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Remote Sensing - a tool for environmental observation

Unfortunately little knowledge of the influence of

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Unformatted text preview: Unfortunately, little knowledge of the influence of different organic soil compounds and the different forms of organic matter on the spectral behaviour of soils is available in the literature. The basic components of soil minerals are silicon, aluminium and oxygen. None of these soil constituents have diagnostic absorption features. Quartz for example, has a large reflectance throughout the short-wave spectrum and absorption features in short-wave quartz spectra are only due to impurities. Soil particle size influences a number of soil properties such as moisture content and soil struc- ture. Both of these properties also influence the reflectance of soils. Consequently, it is very difficult to measure the exact effect of increasing soil particle size on reflectance. Most of the studies concerned with remote sensing and soils follow an empirical approach to distinguish soil types. Vegetation or crop cover always hampers the observation of soils in the optical wavelengths. 90 Figure 6.4 Some examples of soil reflectance spectra (Asrar, 1989). Figure 6.6 Spectral reflectance curves for wet and dry silty loam and wet and dry clay (Curran, 1986). 91 Figure 6.5 Reflectance spectra of carbonate minerals showing features at 2280 and 2340 nm and minerals showing OH en H 2 O absorption bands (Asrar, 1989). 92 6.4 Multi-temporal Remote Sensing & Change Detection Multi-temporal remote sensing techniques refer to the use of images of different dates to study temporal changes of e.g. crop cover or vegetation cover also often referred to as change detection. Sometimes the classification of agricultural crops can significantly be improved by monitoring the different growth stages of the crops. Maize e.g. has a short time span that the soil cover is high, the growing season lasts only from late June to late August. Cereals, in contrast, grow from late April to late July or early August. Hence, if multi-temporal remote sensing techniques are applied, the dates of image acquisition should be matched with the growing stages of the studied crops. A sequence of image acquired in January, June and August are mostly ‘best’ for multi-temporal crop classification. A disadvantage of multi-temporal remote sensing is that it is expensive: multiple images must be acquired and radiometrically and geometrically corrected. Especially, the geometric correction must be very accurate otherwise the temporal change detected might be caused by ‘non-fitting’ agricultural lots. An example of a multi-temporal land use classification in the Po-valley in Italy is described by Azzali (1985). Figure 6.7 shows the temporal profiles of the crops. Figure 6.7 Temporal profile / phenological cycle of winter wheat (Jensen, 2000). 93 Figure 6.7 Cont’d Phenological Cycles of sugar beets, cotton and alfalfa and images collected over a period of 12 months, original in colour (Jensen, 2000). 94 Change detection is one of the most powerul applications of Earth observation. Several regions in the world are currently undergoing rapid, wide-ranging changes in land cover or land use. in the world are currently undergoing rapid, wide-ranging changes in land cover or land use....
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