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

Zhihui Wang; Liangyun Liu; Dailiang Peng; Xinjie Liu; Su Zhang; Yingjie Wang

doi:10.1117/1.JRS.10.036007

19 July 2016 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

Zhihui Wang, Liangyun Liu, Dailiang Peng, Xinjie Liu, Su Zhang, Yingjie Wang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 036007 (July 2016). https://doi.org/10.1117/1.JRS.10.036007

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.

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Zhihui Wang, Liangyun Liu, Dailiang Peng, Xinjie Liu, Su Zhang, and Yingjie Wang "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," Journal of Applied Remote Sensing 10(3), 036007 (19 July 2016). https://doi.org/10.1117/1.JRS.10.036007

Published: 19 July 2016

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