Application of Unmanned Aerial Vehicle 3D Reconstruction for Quantitative Measurements of the Tailing Pond Dry Beach
Source abstract
Abstract
Dry beach parameters are important indicators for measuring the safe operation of tailings ponds. The Unmanned Aerial Vehicle (UAV) is widely used in construction, disaster monitoring, and agriculture. In this paper, a new semi-automatic method is proposed that measures the length and slope of the dry beach in a tailings pond using the point cloud obtained through the 3D reconstruction of UAVs. The programs of dry beach boundary identification, point cloud profile denoising, dry beach length, and slope measurement were established. The proposed method was applied to a case study to monitor the length and slope of a tailings pond dry beach. The results show that the maximum error of the dry beach length measurement is 2.25%, and the error of the slope measurement is 2.67%. The error is within a reasonable range, which can be accepted in practical applications, demonstrating the reliability and accuracy of the proposed method. Compared with traditional methods and image-based dry-beach-monitoring methods, the point cloud model provides comprehensive coverage of the entire dry beach. It enables the measurement of the length and slope of the dry beach at any point while offering an intuitive representation of its surface morphology and topographic features.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · Water Quality Monitoring Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha 410083, China
National Engineering Research Center of High-Speed Railway Construction Technology, Changsha 410083, China
Funding
National Natural Science Foundation of China · Natural Resources Science and Technology Project of Hunan Province · Science and Technology Research and Development Project of China Railway Co., Ltd.
References (43)
Show source-provided references
1. Scarce mineral resources: Extraction, consumption and limits of sustainability10.1016/j.resconrec.2021.105511 ↗
2. Challenges to global mineral resource security and options for future supply10.1144/SP393.13 ↗
3. Metallic Mineral Resources in the Twenty-First Century. I. Historical Extraction Trends and Expected Demand
4. Some developments and new insights for environmental sustainability and disaster control of tailings dam10.1016/j.jclepro.2020.122270 ↗
5. Leaching of heavy metals from abandoned mine tailings brought by precipitation and the associated environmental impact10.1016/j.scitotenv.2019.133893 ↗
6. Why have so many tailings dams failed in recent years?10.1016/j.resourpol.2019.101412 ↗
7. A new look at the statistics of tailings dam failures10.1016/j.enggeo.2022.106657 ↗
8. Floods from tailings dam failures10.1016/j.jhazmat.2007.09.110 ↗
9. Early Warning of Heavy Metal Pollution after Tailing Pond Failure Accident10.1007/s12583-020-1103-6 ↗
10. Global-scale impact analysis of mine tailings dam failures: 1915–202010.1016/j.gloenvcha.2021.102361 ↗
11. Changes in land use and land cover as a result of the failure of a mining tailings dam in Mariana, MG, Brazil10.1016/j.landusepol.2017.10.026 ↗
12. Preliminary reflections on the failure of the Brumadinho tailings dam in January 201910.1680/jdare.19.00004 ↗
13. Tailings Subaerial and Subaqueous Deposition and Beach Slope Modeling10.1061/(ASCE)GT.1943-5606.0001208 ↗
14. Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology10.1016/j.jsg.2014.10.007 ↗
15. Cirillo, D., Cerritelli, F., Agostini, S., Bello, S., Lavecchia, G., and Brozzetti, F. (2022). Integrating Post-Processing Kinematic (PPK)–Structure-from-Motion (SfM) with Unmanned Aerial Vehicle (UAV) Photogrammetry and Digital Field Mapping for Structural Geological Analysis. ISPRS Int. J. Geo-Inf., 11.10.3390/ijgi11080437 ↗
16. Harishidayat, D., Al-Shuhail, A., Randazzo, G., Lanza, S., and Muzirafuti, A. (2022). Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques. Appl. Sci., 12.10.3390/app12199611 ↗
17. Jamšek Rupnik, P., Atanackov, J., Horn, B., Mušič, B., Zajc, M., Grützner, C., Ustaszewski, K., Tsukamoto, S., Novak, M., and Milanič, B. (2024). Revealing Subtle Active Tectonic Deformation: Integrating Lidar, Photogrammetry, Field Mapping, and Geophysical Surveys to Assess the Late Quaternary Activity of the Sava Fault (Southern Alps, Slovenia). Remote Sens., 16.10.3390/rs16091490 ↗
18. Geomorphologic mapping and analysis of fault activities along the northern margin of the Yanfan Basin, China10.1016/j.geomorph.2024.109568 ↗
19. Kinematic analysis of sea cliff stability using UAV photogrammetry10.1080/01431161.2016.1275061 ↗
20. Cirillo, D., Zappa, M., Tangari, A.C., Brozzetti, F., and Ietto, F. (2024). Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area. Drones, 8.10.3390/drones8010031 ↗
21. Rapid seismic risk assessment of bridges using UAV aerial photogrammetry10.1016/j.engstruct.2023.115589 ↗
22. Whole-process survey of a landslide based on UAV tilt photography and 3D reconstruction and spatial analysis techniques10.1007/s10346-024-02396-5 ↗
23. Ge, Y., Guo, B., Zha, P., Jiang, S., Jiang, Z., and Li, D. (2024). 3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision. Remote Sens., 16.10.3390/rs16030473 ↗
24. Luhmann, T., Chizhova, M., and Gorkovchuk, D. (2020). Fusion of UAV and Terrestrial Photogrammetry with Laser Scanning for 3D Reconstruction of Historic Churches in Georgia. Drones, 4.10.3390/drones4030053 ↗
25. A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images10.1016/j.agrformet.2020.108231 ↗
26. 3D reconstruction and characterization of cotton bolls in situ based on UAV technology10.1016/j.isprsjprs.2024.01.027 ↗
27. Jarahizadeh, S., and Salehi, B. (2024). A Comparative Analysis of UAV Photogrammetric Software Performance for Forest 3D Modeling: A Case Study Using AgiSoft Photoscan, PIX4DMapper, and DJI Terra. Sensors, 24.10.3390/s24010286 ↗
28. Gomez, J.A., Kamran-Pishhesari, A., and Sattarvand, J. (2024). Automated Rill Erosion Detection in Tailing Dams Using UAV Imagery and Machine Learning. Arab. J. Sci. Eng., 1–16.10.1007/s13369-024-09550-0 ↗
29. The CNRIEEEMC: A communication-navigation-remote sensing-integrated ecological environment emergency monitoring chain for tailings areas
30. Wang, J., and Wu, Y. (2022, January 23–25). Research on Dry Beach Length Measurement Based on Improved Mask R-CNN Algorithm. Proceedings of the 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi’an, China.10.1109/ICCNEA57056.2022.00012 ↗
31. Duan, Z., Tian, Y., Li, Q., Liu, G., Cui, X., and Zhang, S. (2024). Deep Learning-Based Calculation Method for the Dry Beach Length in Tailing Ponds Using Satellite Images. Appl. Sci., 14.10.3390/app14177560 ↗
32. Real-Time Dry Beach Length Monitoring for Tailings Dams Based on Visual Measurement10.1155/2013/935371 ↗
33. Huang, Q., Li, Q., Wang, Y., and Zhang, J. (2015, January 19–20). Dry beach of tailings dam length measurement based on waterline recognition. Proceedings of the 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.10.1109/IAEAC.2015.7428604 ↗
34. Pushing the envelope of modern methods for bundle adjustment10.1109/TPAMI.2011.256 ↗
35. Modeling the world from Internet photo collections10.1007/s11263-007-0107-3 ↗
36. Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment10.1007/s11263-009-0232-2 ↗
37. Video-based, real-time multi-view stereo10.1016/j.imavis.2011.01.006 ↗
38. Seeded region growing10.1109/34.295913 ↗
39. Region growing method for the analysis of functional MRI data10.1016/S1053-8119(03)00352-5 ↗
40. Optical symbolic substitution: Edge detection using Prewitt, Sobel, and Roberts operators10.1364/AO.28.004644 ↗
41. Design of an image edge detection filter using the Sobel operator10.1109/4.996 ↗
42. Parameter optimization for point clouds denoising based on no-reference quality assessment10.1016/j.measurement.2023.112592 ↗
43. Wang, L., Chen, Y., and Xu, H. (2024). Point Cloud Denoising in Outdoor Real-World Scenes Based on Measurable Segmentation. Remote Sens., 16.10.3390/rs16132347 ↗