The classification of land use and land cover is an essential element of natural resource management and landscape monitoring, particularly in regions such as southern Russia, which exhibit a high degree of ecological diversity and complex land use patterns. Global classification systems like CORINE and USGS LULC lack specificity for southern Russia, where agriculture, urbanization, and industrial zones are dominant. This study proposes a new land use and land cover classification system designed specifically for the southern Russian regions. The system integrates natural and anthropogenic factors, with five primary macro-classes: vegetation, built-up areas, water bodies, bare and barren lands, and snow and ice cover. The system includes sub-classes for each macro-class, allowing for more accurate monitoring of specific land use types. The system is based on the analysis of Landsat data, which allows for the use of spectral bands in the visible, near-infrared and shortwave infrared bands, combined with indices such as NDVI and NDSI, thus enhancing the accuracy of land cover mapping. This system captures the signatures and patterns of diverse land types, enabling dynamic, monthly monitoring, particularly in response to seasonal changes.