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

	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
	Wang, Zhihui 1; Liu, Liangyun 1; Peng, Dailiang 1; Liu, Xinjie 1; Zhang, Su 1; Wang, Yingjie 1
刊名	Journal of Applied Remote Sensing
	2016
卷号	10 期号:3
关键词	DATA CENTERS MODELS SIMULATIONS MAPREDUCE SYSTEM
通讯作者	Liu, Liangyun (liuly@radi.ac.cn)
英文摘要	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)=19.58Mg/ha], the ITA method performs better at estimating AGB density (RMSE=10.56Mg/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 (SPIE).
学科主题	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20163102659673
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39374]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Sciences, Beijing 2.100094, China 3. Yellow River Conservancy Commission, Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 4.450003, China 5. Ministry of Water Resources, Key Laboratory of the Loess Plateau Soil Erosion and Water Process and Control, Zhengzhou 6.450003, China 7. Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, Beijing 8.100101, China
推荐引用方式 GB/T 7714	Wang, Zhihui,Liu, Liangyun,Peng, Dailiang,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]. Journal of Applied Remote Sensing,2016,10(3).
APA	Wang, Zhihui,Liu, Liangyun,Peng, Dailiang,Liu, Xinjie,Zhang, Su,&Wang, Yingjie.(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.Journal of Applied Remote Sensing,10(3).
MLA	Wang, Zhihui,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".Journal of Applied Remote Sensing 10.3(2016).