Abstract
This article presents a spatial assessment of rangeland productivity, forage resources, and the potential carrying capacity of saiga (Saiga tatarica (L., 1766)) habitats within the Korgalzhyn State Nature Reserve (Kazakhstan). This research integrated field geobotanical surveys, remote sensing data, and spatial modelling approaches. During fieldwork conducted in July 2025, 44 geobotanical descriptions were completed. Long-term Sentinel-2 imagery (2015–2025), spectral vegetation indices, and ensemble machine learning algorithms (Random Forest and Gradient Boosted Decision Trees) were used to model rangeland productivity. The study area includes 16 types of rangelands. The forage resource base is primarily formed by dry-steppe communities dominated by Stipa, Festuca, and Artemisia. At the same time, halophytic vegetation containing Atriplex, Bassia, and Halocnemum provides additional forage resources under arid and saline conditions. The total annual forage reserve was estimated at 3,308,921.2 centners, and the potential annual carrying capacity was calculated at 363,033 saiga individuals across 313,868 ha. These values represent the theoretical forage-carrying capacity of the territory and should not be interpreted as actual population estimates. The results demonstrate the effectiveness of integrating remote sensing, field observations, and machine learning methods for assessing forage resources and spatial heterogeneity of rangeland ecosystems in the steppe regions of Kazakhstan.