Planet Earth is distinguished from other Solar System planets by two major categories: Oceans and Land Vegetation. The amount of vegetation within the seas is huge and important in the food chain. But for people the land provides most of the vegetation within the human diet. The primary categories of land vegetation (biomes) and their proportions is shown in this pie chart:
Global maps of vegetation biomes show this general distribution:
Remote sensing has proven a powerful "tool" for assessing the identity, characteristics, and growth potential of most kinds of vegetative matter at several levels (from biomes to individual plants). Vegetation behavior depends on the nature of the vegetation itself, its interactions with solar radiation and other climate factors, and the availability of chemical nutrients and water within the host medium (usually soil, or water in marine environments). A common measure of the status of a given plant, such as a crop used for human consumption, is its potential productivity (one such parameter has units of bushels/acre or tons/hectare, or similar units). Productivity is sensitive to amounts of incoming solar radiation and precipitation (both influence the regional climate), soil chemistry, water retention factors, and plant type. Examine the diagram below to see how these interact, keeping in mind that various remote sensing systems (e.g., meteorological or earth-observing satellites) can provide inputs to productivity estimation:
Because many remote sensing devices operate in the green, red, and near infrared regions of the electromagnetic spectrum, they can discriminate radiation absorption and reflectance properties of vegetation. One special characteristic of vegetation is that leaves, a common manifestation, are partly transparent allowing some of the radiation to pass through (often reaching the ground, which reflects its own signature). The general behavior of incoming and outgoing radiation that acts on a leaf is shown here:
Now, consider this diagram which traces the influence of green leafy material on incoming and reflected radiation.
Absorption centered at about 0.65 µm (visible red) is controlled by chlorophyll pigment in green-leaf chloroplasts that reside in the outer or Palisade leaf. Absorption occurs to a similar extent in the blue. With these colors thus removed from white light, the predominant but diminished reflectance of visible wavelengths is concentrated in the green. Thus, most vegetation has a green-leafy color. There is also strong reflectance between 0.7 and 1.0 µm (near IR) in the spongy mesophyll cells located in the interior or back of a leaf, within which light reflects mainly at cell wall/air space interfaces, much of which emerges as strong reflection rays. The intensity of this reflectance is commonly greater (higher percentage) than from most inorganic materials, so vegetation appears bright in the near-IR wavelengths (which, fortunately, is beyond the response of mammalian eyes). These properties of vegetation account for their tonal signatures on multispectral images: darker tones in the blue and, especially red, bands, somewhat lighter in the green band, and notably light in the near-IR bands (maximum in Landsat's Multispectral Scanner Bands 6 and 7 and Thematic Mapper Band 4 and SPOT's Band 3).
Identifying vegetation in remote-sensing images depends on several plant characteristics. For instance, in general, deciduous leaves tend to be more reflective than evergreen needles. Thus, in infrared color composites, the red colors associated with those bands in the 0.7 - 1.1 µm interval are normally richer in hue and brighter from tree leaves than from pine needles.
These spectral variations facilitate fairly precise detecting, identifying and monitoring of vegetation on land surfaces and, in some instances, within the oceans and other water bodies. Thus, we can continually assess changes in forests, grasslands and range, shrublands, crops and orchards, and marine plankton, often at quantitative levels. Because vegetation is the dominant component in most ecosystems, we can use remote sensing from air and space to routinely gather valuable information helpful in characterizing and managing of these organic systems.
The ability to distinguish different types of vegetation was brought home to the writer (NMS) through a simple study using a densitometer to examine multispectral images of a strip of agricultural land near the Choptank River in the eastern shore of Maryland. These images were part of an experiment by my "boss", Dr. Warren Hovis, at Goddard. He had built a multispectral sensor to fly on an aircraft that would simulate images made by the same four bands on the ERTS-1 (Landsat-1) Multispectral Scanner (MSS). Here are the images:
The relative gray levels are plotted as a four band histogram for each of the numbered features in the above image. It should be evident that there are real differences in these band signatures among the vegetation and other features present; thus Mixed Hardwoods have different relative "brightness" patterns from Soybeans, from Old Hay, etc..
This discrimination capability implies that one of the most successful applications of multispectral space imagery is monitoring the state of the world's agricultural production. This application includes identifying and differentiating most of the major crop types: wheat, barley, millet, oats, corn, soybeans, rice, and others.
This capability was convincingly demonstrated by an early ERTS-1 classification of several crop types being grown in Holt County, Nebraska. This pair of image subsets, obtained just weeks after launch, indicates what crops were successfully differentiated; the lower image shows the improvement in distinguishing these types by using data from two different dates of image acquisition:
This is a good point in the discussion to introduce the appearance of large area croplands as they are seen in Landsat images. We illustrate with imagery that covers the two major crop growing areas of the United States.
The first scene is part of the Great or Central Valley of California, specifically the San Joaquin Valley. Agricultural here is primarily associated with such cash crops as barley, alfalfa, sugar beets, beans, tomatoes, cotton, grapes, and peach and walnut trees. In July of 1972 most of these fields are nearing full growth. Irrigation from the Sierra Nevada, whose foothills are in the upper right, compensates for the sparsity or rain in summer months (temperatures can be near 100° F). The eastern Coast Ranges appear at the lower left. The yellow-brown and blue areas flanking the Valley crops are grasslands and chapparal best suited for cattle grazing. The blue areas within the croplands (near the top) are the cities of Stockton and Modesto.
The second Landsat image is in the Wheat Belt of the Great Plains. The image below is of western Kansas in late August. Most of the scene consists of small farms, many of section size (1 square mile). The principal crop is winter wheat which is normally harvested by June. Spring wheat is then planted, along with sorghum, barley, and alfalfa. This scene is transitional, with nearly all of the right side being heavily planted, but the left side (the High Plains, at higher elevations) contains some unplanted farms and cropfree land, some used for grazing.
Still another example of winter wheat in early growth is this scene in southwestern Australia, east of Perth. Some of the wheat fields are quite large - 5 km (3 miles) or more on a side. The prevailing color is tan but with a faint red cast, implying initial growth. There is a sharp line dividing many fields from the mallee scrub (dark brown) growing on soils derived from Precambrian rocks. This line marks an electrified rabbit fence, keeping these "pests" from nibbling on the wheat and other crops being grown.
Many factors combine to cause small to large differences in spectral signatures for the varieties of crops cultivated by man. Generally, we must determine the signature for each crop in a region from representative samples at specific times. However, some crop types have quite similar spectral responses at equivalent growth stages. The differences between crop (plant) types can be fairly small in the Near-Infrared, as shown in these spectral signatures (in which other variables such as soil type, ground moisture, etc. are in effect held constant). In this illustration, the curves have been offset to make it easier to see each plot; if plotted to the same actual values, they would almost superimpose.
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