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

作者:Zeng, YL (Zeng, Yelu)[ 1,2,3,4 ] ; Li, J (Li, Jing)[ 1,2,3 ] ; Liu, QH (Liu, Qinhuo)[ 1,2,3 ] ; Hu, RH (Hu, Ronghai)[ 1,2 ] ; Mu, XH (Mu, Xihan)[ 1,2 ] ; Fan, WL (Fan, Weiliang)[ 1,2 ] ; Xu, BD (Xu, Baodong)[ 1,2,4 ] ; Yin, GF (Yin, Gaofei)[ 1,2,5 ] ; Wu, SB (Wu, Shengbiao)[ 1,2,4 ]

Remote Sensing

卷: 7  期: 10  页: 13410-13435

DOI: 10.3390/rs71013410

出版年: OCT 2015

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.

通讯作者地址: Li, J (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, 20A Datun Rd, Beijing 100101, Peoples R China.

地址:

[ 1 ] 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