Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

Aims Estimating forest biomass at regional scale with high accuracy is among the pressing challenges in evaluating ecosystem functions and characteristics (e.g., carbon storage). Methods This study is based on airborne small-footprint discrete-return LiDAR data, Landsat 8 OLI multispectral data, and in situ measurements from 55 forest plots in Yushan, Changshu, Jiangsu Province. A total of 87 independent variables (53 from OLI metrics and 34 from LiDAR metrics) were used in the Pearson correlation analysis for estimating aboveground (W_A) and belowground (W_B) biomass by identifying the significant independent variables. Three independent models by using OLI, LiDAR and their combinations (i.e., the combo model) were established through stepwise regression analysis. Important findings The correlation coefficients of determination (R²) for W_A and W_B models are greater than 0.4. The R² seemed much higher when the estimations were type-specific (e.g., coniferous, broad-leaf and mixed forest), with R² of >0.67. The Combo model by forest type yielded an R² of 0.88 for W_A and 0.92 for W_B, while the OLI-based model had R² of 0.73 and 0.81 for W_A and W_B, respectively. The LiDAR-based model has R² of 0.86 and 0.83 for W_A and W_B, respectively.