Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China

Mingyang Li,Chao Li,Yingchang Li

doi:10.3390/f10020104

Abstract

Forest aboveground biomass (AGB) estimation modeling based on remote sensing is an important method for large-scale biomass estimation; the accuracy of the estimation models has been a topic of broad and current interest. In this study, we used permanent sample plot data and Landsat 8 Operational Land Imager (OLI) images of western Hunan. Remote-sensing-based models were developed for different vegetation types, and different crown density classes were incorporated. The linear model, linear dummy variable model, and linear mixed-effects model were used to determine the most effective and accurate method for remote-sensing-based AGB estimation. The results show that the adjusted coefficient of determination (R2adj) and root mean square error (RMSE) of the linear dummy model and linear mixed-effects model were significantly better than those of the linear model; the R2adj increased more than 0.16 and the RMSE decreased more than 2.12 for each vegetation type, and the F-test also showed significant differences between the linear model and linear dummy variable model and between the linear model and linear mixed-effects model. The accuracies of the AGB estimations of the linear dummy variable model and the linear mixed-effects model were significantly better than those of linear model in the thin and dense crown density classes. There were no significant differences in the AGB estimation performance between the linear dummy variable model and linear mixed-effects model; these two models were more flexible and more suitable than the linear model for remote-sensing-based AGB estimation. The results of this study provide a new approach for solving the low-accuracy estimations of linear models.

Highlights

Forest ecosystems provide important ecosystem services and are an important component of the earth’s energy cycle
The application of dummy variable models and mixed-effects models focused on the determination of allometric growth equations, whereas in this study, we considered whether the partition of the crown density classes improved the estimation accuracy of aboveground biomass (AGB) using remote sensing data
The linear model, linear dummy variable model, and linear mixed-effects model were used to determine if the accuracy of the AGB linear estimation model could be improved by considering crown density classes

Summary

Introduction

Forest ecosystems provide important ecosystem services and are an important component of the earth’s energy cycle. Forest biomass is a fundamental parameter for describing the structure and function of forest ecosystems [1,2]. Many ecosystem processes are impacted by forest biomass and, in turn, forest biomass is impacted by these processes [3]. Studies on forest biomass are essential for determining the carbon storage, carbon balance, and carbon cycling at the regional and global levels. Due to difficulties in measuring forest belowground biomass, the majority of previous studies have mostly focused on forest aboveground biomass (AGB). The estimation of AGB is an essential

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