A primary use of remote sensing data is in classifying the myriad features in a scene (usually presented as an image) into meaningful categories or classes. The image then becomes a thematic map (the theme is selectable e.g., land use, geology, vegetation types, rainfall). A farmer may use thematic maps to monitor the health of his crops without going out to the field. A geologist may use the images to study the types of minerals or rock structure found in a certain area. A biologist may want to study the variety of plants in a certain location.
For example, at certain wavelengths, sand reflects more energy than green vegetation while at other wavelengths it absorbs more (reflects less) energy. Therefore, in principle, various kinds of surface materials can be distinguished from each other by these differences in reflectance. Of course, there must be some suitable method for measuring these differences as a function of wavelength and intensity (as a fraction of the amount of radiation reaching the surface). Using reflectance differences, the four most common surface materials (GL = grasslands; PW = pinewoods; RS = red sand; SW = silty water) can be easily distinguished, as shown in the next figure.
When more than two wavelengths are used, the resulting images tend to show more separation among the objects. Imagine looking at different objects through red lenses, or only blue or green lenses. In a similar manner, certain satellite sensors can record reflected energy in the red, green, blue, or infrared bands of the spectrum, a process called multispectral remote sensing. The improved ability of multispectral sensors provides a basic remote sensing data resource for quantitative thematic information, such as the type of land cover. Resource managers use information from multispectral data to monitor fragile lands and other natural resources, including vegetated areas, wetlands, and forests. These data provide unique identification characteristics leading to a quantitative assessment of the Earth’s features.