Next Article in Journal
Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest
Previous Article in Journal
An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery
Remote Sens. 2014, 6(5), 3611-3623; doi:10.3390/rs6053611
Article

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

1,4
,
1,2,3,4,* , 5
,
1
,
1
 and
1,2,3,4
Received: 2 December 2013 / Revised: 10 April 2014 / Accepted: 15 April 2014 / Published: 25 April 2014
View Full-Text   |   Download PDF [1444 KB, uploaded 19 June 2014]   |   Browse Figures

Abstract

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.
Keywords: powdery mildew; winter wheat; SPOT-6; maximum likelihood classifier; mahalanobis distance; artificial neural network powdery mildew; winter wheat; SPOT-6; maximum likelihood classifier; mahalanobis distance; artificial neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Yuan, L.; Zhang, J.; Shi, Y.; Nie, C.; Wei, L.; Wang, J. Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image. Remote Sens. 2014, 6, 3611-3623.

View more citation formats

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert