FOREST ABOVEGROUND BIOMASS ESTIMATION FROM HIGH-RESOLUTION IMAGERY IN WUHAN CITY, CHINA

Current assessments of urban forest carbon storage were missing or largely underestimating their values due to limited spatial resolution. In this study, combining field plot measurements and satellite imagery, a wall-to-wall forest biomass map were generated at a very high spatial resolution (5 m) over urban areas in Wuhan City, China. Specifically, a series of characteristic metrics were extracted from Jilin-1 satellite images, including multispectral reflectances, vegetation indices, and texture features. The estimations of forest aboveground biomass from three machine learning models were evaluated at sampled field plot level. Results demonstrated that the random forest model achieved the highest accuracy using the leave-one-out cross-validation method, with a test set RMSE of 31.84 Mg/ha. However, discrepancies were observed in low biomass areas due to variations in vegetation species, leading to overestimation of lower values.