Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures - A case study of typical grassland in Tajikistan

Climate change and human activities have severely impacted Central Asia's mountainous grasslands' health status, making monitoring aboveground biomass (AGB) crucial for grassland protection. However, a highresolution and low-cost solution for AGB monitoring is lacking for Central Asia's grassland. This research proposes an unmanned aerial vehicle (UAV) based AGB monitoring framework using consumer-level cameras. Texture features from UAV RGB images and vegetation indices from UAV multispectral images are used to predict AGB. As one of the typical mountainous countries in Central Asia, Tajikistan is chosen as the study area. Four different grassland types were chosen to investigate the performance of applying UAV for AGB monitoring. Firstly, correlation analysis was performed to identify important features for AGB estimation. Subsequently, the ground-measured AGB and UAV image features were utilized in multiple linear regression (MLR) and generalized additive model (GAM) to develop an AGB prediction model. The results show that the angular second moment (ASM) of the green band and green normalized difference vegetation index (GNDVI) are the two most important features for AGB estimation in the study area. Furthermore, the GAM-based model demonstrated higher accuracy (R2 = 0.87, RMSE =127.53 g/m2 , rRMSE = 0.17) compared to MLR (R2 = 0.74, RMSE = 181.68 g/m2 , rRMSE = 0.24), highlighting the nonlinear relationship between AGB and UAV image features. This research uses multispectral and RGB images to achieve a high accuracy and efficient AGB monitoring framework for Central Asia grasslands and provides a reference to validate satellite images for providing long -term and large-scale monitoring of grassland ecosystems and reasonable utilization of grassland resources.