Actualización y creación de Nuevos Formatos

At the visible section of the Electro Magnetic Spectrum (EMS), the spectral behaviour is determined by chlorophyll and other plant pigments. The 0.45 – 0.52 µm and 0.63-0.69 µm in the visible portion of the EMS are known to be regions that show greatest chlorophyll absorption and are often referred to as chlorophyll absorption bands (Jensen 2007; Lillesand et al., 2004). The 0.45 – 0.52µm portion is highly sensitive to both carotenoids and chlorophyll while 0.63 – 0.69 µm portion is highly sensitive to

24 chlorophyll (Jensen 2007). In this section, more blue and red wavelengths are absorbed than green wavelengths. Consequently, a small reflectance peak is generated within the visible portion (Smith, 2001; Lillesand et al., 2004; Jensen, 2007). In the case of senescing vegetation and a consequent decrease in chlorophyll absorption, there is often an increase in reflectance values at both the blue and red wavelengths (Lillesand et al., 2004). This portion can thus be used to detect changes in internal leaf structure as well as vegetation health (Jensen, 2007).

The reflectance of healthy green vegetation increases sharply between red and near infrared wavelengths (around 0.7µm) – red edge (Smith, 2001; Lillesand et al., 2004;

Jensen, 2007). In most plants, the distinctive red edge peak into the near infrared wavelength persists to around 1.3µm where 40 to 60 percent of incident near infrared energy is reflected (Jensen, 2007). In this wavelength, the reflectance scatter is dictated by the internal leaf cellular structures (Smith, 2001). Due to high variability in leaf cellular structures of different plants, this wavelength can be used to distinguish between different species (Lillesand et al., 2004). Vegetation stress or senescence often leads to reduction in near infrared reflectance, making this region useful for mapping stressed vegetation (Lillesand et al., 2004; Jensen, 2007). Other important applications of this section include general vegetation mapping, crop condition monitoring, yield estimation, and biomass measurement (Aronoff, 2005).

Generally, reflectance decreases with an increase in wavelength beyond 1.3µm, as leaf incident energy is either absorbed or reflected (Kokaly et al., 2003; Lillesand et al, 2004). However, there are two conspicuous water absorption bands at 1.4µm and 1.9µm within this wavelength (Smith, 2001; Lillesand et al., 2004).

Spectral reflectance curves for soil, rocks and mineral are not markedly dissimilar from those of vegetation (Lillesand, 2004; McCoy, 2005; Aronoff, 2005; Adams and Gillespie, 2006; Jensen, 2007). Richardson and Wiegand (1977) also provide red and near infrared reflectance distinctions between grass, dense vegetation, dry soil, wet soil and water. A typical soil or rock spectral response shows a steady rising curve in the visible and near infrared but may rise less steeply after the near infrared wavelength (Figure 2.4) (McCoy, 2005). Soil reflectance may depend on factors like the soils moisture, texture, organic matter and mineralogy (Jensen, 2007). The influence of these factors on soil reflectance is often interrelated, for instance, coarse

25 sandy soils – often well drained – usually have higher reflectance in comparison to poorly drained soil types (Lillesand et al., 2004; Jensen, 2007). Similar to water absorption bands in vegetation reflectance trends, the effects of moisture on soil spectral response are often apparent around 1.4 and 1.9µm (Figure 2.4). In dry sandy soils however, coarse particles have lower reflectance than fine textured soils (Lillesand et al., 2004). According to McCoy (2005), dry soils are characterised by two reflectance effects; firstly, reflectance increases and secondly water absorption bands become less apparent (Figure 2.5), or may even disappear for extremely dry sandy soils. Drying of clay or silt also leads to a reduction in depth of moisture absorption bands. However, unlike sandy soils, the water absorption band dips may still be visible even after extremely dry conditions (McCoy, 2005).

Figure 2.5: Spectra response of soils at oven dried, 0.03, 0.12, 0.20, 0.30 and 0.42 gravimetric water contents (g/g) (Adapted from Whiting et al., 2004).

An increase in organic matter leads to a decrease in spectral reflectance (Jensen, 2003). According to McCoy (2005), only up to 5% of soil organic matter can affect spectral response often restricted to the visible wavelengths. Reflectances generally increase with increased soils salinity content in the visible and near infrared wavelengths (Jensen, 2007). In iron oxide rich soils, noticeable increase between 0.6-0.7µm and a slight dip between 0.85 and 0.9µm in comparison to soil types without iron oxide are often visible (Jensen, 2007).

26 2.6.5 Importance of spectral derivatives

Whereas it may be easy to distinguish some materials like water from other surfaces using spectral reflectance, some materials have been known to have near similar or overlapping spectra (Curran et al., 1991). Other materials’ spectra like senescing vegetation for instance can be heavily influenced by background soil, shadows or litter (Curran et al., 1991). Derivatives can be used to enhance clarity of such spectra at specific wavelength ranges or within the entire range of wavelengths under investigation (Elvidge and Chen, 1995; Chen et al., 1999). Derivatives aim at identifying inflection points from zero order reflectance curves for different materials.

These inflection points can then be compared against each other (Chen et al., 1999).

Derivatives are achieved by dividing the reflectance difference by an interval of contiguous wavelength, which yields interval slopes of the original spectrum (Becker et al., 2005). According to Becker et al. (2005), areas of sudden change in the spectrum provide better spectral differences than gentle curves. Derivatives have been found to be useful in suppressing background signals, distinguishing closely related signals and reducing differences caused by changes in illumination (Demetriades-Shah et al., 1990; Elvidge and Chen, 1995; Chen et al., 1999). The use of derivatives has also been useful in the identification of the red edge and amount of chlorophyll content by locating its position in the reflectance spectrum (Chen et al., 1999;

Blackburn, 2007).

2. 7 Summary

Investigations of plant invasions along specific ecological and physical gradients provide a better understanding of the invasion process in terms of plant response to varying ecological, physical, climatic and anthropogenic variables. Given that soil moisture flux is heavily influenced by P. incana invasion, moisture regulation can be used to control the invader and restore landscapes whose degradation is a result of the invasion. Notwithstanding the efficacy of pixel based techniques like the PVI, sub-pixel based techniques, for instance the SMA can provide better surface separation.

Image based endmember extraction techniques are preferred by most researchers, as endmembers mirror image conditions. Spectroscopy is important as a data validation

27 tool in land cover mapping. Laboratory based spectroscopy under a controlled environment provides better results than field based spectral measurements. In cases where materials have closely related spectral reflectance, the use of derivatives can be used to provide clarity in spectral differences.

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