Peer-reviewed articles 17,970 +


Title: AN EXPERIMENT OF AUTOMATIC CLASSIFICATION AND MAPPING OF THE LANDFORMS OF THE YAMAL PENINSULA
AN EXPERIMENT OF AUTOMATIC CLASSIFICATION AND MAPPING OF THE LANDFORMS OF THE YAMAL PENINSULA

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Sergey Kharchenko
10.5593/sgem2024/2.1/s08.13
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 investigates the potential of automated geomorphological mapping using geomorphometric analysis and machine learning on the Yamal Peninsula, Russia. The research aims to classify landforms based solely on geomorphometric characteristics, bypassing traditional manual interpretation of aerial imagery and digital elevation models (DEMs). The study utilized a DEM of the Yamal Peninsula and a reference geomorphological map, including 10 distinct landform types. A total of 119 geomorphometric variables, including spectral characteristics of the terrain, were calculated and used for training a Random Forest classifier. The results demonstrate that the model achieved a 65.4% overall accuracy, significantly exceeding the baseline accuracy of 10%. While some landforms, like the first river terrace, were accurately classified with 98% precision, others, such as floodplains, showed lower accuracy. The study identified key geomorphometric variables contributing to the classification, highlighting the importance of "focal" characteristics reflecting the texture and pattern of topographic dissection. The findings suggest that automated classification based on geomorphometric analysis holds promise for geomorphological mapping. It can be used to expedite the creation of geomorphological maps and assist in identifying areas of uncertainty for further investigation. However, future research is necessary to improve the accuracy of specific landform classifications, particularly those with high spatial variability.
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[4] Deng Y., Wilson J., Sheng J., Effects of Variable Attribute Weights on Landform Classification, Earth Surface Processes and Landforms, no. 31, pp. 1452–1462, 2006.
[5] d'Oleire-Oltmanns S., Eisank C., Dragut L., Blaschke T., An Object-based Workflow to Extract Landforms at Multiple Scales from Two Distinct Data Types, IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 947–951, 2013.
[6] Gallant J.C., Dowling T.I., A Multiresolution Index of Valley Bottom Flatness for Mapping Depositional Areas, Water Resources Research, no. 39(12), pp. 1347–1360, 2003.
[7] Iwahashi J., Pike R.J., Automated Classifications of Topography from DEMs by an Unsupervised Nested-Means Algorithm and a Three-Part Geometric Signature, Geomorphology, vol. 86, pp. 409–440, 2007.
[8] Kharchenko S.V., Application of Harmonic Analysis for the Quantitative Description of Earth Surface Topography, Geomorfologiya, Russia, vol. 2, pp. 14-24, 2017. (in Russian).
[9] Kharchenko S.V., Bolysov S.I., Using of the Spectral Geomorphometric Characteristics for Automatized Classification of Landforms (On the Example of Australia), 18th International Multidisciplinary Scientific GeoConference SGEM, Albena, Bulgaria, pp. 719–724, 2018.
[10] Khatib A., Malinnikov V.A., Automated Classification of the Vegetation Cover of Mediterranean Landscape using Spectral-Textural and Topographic Features of High Spatial Resolution Satellite Imagery, Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli Iz Kosmosa, Russia, vol. 18, no. 2, pp. 51-63, 2021.
[11] MacMillan R.A., Martin T.C., Earle T.J., McNabb D.H., Automated Analysis and Classification of Landforms using High-resolution Digital Elevation Data: Applications and Issues, Canadian Journal of Remote Sensing, no. 29(5), pp. 592–606, 2003.
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[14] Weiss A., Topographic Position and Landforms Analysis, ESRI User Conference, San Diego, CA, USA, 2001.
This work was supported by the Russian Science Foundation, project No. 19-77-10036.
[3] Danielson J.J., Gesch D.B., Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, U.S. Geological Survey, 2011, 26 p.
[4] Deng Y., Wilson J., Sheng J., Effects of Variable Attribute Weights on Landform Classification, Earth Surface Processes and Landforms, no. 31, pp. 1452–1462, 2006.
[5] d'Oleire-Oltmanns S., Eisank C., Dragut L., Blaschke T., An Object-based Workflow to Extract Landforms at Multiple Scales from Two Distinct Data Types, IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 947–951, 2013.
[6] Gallant J.C., Dowling T.I., A Multiresolution Index of Valley Bottom Flatness for Mapping Depositional Areas, Water Resources Research, no. 39(12), pp. 1347–1360, 2003.
[7] Iwahashi J., Pike R.J., Automated Classifications of Topography from DEMs by an Unsupervised Nested-Means Algorithm and a Three-Part Geometric Signature, Geomorphology, vol. 86, pp. 409–440, 2007.
[8] Kharchenko S.V., Application of Harmonic Analysis for the Quantitative Description of Earth Surface Topography, Geomorfologiya, Russia, vol. 2, pp. 14-24, 2017. (in Russian).
[9] Kharchenko S.V., Bolysov S.I., Using of the Spectral Geomorphometric Characteristics for Automatized Classification of Landforms (On the Example of Australia), 18th International Multidisciplinary Scientific GeoConference SGEM, Albena, Bulgaria, pp. 719–724, 2018.
[10] Khatib A., Malinnikov V.A., Automated Classification of the Vegetation Cover of Mediterranean Landscape using Spectral-Textural and Topographic Features of High Spatial Resolution Satellite Imagery, Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli Iz Kosmosa, Russia, vol. 18, no. 2, pp. 51-63, 2021.
[11] MacMillan R.A., Martin T.C., Earle T.J., McNabb D.H., Automated Analysis and Classification of Landforms using High-resolution Digital Elevation Data: Applications and Issues, Canadian Journal of Remote Sensing, no. 29(5), pp. 592–606, 2003.
[12] State Geological Map of the Russian Federation (New Series). Scale 1:1,000,000. Geomorphological Map. Sheet R-40-42, 2000. (in Russian).
[13] Voskresenskiy S.S., Anan'ev G.S., Andreeva T.S., Varushchenko S.I., Leont'ev O.K., Luk'yanova S.A., Spasskaya I.I., Spiridonov A.I., Ul'yanova N.S., Geomorphological Zoning of the USSR and Adjacent Seas, Vysshaya Shkola, Moscow, Russia, 1980, 343 p. (in Russian).
[14] Weiss A., Topographic Position and Landforms Analysis, ESRI User Conference, San Diego, CA, USA, 2001.
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https://doi.org/10.5593/sgem2024/2.1/s08.13
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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.
97-104
1 - 7 July, 2024
website
9925
geomorphological map, supervised classification, medium scale, digital elevation model, geomorphometric analysis.

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