TITLE

Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests

AUTHOR(S)
Lin Cao; Coops, Nicholas C.; Hermosilla, Txomin; Innes, John; Jinsong Dai; Guanghui She
PUB. DATE
August 2014
SOURCE
Remote Sensing;Aug2014, Vol. 6 Issue 8, p7110
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
97745477

 

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