Abstract
The aim of the study is to develop a script and intelligent technology for collection and processing of heterogeneous data of field measurements and multispectral space imagery in order to identify and assess the state of natural objects. The core principle of the method is adaptive modeling and adjustment of an identification and evaluation technology of the properties of natural objects based on processing of multispectral space imagery, a geobotanical description and field measurements. As an example, we consider the problem of identification and evaluation of tundra vegetation using imagery from Sentinel-2 or Resource-P satellites. The results of the identification and assessment of the state of tundra vegetation are presented by a part of a vegetation map matching territories suitable for grazing deer in different seasons. The identification and assessment are focused on plant community types and possibility to feed reindeer in different seasons of the year. The testing results demonstrate effectiveness of the proposed technology for collection and processing of heterogeneous data.
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Acknowledgements
The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 16-08-00510, 17-06-00108, state research 0073–2018–0003, International project ERASMUS+, Capacity building in higher education, №. 73751-EPP-1-2016-1-DE-EPPKA2-CBHE-JP, Innovative teaching and learning strategies in open modelling and simulation environment for student-centered engineering education.
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Mochalov, V.F., Grigorieva, O.V., Zelentsov, V.A., Markov, A.V., Ivanets, M.O. (2019). Intelligent Technologies and Methods of Tundra Vegetation Properties Detection Using Satellite Multispectral Imagery. In: Silhavy, R. (eds) Cybernetics and Automation Control Theory Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-030-19813-8_24
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DOI: https://doi.org/10.1007/978-3-030-19813-8_24
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