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Study on Hyper-Spectral Remote Sensing Models for Monitoring Damage of Oedaleus Asiaticus (Orthoptera: Acrididae)

文献类型: 外文期刊

作者: Lu Hui 1 ; Han Jian-guo 1 ; Zhang Lu-da 3 ;

作者机构: 1.China Agr Univ, Coll Anim Sci & Technol, Beijing 100094, Peoples R China

2.Chinese Acad Trop Agr Sci, Environm & Plant Protect Inst, Danzhou 571737, Peoples R China

3.China Agr Univ, Coll Sci, Beijing 100094, Peoples R China

关键词: Hyper-spectral remote sensing;Oedaleus asiaticus;Leaf area index(LAI);Hyper-spectral parameters

期刊名称:SPECTROSCOPY AND SPECTRAL ANALYSIS ( 影响因子:0.589; 五年影响因子:0.504 )

ISSN: 1000-0593

年卷期: 2009 年 29 卷 3 期

页码:

收录情况: SCI

摘要: In the present paper, the ASD Field Spec Pro FRTM spectroradiometer was used for measuring damages of O. asiaticus in Xilin GolLeague, Inner Mongolia. First, the hyper-spectral data were analyzed and the canopy reflectance spectral data,were-compared between the healthy leaves and the infected leaves of Leymus chinensis. Second, the regression models between the leaf area index (LAI) and the hyper-spectral parameters were built, and different varieties of the O. asiaticus damage were used to test its precision. The results showed that there was a high correlation of three hyper-spectral data between the LAI and the first derivative data. Moreover the disease index (DI) model, which is mostly suitable for use in indicating the intensity of grasshoppers damage in the study area, containing the ratio of the sum of first derivative within red peak regions (620-760 mn, SDr) to the sum of first derivative within blue peak regions (430-470 run, SDb) was the best one. From the model, there were no damage if DI was over 72.43, slight damage if DI was between 51.57 and 79.83, and serious damage if DI was less than 51.57. The model had the highest prediction precision with the correlation coefficient of prediction of 0.948, and the mean relative error of 3.928%. These results showed a good prediction of the model and indicated that the grasshopper damage could be estimated at the canopy level using hyper-spectral reflectance.

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