Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture
Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture
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DOI：
10.1016/j.jag.2015.03.017
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发表时间：
2015-12-01
影响因子：
7.5
通讯作者：
Mckee, Mac
中科院分区：
文献类型：
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作者：
Elarab, Manal;Ticlavilca, Andres M.;Mckee, Mac
精准农业需要高分辨率的信息，以便能够更精确地管理生产投入。必须以高空间分辨率和适合于及时响应的时间频率获取关于作物和田地状况的可操作信息。在这项研究中，高空间分辨率的图像是通过使用一个小型的，无人驾驶航空系统称为AggieAir（TM）。在AggieAir飞行的同时，在精确确定的地点对植物叶绿素进行了密集的地面采样。本研究报告的应用程序的相关向量机加上交叉验证和向后消除的数据集组成的反射率从高分辨率多光谱图像（可见光-近红外），热红外图像，植被指数，结合在现场SPAD测量叶绿素浓度的推导，估计叶绿素浓度从遥感数据在15厘米分辨率。结果表明，以LAI、NDVI、热红外波段和红色波段为输入，采用薄板样条核类型和核宽度为5.4的相关向量机，可用于叶绿素浓度的空间估计，均方根误差为5.31 μ g cm（-2），效率为0.76，有9个相关向量。(C)2015作者出版社：Elsevier B. V.
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 AggieAir (TM). 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 mu g cm(-2), efficiency of 0.76, and 9 relevance vectors. (C) 2015 The Authors. Published by Elsevier B.V.