Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science)
International audience For the first time, the geoinformation modelling and machine learning approaches have been used to study the vegetation cover of the mountainous part of North-Eastern Siberia – the Orulgan medium-altitude mountain landscape province. These technologies allowed us to distinguis...
Published in: | BIO Web of Conferences |
---|---|
Main Authors: | , , , |
Other Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2021
|
Subjects: | |
Online Access: | https://hal.science/hal-03430427 https://hal.science/hal-03430427/document https://hal.science/hal-03430427/file/bioconf_napd2021_00142.pdf https://doi.org/10.1051/bioconf/20213800142 |
id |
ftunivavignon:oai:HAL:hal-03430427v1 |
---|---|
record_format |
openpolar |
spelling |
ftunivavignon:oai:HAL:hal-03430427v1 2024-06-23T07:50:28+00:00 Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) Zakharov, Moisey Cherosov, Mikhail Troeva, Elena Gadal, Sébastien North-Eastern Federal University Études 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) Aix Marseille Université (AMU) Institute for Biological Problems of the Cryolithozone (IBPC) Siberian Branch of the Russian Academy of Sciences (SB RAS) FMSH-RSF OSAMA 2021-10-28 https://hal.science/hal-03430427 https://hal.science/hal-03430427/document https://hal.science/hal-03430427/file/bioconf_napd2021_00142.pdf https://doi.org/10.1051/bioconf/20213800142 en eng HAL CCSD EDP Sciences info:eu-repo/semantics/altIdentifier/doi/10.1051/bioconf/20213800142 hal-03430427 https://hal.science/hal-03430427 https://hal.science/hal-03430427/document https://hal.science/hal-03430427/file/bioconf_napd2021_00142.pdf doi:10.1051/bioconf/20213800142 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2273-1709 EISSN: 2117-4458 BIO Web of Conferences https://hal.science/hal-03430427 BIO Web of Conferences, 2021, 38, pp.00142. ⟨10.1051/bioconf/20213800142⟩ https://www.bio-conferences.org/ Vegetation Cover Arctic Mountain Machine Learning Geoinformation approach Spatial Modelling Remote Sensing Siberia Russia [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] info:eu-repo/semantics/article Journal articles 2021 ftunivavignon https://doi.org/10.1051/bioconf/20213800142 2024-06-10T23:49:39Z International audience For the first time, the geoinformation modelling and machine learning approaches have been used to study the vegetation cover of the mountainous part of North-Eastern Siberia – the Orulgan medium-altitude mountain landscape province. These technologies allowed us to distinguish a number of mapping units that were used for creation and analysis of 1:100 000 scale vegetation map of the interpreted key area. Based on the studies, we decided upon the basic principles, approaches and technologies that would serve as a methodology basis for the further studies of vegetation cover of the large region. Relief, slope aspect, genetic types of sediments, and moisture conditions were selected as supplementary factors to the vegetative indices for differentiation of both plant communities and vegetation map units. Article in Journal/Newspaper Arctic Siberia Université d'Avignon et des Pays de Vaucluse: HAL Arctic BIO Web of Conferences 38 00142 |
institution |
Open Polar |
collection |
Université d'Avignon et des Pays de Vaucluse: HAL |
op_collection_id |
ftunivavignon |
language |
English |
topic |
Vegetation Cover Arctic Mountain Machine Learning Geoinformation approach Spatial Modelling Remote Sensing Siberia Russia [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] |
spellingShingle |
Vegetation Cover Arctic Mountain Machine Learning Geoinformation approach Spatial Modelling Remote Sensing Siberia Russia [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Zakharov, Moisey Cherosov, Mikhail Troeva, Elena Gadal, Sébastien Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
topic_facet |
Vegetation Cover Arctic Mountain Machine Learning Geoinformation approach Spatial Modelling Remote Sensing Siberia Russia [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] |
description |
International audience For the first time, the geoinformation modelling and machine learning approaches have been used to study the vegetation cover of the mountainous part of North-Eastern Siberia – the Orulgan medium-altitude mountain landscape province. These technologies allowed us to distinguish a number of mapping units that were used for creation and analysis of 1:100 000 scale vegetation map of the interpreted key area. Based on the studies, we decided upon the basic principles, approaches and technologies that would serve as a methodology basis for the further studies of vegetation cover of the large region. Relief, slope aspect, genetic types of sediments, and moisture conditions were selected as supplementary factors to the vegetative indices for differentiation of both plant communities and vegetation map units. |
author2 |
North-Eastern Federal University Études 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) Aix Marseille Université (AMU) Institute for Biological Problems of the Cryolithozone (IBPC) Siberian Branch of the Russian Academy of Sciences (SB RAS) FMSH-RSF OSAMA |
format |
Article in Journal/Newspaper |
author |
Zakharov, Moisey Cherosov, Mikhail Troeva, Elena Gadal, Sébastien |
author_facet |
Zakharov, Moisey Cherosov, Mikhail Troeva, Elena Gadal, Sébastien |
author_sort |
Zakharov, Moisey |
title |
Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
title_short |
Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
title_full |
Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
title_fullStr |
Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
title_full_unstemmed |
Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
title_sort |
vegetation cover analysis of the mountainous part of north-eastern siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science) |
publisher |
HAL CCSD |
publishDate |
2021 |
url |
https://hal.science/hal-03430427 https://hal.science/hal-03430427/document https://hal.science/hal-03430427/file/bioconf_napd2021_00142.pdf https://doi.org/10.1051/bioconf/20213800142 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Siberia |
genre_facet |
Arctic Siberia |
op_source |
ISSN: 2273-1709 EISSN: 2117-4458 BIO Web of Conferences https://hal.science/hal-03430427 BIO Web of Conferences, 2021, 38, pp.00142. ⟨10.1051/bioconf/20213800142⟩ https://www.bio-conferences.org/ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1051/bioconf/20213800142 hal-03430427 https://hal.science/hal-03430427 https://hal.science/hal-03430427/document https://hal.science/hal-03430427/file/bioconf_napd2021_00142.pdf doi:10.1051/bioconf/20213800142 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1051/bioconf/20213800142 |
container_title |
BIO Web of Conferences |
container_volume |
38 |
container_start_page |
00142 |
_version_ |
1802641374349099008 |