Discriminating Wheat Aphid Damage Degree Using 2-Dimensional Feature Space Derived from Landsat 5 TM
Abstract:Wheat aphid, Sitobion avenae F. infests winter wheat (Triticum aestivum L.) appearing almost annually in northwest China. Wheat aphid feeding causes significant losses in wheat crops, which can be minimized by timely forecasting aphid occurrence and real-time monitoring the degree of damage at different growth stages. Remote sensing has been proved to be an efficient tool in providing spatial estimates of aphid density and damage degree. This study investigates the relationship between the degree of aphid damage and the indicators (normalised difference water index (NDWI), modified normalized difference water index (MNDWI), and land surface temperature (LST)) derived from Landsat 5 TM images. Two 2-dimensional feature spaces are established for LST and MNDWI, as well as for LST and NDWI, separately. From the scattering pattern of datasets in the feature spaces, it is obvious that LST is a driving factor for the aphid occurrence and MNDWI is more sensitive to aphid damage degrees than NDWI. Meanwhile, the uninfected wheat samples are distributed on the left of the LST-MNDWI feature space whereas the infested wheat samples are on the right. Furthermore, the threshold values of LST and MNDWI were determined to discriminate the specific degrees of damage according to the mean value and standard deviation of different samples. The overall accuracy is 84%, and the Kappa coefficient is 0.7567. The results indicate that LST and MNDWI could be used to discriminate the aphid damage degrees over large scale only by thermal infrared band and multi-spectral satellite imagery.
Document Type: Research Article
Publication date: January 1, 2012
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