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Journal of Applied Remote Sensing

Estimating woody aboveground biomass in an area of agroforestry using airborne light detection and ranging and compact airborne spectrographic imager hyperspectral data: individual tree analysis incorporating tree species information
Author(s): Zhihui Wang; Liangyun Liu; Dailiang Peng; Xinjie Liu; Su Zhang; Yingjie Wang
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Paper Abstract

Until now, there have been only a few studies that have made estimates of the woody aboveground biomass (AGB) in an area of agroforestry using remote sensing technology. The woody AGB density was estimated using individual tree analysis (ITA) that incorporated tree species information using a combination of airborne light detection and ranging (LiDAR) and compact airborne spectrographic imagery acquired over a typical agroforestry in northwestern China. First, a series of improved LiDAR processing algorithms was applied to achieve individual tree segmentation, and accurate plot-level canopy heights and crown diameters were obtained. The individual tree species were then successfully classified using both spectral and shape characteristics with an overall accuracy of 0.97 and a kappa coefficient of 0.85. Finally, the tree-level AGB (kg) was estimated based on the ITA; the AGB density (Mg/ha) was then upscaled based on the tree-level AGB values. It is concluded that, compared with the commonly used area-based method combining LiDAR and spectral metrics [root mean square error (RMSE)=19.58  Mg/ha], the ITA method performs better at estimating AGB density (RMSE=10.56  Mg/ha). The tree species information also improved the accuracy of the AGB estimation even though the species are not well diversified in this study area.

Paper Details

Date Published: 19 July 2016
PDF: 20 pages
J. Appl. Rem. Sens. 10(3) 036007 doi: 10.1117/1.JRS.10.036007
Published in: Journal of Applied Remote Sensing Volume 10, Issue 3
Show Author Affiliations
Zhihui Wang, Institute of Remote Sensing and Digital Earth (China)
Yellow River Conservancy Commission (China)
Ministry of Water Resources (China)
Liangyun Liu, Institute of Remote Sensing and Digital Earth (China)
Dailiang Peng, Institute of Remote Sensing and Digital Earth (China)
Xinjie Liu, Institute of Remote Sensing and Digital Earth (China)
Su Zhang, Institute of Remote Sensing and Digital Earth (China)
Yingjie Wang, Institute of Remote Sensing and Digital Earth (China)


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