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Название Obtaining and interpreting geospatial data to build a multi-scale digital model of mining-disturbed areas
DOI 10.17580/gzh.2024.11.14
Автор Reznik A. V., Kolesnikov A. A., Kosarev N. S., Nemova N. A.
Информация об авторе

Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia

A. V. Reznik, Senior Researcher, Candidate of Engineering Sciences
N. A. Nemova, Senior Researcher, Candidate of Engineering Sciences, nemova-nataly@mail.ru

 

Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia1 ; Siberian State University of Geosystems and Technologies, Novosibirsk, Russia2

A. A. Kolesnikov1,2, Candidate of Engineering Sciences, Associate Professor
N. S. Kosarev1,2, Candidate of Engineering Sciences, Associate Professor

Реферат

The article describes methods available for obtaining geospatial data at the current stage of science and technology. A special place is given to aerial photography using UAS as one of the main innovative sources of geospatial data with high-precision spatial metrics. The developed methodology for the integrated use of heterogeneous information, based on the principle of a phased transition from general technological objects to individual areas, and the method for obtaining, processing, displaying and interpreting geospatial data for clustering heterogeneity of mining-altered areas are presented. Integrated with high-precision positioning techniques, they allow combining heterogeneous multiscale data. It is shown that in order to test the developed approach, it is necessary to take into account the specifics of integration of heterogeneous geospatial data. As an example, an abandoned marble quarry located in the Novosibirsk Region was considered. It is demonstrated that for the preliminary assessment of mining-disturbed lands and to identify zones for large-scale surveys, it is enough to have satellite data collected from open sources, archives of remote sensing data and satellite images of the optical spectrum. At the same time, the analysis of these data points at the need for additional geometric processing to obtain more accurate contours of man-made disturbances, which is due to the influence of distortions that arise during image formation under the influence of various factors. It is proved that the precision obtained from the aerial photography is sufficient to solve the tasks of geo-ecological monitoring, and the use of surveying and terrestrial laser scanning is required if it is necessary to increase the accuracy of constructing digital terrain models.

The study was carried out under the grant issued by the Russian Science Foundation (project No. 23-27-10057) and the grant from the Novosibirsk region No. r-60.

Ключевые слова Geospatial data, UAV, GIS, mining-disturbed territories, mining operations, Novosibirsk Region
Библиографический список

1. Available at: http://publication.pravo.gov.ru/Document/View/5400201903210003?index=1 (accessed: 07.06.2024).
2. Usacheva N. E., Bochkareva I. I. State of land in the zone of influence of the industrial enterprise JSC “PA “North”. InterExpo Geo-Sibir. 2020. Vol. 6, No. 2. pp. 118–123.
3. Kolpakova O. P. Scientific-methodical approach to the evaluation of the loss caused by the disturbed and polluted lands. Vestnik KrasGau. 2009. No. 3. pp. 190–196.
4. Vlasyuk L. I. Strategic priority for greening the Kuzbass economy: Land Rehabilitation Fund. Upravlencheskoe consultirovanie. 2021. No. 2. pp. 69–78.
5. Ozhygin D., Šafář V., Dorokhov D., Ozhygina S., Ozhygin S. et al. Terrestrial photogrammetry at the quarry and validating the accuracy of slope models for monitoring their stability. IOP Conference Series: Earth and Environmental Science. 2021. Vol. 906. ID 012062.
6. Akulova E. A., Titov M. O. The methods of acquiring spatial data in modern conditions. Izvestiya vuzov. Gornyi zhurnal. 2017. No. 6. pp. 79–86.
7. Pisarev V. S., Akhmedov B. N., Basargin A. A. Analysis of methods of collecting geodata in geodetic support of mining operations. InterExpo Geo-Sibir. 2019. Vol. 1, No. 1. pp. 197–202.
8. Labant S., Gergelova M. B., Kuzevicova Z., Kuzevic S., Fedorko G. et al. Utilization of geodetic methods results in small open-pit mine conditions: A case study from Slovakia. Minerals. 2020. Vol. 10, Iss. 6. ID 489.
9. Cheskidov V., Kassymkanova K.-K., Lipina A., Bornman M. Modern methods of monitoring and predicting the state of slope structures. Proceedings of IV International Innovative Mining Symposium. 2019. E3S Web of Conferences. 2019. Vol. 105. ID 01001.
10. Shevchuk S. O., Barsukov S. V. Navigational support of aerogeophysical works using RouteNav software. InterExpo Geo-Sibir. 2017. Vol. 1, No. 2. pp. 130–137.
11. Shahbazi M., Sohn G., Théau J., Menard P. Development and evaluation of a UAVphotogrammetry system for precise 3D environmental modeling. Sensors. 2015. Vol. 15, Iss. 11. pp. 27493–27524.
12. Ozhigina S. B., Mozer D. V., Ozhigin D. S., Ozhigin S. G., Bessimbayeva O. G. et al. Monitoring of the undermined territories of Karaganda coal basin on the basis of satellite radar interferometry. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences : XXIII ISPRS Congress. Prague, 2016. Vol. III-6. pp. 37–40.
13. Kosarev N. S., Kolesnikov A. A., Reznik A. V., Nemova N. A., Ozhogin D. S. The use of geospatial data in the industrially disturbed land evaluation. Journal of Mining Science. 2023. Vol. 59, No. 6. pp. 1058–1065.
14. Voskoboynikova A. A. Development of integration architecture for several information systems. Eastern-European Journal of Enterprise Technologies. 2009. Vol. 4, No. 3(40). pp. 12–15.
15. Moomen A.-W., Bertolotto M., Lacroix P., Jensen D. Inadequate adaptation of geospatial informa tion for sustainable mining towards agenda 2030 sustainable development goals. Journal of Cleaner Production. 2019. Vol. 238. ID 117954.
16. Karpik A. P. Trends in the development of science, equipment and technologies in geodesy and cartography in the Russian Federation. Geodesy and Cartography. 2015. No. 12. pp. 55–59.
17. Coppin P., Jonckheere I., Nackaerts K., Muys B., Lambin E. et al. Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing. 2004. Vol. 25, Iss. 9. pp. 1565–1596.
18. Balugin N. V., Marichev V. N., Yushkov V. A., Fomin B. A., Bochkovskiy D. A. Aerosol sounding of the troposphere and stratosphere by Lidar and aerological technologies. Atmospheric and Oceanic Optics. 2024. Vol. 37, No. 3. pp. 338–342.
19. Radke R. J., Andra S., Al-Kofahi O., Roysam B. Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing. 2005. Vol. 14, No. 3. pp. 294–307.
20. Javvadi S. Evaluating the impact of color normalization on kidney image segmentation. International Journal on Cybernetics and Informatics. 2023. Vol. 12, No. 5. pp. 93–105.
21. Rahman S., Rahman M. M., Abdullah-Al-Wadud M., Al-Quaderi G. D., Shoyaib M. An adaptive gamma correction for image enhancement. EURASIP Journal on Image and Video Processing. 2016. Vol. 2016, No. 1. DOI: 10.1186/s13640-016-0138-1
22. Zhu Z., Woodcock C. E. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment. 2014. Vol. 144. pp. 152–171.
23. Minkin A. S., Nikolaeva O. V. Cloud recognition in hyperspectral satellite images using an explainable machine learning model. Atmospheric and Oceanic Optics. 2024. Vol. 37, No. 3. pp. 400–408.
24. Reznik A. V., Nemova N. A., Kosarev N. S. et al. Lands disturbed nu solid mineral mining and use in the area of the Novosibirsk Region. Patent RF, No. 2023624542. Applied: 28.11.2023. Published: 11.12.2023.
25. Sukhikh Ya. A., Pravikov D. I., Kuzichkin A. A. Development of secure architectures for process control systems. Bezopasnost informatsionnykh tekhnologiy. 2020. Vol. 27, No. 2. pp. 97–117.
26. Vystrchil M. G., Gusev V. N., Sukhov A. K. A method of determining the errors of segmented GRID models of open-pit mines constructed with the results of unmanned aerial photogrammetric survey. Journal of Mining Institute. 2023. Vol. 262. pp. 562–570.
27. Novoselova O. V., Volkova G. D., Gavrilov A. G. Modeling of the integrated support environment for applied automated systems creation. Izvestiya vuzov. Povolzhskiy region. Tekhnicheskie nauki. 2014. No. 1(29). pp. 81–91.
28. Reznik A. V., Gavrilov V. L., Nemova N. A. et al. Method for obtaining, processing, displaying and interpreting geospatial data for clustering heterogeneity of technogenically altered territories. Patent RF, No. 2806406. Applied: 30.09.2022. Published: 31.10.2023. Bulletin No. 31.
29. Gavrilov V. L., Nemova N. A., Reznik A. V., Kosarev N. S., Kolesnikov A. A. On the need for a comprehensive geoecological assessment of lands disturbed by mining. Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering. 2023. Vol. 334, No. 10. pp. 76–87.
30. Gavrilov V. L., Nemova N. A., Reznik A. V., Kosarev N. S., Smyk M. I. et al. About land disturbance when developing mineral resource base in the Eastern part of the Novosibirsk region. Fundamentalnye i prikladnye voprosy gornykh nauk. 2022. Vol. 9, No. 2. pp. 69–77.
31. Kolesnikov A. A., Kosarev N. S., Nemova N. A., Reznik A. V., Platonov T. A. Design a database of technologically disturbed and polluted territories of the Novosibirsk Region. Vestnik SGUGiT. 2023. Vol. 28, No. 5. pp. 80–92.
32. Kolesnikov A. A., Kosarev N. S. Monitoring and analysis of changes in technogenously disturbed territories. Processing of Spatial Data in Monitoring of Natural and Anthropogenic Processes (SDM-2023) : All-Russian Conference with International Participation. Novosibirsk : Federalnyi issledovatelskiy tsentr informatsionnykh i vychislitelnykh tekhnologiy, 2023. pp. 263–267.
33. Kramarov S. O., Khramov V. V., Mityasova O. Yu. Satellite identification of mineral deposits under open pit mining. MIAB. 2019. No. 5. pp. 72–79.

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