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Mapping of powdery mildew using multi-spectral HJ-CCD image in Beijing suburban area

文献类型： 外文期刊

作者： Yuan, Lin 1 ; Zhang, Jingcheng 1 ; Zhao, Jinling 1 ; Huang, Linsheng 1 ; Yang, Xiaodong 1 ; Wang, Jihua 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst Applicat, Hangzhou 310029, Zhejiang, Peoples R China

3.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

关键词： Winter wheat;Powdery mildew;HJ-CCD;Spectral information divergence (SID);Spectral angle mapper (SAM)

期刊名称：OPTIK （ 影响因子：2.443； 五年影响因子：1.955 ）

ISSN： 0030-4026

年卷期： 2013 年 124 卷 21 期

页码：

收录情况： SCI

摘要： Powdery mildew is one of the most serious diseases, which has a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study examines the potential of a moderate resolution multispectral satellite image in disease monitoring at regional scale. At the suburban area around Beijing, a large size ground survey sample (n=90) and the corresponding HJ-CCD image were acquired at the grain filling stage of winter wheat. A number of spectral features were found to be sensitive to powdery mildew through an independent t-test. Based on these spectral features, classification models were established using both spectral information divergence (SID) and spectral angle mapper (SAM), respectively. The results showed that the overall accuracies of disease identification and severity estimation were moderate. The estimation of normal and seriously infected samples yielded higher accuracies than slightly infected samples. The single phase HJ-CCD can only be used for locating the infected areas of powdery mildew, whereas is unable to discriminate the severity levels of disease. The presence of several stressors and disturbances other than disease is a possible reason of the unsatisfactory performance of disease monitoring models. Therefore, the integration of multi-phase onboard data and some relevant ancillary data is necessary to improve the accuracy and reliability of disease monitoring at regional scale. (C) 2013 Elsevier GmbH. All rights reserved.

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