1 Kharchenko S.V.

Lomonosov Moscow State University (MSU)

The automatic geomorphological mapping based on the Earth’s surface remote sensing data has been developed in recent years. The aim of the research is an attempt of automatic creation of the Kola Peninsula geomorphological map at the morphogenetic legend’s principle. It was gained based on the random forest classification technique. As input data a several geomorphometric variables were used only (the basic ones – elevation, slope angle, curvatures etc., and many those of surface texture). Developing such algorithms for different territories and hierarchical levels of landforms analysis contributes to propagate the techniques of the fast geomorphological mapping. On the training data covering only 1,3 % of the study area with known labels for one of thirteen probable landform types, the reconstruction of geomorphological boundaries and the automatic creation of the geomorphological map were carried out. The accuracy of the resulting map was 81 %. Elevation has the greatest discrimination power according to Kola Peninsula landform types. In addition, some area-based local geomorphometric variables characterizing terrain pattern has big discrimination power. The lowest power relates with the “classical” local-based geomorphometric variables. The results of the work can be used in the development of automated landforms mapping systems at the level of morphogenetic types.
The study was supported by the Russian Science Foundation grant No. 19-77-10036.
1.   Bronguleev V. V. Sovremennye ekzogeodinamicheskie rezhimy Russkoi ravniny. Geomorfologiya, 2000, no. 4, pp. 11–23.
2.   Geomorfologicheskaya karta. Sost. M. K. Grave, L. M. Grave // Atlas Murmanskoi oblasti. Moskva: GUGK, 1971, pp. 8.
3.   Timofeev D. A. O polimorfizme kak obshchem svoistve zemnoi poverkhnosti. Geomorfologiya, 2006, no. 2, pp. 3–6.
4.   Ufimtsev G. F. Geomorfologicheskaya konvergentsiya. Geomorfologiya, 2009, no. 4, pp. 16–28.
5.   Kharchenko S. V. K voprosu o primenenii garmonicheskogo analiza pri kolichestvennoi kharakteristike rel'efa. Geomorfologiya, 2017, no. 2, pp. 14–24. DOI: 10.15356/0435-4281-2017-2-14-24.
6.   Kharchenko S. V. Novye zadachi morfometrii rel'efa i avtomatizirovannye morfologicheskie klassifikatsii v geomorfologii. Geomorfologiya, 2020, no. 1, pp. 3–21. DOI: 10.31857/S043542812001006X.
7.   Chursin I.N., Aleshina A.R., Gorokhova I.N. (2019) Irrigated soil mapping of Volgograd region (Svetly Yar area) using Landsat-8 and Canopus-B satellite images. Geodezia i Kartografia, 80(12), pp. 31-41. (In Russian). DOI: 10.22389/0016-7126-2019-954-12-31-41.
8.   Yamashkin S.A., Radovanovic M.M., Yamashkin A.A., Vukovich D.V., Frolov A.N. (2016) Using ensemble-based systems for the landscapes mapping. Geodezia i Kartografia, (7), pp. 42-49. (In Russian). DOI: 10.22389/0016-7126-2016-913-7-42-49.
9.   Boehner J., Selige T. (2006) Spatial prediction of soil attributes using terrain analysis and climate regionalization. In SAGA – Analysis and Modelling Applications. Goettingen: Goettinger Geographische Abhandlungen, pp. 13–28. URL: downloads.sourceforge.net/saga-gis/gga115_02.pdf (accessed: 12.03.2019).
10.   Breiman L. (2001) Random forests. Machine learning, Volume 45, no. 1, pp. 5–32.
11.   Florinsky I. V. (2016) Digital Terrain Analysis in Soil Science and Geology. 2nd ed. Amsterdam: Elsevier Academic Press. 486 p.
12.   Gallant J. C., Dowling T. I. (2003) A multiresolution index of valley bottom flatness for mapping depositional areas. Water resources research, no. 39 (12), pp. 4–13.
13.   GMTED 2010. URL: clck.ru/apDWY (accessed: 15.11.2020).
14.   González-Díez A., Barreda-Argüeso J. A., Rodríguez-Rodríguez L., Fernández-Lozano J. (2021) The use of filters based on the Fast Fourier Transform applied to DEMs for the objective mapping of karstic features. Geomorphology, Volume 385, no. 107724, DOI: 10.1016/j.geomorph.2021.107724.
15.   Han H., Guo X., Yu H. (2016) Variable selection using mean decrease accuracy and mean decrease Gini based on random forest. 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). pp. 219–224. DOI: 10.1109/ICSESS.2016.7883053.
16.   Iwahashi J., Pike R. J. (2007) Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, no. 86, pp. 409-440.
17.   Longadge R., Dongre S. (2013) Class imbalance problem in data mining: review. International Journal of Computer Science and Network, Volume 2, no. 1, URL: clck.ru/apDxG (accessed: 15.11.2020).
18.   MacMillan R. A., Pettapiece W. W., Nolan S. C., Goddard T. W. (2000) A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, Volume 113, no. 1, pp. 81–109.
19.   Miska L., Jan H. (2005) Evaluation of current statistical approaches for predictive geomorphological mapping. Geomorphology, no. 67, pp. 3–4.
Kharchenko S.V., 
(2022) Automatic recognition of the landforms origin in the Kola Peninsula based on morphometric variables. Geodesy and cartography = Geodezia i Kartografia, 83(2), pp. 12-25. (In Russian). DOI: 10.22389/0016-7126-2022-980-2-12-25
Publication History
Received: 23.02.2021
Accepted: 26.01.2022
Published: 20.03.2022


2022 February DOI: