SPATIAL STRUCTURE OF THE PERMAFROST LANDSCAPES OF YAKUTIA: GEOINFORMATIONAL MODELING (ON THE EXAMPLE OF MIDDLE TAIGA AND MOUNTAIN PERMAFROST LANDSCAPES
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....
Main Author: | |
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Other Authors: | , , , , , , |
Format: | Doctoral or Postdoctoral Thesis |
Language: | Russian |
Published: |
HAL CCSD
2021
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Subjects: | |
Online Access: | https://hal.science/tel-03281453 https://hal.science/tel-03281453v1/document https://hal.science/tel-03281453v1/file/-%D0%BA%D0%B2%D0%B0%D0%BB%D0%B8%D1%84%D0%B8%D0%BA%D0%B0%D1%86%D0%B8%D0%BE%D0%BD%D0%BD%D0%B0%D1%8F%20%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%B0%20-%D0%97%D0%B0%D1%85%D0%B0%D1%80%D0%BE%D0%B2%20%D0%9C.%D0%98.pdf |
Summary: | 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 ... |
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