The feasibility of risk assessment of a Siberian silk moth (Dendrolimus sibiricus Tschetv.) outbreak was analyzed by means of landscape and weather characteristics and tree condition parameters. Difficulties in detecting forest pest outbreaks (especially in Siberian conditions) are associated with the inability to conduct regular ground surveillance in taiga territories, which generally occupy more than 2 million km². Our analysis of characteristics of Siberian silk moth outbreak zones under mountainous taiga conditions showed that it is possible to distinguish an altitudinal belt between 400 and 800 m above sea level where an outbreak develops and trees are damaged. It was found that to assess the resistance of forest stands to pest attacks, researchers can employ new parameters: namely, characteristics of a response of remote sensing variables to changes in land surface temperature. Using these parameters, it is possible to identify in advance (2–3 years before an outbreak) forest stands that are not resistant to the pest. Thus, field studies in difficult-to-access taiga forests are not needed to determine these parameters, and hence the task of monitoring outbreaks of forest insects is simplified substantially.

The characteristics of Abies sibirica Ledeb.- and Pinus sibirica Du Tour dominated forests stands in outbreak spots formed during a Dendrolimus sibiricus outbreak in 2014–2017 were studied at the stage of population collapse (east of the West Siberian Plain and western foothills of the Yenisei Range). The research was based on the data obtained during ground surveys conducted in 2016 when stands characteristics, the Siberian silk moth population density, and defoliation level were recorded. We classified the studied stands using decision trees and random forest algorithms to identify the key characteristics that determine the formation of outbreak spots. The classification results showed that the characteristics of the detected outbreak spots differ significantly from those previously described for dark coniferous stands of the southern taiga in Siberia. The highest probability of the outbreak spot occurrence in the study area was revealed for stands with the following characteristics: moderately moist site; Siberian stone pine and Siberian fir take 40% or more of stem volume; age of less than 105 years. Another group of forest stands under threat are those sharing the following characteristics: large area (more than 60 ha); stand homogeneity; ground vegetation is dominated by feather mosses; age of more than 120 years; Picea obovata Ledeb. takes a significant share in a stem volume. Such characteristics indicate that during the population collapse, the Siberian silk moth forms outbreak spots in stands that either undergo an early successional stage after previous outbreak or have been slightly damaged during the previous outbreaks due to unfavorable habitat conditions.

In the southern taiga of Siberia, periodic outbreaks of the Siberian moth Dendrolimus sibrircus Tschetv. have been observed. The outbreaks result in the defoliation of Siberian fir Abies sibirica Ledeb. and Siberian pine Pinus sibirica Du Tour. stands across approximately one million hectares, leading to dieback of the affected forests. This is largely attributable to the inability to promptly identify the onset of the pest population growth in a timely manner, particularly in the context of expansive forest areas with limited accessibility. It is feasible to enhance the efficacy of monitoring Siberian moth populations by discerning stands with the highest propensity for damage and concentrating efforts on these areas. To achieve this, we employed machine learning techniques, specifically gradient boosting, support vector machines, and decision trees, training models on two sets of predictors. One of the datasets was obtained through a field study conducted in forest stands during the previous outbreak of the Siberian moth (2015–2018), while the other was derived from the analysis of remote sensing data during the same period. In both 2015 and 2016, the defoliation was most accurately predicted using gradient boosting (XGB algorithm), with ROC-AUC values reaching 0.89–0.94. The most significant predictors derived from the ground data were the proportions of Siberian fir, Siberian spruce Picea obovata Ledeb., and Scots pine Pinus sylvestris L., phytosociological data, tree age, and site quality. Among the predictors obtained from the analysis of remote sensing data, the distance to disturbed forest stands was identified as the most significant, while the proportion of dark coniferous species (A. sibirica, P. sibirica, or Picea obovata Ledeb.), the influx of solar radiation (estimated through the CHILI index), and the position in the relief (mTPI index) were also determined to be important.

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.

In this study, we have analyzed the spatial dynamics of the forests disturbed by Siberian Silk Moth (Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae)) in Central Siberia and obtained model equations that fit these dynamics. We considered three sites that experienced silk moth outbreaks in 1993–1996, 2015–2018, and 2018–2020 and used satellite data (NOAA/AVHRR, Terra/MODIS, Landsat/ETM/OLI), field data, a digital elevation model, and maps of predominant forests. Silk moth-disturbed areas were classified using NDVI, which was calculated for each 15-day period during the growing season (April–September). Time series of disturbed forest areas were obtained for three sites located in the Krasnoyarsk region (Central Siberia, Russia). Total damaged areas for these sites were 41, 430, and 470 thousand hectares. We obtained formalized descriptions for the temporal dynamics of the disturbed area.

Background. The degradation of coniferous forests dominated by siberian fir (Abies sibirica Ledeb.) as a consequence of outbreaks of the Siberian silkmoth (Dendrolimus sibiricus Tschetv.) gives rise to considerable environmental and economic damage at the regional level. An improved understanding of the ecology of the pest population in mountainous terrain will facilitate the development of a more effective monitoring system and the use of a digital terrain model to predict the spread of the outbreak. This will allow the implementation of timely active forest protection measures. Purpose. This study aims to investigate the spatiotemporal patterns of the Siberian silkmoth outbreak by analyzing orographic data from mountainous southern taiga forests in Siberia. Materials and methods. The study was carried out by the method of retrospective analysis based on a geoinformation system combined with data from remote sensing of the Earth. The research was based on a time series of Landsat-8 images obtained from the USGS Earth Explorer data portal (https://earthexplorer.usgs.gov/) with a spatial resolution of 30 m. Results. This was achieved by analyzing time series data from Landsat-8 satellite images. An assessment was conducted of the area of defoliated forests and the relief features of such an area using the ASTER digital elevation model. This was done in order to detail the parameters for predicting the development of the pest outbreaks based on the following orographic characteristics in mid-mountain relief: height above sea level, terrain slope and slope exposure. Conclusion. It is recommended that the early detection of Siberian silkmoth outbreaks in mountainous southern taiga forests should be aimed at the initial monitoring of dark coniferous stands concentrated at altitudes from 400 to 600 m above sea level, located on flat areas and slopes of up to 15 degrees, including exposures predominantly occupied by feather moss forest types. EDN: XLRKHE