Advancing coastal wetland management: a combined UAV-satellite approach for Spartina alterniflora aboveground biomass estimation using interpretable machine learning - Xiaotong Zhang; Mingming Jia; Fengqin Yan; Yongbin Zhang; Weidong Man; Fuping Li; Mingyue Liu; Fenghua Wu; Xuan Yin; Jihang Duan (International Journal of Digital Earth)
Aboveground biomass (AGB) is a key indicator for carbon storage and ecological health in wetland ecosystems. Spartina alterniflora (S.alterniflora), an invasive species in coastal wetlands, threatens biodiversity and blue carbon stocks. This process impacts blue carbon stocks in coastal marshes, making their assessment and management during ecological restoration crucial. While satellite remote sensing is useful for large-scale AGB monitoring, its coarse resolution limits accuracy. UAV technology provides high-resolution plot-scale data, complementing satellite imagery for more precise estimates. This study proposes a combined UAV and satellite remote sensing framework for AGB estimation in coastal wetlands. An AGB inversion map for S.alterniflora was developed at the plot scale using UAV imagery and a Random Forest (RF) model, achieving an R² of 0.64 and an RMSE of 0.321 kg/m². This map was then scaled up to the landscape level by integrating satellite imagery, with an R² of 0.68 and an RMSE of 0.300 kg/m². SHAP analysis was conducted to interpret the model, and the statistical analysis of AGB under different invasion stages was performed using long-term classification data and space-for-time substitution. This approach highlights the potential of combining UAV and satellite data for large-scale, accurate AGB estimation to inform wetland conservation and management efforts.