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An Influence of Snow Covers on the Radar Interferometry Observations of Industrial Infrastructure: Norilsk Thermal Power Plant Case.
- Source :
-
Remote Sensing . Feb2023, Vol. 15 Issue 3, p654. 17p. - Publication Year :
- 2023
-
Abstract
- This manuscript presents the results of the study of snow covers' influence on the interferometric measurements of the stability of industrial infrastructure in the vicinity of Norilsk city, Russia. Fuel tanks of the Norilsk thermal power plant (TPP) were selected as an object of study due to a well-known accident when about 20,000 tons of diesel fuel spilled from one of the tanks. Sentinel-1 synthetic aperture radar data acquired over the territory of Norilsk TPP were used in the DInSAR study of the possible displacements of the tanks that could be the cause of the tank's damage. For twelve days, radar interferograms that were generated in the study covered the cold and warm seasons of 2018–2020, including the catastrophic event—the rupture of the tank with diesel fuel—in order to shed light on the possible impact of the area subsidence because of permafrost thaw under the tanks. As the tank walls and adjacent concrete base constituted the virtual dihedral corner reflector, the accumulation of snow on the surface near the tanks created a distorting effect on the results of monitoring the stability of the tank's location. Three models of snow layer within the dihedral proposed could help explain the deviations in the signal amplitude and phase in the case of snowfalls occurring between radar observations. We propose three ways to minimize the influence of snow on interferometric measurements. One of them, the selection of the radar data acquired in proper observation conditions, made it possible to assess the stability of the mutual location of the tanks. Among the most important processing and analysis results in the paper is a conclusion about the high stability of the fuel tank's location on the yearly time interval, including the troubleshooting tank. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 161870749
- Full Text :
- https://doi.org/10.3390/rs15030654