Remote sensing estimates of stand-replacement fires in Russia, 2002–2011

Alexander Krylov, Jessica L McCarty, Peter Potapov, Tatiana Loboda, Alexandra Tyukavina, Svetlana Turubanova, Matthew C Hansen
2014 Environmental Research Letters  
The presented study quantifies the proportion of stand-replacement fires in Russian forests through the integrated analysis of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data products. We employed 30 m Landsat Enhanced Thematic Mapper Plus derived tree canopy cover and decadal (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) forest cover loss (Hansen et al 2013 High-resolution global maps of 21st-century forest cover change Science 342 850-53) to
more » ... ce 342 850-53) to identify forest extent and disturbance. These data were overlaid with 1 km MODIS active fire ( data/near-real-time-data/firms) and 500 m regional burned area data (Loboda et al 2007 Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data Remote Sens. Environ. 109 429-42 and Loboda et al 2011 Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm Int. J. Wildl. Fire 20 487-96) to differentiate stand-replacement disturbances due to fire versus other causes. Total stand replacement forest fire area within the Russian Federation from 2002 to 2011 was estimated to be 17.6 million ha (Mha). The smallest stand-replacement fire loss occurred in 2004 (0.4 Mha) and the largest annual loss in 2003 (3.3 Mha). Of total burned area within forests, 33.6% resulted in stand-replacement. Light conifer stands comprised 65% of all nonstand-replacement and 79% of all stand-replacement fire in Russia. Stand-replacement area for the study period is estimated to be two times higher than the reported logging area. Results of this analysis can be used with historical fire regime estimations to develop effective fire management policy, increase accuracy of carbon calculations, and improve fire behavior and climate change modeling efforts.
doi:10.1088/1748-9326/9/10/105007 fatcat:tsdtrwyhzragzdayhhmigh7pve