3-1: The peak in the green interval of the visible spectrum would either diminish or disappear, depending on what the dead vegetation looks like (corn stalks become straw yellow, but some other crop types retain a green coloration, usually faded). The high reflectances in the Near-IR diminish but reflections of sunlight from the cell walls still continues, but is decreased, so the height of the spectral curve in the 0-7 to 1.1 µm interval becomes less but still is usually higher than for most inorganic substances. BACK

3-2: The type of crop (corn, wheat, etc.); its shape (especially of its leaves) and color (a pumpkin patch would be interesting); stage of growth, which affects shape and the precentage of the field that is covered by the crop when viewed from above, and may also result in color changes (including any flowering it may produce (e.g., tassles on corn); its water content (color, for example, modifies when drought conditions occur); the color, surface roughness, etc. of the soil that can be seen between plants, and the transient amounts of water in this soil; the spacing between plants (which affects the proportion of vegetation to soil); certain other cultivation practices, including field shapes; atmospheric conditions, time of day and of year during which measurements are made. BACK

3-3: Determination was made mainly by field checking. In other words, members of the experimental team in cooperation with agricultural agents and the farmers themselves, visited representative sampling areas and learned in advance and during the growing season what crops were where. Aerial photos were taken to serve as controls. The signature variations over time could then be followed in the data, so that identification of other fields (not the training ones) becomes feasible. An alternative could be to refer to a "signature bank", in which signatures of different crops, are stored as records, so that signature comparison of unknown field with those being examined can be done; this is normally less reliable than on-site ground truthing that positively identifies crop types in association with specific fields. BACK

3-4: SPOT has higher spatial resolution, a valuable aid to classification since smaller features can be better identified by shape characteristics; it also has off-nadir pointable capability and can provide stereo pairs. SPOT scene sizes are smaller than Landsat. Landsat has more spectral bands and produces larger images. BACK

3-5: The red areas, with forest and grasslands, are higher. The clue is drainage. Note how the headwaters of small streams cut back into these red areas. BACK

3-6: One cannot be sure, since the Idrisi diagram has not geographic labels, but the area near the upper right corner seems to fit the conditions described in the previous answer. BACK

3-7: When a series of circles are packed together, there is space between any three or four that are contiguous. This is land that is probably being wasted although the farmer has the option to plant in this space. There is another type of automated sprinkling system that can be used in square or rectangular fields. This is a straight line sprinkler that has at each end a set of tires and a motor. The two then drive the sprinkler rod forward, in synchrony, from one end of the field to the other. The length of the rod can be varied according to width of the ground to be watered. BACK

3-8: There is a larger town just above the image center. It is evidenced by a speckled red midst blue. Two smaller towns occur to its right. All are below the major highway. BACK

3-9: Panel D shows the southernmost advance of low VI (not necessarily related strictly to drought, as there is some seasonal variation). Panel C indicates that higher VI values have moved northward. BACK

3-10: The Volga drought is widespread, extending well past this scene. The number and density of fields showing low red to yellow-brown color is fairly uniform throughout the scene. The red area within the bend shows no spatial pattern like one expects for fields and is probably forestland. BACK

3-11: The obvious answer is to use space imagery to monitor and inventory crops, as to type, stage of growth, indication of stress, etc. But, there is a problem in this region that is fast becoming serious. The Salton Sea is drying up rather rapidly, and that is causing dangers to wildlife habitats. Recession of shorelines can be watched closely with space imagery. BACK

3-12: Because it is an usual (distorted-looking) projection, one might choose South America as being the most heavily vegetated, but probably Europe (a somewhat artificial continent in that its boundary with Asia is artificial) is even more so. Australia is least vegetated. BACK


3-13: From the images of small areas which show the gridlike clearing one might deduce that stripping of the Amazon forests is widespread - almost epidemic. In reality, the percentages for this clearing up to now are less than 20% of the total forest and several estimates place this closer to 10%. But the pace is quickening, so that if the rate is not slowed and more preservation control emplaced, the Amazon can be largely barren before the end of the next century. Since this forest is the largest supplier of new oxygen to the atmosphere via photosynthesis, one must view that possibility with real alarm. BACK

3-14: The increase is roughly a factor of three. BACK

3-15: Vocanic ash, both on the ground and coming from the smoke plume. BACK

3-16: Yes, large enough ones can be detected with Landsat, SPOT, etc. but not as efficiently as radar. The oil does lower reflectances but for water these are already low. The best band to note differences in reflectance is MSS 4, TM 1 and 2, and SPOT 1. If the ocean pattern is affected by waves, the reduction in local, short wavelength wave action also brings about a small difference in reflectances. The best detector for oil spills is one that senses in certain wavelengths in the ultraviolet. BACK

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Primary Author: Nicholas M. Short, Sr. email:

Collaborators: Code 935 NASA GSFC, GST, USAF Academy,
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