The aim of this study was to develop methods for constructing a simple model describing tree mortality caused by Ips typographus L. using a minimum number of variables. We developed a model for areas spanning natural mountain forests in the Tatra National Park (Slovakia) and the Šumava National Park (Czech Republic), and in managed Czech forests located in four areas varying in environmental conditions. The model describes the time series of tree mortality dynamics caused by I.typographus using two submodels: a long-term dynamics submodel, and a short-term dynamics autoregressive distributed lag(ADL) model incorporating a two year delay and temperature variable averaged over the April-May period. The quality of fit for our models (R² value) ranged from 0.87 to 0.91. The model was formulated to capture the average monthly temperature effect, a key weather factor. We found that for high-elevation stands located at least 1000 ma.s.l., forest damage was predominantly influenced by May temperatures. For lower-elevation managed forests with warmer climates, the weather effect was insignificant.

Methods for analyzing the resistance of large woodlands (such as Siberian taiga forests) to insect attacks based on remote sensing data are proposed. As an indicator of woodland’s resistance, we suggest a function of normalized difference vegetative index (NDVI) susceptibility to changes in the land surface temperature (LST). Both NDVI and LST are obtained via the TERRA/AQUA satellite system. This indicator function was calculated as the spectral transfer function of the response in the integral equation connecting the changes in NDVI and LST. The analysis was carried out for two test sites, both of which are fir stands of the Krasnoyarsk region taiga zone. In the first case, the fir stands have suffered damage inflicted by Siberian silk moth caterpillars, Dendrolimus sibiricus Tschetv. since 2015. Adjacent intact fir forest areas were also analyzed. In the second case, the object of the study was a fir tree site damaged by Black Fir Sawyer Monochamus urussovii Fischer in 2013. It is demonstrated that the above-mentioned indicator function changed significantly 2–3 years prior to the pest population outbreaks, and therefore this indicator function can be used to assess the risk of pest population outbreak. Thereby, the proposed indicator compares favorably with vegetation cover estimates using NDVI, which register significant defoliation of tree stands and cannot be used for forecasting.

Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003–2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities.

The goal of this study is to detect indicators of damage to birch stands in western Siberia by spongy moth (Lymantria dispar L.) using remote sensing methods. The need for such indicators is due to the fact that the size of the study area is about 1 million square kilometers, and ground methods are too laborintensive. It is crucial for these indicators to differentiate the effects of insects from other destructive factors like fires and droughts. During the 2021–2022 fieldwork, we identified 18 areas for trial (intensive damage due to caterpillars) and control. For each area, we obtained seasonal time-series data of vegetation index NDVI mean values within its boundaries. We acquired the data from a Sentinel-2 satellite with a spatial resolution of 10 m. Relative reduction indices of NDVI during the season were introduced for pairs of damaged–control plots. We also considered the effect of foliage regeneration on damaged trees. The obtained indicators demonstrate increased analytical significance in identifying areas affected by pests compared to the simple reduction in vegetative indices.