Predicting water stress induced by Thaumastocoris peregrinus infestations in plantation forests using field spectroscopy and neural networks
Predicting water stress induced by Thaumastocoris peregrinus infestations in plantation forests using field spectroscopy and neural networks
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DOI：
10.1080/14498596.2013.821679
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发表时间：
2014-01-02
影响因子：
1.9
通讯作者：
Mutanga, O.
中科院分区：
文献类型：
--
作者：
Oumar, Z.;Mutanga, O.
现场光谱学在预测由游隼奇虫（Thaumastocoris peregrinus）感染引起的水分胁迫方面进行了测试，这种害虫对国际上的桉树种植园造成了严重损害。将水分指数和根据高光谱场反射率计算出的已知水分吸收带输入神经网络算法中，以预测植物水分含量（PWC）和等效水厚度（EWT）。涉及现场光谱数据和神经网络的集成方法在独立测试数据集上预测 PWC 和 EWT 的相关系数为 0.88 和 0.71。结果表明，高分辨率现场光谱数据在检测由于改变水分含量的生理变化而导致昆虫侵扰的早期阶段方面具有潜力。
Field spectroscopy was tested in predicting water stress induced byThaumastocoris peregrinusinfestations, a pest which is causing significant damage to eucalypt plantations internationally. Water indices and known water absorption bands calculated from hyperspectral field reflectance were input into a neural network algorithm to predict plant water content (PWC) and equivalent water thickness (EWT). The integrated approach involving field spectral data and neural networks predicted PWC and EWT with correlation coefficients of 0.88 and 0.71 on independent test datasets. The results indicate the potential of high-resolution field spectral data in detecting the early stages of insect infestation due to physiological changes that alter water content.