certain land-surface areas. The cost of imagery reduced the user base, and EOSAT had to determine which images would be most marketable prior to acquiring them. That left many scientists with a very limited, high-cost archive of TM data. Thus, for certain areas of the globe there is extensive coverage, but for others it is very poor.

In addition, the more detailed narrative of a senior soil scientist, a member of the interdisciplinary Laboratory for Applications of Remote Sensing at Purdue University, is revealing:

During the mid- to late 1970s and into the early 1980s, our research group was heavily involved in interdisciplinary research (involving electrical engineers, civil engineers, computer scientists, statisticians, meteorologists, crop physiologists, soil scientists, foresters, environmental specialists), collaborating with several federal agencies and other universities. Our research focused on the study of the relationships between the data derived from the Landsat multispectral scanner (MSS) and thematic mapper, the characteristics of agricultural land surfaces, and the changes of these surfaces with time. Specific objectives were to determine the feasibility of using Landsat MSS and TM data for crop inventory and monitoring. Some questions addressed were: What quantitative changes occur in the spectral characteristics of crops (corn, soybeans, winter wheat, spring wheat, rice, many other crops) throughout phenological development? What spectral changes are a result of stress from drought, nutrient deficiency, disease, insect infestation, salinity of the soil, storm damage, wetness, and other causes? Can these changes be identified and delineated by classical pattern recognition analysis applied to multispectral data obtained by aerospace sensors? Many of these questions were addressed by our research during that period and at least partially answered.

One of the many areas of research that came to a complete halt when the price of TM data was Increased manifold was in the application of time-sequential remote sensing data (e.g., MSS, TM, advanced very-high-resolution radiometer (AVHRR), and others) to mapping and monitoring terrestrial ecosystems and to developing models to assess land quality, soil productivity and degradation, and erosion hazards. The anticipation that had begun to build for use of earth observation satellite data for integration with other data sets to provide national, continental, and global resource databases was suddenly dashed. It became impossible to develop the procedures, approaches, and models for doing any credible global monitoring and modeling, especially for terrestrial ecosystems, without such data. Remote sensing research with agricultural crops slowed considerably, and much of it stopped completely as a result of the diminished support for civilian space research in the early 1980s and the subsequent commercialization of Landsat, which resulted in exorbitant data costs. Researchers in remote sensing laboratories and centers around the world, especially in universities, almost overnight went from a 'data rich" situation to a condition of "data poverty." Many of the basic questions that were being addressed in the early 1980s are still being asked because the data became unaffordable to the research community addressing these questions. The resulting nonavailability of data probably played a significant role in the decline of and, in many cases, the closing of remote sensing programs at numerous universities.

It is a pity that the commercialization occurred when it did. The scientific "homework" had not been completed or carried out to the point at which marketable products had been sufficiently demonstrated. Another few years of affordable data and public research support in this area might have made the commercialization process more feasible and ultimately less painful.

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Compiled by Michael D. Jennings, National Biological Service, Gap Analysis Program, University of Idaho, Moscow.



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