Remote Sensing Estimation of Standing Tree Biomass in Pinus massoniana Forest in Typical Red Soil Erosion Area in Southern China

Taking the typical red soil erosion area in the southern Hetian Town, Changting County as an example, and combining the advantages of point cloud data generated by UAV and LiDAR, while later inverting the single wood tree height(H) and canopy radius(R_c) by local maximum and watershed algorithms, fitting the anisotropic growth equation with the combination of H and R_c as variables, and obtaining the the model of standing wood biomass of Pinus massoniana based on new canopy parameters. The evaluation accuracy results showed that the coefficient of determination(R²) and root mean square error(RMSE) of extracted tree height(H) were 0.93 and 0.49 m, respectively; the R² and RMSE of calculated canopy radius(R_c) were 0.88 and 0.64 m, respectively; the R² and RMSE of estimated standing wood biomass were 0.89 and 3.37 kg, respectively. In summary, this paper quantifies by UAV remote sensing image and constructed anisotropic growth equations, the anisotropic growth equation with the combination(H + R_c) as the base has a high accuracy in estimating the standing wood biomass of P. massoniana forest, and can effectively estimate the standing wood biomass of P. massoniana forest. This study can provide methodological reference for the accurate estimation of standing wood biomass in P. massoniana forest in southern typical red soil erosion areas.