Abstract
Kazakhstan’s Ili River delta nourishes a unique wetland ecosystem in arid Central Asia. The delta is dominated by common reed [Phragmites australis (Cav.) Trin. ex Steud.], an ecologically and economically significant species that is sensitive to water levels. We used machine learning methods, including the Random Forest algorithm, to classify common reed-containing wetland vegetation and water surfaces in the delta based on satellite data from 2000 to 2023. The remote sensing results were integrated with field-based geobotanical studies conducted between 2017 and 2019. These studies, which facilitated identification of three classes of vegetation: meadow, marsh and aquatic, also revealed a good correspondence between predicted and measured common reed biomass per unit area. The long-term dynamics of four wetland communities with a common reed content of ≥25% were analyzed with respect to significant interannual variability in the flow of the Ili River. The highest water inflows and water surface areas in the delta were recorded in 2002, 2010 and 2016, and in each case, a statistically significant expansion of common reed was observed one year later. Common reed areas subsequently declined—rapidly or after a lag of several years. Temporal expansion and contraction of these areas following pulses of water differed substantially from that of overall wetland vegetation as measured previously. The identified patterns have important scientific and practical significance for assessing the stability of the surrounding wetlands, preservation of the delta environment, and sustainable use of common reed.