Landsat Imagery-Based Above Ground Biomass Estimation and Change Investigation Related to Human Activities

Chaofan Wu,Muye Gan,Aihua Shen,Jinsong Deng,Huanhuan Shen,Ke Wang

doi:10.3390/su8020159

Abstract

Forest biomass is a significant indicator for substance accumulation and forest succession, and a spatiotemporal biomass map would provide valuable information for forest management and scientific planning. In this study, Landsat imagery and field data cooperated with a random forest regression approach were used to estimate spatiotemporal Above Ground Biomass (AGB) in Fuyang County, Zhejiang Province of East China. As a result, the AGB retrieval showed an increasing trend for the past decade, from 74.24 ton/ha in 2004 to 99.63 ton/ha in 2013. Topography and forest management were investigated to find their relationships with the spatial distribution change of biomass. In general, the simulated AGB increases with higher elevation, especially in the range of 80–200 m, wherein AGB acquires the highest increase rate. Moreover, the forest policy of ecological forest has a positive effect on the AGB increase, particularly within the national level ecological forest. The result in this study demonstrates that human activities have a great impact on biomass distribution and change tendency. Furthermore, Landsat image-based biomass estimates would provide illuminating information for forest policy-making and sustainable development.

Highlights

Being the largest terrestrial ecosystem, the forest ecosystem provides a fundamental wood supply and greatly influences global carbon stocks and carbon exchange with the atmosphere, which is relevant to the greenhouse effect [1]
The main objective of this study is to (1) combine the remote sensing imagery Landsat with detailed forest inventory data to investigate the aboveground biomass (AGB) estimation in Fuyang County, East China, In the past decade; and (2) analyze the AGB spatiotemporal variability across the study area corresponding to hierarchical topography and man-power management activities
Among the vegetation indices, Normalized Difference Vegetation Index (NDVI) is the most widely used index, in the present study, we adopted the improved index named NDVIc as we found that it had a stronger relationship with AGB because it combined the information of shortwave infrared band, which provides valuable information to biomass retrieval [29]

Summary

Introduction

Being the largest terrestrial ecosystem, the forest ecosystem provides a fundamental wood supply and greatly influences global carbon stocks and carbon exchange with the atmosphere, which is relevant to the greenhouse effect [1]. Researchers have explored increasing numbers of studies on forest biophysical parameters, including aboveground biomass (AGB), leaf area index (LAI), and stock volume, to gain a better understanding of forest quality and productivity in recent years [4,5]. As it is an essential biophysical parameter of forest ecosystems, biomass is closely related to timber productivity and carbon cycle, and explicit spatial-temporal information on biomass could provide significant information in the understanding of the process of human–forest interactions [6]. Multi-temporal images and ever-increasing yearly trajectories were introduced to take advantage of phonological information, as well as disturbance and recovery variables, to improve simulation performance [12,13]

Objectives

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Results

Conclusion