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U.S. GEOLOGICAL SURVEY BULLETIN 1583 I 

Department of the Interior 

U.S. Geological Survey 

ERRATA 

APPLICATIONS OF U.S. GEOLOGICAL SURVEY DIGITAL CARTOGRAPHIC PRODUCTS 

1979-1983 

U.S. GEOLOGICAL SURVEY BULLETIN 1583 

Pages 13-17, Figures 4, 6-10 

Illustrations were erroneously printed in reverse tones 

causing a reversal in figure tonal patterns. Light tones 

should appear dark ~nd dark tones should appear light. 

Pages 13-14, Figures 4 and 6 

Captions for figures 4 and 6 are reversed. Correct captions 

are: 

Figure 4. The elevation data used in the study were from 

the Reward quadrangle 7.5-minute digital elevation 

model. In this image, the dark tones represent 

low elevations while the light areas identify high 

elevations. 

Figure 6. This Landsat MSS band 7 (reflected infrared energy) 

subscene of the Reward quadrangle in Pennsylvania 

illustrates the variability of the oak forest spectral 

signature. Notice the changes caused by the topographic 

effect and compare it with figure 10. 

Applications of U.S. Geological 

Survey Digital Cartographic 

Products, 1979-1983 

By T. R. lOVElAND and BEN RAMEY 

U.S. GEOLOGICAL SURVEY BULLETIN 1583 
DEPARTMENT OF THE INTERIOR 

DONALD PAUL HODEL, Secretary 

U.S. GEOLOGICAL SURVEY 

Dallas L. Peck, Director 

UNITED STATES GOVERNMENT PRINTING OFFICE: 1Y86 

For sale by the Distribution Branch, U.S. Geological Survey, 

604 South Pickett Street, Alexandria, VA 22304-4658 

Library of Congress Cataloging-in-Publication Data 

Loveland, Thomas R. 

Applications of U.S. Geological Survey digital cartographic 

products, 1979-1983. 

(U.S. Geological Survey bulletin ; 1583) 

Bibliography : p. 

Supt. of Docs. no.: I 19.3:1583 

1. Digital mapping. 2. Cartography-United States. I. 

Ramey, Benjamin S. II. Title. Ill. Series. 

QE75.B9 no. 1583 [GA139] 557.3 s 85-600231 

[526] 
CONTENTS 

Page 

Introduction ......................................................................................... 1 

Data characteristics and availability 

Digital topographic data ............................................................................... 3 

Digital land use and land cover data ...................................................................... 5 

Digital line graphs .................................................................................... 7 

Applications 

Reduction of the topographic effect on Landsat data .......................................... ~ ............ 13 

Forest fire behavior prediction ......................................................................... 18 

San Francisco Bay Area earthquake hazards mapping ...................................................... 22 

Preliminary soils mapping in eastern Kentucky ............................................................ 26 

Survey of Oregon irrigation development potential. ........................................................ 29 

Arizona rangeland management ........................................................................ 34 

South Carolina coastal zone management. ............................................................... 38 

Environmental assessment of a nuclear power plant 

accident-The Three Mile Island example ............................................................... 42 

III 
FIGURES 

Page 

1. 1 :2,000,000-scale DLG multistate data blocks ......................................................... 8 

2a. 1 :2,000,000-scale DLG feature selection based on size ................................................... 9 

2b. 1 :5,000,000-scale DLG feature selection based on size .................................................. 10 

2c. 1: 10,000,000-scale DLG feature selection based on size ................................................. 10 

3a. 1 :2,000,000-scale DLG feature groups, Florida- Interstate, and Federal and State limited access highways ....... 11 

3b. 1 :2,000,000-scale DLG feature groups, Florida- Major U.S. and State primary non-limited-access highways ..... 11 

3c. 1 :2,000,000-scale DLG feature groups, Florida- Minor U.S., State, and other roads ......................... 11 

4. Landsat MSS Band 7 subscene for Reward, Pa., Quadrangle ............................................ 13 

5. Reward, Pa., Quadrangle location diagram .......................................................... 14 

6. Reward Quadrangle DEM elevation data ............................................................ 14 

7. Reward Quadrangle slope data .................................................................... 15 

8. Reward Quadrangle aspect data ................................................................... 16 

9. Reward Quadrangle solar incidence angle data ....................................................... 16 

10. Corrected Landsat MSS Band 6 subscene for Reward Quadrangle ........................................ 17 

11. Lolo Creek study area, Mont., location map ......................................................... 18 

12. DEM elevation data for Lolo Creek area ............................................................ 19 

13. Percent slope map for Lolo Creek area .............................................................. 19 

14. Forest fuel types map for Lolo Creek area ........................................................... 19 

15. Fire potential model flow diagram ................................................................. 20 

16. Fire behavior maps for Lolo Creek area ............................................................. 20 

17. Coverage of ABAG earthquake mapping program .................................................... 22 

18. San Mateo County percent slope map ............................................................... 23 

19. San Mateo County earthquake-induced landslide susceptibility map ...................................... 23 

20. San Mateo County composite maximum earthquake damage map ........................................ 24 

21. San Mateo County composite risk of earthquake damage map ........................................... 24 

22. Ogle, Ky., Quadrangle location diagram ............................................................ 27 

23. Ogle, Ky., Quadrangle percent slope map ............................................................ 27 

24. Ogle, Ky., Quadrangle aspect map ................................................................. 27 

25. Ogle, Ky., Quadrangle landform map .............................................................. 28 

26. Ogle, Ky., Quadrangle soil unit composite map ....................................................... 28 

27. Umatilla Basin, Oreg., location diagram ............................................................ 29 

28. DTT elevation data for Umatilla Basin .............................................................. 30 

29. Pump lift distance map for Umatilla Basin ........................................................... 31 

30. Friction loss map for Umatilla Basin ................................................................ 31 

31. Potential energy requirements map for Umatilla Basin ................................................. 31 

IV 
FIGURES Page 

32. Umatilla Basin land cover map .................................................................... 31 

33. Umatilla Basin soil irrigability map ................................................................. 32 

34. Umatilla Basin landownership map ................................................................ 32 

35. DTT-based Umatilla Basin percent slope map ........................................................ 32 

36. Umatilla Basin potential energy requirements map .................................................... 32 

37. Umatilla Basin irrigation development potential map .................................................. 33 

38. BLM's Grand Wash Planning Unit, Ariz., location diagram ............................................ 34 

39. DTT elevation map for the Grand Wash Planning Unit. ................................................ 35 

40. Vegetation map produced with, and without, DTT data ................................................ 35 

41. Sagebrush treatment areas map for Grand Wash Planning Unit. ......................................... 36 

42. Pinyon-Juniper cutting areas map for Grand Wash Planning Unit ........................................ 36 

43. Potential antelope habitat map for Grand Wash Planning Unit. ......................................... 36 

44. Location map of South Carolina's coastal counties .................................................... 38 

45. Portion of a land use and land cover map ............................................................ 39 

46. South Carolina coastal zone land use map ........................................................... 40 

47. Three Mile Island power plant, Pa., location diagram .................................................. 42 

48. Land use and land cover and census tract map for Three Mile Island area .................................. 43 

49. Population density map for Three Mile Island area .................................................... 44 

v 

Applications of U.S. Geological 

Survey Digital Cartographic 

Products1 

By T. R. Loveland and Ben Ramey 

INTRODUCTION 

The U.S. Geological Survey prepares and distributes 

fundamental, multipurpose cartographic data to a wide 

range of users throughout the United States. Recognizing 

that traditional cartographic procedures will eventually be 

replaced by digital techniques, the USGS is now actively 

developing computer-based methods to produce digital cartographic products. The digital cartographic products currently being developed parallel the traditional map-based 

information but have the advantage that they can be used in 

a computer environment. The digital cartographic products 

include: 1) digital elevation models (DEM's), 2) digital land 

use and land cover data, and 3) digital line graphs (DLG's). 

In addition, digital terrain tapes (DTT's) that were 

developed by the Defense Mapping Agency are currently 

available through the USGS. As with conventional map 

products, the digital products are suitable for efficient application to a variety of problems. 

The Survey's digital cartographic products offer 

several general advantages for resource studies. The products: 

- cover extensive geographic areas and have relatively 

fine spatial resolution, 

- permit the use of rapid, objective, and repeatable 

computer-based analysis techniques, 

- permit the solving of complex resource problems 

when used in conjunction with other types of digital 

cartographic data, 

- provide the user with the flexibility to address a variety of unrelated problems or alternative scenarios of 

the same problem, and 

- are cost-effective due to the above factors, and 

because of their relatively low-cost per unit of cartographic information. 

In addition, each individual product offers specific advantages based on both its unique attributes and the problems 

to which it may be applied. 

The objective of this report is to briefly describe the 

characteristics and availability of each digital cartographic 

product, and then discuss applications or research studies 

that exemplify both the specific and general benefits gained 

through the use of digital terrain tapes, digital elevation 

1 The report was compiled, prepared, and edited by Thomas R. 

L_oveland, Technicolor Government Services, EROS Data Center, 

S1oux Falls, S.D., and Ben Ramey, National Mapping Division, 

Reston, Va. 

models, and digital land use and land cover data in the mapping and analysis process. 1 The studies collectively illustrate 

the variety of applications and the advantages and unique 

contributions that the digital data offer to resource investigations. The descriptions of the studies are not intended to provide technical details but rather to present typical 

applications and the capabilities the data offered to those 

stu.dies. For readers with specific technical questions, a 

pomt of contact and references to published technical 

reports relating to each study are included in each example. 

The documentation of these examples was made 

possible by assistance provided by representatives from 

several state, regional, and federal government agencies. 

The following people contributed to the production of this 

report: Peter Croswell, Kentucky Department for Natural 

Resources and Environmental Protection; David Cowen, 

University of South Carolina; Paul Wilson and Jeanne 

Perkins, Association of Bay Area Governments; S. W. 

Wharton and B. N. Holben, National Aeronautics and 

Space Administration (Goddard Space Flight Center); 

Lawrence Pettinger, Valerie Milazzo, and James Wray, 

U.S. Geological Survey; and Wayne Miller and Mark 

Shasby, Technicolor Government Services, Inc., and 

William Draeger and David Greenlee, U.S. Geological 

Survey, all from the EROS Data Center, Sioux Falls, 

South Dakota. 

SELECTED REFERENCES 

Anderson, K. E., 1978, Spatial analysis in a data base environment, in Dutton, G., ed., First International Advanced Study Symposium on Topological Data Structures for Geographic Information: Cambridge, 

Massachusetts, Harvard Papers on Geographic Information Systems, v. 2. 

Guptill, S. C., 1980, The development and use of digital 

cartographic data bases, in Blaser, A., ed., Data Base 

Techniques for Pictorial Applications: Berlin, 

Heidelberg, Springer-Verlag, p. 65-77. 

---, 1981, Thematic map production from digital 

spatial data, in Computer Mapping of Natural 

Resources and the Environment and Satellite Derived 

Data Applications: Cambridge, Massachusetts, Harvard University, Laboratory for Computer Graphics 

and Spatial Analysis, v. 15, p. 121-124. 

Hirsch, S. A., 1980, Cartographic considerations for 

merging digital spatial data files, in American Society 

of Photogrammetry, and American Congress on 

S~rveying and Mapping Fall Technical Meeting, 

Ntagara Falls, New York, 1980, Proceedings, p. 

DP2C1-DP2C16. 

2Applications of digital line graphs will not be illustrated in this 

report. Due to their relatively recent availability, they have just begun to 

be applied to resource problems. 
McEwen, R. B., 1979, U.S. Geological Survey digital cartographic data acquisition, in Mapping Software and 

Cartographic Data Bases: Cambridge, 

Massachusetts, Harvard University, Laboratory for 

Computer Graphics and Spatial Analysis, v. 2, p. 

136-142. 

---, 1980, USGS digital cartographic applications 

program: Journal of the Surveying and Mapping 

Division, American Society of Civil Engineers, v. 106, 

SUI, p. 13-22. 

---, 1981, Information potential of a digital cartographic data base, in American Society of Photogrammetry Annual Meeting, 47th, Washington, 

D.C., 1981, Proceedings, p. 150-153. 

McEwen, R. B., and Calkins, H. W., 1982, Digital 

cartography in the USGS National Mapping Division: 

Cartographica, v. 19, no. 2, p. 11-26. 

McEwen, R. B., and Jacknow, H. R., 1980, USGS digital 

cartographic data base, in International Symposium 

on Cartography and Computing, Applications in 

2 

Health and Environment, Auto-Carto IV, Reston, 

Virginia, 1979, Proceedings v. 1, p. 225-235. 

McEwen, R. B., Witmer, R. E., and Ramey, B. S., Editor, 

1983, USGS Digital Cartographic Data Standards, 

U.S. Geological Survey Circular 895-A through G. 

McLaurin, J. D., 1980, U.S. Geological Survey digital 

mapping program, in Cartographic and Statistical 

Data Bases and Mapping Software: Harvard University, Laboratory for Computer Graphics and Spatial 

Analysis, v. 8, p. 53-59. 

Milazzo, V. A., Hallam, C. A., and Anderson, K. E., 

1978, An integrated system of graphic and digital data 

production: Paper presented at Association of 

American Geographers Annual Meeting, 74th, New 

Orleans, Louisiana, 1978. 

Starr, L. E., and Anderson, K. E., 1982, Some thoughts 

on cartographic and geographic information systems 

for the 1980's, in William T. Pecora Symposium, 7th, 

Sioux Falls, SD, 1981, Proceedings: American Society of Photogrammetry, p. 41-55. 
Digital Topographic DataCharacteristics and Availability 

INTRODUCTION 

Digital topographic data, usually in magnetic tape 

form, consist of sampled arrays of elevations for a number 

of ground positions that are usually, but not always, at 

regularly spaced intervals. The USGS currently produces 

and distributes digital elevation models (DEM's) corresponding to standard USGS 7 .5-minute quadrangles. 

The USGS also distributes digital terrain tapes (DTT's) 

and arc-second DEM's produced by the Defense Mapping 

Agency (DMA) from 1 :250,000-scale maps. The 

7 .5-minute DEM's and the arc-second DEM's are 

distributed in the standard USGS DEM tape format, 

designed in the late 1970's. This format has become the 

standard for digital topographic data files. DTT files are 

distributed as produced by the DMA and do not conform 

to the standard USGS DEM tape format. 

CHARACTERISTICS 

7.5-Minute DEM's 

Digital elevation models that correspond to standard 

USGS 7 .5-minute quadrangles may be derived from a 

number of data sources, such as existing contour plates, 

profiles or terrain models scanned in stereo photogrammetric equipment, or from digitizing orthophoto or 

stereo-plotting equipment. Currently, the majority of 

7.5-minute DEM's are created using digitizing orthophoto 

equipment and consist of elevation values spaced at 

regular 30-meter intervals, expressed in meters, and 

referenced in the Universal Transverse Mercator (UTM) 

coordinate system; however, the DEM tape format does 

provide for randomly scattered data values and a variety 

of coordinate referencing systems. 

Digital Terrain Tapes 

Digital terrain tapes (DTT's) were produced by the 

Army Map Service (now Defense Mapping Agency 

Hydrographic and Topographic Center) from the early 

1960's to the late 1970's. Initially, DTT's were produced to 

drive milling machines for production of threedimensional relief maps; however, the versatility of the 

data was soon recognized and DTT's were used in a variety 

of situations, including assessment and analysis of land 

resource problems. 

The DTT's were created by digitizing the contours, 

ridgelines, and drains from 1 :250,000-scale maps and 

subsequently interpolating elevations to the nearest foot at 

.01-inch intervals at map scale. The .01-inch spacing corresponds to a ground distance of 63.5 meters between 

elevation values. 

Each DTT file represents a 1-degree by 1-degree block 

that corresponds to the east or west half of the original 

1 :250,000-scale map. The elevation data are referenced in 

digitizer coordinates at increments of 0.01 inch. 

Several factors must be considered if DTT's are to be 

effectively used for analysis: 

- The 1 :250,000-scale contours may have been mapped 

slightly differently from sheet to sheet, which may 

cause some discontinuity in the elevations in adjoining files. 

- The data are only as accurate as the 1 :250,000-scale 

contours. 

- Because the interpolation was originally performed 

by a planar approximation method, which tends to 

be inadequate if contours are relatively far apart, a 

terracing or stepping effect can be noticed in the data. 

This is especially apparent when topographic slope is 

calculated from the elevations. 

- Mosaicking between sheets can be a significant problem, unless a sophisticated resampling program is 

employed. 

Arc-Second DEM's 

Arc-second DEM's are produced by the Defense 

Mapping Agency by updating and reformatting existing 

DTT's and by scanning contours, ridgelines, and drains 

from 1 :250,000-scale maps where DTT coverage is nonexistent or inadequate. 

Existing DTT's are updated by adding control points, 

interpolating, and subsequently verifying the data by 

generating contour plots to compare to the original source 

materials. Arc-second DEM's are produced from 

1:250,000-scale maps in much the same manner as DTT's 

were created; however, the digitizing equipment and processing methodology is significantly improved and more 

rigorous. 

Like DTT's, arc-second DEM's cover 1-degree by 

1-degree blocks corresponding to the east or west half of 

the 1 :250,000-scale map, except in Alaska, where the 

blocks cover only one third of a 1 :250,000-scale map. The 

spacing between data values is 3 seconds of arc and varies 

with latitude; at the Equator spacing is approximately 90 

meters in each direction, and at 50 degrees latitude, is approximately 90 meters north-south by 60 meters east-west. 

The elevation values are recorded to the nearest meter and 

are referenced horizontally in latitude and longitude coordinates. 

Arc-second DEM's represent an improvement in data 

reliability over the DTT's because of the use of additional 

control and modern verification techniques; however, the 

data are derived from information on the 1 :250,000- scale 

3 
map series and are consequently limited to the accuracy inherent therein. Arc-second DEM's provide useful data for 

analyzing land resource problems where general elevation 

estimates are needed. 

AVAILABILITY 

Completed 7 .5-minute DEM's are being added to 

the data base daily. A status graphic depicting the current 

availability of 7 .5-minute DEM's is produced every six 

months by the National Cartographic Information 

Center. Digital terrain tapes are available for all of the 

conterminous United States and Hawaii. Arc-second 

DEM's are available for nearly all of the United States (including Alaska and Hawaii). The few quadrangles for 

which arc-second DEM coverage is not complete are currently in preparation. 

Descriptions of the technical specifications for 

7 .5-minute and arc-second DEM's, and DTT's are included in U.S. Geological Survey Circular 895-B entitled 

"Digital Elevation Models." Circular 895-B is available 

from NCIC. 

Questions about availability and ordering of all 

forms of digital topographic data should be addressed to: 

4 

U.S. Geological Survey 

EROS Data Center 

User Services Section 

Sioux Falls, SD 57198 

(605) 594-6151/FTS 784-7151 

SELECTED REFERENCES 

Brunson, E. B., and Olsen, R. W., 1978, Digital data 

elevation model collection systems: American Society 

of Photogrammetry Digital Terrain Model Symposium, St. Louis, Missouri, 1978, Proceedings, p. 

72-99. 

Doyle, F. J., 1978, Digital terrain models-An overview: 

Photogrammetric Engineering and Remote Sensing, 

v. 44, n. 12, p. 1481-1485. 

Elassal, A. A., and Caruso, V. M., 1983, USGS Digital 

Cartographic Data Standards: Digital Elevation 

Models, U.S. Geological Survey Circular 895-B. 

USGS National Mapping Division, 1980, Computer files 

for digital elevation models: Reston, Virginia, USGS 

National Mapping Division. 

---, 1977, Digital terrain tapes-users guide (2d ed.): 

Reston, Virginia, USGS. National Cartographic Information Center, 12 p. 
Digital Land Use and Land 

Cover Data-Characteristics 

and Availability 

INTRODUCTION 

Digital land use and land cover data are produced by the 

U.S. Geological Survey from 1 :250,000-and 

1: 1 00,000-scale USGS land use and land cover maps. Four 

associated maps are also available in digital form: (1) 

political units, (2) census county subdivisions, (3) 

hydrologic units (watersheds), (4) federal land ownership. 

These digital files provide comprehensive, standardized 

cartographic and geographic data useful for a variety of 

planning problems. 

CHARACTERISTICS 

Each digital file represents one theme, such as land 

use and land cover or political units, for a specific 

1:250,000- or 1:100,000-scale map. Each file consists of 

lines defining non-overlapping information within the 

map. Numeric codes are used to identify the category to 

which each area (or polygon) on the map belongs. 

The technical specifications for the data are fully 

described in U.S. Geological Survey Circular 895-E 

(Feagas and others, 1983). 

The digital data are stored in a topological file structure within the Geographic Information Retrieval and 

Analysis System (GIRAS). This structure allows the line 

segments (arcs) forming each polygon's perimeter to be 

connected to form areas (or polygons). Pointers are used 

to identify the specific line segments that must be connected in order to form unique polygons. In GIRAS format, the topological data sets can then be manipulated and 

used for many types of geographic analyses and applications. The topographical or line segment file structure is 

efficient for archiving map data and does preserve all 

mapped information. However, for some types of 

analysis, many users convert the data to a polygonal format which is simpler to process for area calculations, 

polygon overlay, and other spatial manipulations. 

LAND USE AND LAND COVER-Files contain up 

to 37 categories of land use and land cover. Small-scale 

aerial photographs are the primary sources of the land use 

and land cover information. The minimum size of the land 

use and land cover polygons is 16 hectares (40 acres) for 

most rural categories and four hectares (10 acres) for all 

urban and built-up, and water classes. The classification 

system is fully described in USGS Professional Paper 964 

(Anderson and others, 1976). 

POLITICAL UNITS-State and county boundaries. 

CENSUS COUNTY SUBDIVISIONS-Boundary 

data for census tracts within Standard Metropolitan 

Statistical Areas (SMSA), and for minor civil divisions in 

non-SMSA's. 

HYDROLOGIC UNITS-Watershed boundaries 

corresponding to USGS-defined drainage basins. 

FEDERAL LAND OWNERSHIP-Federallyowned lands for 28 agencies. Parcels must be at least 16 

hectares (40 acres) to be included. 

AVAILABILITY 

Digital land use and land cover data are currently 

available for approximately one third of the conterminous 

United States. Coverage is complete for most states east of 

the Mississippi and for all files on the Atlantic and Gulf 

coasts. Coverage is also complete for Hawaii, Kansas, 

Louisiana, and Missouri. 

An index to available land use and land cover and 

associated map digital data is available from: 

U.S. Geological Survey 

EROS Data Center 

User Services Section 

Sioux Falls, SD 57198 

(605) 594-6151/FTS 784-7151 

Additional information may be obtained by contacting: 

Office of Geographic and Cartographic Research 

National Mapping Division 

U.S. Geological Survey 

521 National Center 

Reston, Virginia 22092 

(703) 860-6341 

SELECTED REFERENCES 

Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, 

R. E., 1976, A land use and land cover classification 

system for use with remote sensor data: U.S. 

Geological Survey Professional Paper 964, 28 p. 

Anderson, K. E., 1979, A geographic information system 

for land use data in the United States of America: 

Paper presented at U.N. Regional Cartographic Conference for the Americas, 2d, Mexico City, 1979, 16 p. 

Feagas, R. G., and others, 1983, USGS Digital Cartographic Data Standards: Land Use and Land Cover 

Digital Data, U.S. Geological Survey Circular 895-E. 

Fitzpatrick-Lins, K. A., 1978, Accuracy and consistency comparisons of land use and land cover maps 

made from high-altitude photographs and Landsat 

multispectral imagery: U.S. Geological Survey Journal of Research, v. 6, n. 1, p. 23-40. 

---, 1980, Accuracy of selected land use and land 

cover maps at scales of 1:250,000 and 1:100,000: U.S. 

Geological Survey Circular 829, 24 p. 

5 
Guptill, S. C., 1978, The impact of computer graphics, 

data manipulation software, and computing equipment on spatial data structures, in Dutton, G., ed., 

First International Advanced Study Symposium on 

Topological Data Structures for Geographic Information Systems: Cambridge, Massachusetts, Harvard Papers on Geographic Information Systems, 

v. 2. 

---, 1979, A digital cartographic data base for land 

use and land cover and associated maps, in Mapping 

Software and Cartographic Data Bases: Cambridge, 

Mass., Harvard University, Laboratory for Computer Graphics and Spatial Analysis, v. 2, p. 99-106. 

Kleckner, R. L., 1981, Anationalprogramofland use and 

land cover mapping and data compilation, in Planning Future Land Uses: Madison, Wisconsin, 

Agronomy Society of America, p. 7-13. 

Milazzo, V. A., 1980, A review and evaluation of alternatives for updating U.S. Geological Survey land use 

6 

and land cover maps: U.S. Geological Survey Circular 826, 19 p. 

---, 1982, The role of change data in a land use and 

land cover map updating program, in William T. 

Pecora Symposium, 7th, Sioux Falls, SD, 1981, Proceedings: American Society of Photogrammetry, p. 

189-200. 

Mitchell, W. B., Guptill, S.C., Anderson, K. E., Fegeas, 

R. G., and Hallam, C. A., 1977, GIRAS: A 

geographic information retrieval and analysis system 

for handling land use and land cover data: U.S. 

Geological Survey Professional Paper 1059, 16 p. 

Wray, J. R., 1980, Examples of automated cartography in 

presenting land use and land cover maps and data, in 

International Symposium on Cartography and Computing, Applications in Health and Environment, 

Auto-Carto IV, Reston Va., 1979, Proceedings; v. 2, 

p. 156-157. 
Digital Line GraphsCharacteristics and Availability 

INTRODUCTION 

Digital line graphs (DLG's) consist of base-category 

line map information. The USGS currently produces and 

distributes tapes of two types of DLG's: (1) large-scale 

DLG's primarily digitized from 1 :24,000-scale USGS 

topographic quadrangle maps (some large-scale DLG's 

have been digitized from 1 :62,500-scale topographic 

quadrangle maps), and (2) small-scale DLG's digitized 

from the 1 :2,000,000-scale National Atlas sectional maps. 

CHARACTERISTICS 

General Structure 

The DLG file structure is designed to accommodate 

virtually all categories of planimetric data represented on a 

conventional line map. Point, line, and area data types are 

accepted. Each distinct data category (such as boundaries, 

hydrography, transportation) is stored as a separate file or 

subfile which is somewhat equivalent to the printing 

separates used in the conventional map production process. 

The DLG data are classified at one of three levels 

depending on the editing, enhancing, and spatial structuring done to the files: 

DLG-1: Line map information that has been coded to 

prescribed standards and edited to remove 

data acquisition blunders. 

DLG-2: Line map information that has been interactively edited to add additional attribute codes 

and to remove visible errors and inconsistencies. 

DLG-3: DLG-2 data that has been spatially structured 

to define all topological relationships. 

Currently all DLG data distributed by the 

USGS are classified as DLG-3 except for the 

special graphics format available for smallscale DLG's. 

The spatial structuring found in DLG-3 files explicitly maintains, in digital form, the spatial relationships 

among the various elements of the data. In graphic form, 

these relationships are implicitly conveyed and usually 

grasped by even the novice map user. Topology, as defined for cartography and automated spatial data analysis, 

requires that all lines be explicitly linked to nodes (ends of 

lines or line junctions), and that all areas be explicitly coded to the bounding lines. All nodes, lines, and areas are 

completely and unambiguously linked to permit computer 

interpretation without human assistance. Maintaining the 

topology of the data is especially important in applications 

using geographic information systems and spatial data 

analysis. 

Large-Scale DLG's 

The large-scale DLG's contain, in digital form, the 

planimetric data shown on the standard USGS 7 .5-minute 

quadrangle maps. The coding scheme is designed to accommodate the base data categories defined by the National Mapping Program, as well as non-base categories 

such as the U.S. Public Land Survey System. The attribute 

coding scheme is open-ended and can be expanded as 

needs change. 

Production of large-scale DLG's has been authorized 

only for geographic areas and data categories for which 

there has been an expressed need by another Federal agency. To date, these needs have centered on selected themes 

of the data base, especially boundaries and the U.S. Public 

Land Survey System. Large-scale DLG's are being produced at the DLG-3 level in order to maximize future utility. 

The technical specifications for large-scale DLG's are 

available from the U.S. Geological Survey, EROS Data 

Center, User Services Section, Sioux Falls, SD 57198. 

Small-scale (1 :2,000,000-scale) DLG's 

The small-scale DLG's digitized from the 

1 :2,000,000-scale National Atlas maps meet the needs of 

the USGS and other agencies for a small-scale national 

data base suited to practical combination, manipulation 

and analysis. 

The small-scale DLG's are available in DLG-3 format and a special graphics format. The topologically 

structured DLG-3 files are suitable for graphics production and for use with geographic information and spatial 

data analysis systems. The special graphics format is a 

simplified data structure (information defining 

topological structure is omitted) designed especially for 

automated graphics production. 

The data in the small-scale DLG data base are recorded in separate files (or multiple subfiles for large data 

volumes) according to three major categories: (1) boundaries (political and administrative), (2) hydrography 

(streams and water bodies), (3) transportation (roads and 

trails, railroads). Data are also available for selected 

cultural features, such as airports and the Alaska pipeline, 

and hypsography (continental divide only). The coverage 

is nationwide, with the data in 21 multistate regional 

blocks (fig. 1) to limit file size and to minimize edge-join 

problems in applications requiring adjacent blocks. 

7 
Both the DLG-3 format and special graphics format 

contain feature codes assigned to various map features in a 

manner designed to facilitate automated production of 

base maps and special-purpose graphics at scales ranging 

from 1:2,000,000 to 1:10,000,000. Two capabilities are 

built into the feature coding scheme: (1) the size of features 

is encoded to permit selective exclusion of features based 

on the size appropriate for the map's output scale; and (2) 

the type of feature is encoded to permit grouping in 

various ways to produce logical sets of information. 

The capability to select features according to size 

allows the user to produce, from a single data base, maps 

at various scales with appropriate levels of detail. For example, within the hydrography file, single line rivers, 

streams, and canals are coded according to length, and 

water bodies are coded according to length at their maximum dimension, both based on an interval scale. A map 

at 1 :2,000,000 scale might appropriately include single 

line rivers and streams longer than 20 km, canals longer 

than 1 km, and water bodies longer than 2 km (fig. 2a), 

while a map of hydrography at 1:5,000,000 scale might 

appropriately include only rivers and streams exceeding 50 

km, canals exceeding 40 km, and water bodies exceeding 4 

km (fig. 2b). Finally, at a scale of 1:10,000,000, the user 

might choose to show only rivers and streams over 100 km, 

canals over 80 km, and water bodies over 10 km (fig. 2c). 

The choice of breakpoints is, of course, a subjective task, 

and the limits chosen for these illustrations represent the 

preferences of a single user. 

The capability to group features according to type into logical sets of information can be illustrated from the 

transporation file. Within the transportation overlay, 

there are currently over 30 distinct codes assigned to 

various types of roads and trails. Figure 3a is a representation of a logical set made up of all Interstate and Federal 

and State limited access highways (ten distinct classification codes). The logical set represented by figure 3b is 

made up of major U.S. and State primary non-limitedaccess highways (six distinct classification codes), and 

figure 3c is a representation of minor U.S., State, and 

other roads not depicted in figures 3a and 3b. Similar 

manipulations are possible with all files in the small-scale 

DLG data base, providing the user with the capability to 

produce a wide variety of map products from a single data 

source. 

Because of the flexibility permitted by the detailed 

coding of features according to size and type, the smallscale DLG data base is expected to have extensive use. The 

USGS small-scale DLG data base represents the first full 

national coverage since World Data Base II and contains 

significantly more detail. Most of the data are current to 

1980. 

The technical specifications for the small-scale DLG 

data are described in detail in U.S. Geological Survey Circular 895-D (Domaratz and others, 1983). 

INDEX MAP 

1 NORTHEASTERN STATES 

2 MIDDLE ATLANTIC STATES 

3 SOUTHEASTERN STATES 

4 FLORIDA 

5 SOUTHERN MISSISSIPPI VALLEY STATES 

6 CENTRAL MISSISSIPPI VALLEY STATES 

7 NORTHERN GREAT LAKES STATES 

8 SOUTHERN TEXAS 

9 SOUTHERN PLAINS STATES 

10 CENTRAL PLAINS STATES 

11 NORTHERN PLAINS STATES 

12 ARIZONA AND NEW MEXICO 

13 SOUTHERN CALIFORNIA 

14 CENTRAL PACIFIC STATES 

15 NORTHWESTERN STATES 

16 SOUTHEASTERN ALASKA 

17 CENTRAL ALASKA 

18 NORTHERN ALASKA 

19 SOUTHWESTERN ALASKA 

20 ALEUTIAN ISLANDS 

21 HAWAIIAN ISLANDS 

Figure 1. The small-scale DLG data base is divided into 21 multistate blocks to limit file size and to minimize edge-join 

problems in applications requiring several adjacent files. 

8 
0 ~ I 

\ 

Figure 2a. Features in the 1 :2,000,000-scale DLG data base are coded according to size in order to permit selective 

inclusion of features appropriate for the presentation scale. (2a) 1 :2,000,000 scale, Central Florida-streams> 20 

km, canals>1 km, water bodies>2 km. 

9 
10 

Figure 2b. 1:5,000,000 scale, Florida-streams> 50 km, canals > 40 km, water bodies > 4 km. 

J t(/) ~ 

~""-

) ; - ,, 

' -

Figure 2c. 1:10,000,000 scale, Florida-streams > 

100 km, canals> 80 km, water bodies > 10 km. 
Figure 3a. Features in the 1 :2,000,000-scale DLG data 

base are coded according to type in order to permit 

grouping of features into logical units of information for 

presentation. (3a) Florida- Interstate, and Federal and 

State limited access highways. 

Figure 3b. Florida- Major U.S. and State primary nonlimited-access highways. 

Figure 3c. Florida- Minor U.S., State, and other roads. 

11 
AVAILABILITY 

Most of the completed large-scale DLG's contain only boundary and U.S. Public Land Survey System files and 

geographic coverage is limited. A periodically revised index depicting the current status and availability of large-

. scale DLG's is available. 

Small-scale DLG's are available in the DLG-3 and 

special graphics format for all of the United States. 

Questions about availability and ordering of all 

forms of DLG and related documentation should be addressed to: 

I2 

U.S. Geological Survey 

EROS Data Center 

User Services Section 

Sioux Falls, SD 57I98 

(605) 594-6I5I/FTS 784-7I5I 

SELECTED REFERENCES 

Allder, W.R., and Elassal, A. A., I984, USGS Digital 

Cartographic Data Standards: Digital Line Graphs 

from I :24,000-scale maps, U.S. Geological Survey 

Circular 895-C. 

Allder, W.R. and others, I984, USGS Digital Cartographic Data Standards: Digital Line Graph attribute coding standards, U.S. Geological Survey Circular 895-G. 

Beck, F. J., and Plasker, J. R., I979, USGS digital cartographic pilot projects: American Congress on 

Surveying and Mapping Annual Meeting, 39th, 

Washington, D.C., 1979, Proceedings, p. 300-305. 

Domoratz, M. A. and others, 1983, USGS Digital Cartographic Data Standards: Digital Line Graphs from 

1 :2,000,000-scale maps, U.S. Geological Survey Circular 895-D. 

Schmidt, W. E., I98I, Small-scale digital cartographic 

data and transportation planning: Computer 

Graphics in Transportation, Princeton University 

Conference, Princeton, N.J., I981. [in press] 

Stephens, M. J ., I980, The USGS I :2,000,000-scale digital 

data base: American Congress on Surveying and 

Mapping Annual Meeting, 40th, St. Louis, Mo., 

1980, p. 436-443. 
Reduction of the Topographic 

Effect on Landsat Data 

BACKGROUND 

Since the launch of Landsat 1 in July 1972, scientists 

have used multispectral scanner (MSS) data for inventories of the Earth's surface features. A basic assumption 

underlying the use of Landsat MSS image d· ta for 

resource inventories is that similar cover types or conditions reflect electromagnetic energy in a similar fashion, 

thus permitting the use of spectral signatures (measurements of the amount of energy reflected in specific portions of the electromagnetic spectrum) in feature identification and mapping. 

THE PROBLEM 

A basic problem in the use of MSS data for land cover 

mapping lies in the fact that a single land cover type does 

not always exhibit a unique spectral signature. One of the 

primary reasons for this is the topographic effect. The 

topographic effect can be defined as the variation in the 

amount of electromagnetic energy reflected from inclined 

surfaces (such as hillsides) as compared to the energy reflected from horizontal surfaces (such as plains). Thus, an 

oak forest area found on a level plain, for example, will 

reflect less energy than it would if it occupied a hillside facing towards the Sun; the same oak forest found on a 

hillside facing away from the Sun will reflect less energy. 

Therefore, spectral signatures will vary for each cover type 

depending upon the landscape positions they occupy (fig. 

4). This ultimately increases the likelihood that different 

land cover types will share the same spectral signature, increasing the risk of misinterpretation. 

The Earth Resources Branch, National Aeronautics 

and Space Administration (NASA), has conducted extensive experimentation with methods that may mitigate solar 

illumination variations on Landsat multispectral 

images. The results of their research include a model that 

uses digital elevation data to calculate and apply a correction to the Landsat image that decreases the spectral 

variability caused by topographic effects. This process has 

been successfully tested in the Reward quadrangle area of 

Perry County, Pennsylvania (fig. 5). 

Figure 4. This Landsat MSS band 7 (reflected infrared energy) subscene of the Reward quadrangle in Pennsylvania 

illustrates the variability of the oak forest spectral signature. Notice the changes caused by the topographic effect, 

and compare with figure 10. 

13 
PENNSYLVANIA 

Reward Quadrangle'O 

Figure 5. The Reward quadrangle is located in Perry 

County, Pennsylvania. This area consists of ridge and 

valley topography with a maximum elevation of 409 

meters (1 ,342 feet). Much of the mountainous terrain is 

covered by a mixed oak woodland which includes white 

oak, red oak, chestnut oak, and black oak. 

DATA SOURCES 

Topographic Data 

The solar illumination correction process needs very 

precise data describing the slope and aspect of land surfaces.These data were calculated from a 7 .5-minute digital 

elevation model covering the Reward quadrangle (fig. 

6). The availability of the highly accurate elevation data, 

from which the slope and aspect files could be derived, was 

an essential requirement of the correction procedure. 

The linear northeast to southwest ridge examined in 

this study was chosen for its constant slopes and aspects, 

and minimum differences between the two aspects in cover 

type, distribution, and density. These characteristics effectively isolated the topographic effect as the cause of variation in the spectral signature. 

Landsat Data 

The Landsat data used for the Reward quadrangle 

test were acquired July 19, 1976. The correction process 

was illustrated using the reflected infrared channels (bands 

6 and 7 covering the 0.7-0.8, and 0.8-1.1 micrometer range 

of the electromagnetic spectrum respectively) of the 

multispectral scanner. 

MODELING PROCESS 

Several modeling techniques were initially analyzed 

by the NASA scientists. Most approaches used models 

Figure 6. The elevation data used in the study was from the Reward quadrangle 7.5-minute digital elevation model. In 

this image, the dark tones represent low elevation while the light areas identify high elevations. 

14 
that were based on the assumption that the solar energy 

was reflected from a Lambertian surface. A Lambertian 

surface reflects light equally in all directions.lt was found, 

however, that results based on the Lambertian assumption 

were not optimal. A non-Lambertian model provided the 

best results. 

The non-Lambertian model considered the angle of 

the incoming solar energy (incidence angle), the angle that 

the energy departs from the surface (exitance angle), and a 

factor known as Minnaert Constant. The Minnaert Constant provides a measure of the "Lambertianess" of a surface and is adjusted so that it represents true reflectance 

conditions of a surface. In the non-Lambertian model, the 

value chosen as the Minnaert Constant indicated that the 

surface reflectance angle was random rather than equal to 

the incidence angle. 

The first step in the correction process was the 

calculation of the solar incidence angle for each land surface in the Reward quadrangle. This calculation required 

data on terrain slope and aspect, the solar elevation, and 

solar azimuth (figs. 7-9). The solar incidence angle data 

were in turn used in the following equation to correct solar 

illumination variations: 

L = Ln (coski) (cosk-1e), 

where L = Radiance (solar energy departing the 

surface) 

Ln = Radiance when i = e = 0 

i = Incidence angle 

e = Exitance angle 

k = Minnaert Constant 

This equation is applied to each Landsat spectral channel 

individually in order to compensate for the topographic 

effect. 

RESULTS 

The application of the model reduced the overall 

spectral variability in band 6 by 86 percent. The ultimate 

result is corrected Landsat data where cover type data are 

altered to follow the basic assumption of each possessing a 

single spectral signature (fig. 10). 

Figure 7. Slope angle data for Reward quadrangle are illustrated in this image in which dark tones are flatter slopes 

and light tones are steeper slopes. 

15 
Figure 8. Aspect data for Reward quadrangle are illustrated here. Darkest tones indicate south aspects. Light tones 

indicate north aspects. 

Figure 9. Solar incidence angle data for Reward quadrangle. Dark tones indicate small incidence angles (which correspond to directly illuminated surfaces). Light tones indicate large incidence angles (which correspond to shaded 

surfaces). Incidence angles are measured from a line perpendicular to the surface of the Earth. 

16 
Figure 10. Corrected Landsat MSS band 6 (reflected infrared) data for the Reward quadrangle. Note the relatively 

uniform tones of the oak forest areas that indicate a reduction of spectral variability as compared with that evident in 

figure 4. 

While more research is needed to refine the selection 

of Minnaert Constant values, the Reward quadrangle test 

does illustrate the success of the non-Lambertian model 

when used with precise DEM data. 

APPLICATION OF PRODUCTS 

The immediate benefit resulting from the NASA 

research is that the availability of a procedure for correcting solar illumination effects on the corrected spectral 

data should permit more accurate classification of land 

cover, more accurate inventory data, and hence dependable maps from which resource planning and management decision can be made. For more information, contact: 

S. W. Wharton 

Earth Resources Branch, Code 923 

Goddard Space Flight Center 

National Aeronautics and Space Administration 

Greenbelt, Maryland 20771 

SELECTED REFERENCES 

Holben, B. N., and Justice, C. 0., 1980, The topographic 

effect on spectral response from nadir-pointing sensors: Photogrammetric Engineering and Remote Sensing, v. 46, n. 9, p. 1191-1200. 

Justice, C. 0., and Holben, B. N., 1979, Examination of 

Lambertian and non-Lambertian models for 

simulating the topographic effect on remotely-sensed 

data: Goddard Space Flight Center, Greenbelt, 

Maryland, NASA TM 85290, 32 p. 

Justice, C. 0., Wharton, S. W., and Holben, B. N., 1981, 

Application of digital terrain data to quantify and 

reduce the topographic effect on Landsat data: International Journal of Remote Sensing, v. 2, n. 3, p. 

213-230. 

Minnaert, M., 1941, The reciprocity principle in lunar 

photogrammetry: Astrophysics Journal, v. 87, p. 

159-170. 

Smith, J. A., Lin, T. L., and Ranson, K. J., 1980, The 

Lambertian assumption and Landsat data: 

Photogrammetric Engineering and Remote Sensing, 

v. 46, n. 9, p. 1183-1189. 

17 
Forest Fire Behavior Prediction 

BACKGROUND 

Every year forest fires take a devastating toll on our 

Nation's natural resources. Not only do wildfires have a 

significant economic impact through the loss of 

harvestable timber, they also cause the loss of human life, 

wildlife, and property as well as the destruction of the 

aesthetic value of wildlands. However, new legislation and 

advances in fire management technology have resulted in 

policies that direct fire managers to develop management 

plans for wildfire suppression. Both long-term, broadscale prevention plans for national forests and sitespecific suppression plans are now required. In order to 

develop these plans, U.S. Forest Service fire managers require an accurate and consistent method for predicting the 

behavior of forest fires. 

The Northern Forest Fire Laboratory of the U.S. 

Forest Service has developed a mathematical model to 

provide fire managers with realistic estimates of fire 

behavior. If the model variables (fuel types, topography, 

and weather) accurately represent site-specific conditions, 

increased fire-fighting efficiency and decreased timber 

and other losses can be realized. 

THE PROBLEM 

Forest-fuels and digital topographic data needed for 

predicting fire behavior in national forests are generally 

available at only very low resolution for most areas. Few 

site-specific detailed data are available to provide the level 

of information necessary to make realistic estimates of fire 

behavior. In a cooperative project, the U.S. Forest Service 

and the EROS Data Center developed digital analysis 

techniques to provide the basic data needed to model 

forest fire behavior. The data were then incorporated into 

the Forest Service's fire behavior model. The specific objectives of the project were to: 

- Develop a digital data base that provides site-specific 

information describing forest fuel types, topographic 

conditions, and weather conditions, and 

- Produce a map using the mathematical fire behavior 

model displaying predicted fire behavior for a 

specific area. 

The project methods were tested in the Lolo Creek area of 

the Lolo National Forest in western Montana (fig. 11). 

DATA SOURCES 

Topographic Data 

High-resolution elevation data were required for virtually all aspects of the study. Specifically, topographic 

data were used to: (1) develop the fuel-type map, (2) extrapolate weather data collected at a single weather station, and (3) represent terrain conditions in the fire 

behavior model. 

The topographic data needed throughout the study 

were supplied by 7 .5-minute digital elevation models 

(DEM's) (fig. 12). The elevation values, registered to a 

map base, were used to develop slope and aspect data, and 

to improve the accuracy of fuel-type and weathercondition maps. 

Lola National Forest 

Montana 

Figure 11. The Lolo Creek area (1, 180 km) of the Lolo National Forest in the Bitterroot. Mountains of ~estern Montana 

was selected as the project study site. The forest vegetation includes ponderosa p1ne, Dougla~-f1r, w~stern larch, 

subalpine fir, lodgepole pine, grand fir, and Englemann spruce. This diversity created a challenging environment for 

applying project techniques. 

18 
Figure 12. Slope and aspect were calculated from 

digital elevation data, mapped above. The highest 

elevations are displayed in light tones and the lowest in 

dark tones. 

Other Data 

In addition to topographic data, three other types of 

data were required: 

Landsat Data-A July 1974 Landsat 

Computer-Compatible Tape (CCT) from the 

EROS Data Center was used in the development of a fuel-type map. 

Road Network Data-U.S. Forest Service 

engineering maps were used to produce a road 

network map, needed both as a reference aid 

and for fire behavior modeling. 

Weather Data-Daily weather observations, 

recorded at the USFS Missoula Fire Weather 

Station, were used in the fire behavior model. 

DATA ANALYSIS 

The initial analysis task involved the calculation of 

terrain slope and aspect. Digital elevation values at 

60-meter intervals were compared with adjoining values to 

calculate average percent slope (fig. 13). The aspect or 

slope direction was then determined by comparing each 

elevation value with neighboring elevation values. 

The second task was to divide the vegetation of the 

area into forest fuel classes. The classes, based on firerelated characteristics of the vegetation (fuel quantity, 

particle size, and fuel bed depth), were mapped by combining elevation, slope, aspect, and Landsat spectral data 

(fig. 14). The terrain variables permitted the consideration 

of the influence that the complex environmental factors 

Figure 13. Calculated slopes mapped. Darkest tones 

represent the flattest slopes and the lightest tones 

represent the steepest slopes. 

Figure 14. Forest fuel types. 

Yellow- Perennial Grasses 

Dark Green - Decadent Short Needle Conifers 

Brown - Sagebrush and Grasses 

Light Green -Open Pine Stands 

Red - Clearcuts 

Medium Green - Healthy Short Needle Conifers 

White- Non-Vegetation 

Tan- Agricultural Land 

have on forest fire fuel classes. 

The third task involved merging the topographic data 

with daily weather condition observations (wind speed, air 

19 
temperature, relative humidity, and cloudiness) to produce a site-specific estimate of fuel moisture conditions. 

The topographic data were used to extrapolate weather 

conditions to the area around the recording station. 

FIRE BEHAVIOR MODELING 

The mathematical fire behavior model was developed 

by the U.S. Forest Service (Rothermel, 1972). This model 

used site-specific estimates of daily weather conditions, 

fire fuel types, and topographic characteristics to predict 

the following fire behavior components in the Lolo Creek 

area (fig. 15). 

Flame Length - The length of the flame (in feet) 

produced in a forest fire. 

Rate of Spread - The speed (in feet per minute) at 

which a fire would move. 

Energy Release Rate - The amount of energy (in 

BTU's per square foot) released during a forest fire. 

These fire behavior components were then combined to 

identify fire potential classes that illustrate relative dayand site-specific fire dangers. The model, when used with 

digital cartographic data, provides a unique look at the 

role that topography, fuel types, and fuel moisture conditions have on regional fire behavior (fig. 16). 

APPLICATION OF PRODUCTS 

This study demonstrated techniques useful to the 

U.S. Forest Service for broad-scale fire prevention planning, monitoring active forest fires, assessing the damage 

Figure 16. The modeling yielded three descriptive 

maps of specific fire behavior components and a composite map illustrating fire hazards in the Lolo Creek 

area on a typical August day. The blue colors on this 

Fire Hazards map represent the low fire hazards and 

the red colors represent the highest hazard areas. 

potential of incipient fires, and other fire management activities. By using the model to simulate forest fires under a 

variety of weather conditions, a wide range of fire 

behavior patterns and planning regimes can be generated 

and evaluated. 

In addition to their use in the fire behavior model, the 

fuel type, fuel moisture, and topographic data may 

enhance the capability of the lightning detection system 

now being tested by the Forest Service and the U.S. 

FIRE POTENTIAL MODEL 

WEATHER DATA DATE TOPOGRAPHIC DATA FUEL TYPE 

WIND SPEED ELEVATION ASPECT SLOPE 

-~ 

FUEL MOISTURE 

L...------t~~ ADJUSTED FUEL MOISTURE.,.._ ___ ____. AT REMOTE STATION , 

1 

FIRE POTENTIAL ESTIMATES 

Figure 15. The USFS fire behavior model used site-specific data to predict the characteristics of a forest fire in the 

Lolo Creek area. 

20 
Bureau of Land Management. This system, which currently can identify and locate cloud-to-ground lightning 

discharges, must ultimately assess the probability of a fire 

actually starting and what its potential behavior will be. By 

incorporating the data developed in this project with the 

lightning detection system, these evaluations may be provided. 

SELECTED REFERENCES 

Deeming, J. E., Burgan, R. E., and Cohen, J.D., 1977, 

The national fire-danger rating system 1978, in 

Ogden, Utah, Intermountain Forest and Range Experiment Station, USFS General Technical Report, 

INT-39, 63 p. 

Shasby, M. B., Burgan, R. E., and Johnson, G. R., 1981, 

Broad area forest fuels and topography mapping using digital Landsat and terrain data, in Machine Processing of Remotely Sensed Data, West Lafayette, Indiana, 1981, Proceedings: New York, Institute of 

Electrical and Electronics Engineers, p. 529-538. 

Rothermel, R. C., 1972, A mathematical model for fire 

spread predictions in wildland fuels, in Ogden, Utah, 

Intermountain Forest and Range Experiment Station, 

USFS Research Paper INT-115, 40 p. 

21 
San Francisco Bay Area 

Earthquake Hazards Mapping 

BACKGROUND 

The San Francisco Bay Area has a well-known record 

of seismic activity. Earthquakes are potentially a great 

hazard to the lives and properties in the Bay Area. 

Primary effects of seismic activity, such as surface 

rupture and ground shaking, are frequently followed by 

landsliding, liquefaction (the changing of soil from a solid 

to a liquid condition), and occasionally by inundation by 

tsunamis (large, fast-moving sea waves caused by earthquakes) and by dam failure. These processes are all interrelated and their impacts are dependent on both the 

physical environment of the region and the location of 

people and buildings. 

The key to minimizing earthquake damage lies in the 

ability to anticipate and plan for these effects. Much 

research has been done on the reaction of model sites to 

earthquake-related effects. This research provides a 

necessary foundation for other work, but it does not 

describe the variation and distribution of model sites over 

a specific geographic area. There is a definite need to tie 

the theoretical behavior shown in earthquake models to 

specific areas, and to portray this in a form useful to those 

people who make public and private land use decisions in 

the region. 

THE PROBLEM 

The Association of Bay Area Governments (ABAG), 

the comprehensive planning agency for the nine-county 

San Francisco region, is responsible for preparation of 

plans and information that maintain or improve the quality of life in the region. Earthquake preparedness 

assistance is one important area of ABAG's responsibilities, and it includes the collection and mapping of 

technical earthquake hazard data, earthquake legislation 

advocacy, determination of local government liability and 

insurance costs, the preparation of guides for elected officials, and the inclusion of seismic safety concerns in project reviews. 

The mapping and presentation of technical information describing regional earthquake hazards is a key responsibility of ABAG's Bay Area Spatial Information System 

(BASIS). BASIS permits the analysis and presentation of 

complex earthquake data in a spatial format, which is 

useful in making earthquake hazard assessments for entire 

cities, for individual neighborhoods, or for individuals or 

private companies. BASIS contains a comprehensive earthquake data base that is being used to produce: 

1. Basic earthquake hazards maps that explain 

22 

potential hazards related to specific components 

of the earthquake problem, such as landslide 

susceptibility and tsunami hazard areas. 

2. Composite earthquake hazard maps that summarize the total potential hazards for sites 

throughout the region. 

In a cooperative project (Earthquake Mapping Project) with the USGS (Contract No. 14-08-0001-17751, 

-19108, and -19831), ABAG employed BASIS to produce 

several types of digital Earthquake files and resultant 

maps for San Mateo County (fig. 17). The project focused 

on developing an operational Earthquake hazard digital 

mapping capability and demonstrating some sample uses 

for researchers and local geologists and planners. The 

USGS developed much of the basic data and provided 

technical assistance in their use and interpretation. San 

Mateo County was chosen because both ABAG and 

USGS had more data available for that county than any 

other in the Bay Area. For additional information, contact: 

Jeanne Perkins, Earthquake Project Manager 

Association of Bay Area Governments 

Hotel Claremont 

Berkeley, California 94705 

(415) 841-9730 

Figure 17. San Mateo County is an intensely urbanized 

area containing over one-half million residents. The 

danger of earthquake disaster is high due to the San 

Andreas Fault which extends through the county. 
DATA SOURCES 

Topographic Data 

High resolution topographic data were required to 

assist in the identification of the location and severity of 

earthquake-induced landslide susceptibility and overall 

earthquake hazards. The elevation values contained in 

7 .5-minute digital elevation models (DEM's) were used to 

calculate detailed slope data from which accurate maps 

displaying the maximum slopes throughout San Mateo 

County were made (fig. 18). 

PERCENT 

SLOPE 

BASIS 

Figure 18. Digital elevation data were used to calculate 

this percent slope map for San Mateo County using 

ABAG's BASIS. 

Other Data 

Geology-U.S. Geological Survey (USGS) maps of surface 

geology were used in the identification of areas subject to 

ground shaking, liquefaction, and landslide problems. 

Faults - USGS and California Geological Survey maps 

depicting major faults in the Bay Area were used to identify ground shaking, liquefaction, and surface rupture 

hazards. 

Landslides - USGS records of historic landslide locations 

and their characteristics were used to assist in identifying 

liquefaction and earthquake-induced landslide hazards. 

Tsunami Hazard Areas - USGS maps describing the areas 

and depth of potential tsunami inundation were used to 

assess composite earthquake hazards. 

Dam Failure Hazard Areas - Maps produced by the 

California Office of Emergency Services were used to 

identify dams whose total failure would cause injury or 

loss of life. 

HAZARD MAPPING 

Six basic data files contained in BASIS were used to 

produce nine different earthquake hazard files, each 

depicting a specific component of the earthquake problem. 

ABAG used the basic data files representing 

topography, geology, and historic landslides to assess 

regional earthquake-induced landslide susceptibilities. In 

the analysis process originally developed by USGS, percent slope and geologic data were compared to historic 

landslide information in order to rate the landsliding 

susceptibility of each slope class and geologic unit. Ratings 

were then combined to produce a four-category comprehensive earthquake-induced Landslide Susceptibility 

classification (fig. 19). Other hazard maps produced in the 

Earthquake Mapping Project are: 

-Rainfall-Induced Landslide Hazards 

- Maximum Ground Shaking Intensity 

- Risk of Ground Shaking Drainage (for three different types of buildings) 

- Liquefaction Susceptibility 

- Liquefaction Potential 

- Fault Surface Rupture 

- Tsunami Hazard Areas 

- Dam Failure Hazard Areas 

EARTHQUAKE 

INDUCED 

LANDSLIOE 

SU SCE: P TIBIL IT Y 

BASIS 

Figure 19. Earthquake-Induced Landslide Susceptibility map for San Mateo County (produced with BASIS). 

23 
These earthquake hazard files were either used individually to describe specific characteristics of the earthquake 

problem, or composited to identify the combined earthquake risk. 

Composite Hazard Maps 

The individual hazard files are composited to produce additional files that represent the comprehensive, cumulative 

earthquake hazards or risks in a particular geographic 

location. 

Composite Maximum Earthquake Damage 

The maximum percent damage expected during an earthquake can be determined by adding the percent damage 

scores associated with the following individual hazard files 

(fig. 20): 

-Earthquake-Induced Landslide Susceptibility 

- Maximum Ground Shaking Intensity 

- Fault Surface Rupture 

- Liquefaction Potential 

- Tsunami Hazard Areas 

- Dam Failure Hazard Areas 

Because the amount of damage is partially determined by the type of building affected, different assumptions 

regarding type of structures can be made. 

COMPOSITE 

MAXIMUM 

EARTHQUAKE 

DAMAGE 

Figure 20. A map of Composite Maximum Earthquake 

Damage for San Mateo County. This example assumes 

that the area is uniformly covered by woodframe dwellings (produced with BASIS). 

24 

Composite Risk of Earthquake Damage 

The risk of damage from an earthquake is estimated by 

compositing the individual hazard files. The expected 

damage is multiplied by the frequency with which earthquakes are expected to occur, and the resulting average annual damage cost is then discounted to its present value in 

order to permit a comparison with mitigation costs (fig. 

21). The hazard files used to determine composite earthquake risks are: 

- Earthquake-Induced Landslide Susceptibility 

- Fault Surface Rupture 

- Risk of Ground Shaking Damage 

- Liquefaction Potential 

- Tsunami Hazard Areas 

The dam failure data were not used in measuring the risk 

of earthquake damage since the probability of dam 

breakage is low. 

.RISK OF 

EARTHQUAKE 

DAMAGE 

Figure 21. The Composite Risk map of Earthquake 

Damage identifies the statistical losses from earthquakes based on the frequency that this damage can be 

expected to occur for areas throughout San Mateo 

County (produced with BASIS). 

APPLICATION OF PRODUCTS 

The Earthquake Mapping Project illustrates the utility of digital cartographic data and computers for incorporating complex information into the planning and 

decision-making process. The files and maps produced in 
this project have thus far been applied to the following activities: 

- reviewing projects proposing to use State and 

Federal funds, including highways, housing projects, and sewage treatment plants. 

- seismic safety planning by local governments. 

- environmental impact studies. 

- supporting and advocating appropriate legislation 

at both the State and Federal level. 

If additional funds become available, the detailed analysis 

and mapping already completed for San Mateo County 

will be completed for the remainder of the Bay Area. The 

technical approach used to evaluate the earthquake 

hazards throughout the Bay Area has directly contributed 

information that may ultimately result in the saving of 

many lives. 

SELECTED REFERENCES 

Association of Bay Area Governments, 1980, Earthquake 

map applications for automated environmental impact assessment: Berkeley, California, Working 

Paper-8, 15 p. 

---, 1980, Earthquake map applications for composite earthquake hazard mapping: Berkeley, 

California, Working Paper-9, 13 p. 

---, 1980, Slope stability mapping: Berkeley, 

California, Working Paper-5, 13 p. 

---, 1980, A guide to ABAG's earthquake hazard 

mapping capability: Berkeley, California, 50 p. 

---, 1979, BASIS: The Bay Area spatial information 

system: Berkeley, California, 10 p. 

Wilson, P. M., 1980, Earthquake hazards mapping in the 

San Francisco Bay region: Cambridge, Massachusetts, Harvard Computer Graphics Week 809, 21 p. 

25 
Preliminary Soils Mapping in 

Eastern Kentucky 

BACKGROUND 

The highlands of eastern Kentucky are in the heart of 

Appalachia's coal producing region. Development of 

regional coal resources requires detailed information 

describing the physical characteristics of the land. Both 

the selection of mining sites and the evaluation of potential 

environmental impacts require detailed soils data. Because 

various soil bodies have different rates of erosion, varying 

engineering properties, and differing levels of productivity, a knowledge of the soils involved is important prior to 

any construction. 

Unfortunately, soils mapping is not complete for the 

mountainous highlands. Less than 25 percent of the 

30,000-square-kilometer Cumberland Plateau, for example, has been surveyed. Soil mapping is currently underway in this region but is not planned for completion until 

the 1990's. 

THE PROBLEM 

Because the need for soils data that satisfy the planning and regulatory activities in eastern Kentucky is current, surrogates to traditional soil survey maps are needed 

to fill the data gap. 

The Lands Unsuitable for Mining Program of the 

Kentucky Department for Natural Resources and Environmental Protection has begun investigating alternative approaches for providing soils information. The 

large land area without soils data combined with the need 

for maps with detailed resolution suggested the use of 

digital data and computer-based analysis techniques. It 

became apparent that if appropriate data were available, 

the geographic overlay modeling capabilities of the Kentucky Natural Resources Information System (KNRIS) 

could be used to identify broad associations of soils. Thus, 

a project was initiated to assemble digital cartographic 

data related to geomorphology or terrain and to produce 

preliminary soils maps using geographic modeling techniques. 

This project was conducted by personnel with the 

Lands Unsuitable for Mining Program and the Division of 

Conservation of the Kentucky Department for Natural 

Resources and Environmental Protection, with assistance 

from the department's KNRIS staff. For more information, contact: 

26 

Lands Unsuitable for Mining Program 

Kentucky Department for Natural Resources and 

Environmental Protection 

14th Floor, Capitol Plaza Tower 

Frankfort, Kentucky 40601 

(502) 564-5174 

DATA SOURCES 

Topographic Data 

The form of individual soil bodies is closely related to 

topography, since it effects soil development. In particular, the steepness and direction of slopes influence the 

characteristics of each soil unit. As slopes become steeper, 

soil profile development is slowed. The slope direction 

(aspect) affects soil temperature, soil moisture, and surface vegetation. The combined effect of all topographic 

characteristics on soil conditions is significant. 

A high-resolution digital elevation model of the Ogle 

7 .5-minute quadrangle was used for the soil modeling 

study (fig. 22). The elevation data served as the basis for 

percent slope and aspect calculations. The precision of the 

DEM was critical since minor topographic variations 

cause different soils conditions. 

Landform Data 

Landforms are important factors in defining unique 

soil bodies. Digital landform data (such as floodplain, 

terrace, toeslope) existing in KNRIS were used in the soil 

modeling process. These data had originally been 

manually interpreted from stereoscopic 1 :24,000-scale 

aerial photographs, digitized, and incorporated into the 

KNRIS data base. 

SOILS MODELING AND MAPPING 

The aspect and percent slope data, developed using 

the DEM and the digital landform files were used in a 

composite model to delineate landform based soil units. 

Thus, the soil bodies are defined primarily on the basis of 

geomorphic soil forming factors (relief and parent 

material) and only indirectly consider the influence of 

climate, time, and organisms on soil formation. 

The automated soil mapping involved three steps. 

First, the digital elevation data were converted to slope 

and aspect files using KNRIS-based algorithms. These 

files were used to produce a digital slope map containing 

seven slope categories that were consistent with Soil Conservation Service (SCS) soil survey percent slope classes 

(fig. 23). Percent slope was based on the steepest slope for 
APPALACHIAN 

HIGHLANDS 

Figure 22. The area included in the USGS Ogle 

7.5-minute quadrangle is typical of lands in the eastern 

Kentucky highlands. The area is in the rugged Cumberland Plateau which is characterized by highly dissected 

topography. Because of the presence of extractable coal 

resources, considerable interest exists to expand surface mining in the region. 

I_ 

Figure 23. The percent slope file contains the maximum slopes existing throughout the Ogle 7.5-minute 

quadrangle. 

each 67-meter-square grid cell. The aspect calculations 

identified one of eight compass directions for each grid 

cell. The aspect data were then aggregated into "northslope" and "southslope" categories which are used by the 

SCS to map soil units in rugged areas of eastern Kentucky 

(fig. 24). 

The definition of decision rules governing the soil 

modeling process was the second analysis step. This was 

done by examining the relationships between topography, 

landforms (fig. 25), and soil units in a surveyed area of 

Kentucky. Preliminary assumptions were evaluated in the 

field and the final soil model was defined. 

The third and final task involved the actual soil 

modeling using an overlay analysis procedure. The 

geographically registered slope, aspect, and landform files 

were automatically composited, using the KNRIS computing system, to produce the desired seven-category 

preliminary soil map (fig. 26). The soil unit categories 

defined areas with unique combinations of landform, 

slope, and aspect conditions. Slope and landform conditions affect such factors as the amount of soil profile 

development and potential erosion rates. Aspect affects 

the amount and types of organic matter contributed to the 

soil body, soil moisture conditions, and soil temperatures. 

Figure 24. The aspect file contains the direction that 

each slope faces in the Ogle 7.5-minute quadrangle. 

27 
Figure 25. Primary landforms in the Ogle 7.5-minute 

quadrangle. 

Figure 26. The soil map for the Ogle 7.5-minute 

quadrangle resulted from the compositing of slope, 

aspect, and landform data. 

28 

Thus, each of the seven soil units have unique erosion 

characteristics, productivity levels, and development 

suitabilities. In the absence of conventional soil survey 

maps, the preliminary soil maps provide a starting point 

from which soil use decisions can be made. 

APPLICATION OF PRODUCTS 

The preliminary soil maps and statistics produced using the automated soil modeling procedure have a variety 

of potential uses. As input to the planning process, they 

may be applied to problems such as: 

- Land use conflicts between important agricultural 

lands and potential urban or surface mining 

developments. 

- Coordinating planting and land acquisition activities in the Daniel Boone National Forest. 

- Wildlife habitat modeling. 

- Regional land capability or suitability development 

assessments. 

The preliminary soils maps may also be useful tools for 

planning and conducting SCS field soil surveys. They may 

expedite initial phases of soil survey work such as terrain 

mapping and locating field transects. The preliminary soil 

maps may ultimately decrease the costs and efforts required to produce the detailed soil surveys, if significant 

correlations are found to exist between the preliminary soil 

units and field-verified soil characteristics. 

The utility of the preliminary soil maps is obviously 

more limited than detailed SCS soil surveys. However, 

they do provide a low-cost alternative during the period 

before the SCS maps are available. 

SELECTED REFERENCES 

Croswell, Peter, 1981, Automated soil modeling with 

the Kentucky Natural Resources Information System 

(KNRIS): Frankfort, Kentucky, Kentucky Department for Natural Resources and Environmental Protection, 8 p. 
Survey of Oregon Irrigation 

Development Potential 

BACKGROUND 

The Columbia River system serves as the lifeline for 

the Pacific Northwest. The river supplies the region with 

food and fiber through irrigation, with electricity through 

hydroelectric plants, and provides transportation, recreation, and primary municipal and industrial water supplies. 

Dependence on the Columbia River system for these water 

uses obviously requires effective management so that no 

one use seriously interferes with the others. Water 

management decisions must be based on accurate information describing both present and future water use. 

Irrigation is the largest consumptive use of Columbia 

River water, so it must be managed both to maximize 

agricultural benefits and to insure the wisest use of water 

and power supplies. Hydropower generation, in particular, is most adversely affected by irrigation development. The development of 4000 hectares (10,000 acres) of 

irrigation causes an average annual loss of $310,000 (1976 

dollars) of electrical energy. The exact amount of lost 

energy is dependent upon the characteristics of the land 

developed for irrigation agriculture. Future irrigation 

developments, therefore, must take place in areas where 

the loss of power revenues is more than compensated by 

the benefits derived from the new irrigation. 

THE PROBLEM 

The U.S. Army Corps of Engineers, North Pacific 

Division, has the responsibility for preparing a Columbia 

River system plan that maximizes the use of the region's 

resources. This plan must permit the development of lands 

having the greatest irrigation potential both in terms of 

land capability and efficient energy use. In order to 

prepare a plan that strikes a balance between irrigation 

growth and energy reduction, the Corps and the EROS 

Data Center designed techniques that provided the following in(ormation: 

1. A potential-energy-costs map that illustrated 

power estimates for pumping irrigation water to 

sites throughout the study area. 

2. An irrigation-development-potential map that 

identified land suitability for future irrigation. 

The project methods were tested in the Umatilla River 

Basin of north-central Oregon (fig. 27) in order to determine their utility for supplying information used in the 

preparation of the Columbia River systems plan. The 

Corps assisted in the planning and review of the analysis, 

and assisted in the collection of supporting data. EROS 

completed the technical aspects of the project. 

Two factors influenced the selection of the data and 

OREGON 

M ILES 

0 60 

Figure 27. The study area included 400,000 hectares 

(one million acres) in the north portion of Oregon's 

Umatilla River Basin. 

analysis techniques used in this study. First, the Corps of 

Engineers needed spatial or map-based information for its 

planning process. Second, the portion of the Umatilla 

Basin that was to be evaluated was too large (approximately 1 million acres), and the level of ground detail needed 

was too fine ( Y2 hectare resolution) to be efficiently 

analyzed without the use of a digital computer. Therefore, 

an analysis process using digital cartographic data was 

selected for use in this project. 

DATA SOURCES 

Topographic Data 

Solutions to both problems are partially based on the 

influence of topography. Energy requirements for irrigation are dependent on the distance water is lifted from its 

source to its point of use. Irrigation development potential 

of the land is partially based on the steepness of the landscape under consideration. As slopes become steeper, irrigation potential decreases due to increasing water runoff problems. 

Digital terrain tapes (DTT) supplied the topographic 

data needed to prepare both maps. The data, when 

registered to a base map, provided elevation values for 

every acre in the study area (fig. 28). Computer analysis 

techniques can both convert the elevation data into the 

pump lift estimates needed to calculate energy requirements and transform them into the percent slope 

classes needed to determine irrigation suitability. Because 

the transformed data are also in digital form, they can 

readily be merged with other pertinent digital cartographic 

data to efficiently produce solutions to complex questions. 

Other Data 

In addition to topographic data, several other types 

29 
Figure 28. Digital elevation data for the Umatilla Basin provide a unique view of the region's topographic patterns. For 

example, the elevation data clearly identify the level tablelands that parallel the Columbia Basin to the north. 

of data were required to accurately estimate energy requirements and determine irrigation potential. 

Pumping Plant Data -Corps of Engineers records described the location and operating efficiency of pumping plants 

along the Columbia River. 

Land Cover- A 1979 Landsat scene was interpreted to produce a land cover map. 

Landownership - Both Corps of Engineers and USGS 

maps were used to identify landownership parcels. 

Soil Survey Maps - USDA Soil Conservation Service 

detailed county soil surveys provided soil irrigability 

ratings. 

These data, originally available in tabular and map formats, were digitized so that they could be used with the 

digital topographic data. 

ENERGY REQUIREMENTS MAPPING 

The amount of energy required for successful irrigation is dependent not only upon the lift and distance between the irrigated field and source of water, but also upon 

the type of crop being irrigated and the operating efficiency of the pump supplying the water. The Corps of 

Engineers traditionally has used an equation that considers these parameters to estimate energy needs in 

kilowatt hours per acre per year. The equation is as 

follows: 

KWH = (1.024)(Diversion)(TDH) 

Epp 

Where, 1.024 - A constant that standardizes the equatio11. 

30 

Diversion - The amount of irrigation water required. 

(2.81 acre-feet per year in this area.) 

TDH- The total dynamic head. This factor is the 

sum of the pump lift distance in feet, the 

friction loss per linear mile (at 11.12 feet 

per mile), and system operating pressure. 

Epp - Efficiency of pumping plant supplying irrigation water (85 percent in this study). 

The TDH component of the equation is critical since 

it represents the characteristics of the landscape. In the 

past, average vertical and linear distances between the 

source and point of use of the irrigation waters were used 

to estimate basin-wide energy requirements. In variable 

terrain, however, the average results will in fact vary 

significantly on a field-by-field basis. For assessing the 

energy impact of future development, the Corps needed 

more site-specific information. 

Digital topographic data provided an efficient means 

for calculating pump-lift to lands throughout the Umatilla 

Basin. By identifying the elevation of the water source and 

the elevation of the point of use, the pump-lift in feet was 

quickly computed (fig. 29). The resulting lift data provided acre-by-acre estimates that were combined with 

friction-loss estimates (fig. 30) also developed with 1-acre 

detail, to produce the TDH portion of the energy equation. With TDH in a spatial format that accurately 

represents the landscape character, the remaining nonsite-specific variables (Diversion and Epp) can be included 

in the equation to produce the classification of potential 

energy requirements (fig. 31). 
Figure 29. Pump lift distances in feet were calculated 

using the digital terrain data. 

Figure 30. Friction loss was estimated by calculating 

the mileage from the pumping plants located along the 

Columbia River. 

Figure 31. The potential energy requirements map provides an estimate of the energy required for irrigation development and an estimate of the lost power revenues as a result of the new irrigation. The map clearly illustrates the role of 

land position in determining energy costs. 

IRRIGATION POTENTIAL MAPPING 

To evaluate irrigation development potential for 

lands in the Umatilla Basin, a variety of physical 

characteristics of the landscape as well as potential energy 

requirements for future development must be considered 

(figs. 32-36). The physical factors that were identified as 

important were percent slope, soil irrigability, and existing 

land cover. The energy requirements data derived in the 

previously discussed phase of the project were to represent 

partial operating costs. One additional variable, landownership, was included since several areas in the basin 

had regulatory restrictions prohibiting irrigation development. 

The five variables were first digitally composited to 

produce summary data describing irrigation development potential in the Umatilla Basin. The summary data 

Figure 32. Land cover, interpreted from a 1979 Landsat 

scene, was used to identify lands that are already being 

irrigated, could be irrigated (dryland agriculture and 

rangeland), and cannot be irrigated (water bodies, 

wetlands, forests, and urban areas). 

31 
Figure 33. Soil irrigability was determined using county 

soil survey data prepared by the USDA's Soil Conservation Service. The irrigability ratings are based on the 

physical and chemical characteristics of each soil body. 

Figure 35. Digital terrain data were used to produce this 

percent slope classification. The slope data identify 

areas where land steepness limits irrigation development. 

provides ratings of development potential based on the 

physical characteristics of the landscape, the economic impact of energy costs, and government restrictions prohibiting irrigation (fig. 37). 

APPLICATION OF PRODUCTS 

The map products of this study can be applied to a 

variety of planning problems. Of primary importance is 

their utility for evaluating assumptions used in the 

preparation of the Corps of Engineers Columbia River 

System Plan. The maps provide a baseline for measuring 

the validity of the irrigation development-hydropower 

generation balance strived for in the plan. Other applications of study results might include: 

32 

1. The potential-energy-costs map can be used to 

estimate energy impacts of new irrigation 

developments. By locating possible development areas on the energy map, both the 

developer's energy costs and the amount of 

lost hydropower can be estimated. 

2. The irrigation-development-potential map can 

be used to identify lands that offer the greatest 

return on investment. Both private and 

government developers can benefit from this 

knowledge. 

Figure 34. Land ownership, produced using several 

sources of data, identifies land areas with administrative regulations prohibiting irrigation development. 

Figure 36. Energy requirements are also based on terrain data. 

Whereas the maps produced in this study will never 

replace detailed on-site surveys of irrigability, they do provide a starting point for planning future regional uses of 

land, water, and power resources. From this starting 

point, more cost-effective field studies will be possible, 

and more resource-efficient water development decisions 

can be made. 

SELECTED REFERENCES 

Johnson, G. E., Vining, R. F., and Loveland, T. R., 1980, 

Remote sensing applied to irrigation engineering: 

Proceedings of the 1st International Conference on 

Computing in Civil Engineering, New York, N.Y., p. 

545-560. 

Loveland, T. R., and Johnson, G. E., 1982, The role of 

remotely sensed and other spatial data in predictive 

modeling: the Umatilla, Oregon example, in William 

T. Pecora Symposium, 7th, Sioux Falls, South 

Dakota, 1981, Proceedings: American Society of 

Photogrammetry, p. 442-454. 
Figure 37. The Irrigation Development Potential Map represents the cumulative impact that energy requirements, 

slope, soils, land cover, and land ownership have on irrigation development. 

33 
Arizona Rangeland 

-Management 

BACKGROUND 

The U.S. Bureau of Land Management (BLM) is 

responsible for the administration of nearly 80 million 

hectares of public lands in the continental United States. 

The agency must manage all resources found on these 

lands so that the greatest public benefit is realized. The 

many uses of public lands, including livestock grazing, 

timber production, minerals development, fish and 

wildlife habitat, and outdoor recreation, require proper 

planning in order to insure long-term benefits. 

As pressures for increased access and use of public 

lands mount, timely resource information is needed. This 

need was further recognized when the 94th Congress passed the ''Federal Land Policy and Management Act of 

1976" which stated: 

"The Secretary [of the Interior] shall prepare and 

maintain on a continuing basis an inventory of all 

public lands and their resource and other values, giving priority to areas of critical environmental concern. 

This inventory shall be kept current so as to reflect 

changes in conditions and to identify new and emerging resource and other values." (Public Law 94-579, 

Title II Section 201(a), 43 USC 1711, October 21, 

1976.) 

Because of the need for natural resource data, the BLM 

has updated its traditional inventory procedures so that it 

can apply the most efficient data collection methods. 

THE PROBLEM 

The BLM needs resource inventory and evaluation 

procedures that permit not only the collection of baseline 

data, but also the efficient analysis of complex planning 

problems related to multiple uses of the public lands. 

Because of the tremendous land area that must be managed, new technologies must be adopted if timely information are to be gathered efficiently. The BLM and the Earth 

Resources Observation Systems (EROS) Data Center 

undertook a cooperative project to evaluate the suitability 

of remote sensing and geographic data base technologies 

for resource mapping and analysis. Remote sensing would 

provide the inventory data, and these, plus related 

resource data, would be stored in a computerized 

geographic data base and manipulated to produce the required maps and information. The specific objectives of 

the project were: 

34 

(1) to develop procedures for inventorying and mapping vegetation resources found on BLM lands. 

(2) to develop procedures for combining several types 

of resource data to produce rangeland and wildlife 

management opportunity maps. 

The methods developed in the project were tested in the 

BLM Grand Wash Planning Unit in northwest Arizona 

(fig. 38). 

UTAH 

~ GRAND WASH 

:; PLANNING 

w UNIT 

z 

Figure 38. The BLM Grand Wash Planning Unit encompasses approximately 750,000 hectares in northwest 

Arizona. Broad, arid rangeland valleys dominate the 

area. Several tree-covered mountainous regions lie 

within the planning unit. The elevation ranges from 300 

to over 2000 meters. 

The Grand Wash Planning Unit rangeland management project was a part of the Arizona Vegetation 

Resource Inventory (A VRI) project. Bureau of Land 

Management personnel representing the Scientific 

Systems Division (Denver, Colorado), the Arizona State 

Office (Phoenix, Arizona), and the Arizona Strip District 

(St. George, Utah) provided expertise related to both 

inventory sampling procedures and rangeland management practices. The EROS Data Center provided the majority of the remote sensing and geographic data analysis 

expertise. 

DATA SOURCES 

Topographic Data 

Digital terrain tape (DTT) elevation data and derived 

slope and aspect data were used throughout the project 

(fig. 39). Topographic data played an important role in 

improving the vegetation map of the Grand Wash Planning Unit area, and topographic factors were also used, 

with other data, to identify rangeland management opportunities, wildlife habitats, and grazing suitabilities. 
Figure 39. The DTI elevation map for the Grand Wash 

Planning Unit. 

Other Data 

Landsat- A Landsat Computer-Compatible Tape (CCT) 

was used in the vegetation inventory phase of the project. 

Road Network - BLM road maps were used to produce 

selected management opportunity maps, although they 

first had to be converted to a digital format so that all project data could be analyzed collectively. 

Field Data- Reference data collected in the field describing 

vegetation resources were converted to a digital form and 

used to precisely define the characteristics of the vegetation map. 

VEGETATION MAPPING 

A comprehensive inventory of vegetation resources 

was needed in order to provide a baseline from which 

future vegetation changes could be measured. The inventory data were also required to assist in identifying 

rangeland management opportunities. 

The initial task was the development of a detailed 

vegetation map for the Grand Wash Planning Unit. The 

vegetation mapping originally done using only the Landsat data yielded 54 percent accuracy, so were too inaccurate to be useful. However, by combining the Landsat 

data with elevation values supplied by the DTT, a more accurate and useful vegetation map was created, as the elevation data permitted the inclusion of information on the 

distributional characteristics of specific plant communities. For example, a Landsat class identified as 

Mohave desert shrub vegetation was initially mapped at 

elevations from 365 to 2000 meters. However, in the field, 

it was found that this vegetation type seldom occurs at 

elevations above 1500 meters. Therefore, the areas 

classified as Mohave desert shrub above 1500 meters were 

reassigned to another vegetation category. The final 

vegetation map accuracy improved from 54 percent when 

only Landsat data were used, to 73 percent when both 

Landsat and elevation data were used (fig. 40). 

RANGELAND MANAGEMENT 

OPPORTUNITIES 

The final vegetation map, which provided useful information on the distribution of plant communities in the 

Grand Wash Planning Unit was used with other mapbased data to produce a series of management opportunity 

maps. The flexibility of the digital data base (vegetation, 

topography, and road network) permitted efficient 

generation of maps describing range conditions, public 

use options, and wildlife habitat. 

Sagebrush Treatment Areas 

Sagebrush is unpalatable to livestock and competes 

with and eventually forces out desirable feed plants. Burning is a common treatment in areas dominated by 

Figure 40. The use of topographic data with Landsat data to classify vegetation improved the accuracy of the vegetation map from 54 percent using only Landsat data to 73 percent. 

35 
sagebrush since it destroys seeds and permits the regrowth 

of beneficial plant species. However, burning has adverse 

impacts because a burned area is susceptible to serious soil 

erosion. Based on these considerations, a composite map 

was created using vegetation, slope, and elevation data to 

show the location of sagebrush (using the vegetation and 

elevation maps) and define (using slope data) treatment 

areas with low soil erosion potential (fig. 41). 

Figure 41. This Sagebrush Treatment Areas map was 

produced using vegetation, slope, and elevation data. 

Treatment areas are displayed in yellow. 

Pinyon-Juniper Cutting Areas 

The BLM has a program designed to make the PinyonJuniper vegetation, commonly cut for use as firewood by 

local residents, available to the public. A composite map 

that identifies areas where cutting can be done with a 

minimal impact on other resources, was created based on 

vegetation, elevation, slope, and road access (fig. 42.). 

Areas dominated by Pinyon-Juniper vegetation, with elevations between 1,500 and 1,800 meters, with slopes under 15 

percent, and that lie within 0.8 km of a road were identified 

as Pinyon-Juniper cutting areas for issuance of permits. 

Potential Antelope Habitat 

Maps showing major habitat areas for antelope were 

needed to assist in preparing wildlife habitat management 

plans. By locating areas that could potentially carry 

significant antelope populations, the BLM can develop 

habitat management or species reestablishment plans that 

recognize the carrying capacity of the rangelands. The 

Potential Antelope Habitat composite map (fig. 43) was 

created by identifying areas with Great Basin desert shrub 

and Plains grassland vegetation, elevations between 1200 

and 1800 meters and slopes under 20 percent. 

36 

Figure 42. Pinyon-Juniper Cutting Areas, shown in 

yellow, were identified using vegetation, elevation, slope, 

and road network data. 

Figure 43. This Potential Antelope Habitat composite 

map was developed using vegetation, elevation, and 

slope data. Prime habitat is represented in blue. 

Other Management Opportunity Maps 

Six additional composite maps were produced using 

specific combinations of topographic, vegetation, and 

road network data. They were: 

- Blackbrush Treatment Areas 

-Pinyon-Juniper Burning Areas 

- Mule Deer Spring/Fall Range 

-Mule Deer Summer Range 

- Mule Deer Winter Range 

- Bighorn Desert Sheep Habitat 
APPLICATION OF PRODUCTS 

The map products of the Grand Wash project were 

immediately applied to BLM planning and management 

activities. Both the vegetation map and the management 

opportunity maps have been used for land use planning, 

environmental analysis, and resource management. 

Specifically: 

- Tree cutting permits are being granted using the 

Pinyon-Juniper Cutting Areas map. 

- Habitat management plans have been prepared using the Potential Antelope Habitat map. 

-The vegetation inventory map is being used for daily rangeland planning and analysis. 

The digital data used in this project provide a flexible, 

powerful tool for range managers. 

SELECTED REFERENCES 

Bonner, W. G., Jr., 1978, Information requirements for 

natural resource inventories, in National Computer 

Conference, Anaheim, California, June 1978, Proceedings: Montvale, New Jersey, American Federation of Processing Societies, p. 87-91. 

Ramey, George, Moore, Ken, Walker, L. D., and Wolfe, 

Cub, 1980, Executive summary- Arizona vegetation 

resource inventory and mapping project (Phase 2 

ASVT Project) Final Report: Phoenix, Arizona, U.S. 

Bureau of Land Management, 16 p. 

Rohde, W. G., and Miller, W. A., 1981, AVRI Arizona 

vegetation resource inventory final report: Unpublished report, Sioux Falls, SD, USGS EROS Data 

Center, 80 p. 

37 
South Carolina Coastal Zone 

Management 

BACKGROUND 

The South Carolina coastal zone is comprised of approximately 200,000 hectares along a 320 kilometer coastline. 

This fragile natural resource is important as a fishery and 

as wildlife habitat; in addition, the region's aesthetic 

qualities have contributed to increased tourism and 

second-home ownerships. The developments associated 

with the recreational growth are of special concern. Orderly growth, that balances economic development while still 

preserving the ecological integrity of the coastal zone, is 

desired by State officials. 

The enactment of Public Law 92-583, the Coastal 

Zone Management Act of 1972, by the U.S. Congress provided the framework needed by coastal States such as 

South Carolina to develop long-term management plans 

for orderly growth in the coastal zone. PL 92-583 required 

States to prepare a coastal zone management plan and enforcement procedures that identify: 

1. Coastal zone boundaries 

2. Permissible uses 

3. Areas of particular concern 

4. Areas of preservation 

5. Areas of priority uses 

6. Organizational structures 

These components can be efficiently developed only 

if adequate information describing the coastal resources is 

available. Therefore, South Carolina initiated a program 

that would collect data relating to coastal resources and 

present it in a manner conducive to planning and management activities. 

THE PROBLEM 

The South Carolina Coastal Council was given the responsibility to establish an information system that provided 

the baseline data needed to evaluate coastal zone problems. Because of the large area involved, and because 

geographic data describing the location and characteristics 

of coastal resources were required, it was determined that 

a computer-based geographic information system would 

be the most effective tool for coastal zone planning and 

management. To minimize expenditures, it was decided 

that whenever possible, existing sources of data would be 

utilized. 

The University of South Carolina's (USC) Computer 

Services Division, with program coordination provided by 

the State Division of Research and Statistical Services, Office of Geographical Statistics, was selected to develop and 

house the information system. Their initial objective was 

38 

to produce maps and accompanying area statistics 

describing land use and wetland conditions in the coastal 

counties (fig. 44). Maps of general land use and land 

cover, and land use and detailed wetlands within the 

critical area of the coastal zone were needed. 

North Carolina 

South Carolina 

Coastal Zone 

Counties 

Figure 44. Eight counties comprise the ~ou~h C~roli~a 

Coastal Zone. This 200,000 hectare reg1on 1s pnmanly 

forested, but has significant areas of marshes and open 

water. 

Under the direction of the University's Computer 

Services Division, the Geographic Information Retrieval 

and Analysis System (GIRAS) developed by the U.S. 

Geological Survey was used to produce the maps. 

The South Carolina coastal mapping program involved several State agencies. Overall responsibility for 

the definition and utilization of the program's information resources belongs to the South Carolina Coastal 

Council; the software system maintenance and information production tasks are handled by the University of 

South Carolina Computer Services Division; and system 

direction and policies are the responsibility of the State 

Division of Research and Statistical Services, Office of 

Geographical Statistics. The U.S. Geological Survey provided technical assistance in the installation and use of the 

Geographic Information Retrieval and Analysis System 

(GIRAS). 

This case study is based on materials prepared by the 

coastal mapping project's major investigator, Dr. David 

J. Cowen. For more information, contact: 

Dr. David J. Cowen 

Department of Geography 

University of South Carolina 

Columbia, SC 29621 

(802) 777-6803 
DATA SOURCES 

Land Use and Land Cover 

Land use and land cover data describing the location 

and types of land uses and cover types in South Carolina's 

eight coastal counties were needed for understanding the 

relationships between developed and natural areas. Accurate land use and land cover maps and statistics would 

provide insight into potential conflicts that might currently exist or arise with future development. 

Land use and land cover data, produced by the U.S. 

Geological Survey were selected as the source of the 

baseline land use information (fig. 45). The USGS maps 

provided complete coverage of the eight coastal counties 

and thus served as an excellent tool for preparing the land 

use component of the South Carolina Coastal Zone 

Management Plan. The data were particularly useful 

because 40 percent of the coastal zone land use data were 

available in a digital form. 

Other Data 

Detailed wetlands data (information showing locations of salt marshes, brackish marshes, freshwater 

marshes, etc.) were needed to thoroughly understand the 

fragile environmental conditions of the coastal zone. 

Under the Coastal Council's direction, extensive field 

surveys and interpretations of low-altitude color-infrared 

aerial photographs were used to produce these data, which 

were then digitized so that they could be used in conjunction with other digital geographic data. 

COASTAL ZONE MAPPING 

The mapping of land uses in the eight coastal counties 

was completed in two stages. In the initial stage, the USC 

Computer Services Division used USGS land use and land 

cover data to produce a Level I land use and land cover 

map which displayed: (1) urban or built-up lands, (2) 

agricultural lands, (3) rangeland, (4) forest land, (5) water, 

(6) wetlands, and (7) barren lands. The map was produced 

by first digitizing the land use and land cover maps that 

were not already in a computer-compatible format, then 

merging these data with the existing digital files, and finally using the automated mapping capabilities of GIRAS to 

produce color separates of the land use and land cover 

categories. The separates were then combined to produce 

a four-color land use map. Area statistics for each land use 

and land cover category were also generated. Thus, arecent and unbiased inventory map of general land uses in 

the eight coastal counties was produced. 

The second mapping stage consisted of increasing the 

information contained in the Level I map by subdividing 

the general wetlands category into the detailed wetlands 

categories mapped by the South Carolina Coastal Coun39 
cil. In addition, critical areas of the coastal zone were 

defined. The result was a map displaying the Council's 

detailed wetlands categories and the USGS's general land 

use classes and accompanying area statistics summarizing 

category acreages for both the critical and noncritical 

areas of each of the eight South Carolina coastal counties 

(fig. 46). This final product is an innovative combination 

of the basic USGS land use and land cover data with data 

collected by local agencies. By augmenting existing data 

with the detailed, locally collected data required in the 

planning process, a cost-effective approach was designed 

to meet South Carolina's information requirements. 

APPLICATION OF PRODUCTS 

The South Carolina Coastal Zone land use and land 

cover maps provided the Coastal Council with information that was used to prepare components of the management plan required by the Coastal Zone Management Act 

of 1972. The maps and statistics are also valuable tools for 

carrying out the directives of South Carolina's Coastal 

Zone Management Act of 1977 (Code of Laws of South 

Carolina, 1976, v. 16, chapter 39 1980 Supplement). 

Specific requirements of the State Act that can use the land 

use maps include: 

(1) Comprehensive coastal zone management planning; 

(2) Examining, modifying, improving, or denying applications for permits for activities. 

(3) Managing and regulating all activities within 

estuaries and marine sanctuaries. 

(4) Establishing, controlling, and administering 

pipeline corridors and the location of pipelines in 

critical areas; 

(5) Directing and coordinating the beach and coastal 

shore erosion control activities. 

The flexibility of the coastal mapping program, best 

illustrated by the ability to combine data from a variety of 

existing sources, provides a useful tool for managing both 

current and future problems facing South Carolina's 

coastal zone. 

SELECTED REFERENCES 

Cowen, D. J., Crosley, Powel, and Holland, Michael, 

1979, Automated mapping of South Carolina's 

coastal zone: Proceedings of the American Congress 

on Surveying and Mapping, Washington, D.C., p. 

247-253 . 

Cowen, D. 1., Walters, George, Feinberg, Kenneth, 

Holland, Michael, and Yang, Alfred, 1979, The 

evolution of the South Carolina coastal mapping program, in Computer Mapping in Education, Research 

SOUTH CAROLINA'S COASTAL ZONE 

LAND USE AND LAND COVER 

Figure 46. South Carolina Coastal Zone land use map produced using both USGS and Coastal Council land use and 

land cover data identifies both the uses of the coast's critical zone and provides a detailed summary of acreage 

statistics. 

40 
and Mapping, Cambridge, Massachusetts, Harvard 

Laboratory for Computer Graphics and Spatial 

Analysis, p. 29-36. 

Cowen, D. 1., Yang, A. H., and Waddell, J. M., Jr., 

1980, The South Carolina geographical information 

system: the role of the State and the University in 

system implementation, in Urban, Regional, and 

State Government Applications of Computer Mapping, Cambridge, Massachusetts, Harvard 

Laboratory for Computer Graphics and Spatial 

Analysis, p. 48-58. 

Mitchell, W. B., Guptill, S.C., Anderson, K. E., Fegeas, 

R. G., and Hallam, C. A., 1977, GIRAS- A geographic information retrieval and analysis system for 

handling land use and land cover data: U.S. 

Geological Survey Professional Paper 1059, p. 16. 

41 
Environmental Assessment of a 

Nuclear Power Plant AccidentThe Three Mile Island Example 

BACKGROUND 

A nuclear accident occurred in late March 1979 at the 

Three Mile Island thermal nuclear power plant on the Susquehanna River, 10 miles southeast of Pennsylvania's 

State capital, Harrisburg. Although the plant is located in 

an area dominated by forests, agricultural land, and open 

water, it is at the center of a triangle formed by populous 

metropolitan centers at Harrisburg, York and Lancaster 

(fig. 47). 

The incident raised questions regarding the need for 

and availability of data upon which disaster assessments 

and evacuation plans could be made. When an accident 

occurs, a cartographic data base must already exist if 

politicians, planners, utility company personnel, and involved laymen can be expected to quickly implement the 

most desirable course of action. A variety of data types 

could be maintained in this data base, including land use 

and land cover, topography, hydrology, landownership, 

transportation, and population distribution, keyed to census statistical tracts and local political units. When 

population data are combined with land use and transportation information, different evacuation plans can be 

developed which take into account the daily patterns of 

movement of various segments of the population (for example, school children, urban workers, and farmers). 

PENNSYLVANIA 

Figure 47. Three Mile Island is located on the Susquehanna River in eastern Pennsylvania. It lies in the 

middle of a triangle formed by the cities of York, Harrisburg, and Lancaster. 

42 

THE PROBLEM 

A comprehensive data base of natural and cultural 

resource information did not exist in a readily accessible 

format at the time of the Three Mile Island accident. 

When hearings were held by the Atomic Safety and Licensing Board of the Nuclear Regulatory Commission following the accident, a request for preparation of relevant map 

products was made to the U.S. Geological Survey. In 

response to this request, two types of digital cartographic 

products were produced using the USGS Geographic Information Retrieval and Analysis System (GIRAS): 

(1) computer-plotted maps of land use and land cover, 

with census tracts, for 20- and 50-mile radius areas. 

(2) population density maps for 20- and 50-mile radius 

areas. 

DATA SOURCES 

Land Use and Land Cover 

Regional land use and land cover patterns are important input to determining the types of impacts which 

would result from a nuclear accident. Land use data can 

provide a means to determine whether the affected area is 

predominantly populated, agricultural, or undeveloped. 

This information, when combined with other data (such as 

radiation released and wind speed and direction) can assist 

officials in predicting the magnitude of the impacts on 

area residents. 

The land use and land cover data were contained in a 

statewide digital data base that was developed as part of a 

cooperative effort between the Pennsylvania Department 

of Environmental Resources and the U.S. Geological 

Survey. The Geological Survey compiled the data as part 

of its land use and land cover mapping program. Nineteen 

categories were mapped in the Three Mile Island area. 

This data base was maintained on GIRAS, which can 

be used to perform computer mapping as well as to correlate land use area measurements with census and other 

data, to update data sets, and to retrieve and analyze data. 

Census Areas 

U.S. Bureau of the Census data, which identify census area boundaries, were needed to assess the 

demographic characteristics of the impacted region. Census County Subdivision boundaries were available in 

digital form through the USGS land use and land cover 

data analysis program. 

Population 

Census Bureau population estimates for 1977 were 

used to determine population density. 
IMPACT MAPPING 

The initial analysis produced a map displaying the 

land use and land cover and census tracts of the Three Mile 

Island region (fig. 48). The digital nature of the data allowed the use of the automated mapping capabilities of 

GIRAS, which is designed to accept digitized input, provide comprehensive editing facilities, produce cartographic and statistical output, permit retrieval and 

analysis of data, and exercise data-base management 

tasks. For this application, the data were plotted on base 

maps that covered areas within 32 km and 80 km of the 

Three Mile Island facility. The final maps have the radius 

information displayed in 5-mile increments. 

The presentation of land use, census, and radius data 

on one map provides a unique tool for identifying the affected areas. For example, by comparing the demographic 

characteristics of census tracts in the residential land uses 

areas, it is possible to estimate the number of people who 

might be affected. The consideration of cropland and 

pasture locations in relationship to the distance from the 

power plant permits an assessment of the extent to which 

food supplies might be affected by a nuclear accident. 

ttoo.o» 

'L---~--~=====----··~~ ~~~~~~· 

The second map displayed 1977 population density 

estimates by Census County Subdivision, with 5-mile 

radius increments. It was produced by calculating the 

population densities for the region and using the GIRAS 

automated mapping capabilities to plot the data (fig. 49). 

POTENTIAL APPLICATIONS OF 

PRODUCTS 

The set of maps produced for the Three Mile Island 

area is a prototype demonstration of a working atlas for 

hazard mitigation and emergency response. It could be used to plan for different scenarios of potential disasters, or 

as a basis for reacting to an actual disaster such as might 

have occurred at Three Mile Island in 1979. For example, 

if wind patterns and other meteorological phenomena are 

known, then fallout patterns from radiation leaks can be 

plotted and their impact on human populations and 

agricultural resources can be assessed. The land use and 

population density information can be manipulated to 

portray the movement of various segments of the population during a typical day. In this way, the impacts of 

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Figure 48. Land use and land cover and census tract map area around Three Mile Island (original scale 1 :100,000). 

Nineteen categories of land use and land cover have been mapped. Census county subdivision boundaries are 

shown by black lines and upper case lettering. 

43 
Three MOe Island 

Nw:leat ~Statton 

Mlddleto.D,. ~ 

20-Mile Radios Area 

Population Density 

~~~ 

1917~~ 

Figure 49. Population density for a 20-mile radius area around Three Mile Island (original scale 1:100,000). The two 

densest categories represent most of the urban/suburban population 

fallout can be made more time-and-location specific, and 

response to a disaster can be more effective. 

This data base would be useful for planning evacuation routes and other emergency response actions. 

Population distributions can be correlated with patterns 

of radiation dispersion to identify transportation routes 

which would be most effectively used as evacuation 

routes. Those routes which do not have adequate capacity, 

or would pass through areas of excessively high radiation, 

could be identified and avoided. The data base of land use, 

population, and associated transportation networks 

would also be appropriate for identifying sites for fallout 

shelters and host areas for relocation of the affected 

population. 

Because much of the information needed for such a 

data base is customarily used in the power plant site selection process, and thus would already be available upon 

completion of construction of the nuclear power plant, it 

44 

would be logical to maintain and update it in a data base 

for disaster impact assessment and planning purposes. 

Computer-based geographic information systems are 

available to facilitate data entry, storage, manipulation, 

update, monitoring, and display. 

The map series discussed here was designed and produced by James R. Wray, Robin Fegeas, and Bruce 

Wright, Office of Geographic and Cartographic 

Research, U.S. Geological Survey, to meet the needs of 

agencies that were assessing the implications of the Three 

Mile Island accident for future power plant disaster planning. For further details, contact: 

Office of Geographic and Cartographic Research 

National Mapping Division 

U.S. Geological Survey 

521 National Center 

Reston, Virginia 22092 

(703) 860-7796 

* U.S. GOVERNMENT PRINTING OFFICE: 1986 -4 91 - 407:20013