Methodology for the study of the spatio-temporal dynamics of vegetation cover in the Northern regions of Kazakhstan (2000–2023)

Vegetation cover dynamics is one of the key indicators of ecosystem functioning, land degradation processes, and climate-driven environmental changes, especially in regions with sharply continental and semi-arid climates. Many existing drought-related studies mainly focus on short-term anomalies, seasonal variability, or individual drought events, which often limits the ability to identify persistent long-term trends and the cumulative vegetation response to hydrothermal conditions. For regions characterized by pronounced interannual climate variability, this limitation may lead to an underestimation of gradual processes of vegetation degradation or recovery. This study presents a methodology for monitoring the spatio-temporal dynamics of vegetation cover on rainfed lands in Northern Kazakhstan for the period 2000–2023. The proposed approach is based on long-term time series of vegetation indices. Satellite-derived vegetation indices are widely used for vegetation monitoring, drought assessment, and the analysis of long-term environmental trends at regional and global scales. Among them, Normal Differential Vegetation Index (NDVI), Vegetation Condition Index (VCI), have proven to be effective tools for detecting vegetation stress during the growing season. The Integral Vegetation Index (IVI) and the Integral Vegetation Condition Index (IVCI) are designed to rank growing seasons according to drought severity within a long-term observation period and to identify interseasonal vegetation trends associated with climatic factors. Their computational and interpretative simplicity makes them efficient analytical tools. The use of IVCI enables a comprehensive assessment of drought intensity through vegetation response to weather-related stress, facilitates the analysis of interseasonal changes in the influence of meteorological conditions on vegetation, and provides a more complete representation of vegetation dynamics compared to traditional approaches. In addition, this approach allows the identification of persistent zones of vegetation change and supports spatial zoning of territories according to their sensitivity to drought conditions in the northern regions of Kazakhstan. All indices were calculated using 8-day MODIS composite data (MOD09Q1) with a spatial resolution of 250 m for the growing seasons from 2000 to 2023. The results were validated using correlation analysis between IVCI and the ground-based Selyaninov Hydrothermal Coefficient (HTC). The findings revealed strong to very strong correlations in Kostanay region (at more than 75% of meteorological stations) and moderate to strong correlations in North Kazakhstan and Akmola regions. Shifting the focus from the analysis of individual extreme events to the investigation of long-term vegetation dynamics contributes to the advancement of satellite-based vegetation monitoring methods and provides scientifically grounded results relevant for drought assessment, land resource management, and climate impact analysis in regions with continental climates.