Rural System's

The Potentials of GIS

Robert H. Giles, Jr., Ph.D., Professor Emeritus,
College of Natural Resources, Virginia Tech,
Blacksburg, Virginia 24061

I began work with computer mapping systems in 1969 before they were called GIS or Geographic Information Systems. The work is poorly labeled. There are computers needed to make maps but entire systems are needed - space, staff, computer hardware, computer software, statistical and other analytical packages, and plotters/printers ... and data of many types, and ideas. The emphasis of "geographic" seems misguided except that all data seem to have to have (or have access to) some geographic coordinates. The debate over the name seems wasteful. GIS is the code for that whole, large, expensive system. Herein I have made gross suggestions and have shown a few applications of suggested uses in

I have been critical of hardware and software sales groups for after the singular needful application, there is little further application of the system purchased. Expertise is easily lost to another project. The system, without an operator, languishes, wasteful and not seeming to fulfill promises.

There have been many applications of GIS and these are now regularly reported in the Journal of Forestry and elsewhere. Editors of wildlife journals rejected early submissions since the papers seems to describe the system rather than wildlife topics.

The following are notes on potential uses for GIS work. By thinking of things (all things ) as systems, uses in one area can easily be duplicated for other areas. For example, a GIS powerline corridor location procedure was turned (by A. Blair Jones) into a trail-corridor location procedure.

Consider that everything is either a point, line, area, or volume and similarities in software applications become evident.


  1. Intensity of use (user counts) areas
  2. Recreational opportunities
  3. Reported problem spots (trash, bad campsites, animal attacks, thefts, vandalism)
  4. Use patterns radiating from a point
  5. Percentage of users by areas
  6. Minimum pathways between Designated points
  7. Lost-hiker locations with time-rate-distance estimates
  8. Area affected by road or trail closures
  9. Maps showing potential for trail erosion and compaction
  10. Viewscape maps from trail points (e.g., every 50 meters) and areas seen(cumulatively)
  11. Intensity of road or trail use (thus impact of closures)
  12. Suitability for different types of recreation
  13. Areas of use by recreationists (and thus potential disturbance to some faunal species)
  14. Suitability of trail reaches for hiking, horseback, biking, etc.
  15. Sampling and extrapolating trail use rates by seasons
  16. Areas of conflict between and among forest users
  17. Education and sign areas
  18. Posters for sale for many topics such as those of Defense Mapping
  19. Changes in many of the above over time.

    The Fishery and Watersheds

  20. Ponds, lakes, wetlands, streams, rivers - reaches and shoreline segments
  21. Three-D representations of ponds and lake/stream cross-sections
  22. Water-surface area within zones of the shore (shoreline-fishing opportunities) adjusted to shore-line conditions
  23. Species maps (probable occurrence and fish size)
  24. Pollution areas with concentrations of each pollutant
  25. Erosion maps
  26. Runoff probability maps
  27. Zones contributing water to a lake or pond (by %)
  28. Storm water control potentials
  29. Distance-to-stream map
  30. Stream reach-to-reach links (adjacent and with same or lower elevation) Following pollution or migration potentials
  31. Distance from water map (for wildlife, and anglers)
  32. Potential flooding from a waste storage lagoon (Geospatial Solutions May, 2002, page 40)
  33. Ground water pollution potentials from slope and soil maps
  34. Flatness maps - assuming surface slopes of 6 degrees or more (greater than 10.5 percent) is of steep hydraulic gradient
  35. Pollution potential as a radius distance (a buffer zone width) from the water edge, the distance being proportional to the soil permeability rate (2.8 cm/hour or greater)
  36. Consequence to pond, lake or stream-reach of modifying a land unit within a watershed, changing its "cover type" from A to B. See MS thesis of Steve Findley

    a part of an elevation image by Steve Brown, Univ. Montana -in Landres et al. 2001Three-D Representations

  37. Elevations can be depicted in three dimensions, but more useful will be presentations of computed values for each cell such as cubic feet of wood, tons of erosive soil, fire hazard index, water runoff, animal abundance, recreational use values, suitability or primeness, site index, estimated land value (dollars), or results of distribution of users based on a quota system.

    Trained Images

  38. Supervised classification is the fancy expression. Given that you have only a few samples from known spots, ask the computer to show you all sites that have approximately the same 10-to-20 characteristics. This may be the habitats of threatened species (as done by McCombs for the flying squirrel using logistic raegression to estimate the probability of occurrence)

    Excluded Spaces

  39. Use simple logic with expert knowledge. You know the creature does not live in water (exclude all water map sells) enter a value of 1 for all of these cells; it does not live very near humans (exclude those cells and add this map values (all 1's) to the last map (you're adding the "known" exclusions, increasing in confidence and cell color darkness correlated with the growing number in excluded cells; it is known to live in relatively high areas (exclude all lower elevation cells - if not sure, make several runs with your 2-3 best estimates), exclude all highways, all mines, all powerlines, all non-forested areas ... then within a limited window, the area of concern for the decision. The proportion of the area excluded with each exclusion act can be calculated and graphed. The resulting area is usually quite small, (an hypothesis can be tested to compare its size to a priori estimated areas). Often the remaining area can be discussed and if impacts are hypothesized, then as much of the residual area as possible can be avoided by a developer (such as for a powerline).

    Regression Maps

  40. Simple linear equations exist such as

    Y= a + bX

    with a and b coefficients having been determined from research analyses using regression packages. By using the value of X in each map cell (for example the slope percent) then some other value (like Y) can be estimated for the map cell. The computer can readily create a new map with the values of Y in each map cell (pixel). Erosion potential maps can be created but they use several map layers and the equations have non linear components. These types of maps need to replace the past emphases on simple overlays (the hidden assumption being that each map layer value is of equal value. Weighting of layers (count this one layer five times!) can be done but that adds a human dimension that may not be computed properly and may not be representative of the appropriate decision makers).

    Fly Overs

  41. Part of many small modern GIS systems is the ability to make multiple maps and then to move among them quickly giving the appearance of flying over an area or landscape. Flying over a complex data-scape seems necessary to show optimum search strategies, the false peaks and troughs that give decision trouble, the constraint space, the risk zones. Flying over a terrain is now well known and often seen in hand-held children games.
  42. Workers need to "fly" through forest stands and especially fly through a stream, a lake, or back and forth through several reaches of a stream or river. Slow motion "flys" up and over an elevational sequence may be especially instructive for workers new to an area. Flying progressively through all aspects, all elevations, and all slope classes in an area with ancillary photographs should make an effective training unit for new workers to a refuge, and a large natural resource area ... virtually to every county within a region.

    Nearness-to Maps

  43. Water may be known for springs, seeps, waterholes, ponds, etc. These can be mapped. Water may not be present in adjacent map cells but water is available for wildlife, etc. A map can be made of water and nearness to water. The areas missing then can become of interest to managers trying to supply water to many species of wildlife. This can be formulated as buffer-zones around water or simply water cells and all contiguous cells.
  44. Similar areas can be contrived for low noise areas, areas likely to have free-running dogs, areas likely to have poaching, areas likely to have air-borne pollutants.

Landres et al. (2001) seem overly cautious about recommending GIS for wilderness management work. Large area work requires it. Not to become highly involved with it is proximal to mismanagement. Using GIS is a step on the way to improving wilderness and other natural resource management. It has to be pulled from a concept of maps and map pages and pictures. The results need to be in electronic form for the Internet and for sending to the field by wireless technologies. More than a system for making maps it needs to be an information storage system, a retrieval system, an analysis system, a updating system, and a prognostic system ... and, oh yes, occasionally a map printing system. It has to be seen as a unit of an information and education system,a communication system, a multi-media system. Importantly, most importantly over the long run, it is an alternative way of thinking about management for the whole can be seen and mastered and the future foretold ... and improvements made over time. They are a way of dealing with history, conditions (especially baseline conditions) and services, likely transitions, uses, threats (like exotic plants and insects or new mammal abundance like that of the eastern coyote), and the relations among these. They provide a work space for notes, observations, ideas, and plans. They prevent the loss of information as staff retire or move. They provide economies in training for the new staff person. Where questions in the past were never asked because the answer would never be available, now new questions can be aired such as:

Perhaps real scientists can never prove a negative, but the presence of a GIS and its potential use has stopped or greatly delayed proposals for projects that would impact natural resource areas since the impact could be very well analyzed and the case clearly made for an agency or court not to approve the proposed development. In wilderness work, it can be used to oppose (or support) de-classification or land trades.


See for county and topo data

See: Robert Meese's (rjmeese@UCDAVIS.EDU) international species databases of The Information Center for the Environment (ICE), in cooperation with the United States Man and the Biosphere program (U.S. MAB), the Man and the Biosphere (MAB) program of the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the National Biological Information Infrastructure, the U.S. National Park Service, and the Biological Resources Discipline of the U.S.G.S., has produced databases containing documented, taxonomically standardized species inventories of plants and animals reported from the world's protected areas. These databases ( contain documented, standardized species inventories of over 1,200 protected areas in ca. 130 countries and are updated monthly.

Possible data source for digital maps:Tele Atlas (that bought GDT in 2004)

For instruction on remotely sensed information

See alsy yahoo!maps at and MapQuest that includes locations outside of the US

Also see for Wise Co Va online GIS

Small projects

The following references (Geo Info Systems, November/December 1992) form the ideas for expert system work with GIS:

Dabrowski, CE., and E.N. Fong. 1991. Guide to Expert System Building Tools for Microcomputers. Gaithersburg, Maryland: National Institute of Standards and Technology.

Fisher, P.E, et al. 1988. Artificial Intelligence and Expert Systems in Geodata Processing. Progress in Physical Geography 12(3):371-388.

Freeman, H., and J. Ahn. 1984. AUTONAP - An Expert System for Automatic Map Name Placement. Proceedings of the International Symposium on Spatial Data Handling. Ziirich. Friederich, S., and M. Gargano. 1989. Expert System Design and Development Using VPExpert. New York: John Wiley & Sons.

Glick, B., S.A. Hirsch, and N.A. Mandico. 1985. Hybrid Knowledge Representation for a Geographic Information System. Proceedings of AutoCarto-7. Washington, D.C.

Loh, D.K., et al. 1991. INFORMS DEMO: The Design Concept of Integrated Resource Management Systems. Proceedings of International Conference on Decision Support Systems for Resource Management. College Station, Texas: Texas A and M University.

Luckey, S. 1991. GIS/Expert System Demonstration: NOAA's CartAssociate. Technology Integration Workshop. Selected papers. H. Tom, editor. Gaithersburg, Maryland: U.S. Department of Commerce, National Institute of Standards.

Maidment, D.R., and T.A. Evans. 1991. Expert GIS: Linking ARC/INFO to the Nexpert Object Expert System Shell. ARC News. Fall. Redlands, California: Environmental System Research Institute.

Mitchell, L.C., et al. (the Environmental Work Group, Coastal Wetland Planning, Protection, and Restoration Act Technical Committee). 1992. Wetland Value Assessment Methodology and Community Models. Unpublished report. Lafayette, Louisiana: U.S. Fish and Wildlife Service.

Pereira, LM., P. Sabatier, and E. de Oliveira. 1982. ORB I - An Expert System for Environmental Resource Evaluation through Natural Language. Report FCT/DI. Lisbon, Portugal: Departmento de Informatica, Universidade Nova de Lisboa.

Robinson, V.B., A.U. Frank, and M.A. Blaze. 1986. Introduction to Expert Systems for Land Information Systems. Journal of Surveying Engineering 112(2):1O9-119.

Robinson, VB., A.U. Frank, and M.A. Blaze. 1988. Expert Systems and Geographic Information Systems: Review and Prospects. Expert System in Engineering, AI in Industry Series. D.T. Pham, editor. Kempston, United Kingdom: IFS Ltd.

Smith, T.R., and M. Pazner. 1984. Knowledgebased Control of Search and Learning in a Largescale GIS. Proceedings of International Symposium on Spatial Data Handling, Ziirich 2:498-519.

Sumic, Z., and T. Pistorese. 1991. Automating Electrical Plat Design Process Combining GIS and Artificial Intelligence Approach. Training materials, expert GIS training course. Redlands, California: Environmental Systems Research Institute. .


Landres, P, D.R. Spildie, and L.P. Queen. 2001. GIS applications to wilderness management: potential uses and limitations. USDA For. Serv, Rocky Mt. Research Station, Gen Tech Rpt RMRS-GTR-80, Ft Collins, CO 9p.

Anderson, D. R.1981. Estimating climatological parameters for land use planning and wildlife management. M.S. Thesis, Virginia Polytechnic Institute and State Univ., Blacksburg.

Anonymous. 1979. Listing of state natural resource information systems.Remote Sensing 2(9):3.

Antenucci,J. S.A.Miller,and C.R.Brunori.1979.Maryland wildliferesources information retrieval system.Trans.N.Am.Wildl.Nat.Resour.Conf.11:330-338.

Cross,R. H. /III.1979. A case study of computer-aided transmissionline siting.Pages13-33 in Computer mapping in natural resources and the environment. Laboratory for Computer Graphics and Spatial Analysis, Harvard Univ., Cambridge, Mass.Environmental ResearchInstitute of Michigan. 1979.

Description of ERIM wildlife habitat evaluation services. Environmental Research Institute of Michigan,Ann Arbor. 8pp (Mimeo).

Fabos, J. G. and S. J. Caswell.1977. Composite landscape assessment. Res. Bull. No. 637, Agr. Exp. Stat., Univ. Massachusets, Amherst. 323pp.

Frigerio, N. A., P. F. Gustafson, and J. M. Cobian. 1975.Power plant siting: evaluation of optimization. Pages 58-83 in Proc.Conf.on Computer Support on Environmental Science and Analysis. Lawrence Livermore Laboratory, Livermore, Calif.

Giles, R. H., Jr. 1977. Simulating the environmental impacts of a high voltage transmission line. Proc.1977 Winter Simulation Conf. 319-321.

Hoar, A. R. 1980. A methodology for mapping probable ranges of endangered terrestrial mammals within selected areas of Virginia. M.S.Thesis,Virginia Polytechnic Institute and State Univ., Blacksburg, 191pp.

Holloran, R.L.1978. A white-tailed deer harvest data analysis and information system for Virginia. M.S. Thesis, Virginia Polytechnic Institute and State Univ.,Blacksburg, xi + 276pp.

Johnston, D. R. 1979. A case study of a computer application using G.I. S. for forest management inventory. Pages 51-55 in Computer mapping in natural resources and the environment. Laboratory for Computer Graphics and Spatial Analysis, Harvard Univ., Cambridge, Mass.

Jones, A. B. III. 1976. Power: a computer information system for land use decisions. M.S.Thesis,Virginia Polytechnic Institute and State Uni v. , Blacksburg. vi+194pp.

Knapp,E.M. and D.Rider.1979. Automated geographic information systems and Landsat data: a survey.Pages 57-68 in Computer mapping in natural resources and the environment. Laboratory for Computer Graphics and Spatial Analysis, Harvard Univ., Cambridge, Mass.

Koeln,G. T.1980a. A computer-assisted general aviation airport location and evaluation system for Virginia. Ph.D. Dissertation, Virginia Polytechnic Institute and State Univ., Blacksburg. x + 235pp.

Koeln,G.T. 1980b.A computer technique for analyzing radio-telemetry data. Pages 262-271 in J. M. Sweeney (ed.), Proc. Fourth Natl. Wild Turkey Symp., Arkansas Wildlife Society, Little Rock.

Koeln, G.T. ,R.H.Giles,Jr., E.G.Okie, and N.Saelinger.1980. Assessment of airport noise usingVirginia's state information system. Proc.Third Annual Meeting of Nat. Ass. Noise Control Officials, Boulder, Colo.

McDonald, M.V.1977. A computer information system for Virginia counties. M.S.Thesis, Virginia Polytechnic Institute and State Univ., Blacksburg. vii + 99pp.

McHarg,I.1969. Design with nature. Doubleday/Natural History Press, Garden City, N.Y.

Reed, W. E. and J. E. Lewis. 1978. Land use and land cover information and air-quality planning. U.S.G.S., Geological Survey Professional Paper 1099-B,U.S.Government Printing Off., Washington, D.C. 43pp.

Ross. D.I. 1979. A case study of water quality mapping in the Canadian coastal zone of the Great Lakes.Pages 97-104 in Computer mapping in natural resources and the environment.,Laboratory for Computer Graphics and Spatial Analysis, Harvard Univ., Cambridge, Mass.

Schlesinger, J. B. Ripple,and T. R. Loveland. 1979. Land capability studies of the South Dakota automated geographic information system (AGIS). Pages 105-114 in Computer mapping in natural resources and the environment. Laboratory for Computer Graphics and Spatial Analysis, Harvard Univ., Cambridge, Mass.

Smart,C.W.1976.A computer-assisted technique for planning minimum impact transmission right of way routes. Ph.D. Dissertation,Virginia Polytechnic Institute and State Univ., Blacksburg. xiii + 192pp.

Svatos, V.C. 1979. A case study of the analysis of the capability of undeveloped lands to contribute to water quality degradation. Pages 115-133 in Computer mapping in natural resources and the environment. Laboratory for Computer Graphics and Spatial Analysis, Harvard Univ., Cambridge, Mass.

Tomlin, C. D., S. H. Berwick,and S. M. Tomlin. 1979. Paper Presented at Second Ann. Int. Users' Conf. on Computer Mapping, Hardware,Software, and Data Bases.July 15-20, 1979, Cambridge, Mass. 18pp. (Mimeo.)

Williamson, J. F., Jr. and G. T. Koeln. 1980. A computerized wild turkey habitat evaluation system. Pages 233-239 in J. M. Sweeney (ed.), Proc. Fourth Natl. Wild Turkey Symp., Arkansas Wildlife Society, Little Rock.

Williamson,J.F.,Jr. and Whelan,J.B.1981.Computer assisted black bear Management in Shenandoah National Park. Proc. Fifth Int. Conf. on Bear Res. and Manage., Feb 10-13, 1980. Madison, WI

September 14, 2004