A Method for Estimating Forest Aboveground Biomass at the Plot Scale Combining the Horizontal Distribution Model of Biomass and Sampling Technique

Abstract

Accurate estimation of small-scale forest biomass is a prerequisite and basis for trading forest carbon sinks and optimizing the allocation of forestry resources. This study aims to develop a plot-scale methodology for estimating aboveground biomass (AGB) that combines a biomass horizontal distribution model (HDM) and sampling techniques to improve efficiency, reduce costs and provide the reliability of estimation for biomass. Simao pine (Pinus kesiya var. langbianensis) from Pu’er City, Yunnan Province, was used as the research subject in this study. A canopy profile model (CPM) was constructed based on data from branch analysis and transformed into a canopy biomass HDM. The horizontal distribution of AGB within the sample plots was simulated using the HDM based on the data from the per-wood survey and compared with the results from the location distribution model (LDM) simulation. AGB sampling estimations were carried out separately by combining different sampling methods with the AGB distribution of sample plot simulated by different biomass distribution models. The sampling effectiveness of all sampling schemes was compared and analyzed, and the best plan for the sampling estimation of AGB in plot-scale forests was optimized. The results are as follows: the power function model is the best model for constructing the CPM of the Simao pine in this study; with visual comparison and the analysis of the coefficient of variation, the AGB simulated by HDM has a larger and more continuous distribution than that simulated by LDM, which is closer to the actual distribution; HDM-based sampling plans have smaller sample sizes and sampling ratios than LDM-based ones; and lastly, the stratified sampling method (STS)-HDM-6 plan has the best sampling efficiency with a minimum sample size of 10 and a minimum sampling ratio of 15%. The result illustrates the potential of the method for estimating plot-scale forest AGB by combining HDM with sampling techniques to reduce costs and increase estimation efficiency effectively.