Remote Sensing - a tool for environmental observation

57 spectral ratioing spectral ratioing refers to

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5.7 Spectral Ratioing Spectral ratioing refers to enhancement techniques by combining pixel values from different spectral bands. Ratio images are prepared by dividing the digital numbers in one band by the corresponding digital numbers in another band for each pixel, stretching the resulting values, and plotting the new values as an image. The most widely used ratio techniques are ratios to enhance the vegetation cover or to distinguish bare soil from vegetation . Spectral indices used for these purposes are e.g. (Huete, 1989; Sellers, 1989; Tucker, 1979), the VI: Vegetation Index, the NDVI: Normalized Difference Vegetation Index , the GEMI, the SAVI: Soil Adjusted Vegetation Index, the PVI: Perpendicular Vegetation Index, the GVI: Green Vegeta- tion Index, or the TVI: Transformed Vegetation Index. Figure 1.9 and figure 5.12 illustrates the principle of the vegetation indices. Most vegetation indices combine one infrared spectral band with one visible band.
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75 Vegetation indices are often used to estimate from remote sensing images the leaf area index, the vegetation cover or the biomass for specific areas (De Jong 1994; Huete, 1989). Although there is a correlation between biomass, leaf area index, vegetation cover and spectral vegetation indices, some caution is necessary. The relations between spectral indices and these vegetation properties is often area-specific (that means that functions developed in one study area cannot be used directly in other areas), the condition of the soil and the type of soil beneath the vegetation or crop has a certain impact on the value of the spectral indices: although the vegetation cover remains the same, the value of the spectral index might change due to a change in underlying soil. Figure 5.12 Reflectance spectra of vegetation and dry soil and their computed vegetation index (Sabins, 1987). An advantage of using spectral ratioing techniques is that they express the spectral character- istics of image features regardless the intensity of illumination. Consequently, the same object at sunlit sites or at shadow sites will appear different in the original spectral bands but will have the same spectral brightness after spectral ratioing. Figure 5.13 shows the principle with an computational example.
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76 Figure 5.13 Reduction of scene illumination effects through spectral ratioing (Lillesand & Kiefer, 1994). 5.8 Digital Image Transformation Principal Component Transformation: PCA Multi-spectral bands of e.g. Landsat TM or SPOT show often a very high correlation especially among the visible bands or among the infrared bands. That indicates that the different spectral images are to a large extent similar. Principal Component Analysis (PCA) is a technique to reduce the redundancy of information in spectral data. The purpose of PCA is to compress all of the information contained in e.g. 6 Landsat TM bands into fewer bands. The statistical distri- bution of the pixel values over the multi-spectral bands is used to compute a new, most effective, abstract coordinate system. Figure 5.14 shows the principle for a two-dimensional situation i.e. 2
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