Estimation of forest aboveground biomass by using mixed-effects model. Issue 22 (17th November 2021)

• Record Type: Journal Article
• Authors: Feng, Haoning; Chen, Qi; Hu, Yueming; Du, Zhiguo; Lin, Guantu; Wang, Changwei; Huang, Youju
• Is Part Of: International journal of remote sensing. Volume 42:Issue 22(2021)
• Page Start: 8675
• Page End: 8690
• Publication Date: 2021-11-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1984611
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Using remote sensing data to estimate forest aboveground biomass (FAB) is of great significance for understanding the terrestrial C dynamics and making regional policies of forest management. The vegetation type-specified approach can improve the accuracy of estimating FAB, and it is difficult to obtain enough field plots for each vegetation type to fit a statistic model in practice. In this study, we tried to solve this problem by fitting a linear mixed-effects model based on the Landsat TM image and vegetation types. For developing the most parsimonious mixed-effect models, we used stepwise regressions to fit a linear regression model at the beginning and then we extended it into a linear mixed-effects model with the backward elimination method by adding and testing random effects to the explanatory variables in the linear regression model. The results showed that the linear mixed-effects model, compared with the linear regression model, has higher estimation accuracy with fewer parameters and smaller RMSE (R 2 = 0.63, RMSE reduced by 14.81%). The linear mixed-effects model is a feasible way to fit a species-level FAB estimation model when the field plots data are limited and uneven for different vegetation types.