The following points highlight the top six factors affecting remote sensing in vegetation classification. The factors are: 1. Brightness 2. Greenness 3. Moisture 4. Leaf Optical Properties 5. Electromagnetic Wavelengths 6. Other Factors.
Vegetation Classification: Factor # 1.
It is calculated as a weighted sum of all the bands and is defined in the direction of principal variation in soil reflectance.
Vegetation Classification: Factor # 2.
It is related to the amount of green vegetation in the scene. It is orthogonal to brightness and is a contrast between the near-infrared and visible bands.
Vegetation Classification: Factor # 3.
Moisture in vegetation reflects more energy than dry vegetation.
Vegetation Classification: Factor # 4.
Leaf Optical Properties:
It is influenced by leaf properties such as internal or external structure, age, the health of the leaf, water status, and mineral stresses. The reflectance of the optical properties of leaves remains the same, regardless of the species. It is only the typical spectral features recorded for the three main optical spectral domains viz. leaf pigments, cell structure and water content, that may differ for each leaf.
Vegetation Classification: Factor # 5.
It affects different parts of the plant and trees, which include their leaves, stem, stalks and limbs. The amount of reflection that occurs also depends on the length of the wavelengths.
It has been found that the tree leaves and crop canopies reflect more in the shorter radar wavelengths, while tree trunks and limbs reflect more in the longer wavelengths. Further, the scattering of the wavelengths is affected by the density of the tree or plant canopy.
Within the electromagnetic spectrum, bands will produce different levels of reflectance rates. For example, a lower reflectance will occur in the visible bands (400 – 700 nm) as more light will be absorbed by the leaf pigments than reflected. The blue (450 nm) and red (670 nm) wavelengths include two main absorption bands that absorb two main leaf pigments.
Vegetation Classification: Factor # 6.
Seasonal differences, plant transitional zones, quality, scale and season of photography, film type and background will influence the remote sensing image. It is affected by the time of the day, sun angle, clouds, atmospheric haze, processing errors of transparencies/ prints and errors in interpreting the images.
Photographic texture (smoothness and coarseness of images), total contrast or color, relative sizes of crown images at a given photo scale and topographic location, help to determine the cover types of vegetation.
If the researcher has a sound knowledge of what plants and tree species are native to a particular area, and what influences their growth and distribution, it becomes easier for him to interpret the satellite images of vegetation.
Remote sensing images can be further supplemented by aerial photographs, color infrared and black and white infrared photographs, which help to identify species. Details of branching characteristics, crown shapes, spatial distribution and patterns of species can be interpreted from such photographs, which may provide useful data for the interpreter.
Satellites that successfully identify vegetation types include the AVHRR (Advance Very High Resolution Radiometer), Land sat MSS, Land sat TM, SPOT HRV and RADARSAT. Satellite images can be combined with topographic data (ancillary data), to identify plant species with relation to slope direction, sun angles and other spectral characteristics.
This is known as Multitemporal Image Classification. The technique of combining multi-spectral and ancillary information into a classification algorithm is referred to as Multidimensional Image Classification.