Remote Sensing of Vegetation

Vegetation has unique characteristics which make it very useful to study using remote sensing. Chlorophyll, for example, is a green pigment used for photo-synthesis in plants (found mostly in the leaves). Chlorophyll absorbs radiation strongly at the wavelengths0.45µm(blue) and0.67µm(red). Figure 2.11 shows a general spectral reflectance curve for most vegetation types, the absorption areas can be seen as dips in the curve. The small reflectance peak in the green region

between0.5-0.6µmgives rise to the visible green colour of vegetation.

Another unique characteristic is seen in the NIR region between 0.7 - 0.9µm, where the reflectance is much higher than that in the visible bands. This is due to scattering in the cellular structure of the leaves as well as scattering in the vegetation canopy. Such a steep gradient between the low reflection in the red and high reflection in the NIR region, is only produced by vegetation (Hashimoto et al., 1993; CRISP, 2001; Fiella and Penuelas, 1994). Because plants reflect far more in the NIR compared to all other visible bands, this band is most often used to look at vegetation as apposed to the green band. See figure 2.7 for a comparison between the reflectance characteristics of vegetation, soil and water.

Figure 2.11: A general vegetation spectral signature. It is labeled with the main sections of the EM spectrum which have unique vegetation characteristics. Vegetation is characterised by high reflectance in the NIR region due to scattering in the cellular structure of the leaves.

Chlorophyll in plant leaves is responsible for the high absorption in the blue and red regions.

Vegetation reflects the most in the green region of the visible spectrum and is the reason we then see vegetation as green. The absorption bands in the SWIR region are affected by the plants water content. Copied and modified from CRISP (2001).

The reflectance of vegetation in the SWIR region (e.g. band 5 Landsat TM) is more varied depending on the types of plant and the plant’s water content. Water has strong absorption bands around1.45,1.95and2.50µm. Outside these absorp-tion bands in the SWIR region, the reflectance from leaves generally increases when the liquid water content of the leaves decreases. This can be seen in figure 2.11 as dips in the curve.

The shape of a spectral reflectance curve can be used for identifying different vegetation types. Even though most vegetation exhibit the above mentioned acteristics of low reflectance in the red region and high in the NIR, these char-acteristics vary slightly between plants and can be used to identify plant species, leaf moisture content, and plant health. The SWIR region for example can be

2.3 Remote Sensing Theory 23

used in detecting plant drought stress and delineating burnt areas and fire-affected vegetation (Fiella and Penuelas, 1994). Figure 2.12 shows the spectral reflectance curve for 2 tree types, a maple and a pine. Notice that their spectral signatures are slightly different and that with real data the signatures cover a narrow (or some-times wide) range of spectral values.

Figure 2.12: Examples of spectral signatures for deciduous maple trees and coniferous pine trees. Notice the small range of spectral reflectance values per curve rather than just a line of data as has been shown by the other spectral reflectance plots. In reality this is how a veg-etation type would appear. Copied and modified from Lillesand et al. (2004) with permission from Wiley & sons Inc.

The red edge is a term used to describe the point of maximum slope in the vegeta-tion reflectance spectra. It occurs between the wavelengths0.68to0.75µm, where the reflectance changes from very low in the red region to high in the NIR region.

The wavelength and amplitude of this red edge point can be used to determine

chlorophyll content. It has been shown that as the chlorophyll content increases the red edge peak moves to longer wavelengths because the absorption band be-comes broader. There is also a strong link between the area of the red edge peak and leaf area index (LAI) or total biomass (Fiella and Penuelas, 1994).

Various mathematical combinations of the red and NIR band have been found to be sensitive indicators of the presence and condition of green vegetation. These mathematical quantities are referred to as vegetation indices and one such index is the normalised difference vegetation index (NDVI). NDVI is a measure of

”greenness”, whose values range from−1to+1. Vegetated areas will give high values because of their relatively high NIR reflectance and low visible reflectance.

In contrast, for example, water, clouds and snow have higher visible reflectance than NIR reflectance, and hence result in negative values. Rock and bare soil areas have similar reflectances in the two bands and hence result in an NDVI value of around 0.

The reason NDVI is such an effective indicator of vegetation because no other land cover has the characteristic high reflectance in NIR and low in red as vegetation does. It has been related to several vegetation phenomena that range from LAI measurement, biomass estimation, percentage ground cover determination, trop-ical forest clearance, and vegetation seasonal dynamics at global and continental scales. In turn, these vegetation attributes are used in various models to study pho-tosynthesis, carbon budgets, water balance, and related processes (Lillesand et al., 2004; Fiella and Penuelas, 1994). See figures 2.7 and 2.11 for an illustration of typical spectral reflection curves for vegetation and other landcover types.