| Abstrak/Abstract |
The HyMap hyper-spectral data was used to classify photosynthetic vegetation (PV), non-photosybthetic vegetation (NPV), and exposed soils in a semi-arid savannah environment of McKinlay, northern Queensland, and Australia. This study aimed to understand how effective the sub-pixel classification approach applied on hyper-spectral data to distinguish the vegetation and soil features in semi-arid environment. In contrast to the per-pixel approach, this approach treats the pixel value as refletance sum of its composite features, and shows its component abundance. The most commonly used sub-pixel classification technique was used in this research, namely linear spectral Unmixing (LSU). End members were used as the input class, and the result was compared with the standart maximum likelihood classification (MLC) using post-classification comparison method. The result of this study shows that LSU produced apatchy distribution of classes. NPV features have problem with domination of exposed soil reflectance. This is equivalent to the previous studies result that background soil dominates the spectral reflectance in this environment. According to the qualitative accuracy assessment, LSU has higher accuray in representing PV and NPV compare to the traditional MLC classification. |
