Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

Xiandie Jiang,Erxue Chen,Guiying Li,Xinliang Wei,Dengsheng Lu

doi:10.3390/rs12071101

Abstract

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

Highlights

The Chinese subtropical regions with rich tree species have a higher carbon sequestration capacity than tropical and temporal forests in the rest of Asia and regions at the same latitude in Europe, Africa, and North America [1] and play important roles in regional and global carbon regulations and climate changes [2,3]
We selected the Gaofeng Forest Farm as a case study to explore whether different stratification scenarios of forest types improve aboveground biomass (AGB) estimation and which algorithm, i.e., linear regression (LR) or random forest (RF), performs better corresponding to a specific forest type
A comparative analysis of the AGB models indicates that in the LR-based AGB models, (1) HME is often selected for the AGB estimation models and has a higher beta value than other selected variables, implying that it is a more important variable and (2) for individual tree species such as Chinese fir, eucalyptus, and star anise with relatively simple stand structures, only one variable is selected, but for the complex forest types such as coniferous or broadleaf forests, more variables related to forest stand structure, in addition to HME, are needed

Summary

Introduction

The Chinese subtropical regions with rich tree species have a higher carbon sequestration capacity than tropical and temporal forests in the rest of Asia and regions at the same latitude in Europe, Africa, and North America [1] and play important roles in regional and global carbon regulations and climate changes [2,3]. These subtropical regions have complex topographies with mountains, hills, and plains, as well as high forest coverage but fragmental and diverse forest patches due to intense disturbances from humans and nature [4,5]. The Lidar data method with its ability to capture forest vertical features, that is, tree height, effectively solves the data saturation problem in optical and radar data and provides better AGB estimation than other sensor data systems [23,24,25,26]

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Conclusion