Algorithm for predicting Siberian silkmoth outbreaks in taiga forests of Krasnoyarsk Krai

Abstract

Forests disturbances by pests and diseases remains one of the major problems for forestry. These factors, combined with logging, fires and other human impacts, lead to degradation of forest ecosystems. Nowadays, forest health in Russia is assessed using forest inventory data and state forest health monitoring data. At the beginning of the 2020s, the Siberian silkmoth (Dendrolimus superans sibiricus Tscetv.) population continues to rise and damage taiga forests. The present study is dedicated to one of the methods for improving forest health monitoring. The method is based on remote sensing (in order to cover large forest areas), combined with GIS-based forecast models. The models make it possible to predict the risk of the Siberian silkmoth outbreak based on previously studied dependences between the pest population characteristics and environmental factors.