Modeling Forest Carbon Stock Based on Sample Plots and UAV Lidar Data from Multiple Sites and Examining Its Vertical Characteristics in Wuyishan National Park

The accurate estimation of forest carbon stocks with remote sensing technologies helps reveal the spatial patterns of forest carbon stocks within national parks, but the limited number of sample plots in one site often results in difficulty in developing robust estimation models. This study employed a Bayesian hierarchical model to estimate forest carbon stock based on data from 193 sample plots collected across 37 UAV (unmanned aerial vehicle) Lidar sites. The developed model was employed to predict the carbon stock distribution in 17 Lidar sites within Wuyishan National Park (WNP). Then, the carbon stock characteristics along vertical zones of vegetation distribution (VZsVD) were examined. The results showed an overall coefficient of determination (R2) of 0.84 for forest carbon stock estimation across four regions, with a root mean square error (RMSE) of 12.09 t/ha. Within WNP, the overall R2 was 0.73, with specific values of 0.83 for broadleaf forests, 0.61 for mixed forests, 0.53 for Masson pine forests, and 0.46 for Chinese fir forests. Despite variations in R2, the relative RMSE (rRMSE) averaged 20.15%, ranging from 10.83% to 23.57%. The average carbon stock was 52.15 t/ha. Forest diversity and structural complexity emerged as key factors influencing the vertical distribution of carbon stocks. Regions with complex and diverse forest types exhibited higher and more evenly distributed carbon stocks. Chinese fir and Masson pine showed higher carbon stocks in low-altitude regions (350–850 m) than other vegetation types. In medium- to high-elevation regions (1350–1600 m), the carbon stocks of mixed forest and broadleaf forests remained relatively stable. Conversely, coniferous forests at high altitudes (above 1600 m) had lower carbon stocks due to extreme climatic and terrain conditions. This study provided a comprehensive analysis of carbon stock distribution across different VZsVD in WNP, offering valuable insights for enhancing the management of national parks.