Keywords :
Enhancing Multispectral Discrimination
บทคัดย่อ :
In this study at the Sakaerat Biosphere Reserve of Thailand, 15 derivative grayscale imageswere generated from grayscale images, for redness and greenness, among 7 Landsat grayscale images.The use of all 22 grayscale images provided an additional principal component and a larger number ofpixel clusters. As evergreen forest is the natural vegetation type of Sakaerat, all pixels in the imagewere grayscaled based on the principal component score as an indicator of the clusters evergreenforest-likelihood. Using separate sets of the 7 Landsat images and all 22 grayscale images Dunnett s f-test completely discriminated values of evergreen forest-likelihood for seven 300 m x 300 m plots withdifferent vegetation types, favored by a difference in the pattern of mean separation between the imagesets. With all 22 grayscale images, the evergreen forest plot was more significantly discriminated fromthat of fire-protected deciduous forest compared to the Landsat images alone. Thus, differences invisible reflectance revealed by the derivative grayscale images quantified the degree of ecologicalrestoration more strictly than the conventional Landsat images. The proposed imaging method wouldthus improve the real-time observation of forest and other canopies when used together withmultispectral sensors.
เอกสารอ้างอิง :
Doi, R. (2007). Enhancing multispectral discrimination among vegetation types with a new pseudo-color imaging method. H. Muteia, 24(27), 7.
