Predictive Analysis of Forest Cover in the Middle Volga Region Based on Time Series and Climate Scenarios

In this study, a comprehensive predictive analysis of the Middle Volga region’s forest cover was conducted from 2021 to 2050 according to Normalized Difference Vegetation Index (NDVI), average monthly land surface temperature (LST), and precipitation (Pr) data. A temporal convolutional network model was employed for data predicting. A multiple linear regression model was subsequently developed to examine the relationship between NDVI, LST, and Pr within the study area. The model was then used to simulate the NDVI dynamics in response to three climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC), which are based on representative concentration pathways (RCPs) for greenhouse gases. To assess the spatiotemporal trends of the indicators within the QGIS (Quantum Geographic Information System), the nonparametric Mann–Kendall correlation test was applied. The NDVI analysis, covering all three IPCC scenarios, revealed a generally increasing trend in forest cover within the Middle Volga region by 2050, with the RCP8.5 (high emissions) scenario demonstrating the most pronounced growth. The central and eastern areas of the study region displayed the highest NDVI spatial trends. LST trends indicated an upward trajectory by the mid-21st century across all scenarios, while precipitation trends, particularly under the RCP8.5, showed a noticeable decrease. The observed trends indicate a reduction in the time interval between drought seasons from 10 to 4–6 years, which poses significant risks for the forest plantations in the Middle Volga. The models and trends obtained provide valuable insights for long-term sustainable forest management planning in the Middle Volga region.