摘要
为实现烟草花叶病快速、无损监测。基于染病植株冠层高光谱数据,优选病情指示变量,利用偏最小二乘法建立病害程度估测模型,并进一步将模型应用于资源3号卫星遥感数据,建立烟草花叶病病害等级分布图。结果显示:1)以比值植被指数(RVI),差值植被指数(DVI),再归一化植被指数(RDVI),变换植被指数(TVI),土壤可调植被指数(SAVI)作为烟草花叶病病情的指示因子,能有效估测花叶病的严重程度,决定系数达0.8165。2)在烟草花叶病病害等级分布图上,随机抽取140个样点进行实测,并将实测值与估测值进行线性拟合,拟合优度R2达77.13%。因此该方法能实现对烟草花叶病的大范围实时监测。
Disease severity estimation model was established using partial least squares method to find best indicator variable for disease condition on the basis of canopy hyper spectral data of mosaic tobacco so as to realize fast and nondestructive monitoring of tobacco mosaic virus. Tobacco mosaic disease grade map was obtained by applying the model to remote sensing data of ZY-3 satellite. Results showed that： 1）the ratio vegetation index（RVI）, difference vegetation index（DVI）, and normalized difference vegetation index（RDVI）, transformed vegetation index（TVI）, soil adjusted vegetation index（SAVI） as indicators of mosaic tobacco could effectively estimate the disease severity. The determination coefficient of the model based on the above 5 index was 0.8165. 2） Randomly selected 140 samples were measured on tobacco mosaic disease grade map. Then linear fitting of measured value and the estimated value was implemented and its R2 reached 77.13%. It was suggested that this method could achieve real-time monitoring of mosaic tobacco over large areas.
出处
《中国烟草学报》
EI
CAS
CSCD
北大核心
2016年第1期76-83,共8页
Acta Tabacaria Sinica
基金
国家自然科学基金(No:41171425)
山东省烟草公司重点项目资助(No:2014-7-1)
上海烟草集团公司重点科技项目(No:SZBCW2013-01140)
关键词
烟草花叶病
高光谱
遥感监测
资源3号卫星
tobacco mosaic disease
hyper spectrum
remote sensing monitoring
ZY-3 satellite
