Arizona Geophysical Data Base

A series of digital data sets were compiled for input into a geophysical data base for a one degree quadrangle in Arizona. Using a Landsat digital mosaic as a base, information on topography, geology, gravity as well as Seasat radar imagery were registered. Example overlays and tabulations are performed. The role of remote sensing in environmental inventory has evolved into an important supplement to field work and ground survey. The cost and time required to conduct a project can be reduced when remotely sensed imagery is used to identify and pinpoint areas of critical environmental concern. The initial stratification allows researchers to concentrate efforts in these areas and conduct the slow and tedious ground data collection process only where absolutely necessary. In this respect, environmental analysis benefits from the synoptic coverage of remote sensor data where a complete eco-zone or geomorphic province can be viewed in context. Remote sensor data can easily provide information about the landscape that would not otherwise be obtained except by long and difficult ground data collection efforts. Often, environmental analysis is hampered by a host of problems. Lack of data is a major concern to planners because of the obvious difficulty on what to base land management and resource all 0-::-Gation decisions. Quality of data is also a problem especially when the information is gathered from a number of sources. The very fact that much of the information obtained is from several sources leads to data type incompatibility which is not a trivial problem. To illustrate how these problems can be understood and overcome, a good example is the California Desert Project. l The goal in the California Desert Project was to provide basic information on soils, land forms and vegetation throughout the 25 million acres of the California Desert Conservation Area (CDCA) to be used as input to the CDCA Master Plan and Environmental Impact Statement. In the soils and landform inven~ory process, U-2 color IR imagery was flown for the desert and manually interpreted into basic class types. This process was assisted with digital data from Landsat to isolate problem or anomalous areas to help improve the classification. The classification yielded 15 basic soil classes. The vegetation inventory sequence consisted of two specific objectives. First, a complete vegetation type map of the CDCA was needed. This meant not only a map depicting the spatial distribution of vegetation types, but also summary tabulations of acres of vegetation types. Secondly, the amount of vegetation present had to be known in terms of kilograms of forage intended for grazing animals. This information was obtained with the integration of data from several sources; Landsat, aerial photography (both large and small scale), ground gathered data, conventional maps, and historical records. It is with the integration of several data types that makes such a project possible and successful for a relatively small amount of money. "For the purposes of consistency and simplicity, the values obtained from the recent multi-stage sampling and remote sensing survey are presented ... as the most current estimate of range carrying capacity. The reliability of these estimates is considered satisfactory when reviewed in compari-son with earlier survey results from common areas.,,2,p.S2 It is the very fact that these conventional data types and derived information were used in a Federal plan and program that lends credibility to its ,use and development. While the techniques worked well for soils and vegetation studies, they are also applicable to other types of applications such as water quality monitoring 3, forest type mapping4 and urban applications. S One particularly interesting application is the development of a geophysical data base. While the CDCA project dealt chiefly with renewable resources, a geophysical data base such as the one compiled here for a one degree quadrangle in Arizona deals with non-renewable resources. Since most of our energy sources are non-renewable, the application of image processing to this topic can be valuable. I. ARIZONA DATA BASE DEVELOPMENT As with the data base developed for the California Desert, the Arizona geophysical data base described here was compiled using the same methods and techniques but with differing data types. Data variables included are: 1. Landsat multispectral data, 2. Digital terrain information, 3. Seasat radar data, 4. Conventional geologic map information, and 5. Geophysical data. LANDSAT MULTISPECTRAL DATA More often than not, study areas of interest do not conform to the Landsat framing convention, 1981 Machine Processing of Remotely Sensed Data Symposium