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Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions
Zeng, Yelu1,2,3,4; Li, Jing1,2,3; Liu, Qinhuo1,2,3; Hu, Ronghai1,2; Mu, Xihan1,2; Fan, Weiliang1,2; Xu, Baodong1,2,4; Yin, Gaofei1,2,5; Wu, Shengbiao1,2,4
Corresponding AuthorLi, Jing ; Liu, Qinhuo
2015
Source PublicationRemote Sensing
ISSN2072-4292
Volume7Issue:10Pages:13410-13435
SubtypeArticle
AbstractThe 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.
KeywordLeaf Area Index Near-surface Remote Sensing Digital Photography Gap Fraction Clumping Index Sunlit Foliage Component Clear-sky Conditions
WOS HeadingsScience & Technology ; Technology
DOI10.3390/rs71013410
WOS Subject ExtendedRemote Sensing
Indexed BySCI
WOS KeywordRADIATIVE-TRANSFER MODEL ; HEMISPHERICAL PHOTOGRAPHY ; BOREAL FORESTS ; CORRECT ESTIMATION ; ANGLE DISTRIBUTION ; CANOPY STRUCTURE ; GAP FRACTION ; NEAR-SURFACE ; REFLECTANCE ; CROPS
Language英语
Quartile2区
TOP
WOS SubjectRemote Sensing
WOS IDWOS:000364328600037
Funding OrganizationNational Basic Research Program of China(2013CB733401) ; National Natural Science Foundation of China(41271366) ; National High Technology Research and Development Program of China(2012AA12A304) ; CAS/SAFEA International Partnership Program for Creative Research Teams(KZZD-EW-TZ-09)
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/13840
Collection数字山地与遥感应用中心
Affiliation1.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
2.Beijing Normal Univ, Beijing 100101, Peoples R China
3.Joint Ctr Global Change Studies, Beijing 100875, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
Recommended Citation
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):13410-13435.
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),13410-13435.
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):13410-13435.
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