Abstract
An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D canopy structural information by discrete (XYZ) LiDAR metrics (DR-metrics), and (ii) the detailed geometric and radiometric information of the returned waveform by full-waveform LiDAR metrics (FW-metrics), and to evaluate the capacity of these metrics in predicting biomass and its components in subtropical forest ecosystems. This study was undertaken in a mixed subtropical forest in Yushan Mountain National Park, Jiangsu, China. LiDAR metrics derived from DR and FW LiDAR data were used alone, and in combination, in stepwise regression models to estimate total as well as above-ground, root, foliage, branch and trunk biomass. Overall, the results indicated that three sets of predictive models performed well across the different subtropical forest types (Adj-R2 = 0.42–0.93, excluding foliage biomass). Forest type-specific models (Adj-R2 = 0.18–0.93) were generally more accurate than the general model (Adj-R2 = 0.07–0.79) with the most accurate results obtained for coniferous stands (Adj-R2 = 0.50–0.93). In addition, LiDAR metrics related to vegetation heights were the strongest predictors of total biomass and its components. This research also illustrates the potential for the synergistic use of DR and FW LiDAR metrics to accurately assess biomass stocks in subtropical forests, which suggest significant potential in research and decision support in sustainable forest management, such as timber harvesting, biofuel characterization and fire hazard analyses.
Highlights
As a primary reservoir of carbon dioxide (CO2) in terrestrial ecosystems, forests play a key role in the global carbon cycle [1]
The waveform metrics related to canopy height (e.g., waveform distance (WD)), vertical arrangement (e.g., height of median energy (HOME)) and roughness of upper-most canopy (e.g., roughness of outermost canopy (ROUGH)) had a more significant relationship with biomass components than others
For the discrete and full-waveform metrics analyzed in this study, these metrics related to vegetation heights, i.e., mean height, upper height percentiles (i.e., h50, h75, h95), HOME and vertical distribution ratio (VDR), were the strongest predictors of biomass and its components
Summary
As a primary reservoir of carbon dioxide (CO2) in terrestrial ecosystems, forests play a key role in the global carbon cycle [1]. The subtropical forests biome accounts for approximately a quarter of the area of China and are important for improving regional ecological environment and maintaining global carbon balance [3]. Despite their importance, there is still considerable uncertainty about carbon budgets within these subtropical forests, despite several national-scale studies using historical forest inventory data [4]. The partitioned biomass components (i.e., trunk, branch, foliage and root) provide important information for forest management decisions such as the estimation of stem and branch biomass for biofuels calculations, as well as analyzing foliage biomass for studying forest growth and assessing crown biomass (i.e., branch and foliage biomass) for predicting fire hazard [5,6].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
