Texture Augmented Analysis of High Resolution Satellite Imagery in Detecting Invasive Plant Species
Fuan Tsai, Ming-Jhong Chou
During recent decades, a considerable number of alien species have been brought into Taiwan and have caused significant impacts to local ecosystems and biodiversity. High resolution satellite imagery can provide detailed spatial characteristics over a large area and has a great potential for accurate vegetation mapping. However, most traditional multispectral image classification techniques focus on spectral discrimination of ground objects and may overlook useful spatial information provided by high resolution images. To achieve the best result, analysis of high resolution imagery should also incorporate spatial variations of the data. Therefore, this paper has looked into using a texture augmented procedure to analyze a high resolution satellite (QuickBird) image in order to detect an invasive plant species (Leucaena leucocephala) in southern Taiwan. Samples of primary vegetation covers were selected from the image to determine suitable texture analysis parameters for extracting texture features helpful for classification. Validation with ground truth data showed that the analysis produced high accuracies in detecting the target plant species and overall classification for primary vegetation types within the study site.
keywords:remote sensing, texture analysis, GLCM, vegetation mapping.