Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA

Abstract

Quantifying above-ground biomass changes, ΔAGB, is key for understanding carbon dynamics. National Forest Inventories, NFIs, aims at providing precise estimates of ΔAGB relying on model-assisted estimators that incorporate auxiliary information to reduce uncertainty. Poststratification estimators, PS, are commonly used for this task. Recently proposed endogenous poststratification, EPS, methods have the potential to improve the precision of PS estimates of ΔAGB. Using the state of Oregon, USA, as a testing area, we developed a formal comparison between three EPS methods, traditional PS estimators used in the region, and the Horvitz-Thompson, HT, estimator. Results showed that gains in performance with respect to the HT estimator were 9.71% to 19.22% larger for EPS than for PS. Furthermore, EPS methods easily accommodated a large number of auxiliary variables, and the inclusion of independent predictions of ΔAGB as an additional auxiliary variable resulted in further gains in performance.

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

“…They can be computationally expensive, but they can be effective for high-dimensional datasets. The most common embedded methods include principal component analysis (PCA), Lasso Regression, Ridge Regression, Elastic Net, Tree-Based Models, and Boosting models [207,207,[219][220][221]. Figure 19 shows the frequency distribution of different feature selection methods used for above-ground biomass (AGB) estimation.…”