Remote Sensing Applied to Vegetation Classification

Vegetation is important because it provides a basic foundation for all living beings. Classifying vegetation, using remote sensing, is valuable because it can determine vegetation distribution and occurrence and how such factors as moisture, latitude, elevation above sea level, length of the growing season, solar radiation, temperature regimes, soil type and drainage conditions, topographic aspect and slope, prevailing winds, salt spray and air pollutants influence it. Remote sensing can also be used to detect and prevent the spread of damaged and stressed plants.

Components that are involved in classifying vegetation include images received from satellites, remote sensing images and airphotos, chemical properties and physical properties recorded for the vegetation (including surface texture, roughness and local slope properties).

Interpretation of satellite images of vegetation becomes easier if the researcher understands what plants and tree species are native to the area , and what influences their growth and distribution. Seasonal differences and plant transitional zones will influence the remote sensing image. Of course, if the money is available, field work should still be undertaken, to provide specific information that can't be obtained from the images.

There are several factors that influence the reflectance quality of vegetation on satellite and remote sensing images. These include brightness, greeness and moisture. Brightness is calculated as a weighted sum of all the bands and is defined in the direction of principal variation in soil reflectance. Greeness is orthogonal to brightness and is a contrast between the near-infrared and visible bands. It is related to the amount of green vegetation in the scene. Moisture in vegetation will reflect more energy than dry vegetation.

Leaf properties that influence the leaf optical properties are the internal or external structure, age, water status, mineral stresses, and the health of the leaf. It is important to note that the reflectance of the optical properties of leaves are the same, regardless of the species. What may differ for each leaf, is the typical spectral features recorded for the three main optical spectral domains; leaf pigments, cell structure and water content.

Electromagnetic wavelengths affect different parts of plant and trees. These parts include leaves, stems, stalks and limbs of the plants and trees. The length of the wavelengths also play a role in the amount of reflection that occurs. Tree leaves and crop canopies reflect more in the shorter radar wavelengths, while tree trunks and limbs reflect more in the longer wavelengths. The density of the tree or plant canopy will affect the scattering of the wavelengths.

Within the electromagnetic spectrum, bands will produce different levels of reflectance rates. For example, in the visible bands ( 400 - 700 nm), a lower reflectance will occur 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.

 

Typical Spectral Response Characteristics of Green Vegetation (Hoffer, 1978)

Leaf Pigments Cell Structure Water Content
Chlorophyll Absorption Water Absorption
Visible Near Infrared Shortwave Infrared
Blue Green Red


The images created by remote sensing will be influenced by these factors: quality, scale and season of photography, film type and background. Other factors that influence vegetation classification are time of day, sun angle, atmospheric haze, clouds, processing errors of transparencies/prints and errors in interpretating the images.

Photographic texture (smoothness and coarseness of images), total contrast or colour, relative sizes of crown images at a given photo scale and topographic location help to determine the cover types of vegetation.

Aerial photographs, color infrared and black and white infrared photographs help to identify species(plants and trees). Species are more easily distinguished if they occur in pure, even-aged stands. Through the photographs, details of branching characteristics, crown shapes, spatial distribution and patterns of species may show, providing useful data for the interpreter. This information can then be combined and added to the remote sensing images. Regression equations can then be developed for each species or species group for use in volume estimation.

Different types of images will display diverse characteristics of vegetation. For example, AVHRR (Advance Very High Resolution Radiometer) will include bands that produce different results:

Band 1 of AVHRR will allow chlorophyll to be absorbed in a red wavelength. A low value indicates a high concentration of chlorophyll.

Band 2 of AVHRR includes infrared wavelengths (IR, RIR) and records the cell structure of the leaves. High values are indicated by dark green signatures and more growth, while low values are indicated by orange signatures.

Other satellites that successfully identify vegetation types include the Landsat MSS, Landsat 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 multispectral and ancillary information into a classification algorithm is referred to as "Multidimensional Image Classification".

An Example of Remote Sensing and Vegetation

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