Geoinformation modeling of permafrost landscapes of North-Eastern Siberia
International audience The landscape-indicative approach makes it possible to determine permafrost landscapes based on the identification of two physiognomic indicators-variables of relief and vegetation, as well as stratigraphic-genetic sediment complexes. For permafrost characteristics of landscap...
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ftunivcotedazur:oai:HAL:hal-03897646v1 2024-04-14T08:07:51+00:00 Geoinformation modeling of permafrost landscapes of North-Eastern Siberia Zakharov, Moisei, Ivanovich Gadal, Sébastien Danilov, Yuri Étude des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA) North-Eastern Federal University Aix Marseille Université (AMU) Russian Science Foundation No. 21-17-00250. V.B. Sochava Institute of Geography SB RAS Valdimitrov Igor Nikolaevich Irkutsk, Russia 2022-08-23 https://hal.science/hal-03897646 https://hal.science/hal-03897646/document https://hal.science/hal-03897646/file/Zakharov%20et%20al.,%202022Irkutsk.pdf en eng HAL CCSD hal-03897646 https://hal.science/hal-03897646 https://hal.science/hal-03897646/document https://hal.science/hal-03897646/file/Zakharov%20et%20al.,%202022Irkutsk.pdf http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess Resources, Environment and Regional Sustainability in Northeast Asia https://hal.science/hal-03897646 Resources, Environment and Regional Sustainability in Northeast Asia, V.B. Sochava Institute of Geography SB RAS, Aug 2022, Irkutsk, Russia. pp.171 http://irigs.irk.ru/resources2022/en/ Landscape permafrost Remote sensing Machine learning GEE Land cover change Arctic Mountains ASTER GDEM Siberian Eastern Yakutia [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] info:eu-repo/semantics/conferenceObject Conference papers 2022 ftunivcotedazur 2024-03-21T18:05:33Z International audience The landscape-indicative approach makes it possible to determine permafrost landscapes based on the identification of two physiognomic indicators-variables of relief and vegetation, as well as stratigraphic-genetic sediment complexes. For permafrost characteristics of landscapes, multilevel combinations of environmental variables (criteria) for identifying landscapes are used. All this is the result of the analysis and processing of a huge array of field data in various landscapes of Yakutia (Fedorov, 2022). One of the main methods for obtaining these variables is geoinformation modeling as a methodological solution based on the construction and use of models of spatial objects, their relationships, and the dynamics of processes using GIS tools (Zhurkin et al., 2012). The aim of our study is to develop a geoinformation modeling technique for studying mountainous permafrost landscapes using the Orulgan ridge as the study case. The Orulgan Ridge is characterized by a meridional orographic structure and the presence of all varieties of mountain permafrost landscapes in the Arctic, which makes it a reasonable choice among the other mountain regions in terms of coverage and diversity. Cloud calculator platforms such as Google Earth Engine(GEE) are becoming the main tool for land cover-based landscape indication (DeLancey et al, 2019). The core of geoinformation modeling is the data of multitemporal multi-zone satellite images and a digital elevation model, the synthesis and processing of which make it possible to carry out landscape indication. The methodological workflow for obtaining data on the spatial structure of permafrost landscapes consists of the sequential compilation and synthesis of vegetation cover data, using the supervised phenology-based classification of the time series of Sentinel 2 MSI and Landsat 8 OLI data. Landform classification is performed on the basis of GIS-based terrain analysis by the ASTER GDEM scenes. The characteristics of the relief and genetic deposits were ... Conference Object Arctic permafrost Yakutia Siberia HAL Université Côte d'Azur Arctic |
institution |
Open Polar |
collection |
HAL Université Côte d'Azur |
op_collection_id |
ftunivcotedazur |
language |
English |
topic |
Landscape permafrost Remote sensing Machine learning GEE Land cover change Arctic Mountains ASTER GDEM Siberian Eastern Yakutia [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
spellingShingle |
Landscape permafrost Remote sensing Machine learning GEE Land cover change Arctic Mountains ASTER GDEM Siberian Eastern Yakutia [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Zakharov, Moisei, Ivanovich Gadal, Sébastien Danilov, Yuri Geoinformation modeling of permafrost landscapes of North-Eastern Siberia |
topic_facet |
Landscape permafrost Remote sensing Machine learning GEE Land cover change Arctic Mountains ASTER GDEM Siberian Eastern Yakutia [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
description |
International audience The landscape-indicative approach makes it possible to determine permafrost landscapes based on the identification of two physiognomic indicators-variables of relief and vegetation, as well as stratigraphic-genetic sediment complexes. For permafrost characteristics of landscapes, multilevel combinations of environmental variables (criteria) for identifying landscapes are used. All this is the result of the analysis and processing of a huge array of field data in various landscapes of Yakutia (Fedorov, 2022). One of the main methods for obtaining these variables is geoinformation modeling as a methodological solution based on the construction and use of models of spatial objects, their relationships, and the dynamics of processes using GIS tools (Zhurkin et al., 2012). The aim of our study is to develop a geoinformation modeling technique for studying mountainous permafrost landscapes using the Orulgan ridge as the study case. The Orulgan Ridge is characterized by a meridional orographic structure and the presence of all varieties of mountain permafrost landscapes in the Arctic, which makes it a reasonable choice among the other mountain regions in terms of coverage and diversity. Cloud calculator platforms such as Google Earth Engine(GEE) are becoming the main tool for land cover-based landscape indication (DeLancey et al, 2019). The core of geoinformation modeling is the data of multitemporal multi-zone satellite images and a digital elevation model, the synthesis and processing of which make it possible to carry out landscape indication. The methodological workflow for obtaining data on the spatial structure of permafrost landscapes consists of the sequential compilation and synthesis of vegetation cover data, using the supervised phenology-based classification of the time series of Sentinel 2 MSI and Landsat 8 OLI data. Landform classification is performed on the basis of GIS-based terrain analysis by the ASTER GDEM scenes. The characteristics of the relief and genetic deposits were ... |
author2 |
Étude des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA) North-Eastern Federal University Aix Marseille Université (AMU) Russian Science Foundation No. 21-17-00250. V.B. Sochava Institute of Geography SB RAS Valdimitrov Igor Nikolaevich |
format |
Conference Object |
author |
Zakharov, Moisei, Ivanovich Gadal, Sébastien Danilov, Yuri |
author_facet |
Zakharov, Moisei, Ivanovich Gadal, Sébastien Danilov, Yuri |
author_sort |
Zakharov, Moisei, Ivanovich |
title |
Geoinformation modeling of permafrost landscapes of North-Eastern Siberia |
title_short |
Geoinformation modeling of permafrost landscapes of North-Eastern Siberia |
title_full |
Geoinformation modeling of permafrost landscapes of North-Eastern Siberia |
title_fullStr |
Geoinformation modeling of permafrost landscapes of North-Eastern Siberia |
title_full_unstemmed |
Geoinformation modeling of permafrost landscapes of North-Eastern Siberia |
title_sort |
geoinformation modeling of permafrost landscapes of north-eastern siberia |
publisher |
HAL CCSD |
publishDate |
2022 |
url |
https://hal.science/hal-03897646 https://hal.science/hal-03897646/document https://hal.science/hal-03897646/file/Zakharov%20et%20al.,%202022Irkutsk.pdf |
op_coverage |
Irkutsk, Russia |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic permafrost Yakutia Siberia |
genre_facet |
Arctic permafrost Yakutia Siberia |
op_source |
Resources, Environment and Regional Sustainability in Northeast Asia https://hal.science/hal-03897646 Resources, Environment and Regional Sustainability in Northeast Asia, V.B. Sochava Institute of Geography SB RAS, Aug 2022, Irkutsk, Russia. pp.171 http://irigs.irk.ru/resources2022/en/ |
op_relation |
hal-03897646 https://hal.science/hal-03897646 https://hal.science/hal-03897646/document https://hal.science/hal-03897646/file/Zakharov%20et%20al.,%202022Irkutsk.pdf |
op_rights |
http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess |
_version_ |
1796305251142205440 |