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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 6, pp. 129-143

Detection of perennial heaving mounds in digital elevation model images using decomposition by topological features

S.V. Eremeev 1 , A.V. Abakumov 1 , D.E. Andrianov 1 , I.V. Bogoyavlensky 2 , R.A. Nikonov 2 
1 Murom Institute (Branch) of Vladimir State University, Murom, Russia
2 Oil and Gas Research Institute RAS, Moscow, Russia
Accepted: 27.11.2023
DOI: 10.21046/2070-7401-2023-20-6-129-143
In the past decade, interest in studying the geocryological features of the Arctic has increased significantly, which is associated with the expansion of the search for and development of mineral resources, especially hydrocarbons. The article describes the technology for automated detection of perennial heaving mounds (PHM), often posing the threat of powerful gas blowouts and explosions, on digital elevation models (DEMs). ArcticDEM high-precision DEMs of the Arctic, built by photogrammetric processing of high-resolution satellite images, are used as initial data. The developed PHM search technology, including software, is based on the use of image decomposition based on topological features. This method allows one to generate a set of topological properties from the input matrix of DEM points and carry out their further analysis with subsequent filtering according to specified parameters. The features of PHM presentation are shown, as well as their differences from other relief-forming objects of the Arctic. Numerical and graphical results of PHM detection using real data from the north of Western Siberia are presented. The proposed approach makes it possible to analyze the DEM by geometric and topological features. With the use of geometric features, the average accuracy of correctly selected objects is 78.2 %, and with the addition of topological properties, this value increases to 81.8 %.
Keywords: perennial heaving mounds (PHM), digital elevation models (DEM), ArcticDEM, image decomposition, topological features
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References:

  1. Belyaev M. Yu., Vinogradov P. V., Desinov L. V., Kumakshev S. A., Sekerzh-Zen’Kovich S. Ya., Identifica­tion of a source of oceanic ring waves near Darwin’s island based on space photos, J. Computer and Systems Sciences Intern., 2011, No. 1, pp. 70–83 (in Russian).
  2. Bogoyavlensky V. I. (2014a), The threat of catastrophic gas blowouts from the Arctic cryolithozone. Yamal craters, Oil and Drilling, 2014, No. 9, pp. 13–18 (in Russian).
  3. Bogoyavlensky V. I. (2014b), Arctic and the World Ocean: current state, perspectives and challenges of hydrocarbon production, Trudy VEO Russia, 2014, Vol. 182, No. 3, pp. 12–175 (in Russian).
  4. Bogoyavlensky V. I., Fundamental aspects of the catastrophic gas blowout genesis and the formation of giant craters in the Arctic, Arctic: Ecology and Economy, 2021, Vol. 11, No. 1, pp. 51–66 (in Russian), DOI: 10.25283/2223-4594-2021-1-51-66.
  5. Bogoyavlensky V. I., Sizov O. S., Bogoyavlensky I. V. et al., Earth Degassing in the Arctic: Comprehensive Studies of the Distribution of Frost Mounds and Thermokarst Lakes with Gas Blowout Craters on the Yamal Peninsula, Arctic: Ecology and Economy, 2019, No. 4(36), pp. 52–68 (in Russian), DOI: 10.25283/2223-4594-2019-4-52-68.
  6. Bogoyavlensky V. I., Bogoyavlensky I. V., Kargina T. N., Nikonov R. A., Digital technologies for remote detection and monitoring of the development of heaving mounds and craters of catastrophic gas blowouts in the arctic, Arctic: Ecology and Economy, 2020, Vol. 40, No. 4, pp. 90–105 (in Russian), DOI: 10.25283/2223-4594-2020-4-90-105.
  7. Bogoyavlensky V. I., Bogoyavlensky I. V., Kargina T. N., Catastrophic gas blowout in 2020 on the Yamal Peninsula in the Arctic. Results of comprehensive analysis of aerospace RS data, Arctic: Ecology and Economy, 2021, Vol. 11, No. 3, pp. 362–374 (in Russian), DOI: 10.25283/2223-4594-2021-3-362-374.
  8. Bondur V. G., Chimitdorzhiev T. N., Kirbizhekova I. I., Dmitriev A. V., Radar detection of anomalous dynamics of perennial heaving mounds from the example of the Yamal mound/crater in 2020, Doklady Earth Sciences, 2022, Vol. 506, No. 1, pp. 65–72 (in Russian), DOI: 10.31857/S2686739722602575.
  9. Eremeev S. V., Abakumov A. V., Andrianov D. E., Titov D. V., Image decomposition method by topological features, Computer Optics, 2022, Vol. 46, No. 6, pp. 939–947 (in Russian), DOI: 10.18287/2412-6179-CO-1080.
  10. Zvyagin K. N., Maltsev D. D., Revisiting the digital noise reduction in automatic contouring of “ice-water” objects, Arctic and Antarctic Research, 2020, Vol. 66, No. 1, pp. 102–114 (in Russian), DOI: 10.30758/0555-2648-2020-66-1-102-114.
  11. Ivanov M. A., Yermolaev O. P., Geomorphometric analysis of river basins of the Volga Federal District using SRTM and Aster GDEM data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 2, pp. 98–109 (in Russian), DOI: 10.21046/2070-7401-2017-14-2-98-109.
  12. Lisitsyn V. M., Obrosov K. V., Safonov V. A., Detection of Cylindrical and Spherical Surfaces and Determining Their Parameters in Segmentation of Range Images, J. Computer and Systems Sciences Intern., 2019, Vol. 58, No. 6, pp. 945–959, DOI: 10.1134/S106423071906011X.
  13. Lobanov G. V., Zvereva A. Yu., Protasova A. P. et al., Geomorphological zonation based on digital elevation models (from SRTM data over the Upper Dnieper region), Geomorfologiya, 2020, No. 3, pp. 44–55 (in Russian), DOI: 10.31857/S0435428120030074.
  14. Savchik A. V., Nikolaev P. P., Projective correspondence method for an oval with two fixed points, J. Information Technologies and Computing Systems, 2018, No. 1, pp. 60–67 (in Russian).
  15. Barr I., Dokukin M., Kougkoulos I. et al., Using ArcticDEM to analyse the dimensions and dynamics of debris-covered glaciers in Kamchatka, Russia, Geosciences, 2018, Vol. 8, No. 6, pp. 216–233, DOI: 10.3390/geosciences8060216.
  16. Bogoyavlensky V., Bogoyavlensky I., Nikonov R. et al., Permanent gas emission from the Seyakha crater of gas blowout, Yamal Peninsula, Russian Arctic, Energies, 2021, Vol. 14, Article 5345, DOI: 10.3390/en14175345.
  17. Chang Y., Bailey D., Le Moan S., The shape of patterns tells more: Using two dimensional Hough transform to detect circles, In: Proc. 5th Asian Conf. Pattern Recognition (ACPR 2019), 2019, Vol. 12047, pp. 64–75.
  18. Ishalina O. T., Bliakharskii D. P., Florinsky I. V., Recognition of crevasses with high-resolution digital elevation models: application of geomorphometric modeling and texture analysis, Trans. GIS, 2021, Vol. 25, No. 5, pp. 2529–2552.
  19. Martin M. D., Barr I., Edwards B. et al., Assessing the use of optical satellite images to detect volcanic impacts on glacier surface morphology, Remote Sensing, 2021, Vol. 13, No. 17, Article 3453, DOI: 10.3390/rs13173453.
  20. Porter C., Morin P., Ian H. et al., ArcticDEM, Version 3, Harvard Dataverse, V1, 2018, DOI: 10.7910/DVN/OHHUKH.
  21. Shiggins C. J., Lea J. M., Brough S., Automated ArcticDEM iceberg detection tool: insights into area and volume distributions, and their potential application to satellite imagery and modeling of glacier–iceberg–ocean systems, The Cryosphere, 2023, Vol. 17, pp. 15–32, DOI: 10.5194/tc-17-15-2023.