Siberian Federal University, Krasnoyarsk, Russia:

Zenkov I. V., Doctor of Engineering Sciences, Professor, zenkoviv@mail.ru
Morin A. S., Doctor of Engineering Sciences, Professor
Vokin V. N., Candidate of Engineering Sciences, Professor
Kiryushina E. V., Candidate of Engineering Sciences, Associate Professor

Remote sensing data have allowed detecting and monitoring of arrangement of mining and haulage machines in open pit mines producing coal and iron ore in Russia. In coal mining, the highest concentration of mining and haulage equipment is revealed in open pit mines in Kuzbass; in the iron ore industry, open pit mines in the Belgorod and Kursk Regions operate 70% of the total equipment employed in the mining sector. The authors draw a conclusion on the essentiality of strengthening of in-house mining machine engineering in Russia and on creation of interregional centers for maintenance and repair of mining and haulage equipment.

1. Proceedings of XXVI Congress of the Communist Party of the Soviet Union. Moscow: Politizdat, 1981. 384 p.
2. Zenkov I. V., Zayats V. V., Nefedov B. N. et al. Open pit coal mines in Russia from space. Mining and Disturbed Land Ecology. Krasnoyarsk : SFU, 2017. 519 p.
3. Zenkov I. V., Zayats V. V., Nefedov B. N. et al. Open pit iron ore mines in Russia from space. Mining and Disturbed Land Ecology. Krasnoyarsk : SFU, 2018. 664 p.
4. Zenkov I. V., Zayats V. V., Nefedov B. N. et al. Open pit mines in nonferrous metallurgy of Russia from space. Mining and Disturbed Land Ecology. Krasnoyarsk : SFU, 2019. 604 p.
5. Bullock E. L., Nolte Ch., Segovia A. L. R., Woodcock C. E. Ongoing forest disturbance in Guatemala’s protected areas. Remote Sensing in Ecology and Conservation. 2020. Vol. 6, Iss. 2. pp. 141–152.
6. Oeser J., Heurich M., Senf C., Pflugmacher D., Belotti E. et al. Habitat metrics based on multi-temporal Landsat imagery for mapping large mammal habitat. Remote Sensing in Ecology and Conservation. 2020. Vol.6, Iss.1. pp. 52–69.
7. Lopatin J., Dolos K., Kattenborn T., Fassnacht F. E. How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 4. pp. 302–317.
8. Glover-Kapfer P., Soto-Navarro C. A., Wearn O. R. Cameratrapping version 3.0: Current constraints and future priorities for development. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 3. pp. 209–223.
9. Nahirnick N. K., Reshitnyk L., Campbell M., Hessing-Lewis M., Costa M. et al. Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS). Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 2. pp. 121–135.
10. Van Den Broeke M. S. Radar quantification, temporal analysis and influence of atmospheric conditions on a roost of American Robins (Turdus migratorius) in Oklahoma. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 2. pp. 193–204.
11. Abdullah H., Skidmore A. K., Darvishzadeh R., Heurich M. Sentinel-2 accurately maps green-attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat-8. Remote Sensing in Ecology and Conservation. 2019. Vol.5 , Iss. 1. pp. 87–106.
12. Jacobs C. E., Ausband D. E. An evaluation of camera trap performance—What are we missing and does deployment height matter? Remote Sensing in Ecology and Conservation. 2018. Vol. 4, Iss. 4. pp. 352–360.
13. Cordell S., Questad E. J., Asner G. P., Kinney K. M., Thaxton J. M. et. al. Remote sensing for restoration planning: how the big picture can inform stakeholders. Restoration Ecology. 2017. Vol. 25, Iss.2. pp. 147–154.
14. O’Brien T. G., Ahumada J., Akampurila E., Beaudrot L., Boekee K. et al. Camera trapping reveals trends in forest duiker populations in African National Parks. Remote Sensing in Ecology and Conservation. 2020. Vol. 6, Iss. 2. pp. 168–180.
15. LaRue M. A., Ainley D. G., Pennycook J., Stamatiou K., Salas L., et.al. Engaging ‘the crowd’ in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica. Remote Sensing in Ecology and Conservation. 2020. Vol. 6, Iss. 1. pp. 70–78.
16. Elkind K., Sankey T. T., Munson S. M., Aslan C. E. Invasive buffelgrass detection using high-resolution satellite and UAV imagery on Google Earth Engine. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 4. pp. 318–331.
17. Fortin J. A., Fisher J.T., Rhemtulla J. M., Higgs E. S. Estimates of landscape composition from terrestrial oblique photographs suggest homogenization of Rocky Mountain landscapes over the last century. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 3. pp. 224–236.
18. Md Rahman M., Lagomasino D., SeungKuk Lee, Fatoyinbo T., Ahmed I. et al. Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using World-View 2 and TanDEM-X high resolution imagery. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 2. pp. 136–149.
19. Kattenborn T., Fassnacht F. E., Schmidtlein S. Differentiating plant functional types using reflectance: which traits make the difference? Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 1. pp. 5–19.
20. Leitão P. J., Schwieder M., Pedroni F., Sanchez M., Pinto J. R. R. et al. Mapping woody plant community turnover with spaceborne hyperspectral data – A case study in the Cerrado. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 1. pp. 107–115.
21. Pen-Yuan Hsing, Bradley S., Kent V. T., Hill R. A., Smith G. C. et al. Economical crowdsourcing for camera trap image classification. Remote Sensing in Ecology and Conservation. 2018. Vol. 4, Iss. 4. pp. 361–374.
22. Available at: https://www.google.com/earth (accessed at: 24.01.2017–24.12.2019).