Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics

Zhibin Sun,Haomiao Lu,Qiangxin Ou,Xuehai Tang,Wenqi Qian,Qingfeng Huang,Dagui Yu,Haiyan Lv

doi:10.3390/rs14051066

Abstract

Forest is the largest vegetation carbon pool in the global terrestrial ecosystem. The spatial distribution and change of forest biomass are of importance to reveal the surface spatial variation and driving factors, to analyze and evaluate forest productivity, and to evaluate ecological function of forest. In this study, broad-leaved forests located in a typical state nature reserve in northern subtropics were selected as the study area. Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass. The biomass of broad-leaved forest with 30-m resolution in the study area from 1998 to 2016 was estimated by using the best model about every two years. With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations. Our results showed that the artificial neural network (ANN) model was the best (R2 = 0.8742) among the three, and its accuracy was also much higher than that of the traditional linear or nonlinear models. The mean biomass of the broad-leaved forest in the study area from 1998 to 2016 ranged from 90 to 145 Mg ha−1, showing an obvious temporal variation. Instead of biomass, biomass change (BC) was studied further in this research. Significant correlations were found between BC in broad-leaved forest and three climate factors, including average daily maximum surface temperature, maximum precipitation, and maximum mean temperature. It was also found that BC has a strong correlation with the biomass at the previous time (i.e., two years ago). Those quantitative correlations were used to construct a linear model of BC with high accuracy (R2 = 0.8873), providing a new way to estimate the biomass change of two years later based on the observations of current biomass and the three climate factors.

Highlights

Licensee MDPI, Basel, Switzerland.Forest biomass is a fundamental characteristic factor for evaluating forest ecosystems, as well as one of the important variables for quantifying the structure and function of forest ecosystems [1,2]
The results of the three models (RF, support vector machine (SVM), and artificial neural network (ANN)) are list in Table 4, which indicates that ANN has the best performance, with high R2 and low root mean square error (RMSE)
Please note that both SVM and ANN models were from the Statistics and Machine Learning Toolbox of Matlab R2019b, and random forest (RF) model was from

Summary

Introduction

Forest biomass is a fundamental characteristic factor for evaluating forest ecosystems, as well as one of the important variables for quantifying the structure and function of forest ecosystems [1,2]. Forest biomass accounts for about 85% of global terrestrial vegetation biomass, which is the energy base and source of nutrients for the operation of the entire forest ecosystem [3,4]. Based on forest biomass estimation, many studies have been conducted on forest ecosystem productivity, terrestrial ecosystem carbon cycling, and global climate published maps and institutional affil-. In-depth quantitative analysis of key factors will provide a basis for establishing forest biomass prediction models, and a scientific basis for the evaluation and prediction of forest ecosystem evolution and carbon cycle effects under future climates [6]

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Conclusion