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Title: IMPORTANCE OF GEOMORPHOMETRIC AND GEOLOGIC VARIABLES FOR AUTOMATED DELINEATION OF GEOMORPHOLOGICAL UNITS (ON THE EXAMPLE OF YAMAL PENINSULA)
IMPORTANCE OF GEOMORPHOMETRIC AND GEOLOGIC VARIABLES FOR AUTOMATED DELINEATION OF GEOMORPHOLOGICAL UNITS (ON THE EXAMPLE OF YAMAL PENINSULA)

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Sergey Kharchenko
10.5593/sgem2024/2.1/s08.14
10.5593/sgem2024/2.1
1314-2704
978-619-7603-69-9
English
24
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Geoinformatics
This study assesses the efficiency of various geomorphometric terrain characteristics and a number of categorical geological parameters in training statistical models to create geomorphological maps. The study area is the central sector of the western coast of Yamal Peninsula (S = 10 400 km2). The terrain is exceptionally flat and morphologically similar, but the genesis and age of the surfaces are diverse. The background surface type is marine terraces of various (Middle Pleistocene to Holocene) ages, subsequently reworked by fluvial, cryogenic, and other exogenous processes. The classification method used is random forest. For representativeness assessment, we used 211 raster images reflecting the spatial distribution over the area of quantitative characteristics of the earth's surface morphology, as well as substrate parameters (the age of different sediment packs, basement structures), geophysical anomalies, etc. It has been established that using sample data covering fractions of the area percent (0.04–1%) allows for the automatic construction of a geomorphological map for the rest of the area with an accuracy of 78–79%, which is more than 7 times higher than the accuracy of random guessing. The most representative parameters are elevation, various metrics of relative heights/depths, supplemented by texture characteristics of topographic dissection: "non-fractality" and "harmony" of the terrain, etc.
[1] Breiman L. Random forests. Machine learning. 2001, vol. 45, no. 1, pp. 5–32. DOI:10.1023/A:1010950718922.
[2] Bugnicourt P., Guitet S., Santos V. F., Blanc L., Sotta E. D., Barbier N., Couteron P. Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield. Geomorphology, 2018, vol. 317, pp. 23–44. DOI:10.1016/j.geomorph.2018.03.017.
[3] Hargrove W. W., Hoffman F. M., Hessburg P. F. Mapcurves: a quantitative method for comparing categorical maps. Journal of Geographical Systems, 2006, vol. 8, no. 2, pp. 187–208. DOI:10.1007/s10109-006-0025-x.
[4] Kharchenko S.V. Automatic recognition of the landforms origin in the Kola Peninsula based on morphometric variables, Geodesy and Cartography, 2022, vol. 83, no. 2, pp. 12–25. DOI: 10.22389/0016-7126-2022-980-2-12-25. (In Russian) DOI: 10.22389/0016-7126-2022-980-2-12-25.
[5] Kharchenko S.V. New Challenges of Geomorphometry and Automatic Morphological Classifications in Geomorphology. Geomorfologiya, 2020, no. 1, pp. 3–21. (In Russian) DOI: 10.31857/S043542812001006X.
[6] Kozlov E. P., Cherdantsev S. G., Sokolovsky A. P., Novoseltseva R. G., Nikitin Yu. N., Voronov V. N., Suslov S. L. State Geological Map of the Russian Federation. Scale 1: 200,000. West Siberian series. Subseries Tyumen-Salekhardskaya. Sheets R-42-VII-IX, XIII-XV. Explanatory note. Moscow, MF VSEGEI (Publ.), 2015. 103 p. (In Russian)
[7] Maxwell A. E., Shobe C. M. Land-surface parameters for spatial predictive mapping and modeling. Earth-Science Reviews, 2022, vol. 226, pp. 103944. DOI: 10.1016/j.earscirev.2022.103944.
[8] McDermid G. J., Franklin S. E. Spectral, spatial, and geomorphometric variables for the remote sensing of slope processes. Remote Sensing of Environment, 1994, vol. 49, no. 1, pp. 57–71. (In Russian) DOI:10.1016/0034-4257(94)90059-0.
[9] Mel'nik M.A., Pozdnyakov A.V. Fractals in the erosion dissection and self-oscillations in geomorphosystems' dynamics. Geomorfologiya, 2008, no. 3, pp. 86–95. (In Russian). DOI: 10.15356/0435-4281-2008-3-86-95.
[10] Ng V. W., Breiman L. Bivariate variable selection for classification problem. Technical report. Berkeley: Department of Statistics, University of California-Berkeley, 2005. 22 p.
[11] Open Topography – ALOS World 3d – 30 m. – 2021. – URL: https://portal.opentopography.org/raster?opentopoID=OTALOS.112016.4326.2. Available at 01.01.2022.
[12] Serebryanny L.R., Chuklenkova I.N. Density of lakes as an age indicator of glacigenetic morphosculpture: an application of morphometric analysis in the northwestern areas of the Russian Plain. Geomorfologiya, 1973 no. 4, pp. 79–85. (In Russian) DOI: 10.15356/0435-4281-1973-4-.
This work was supported by the Russian Science Foundation, project No. 19-77-10036.
[2] Bugnicourt P., Guitet S., Santos V. F., Blanc L., Sotta E. D., Barbier N., Couteron P. Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield. Geomorphology, 2018, vol. 317, pp. 23–44. DOI:10.1016/j.geomorph.2018.03.017.
[3] Hargrove W. W., Hoffman F. M., Hessburg P. F. Mapcurves: a quantitative method for comparing categorical maps. Journal of Geographical Systems, 2006, vol. 8, no. 2, pp. 187–208. DOI:10.1007/s10109-006-0025-x.
[4] Kharchenko S.V. Automatic recognition of the landforms origin in the Kola Peninsula based on morphometric variables, Geodesy and Cartography, 2022, vol. 83, no. 2, pp. 12–25. DOI: 10.22389/0016-7126-2022-980-2-12-25. (In Russian) DOI: 10.22389/0016-7126-2022-980-2-12-25.
[5] Kharchenko S.V. New Challenges of Geomorphometry and Automatic Morphological Classifications in Geomorphology. Geomorfologiya, 2020, no. 1, pp. 3–21. (In Russian) DOI: 10.31857/S043542812001006X.
[6] Kozlov E. P., Cherdantsev S. G., Sokolovsky A. P., Novoseltseva R. G., Nikitin Yu. N., Voronov V. N., Suslov S. L. State Geological Map of the Russian Federation. Scale 1: 200,000. West Siberian series. Subseries Tyumen-Salekhardskaya. Sheets R-42-VII-IX, XIII-XV. Explanatory note. Moscow, MF VSEGEI (Publ.), 2015. 103 p. (In Russian)
[7] Maxwell A. E., Shobe C. M. Land-surface parameters for spatial predictive mapping and modeling. Earth-Science Reviews, 2022, vol. 226, pp. 103944. DOI: 10.1016/j.earscirev.2022.103944.
[8] McDermid G. J., Franklin S. E. Spectral, spatial, and geomorphometric variables for the remote sensing of slope processes. Remote Sensing of Environment, 1994, vol. 49, no. 1, pp. 57–71. (In Russian) DOI:10.1016/0034-4257(94)90059-0.
[9] Mel'nik M.A., Pozdnyakov A.V. Fractals in the erosion dissection and self-oscillations in geomorphosystems' dynamics. Geomorfologiya, 2008, no. 3, pp. 86–95. (In Russian). DOI: 10.15356/0435-4281-2008-3-86-95.
[10] Ng V. W., Breiman L. Bivariate variable selection for classification problem. Technical report. Berkeley: Department of Statistics, University of California-Berkeley, 2005. 22 p.
[11] Open Topography – ALOS World 3d – 30 m. – 2021. – URL: https://portal.opentopography.org/raster?opentopoID=OTALOS.112016.4326.2. Available at 01.01.2022.
[12] Serebryanny L.R., Chuklenkova I.N. Density of lakes as an age indicator of glacigenetic morphosculpture: an application of morphometric analysis in the northwestern areas of the Russian Plain. Geomorfologiya, 1973 no. 4, pp. 79–85. (In Russian) DOI: 10.15356/0435-4281-1973-4-.
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https://doi.org/10.5593/sgem2024/2.1/s08.14
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Proceedings of 24th International Multidisciplinary Scientific GeoConference SGEM 2024
24th International Multidisciplinary Scientific GeoConference SGEM 2024, 1 - 7 July, 2024
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
105-112
1 - 7 July, 2024
website
9926
geomorphological mapping, predictive modeling, supervised classification, terrain geomorphometric analysis, spectral terrain variables.

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