Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 3, pp. 84-94
Identification of tornado cases in a forest region using long-term series of remote sensing data
A.N. Shikhov
1 , A.V. Tarasov
1
1 Perm State National Research University, Perm, Russia
Accepted: 18.04.2016
DOI: 10.21046/2070-7401-2016-13-3-84-94
The article describes a method of tornado track identification and mapping in forested regions using long-term series of LANDSAT data and LANDSAT-based Global Forest Change Map. The method is based on the identification of narrow and elongated areas of total forest windfall disturbances, with their subsequent verification by high-resolution satellite images, received from open map services. To determine the date of tornado occurrence (the appearance of windfall) we have used all available images from LANDSAT and Terra/Aqua MODIS sensors and CFS model reanalysis data. The proposed approach makes it possible to objectively (that is independent of the population density and observation network) estimate the spatial and temporal distribution of tornado cases in forested regions. It also allows identifying areas of high frequency of tornado appearance, and estimating some tornado parameters (path length, the average and maximum width of tornado, and sense of rotation).
We compared the geometrical parameters of tornado tracks identified by Global Forest Change and digitized manually for high resolution satellite images. A very good agreement of the results proved the relevance of using Global Forest Change data for tornado tracks identification, and low probability of missing objects.
The proposed method of tornado tracks identification was implemented for the eastern part of the European Russia. In total, in this area we identified 35 tracks of tornadoes occurred in the period from 2003 to 2014. The highest tornadoes frequency is observed on the north of Perm and Kirov Regions, in the southern and eastern parts of Komi Republic.
Keywords: tornadoes, forest windfalls disturbances, remote sensing data, LANDSAT images, Global Forest Change Map
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