Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Yan Zhu,Zhongke Feng,Jing Lu,Jincheng Liu

doi:10.3390/f11020163

Abstract

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

Highlights

Forest ecosystems are an important component of the terrestrial ecosystem
The R2 values of the multiple linear regression (MLR) models of three forest types were greater than 0.5, and root mean square error (RMSE) were less than 15.5 Mg ha−1, The R2 values of the Random forest (RF) models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1
Previous studies have typically analyzed the relationship between a single remote sensing variable and the forest biomass or have used the original band and variables transformed from images for feature selection [46,47]

Summary

Introduction

Forest ecosystems are an important component of the terrestrial ecosystem. Of the organic carbon in terrestrial ecosystems [1] and play an irreplaceable role in mitigating global warming caused by the increase in atmospheric carbon dioxide [2]. Forests 2020, 11, 163 cycle of forest ecosystems and is an important indicator for measuring changes in forest structure and function. Forest biomass is closely related to the carbon sources and sinks in forest ecosystems [3]. Because the monitoring of forest biomass resources is expensive and time consuming, most countries do not have effective monitoring systems. Accurate estimations of forest biomass can effectively replace forest monitoring systems and are an important basis for assessing ecosystem processes, the carbon balance of ecosystems and climate change [4]

Objectives

Methods

Results

Discussion

Conclusion