15-1: General geography maps (showing states or countries and major cities) and road maps (showing transportation routes, cities and towns, rivers/lakes, and places of interest, along with mileage between points). BACK

15-2: The types are many; just a few: Land Ownership; Political Units (Wards, Townships, Zoning, etc.); Housing Density; Recreational Facilities; Archaeological Sites; Mineral Resources; Energy Distribution Infrastructure; Depth to Water Table. BACK

15-3: The general land use map will tell you what kind of neighborhood your future home would be in. If you are close to an industrial or business area, perhaps you may want to sell the property or trade for a residential lot. The vegetation map will warn you that some expensive clearing might be necessary. The slope map will indicate the type of house you should develop - if there is a moderate slope, the excavation, which also be influenced by the soil type, can be tricky. Slope also indicates whether there could be landslide or runoff flooding problems. This simple case begins to illustrate what a Geographic Information System can do: the various thematic map imput data are important in solving a problem or making a decision as to what needs to be considered in getting something done. BACK

15-4: Land ownership; Topographic relief; Proximity to Railroads; Direction of Prevailing Winds; Slope Stability; Forest Cover; Access to power lines; Bearing strength of soil; Existing buildings; Proximity to Housing; EPA requirements; Cost of living; Present highway locations; Ease of excavation; Land Costs; Tax rates; Zoning laws. BACK

15-5: This is not a trivial deal - in fact, a lot of work will be involved before you try to convince some "venture capitalists" to underwrite your opening mining operations. First, you need to tap some data sources: Land ownership is critical. Is the land public or private? Records in the county seat where the possible ore body is a start. If it turns out to be Federal land, then the Bureau of Land Management, the Bureau of Reclamation, and the National Park Service are good sources. Similar agencies manage state lands. If it is private land, the problem compounds. You need to track down ownership but buying the land may involve some ethical problems. Let's assume that the land turns out to be federal. Now you need some data elements that will tell you if anyone has already found "suspicious" types of information that suggest potential mineralization. The U.S. Geological Survey (or maybe the State Geological Survey) may have produced geological or mineral maps that include, and perhaps even detail, the land you are speculating on. See if any mining companies have run geological mapping or geophysical surveying over the area. If not, you are going to have to hire some company to map the area and conduct appropriate surveys. This will include mineral assays (check the state or county assayer's office to see if any are on file). The cap to this must be drilling to get at the ore body. At this point you should have gotten "seed" money from your bankrollers to pay for this. The prime output map will be a three-dimensional display of the extent of the body and the percentage variability of the silver it contains. Now, you need the "big cash" to develop it. So, other interpretive maps may be needed to present your case to your backers. These include maps that define the conditions that involve setting up the mining operations, including environmental protection guarantees and haulage routes to smelters. This answer is rather generalized and leaves out some essentials, but you get the idea that becoming a millionaire this way isn't easy. And you can appreciate the role of maps and data accumulation critical to decision-making. GIS has a key part to play in this, as you will see. BACK

15-6: The first M is Measurement - observing, identifying, and quantifying the parameters needed in a GIS. The second is Mapping - portraying the characteristics of the Earth's surface as a series of themes. The third is Monitoring - updating existing maps, discovering changes, and, in some applications, getting essential near real time information. The fourth is Modeling - defining how things work (processes) or relations (actions) vital to the decision-making interact. BACK

15-7: Clearly, the ultimate factor that determines how a GIS will be involved in Data Management is the user requirements. These govern what inputs are to be sought out and integrated. One can also argue that a second driver is the specific outputs to be developed, usually a combination of statistics, graphical (including maps) displays, and written reports that should include interpretations and bases for the decisions that are the final goal. BACK

15-8: These five methods of encoding are common alternatives: 1) The dominant soil type within the grid cell can be selected for the cell, namely the type that occupies the largest amount of land in that cell; 2) The percentage of each soil type within the cell, which achieves a high degree of detail but at a higher cost; 3) The presence or absence of each soil within the cell can be encoded; 4) The soil type found at the centroid of the cell is encoded to represent the value for the entire cell; 5) The Corner designation method, in which data values are recorded at each corner of the grid cell, or systematically at some one corner. BACK

15-9: The gray, purple, and dark green colors are there on each side of the river. They are so close-spaced that they are barely visible. This thinning along the river is just the fact that the river has very steep banks (perhaps it is somewhat incised). BACK

15-10: The areas in dark green, purple, and gray in the elevation map are the lower parts in the scene. When the river floods, it spills its banks and carries across the medium green strip into these lower areas but doesn't invade the other areas that are medium green and higher. BACK

15-11: The floodwaters are the key factor. Where they cover the clays, and over a period of time saturated these fine-grained soils, they produce optimum soil moisture contents that are higher the water amounts held in the other soils. Of course, there can be years when flooding doesn't occur but hopefully some moisture will be concentrated preferentially in the clay soils anyway. BACK

15-12: Location and identification of major crops; Distribution and identification of forests; Location and status of lakes; Major categories of land use; Broad patterns of urban development; Characteristics and interrelations of landforms; Indications of offshore sediment concentrations; Wildlife habitat; Range land conditions; Disaster assessment; Mineral/Petroleum exploration; Meteorological conditions. BACK

15-13: The vector method is definitely more accurate. The raster cell method is based on which feature occupies more of a cell's area - this causes loss of information about the lesser occupants of cells containing two or more features (or categories or classes). BACK

15-14: Despite its greater inaccuracy (less precise location of individual map categories), the raster method is much easier to process, assuming the same cell size is maintained for each data element or layer. When vectors are used, they will almost invariably have different outlines from one theme map to any other (each such map will have its own vector patterns). These are difficult to overlay but computer programs do exist to allow analysis based on point arrays or other decision layouts. BACK

15-15: Any GIS product - including a series of thematic maps - that PP&L undertook to construct would be subject to "coarse" resolution (23 acres or about 300 meters on a side), that is, the map categories would have to be those which tend to extend more or less continuously over large areas, so that essential details for some purposes would not be discernible. The features mapped would be restricted to those of the Level 1 categories in the Anderson Land Use/Land Cover Classification. Particularly limited would be expressions of transportation (road networks) .and energy flow (power line) infrastructures, since these linear features tend to be narrow (less than a square acre's width). Landsat opened up the possibility of a much more detailed set of end products with many more categories specifiable at its resolution (even better with TM). Realizing this, PP&L in 1980 approached the ERRSAC group at NASA Goddard with the (accepted) offer to conduct a joint study on the applicability of remote sensing imagery to improving the GIS data base and maintaining currency on the new sets of categories chosen. This led to the Harrisburg and Berwick projects, the latter of which involved using Landsat MSS data to develop, analyze, and produce GIS data elements and maps of a nuclear power plant on the Susquehanna River near Berwick, PA at a site about 20 miles east (and normally downwind) from the writer's residence in Bloomsburg. Unfortunately, the data tapes that have the interesting results were unreadable in 1999 so that the demonstration, which proved quite successful, cannot be included in this Tutorial. BACK

15-16: A photointerpreter, examining Landsat images, could have been able to produce maps of A., Landforms, and C., Stream Order (probably only as far as the 3rd or 4th orders) without resort to additional data sources. Map B., could be constructed if DEM data or a digitized topographic map data set (done by the PP&L/Goddard team) were available. Map D requires ground measurements and Landsat would offer little help. Landsat, or other space observations, would be of some aid in putting together the other maps (D = Flood Prone Areas; E = Agricultural Potential) only if there were auxiliary information and, for D, 20 or more years of observations. BACK

15-17: The broad land cover pattern evident in the PP&L map is effectively duplicated in the Landsat classification. The big differences are in details, level of classification, and accuracy - Landsat being much more specific for some classes. Thus, for convenience, the PP&L map combines several urban categories that are subdivided into three plausible classes in the Landsat map. However, the PP&L map contains several classes, such as forested and non-forested wetlands, that were chosen from ground surveys for specialized purposes not considered germane in the Landsat classification (in principle, they probably could have been singled out in Landsat but weren't). One big reason, as already mentioned, for differences between the two maps is the "coarser" mapping level adopted for the PP&L map: 23 acres compared with the 1.1 acre for Landsat. Differences also result from arbitrary class selections and intended use. The PP&L choices tended to lump together some diverse natural features into general categories; the Landsat map is more of an indication of real spectral classes that properly correspond to bonafide land cover classes. BACK

15-18: As it appears, for me (NMS) this map raises some concerns and doubts. It was made by others nearly 20 years ago - individuals no longer available to ask about details. While the red pattern in the white square represents a set of conditions that are said to meet all major criteria, and its selection as located seems based on ruling out red areas outside because they don't satisfy some unspecified criteria, there arises the question of why the large areas in the scene that are shown as black may not have also had some favorable localities. Sometime one cannot easily accept at face value a reported result; this Tutorial contains several instances but better examples just weren't available. BACK

15-19: You can clearly see the outline of individual rooms in this large Roman villa. The villa is a large (100m on a side) farm community structure, with stone and cement walls. The walls are now about 60 cm below the current surface. Individual rooms served as living quarters, workshops, granaries, etc. The green square is the remains of a wooden structure associated with the villa. It may have been an animal pen, as the soil there is more fertile, causing better modern soil and therefore more crop growth. The curving patterns at bottom right are where a tractor with a disk plow drove over a pile of building stones from the villa that have been piled up by the farmers.BACK

15-20: If you know it is there, you can clearly see the geometric pattern of the negative crop marks on the ground, but the farmer who owned this land never suspected that he had an entire Roman era villa complex in this field. BACK

15-21:You can see the vestige of an ancient road continuing to the upper right of the picture. This is an example of a 'soil' mark, where differences in soils type and structure are caused by the use of the road over time. The road is a continuation of the modern road coming from the lower left. Such marks often only show up when the ground is bare, before planting or after the harvest. Flying at different times of the year and different times of the day are important in finding such features.BACK

15-22:There are many differences, and also many things have remained the same. The borders of the mountain's forest cover have clearly changed over time, with some areas being larger and others smaller. One major difference is the Celtic rampart (which stood some 4-5 meters tall then) is very difficult to see on the map, but is clearly visible on the aerial photo. This is an excellent example of the use of archival aerial photography, as the modern aerials and maps do not show the ramparts at all.BACK

15-23:You can see a long, curving line in one field to the left of the star. This feature has not been field verified yet, so its origin remains unknown. It may be a part of a large ancient enclosure, or it may be of more modern origin. The large size and regular shape mean it is certainly of human (and not natural) origin.BACK

15-24:Heat differences in the soil can be an indicator of buried stone structures because large masses of stones act as 'heat sinks' and collect solar radiation throughout the day and then re-radiate the energy in the evening and at night. Such effects are often seen after a snowfall, when square or linear patterns melt the snow cover faster and are visible from the air.BACK

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