Remote Sensing Assessment and Modeling of the Spatial Dynamics of Tree Stand Disturbance after the Impact of Siberian Silk Moth (Dendrolimus sibiricus)

Abstract

In this study, we have analyzed tree stand disturbance by hthe Siberian Silk Moth (Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae)) in Central Siberia (Krasnoyarsk region, Russia) in 2015–2020. We considered two plots that experienced silk moth outbreaks in 2015–2018 and 2018–2020 and used satellite data (Terra/MODIS, Landsat/ETM/OLI), field forest inventory data, a meteorological data set, and a vegetation cover vector layer. Silk moth-disturbed areas were classified using NDVI, which was calculated for each 15-day period during the growing season (April–September). We obtained formalized descriptions of the temporal dynamics of the disturbed area. Next, we classified the degree of disturbance of the forest stand after the impact of the silk moth by the threshold method according to the ranges of NDVI anomalies. Based on the generalized data from the forest inventory, we performed a correlation analysis of the relationship between the main characteristics of forests and the classes of disturbance. Finally, using a series of regression equations, we described a procedure for predicting the degree of impact on the stand during the time of silk moth outbreaks in the dark-needle coniferous stands of Central Siberia.

Remote Sensing Indicators of Spongy Moth (Lymantria dispar L.) Damage to Birch Stands in Western Siberia

“…The resulting indices provide greater analytical value than the simple decrease in vegetative indices observed by remote sensing methods in the study area. Single-step assessments of forest condition based on remote indicators are subject to observation noise, e.g., due to high cloud cover during the measurement period, and may change under the influence of a large number of external factors [36].…”

“…In different climatic conditions, when days with high cloudiness are frequent enough, it is suggested to use the NDWI index constructed from the data of two bands of infrared study or radar imaging, which allows eliminating the influence of cloudiness [37,38]. Most contemporary studies on the use of remote sensing techniques to detect forest damage do not include the "control-experiment" pair and therefore include short-term fluctuations of vegetative indices in the method error [22,23,36,37].At the same time, the method proposed by the authors allows separating tree damage from local fluctuations of environmental factors.…”