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Identification of oil mining technogenesis based on aerial photography data

  • Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • Published:
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Abstract

Purpose

The paper studies the applicability of aerial photography materials for identifying the consequences of oil mining technogenesis.

Materials and methods

Multi-seasonal aerial photography of a karst river basin, where oil is being produced, was carried out using an unmanned aerial vehicle (UAV). Visual photographic delineation revealed the consequences of mechanical transformations, and some hydrocarbon inputs (bitumisation) and salts (technogenic salinisation) were also identified.

Results

As a rule, it has been established that mechanical transformations are detected by the colour and shape of objects. Occasionally, it is also necessary to analyse indirect signs of photographic delineation: the shape of the shadow, the configuration of the borders, the traces of passage, etc. Signs of photographic delineation of technogenic salinisation are turbidity of water and the acquisition of a blue-white colour; the change of colour in the water body to green-yellow; and white salt manifestations on the soil surface. The bitumisation process is sufficiently reliable to identify the presence of open oil spills on the surface of soil or water.

Conclusion

The use of orthophotos to detect the processes of bitumisation and technogenic salinisation is effective, especially in combination with direct field studies. The conditions for using aerial photography to identify the consequences of oil mining technogenesis are pixel resolution ≥ 20 cm/pixel (optimal ≥ 10 cm/pixel), snowless shooting season, cloudlessness and relatively low forest cover. The spatial distribution of the identified areas of all types of technogenesis indicates a close relationship with the location of oil mining facilities.

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References

  • Agisoft PhotoScan User Manual: Professional Edition, Version 1.4. https://www.agisoft.com/pdf/photoscan-pro_1_4_en.pdf. Accessed 1 Jun 2021

  • Andreev DN, Dziuba EA, Khotyanovskaya YV (2017) Biotic monitoring in the karst region of oil production (Perm region). Anthropogenic Transform Nat 3:87–89 [In Russian]

    Google Scholar 

  • Bayramov E, Kada M, Buchroithner M (2018) Monitoring oil spill hotspots, contamination probability modelling and assessment of coastal impacts in the Caspian Sea using SENTINEL-1, LANDSAT-8, RADARSAT, ENVISAT and ERS satellite sensors. J Oper Oceanogr 11(1):27–43. https://doi.org/10.1080/1755876X.2018.1438343

    Article  Google Scholar 

  • Buzmakov S, Andreev D, Sannikov P (2015) Applying of unmanned aerial vehicle in the study of forest conditions. Geol Geogr Glob Energy 4(59):60–69 [In Russian]

    Article  Google Scholar 

  • Buzmakov SA, Andreev DN, Zaytsev AA, Khotyanovskaya YV, Voronov GA (2019) Possible sources of pollution by oil products of water body in karst area. IOP Conf Ser Earth Environ Sci 321:012051. https://doi.org/10.1088/1755-1315/321/1/012051

  • Buzmakov SA, Sannikov PYu, Sivkov DE, Dziuba EA, Khotyanovskaya YV, Egorova DO (2021) Development of geoinformation systems for environmental management and environmental safety in the areas of exploited oil deposits. Anthropogenic Transform Nat 7(1):102–127. https://doi.org/10.17072/2410-8553-2021-1-102-127 [In Russian]

  • Correa Pabón RE, Souza Filho CR (2016) Spectroscopic characterization of red latosols contaminated by petroleum-hydrocarbon and empirical model to estimate pollutant content and type. Remote Sens Environ 175:323–336. https://doi.org/10.1016/j.rse.2016.01.005

    Article  Google Scholar 

  • Dashpurev B, Bendix J, Lehnert L (2020) Monitoring oil exploitation infrastructure and dirt roads with object-based image analysis and random forest in the Eastern Mongolian Steppe. Remote Sens 12:144. https://doi.org/10.3390/rs12010144

    Article  Google Scholar 

  • De Smet TS, Nikulin A, Romanzo N, Graber N, Dietrich C, Puliaiev A (2021) Successful application of drone-based aeromagnetic surveys to locate legacy oil and gas wells in Cattaraugus county New York. J Appl Geophys 186:104250. https://doi.org/10.1016/j.jappgeo.2020.104250

    Article  Google Scholar 

  • Digital photogrammetric system Photomod: Version 7.2. User Manual. https://en.racurs.ru/upload/iblock/1a8/65ocqahsl2kxd463tyui3mzg2uvuc90r/project.pdf. Accessed 3 Jun 2021

  • Farr T, Kobrick M (2000) Shuttle radar topography mission produces a wealth of data. EOS Trans Am Geophys Union 81(48):583–585. https://doi.org/10.1029/EO081i048p00583

    Article  Google Scholar 

  • Getzin S, Nuske RS, Wiegand K (2014) Using unmanned aerial vehicles (UAV) to quantify spatial gap patterns in forests. Remote Sens 6:6988–7004. https://doi.org/10.3390/rs6086988

    Article  Google Scholar 

  • Gorbunova КA, Andreytchuk VN, Kostarev VP, Maximovich NG (1992) Karst and caves of Perm region. Publishing House of Perm University, Perm [In Russian]

    Google Scholar 

  • Gurumoorthi K, Suneel V, Trinadha Rao V, Thomas AP, Alex MJ (2021) Fate of MV Wakashio oil spill off Mauritius coast through modelling and remote sensing observations. Mar Pollut Bull 172:112892. https://doi.org/10.1016/j.marpolbul.2021.1128

    Article  CAS  Google Scholar 

  • Hansen M, Potapov P, Moore R, Hancher M, Turubanova S, Tyukavina A, Thau D, Stehman S, Goetz S, Loveland T, Kommareddy A, Egorov A, Chini L, Justice C, Townshend J (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853. https://doi.org/10.1126/science.1244693

    Article  CAS  Google Scholar 

  • Kilin YuA, Minkevich II (1999) Cavities of the Krasnoyasyl karst field. Caves. Perm State University, Perm 52–57. [In Russian]

  • Kilin YuA, Minkevich II (2021) Features of oil pollution of underground and surface waters in the karst regions of the south of the perm territory. Geol Mineral Resources Western Urals 4(41):256–262 [In Russian]

    Google Scholar 

  • Kostarev SM (2015) Oilfield pollution aspects of the karst areas geological environment in Perm. Environmental safety and construction in karst areas. Perm State University, Perm. 317–322. [In Russian]

  • Krestenitis M, Orfanidis G, Ioannidis K, Avgerinakis K, Vrochidis S, Kompatsiaris I (2019) Oil spill identification from satellite images using deep neural networks. Remote Sens 11:1–22. https://doi.org/10.3390/rs11151762

    Article  Google Scholar 

  • Lassalle G, Elger A, Credoz A, Hedacq R, Bertoni G, Dubucq D, Fabre S (2019) Toward quantifying oil contamination in vegetated areas using very high spatial and spectral resolution imagery. Remote Sens 11:2241. https://doi.org/10.3390/rs11192241

    Article  Google Scholar 

  • Löw F, Stieglitz K, Diemar O (2021) Terrestrial oil spill mapping using satellite earth observation and machine learning: a case study in South Sudan. J Environ Manage 298:113424

    Article  Google Scholar 

  • Magalhães LA, Correa Pabón RE, Sanches ID, Alves MN, Oliveira WJ, Souza Filho CR (2013) Ultra and hyperspectral data as a tool to discriminate between contaminated soils with hydrocarbon fuels and senescent vegetation. Latinoamerican Remote Sens Week – LARS

  • Mahdianpari M, Salehi B, Mohammadimanesh F, Larsen G, Peddle DR (2018) Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle imagery and electromagnetic induction survey data. J Appl Remote Sens 12(3):036015. https://doi.org/10.1117/1.JRS.12.036015

    Article  Google Scholar 

  • Negara T, Jaya I, Kusmana C, Mansur I, Santi N (2021) Drone image-based parameters for assessing the vegetation condition the reclamation success in post-mining oil exploration. Telkomnika (Telecommunication Computing Electronics and Control) 19(1):105–114. https://doi.org/10.12928/TELKOMNIKA.V19I1.16663

  • Nikulin A, de Smet TS (2019) A UAV-based magnetic survey method to detect and identify orphaned oil and gas wells. Lead Edge 38(6):447–452. https://doi.org/10.1190/tle38060447.1

    Article  Google Scholar 

  • Ozigis MS, Kaduk JD, Jarvis CH, Da Conceição BP, Balzter H (2020) Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods. Environ Pollut 256:113360. https://doi.org/10.1016/j.envpol.2019.113360

    Article  CAS  Google Scholar 

  • Paneque-Gálvez J, McCall MK, Napoletano BM, Wich SA, Koh LP (2014) Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests 5(6):1481–1507. https://doi.org/10.3390/f5061481

    Article  Google Scholar 

  • Perepelica DI (2019) To the question of sources of oil production in the surface water objects r. Ysyl Anthropogenic Transformation of Nature No 5:38–44 [In Russian]

    Google Scholar 

  • Plotnikova MD, Medvedeva NA, Bortnik AG (2019) About the causes of water turbidity in the river Yasyl. Anthropogenic Transformation of Nature No 5:45–50 [In Russian]

    Google Scholar 

  • Procedure for determining scale of damage from chemical pollution of lands: Approved by Russian Federation Minprirody 18.11.1993. https://meganorm.ru/Index2/1/4294845/4294845895.htm [in Russian]. Accessed 14 Jun 2021

  • Rajendran S, Sadooni FN, Al-Kuwari HAS, Oleg A, Govil H, Nasir S, Vethamony P (2021) Monitoring oil spill in Norilsk. Russia Using Satellite Data Sci Rep 11(1):3817. https://doi.org/10.1038/s41598-021-83260-7

    Article  CAS  Google Scholar 

  • Sanches ID, Souza Filho CR, Magalhães LA, Quitério GCM, Alves MN, Oliveira WJ (2013a) Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy. ISPRS J Photogramm Remote Sens 78:85–101. https://doi.org/10.1016/j.isprsjprs.2013.01.007

    Article  Google Scholar 

  • Sanches ID, Souza Filho CR, Magalhães LA, Quitério GC, Alves MN, Oliveira WJ (2013b) Unravelling remote sensing signatures of plants contaminated with gasoline and diesel: an approach using the red edge spectral feature. Environ Pollut 174:16–27. https://doi.org/10.1016/j.envpol.2012.10.029

    Article  CAS  Google Scholar 

  • Sannikov P, Andreev D, Buzmakov S (2018) Identification and analysis of deadwood using an unmanned aerial vehicle. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 15(3):103–113. https://doi.org/10.21046/2070-7401-2018-15-3-103-113 [In Russian]

  • Scafutto RDM, de Souza Filho CR, de Oliveira WJ (2017) Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: implications for onshore exploration and monitoring. ISPRS J Photogramm Remote Sens 128:146–157. https://doi.org/10.1016/j.isprsjprs.2017.03.0

    Article  Google Scholar 

  • Soromotin AV (2011) Ecological consequences of different stages of the development of oil and gas deposits in the taiga zone of the Tyumen’ oblast. Contemp Probl Ecol 4:600–607. https://doi.org/10.1134/S1995425511060063

    Article  Google Scholar 

  • Stock materials of the Federal State Statistics Service. https://bdex.ru/naselenie/permskiy-kray/n/ordinskiy/krasnyy-yasyl/ [In Russian] Accessed 20 Jun 2022

  • Stock materials «PermNIPIneft» – Environmental Impact Assessment Project, section Environmental Protection

  • The Shuttle Radar Topography Mission (SRTM) (2019) 90m DEM Digital Elevation Database. http://srtm.csi.cgiar.org/srtmdata/

  • Wanasinghe T, Gosine R, De Silva O, Mann G, James L, Warrian P (2020) Unmanned aerial systems for the oil and gas industry: overview, applications and challenges. IEEE Access 8:166980–166997. https://doi.org/10.1109/ACCESS.2020.3020593

    Article  Google Scholar 

  • Web application “public cadastral map” (version 6). Developed by Rosreestr. https://pkk.rosreestr.ru/ [In Russian]

  • Year of gross forest cover loss event – global dataset. Global Forest Change 2000–2019. http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html. Accessed 28 Jun 2021

Download references

Funding

The reported study was funded by the Russian Foundation for Basic Research (RFBR) and Perm Territory, project number 20–45-596018.

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Correspondence to Yuliya Khotyanovskaya.

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Responsible editor: Jun Zhou

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Khotyanovskaya, Y., Buzmakov, S. & Sannikov, P. Identification of oil mining technogenesis based on aerial photography data. J Soils Sediments 23, 973–988 (2023). https://doi.org/10.1007/s11368-022-03357-y

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