Evaluating the effectiveness of forest type stratification for aboveground biomass inference

Abstract

Accurate quantification of aboveground biomass (AGB) in heterogeneous forest ecosystems is critical for reliable carbon cycle modeling and the effective climate policy development. Although remote sensing-assisted methods have significantly enhanced estimation efficiency, the impact of forest type stratification on estimation accuracy remains insufficiently investigated, especially when classified forest types from remote sensing data are used. In this study, we conducted a comprehensive comparison between model-assisted (MA) and model-based (MB) estimators and conventional simple random sampling (SRS) estimators under three different stratified or nonstratified scenarios: (A) a nonstratified estimation framework; (B) stratified estimation employing error-free forest type maps; and (C) stratified estimation predicated on classification results from remote sensing. Additionally, we assessed the effect of model specification—whether using a general model or strata-specific models—on estimation accuracy within stratified frameworks. The results showed that both the MA and MB estimators outperformed the SRS estimator. Stratification with ground truth reference maps significantly enhanced estimation accuracy, especially for the variance of the MB estimator employing strata-specific models is reduced from 13.65 t/ha to 10.42 t/ha, with the highest relative efficiency (RE = 2.95) achieved by the error-free stratified MA estimator using a general model. However, classification errors in remote sensing-derived maps substantially reduced these benefits, often leading to estimation variances exceeding those of the unstratified approach. Specifically, the variances of estimators MA and MB have increased from 8.89 t/ha to 24.17 t/ha, and from 10.42 t/ha to 23.65 t/ha, respectively. The predominant source of error was model misassignment due to misclassified forest types. This study provides a practical framework for estimating regional forest AGB using remote sensing data and offers decision support for the scientific formulation of forest management and sustainable utilization plans.