Analysis of forest stands resistance to Siberian silkmoth attack, according to remote sensing data

• Published in: Сибирский лесной журнал
• Main Author: A. V. Kovalev
• Format: Article in Journal/Newspaper
• Language: English; Russian
• Published: Russian Academy of Sciences, Siberian Branch Publishing House 2021
• Subjects: forest insects; assessment of the forest state; population outbreaks; ground-based remote sensing methods; Forestry; SD1-669.5; taiga; Siberia
• Online Access: https://doi.org/10.15372/SJFS20210508; https://doaj.org/article/3f934c0bc78b4da5bca800bff8bab205

To assess the state of plantations in vast areas of boreal forests, modern methods are needed that allow obtaining information quickly with minimal labor costs. The existing assessment methods are either associated with labor-consuming ground-based observations, or they make it possible to measure the damage that has already occurred using remote sensing data (satellite, aeronautical observation methods). Methods for analyzing the state of forest stands in large areas (such as taiga forests in Siberia) based on remote sensing data are proposed. As an indicator of the state of stands, it is proposed to use the susceptibility index of vegetation index during the season (NDVI) to changes in the radiation temperature (LST), obtained from satellite data of the Terra/Aqua system. The index was calculated as the transfer spectral response function in the integral equation between NDVI and LST. The analysis was made for two types fir stands of Krasnoyarsk Region taiga zone – territories that since 2015 were damaged by of the Siberian silkmoth Dendrolimis sibiricus Tschetv. caterpillars and nearest intact areas. It is shown that indicators of stands’ susceptibility to environmental changes on the studied test plots changed significantly 2–3 years before pest population outbreaks and can be taken into account when assessing the risk of outbreaks. This distinguishes proposed indicator from assessments of the vegetation cover state, which register a significant defoliation of forest stands and cannot be used for forecasting.