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)

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 u...

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Autores principales:	Zakharov Moisey, Cherosov Mikhail, Troeva Elena, Gadal Sebastien
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Microbiology QR1-502 Physiology QP1-981 Zoology QL1-991
Acceso en línea:	https://doaj.org/article/db3d81368256457392df244e0527747a
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spelling	oai:doaj.org-article:db3d81368256457392df244e0527747a2021-11-08T15:17:41ZVegetation 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)2117-445810.1051/bioconf/20213800142https://doaj.org/article/db3d81368256457392df244e0527747a2021-01-01T00:00:00Zhttps://www.bio-conferences.org/articles/bioconf/pdf/2021/10/bioconf_napd2021_00142.pdfhttps://doaj.org/toc/2117-4458For 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.Zakharov MoiseyCherosov MikhailTroeva ElenaGadal SebastienEDP SciencesarticleMicrobiologyQR1-502PhysiologyQP1-981ZoologyQL1-991ENFRBIO Web of Conferences, Vol 38, p 00142 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Microbiology QR1-502 Physiology QP1-981 Zoology QL1-991
spellingShingle	Microbiology QR1-502 Physiology QP1-981 Zoology QL1-991 Zakharov Moisey Cherosov Mikhail Troeva Elena Gadal Sebastien 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)
description	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.
format	article
author	Zakharov Moisey Cherosov Mikhail Troeva Elena Gadal Sebastien
author_facet	Zakharov Moisey Cherosov Mikhail Troeva Elena Gadal Sebastien
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	EDP Sciences
publishDate	2021
url	https://doaj.org/article/db3d81368256457392df244e0527747a
work_keys_str_mv	AT zakharovmoisey vegetationcoveranalysisofthemountainouspartofnortheasternsiberiabymeansofgeoinformationmodellingandmachinelearningbasicprinciplesapproachestechnologyandrelationtogeosystemscience AT cherosovmikhail vegetationcoveranalysisofthemountainouspartofnortheasternsiberiabymeansofgeoinformationmodellingandmachinelearningbasicprinciplesapproachestechnologyandrelationtogeosystemscience AT troevaelena vegetationcoveranalysisofthemountainouspartofnortheasternsiberiabymeansofgeoinformationmodellingandmachinelearningbasicprinciplesapproachestechnologyandrelationtogeosystemscience AT gadalsebastien vegetationcoveranalysisofthemountainouspartofnortheasternsiberiabymeansofgeoinformationmodellingandmachinelearningbasicprinciplesapproachestechnologyandrelationtogeosystemscience
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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)

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