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)

• Published in: BIO Web of Conferences
• Main Authors: Zakharov, Moisey, Cherosov, Mikhail, Troeva, Elena, GADAL, Sébastien
• Other Authors: North-Eastern Federal University, Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), 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
• Language: English
• Published: HAL CCSD 2021
• Subjects: 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/Environmental 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]; Arctic
• Online Access: https://hal.archives-ouvertes.fr/hal-03430427; https://hal.archives-ouvertes.fr/hal-03430427/document; https://hal.archives-ouvertes.fr/hal-03430427/file/bioconf_napd2021_00142.pdf; https://doi.org/10.1051/bioconf/20213800142

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.