot reliably monitor surface subsidence in coal mining, a novel method has been developed for monitoring subsidence in mining areas using time series unmanned aerial vehicle (UAV) photogrammetry in combination with LiDAR. A dynamic subsidence basin...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/18/9374 |
| _version_ | 1797491396808015872 |
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| author | Jibo Liu Xiaoyu Liu Xieyu Lv Bo Wang Xugang Lian |
| author_facet | Jibo Liu Xiaoyu Liu Xieyu Lv Bo Wang Xugang Lian |
| author_sort | Jibo Liu |
| collection | DOAJ |
| description | Addressing the problem that traditional methods cannot reliably monitor surface subsidence in coal mining, a novel method has been developed for monitoring subsidence in mining areas using time series unmanned aerial vehicle (UAV) photogrammetry in combination with LiDAR. A dynamic subsidence basin based on the differential digital elevation model (DEM) was constructed and accuracy of the proposed method was verified, with the uncertainty of the DEM of difference (DoD) being quantified via co-registration of a dense matching point cloud of the time series UAV data. The root mean square error calculated for the monitoring points on the subsidence DEM was typically between 0.2 m and 0.3 m with a minimum of 0.17 m. The relative error between the maximum subsidence value of the extracted profile line on the main section after fitting and the measured maximum subsidence value was not more than 20%, and the minimum value was 0.7%. The accuracy of the UAV based method was at the decimeter level, and high accuracy in monitoring the maximum subsidence value was attained, confirming that an innovative strategy for monitoring mining subsidence was realized. |
| first_indexed | 2024-03-10T00:46:53Z |
| format | Article |
| id | doaj.art-3b86ed57b65f415da8084cc865fe85c3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:46:53Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3b86ed57b65f415da8084cc865fe85c32023-11-23T14:57:37ZengMDPI AGApplied Sciences2076-34172022-09-011218937410.3390/app12189374Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and PhotogrammetryJibo Liu0Xiaoyu Liu1Xieyu Lv2Bo Wang3Xugang Lian4College of Mining Engineering, Guizhou University of Engineering Science, Bijie 551700, ChinaSchool of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaInspection and Testing Center of Shanxi Province (Institute of Standard Metrology of Shanxi Province), Taiyuan 030012, ChinaCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaAddressing the problem that traditional methods cannot reliably monitor surface subsidence in coal mining, a novel method has been developed for monitoring subsidence in mining areas using time series unmanned aerial vehicle (UAV) photogrammetry in combination with LiDAR. A dynamic subsidence basin based on the differential digital elevation model (DEM) was constructed and accuracy of the proposed method was verified, with the uncertainty of the DEM of difference (DoD) being quantified via co-registration of a dense matching point cloud of the time series UAV data. The root mean square error calculated for the monitoring points on the subsidence DEM was typically between 0.2 m and 0.3 m with a minimum of 0.17 m. The relative error between the maximum subsidence value of the extracted profile line on the main section after fitting and the measured maximum subsidence value was not more than 20%, and the minimum value was 0.7%. The accuracy of the UAV based method was at the decimeter level, and high accuracy in monitoring the maximum subsidence value was attained, confirming that an innovative strategy for monitoring mining subsidence was realized.https://www.mdpi.com/2076-3417/12/18/9374subsidence monitoringUAV photogrammetryairborne LiDARco-registrationdynamic subsidence basin |
| spellingShingle | Jibo Liu Xiaoyu Liu Xieyu Lv Bo Wang Xugang Lian Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry Applied Sciences subsidence monitoring UAV photogrammetry airborne LiDAR co-registration dynamic subsidence basin |
| title | Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry |
| title_full | Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry |
| title_fullStr | Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry |
| title_full_unstemmed | Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry |
| title_short | Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry |
| title_sort | novel method for monitoring mining subsidence featuring co registration of uav lidar data and photogrammetry |
| topic | subsidence monitoring UAV photogrammetry airborne LiDAR co-registration dynamic subsidence basin |
| url | https://www.mdpi.com/2076-3417/12/18/9374 |
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