Remote Sensing Applications and Decision Support

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 Affiliations
Zhihui Wang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Sciences, Beijing 100094, China

Yellow River Conservancy Commission, Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China

Ministry of Water Resources, Key Laboratory of the Loess Plateau Soil Erosion and Water Process and Control, Zhengzhou 450003, China

Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China

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

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Sciences, Beijing 100094, China

J. Appl. Remote Sens. 10(3), 036007 (Jul 19, 2016). doi:10.1117/1.JRS.10.036007
History: Received February 3, 2016; Accepted June 24, 2016
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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 meansquareerror(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.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Zhihui Wang ; Liangyun Liu ; Dailiang Peng ; Xinjie Liu ; Su Zhang, et al.
"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", J. Appl. Remote Sens. 10(3), 036007 (Jul 19, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036007


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