Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.

The match relationship between urbanization and ecosystem services (ESs) is a cornerstone of achieving sustainable development. However, the evolution patterns of urbanization/ecosystem service (UES) synergies under economic polarization in the rapid urbanization process remain poorly understood. This study integrates bivariate local Moran’s index and correlation analysis methods to examine the match relationship between urbanization and three key ESs (water yield, carbon sequestration, and food production) from 2000 to 2020 and explores the impact of intra-city disparities on the match relationship of urbanization and ESs. The findings revealed that urbanization and three ecosystem services showed increasing trends during 2000–2020 simultaneously. The spatial aggregation pattern of urbanization and ecosystem services showed smaller variations from 2000 to 2020. There was a High-High aggregation between urbanization and water yield in urban built-up areas and primarily High-Low aggregations between urbanization, carbon sequestration, and food production. Furthermore, the impact of urbanization on ESs decreased with increasing urban polarization. In particular, the Beijing–Tianjin–Tangshan region still demonstrated pronounced economic polarization, suggesting disparities in economic development within its urban core. This study highlights the importance of mitigating the adverse effects of urban polarization on ESs and fostering resilient and sustainable urban ecosystems in rapidly developing regions.