Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley

The landscape taxonomy has a complex structure and hierarchical classification with indicators of their recognition, which is based on a variety of heterogeneous geographic territorial and expert knowledge. This inevitably leads to difficulties in the interpretation of remote sensing data and image...

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Published in:Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management
Main Authors: Zakharov, Moisei, Gadal, Sébastien, Danilov, Yuri, Kamičaitytė, Jūratė
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Online Access:https://vb.ktu.edu/KTU:ELABAPDB95107052&prefLang=en_US
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spelling ftkaunastuniv:oai:elaba:95107052 2023-05-15T15:09:40+02:00 Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley Zakharov, Moisei Gadal, Sébastien Danilov, Yuri Kamičaitytė, Jūratė 2021 application/pdf https://vb.ktu.edu/KTU:ELABAPDB95107052&prefLang=en_US eng eng info:eu-repo/semantics/altIdentifier/doi/10.5220/0010448801250133 https://epubl.ktu.edu/object/elaba:95107052/95107052.pdf https://vb.ktu.edu/KTU:ELABAPDB95107052&prefLang=en_US info:eu-repo/semantics/openAccess GISTAM 2021: proceedings of the 7th international conference on geographical information systems theory, applications and management, April 23-25, 2021 / edited by C. Grueau, R. Laurini, L. Ragia, Setúbal : Scitepress - Science and technology publications, 2021, vol. 1, p. 125-133 ISSN 2184-500X ISBN 9789897585036 permafrost landscape remote sensing modeling landscape mapping terrain landsat ASTER GDEM Yakutia info:eu-repo/semantics/article 2021 ftkaunastuniv https://doi.org/10.5220/0010448801250133 2022-01-20T00:10:50Z The landscape taxonomy has a complex structure and hierarchical classification with indicators of their recognition, which is based on a variety of heterogeneous geographic territorial and expert knowledge. This inevitably leads to difficulties in the interpretation of remote sensing data and image analysis in landscape research in the field of classification and mapping. This article examines an approach to the analysis of intraseason Landsat 8 OLI images and modeling of ASTER GDEM data for mapping of mountain permafrost landscapes of Northern Siberia at the scale of 1: 500,000 as well as its methods of classification and geographical recognition. This approach suggests implementing the recognition of terrain types and vegetation types of landscape types. The 8 types of the landscape have been identified by using the classification of the relief applying Jenness's algorithm and the assessment of the geomorphological parameters of the valley. The 6 vegetation types have been identified in mountain tundra, mountain woodlands, and valley complexes of the Adycha river valley in the Verkhoyansk mountain range. The results of mapping and the proposed method for the interpretation of remote sensing data used at regional and local levels of studying the characteristics of the permafrost distribution. The work contributes to the understanding of the landscape organization of remote mountainous permafrost areas and to the improvement of methods for mapping the permafrost landscapes for territorial development and rational environmental management. Article in Journal/Newspaper Arctic permafrost Tundra Yakutia Siberia KTU ePubl (Kaunas University of Technology) Adycha ENVELOPE(134.773,134.773,68.217,68.217) Arctic Verkhoyansk ENVELOPE(133.400,133.400,67.544,67.544) Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management 125 133
institution Open Polar
collection KTU ePubl (Kaunas University of Technology)
op_collection_id ftkaunastuniv
language English
topic permafrost landscape
remote sensing modeling
landscape mapping
terrain
landsat
ASTER GDEM
Yakutia
spellingShingle permafrost landscape
remote sensing modeling
landscape mapping
terrain
landsat
ASTER GDEM
Yakutia
Zakharov, Moisei
Gadal, Sébastien
Danilov, Yuri
Kamičaitytė, Jūratė
Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley
topic_facet permafrost landscape
remote sensing modeling
landscape mapping
terrain
landsat
ASTER GDEM
Yakutia
description The landscape taxonomy has a complex structure and hierarchical classification with indicators of their recognition, which is based on a variety of heterogeneous geographic territorial and expert knowledge. This inevitably leads to difficulties in the interpretation of remote sensing data and image analysis in landscape research in the field of classification and mapping. This article examines an approach to the analysis of intraseason Landsat 8 OLI images and modeling of ASTER GDEM data for mapping of mountain permafrost landscapes of Northern Siberia at the scale of 1: 500,000 as well as its methods of classification and geographical recognition. This approach suggests implementing the recognition of terrain types and vegetation types of landscape types. The 8 types of the landscape have been identified by using the classification of the relief applying Jenness's algorithm and the assessment of the geomorphological parameters of the valley. The 6 vegetation types have been identified in mountain tundra, mountain woodlands, and valley complexes of the Adycha river valley in the Verkhoyansk mountain range. The results of mapping and the proposed method for the interpretation of remote sensing data used at regional and local levels of studying the characteristics of the permafrost distribution. The work contributes to the understanding of the landscape organization of remote mountainous permafrost areas and to the improvement of methods for mapping the permafrost landscapes for territorial development and rational environmental management.
format Article in Journal/Newspaper
author Zakharov, Moisei
Gadal, Sébastien
Danilov, Yuri
Kamičaitytė, Jūratė
author_facet Zakharov, Moisei
Gadal, Sébastien
Danilov, Yuri
Kamičaitytė, Jūratė
author_sort Zakharov, Moisei
title Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley
title_short Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley
title_full Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley
title_fullStr Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley
title_full_unstemmed Mapping Siberian Arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of Adycha river valley
title_sort mapping siberian arctic mountain permafrost landscapes by machine learning multi-sensors remote sensing: example of adycha river valley
publishDate 2021
url https://vb.ktu.edu/KTU:ELABAPDB95107052&prefLang=en_US
long_lat ENVELOPE(134.773,134.773,68.217,68.217)
ENVELOPE(133.400,133.400,67.544,67.544)
geographic Adycha
Arctic
Verkhoyansk
geographic_facet Adycha
Arctic
Verkhoyansk
genre Arctic
permafrost
Tundra
Yakutia
Siberia
genre_facet Arctic
permafrost
Tundra
Yakutia
Siberia
op_source GISTAM 2021: proceedings of the 7th international conference on geographical information systems theory, applications and management, April 23-25, 2021 / edited by C. Grueau, R. Laurini, L. Ragia, Setúbal : Scitepress - Science and technology publications, 2021, vol. 1, p. 125-133
ISSN 2184-500X
ISBN 9789897585036
op_relation info:eu-repo/semantics/altIdentifier/doi/10.5220/0010448801250133
https://epubl.ktu.edu/object/elaba:95107052/95107052.pdf
https://vb.ktu.edu/KTU:ELABAPDB95107052&prefLang=en_US
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5220/0010448801250133
container_title Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management
container_start_page 125
op_container_end_page 133
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