When a spectral band which contains reflectances in the wavelength interval between 0.7 – 1.1 µm is projected through a red filter, healthy, active vegetation will appear bright (light tones) in a black and white image or red in a false color image.
We can use color-filtered b & w pictures as transparencies projected through color filters and superposed (registered) to produce color composites. To do this, imagine this setup: Pick any scene containing many features and classes of differing colors. First, replace the prints with positive transparencies (tonally analogous to prints). Work with three b & w transparencies, each representing its spectral band. Shine white light through each one mounted in its own lamp projector (total of three) on to a screen. Project the blue band b & w transparency through a blue filter, the green through green, and the red through red. Blue features on the ground are clear areas in the blue spectral band. When the blue band transparency is projected through the blue filter, the blue features will be blue on an observing screen, likewise the green band projects green objects through its filter as green, and red as red. Co-register (line up) the three projections by superimposing several distinctive patterns that are common within the photographed scene. The result will be a simulated natural color image. with any red, green, and blue objects or classes showing in the projected image as these colors respectively. Other colors present are additive mixes of two or more primaries (e.g., yellow is a mix of red and green; orange is a mix of more red and some green; white is an equal mix of all three primaries, and black is simply the absence of any colored light of any wavelength). The colors that result from combinations of blue, green, and red (the primaries) are indicated in additive color diagram.
For any given material, the amount of solar radiation that it reflects, absorbs, transmits, or emits varies with wavelength. When that amount (usually intensity, as a percent of maximum) coming from the material is plotted over a range of wavelengths, the connected points produce a curve called the material’s spectral signature (spectral response curve). Here is a general example of a reflectance plot for some (unspecified) vegetation type (bio-organic material), with the dominating factor influencing each interval of the curve so indicated:
This important property of matter makes it possible to identify different substances or classes and to separate them by their individual spectral signatures, as shown in the figure below. *
For example, at some wavelengths, sand reflects more energy than green vegetation but at other wavelengths it absorbs more (reflects less) than does the vegetation. In principle, we can recognize various kinds of surface materials and distinguish them 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 [normally in percent] of the amount of irradiating radiation). Using reflectance differences, we may be able to distinguish the four common surface materials in the above signatures (GL = grasslands; PW = pinewoods; RS = red sand; SW = silty water) simply by plotting the reflectances of each material at two wavelengths, commonly a few tens (or more) of micrometers apart. Note the positions of points for each plot as a reflectance percentage for just two wavelengths:
In this instance, the points are sufficiently separated to confirm that just these two wavelengths (properly selected) permit notably different materials to be distinguished by their spectral properties. When we use more than two wavelengths, the plots in multi-dimensional space (3 can be visualized; more than 3 best handled mathematically) tend to show more separability among the materials. This improved distinction among materials due to extra wavelengths is the basis for multispectral remote sensing .