Physical Sciences and Mathematics Commons^™

Mapping Robinia Pseudoacacia Forest Health Conditions By Using Combined Spectral, Spatial, And Textural Information Extracted From Ikonos Imagery And Random Forest Classifier, Hong Wang, Yu Zhao, Ruiliang Pu

School of Geosciences Faculty and Staff Publications

The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) were first calculated from …