globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2015.03.017
Scopus记录号: 2-s2.0-84943609503
论文题名:
Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture
作者: Elarab M; , Ticlavilca A; M; , Torres-Rua A; F; , Maslova I; , McKee M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 43
起始页码: 32
结束页码: 42
语种: 英语
英文关键词: Chlorophyll concentration ; High spatial resolution imagery ; Precision agriculture ; Relevance vector machine ; Remote sensing
Scopus关键词: chlorophyll ; infrared imagery ; multispectral image ; precision agriculture ; remote sensing ; spatial resolution ; vegetation index
英文摘要: Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS-NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm-2, efficiency of 0.76, and 9 relevance vectors. © 2015 The Authors.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79562
Appears in Collections:气候变化事实与影响

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作者单位: Civil and Environmental Engineering Department, Utah State University, United States; Utah Water Research Laboratory, Utah State University, United States; American University, Washington, DC, United States

Recommended Citation:
Elarab M,, Ticlavilca A,M,et al. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,43
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