Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

	Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions
	Zeng, Yelu 1; Li, Jing 1; Liu, Qinhuo 1; Hu, Ronghai 1; Mu, Xihan 1; Fan, Weiliang 1; Xu, Baodong 1; Yin, Gaofei 1; Wu, Shengbiao 1
刊名	Remote Sensing
	2015
卷号	7 期号:10 页码:4489-4501
关键词	leaf area index near-surface remote sensing digital photography gap fraction clumping index sunlit foliage component clear-sky conditions
通讯作者	Li, J (reprint author), Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, 20A Datun Rd, Beijing 100101, Peoples R China.
英文摘要	The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.
研究领域[WOS]	Remote Sensing
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000364328600037
内容类型	期刊论文
源URL	[http://ir.ceode.ac.cn/handle/183411/38091]
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.[Zeng, Yelu 2.Li, Jing 3.Liu, Qinhuo 4.Hu, Ronghai 5.Mu, Xihan 6.Fan, Weiliang 7.Xu, Baodong 8.Yin, Gaofei 9.Wu, Shengbiao] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 10.[Zeng, Yelu
推荐引用方式 GB/T 7714	Zeng, Yelu,Li, Jing,Liu, Qinhuo,et al. Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions[J]. Remote Sensing,2015,7(10):4489-4501.
APA	Zeng, Yelu.,Li, Jing.,Liu, Qinhuo.,Hu, Ronghai.,Mu, Xihan.,...&Wu, Shengbiao.(2015).Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions.Remote Sensing,7(10),4489-4501.
MLA	Zeng, Yelu,et al."Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions".Remote Sensing 7.10(2015):4489-4501.