It is essential to analyze the spatial autocorrelation and heterogeneity of aboveground biomass (AGB). But it is difficult to accurately describe due to the lack of data in clear-cutting plots. Thus, measuring the AGB directly in a clear-cutting plot can provide a reference for accurately describing the spatial variation. Therefore, a 0.3-hectare clear-cutting sample plot of Pinus kesiya var. langbianensis natural forest was selected, and the AGB was calculated by each component. The intra-group variance was quantitatively described in terms of spatial heterogeneity, and the spatial autocorrelation was explored by global and local Moran’s I. The results indicated that (1) there was different spatial heterogeneity for the different trees and organs. The intra-group variance tended to be stable after 20 m for P. kesiya var. langbianensis (PK) and other upper trees (UPs) and after 10 m for the other lower trees (LTs). (2) The spatial autocorrelation of AGB and wood biomass was similar, while the bark biomass and foliage biomass were consistent. PK and other UPs also exhibited strong spatial autocorrelation, with maximum Moran’s I values of 0.1537 and 0.1644, respectively. (3) There was spatial heterogeneity in the different components except for the bark of PK. The lowest spatial heterogeneity was found for LT.

Forest biomass measurement or estimation is critical for forest monitoring at the stand scale, but errors among different estimations in stand investigation are unclear. Thus, the Pinus densata natural forest in Shangri-La City, southwestern China, was selected as the research object to investigate the biomass of 84 plots and 100 samples of P. densata. The stand biomass was calculated using five methods: stand biomass growth with age (SBA), stem biomass combined with the biomass expansion factors (SB+BEF), stand volume combined with biomass conversion and expansion factors (SV+BCEF), individual tree biomass combined with stand diameter structure (IB+SDS), and individual tree biomass combined with stand density (IB+SD). The estimation errors of the five methods were then analyzed. The results showed that the suitable methods for estimating stand biomass are SB+BEF, M+BCEF, and IB+SDS. When using these three methods (SB+BEF, SV+BCEF, and IB+SDS) to estimate the biomass of different components, wood biomass estimation using SB+BEF is unsuitable, and root biomass estimation employing the IB+SDS method was not preferred. The SV+BCEF method was better for biomass estimation. Except for the branches, the mean relative error (MRE) of the other components presented minor errors in the estimation, while MRE was lower than other components in the range from −0.11%–28.93%. The SB+BEF was more appealing for branches biomass estimation, and its MRE is only 0.31% lower than SV+BCEF. The stand biomass strongly correlated with BEF, BCEF, stand structure, stand age, and other factors. Hence, the stand biomass growth model system established in this study effectively predicted the stand biomass dynamics and provided a theoretical basis and practical support for accurately estimating forest biomass growth.