Аннотация
Pasture degradation under increasing climatic aridity threatens sustainable land use, livestock production, and ecosystem stability in southern Kazakhstan. This study assessed drought-related pasture degradation in Zhambyl District, Almaty Region, using Sentinel-2 imagery, climatic data, and GIS analysis to support sustainable pasture management and climate-adaptive land-use planning. The study area was subdivided into four zones: semi-desert, arid foothill, typical foothill, and mountain. Zone-specific seasonal windows were defined to account for differences in relief, aridity, and vegetation phenology. Pasture conditions were evaluated using NDVI, NDMI, and BSI, while precipitation, air temperature, and the hydrothermal coefficient (HTC) were used to characterize drought conditions for 2021–2025. Multi-year anomalies, repeatability of unfavorable signals, and an integrated degradation indicator were used for GIS-based mapping of drought-related pasture degradation signals. The results showed pronounced spatial differentiation of degradation processes. The semi-desert and arid foothill zones contained the largest total mapped areas of degradation, whereas the typical foothill zone showed a relatively higher share of moderate degradation. Weak and moderate degradation predominated in all zones, while severe degradation was confined to small local areas. In the semi-desert zone, weak and moderate degradation occupied 891,169 and 95,414 ha, respectively. In the arid foothill zone, these classes covered 303,283 and 43,185 ha; in the typical foothill zone, 134,966 and 67,163 ha; and in the mountain zone, 66,376 and 17,286 ha. HTC showed a more consistent correlation pattern with pasture conditions than precipitation alone. The proposed approach can support sustainable pasture management by identifying drought-sensitive areas, prioritizing zones for monitoring and restoration, and informing adaptive grazing and land-use decisions under increasing climatic aridity. Because formal accuracy assessment was limited by the available field data, the resulting maps should be interpreted as exploratory maps of drought-related pasture degradation signals rather than as a fully validated categorical classification of pasture degradation.