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This article is cited in 6 scientific papers (total in 6 papers)
Computer modelling
The flooding dynamics of the Volga–Akhtuba floodplain during spring flood using space monitoring
A. N. Presnyakovaa, A. V. Pisarevb, S. S. Khrapovb a Volgograd State University, Institute of Mathematics and Information Technologies
b Volgograd State University
Abstract:
Volga–Akhtuba floodplain is exposed to annual flooding. Depending on several factors (hydrological regime, hydrographic network, the weather situation and socio-economic activities) flood events may lead to various consequences both positive and negative. Monitoring is necessary to eliminate unwanted options for the growth of flood areas and to predict potentially dangerous situations. One of the methods of studying phenomena in the Volga–Akhtuba floodplain is a space monitoring. Science programs such as Landsat and Sentinel allow to receive spatial data with a resolution of 10–30 meters with intervals 3–10 days. Thus, it becomes possible to track the dynamics of flooding and water distribution for the assessment of flood zones.
Satellite images were taken of different time intervals corresponding to the period of 2013–2016 years floods. The technology of remote sensing data decryption based on the algorithms of search quality hydrographic objects was developed. The software module for multi-layer processing of satellite data in GRASS GIS system was developed. The integral indicators of flooding of the Volga–Akhtuba floodplain for several years were analyzed. Thus, research of flood events with floodplain systems using modern satellite monitoring equipment is promising.
Keywords:
GIS, flood detection, ERS, GRASS GIS, Landsat, Sentinel.
Citation:
A. N. Presnyakova, A. V. Pisarev, S. S. Khrapov, “The flooding dynamics of the Volga–Akhtuba floodplain during spring flood using space monitoring”, Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 1. Mathematica. Physica, 2017, no. 1(38), 66–74
Linking options:
https://www.mathnet.ru/eng/vvgum164 https://www.mathnet.ru/eng/vvgum/y2017/i1/p66
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