Aspirantura / Moisei ZAKHAROV
Moisei ZAKAROV soutiendra son “Aspirantura” (doctorat russe de géographie) le samedi 19 juin 2021 à Aix-Marseille Université.
« The Spatial Structure of the Permafrost Landscapes of Yakutia: Geoinformation Modeling (Example of the Middle Taiga and Mountain Permafrost Landscapes) »
Ce travail a été réalisé sous la direction de Sébastien Gadal (Professeur, Aix-Marseille Université, UMR 7300 ESPACE) et Yuri Danilov (Université fédérale du Nord-Est à Iakoutsk, Russie).
Le jury sera composé de :
Dmitry CHERNYKH, Professor, Doctor of Science in Geography, Siberian Federal University, Institute For Water and Environmental Problems, SB RAS, President
Yuri DANILOV, Professor, Candidate of Science in Geography, North-Eastern Federal University, Co-supervisor
Alexander FEDOROV, Chief Researcher, Doctor of Science in Geography, Melnikov Permafrost Institute SB RAS, Examiner
Sébastien GADAL, Professor, HDR of Geography, Aix-Marseille University, North-Eastern Federal University, CNRS ESPACE UMR 7300, Co-supervisor
Alexey GALANIN, Chief Researcher, Doctor of Science in Geography, Melnikov Permafrost Institute, SB RAS, Examiner
Résumé de la thèse
The solution of many issues of rational land use management is based on information about the state of the landscape complexes. The possibilities of obtaining reliable information on the spatial structure of permafrost landscapes become especially relevant for state assessment and evolution trends. Increasing environmental changes in the cryolithozone zone are associated with trends in climate change and vulnerability to anthropogenic impacts. The aim of this dissertation is to study the spatial structure of the permafrost landscapes of Yakutia on the basis of geoinformation modeling, taking as the object of research the middle taiga permafrost landscapes of Central Yakutia and the mountain permafrost landscapes of the Verkhoyansk region. To achieve this aim, the methods of the analysis and the processing of the time series of multi-sensor remote sensing data and digital elevation model has been developed. The methods allow modeling to map the visible morphological features of permafrost landscapes (relief and vegetation) with the implementation of ontological properties with the permafrost and lithogenic base. Time series of Sentinel 2 and Landsat 8 OLI images for the period 2015-2020 are used to map vegetation classes and analyze the state of vegetation cover. The vegetation classes are recognized by the variation in the photosynthetic activity of plant associations on the spectral reflectance characteristics. This allows us to use differences in phenological phases to increase the recognizable classes of plant associations. The accuracy assessments of the classification results are calculated by the comparison with high resolution images and field data to ensure a high level of validation confidence. Based on the ASTER GDEM relief data, we determined the genetic type of Quaternary sediments according to the landform classification calculated by the Topographic Position Index (TPI) and GIS analysis. Thus, modeling these two morphological features allows the development of a geospatial database of test study area. The database includes information on the existence of intensity transformation factors for the identified landscape complexes. The GIS models of permafrost landscapes provide information on the type of soil cover, soil conditions (moisture, temperature) and cryogenic texture. Obtaining such data demonstrates the effectiveness of the modeling for assessing the spatial structure of permafrost landscape in local level.
Keyword: Permafrost landscape, geoformation modeling, Remote sensing, Satellite image time series, terrain analysis, spatial structure, Yakutia