Mining of Mineral Deposits

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Methods of mapping the lands disturbed by mining operations and accuracy of cartographic images obtained from Unmanned Aerial Vehicles: A review

Ada Zuska1, Alla Goychuk1, Valery Riabchii1, Vladyslav Riabchii1

1Dnipro University of Technology, Dnipro, Ukraine


Min. miner. depos. 2022, 16(1):58-67


https://doi.org/10.33271/mining16.01.058

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      ABSTRACT

      Purpose. Analyzing the land disturbance consequences caused by surface mining operations and methods for mapping these lands, as well as studying the accuracy of point coordinates of digital images obtained from materials of aerial photographic surveys using Unmanned Aerial Vehicles (UAVs). Performing a quantitative assessment of the Root Mean Square Error (RMSE) of point coordinates on cartographic images and determining the dependences of the RMSE of point coordinates on the photogrammetric parameters.

      Methods. The review of previous research publications within the framework of the presented subject is performed in the following sequence: analysis of ecosystem disbalance as a result of surface mining operations; based on previous studies, collecting the data for quantitative assessment of accuracy in the form of RMSE of point coordinates on cartographic images obtained from the materials of aerial photographic survey using UAVs; statistical study of the relationship between the RMSE and photographic survey parameters.

      Findings. The methods for mapping the disturbed lands to return them to their natural state after the consequences of surface mining operations are presented, based on a review of previous research publications on the subject of the work. According to the previous studies, the RMSE of point coordinates of cartographic images has been systematized, and, based on this, the accuracy of topographic plans has been determined for them. Statistical studies of the relationship between the quantitative assessment of the RMSE (xy) and RMSE (z) accuracy in relation to the photographic survey parameters have been performed. In addition, the scattering diagrams of the correlation dependence and the range of RMSE relative frequency have been presented.

      Originality. Based on a critical analysis of previous studies on the lack of quantitative accuracy regulation of cartographic images obtained from aerial photographic survey using UAVs, the RMSE systematics has been performed in terms of the photographic survey height. Based on this, the accuracy of topographic plans, the relative frequency of horizontal and vertical distribution of errors, the mean value and the root mean square error (σ) have been determined.

      Practical implications. The systematics of the RMSE values of cartographic image point coordinates for certain photographic survey parameters and the scale of topographic images makes it possible to take this into account in the project of aerial photographic survey using UAVs of lands for various purposes, as well as to choose the height and photographic equipment according to the required accuracy.

      Keywords: surface mining, cartographic image, aerial photographic survey using UAVs, ground control points, root mean square error, orthophotomap accuracy


      REFERENCES

  1. Malanchuk, Z., Korniyenko, V., Malanchuk, Y., & Khrystyuk, A. (2016). Results of experimental studies of amber extraction by hydromechanical method in Ukraine. Eastern-European Journal of Enterprise Technologies, 3(10(81)), 24-28. https://doi.org/10.15587/1729-4061.2016.72404
  2. Malanchuk, Z., Moshynskyi, V., Malanchuk, Y., & Korniienko, V. (2018). Physico-Mechanical and Chemical Characteristics of Amber. Solid State Phenomena, (277), 80-89. https://doi.org/10.4028/www.scientific.net/ssp.277.80
  3. Malanchuk, Z., Korniienko, V., Malanchuk, Y., & Moshynskyi, V. (2019). Analyzing vibration effect on amber buoying up velocity. E3S Web of Conferences, (123), 01018. https://doi.org/10.1051/e3sconf/201912301018
  4. Ivanov, Ye.A. (2019). Analiz ekolohichnoi sytuatsii u raionakh nezakonnoho vydobuvannia burshtynu. Nadrokorystuvannia v Ukraini. Perspektyvy Investuvannia, 101-107.
  5. Malanchuk, Z., Korniyenko, V., Malanchuk, Y., Khrystyuk, A., & Kozyar, M. (2020). Identification of the process of hydromechanical extraction of amber. E3S Web of Conferences, (166), 02008. https://doi.org/10.1051/e3sconf/202016602008
  6. Gurung, K., Yang, J., & Fang, L. (2018). Assessing ecosystem services from the forestry-based reclamation of surface mined areas in the north fork of the Kentucky river watershed. Forests, 9(10), 652. https://doi.org/10.3390/f9100652
  7. Li, X., Barton, C., & Yang, J. (2018). Valuing the environmental benefits from reforestation on reclaimed surface mines in Appalachia. Journal American Society of Mining and Reclamation, 7(1), 1-29. https://doi.org/10.21000/JASMR180100029
  8. Cinar, N.C., & Ocalir, E.V. (2019). A reclamation model for post-mining marble quarries. Gazi University Journal of Science, 32(3), 757-774. https://doi.org/10.35378/gujs.475391
  9. Merugu, S., & Kamal, J. (2013). Change detection and estimation of illegal mining using satellite images. Proceedings of 2nd International Conference on Innovations in Electronics and Communication Engineering, 246-251.
  10. Kirilov, I., & Banov, M. (2016). Reclamation of lands disturbed by mining activities in Bulgaria. Agriculture and the Environment, 8(4). https://doi.org/10.15547/ast.2016.04.066
  11. Padró, J.C., Carabassa, V., Balagué, J., Brotons, L., Alcañiz, J.M., & Pons, X. (2019). Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery. Science of the Total Environment, (657), 1602-1614. https://doi.org/10.1016/j.scitotenv.2018.12.156
  12. Zvit pro otsinku vplyvu na dovkillia rekultyvatsii zemel, porushenykh vnaslidok nelehalnoho vydobutku burshtynu. (2018). Zvit pro doslidzhennia 2018516794. Kyiv, Ukraina: Instytut heokhimii navkolyshnoho seredovyshcha NAN Ukrainy.
  13. Volokh, P., Kobets, A., & Hrytsan, Yu. (2017). Rekultyvatsiia porushenykh zemel pry vydobutku burshtynu. Zemlevporiadnyi Visnyk, (1), 27-29.
  14. Wang, J., Zhao, F., Yang, J., & Li, X. (2017). Mining site reclamation planning based on land suitability analysis and ecosystem services evaluation: A case study in Liaoning Province, China. Sustainability, 9(6), 890. https://doi.org/10.3390/su9060890
  15. Merugu, S., & Kamal, J. (2013). Change detection and estimation of illegal mining using satellite images. Proceedings of 2nd International Conference on Innovations in Electronics and Communication Engineering.
  16. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2011). UAV photogrammetry for mapping and 3D modelling-current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (38), 22-25. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-25-2011
  17. Nex, F.C., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1-1-2015. https://doi.org/10.1007/s12518-013-0120-x
  18. Iizuka, K., Watanabe, K., Kato, T., Putri, N.A., Silsigia, S., Kameoka, T., & Kozan, O. (2018). Advantages of Unmanned Aerial Vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: A case study of postmining sites in IndonesiaPilot Test Using Unmanned Aerial Systems (UASs). Remote Sensing, 10(9), 1345. https://doi.org/10.3390/rs10091345
  19. Sechin, A.Yu., Drakin, M.A., & Kiseleva, A.S. (2013). Bespilotnyy letatelnyy apparat: Primenenie v celyah aerofotos’emki dlya kartografirovaniya (Chast 2). Avtomatizirovannye Tehnologii Izyskaniy i Proektirovaniya, 3(50), 56-58.
  20. Ahmad, A. (2011). Digital mapping using low altitude UAV. Journal of Science & Technology, 19(S), 51-58.
  21. Udin, W.S., & Ahmad, A. (2014). Assessment of photogrammetric mapping accuracy based on variation flying altitude using Unmanned Aerial Vehicle. 8th International Symposium of the Digital Earth (ISDE8) IOP Publishing. Series: Earth and Environmental Science, (18), 1-7. https://doi.org/10.1088/1755-1315/18/1/012027
  22. Tang, L.N., & Shao, G.F. (2015). Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26(4), 791-797. https://doi.org/10.1007/s11676-015-0088-y
  23. Ishaq Deliry, S., & Avdan, U. (2021). Accuracy of unmanned aerial systems photogrammetry and structurefrom motion in surveying and mapping: A review. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-021-01366-x
  24. Haskins, J., Endris, C., Thomsen, A.S., Gerbl, F., Fountain, M.C., & Wasson, K. (2021). UAV to inform restoration: A case study from a California Tidal Marsh. Frontiers in Environmental Science, (9). https://doi.org/10.3389/fenvs.2021.642906
  25. Lechner, A.M. (2009). Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping. Remote Sensing of Environment, (113), 2194-2204. https://doi.org/10.1016/j.rse.2009.06.002
  26. Shevnya, M.S. (2013). Ispolzovanie bespilotnykh letatelnykh apparatov dlya polucheniya materialov distantsionnogo zondirovaniya Zemli. Geodeziya i Kartografiya, (1), 44-50.
  27. Vovk, A., Hlotov, V., Hunina, A., Malitskyi, A., Tretyak, K., & Tsyrklevych, A. (2015). Analysis of the results of the use UAV Trimble UX-5 for creation of orthophotomaps and digital model of relief. Heodeziia, Kartohrafiia ta Aerofotoznimannia, (81), 90-103. https://doi.org/10.23939/istcgcap2015.01.090
  28. Shults, R.V., Voitenko, S.P., Krelshtein, P.D., & Malina, I.A. (2015). Do pytannia rozrakhunku tochnosti vyznachennia koordynat tochok pid chas aerofotoznimannia z bezpilotnykh litalnykh aparativ. Inzhenerna Heodeziia, (62), 124-135.
  29. Ajayi, O.G., Salubi, A.A., Angbas, A.F., & Odigure, M.G. (2017). Generation of accurate digital elevation models from UAV acquired low percentage overlapping images. International Journal of Remote Sensing, 38(8-10), 3113-3134. https://doi.org/10.1080/01431161.2017.1285085
  30. Harwin, S., & Lucieer, A. (2012). Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from Unmanned Aerial Vehicle (UAV) imagery. Remote Sensing, 4(6), 1573-1599. https://doi.org/10.3390/rs4061573
  31. Lucieer, A., De Jong, S., & Turner, D. (2013). Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography, 38(1), 97-116. https://doi.org/10.1177/0309133313515293
  32. Sanz-Ablanedo, E, Chandler, J.H., Rodríguez-Pérez, J.R., & Ordóñez, C. (2018). Accuracy of Unmanned Aerial Vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, 10(10), 1606. https://doi.org/10.3390/rs10101606
  33. Carrera-Hernandez, J.J., Levresse, G., & Lacan, P. (2020). Is UAVSfM surveying ready to replace traditional surveying techniques? International Journal of Remote Sensing, 41(12), 4820-4837. https://doi.org/10.1080/01431161.2020.1727049
  34. Zhang, H., Aldana-Jague, E., Clapuyt, F., Wilken, F., Vanacker, V., & Van Oost, K. (2019). Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection. Earth Surface Dynamics, (7), 807-827. https://doi.org/10.5194/esurf-7-807-2019
  35. Rehak, M., Mabillard, R., & Skaloud, J. (2013). A micro-UAV with the capability of direct georeferencing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (40), 317-323. https://doi.org/10.5194/isprsarchives-XL-1-W2-317-2013
  36. Rehak, M., & Skaloud, J. (2015). Fixed-wing micro aerial vehicle for accurate corridor mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 23-31. https://doi.org/10.5194/isprsannals-II-1-W1-23-2015
  37. Oniga, V.-E., Breaban, A.-I., Pfeifer, N., & Chirila, C. (2020). Determining the suitable number of ground control points for UAS images georeferencing by varying number and spatial distribution. Remote Sensing, 12(5), 876. https://doi.org/10.3390/rs12050876
  38. Fazeli, H., Samadzadegan, F., & Dadrasjavan, F. (2016). Evaluating the potential of RTK-UAV for automatic point cloud generation in 3D rapid mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 221-226. https://doi.org/10.5194/isprs-archives-XLI-B6-221-2016
  39. Myishlyaev, V.A. (2005). Otsenka tochnosti tsifrovykh ortofotoplanov. Geodeziya i Kartografiya, (5), 25-28.
  40. Nakaz Ukrheodezkartohrafii “Pro zatverdzhennia instruktsii z topohrafichnoho znimannia u masshtabakh 1:5000, 1:2000, 1:1000 ta 1:500 (HKNTA-2.04-02-98)”. (1998). Sposib dostupu: http://zakon2.rada.gov.ua/laws/show/z0393-98
  41. Hlotov, V.M., & Hunina, A.V. (2016). Analysis of modern methods surveying in the processing large-scale plans. Heodeziia, Kartohrafiia i Aerofotoznimannia, (83), 53-63. https://doi.org/10.23939/istcgcap2016.01.053
  42. Shinkevich, M.V., Vorobeva, N.G., Altyintsev, M.A., Popov, R.A., Arbuzov, S.A., & Florov, A.V. (2015). Otsenka tochnosti plotnoy tsifrovoy modeli poverhnosti i ortofotoplanov, poluchennyih po materialam aerofotos’emki s BLA serii Supercam. Geomatics, (4), 37-41.
  43. Reshetyuk, Yu., & Mårtensson, S.-G. (2016). Generation of highly accurate digital elevation models with Unmanned Aerial Vehicles. Photogrammetric Record.
  44. Agüera-Vega, F., Carvajal-Ramírez, F., & Martínez-Carricondo, P. (2017). Accuracy of digital surface models and orthophotos derived from Unmanned Aerial Vehicle photogrammetry. Journal of Surveying Engineering, 143(2). https://doi.org/10.1061/(ASCE)SU.1943-5428.0000206
  45. Martínez-Carricondo, P., Agüera-Vega, F., Carvajal-Ramírez, F., Mesas-Carrascosa, F.-J., Garcia-Ferrer, A., & Perez-Porras, F.-J. (2018). Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International Journal of the Indian Society of Remote Sensing Journal of Applied Earth Observation and Geoinformation, (72), 1-10. https://doi.org/10.1016/j.jag.2018.05.015
  46. Ferrer-González, E., Agüera-Vega, F., Carvajal-Ramírez, F., & Martínez-Carricondo, P. (2020). UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points. Remote Sensing, 12(15). https://doi.org/10.3390/rs12152447
  47. Hill, A. (2019). Economical drone mapping for archaeology: Comparisons of efficiency and accuracy. Journal of Archaeological Science: Reports, (24), 80-91. https://doi.org/10.1016/j.jasrep.2018.12.011
  48. Pessoa, G.G., Carrilho, A.C., Miyoshi, G.T., Amorim, A., Galo, M. (2021). Assessment of UAV-based digital surface model and the effects of quantity and distribution of ground control points. International Journal of Remote Sensing, (42). https://doi.org/10.1080/01431161.2020.1800122
  49. Chaudhry, M.Н., Anuar, A., Qudsia, G., Muhammad, S., Farid, H.S., & Al-Ansari, N. (2021). Assessment of DSM based on radiometric transformation of UAV data. Sensors, 21(5), 1649. https://doi.org/10.3390/s21051649
  50. Forlani, G., Dall’Asta, E., Diotri, F., Cella, U., Roncella, R., & Santise, M. (2018). Quality assessment of DSMs produced from UAV flights georeferenced with on-board RTK positioning. Remote Sensing, 10(2), 311. https://doi.org/10.3390/rs10020311
  51. Yu, J.J., Kim, D.W., Lee, E.J., & Son, S.W. (2020). Determining the optimal number of ground control points for varying study sites through accuracy evaluation of unmanned aerial system-based 3D point clouds and digital surface models. Drones, 4(3), 49. https://doi.org/10.3390/drones4030049
  52. Laporte-Fauret, Q., Marieu, V., Castelle, B., Michalet, R., Bujan, S., & Rosebery, D. (2019). Low-cost UAV for high-resolution and large-scale coastal dune change monitoring using photogrammetry. Journal of Marine Science and Engineering, 7(3), 63. https://doi.org/10.3390/jmse7030063
  53. Padróa, J.C., Carabassab, V., Balaguéc, J., Brotonsd, L., Alcañize, J.M., & Ponsf, X. (2018). Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery. Science of the Total Environment, (657), 1602-1614. https://doi.org/10.1016/j.scitotenv.2018.12.156
  54. Cryderman, C., Mah, S., & Shufletoski, A. (2014). Evaluation of UAV photogrammetric accuracy for mapping and earthworks computations. Geomatica, (68), 309-317. https://doi.org/10.5623/cig2014-405
  55. Whitehead, K., & Chris, H. (2015). Applying ASPRS accuracy standards to surveys from Small Unmanned Aircraft Systems (UAS). Photogrammetric Engineering & Remote Sensing, 81(10), 787-793. https://doi.org/10.14358/PERS.81.10.787
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