Journal of Environmental Biology

Mapping natural forest disturbances in the Western Urals region using remote sensing

 Sergey V. Pyankov and Andrey N. Shikhov*

Department of Cartography and Geoinformatics, Perm State University, Perm-614 990, Russian Federation

*Corresponding Author E-mail: and3131@inbox.ru

Publication Data

Paper received:

15 October 2015

Revised received:

31 March 2016

Re-revised received:

19 April 2016

Accepted:

10 June 2016

Abstract

The present study investigated the damage caused by forest fires, strong winds and tornadoes to boreal forest of the Western Ural from 2001 to 2015. Long-term series of satellite imagery LANDSAT were used to detect forest disturbance caused by severe storms, tornadoes and large forest fires. The main method of forest disturbance identification was based on short-wave vegetation index (SWVI). Validation of SWVI-based automatized detection results is performed using the satellite images of high and very high resolution (SPOT-5 and WorldView-2). Nearly 70,000 ha of forest areas disturbed by forest fires, severe winds and tornadoes were revealed within the boundaries of the study area (0.6% of the total forested area in the study region). During 2001-2015, damage caused by forest fires was significantly greater than that caused by severe winds and tornadoes. Large forest fires were observed mainly in the north-western part of the study area with pine dominated forests. The damage caused by severe storms and tornadoes was distributed more equally on the study area. However, their frequency was also higher in the northern parts of the region. Identification of several tornado tracks allowed to estimate tornado frequency in the study region. Assessment of species composition and age structure of forests affected by fires and destructive storms, showed that the greatest damage was observed in mature coniferous forests. This caused great damage to regional timber industry.?????????

 Key words

Forest fires, Image processing and interpretation, Natural forest disturbances, Remote sensing data, Severe winds, Tornadoes

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