Title screen

[References: 82 tit.]

In recent years, the vegetation cover in urban agglomerations has been changing very rapidly due to technogenic influence. Satellite images play a huge role in studying the dynamics of forest vegetation. Special programs are used to process satellite images. The purpose of the study is to analyze forest vegetation within the territory of the Tomsk agglomeration based on Landsat remote sensing data for the period from 1990 to 2022. The novelty of the study is explained by the development of a unique program code for the analysis of Landsat satellite data on the previously unexplored territory of the Tomsk agglomeration with the prospect of moving to the scale of the entire state in the future. In this study, the authors present an algorithm implemented in Python to quantify the change in the area of vegetation in an urban agglomeration using Landsat multispectral data. The tool allows you to read space images, calculate spectral indices (NDVI, UI, NDWI), and perform statistical processing of interpretation results. The created tool was applied to study the dynamics of vegetation within the Tomsk urban agglomeration during the period 1990–2022. Key findings and conclusions: (1) The non-forest areas increased from 1990 to 1999 and from 2013 to 2022. It is very likely that this is due to the deterioration of the standard of living in the country during these periods. The first time interval corresponds to the post-Soviet period and the devastation in the economy in the 1990s. The second period corresponds to the implementation and strengthening of sanctions pressure on the Russian Federation. (2) The area of territories inhabited by people has been steadily falling since 1990. This is due to the destruction of collective agriculture in the Russian Federation and the outflow of the population from the surrounding rural settlements to Tomsk and Seversk.