Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest. (July 2017)

• Record Type: Journal Article
• Authors: Nie, Sheng; Wang, Cheng; Zeng, Hongcheng; Xi, Xiaohuan; Li, Guicai
• Is Part Of: Ecological indicators. Volume 78(2017)
• Page Start: 221
• Page End: 228
• Publication Date: 2017-07
• Subjects: LiDAR -- Discrete-return LiDAR -- Full-waveform LiDAR -- Above-ground biomass (AGB) -- Pseudo-waveform; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2017.02.045
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: A few metrics derived from aggregated pseudo-waveforms were used to estimate AGB. Structure parameters were proposed to predict forest AGB. A new way of combing discrete-return and full-waveform LiDAR data was designed. Abstract: The estimation of forest aboveground biomass (AGB) is critical for quantifying carbon stocks and essential for evaluating global carbon cycle. Many previous studies have estimated forest AGB using airborne discrete-return Light Detection and Ranging (LiDAR) data, while fewer studies predicted forest AGB using airborne full-waveform LiDAR data. The objective of this work was to evaluate the utility of airborne discrete-return and full-waveform LiDAR data in estimating forest AGB. To fulfill the objective, airborne discrete-return LiDAR-derived metrics (DR-metrics), full-waveform LiDAR-derived metrics (FW-metrics) and structure parameters (combining height metrics and canopy cover) were used to estimate forest AGB. Additionally, the combined use of DR- and FW-metrics through a nonlinear way was also evaluated for AGB estimation in a coniferous forest in Dayekou, Gansu province of China. Results indicated that both height metrics derived from discrete-return and full-waveform LiDAR data were stronger predictors of forest AGB compared with other LiDAR-derived metrics. Canopy cover derived from discrete-return LiDAR data was not sensitive to forest AGB, while canopy cover estimated by full-waveform LiDAR data (CC WF ) showed moderate correlation with forest AGB. Structure parameters derived from full-waveform LiDAR data, such as H75 FW * CC FW, were closely related to forest AGB. In contrast, structure parameters derived from discrete-return LiDAR data were not suitable for estimating forest AGB due to the less sensitivity of canopy cover CC DR 2 to forest AGB. This research also concluded that the synergistic use of DR- and FW-metrics can provide better AGB estimates in coniferous forest.