Open-pit mine geomorphic changes analysis using multi-temporal UAV survey

Abstract

Mining activities, and especially open-pit mines, have a significant impact on the Earth’s surface. They influence vegetation cover, soil properties, and hydrological conditions, both during mining and for many years after the mines have been deactivated. Exploring a fast, accurate, and low-cost method to monitor changes, through years, in such an anthropogenic environment is, therefore, an open challenge for the Earth Science community. We selected a case study located in the northeast of Beijing, to assess geomorphic changes related to mining activities. In 2014 and 2016, an unmanned aerial vehicle (UAV) collected two series of high-resolution images. Through the structure-from-motion photogrammetric technique, the images were used to generate high-resolution digital elevation models (DEMs). The assessment of geomorphic changes was carried out by two methodologies. At first, we quantitatively estimated the detectable area, volumetric changes, and the mined tonnage by using the DEM of difference (DoD), which calculated the differences between two DEMs on a cells-by-cells basis. Secondly, the slope local length of autocorrelation (SLLAC) allowed determining the surface covered by open-pit mining by using an empirical model extracting the extent of the open-pit. The analysis of the DoD allows estimating the areal changes and the volumetric changes. The analysis of the SLLAC and its derived parameter allows for the accurate depiction of terraces and the extent of changes within the open-pit mine. Our results underlined how UAVs equipped with high-resolution cameras can be fast, precise, and low-cost instruments for obtaining multi-temporal topographic information, especially when combined with suitable methodologies to analyze the surface geomorphology, for dynamic monitoring of open-pit mines.

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References

  1. Asner GP, Llactayo W, Tupayachi R, Luna ER (2013) Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring. Proc Natl Acad Sci USA 110:18454–18459. https://doi.org/10.1073/pnas.1318271110

    Article  Google Scholar 

  2. Bennett GL, Molnar P, Eisenbeiss H, Mcardell BW (2012) Erosional power in the Swiss Alps: characterization of slope failure in the Illgraben. Earth Surf Process Landf 37:1627–1640. https://doi.org/10.1002/esp.3263

    Article  Google Scholar 

  3. Brasington J, Rumsby BT, McVey RA (2000) Monitoring and modelling morphological change in a braided gravel-bed river using high resolution GPS-based survey. Earth Surf Process Landf 25:973–990. https://doi.org/10.1002/1096-9837(200008)25:9<973:AID-ESP111>3.0.CO;2-Y

    Article  Google Scholar 

  4. Brasington J, Langham J, Rumsby B (2003) Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport. Geomorphology 53:299–316

    Article  Google Scholar 

  5. Brown AG, Tooth S, Bullard JE et al (2017) The geomorphology of the Anthropocene: emergence, status and implications. Earth Surf Process Landf 42:71–90. https://doi.org/10.1002/esp.3943

    Article  Google Scholar 

  6. Chen J, Li K, Chang KJ et al (2015) Open-pit mine geomorphic changes analysis using multi-temporal UAV survey. Int J Appl Earth Obs Geoinf 42:76–86. https://doi.org/10.1016/j.jag.2015.05.001

    Article  Google Scholar 

  7. Chen J, Xiang J, Hu Q et al (2016) Quantitative geoscience and geological big data development: a review. Acta Geol Sin 90:1490–1515. https://doi.org/10.1111/1755-6724.12782

    Article  Google Scholar 

  8. Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens 92:79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013

    Article  Google Scholar 

  9. Cook KL (2017) An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection. Geomorphology 278:195–208. https://doi.org/10.1016/j.geomorph.2016.11.009

    Article  Google Scholar 

  10. Ellis EC (2011) Anthropogenic transformation of the terrestrial biosphere. Philos Trans A Math Phys Eng Sci 369:1010–1035. https://doi.org/10.1098/rsta.2010.0331

    Article  Google Scholar 

  11. Eltner A, Kaiser A, Castillo C et al (2016) Image-based surface reconstruction in geomorphometry-merits, limits and developments. Earth Surf Dyn 4:359–389. https://doi.org/10.5194/esurf-4-359-2016

    Article  Google Scholar 

  12. Esposito G, Mastrorocco G, Salvini R et al (2017) Application of UAV photogrammetry for the multi-temporal estimation of surface extent and volumetric excavation in the Sa Pigada Bianca open-pit mine, Sardinia, Italy. Environ Earth Sci 76:103. https://doi.org/10.1007/s12665-017-6409-z

    Article  Google Scholar 

  13. Evans IS (1980) An integrated system of terrain analysis and slope mapping. Z Geomorphol 36:274–295

    Google Scholar 

  14. Fernández T, Pérez JL, Cardenal J et al (2016) Analysis of landslide evolution affecting olive groves using UAV and photogrammetric techniques. Remote Sens 8:837–865. https://doi.org/10.3390/rs8100837

    Article  Google Scholar 

  15. Francioni M, Salvini R, Stead D et al (2015) An integrated remote sensing-GIS approach for the analysis of an open pit in the Carrara marble district, Italy: slope stability assessment through kinematic and numerical methods. Comput Geotech 67:46–63. https://doi.org/10.1016/j.compgeo.2015.02.009

    Article  Google Scholar 

  16. Ghosh R, Chakraborty D, Halder M, Baidya TK (2015) Manganese mineralization in Archean greenstone belt, Joda-Noamundi sector, Noamundi basin, East Indian Shield. Ore Geol Rev 70:96–109. https://doi.org/10.1016/j.oregeorev.2015.04.007

    Article  Google Scholar 

  17. Haas F, Hilger L, Neugirg F et al (2016) Quantification and analysis of geomorphic processes on a recultivated iron ore mine on the Italian island of Elba using long-term ground-based lidar and photogrammetric SfM data by a UAV. Nat Hazards Earth Syst Sci 16:1269–1288. https://doi.org/10.5194/nhess-16-1269-2016

    Article  Google Scholar 

  18. Hancock GR, Grabham MK, Martin P et al (2006) A methodology for the assessment of rehabilitation success of post mining landscapes–sediment and radionuclide transport at the former Nabarlek uranium mine, Northern Territory, Australia. Sci Total Environ 354:103–119. https://doi.org/10.1016/j.scitotenv.2005.01.039

    Article  Google Scholar 

  19. Hancock GR, Crawter D, Fityus SG et al (2008) The measurement and modelling of rill erosion at angle of repose slopes in mine spoil. Earth Surf Process Landf 33:1006–1020. https://doi.org/10.1002/esp.1585

    Article  Google Scholar 

  20. Haralock RM, Shapiro LG (1991) Computer and robot vision. Addison-Wesley Longman Publishing Co., Inc, Boston

    Google Scholar 

  21. Heipke C, Mayer H, Wiedemann C, Jamet O (1997) Automated reconstruction of topographic objects from aerial images using vectorized map information. Int Arch Photogramm Remote Sens 23:47–56

    Google Scholar 

  22. Hsieh YC, Chan YC, Hu JC (2016) Digital elevation model differencing and error estimation from multiple sources: a case study from the Meiyuan Shan landslide in Taiwan. Remote Sens. https://doi.org/10.3390/rs8030199

    Article  Google Scholar 

  23. Hu W, Wu L, Zhang W et al (2017) Ground deformation detection using China’s ZY-3 stereo imagery in an opencast mining area. ISPRS Int J Geo-Inf 6:361. https://doi.org/10.3390/ijgi6110361

    Article  Google Scholar 

  24. Huang X, Zhu Y, Ji H (2013) Distribution, speciation, and risk assessment of selected metals in the gold and iron mine soils of the catchment area of Miyun Reservoir, Beijing, China. Environ Monit Assess 185:8525–8545. https://doi.org/10.1007/s10661-013-3193-4

    Article  Google Scholar 

  25. Hugenholtz CH, Walker J, Brown O, Myshak S (2015) Earthwork volumetrics with an unmanned aerial vehicle and softcopy photogrammetry. J Surv Eng 141:6014003. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000138

    Article  Google Scholar 

  26. Immerzeel WW, Kraaijenbrink PDA, Shea JM et al (2014) High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. Remote Sens Environ 150:93–103. https://doi.org/10.1016/j.rse.2014.04.025

    Article  Google Scholar 

  27. ISO B (2013) 25178-2: geometrical product specifications (GPS)-surface texture: areal-part 2: terms, definitions and surface texture parameters

  28. Jaakkola A, Hyyppä J, Kukko A et al (2010) A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J Photogramm Remote Sens 65:514–522. https://doi.org/10.1016/j.isprsjprs.2010.08.002

    Article  Google Scholar 

  29. James MR, Robson S (2012) Straightforward reconstruction of 3D surfaces and topography with a camera: accuracy and geoscience application. J Geophys Res Earth Surf. https://doi.org/10.1029/2011jf002289

    Article  Google Scholar 

  30. Kobayashi H, Watando H, Kakimoto M (2014) A global extent site-level analysis of land cover and protected area overlap with mining activities as an indicator of biodiversity pressure. J Clean Prod 84:459–468. https://doi.org/10.1016/j.jclepro.2014.04.049

    Article  Google Scholar 

  31. Lane SN, Richards KS, Chandler JH (1994) Developments in monitoring and modelling small-scale river bed topography. Earth Surf Process Landf 19:349–368

    Article  Google Scholar 

  32. Lane SN, Westaway RM, Hicks DM (2003) Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing. Earth Surf Process Landf 28:249–271. https://doi.org/10.1002/esp.483

    Article  Google Scholar 

  33. Lee S, Choi Y (2015) Topographic survey at small-scale open-pit mines using a popular rotary-wing unmanned aerial vehicle (drone). Tunn Undergr Space 25:462–469

    Article  Google Scholar 

  34. Lewin J, Macklin MG (2014) Marking time in geomorphology: should we try to formalise an Anthropocene definition? Earth Surf Process Landf 39:133–137. https://doi.org/10.1002/esp.3484

    Article  Google Scholar 

  35. Lewis JP (1995) Fast template matching. Vision interface 95, Canadian image processing and pattern recognition society. Quebec City, Canada, May 15–19, pp 120–123

  36. Lucieer A, de Jong SM, Turner D (2014) Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr 38:97–116. https://doi.org/10.1177/0309133313515293

    Article  Google Scholar 

  37. McLeod T, Samson C, Labrie M et al (2013) Using video acquired from an unmanned aerial vehicle (UAV) to measure fracture orientation in an open-pit mine. Geomatica 67:173–180

    Article  Google Scholar 

  38. Messinger M, Asner G, Silman M (2016) Rapid assessments of amazon forest structure and biomass using small unmanned aerial systems. Remote Sens 8:615. https://doi.org/10.3390/rs8080615

    Article  Google Scholar 

  39. Mossa J, James LA (2013) Impacts of mining on geomorphic systems. In: Treatise on geomorphology, vol. 13. Geomorphology of human disturbances, Climate change, and natural hazards. Academic Press, San Diego, CA, pp 74–95 

  40. Neugirg F, Stark M, Kaiser A et al (2016) Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multi-temporal high-resolution terrestrial LiDAR and UAV surveys. Geomorphology 269:8–22. https://doi.org/10.1016/j.geomorph.2016.06.027

    Article  Google Scholar 

  41. Niethammer U, James MR, Rothmund S et al (2012) UAV-based remote sensing of the super-sauze landslide: evaluation and results. Eng Geol 128:2–11. https://doi.org/10.1016/j.enggeo.2011.03.012

    Article  Google Scholar 

  42. Osterkamp WR, Joseph WL (2000) Climatic and hydrologic factors associated with reclamation. In: Barnhisel RI, Darmody RG, Daniels WL (eds) Reclamation of drastically disturbed lands. American Society of Agronomy, Madison, pp 193–215

  43. Passalacqua P, Belmont P, Staley DM et al (2015) Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: a review. Earth Sci Rev 148:174–193. https://doi.org/10.1016/j.earscirev.2015.05.012

    Article  Google Scholar 

  44. Prosdocimi M, Calligaro S, Sofia G et al (2016) Bank erosion in agricultural drainage networks: new challenges from structure-from-motion photogrammetry for post-event analysis. Earth Surf Process Landf 40:1891–1906. https://doi.org/10.1002/esp.3767

    Article  Google Scholar 

  45. Shahbazi M, Sohn G, Théau J, Ménard P (2015) UAV-based point cloud generation for open-pit mine modelling. Int Arch Photogramm Remote Sens Spat Inf Sci ISPRS Arch 40:313–320. https://doi.org/10.5194/isprsarchives-XL-1-W4-313-2015

    Article  Google Scholar 

  46. Sibson R et al (1981) A brief description of natural neighbour interpolation. Interpret Multivar Data 21:21–36

    Google Scholar 

  47. Sofia G, Marinello F, Tarolli P (2014) A new landscape metric for the identification of terraced sites: the slope local length of auto-correlation (SLLAC). ISPRS J Photogramm Remote Sens 96:123–133. https://doi.org/10.1016/j.isprsjprs.2014.06.018

    Article  Google Scholar 

  48. Sofia G, Bailly J-S, Chehata N et al (2016) Comparison of pleiades and LiDAR digital elevation models for terraces detection in farmlands. IEEE J Sel Top Appl Earth Obs Remote Sens 9:1567–1576. https://doi.org/10.1109/JSTARS.2016.2516900

    Article  Google Scholar 

  49. Sofia G, Masin R, Tarolli P (2017) Prospects for crowd sourced information on the geomorphic “engineering” by the invasive Coypu (Myocastor coypus). Earth Surf Process Landf. https://doi.org/10.1002/esp.4081

    Article  Google Scholar 

  50. Stout KJ, Blunt L, Dong WP et al (2000) Development of methods for the characterisation of roughness in three dimensions, 1st edn. Penton Press, Luxembourg

    Google Scholar 

  51. Tarolli P (2014) High-resolution topography for understanding earth surface processes: opportunities and challenges. Geomorphology 216:295–312. https://doi.org/10.1016/j.geomorph.2014.03.008

    Article  Google Scholar 

  52. Tarolli P, Sofia G (2016) Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology 255:140–161. https://doi.org/10.1016/j.geomorph.2015.12.007

    Article  Google Scholar 

  53. Tarolli P, Sofia G, Dalla Fontana G (2012) Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion. Nat Hazards 61:65–83. https://doi.org/10.1007/s11069-010-9695-2

    Article  Google Scholar 

  54. Tarolli P, Sofia G, Ellis E (2017) Mapping the topographic fingerprints of humanity across earth. Eos (Washington DC). https://doi.org/10.1029/2017EO069637

    Article  Google Scholar 

  55. Tong X, Liu X, Chen P et al (2015) Integration of UAV-based photogrammetry and terrestrial laser scanning for the three-dimensional mapping and monitoring of open-pit mine areas. Remote Sens 7:6635–6662. https://doi.org/10.3390/rs70606635

    Article  Google Scholar 

  56. Townsend PA, Helmers DP, Kingdon CC et al (2009) Changes in the extent of surface mining and reclamation in the central appalachians detected using a 1976–2006 Landsat time series. Remote Sens Environ 113:62–72. https://doi.org/10.1016/j.rse.2008.08.012

    Article  Google Scholar 

  57. Toy TJ, Hadley RF (1987) Geomorphology of disturbed lands. Academic Press, New York

    Google Scholar 

  58. Turner D, Lucieer A, de Jong SM (2015) Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sens 7:1736–1757. https://doi.org/10.3390/rs70201736

    Article  Google Scholar 

  59. Verhoeven G (2011) Taking computer vision aloft–archaeological three-dimensional reconstructions from aerial photographs with photoscan. Archaeol Prospect 18:67–73

    Article  Google Scholar 

  60. Vidal O, Goffé B, Arndt N et al (2013) Metals for a low-carbon society. Nat Geosci 6:894–896. https://doi.org/10.1038/ngeo1993

    Article  Google Scholar 

  61. Westaway RM, Lane SN, Hicks DM (2000) The development of an automated correction procedure for digital photogrammetry for the study of wide, shallow, gravel-bed rivers. Earth Surf Process Landf 25:209–226

    Article  Google Scholar 

  62. Westoby MJ, Brasington J, Glasser NF et al (2012) “Structure-from-Motion” photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314. https://doi.org/10.1016/j.geomorph.2012.08.021

    Article  Google Scholar 

  63. Wheaton JM, Brasington J, Darby SE, Sear DA (2010) Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surf Process Landforms. https://doi.org/10.1002/esp.1886

    Article  Google Scholar 

  64. Wilkinson BH, McElroy BJ (2007) The impact of humans on continental erosion and sedimentation. Geol Soc Am Bull 119:140–156

    Article  Google Scholar 

  65. Woodget AS, Carbonneau PE, Visser F, Maddock IP (2015) Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surf Process Landf 40:47–64. https://doi.org/10.1002/esp.3613

    Article  Google Scholar 

  66. Yucel MA, Turan RY (2016) Areal change detection and 3D modeling of mine lakes using high-resolution unmanned aerial vehicle images. Arab J Sci Eng 41:4867–4878. https://doi.org/10.1007/s13369-016-2182-7

    Article  Google Scholar 

  67. Zhao Y, Feng C, Li D (2014) The major ore clusters of super-large iron deposits in the world, present situation of iron resources in China, and prospect. Acta Geol Sin (English Ed) 88:1895–1915

    Article  Google Scholar 

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Acknowledgements

The author would like to acknowledge the Miyun mine company for their cooperation, and we would like to thank Zhen Yanwei for manipulation of UAV, Zheng Yongxin, Lai Zili and Huang Haozhong for assisting with the processing of the data. The technical support for the UAV 2014 survey was provided by Sky View Technology Co., Ltd. (Taiwan). This research was financially supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2006BAB01A01), Joint Evaluation of Geological Hazards in Beijing by Beijing Education Commission (2015282-49), and China MOST project “Method and model for quantitative prediction of deep geologic anomalies” (2017YFC0601502). The algorithms used in this works were elaborated and tested by the digital terrain analysis research group at University of Padova (Italy), and supported by the grant 60A08-5455/15 “the analysis of the topographic signature of anthropogenic processes”.

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Correspondence to Jianping Chen.

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This article is part of a Topical Collection in Environmental Earth Sciences on “Learning from spatial data: unveiling the geo-environment through quantitative approaches” guest edited by Sebastiano Trevisani, Marco Cavalli, Jean Golay, and Paulo Pereira.

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Xiang, J., Chen, J., Sofia, G. et al. Open-pit mine geomorphic changes analysis using multi-temporal UAV survey. Environ Earth Sci 77, 220 (2018). https://doi.org/10.1007/s12665-018-7383-9

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Keywords

  • UAV
  • DEM
  • SLLAC
  • DoD
  • Open-pit mine