Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

文献类型： 外文期刊

第一作者： Yuan, Lin

作者： Yuan, Lin;Zhang, Jingcheng;Nie, Chenwei;Wei, Liguang;Wang, Jihua;Zhang, Jingcheng;Wang, Jihua;Zhang, Jingcheng;Wang, Jihua;Yuan, Lin;Zhang, Jingcheng;Wang, Jihua;Shi, Yeyin

作者机构：

关键词： powdery mildew;winter wheat;SPOT-6;maximum likelihood classifier;mahalanobis distance;artificial neural network

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2014 年 6 卷 5 期

页码：

收录情况： SCI

摘要： Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km(2) typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods-artificial neural network, mahalanobis distance, and maximum likelihood classifier-were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.

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