Resource Characteristics of Common Reed (Phragmites australis) in the Syr Darya Delta, Kazakhstan, by Means of Remote...

Reed beds, often referred to as dense, nearly monotonous extensive stands of common reed (Phragmites australis), are the most productive vegetation form of inland waters in Central Asia and exhibit great potential for biomass production in such a dryland setting. With its vast delta regions, Kazakhstan has the most extensive reed stands globally, providing a valuable case for studying the potential of reed beds for the bioeconomy. However, accurate and up-to-date figures on available reed biomass remain poorly documented due to data inadequacies in national statistics and challenges in measuring and monitoring it over large and remote areas. To address this gap in knowledge, in this study, the biomass resource characteristics of common reed were estimated for one of the significant reed bed areas of Kazakhstan, the Syr Darya Delta, using ground-truth field-sampled data as the dependent variable and high-resolution Sentinel-2 spectral bands and computed spectral indices as independent variables in multiple Random Forest (RF) regression models. An analysis of the spatially detailed yield map obtained for Phragmites australis-dominated wetlands revealed an area of 58,935 ha under dense non-submerged and submerged reed beds (with a standing biomass of >10.5 t ha^-1) and an estimated 1,240,789 tons of reed biomass resources within the Syr Darya Delta wetlands. Our findings indicate that submerged dense reed exhibited the highest biomass at 28.21 t ha^-1, followed by dense non-submerged reed at 15.24 t ha^-1 and open reed at 4.36 t ha^-1. The RF regression models demonstrated robust performance during both calibration and validation phases, as evaluated by statistical accuracy metrics using ten-fold cross-validation. Out of the 48 RF models developed, those utilizing the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) as key predictors yielded the best standing reed biomass estimation results, achieving a predictive accuracy of R² = 0.93, Root Mean Square Error (RMSE) = 2.74 t ha^-1 during the calibration, and R² = 0.83, RMSE = 3.71 t ha^-1 in the validation, respectively. This study highlights the considerable biomass potential of reed in the region's wetlands and demonstrates the effectiveness of the RF regression modeling and high-resolution Sentinel-2 data for mapping and quantifying above-ground and above-water biomass of Phragmites australis-dominated wetlands over a large extent. The results provide critical insights for managing and conserving wetland ecosystems and facilitate the sustainable use of Phragmites australis resources in the region.