Estimation of carbon sequestration capacity of urban green infrastructure by fusing multi-source remote sensing data

Abstract

Urban green infrastructure significantly contributes to the carbon storage functions of urban ecosystems. Accurate selection and efficiently integrating remote sensing data are paramount for evaluating carbon storage at the small-scale of urban green infrastructure. In this study, evaluating the precision of carbon storage estimation by integrating UAV-acquired multi-view spectral images and LiDAR data, complemented by ground-truth validation data. The allometric equations and carbon ratios specific to the tree species. The accuracy evaluation reveals that the R² value and RMSE for the extracted individual tree height variables were 0.75 and 1.76 m. For the estimated carbon storage, the R² reached 0.86, with an RMSE of 28.88 kg C. Additionally, the spatial arrangement and structure of tree species within green infrastructure notably affected carbon storage heterogeneity. This study demonstrates the effectiveness of integrating multi-view spectral imagery and LiDAR in accurately estimating carbon storage in urban green infrastructure. Furthermore, incorporating the small-scale spatial patterns of tree species can enhance the precision of carbon storage estimation.