Predicting wheat aphid using 2-dimensional feature space based on multi-temporal Landsat TM

Aphid (Hemiptera: Aphididae) outbreaks appear in wheat (Triticum aestivum L.) planting area in China, and had significant economic impacts on wheat. It has severe impact on both winter wheat yield and grain quality. The aim of this study was to monitor and predict of wheat aphid by analyzing the relationship between land surface temperature (LST), modified normalized difference water index (MNDWI) and the occurrence and prevalence of aphid in wheat. The results showed that LST was an important driving factor for occurrence of aphid, and MNDWI was sensitive to aphid damage degree. Meanwhile, a 2-dimensional feature space was established based on LST and MNDWI derived from Landsat TM images, and discrimination model of aphid damage degrees was established according to the distribution of samples in the feature space. It was verified that the overall accuracy of discrimination model is 82.5%, and kappa accuracy is 73.88%.