Full text: Proceedings of Symposium on Remote Sensing and Photo Interpretation (Volume 2)

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A second advantage of the periodic coverage is the ability to monitor the 
development of agricultural crops. While cloud coverage over agricultural 
areas significantly influences the amount of periodic coverage available, the 
coverage can still be used. The potential exists for crop yield estimation 
and prediction, by comparing the crop development, as sensed from ERTS, with 
a crop calendar of normal development. 
TABLE 4. SIGNATURES OF TWO VEGETATION CLASSES ON TWO DAYS 
JUNE 
MSS-5 
MSS-5 
MSS-6 
MSS-7 
Hdw/Conif/Grass 
27.32 
(0.91) 
17.82 
(1.45) 
49.62 
(5.52) 
27.53 
(3.88) 
Shrub/Swamp 
27.69 
(1.20) 
17.25 
(1.53) 
47.06 
(5.69) 
25.75 
(3.94) 
MARCH 
Hdw/Conif/Grass 
29.96 
(3.75) 
28.07 
(3.11) 
29.93 
(2.96) 
15.82 
(1.28) 
Shrub/Swamp 
23.69 
(1.74) 
20.25 
(4.04) 
22.19 
(3.60) 
11.38 
(2.60) 
Mean signature values are shown, with standard deviations in parenthesis. 
Although there is not sufficient space here to describe the techniques, two 
additional techniques are worthy of comment. These are proportions estima 
tion techniques (Horwitz, 1974) which have major importance for accuracy of 
area determination (Malila, 1973) with coarse ground resolution satellite MSS 
systems and adaptive decision-directed classification techniques (Crane, 1974) 
for overcoming gradual changes and variations which cause degradation in 
performance. 
USER APPLICATION MODEL DEVELOPMENT 
One critical portion of the Earth Resources Survey System is the User 
Application Model, which relates the output of the extractive processors and 
ancillary data to generate information which a user can employ directly. User 
models may be very simple — if the user wants a map of vegetation types, the 
output of the extractive processing may serve him directly, and the 
user application model is absent. But if a Department of Agriculture official 
wants to know what is the projected wheat production in Kansas, the user model 
may combine the total productive acreage of wheat (obtained from a remote 
sensing system), with some farmer’s estimates of the yield of particular fields, 
and some estimates from the weather service of future weather trends, to 
calculate the total production of wheat in Kansas. 
As an example of a user application model, consider Figure 3. This model 
predicts the population of migratory waterfowl, given the water supply conditions, 
the food supply conditions, and ancillary variables such as number of nesting 
pairs, predation, and mortality. Also shown in Fig. 3 on the right are elements of
	        
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