Development,evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat |
| |
Authors: | Jingcheng Zhang Wenjiang Huang Jiangyuan Li Guijun Yang Juhua Luo Xiaohe Gu Jihua Wang |
| |
Institution: | (1) National Engineering Research Center for Information Technology In Agriculture, Beijing, 100097, China;(2) Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou, 310029, China;(3) School of Electronic and Information Engineering, Tong Ji University, Shanghai, 201800, China;(4) School of Engineering, Shanxi Normal University, Linfen, 041000, China |
| |
Abstract: | In most cases, statistical models for monitoring the disease severity of yellow rust are based on hyperspectral information.
The high cost and limited cover of airborne hyperspectral data make it impossible to apply it to large scale monitoring. Furthermore,
the established models of disease detection cannot be used for most satellite images either because of the wide range of wavelengths
in multispectral images. To resolve this dilemma, this paper presents a novel approach by constructing a spectral knowledge
base (SKB) of diseased winter wheat plants, which takes the airborne images as a medium and links the disease severity with
band reflectance from environment and disaster reduction small satellite images (HJ-CCD) accordingly. Through a matching process
with a SKB, we estimated the disease severity with a disease index (DI) and degrees of disease severity. The proposed approach
was validated against both simulated data and field surveyed data. Estimates of DI (%) from simulated data were more accurate,
with a coefficient of determination (R
2) of 0.9 and normalized root mean square error (NRMSE) of 0.2. The overall accuracy of classification reached 0.8, with a
kappa coefficient of 0.7. Validation of the estimates against field measurements showed that there were some errors in the DI value
with the NRMSE close to 0.5. The result of the classification was more encouraging with an overall accuracy of 0.77 and a
kappa coefficient of 0.58. For the matching process, Mahalanobis distance performed better than the spectral angle (SA) in all
analyses in this study. The potential of SKB for monitoring the incidence and severity of yellow rust is illustrated in this
study. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|