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

Remote Sensing

Lin Yuan²  Jingcheng Zhang²  Yeyin Shi¹  Chenwei Nie²  Liguang Wei² 

[1] Department of Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agricultural Hall, Stillwater, OK 74078, USA; E-Mail:;Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; E-Mails:

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

DOI  :  10.3390/rs6053611

来源: mdpi

【 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 km² 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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland