The Siberian silkmoth is one of the most dangerous coniferous forests pests. Siberian silkmoth outbreaks cause massive defoliation and subsequent forest fires over vast areas. Remote forest disturbance assessments performed after an outbreak make it possible to assess carbon emissions and the potential for natural regeneration, estimate forest fire danger, and reveal the need to implement forest management practices. The goal of the present research was to investigate the use of modern satellite imagery of medium spatial resolution to estimate the percentage of dead trees in a given area. The subject of this study is the Siberian silkmoth outbreak that occurred in 2018–2020 and covered 42 thousand ha in the Irbey region of the Krasnoyarsk Krai. Imagery from the Sentinel-2/MSI sensor was used to calculate a number of spectral indices for images received before and after the outbreak. Field study data were used to create regression models relating the index values to the percentage of dead trees. A number of spectral indices, such as NDVI, dNDVI, NBR, dNBR, NDMI, EVI, and TCG, were used. As a result, spectral indices based on the data from NIR/SWIR bands (NBR, NDMI, dNBR) demonstrated the best correlations with field-measured tree mortality. Therefore, these indices may be used to accurately estimate the percentage of dead trees by remote sensing data. The best was the NBR index with an R² equal to 0.87, and the lowest RMSE and MAE errors. Consequently, Sentinel-2 imagery can be successfully used for tree mortality assessment over large inaccessible areas disturbed by Siberian silkmoth outbreaks at a relatively low cost.

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.

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

The article is devoted to studying the siberian silkmoth ( Dendrolimus sibiricus Tschetverikov, 1908) outbreaks, which is highly relevant today. The study was aimed at improving the system of forest pathology monitoring by revealing factors contributing to forest stand resistance to the pest attacks. We analyze the habitat conditions that are favorable for the siberian silkmoth in the early stages of its development and during the outbreak in dark coniferous taiga in the mountains of Southern Siberia (particularly, the Krasnoyarsk region). The research was based on remote sensing data combined with field data obtained in disturbed forests where we also con-ducted a pest population assessment. The habitat characteristics were based on the forest type and silvicultural and forest inventory details. We an alyzed environmental conditions and their influence on the pest reproduction. As a result, we divided the forest stands by their resistance to insect attacks. Primary habitats are stands with moss-covered forest floor, secondary habitats are stands with herbs-covered forest floor, and tertiary habitats are depressions with wet soils (forests along streams or swamp forests). We revealed the D. sibiricus population density dynamics related to the forest stands structure. The present research lets us determine forest sites vulnerable to pest attacks and conduct spatial monitoring of their dynamics and resistance.

This article is concerned with a recent problem of an outbreak of the Siberian silk moth (Dendrolimus sibiricus Tschetverikov, 1908). This study is focused on advancement of a system of forest pathology monitoring through determination of tolerance for the harmful phytophage by forest plots. The results of this analysis are reported with respect to suitable habitats for the vital activity of the Siberian silk moth at the early phases and during the outbreak period in the conditions of mountain–southern boreal dark coniferous forest stands (phytocenoses) in Krasnoyarsk krai. This study employs data on remote sensing documents combined with the results of field survey of damaged plots together with populational characteristics of the phytophage. The habitat description is based on the forest type and silvicultural–mensuration parameters of forest stands. As a result of the analysis of ecological conditions for pest propagation, the forest stands are differentiated based on their tolerance for phytophage exposure. The first-order reservations (areas of maximum sustained numbers between outbreaks) are the Bryidae (green-moss) group of forest types; second-order, the grass group of forest types; and third-order, moist habitats in low topographic relief (riverine “near a brook” and herb–swamp forest type). The dynamics of the Siberian silk moth density is determined depending on the structural characteristics of tree stands. The results allow for territorial tracking of the dynamics and tolerance of forest plots susceptible to phytophage exposure.