Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 176-193
Methodology of post-fire tree mortality monitoring and prediction using remote sensing data
S.A. Bartalev
1 , F.V. Stytsenko
1 , S.A. Khvostikov
1 , E.A. Loupian
1 1 Space Research Institute RAS, Moscow, Russia
Accepted: 15.12.2017
DOI: 10.21046/2070-7401-2017-14-6-176-193
Until recently knowledge about post-fire tree mortality in Russia was fragmentary and limited, despite significant quantity and area of wildfires in our country and serious ecological and socio-economical damage inflicted by them. Over the last few years widespread implementation of remote sensing methods allowed regular estimation of post-fire tree mortality for the whole territory of Russia. This development made it possible to create a data bank with annual geospatial data on fire severity in the forests of Russia, identify new patterns in associated tree mortality, and form a basis for regular monitoring of post-fire effects on forest ecosystem. This data bank spans the period from 2006 to 2017 and offers unique source of information for the development of new models of fire induced mortality that account for fire conditions and other natural factors. This type of models in turn provides new opportunities to develop methods for prediction of post-fire tree mortality. Predictions provided by those methods can play important part in the decision-making process regarding wildfire protection in Russia. Analysis provided in this paper shows significant spatiotemporal variability in fire-induced changes in forests of Russia, existence of trends in fire regimes, and consistent seasonal patterns in probabilities of post-fire mortality for various tree species. Those patterns form a basis for new methodology of probabilistic prediction and real time estimation of post-fire tree mortality. Predictions and estimates produced by new methodology show high skill, and thus can be used to make decisions about firefighting planning and post-fire management.
Keywords: wildfires, post-fire tree mortality, remote sensing, monitoring, prediction
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