Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery

Yinghui Zhao,Ye Ma,Lindi J Quackenbush,Zhen Zhen

doi:10.3390/rs14020271

Abstract

Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the performance of three machine learning and three ensemble learning algorithms in tree species classification based on airborne laser scanning (ALS) and WorldView-3 imagery, inversed the diameter at breast height (DBH) using an optimal tree height curve model, and mapped individual tree AGB for a site in northeast China using additive biomass equations, tree species, and inversed DBH. The results showed that the combination of ALS and WorldView-3 performed better than either single data source in tree species classification, and ensemble learning algorithms outperformed machine learning algorithms (except CNN). Seven tree species had satisfactory accuracy of individual tree AGB estimation, with R2 values ranging from 0.68 to 0.85 and RMSE ranging from 7.47 kg to 36.83 kg. The average individual tree AGB was 125.32 kg and the forest AGB was 113.58 Mg/ha in the Maoershan study site in Heilongjiang Province, China. This study provides a way to classify tree species and estimate individual tree AGB of NSFs based on ALS data and WorldView-3 imagery.

Highlights

Forests are essential resources and require sustainable management approaches to monitor forest structures and understand the impacts of global climate change on terrestrial ecosystems
Since region-hierarchical cross-sectional analysis (RHCSA) was based on canopy height model (CHM) for individual tree crown delineation, the resolution of the CHM had a significant impact on the results
This study investigated the effect of the spatial resolution of CHM on the individual tree detection rate and the overall accuracy of the RHCSA algorithm, with the goal of determining the optimal resolution where the numbers of detected individual trees best matched the counts in the field and the overall accuracy was highest

Summary

Introduction

Forests are essential resources and require sustainable management approaches to monitor forest structures and understand the impacts of global climate change on terrestrial ecosystems. Forest biomass is defined as the total dry weight of organic material, both aboveground and belowground, in forests. The accurate estimation of forest biomass has substantial significance in understanding the ecological and global changes [6]. There are three main methods of estimating forest biomass: physiological model-based simulation [7], measurements from traditional field survey data [8], and inversion from remote sensing datasets [9,10]. Physiological model-based simulation methods usually estimate forest biomass at local or regional scales and rely on various input variables (e.g., radiation, climate conditions, and altitude) [11]. National Forest Inventory (NFI) programs often rely upon the plot-based field inventory and monitoring of forest

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