屈永华等:Preliminary study on integrated wireless smart terminals for leaf area index measurement
被阅读 788 次
2016-12-09
Preliminary study on integrated wireless smart terminals for leaf area index measurement
作者:Qu, YH (Qu, Yonghua)[ 1 ] ; Meng, JH (Meng, Jihua)[ 2 ] ; Wan, HW (Wan, Huawei)[ 3 ] ; Li, YT (Li, Yetao)[ 1 ]
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷: 129  页: 56-65
DOI: 10.1016/j.compag.2016.09.011
出版年: NOV 1 2016
 
摘要
The last few decades have seen the emergence of many new methods for measuring leaf area index (LAI). One attractive method is to measure LAI by using a readily available simple instrument, such as a smart phone, as an alternative to traditional complicated and thus costly commercial instruments. We designed a novel integrated LAI measurement system, LAISmart, which is implemented by using two smart terminals communicating through a wireless connection in an integrated support infrastructure. The LAI is calculated by using the Beer gap fraction model on the assumption of spherical leaf angle distribution that takes the G value (the projection of unit foliage area on the plane perpendicular to the view direction 9) as constant at 0.5. The gap fraction is calculated by segmenting the image pixel into canopy and background. For the selection of image segment features, LAISmart provides two options, greenness index and blue band, which may be suited to different vegetation types. The proposed LAISmart was validated by measurements on four types of vegetation, i.e., Evergreen Needleleaf Forest (ENF), Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), and broadleaf crop, on which 114 LAI values were calculated in the eight datasets. A pairwise comparison between LAISmart and LAI-2000 showed that the LAI values derived from LAISmart correlated strongly with those of the LAI-2000, with an R-2 value of 0.97, and had high accuracy in total, with a root-mean-square error (RMSE) of 0.45 m(2)/m(2). The validation results revealed that the overestimation on LAI caused by underestimation of the G value might complement the bias caused by misclassification. Moreover, although there is no limitation on the shooting angle of LAISmart, it is recommended that LAISmart be used at the low zenith angle mode, e.g., upward- or downward-looking mode, rather than at the large zenith angle. Further validation will focus on other vegetation types such as grass and shrubs in order to improve the performance of LAISmart for different vegetation types. (C) 2016 Elsevier B.V. All rights reserved.
 
通讯作者地址: Qu, YH (通讯作者)
Beijing Normal Univ, Sch Geog, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Sch Geog, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100101, Peoples R China
[ 3 ] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China