Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

Linghan Gao,Xiaoli Zhang

doi:10.3390/f12121713

Abstract

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.

Highlights

Forests have the highest biological storage capacity and play a key role in the global carbon cycle
Among the 63 feature variables extracted from the point cloud data, the top four feature variables of importance ranking were selected for modeling for Chinese fir, while the top four, top six and top three feature variables of importance ranking were selected for pine tree, eucalyptus and other broadleaved trees
Based on LiDAR point cloud data, the features of point cloud data were extracted from different aspects and the optimal feature combination of each tree species was obtained through feature screening

Summary

Introduction

Forests have the highest biological storage capacity and play a key role in the global carbon cycle. Forest biomass is an important indicator of terrestrial ecosystem function evaluation, and an important parameter of forest carbon sink assessment [3]. Accurate assessment of the forest biomass is beneficial to quantify forest carbon storage, and can provide a key reference for forest resource management. The lack of forest biomass information is one of the uncertain factors of the global carbon budget [4]. Real-time and accurate monitoring of the forest biomass of China’s plantations will promote the assessment accuracy of forest carbon storage, but it has important significance for evaluating the sustainable development potential of China’s plantations

Methods

Results

Discussion

Conclusion