United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-430

October 1998

The Starkey Habitat

Database for Ungulate

Research: Construction,

Documentation, and Use

Mary M. Rowland, Priscilla K. Coe, Rosemary J. Stussy,

Alan A. Ager, Norman J. Cimon, Bruce K. Johnson, and

Michael J. Wisdom
Authors MARY M. ROWLAND is a contracting wildlife biologist, La Grande, OR 97850;

PRISCILLA K. COE, BRUCE K. JOHNSON, and ROSEMARY J. STUSSY are

wildlife research biologists, Oregon Department of Fish and Wildlife, 1401 Gekeler

Lane, La Grande, OR 97850; ALAN A. AGER is an operations research analyst,

U.S. Department of Agriculture, Forest Service, Umatilla National Forest, 2517

Hailey Avenue, Pendleton, OR 97801; NORMAN J. CIMON is a supervisory

computer systems analyst, U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station, Forestry and Range Sciences Laboratory,

1401 Gekeler Lane, La Grande, OR 97850; and MICHAEL J. WISDOM is regional

wildlife ecologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station, Forestry and Range Sciences Laboratory, 1401 Gekeler Lane,

La Grande, OR 97850.
Abstract Rowland, Mary M.; Coe, Priscilla K.; Stussy, Rosemary J. [and others]. 1998.

The Starkey habitat database for ungulate research: construction, documentation,

and use. Gen. Tech. Rep. PNW-GTR-430. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 48 p.

The Starkey Project, a large-scale, multidisciplinary research venture, began in 1987

in the Starkey Experimental Forest and Range in northeast Oregon. Researchers are

studying effects of forest management on interactions and habitat use of mule deer

(Odocoileus hemionus hemionus), elk (Cervus elaphus nelsoni), and cattle. A habitat

database was compiled, using GIS (geographic information systems), to examine relations of environmental variables to ungulate distribution and habitat use. The database contains over 100 variables associated with vegetation, water, soils, roads, topography, and structural features such as fences. We describe database construction

and documentation of GIS layers from 1987 to 1997. Error estimates associated with

each variable or layer and sample applications of the database also are presented.

Keywords: Habitat database, GIS, spatial data, ungulate, cattle, elk, mule deer,

northeast Oregon, Starkey Project, accuracy assessment, Blue Mountains.
Contents 1 Introduction

3 Database Construction and Organization

5 Vegetation

8 Roads

12 Fences

12 Soils

12 Water

14 Topography

14 Other Variables

14 Sources of Error and Accuracy Assessment

15 Types of Error

15 Accuracy of Starkey Data

16 Accuracy Assessment Ongoing or Completed

18 Suggestions for Future Accuracy Assessment and Monitoring

19 Using the Habitat Database—Sample Applications

19 Road Densities and Elk Habitat Effectiveness

20 Forage Allocation Modeling

22 Deriving Variables of Traffic Rate

24 Recommendations

24 Acknowledgments

25 Equivalents

25 References

30 Appendix 1

30 Variables in the Starkey Habitat Database, Their Definitions, and Codes

40 Appendix 2

40 Documentation and Creation of Variables, Source Maps, and Tables in the

Starkey Habitat Database

44 Appendix 3

44 Computer Software Used

45 Glossary
Introduction Geographic information systems (GISs) are widely used to develop databases for

modeling and analyzing wildlife habitat relations and conducting wildlife research

(Butler and others 1995, Clark and others 1993, Donovan and others 1987, Duncan

and others 1995, Koeln and others 1994). These systems allow efficient compilation

and analysis of habitat data on a landscape scale. A habitat database was created

with several GIS sources for the Starkey Project, a long-term, multidisciplinary, cooperative endeavor between the USDA Forest Service (FS) and the Oregon Department

of Fish and Wildlife (ODFW). The project specifically addresses effects of forest and

rangeland management on elk (Cervus elaphus nelsoni), mule deer (Odocoileus

hemionus hemionus), and cattle. Several studies are underway (Johnson and others

1991, Rowland and others 1997), and hypothesis testing relies heavily on accurate

assessment of environmental conditions in relation to ungulate distributions. Thus,

a comprehensive, spatially explicit habitat database was needed to store environmental data in relation to elk, deer, and cattle occurrence.

Most of the study area lies within the Starkey Experimental Forest and Range

(SEFR), the site of long-term range and wildlife research (Skovlin 1991; fig. 1).

The term “Starkey” will be used hereafter to refer to the portion of the SEFR

specifically fenced for Starkey Project research. This game-proof fence encloses

10 102 ha of historic summer range (Bryant and others 1993), with estimated

post-partum populations of 850 elk, 500 mule deer, and 600 cow-calf pairs grazed

annually. The area is representative of many plant communities and management

activities in National Forests (NFs) of the Blue Mountains.

Radio locations of deer, elk, and cattle are recorded with an automated animal telemetry system (AATS) that can page and locate a radio-collared animal every 20 seconds (Dana and others 1989, Rowland and others 1997; see “Glossary” for terms

used in this paper). The resolution of Starkey habitat data should meet or exceed

that of the AATS (about 3.1 ha, 90 percent confidence interval; Findholt and others

1996), particularly in studies of ungulate-habitat interactions.

Construction of the database began in 1989. It is continually updated as better information becomes available or habitat features change (for example, road construction

or changes in forest cover from insect outbreaks or timber harvest). In this report, we

describe compilation of the database, including its organization and use, and sources

for habitat variables as established from 1987 to 1997. Accuracy assessment and the

need for better error estimates also are addressed. Appendix tables provide more

detail, including a complete listing of variables now in the database and associated

codes (appendix 1), documentation of creation of the database (appendix 2), and

applicable software (appendix 3).

1
Figure 1—The Starkey Project is located within the Starkey Experimental Forest and Range in the Blue Mountains, northeast Oregon.

2
Database

Construction and

Organization

The habitat database contains maps for all major resource themes (vegetation,

fences, topography, water, soils, and roads) and more than 100 variables related

to distribution of deer, elk, and cattle at Starkey (table 3, appendix 1). Many sources

and techniques were used to create the database, depending on the habitat resource

(tables 8-11, appendix 2). The database exists in two principal platforms: UTOOLS

and Arc/Info1 (see appendix 3). The UTOOLS portion is primarily raster based,

whereas the Arc/Info portion is predominately vector based. Because the UTOOLS

database is most often used for exploration and analysis, this platform is emphasized

in our paper. Arc/Info, however, is superior for its display capabilities and analysis of

certain types of vector data, such as area perimeters or road networking and routing.

Source maps for the habitat database were created with several mapping techniques

(see tables 9 and 10) that use input programs such as GeoBased STRINGS, LTPlus,

or Trimble Pfinder GPS (global positioning system) software (appendix 3). These

layers were output in MOSS import-export format, which was the GIS used by the

FS when the Starkey database was constructed. Starkey staff converted the MOSS

files to Arc/Info, where they are currently stored. Data acquisition techniques for

base maps include remote sensing (satellite imagery and aerial photography), onthe-ground mapping with GPS, and digitizing from topographic maps and orthophoto

quadrangles. Base maps exist in two formats, vector and raster; most variables are

accessible in both formats.

The main archival storage for all digital maps of the Starkey Project is the FS GIS,

which uses both Arc/Info GIS and Oracle database programs residing on the IBM

RS6000 (see table 9, appendix 2). Oracle elements are used primarily to qualify map

characteristics. The only geographic data not stored in Arc/Info are animal telemetry

observations, which are in Oracle. There are several data structures, or models,

supported by Arc/Info. Most Starkey maps are stored in the vector data model of

Arc/Info, but a few are stored in the raster data model. Some vector map layers

have never been rasterized for use in UTOOLS (table 11, appendix 2), primarily

because they contain features recorded in too few pixels to warrant conversion

to a raster database, or they are used for display only, rather than analysis.

The primary software used to build the raster portion of the database is UTOOLS, a

DOS-based collection of spatial analysis programs for the PC (Ager and McGaughey

1997; McGaughey 1995). The UTOOLS programs combine raster, vector, and attribute data that are then stored as spatial databases in Paradox software, where

they can be analyzed in several ways (fig. 2). By using Paradox, many complex

operations in GIS, such as unions and overlays, can be completed with simple

queries. Spatial operations in UTOOLS, such as routines for buffers, rasterization,

slope and aspect, and landscape diversity, are performed with UCELL5 (appendix 3;

Ager and McGaughey 1997). UTOOLS also includes a visualization program, UVIEW,

that constructs two- and three-dimensional images of attribute data, vegetation

patterns, and elevation. This analysis and display tool creates depictions of such

combinations as elk and deer locations in relation to roads and topography (see

“Using the Habitat Database—Sample Applications”).

1 The use of trade or firm names in this publication is for

reader information and does not imply endorsement by the

U.S. Department of Agriculture of any product or service.

3
Figure 2—Data operations with UTOOLS software.

4
All spatial variables in the database except elk habitat categories and elevation were

originally recorded in vector format. Vector data were converted in UCELL5 to raster

format, creating a spatial database in Paradox with 112,246 records for the Starkey

area. Each record in the Paradox database represents a single 30- by 30-m pixel

within Starkey, with corresponding fields for each map layer or attribute associated

with that pixel (fig. 3). Pixels in a buffer about 0.8 km wide around the perimeter of

the Starkey Project area also are included in many analyses; this buffer contains an

additional 50,725 pixels. The UTM (Universal Transverse Mercator) coordinates for

the center of each pixel (variable “UTMGrid”) comprise the key field linking tables

together in the database (table 3, appendix 1). All locations at Starkey are recorded

in NAD83 (a geographic datum; see “Glossary”).

Each primary resource group is associated with lookup tables in Paradox that

contain the codes (and their descriptions) that correspond with each variable:

AREACODE.DB, TOPOCODE.DB, WATRCODE.DB, ROADCODE.DB, PICODE.DB,

HARVCODE.DB, TRAFCODE.DB, ECOCODE.DB, SPECCODE.DB, LNSTCODE.-

DB, and SOILCODE.DB (table 1). Similar code descriptions also are presented in

tables 4-7 (appendix 1). To accommodate changes in habitat variables, such as

roads, portions of the database are updated yearly. Such updates permit more

accurate evaluation of habitat variables in relation to ungulate locations.

Vegetation Aerial photointerpretation—Dave Motanic (Umatilla NF) interpreted two sets of

aerial, natural color photographs (1:15,840) from 1987 to 1988 and 1993 specifically

for the Starkey Project, by using standard Pacific Northwest Region (FS) guidelines

for photointerpretation (USDA Forest Service 1992). Variables from this work are

in the “STKYPI” directory (tables 1 and 3). Stands were delineated on aerial photographs, transferred to 1:24,000 orthophotos, and scanned and edited in LTPlus.

Stand identification numbers and polygon boundaries associated with these variables

are identical to those from earlier, intensive field data collection in the Starkey area

by the La Grande Ranger District (LGRD), known as the existing vegetation database. The stand number serves as the key field linking information among the

vegetation databases.

Figure 3—Sample of environmental variables recorded for each 30- by 30-m pixel in Starkey.

5
Satellite interpretation—An unsupervised classification of a 1991 Landsat Thematic

Mapper (TM) scene was completed to produce a digital map of elk habitat categories

and canopy defoliation in the SEFR (Noyes and others 1994). This classification was

performed jointly by personnel from the LGRD (Wallowa-Whitman NF), ODFW, and

the Forestry and Range Sciences Laboratory (Pacific Northwest Research Station,

FS), using PC-based image processing programs. Previously, much of the Blue

Mountains province had been classified into elk habitat categories from 1979 and

1980 Landsat scenes (Leckenby and others 1985). Also, a 1988 Landsat TM scene

was classified into initial spectral groupings, but no further classification was completed. Severe defoliation of trees has since occurred, however, primarily because

of outbreaks of western spruce budworm (Choristoneura occidentalis Freeman;

Wickman 1992). Thus, a more current classification of elk habitat was needed.

Landsat TM data were recorded in 28.5-m pixels (raster format) and resampled to

30-m resolution (Noyes and others 1994). The Landsat data were originally used to

define six categories of elk habitat, based primarily on percentage of canopy cover:

forage in natural openings with less than 10 percent canopy cover; forage in clearcuts

with less than 10 percent canopy cover; timbered forage (10 to 39 percent canopy

cover); marginal cover (40 to 69 percent canopy cover); marginal-satisfactory cover

(40 to 69 percent canopy cover, but would equal at least 70 percent if defoliation had

not occurred); and satisfactory cover (at least 70 percent canopy cover). In a second

classification, the three forage classes were combined, and marginal-satisfactory

cover was combined with satisfactory, producing three elk habitat categories (forage,

marginal cover, and satisfactory cover; see Thomas and others 1988). These habitat

categories can be used to calculate habitat effectiveness for elk on winter ranges in

the Blue Mountains (Hitchcock and Ager 1992).

Table 1—Organization of primary tables in the Paradox portion of the Starkey habitat database (NEWHAB)

Directory Paradox tables Remarks

STKYAREA AREAAFTR.DB, AREAB4.DB Entries are assigned to a table based on whether they apply before

AREACODE.DB or after construction of the Campbell Flat fence in September 1992.

STKYHARV STKYHARV.DB, HARVCODE.DB Timber harvest data from the La Grande Ranger District.

STKYPI 1987PI.DB, 1993PI.DB, PICODE.DB, Contain vegetation data from interpretation of aerial photographs in

LNSTCODE.DB, LANSAT91.DB, 1987 and 1993; additional tables refer to elk habitat classified from

SPECCODE.DB a Landsat Thematic Mapper scene.

STKYROAD 88.DB, 89.DB, 90.DB, 91.DB, 92.DB, Roads data differentiated by year, as road upgrades and construction

93-95.DB, 88DIST.DB, 89DIST.DB, occur; distance tables were created from buffering routines in UTOOLS.

90DIST.DB, 91DIST.DB, 92DIST.DB, The TRAFFIC directory will contain temporary databases created

9395DIST.DB, ROADCODE.DB, as needed for derived variables of distance to nearest road of a

COUNTERS.DB; TRAFFIC given traffic rate, for day and night categories.

STKYSOIL STKYSOIL.DB, SOILCODE.DB Soil polygons and codes.

STKYTOPO STKYTOPO.DB, TOPOCODE.DB All topographic variables (for example, slope and convexity) and codes.

STKYWATR STKYWATR.DB, CDISTB4.DB, Includes tables with distance to water for elk versus cattle, before and

CDISTAFT.DB, EDISTB4.DB, after construction of the Campbell Flat fence.

EDISTAFT.DB, WATRCODE.DB

STKYEVG ECOCLASS.DB, ECOCODE.DB, Vegetation data from the La Grande Ranger District “EVG” database;

FORPROD.DB more variables to be added later.

6
Ground-truthing of selected sites confirmed the fit of spectral classes with elk habitat

categories and that canopy height was adequate for marginal and satisfactory cover,

as defined by Thomas and others (1988). Currently the only variable entered in the

Starkey habitat database from this classification is “Lansat91,” the three categories

of elk habitat type (table 3). However, the original classification data for canopy defoliation and other elk habitat categories can be incorporated as needed.

The Integrated Satellite Vegetation Mapping Project (ISAT) used the same 1991

Landsat scene to classify vegetation across the Pacific Northwest Region (Pacific

Meridian Resources [1997]). Five layers (25- by 25-m cells) were produced, stored

by 1:24,000 U.S. Geological Survey (USGS) topographic maps: canopy cover class,

size-structure, tree species class, species groups, and nontree range and understory class. These cell layers may be entered into the Starkey habitat database in

the future to supplement photointerpreted and other vegetation data.

Intensive field data collection—Vegetation variables from the EVG (existing vegetation) database of the LGRD, with the exception of “ecoclass,” have not been physically transferred into the Starkey database but are accessed as needed over the

network (fig. 4). Attributes such as cover (tree, browse, and herbaceous), forage

condition, and site index are available and represent on-site field interpretation for

most stands.

The CIMS (continuous inventory and monitoring survey; USDA Forest Service

1994) information being compiled by NFs in the Blue Mountains also may be used

by project staff. The 1-ha, permanent sampling plots have been visited in the last

3 years and represent the most comprehensive vegetation data in this area. There

are 15 CIMS plots within Starkey, either 2.7 km or 5.5 km apart.

Starkey staff have sampled vegetation as well, including transects in the northeast

study area (Northeast) of Starkey (see Rowland and others 1997). Vegetation phenology data have been collected at 11 sites within Starkey (table 11, appendix 2;

Rowland and others 1997). These data will probably not be incorporated into the

Starkey habitat database but be used in forage allocation models for specific years

(Johnson and others 1996).

Roads Field personnel from the Grande Ronde Engineering Zone (GREZ) and LGRD

recorded locations of roads at Starkey by walking or driving along all roads with a

hand-held GPS receiver. Locations at road junctions were differentially corrected and

averaged (at least 120 data points were collected); sections between road junctions

were only differentially corrected (table 2). Road locations were entered in the habitat

database by road segments, which are defined by intersections of roads. Each road

segment was assigned a unique identification number (RoadSeg#), which is the key

field for the roads database. Attribute data for roads were then assigned to each

segment.

All roads tables in the STKYROAD directory are distinguished by year, because

road attributes may change or new roads may be built (table 1). Road segments

were placed in one of seven categories, according to level of use (“ROAD USE,”

table 4, appendix 1). Portions of the roads data are stored either in the transportation management system (TMS) database of the Pacific Northwest Region or the

GREZ roads database. Some variables, such as “WIDTH” and “ALIGNMNT,” were

created specifically for the Starkey roads database.

7
The distance-to-roads tables are created with buffering routines in UTOOLS that

calculate the distance to specific types of roads, in a given season and year, from

each pixel (table 1). Roads in the buffer zone outside the perimeter fence also were

mapped and categorized because of their potential influence on animal distribution

within the fence.

Figure 4—Variables and their relations in the existing vegetation (EVG) database for the

Wallowa-Whitman National Forest.

8
Table 2—Accuracy of principal variables and vector layers in the Starkey habitat database

Variable or Source(s) Estimated error

layera of error Error type or % accuracy Remarks

Fences:

FENCGPS3 DGPSb Spatial ± 5 m All fencelines walked or driven and mapped with portable GPS unit.

Convert3,c Spatial Unestimated

convert2d

Fenccode Human Classification 100% Assignment of fence type (barbed wire or elk fence).

ElkFence, Rasterization Spatial 9 m average Fields in the spatial (rasterized) database.

CowFence

Roads:

TRN2OBT, DGPS Spatial ± 5 m Road segments mapped as vector layer with portable GPS units

TRN2INT, Convert3, Spatial Unestimated and assigned unique 12-digit numbers.

TRN2EXT convert2

RoadSeg# Rasterization Spatial 9 m average Road segment ID number in the spatial database.

ROAD USE, Human Classification 90%e Field names in the database; description of road use (for example,

LENGTH, green dot or closed), segment length, or surface type assigned by

SURF engineer may be incorrect, or entered incorrectly in database.

Soils:

SOILS3 Human Inherent Unestimated Soil polygon boundaries mapped as vector layer on aerial photographs

and transferred to USGS quads. Boundary location could be wrong.

Scanning,f Spatial Unestimated Polygon boundaries scanned and edited (WAW SO, Baker City).

Convert1,g

convert2

SoilCode Human Classification Unestimated Field in spatial database; wrong soil type could be assigned to polygon

Rasterization Spatial 9 m average (map unit). Starkey was mapped as a 2d-order (intensive) survey,

that is, about 75% of polygons were visited in the field, and the

remainder mapped from aerial photographs.

Topography:

TOPOGSTK Human Inherent Unestimated Vector layer produced by digitizing 20-ft contour lines on USGS

Digitizing Spatial 0.0025 cm acetate overlays.

Convert4,h Spatial Unestimated

convert2

Rasterization Spatial 9 m average

DEMSTKWI Source map Inherent RMSEi<7 m Original raster map, a level 1 DEM,j produced by FS Geometronics

Digitizing Spatial Service Center by digitizing 20-ft contour lines on 7.5-min USGS

Rasterization Spatial maps (see Thompson 1987 for map accuracy standards). Rasterized

to 30- by 30-m cells. Accuracy may be less in areas of steep slope

or along ridge tops.

Elev Field in spatial database for elevation, from DEMSTKWI.

% slope DEM Classification See Skidmore Field in spatial database; derived from DEM with AVERAGE method

1989 in UTOOLS (see USDI Bureau of Land Management 1990). Accuracy

depends in part on DEM accuracy.

Aspect DEM Classification Proportional to Field in spatial database; calculated in UTOOLS with AVERAGE

slope error method (see USDI Bureau of Land Management 1990 and Skidmore

1989). No additional error introduced through aspect calculation.

Convex3 DEM Classification = DEM error Field in spatial database; calculated in UTOOLS (see Kvamme 1988 for

details). No additional error introduced through convexity calculation.

9
Table 2—Accuracy of principal variables and vector layers in the Starkey habitat database (continued)

Variable or Source(s) Estimated error

layera of error Error type or % accuracy Remarks

Water:

STRSTK2 Human Inherent Unestimated Vector layer of streams; streams and crenulations were hand manuScanner Spatial Unestimated scripted on 7.5-min maps and then scanned at Umatilla SO (cell size

Convert1, Spatial Unestimated about 6.1 by 6.1 m). (See Thompson 1987 for national map accuracy

convert2 standards.)

StrmSeg# Rasterization Spatial 9 m average Vector maps rasterized in UTOOLS software; each stream segment

assigned a unique ID number.

StrmClas Human Classification Unestimated Possible misclassification of stream class and other attribute data.

Stream class accuracy for all of Starkey was coded as “B,” that is,

estimates used information that is less than complete (at least 1 value

between “best" and “worst,” or no knowledge of domestic use).

WATERPT4 DGPS Spatial ± 5 m Vector layer of water points mapped in field with portable GPS units.

Convert3, Spatial Unestimated

convert2

Waterpt6 Rasterization Spatial 9 m average Water points rasterized in UTOOLS. Type of water source (for example,

Human Classification Unestimated spring or seep) could be incorrectly assigned.

Vegetation:

Lansat91 Image Classification 82% Originated as raster map. Elk habitat categories assigned from an

resolution unsupervised classification of a Landsat Thematic Mapper scene; field

and map testing for accuracy assessment (Noyes and others 1994).

Image Spatial 0.87 pixels Scene georectified with ground control points using UTM coordinates

placement on 7.5-min USGS quads (Noyes and others 1994).

MRISTK3 Human Inherent 90%k Vegetation stand boundaries delineated by hand on aerial photographs,

Scanner Spatial Unestimated transferred to orthophoto quadrangles, and then digitized by scanning

Convert1, Spatial Unestimated at Umatilla SO using half-quads (see USDA Forest Service 1992 for

convert2 vegetation mapping standards). Cell size was about 2.4 m in LTPlus.

STANDID Rasterization Spatial 9 m average Vegetation stand ID numbers; polygons rasterized in UTOOLS. All other

vegetation variables are attributes assigned to these stand numbers.

Ecoclass, Veg Human Classification 70-75%k May have incorrectly assigned vegetation attributes to stand polygons;

Code, Total all vegetation variables entered from photointerpretation only, except

CC, #Layers, ecoclass (from LGRD EVG database). EVG_EXAM contains information

LYRCODE1-3, about what type of exam was used for the LGRD vegetation variables.

others

a Variable names are described in table 3, appendix 1; vector layer names are in table 9, appendix 2. b Differentially corrected Global Positioning System; all locations differentially corrected to at least 5 m CEP (circular error probable; see glossary).

Locations of road junctions and traffic counters were differentially corrected and averaged (reference base station in Portland), resulting in potential

2-m accuracy.

c Conversion routine for Trimble Pathfinder Standard Storage Format to MOSS Export Format. d Conversion routine for MOSS Export Format to Arc/Info binary format. e Estimate provided by Richard Collins, formerly engineer with the Grande Ronde Engineering Zone. f IDEAL monochrome scanner, Wallowa-Whitman and Umatilla NF SO; resolution for most images is 150 dots per inch.

g MOSS binary vector format to MOSS Export format. h STRINGS export format to MOSS Export format. i Root-mean-square error.

j Digital elevation model.

k Estimates for vegetation accuracy provided by Glenn Fischer, Umatilla NF.

10
Traffic—Extensive data on traffic frequency have been gathered in the SEFR

since 1988 with traffic counters on over 90 road segments (Cimon and others

1996, Rowland and others 1997). Counter locations are stored in a separate table

(COUNTERS.DB; see table 1). Cameras at two sites also record vehicle type. Traffic

frequencies have been analyzed from these counters for each season. Count data

was summed for each counter for a given season. A moving window analysis (with

one-half hour shifts) was conducted to determine the day-night combination that

yields the highest ratio of day to night vehicle counts. The highest ratio was used

to define the day and night periods for that season and year for any traffic-ungulate

analysis of interest.

Each traffic counter was then characterized as belonging to a specific category of

traffic frequency during the day and night periods for a given season and year. For

example, each traffic counter was characterized as belonging to one of five traffic

rate categories (very high, high, medium, low, or zero) during the day and one of two

categories during the night (zero or nonzero). This designation of traffic categories,

one for day and one for night for each counter, was then assigned to the applicable

road segments, and corresponding pixels, near each counter. Buffering routines in

UTOOLS created new derived variables for distance from a pixel to each of the road

traffic categories for day and night (for example, distance to nearest pixel with “high”

daytime road use; fig. 5, table 1). Vectors of use for deer and elk for these traffic

categories, distinguished by day and night within a season, were then calculated and

compared against random vectors of use under a multivariate analysis of variance

(Wisdom 1998). Results indicate ungulates are selecting areas disproportionately far

or close to roads of a specified traffic rate, in contrast to random use of the study

area. Further studies will manipulate traffic to test these results.

Figure 5—Distance to the nearest road characterized by a given traffic rate is

recorded for each pixel in Starkey.

11
Fences An 8-foot-high game-proof fence encircles the perimeter of the SEFR (Bryant and

others 1993). This fencing also separates the main study area (Main) and the winter

feeding and handling area and its pastures, Campbell Flat research pasture, several

game-proof exclosures, and Northeast. Barbed wire fences create additional grazing

pastures for cattle within Main (fig. 6). (Cattle are grazed in the SEFR, except for

the winter feeding and handling area, from June-October.) Fenceline locations were

recorded with a portable GPS unit. Each pixel at Starkey is assigned to one of four

elk pastures defined by the game-proof fence (MAIN, NORTHEAST, FEEDGROUND,

and CAMPBELL), as well as to one of seven cattle pastures (table 3, appendix 1).

Before construction of the Campbell Flat fence in fall 1992, Campbell was a

cattle pasture in Main. This distinction is reflected in the “AREAB4.DB” and

“AREAAFTR.DB” tables in the STKYAREA directory (table 1).

Soils Soils information was abstracted from a database begun in 1986 by the WallowaWhitman NF (WAW). Data were compiled by mapping soil polygons on aerial

photographs and transferring these polygons to 1:24,000 USGS topographic

maps for digitizing. The Starkey soils information was collected with a secondorder survey.2 These mapping sites were classified from the following sources:

20 percent delineated only on aerial photographs; 15 percent determined from

transects run at intervals (10 observations per transect, with 3 transects per

map unit); 40 percent from traverses; and 25 percent from field observations

(less standardized sampling than traverses or transects). Minimum size delineation for a second-order survey is about 2.4 ha. The Starkey area encompasses

37 map units (table 5, appendix 1). Additional information about each soil series

can be accessed through the series number in the WAW soils database (table 10,

appendix 2).

Water Streams and rivers in the SEFR were mapped as line vectors by WAW personnel

following mapped watercourses and also using contour lines and crenulations on

USGS topographic maps (table 9, appendix 2). Streams were merged into subwatersheds to simplify coding; cell size was about 6.1 m. Mapped streams were

then scanned (Umatilla NF), edited in LTPlus, and later rasterized in UTOOLS. Each

stream segment was assigned a unique identification number; at a minimum, stream

segments end at confluences (USDA Forest Service 1991). A subset of the WAW

stream variables is used in the Starkey habitat database (table 3, “STKYWATR.DB”;

see the Tri-Forest Data Dictionary, USDA Forest Service 1991, for a complete

listing). Locations of water sources other than streams, such as stock ponds or

springs, were mapped with GPS recorders or by digitizing these features from

FS grazing allotment maps (enlarged 7.5-minute topographic quadrangles).

2 Personal communication. 1995. Art Kreger, soil scientist,

Wallowa-Whitman National Forest, P.O. Box 907, Baker City,

OR 97814.

12
Figure 6—A combination of barbed wire and game-proof fences divides Starkey into pastures for

more efficient management and research.

13
In calculating derived variables for distance to nearest water (for example, to nearest

class II stream), ungulate species, pasture fencing, and year had to be accounted

for. For example, cattle in Bear pasture do not have access to water in Smith-Bally

pasture, even though some water in Smith-Bally may be closer to those cattle. Similarly, elk in Northeast cannot reach water in Main. It is assumed, however, that deer

and elk cross cattle fences to reach water. Derived variables for distance to water

have been created for elk and cattle thus far. In the future, a “generic” distance to

water variable likely will be created that reflects the closest water source (regardless

of type or order of stream) for a deer or elk, before and after the Campbell fence was

built.

Topography Elevation data for the SEFR were transferred from a digital elevation model (DEM)

produced by the FS Geometronics Service Center. The DEM covering Starkey was

created by digitizing contour lines on 7.5-minute USGS quadrangles; output is in 30-

by 30-m raster format.3 Slope, aspect, convexity, and other topographic variables

(table 3) were derived from the DEM with routines in UCELL5. These programs use

the “AVERAGE” method to calculate percentage of slope and aspect for the home

pixel (Skidmore 1989) by using a roving 3- by 3-cell matrix. Convexity, which conveys

variety in terrain, is derived by calculating the difference between elevation in the

home pixel and an imaginary plane some specified distance above the surrounding

pixels (Kvamme 1988).

Other Variables Additional features have been, or will be, located with GPS units and entered in the

habitat database. One is a subset of salt block sites that have been consistently used

in the SEFR, partially mapped in December 1993. (These sites are not necessarily

current salting locations). Another GIS layer maps the locations of microwave relay

towers at Starkey. In the Northeast, perimeters of former timber sale units will be

mapped with GPS. Additional data for possible inclusion in the habitat database are

weather records, thermal neutrality zones, edge calculations (for example, between

cover and forage areas), and forage productivity or biomass. A new set of digital

orthophoto quadrangles also is available (see “orthophoto quadrangle” in the

“Glossary”).

Sources of Error

and Accuracy

Assessment

Interest in accuracy of spatial databases has burgeoned in recent years (Mowrer

and others 1996), as users need to assess the reliability of analyses using databases constructed with GIS applications. All GIS products should present error

rates to warn the user of potential pitfalls when making decisions based on these

products (Goodchild and Gopal 1992). The following discussion highlights sources

of error in the Starkey database as it now exists and offers suggestions for accuracy

assessment that will ensure the legitimacy of the database as the foundation of the

Starkey analyses (see Rowland 1996 for further elaboration of accuracy assessment

of the Starkey habitat database).

3 Personal communication. 1995. Rodney Dawson,

geometronics engineer, Pacific Northwest Region, USDA

Forest Service, 333 SW First Avenue, P.O. Box 3623,

Portland, OR 97208.

14
Types of Error Experts in GIS classify and partition error in several ways (Goodchild and Gopal

1992); however, the following terminology is commonly used. Inherent error is

error present in source materials, such as topographic maps or aerial photos (fig. 7).

Inherent error can sometimes be quantified (for example, national mapping standards

exist for U.S. maps [Thompson 1987]) but often remains unknown and uncorrectable.

Operational error is error introduced through data capture and manipulation. It has

two distinct components—spatial (positional, locational) error and identification

(classification) error (Walsh and others 1987). Spatial error refers to geographic

accuracy of data in a GIS, or the horizontal or vertical placement of boundaries.

Digitizing, GPS, plotting, and scanning errors are types of spatial error introduced

through data capture. Spatial error introduced through data manipulation includes

errors from rasterizing vector data or using buffering routines to calculate distance

to various features (for example, roads, water). Identification error refers to the

misclassification of attribute data in the database, such as soil type or ecoclass.

These errors are generally unknown but presumed to be negligible in most analyses.

Accuracy of Starkey

Data

Most layers in the database were originally entered as vector maps, including

vegetation, roads, streams, soils, fences, and some elevation data. Vector maps

of features located with differentially corrected GPS (fences, roads, and water

points) are accurate to about 2 to 5 m (see table 2 and Trimble 1992). In addition

to the error inherent in this technique, software conversion routines add an unknown

error (table 2). Finally, rasterization of these vector maps to create spatial databases

adds an average spatial error of 9 m (see following discussion of rasterization errors).

Vector maps not created using GPS (vegetation and soils polygons, streams, and

elevation) were produced by first digitizing, and editing, these features (manually or

by scanning) on orthophotos or aerial photographs. Again, conversion routines and

rasterization add spatial error when these layers are converted to spatial databases

in Paradox. In addition, any delineation of lines or polygon boundaries includes

human or conceptual error in deciding where to draw lines. Attributes assigned

to polygons also may be incorrect (table 2).

Figure 7—Types of error typical in GIS products (adapted from Goodchild and Gopal 1992 and

Walsh and others 1987).

15
The UTM grid that underlies the row-column structure of the spatial database was

created by rasterizing all vector maps in UTOOLS to a 30- by 30-m grid structure.

The maximum error associated with this process is half the diagonal length of a

pixel (or about 21 m), with an average error of 7.5 to 10.5 m. For the relative

distance between two objects, the maximum error introduced through the raster

process is 37 m. The maximum error associated with buffering routines (specifically,

the distance between a pixel center point and some fixed feature such as a road)

in UTOOLS is about 17 m.4 Because some objects will in reality be closer than

recorded in the database, and others farther away, average buffering error is likely

around 0 m. Positions of features commonly buffered (fences, roads, water points,

and salt locations) have all been determined with differentially corrected GPS (DGPS)

and thus are as accurate as possible with any technology now in use at Starkey.

The scale of analysis influences acceptable levels of error by determining the compatibility of the habitat data with the questions being posed (Donovan and others

1987). The habitat database is used primarily with analyses that incorporate animal

location data. Because the current error polygon for AATS locations is about 3.1 ha

(90 percent confidence interval; Findholt and others 1996), resolution of habitat variables at this scale is likely adequate for most Starkey studies. Cumulative error may

occur when combining map layers, however, justifying collection of habitat data at a

finer scale.

“Veg code” (life form of vegetation) is one of several vegetation variables in the

database that have never been field checked but were based entirely on photointerpretation. Another variable, ecoclass (from the LGRD vegetation database),

is used extensively in the animal-unit-equivalencies study and forage allocation

modeling (Johnson and others 1996). Although ecoclass was determined mainly

from stand examinations, both ecoclass and “veg code,” as well as some of the

canopy cover variables, are priorities for field verification because of their prominent role in future analyses.

Accuracy Assessment

Ongoing or Completed

Some accuracy assessment of the habitat database has been accomplished by

Starkey Project and LGRD personnel:

• The Landsat scene used to classify elk habitat was georectified to UTM coordinates

with orthophoto points; the resulting root-mean-square error (RMSE) was 0.87

pixels, within the recommended error of less than 1.0 pixel (Noyes and others

1994). Verification of elk habitat categories (satisfactory cover, marginal cover,

or forage areas) derived from classification of this scene was completed in 1993

(table 2; Noyes and others 1994). Classification accuracy (82 percent) was typical

for vegetation classification from Landsat TM scenes.

4 Features are buffered relative to the center point of the

pixel in question. Thus, the maximum distance between

a center point and a feature in an adjacent pixel is 47 m

(distance from center point of one pixel to the far corner of

an adjacent pixel). The buffered feature will be assigned to

the center point of its pixel (30 m from the center point of the

home pixel); therefore the maximum error is 47 - 30 = 17 m.

16
• Twenty vegetation polygons in Starkey were visited in fall 1996 for validation of

vegetation variables. Test polygons were randomly selected, outlined on aerial

photographs, and stratified by ecoclass. Data collected included canopy closure,

downed wood, and primary vegetation species.

• Forty-seven vegetation macroplots have been established in Northeast (Coleman

and Bobowski 1991) and were sampled in 1991 and 1995. These data will be

used for validation of some of the photointerpreted vegetation variables, such

as canopy cover and primary and secondary species by layer.

• Twenty-nine plots were sampled in summer 1995 for accuracy assessment.

The circular plots (30-m diameter) were centered on points 50 m north of 29

traffic counters, which have known UTM coordinates from DGPS readings

(see Rowland 1996 for further details).

• Spatial accuracy of some vegetation polygon boundaries at Starkey was

checked by delineating obvious stand boundaries (29 segments total) on

orthophotos (1:24,000) and digitizing them (STRINGS software). These segments were visually compared to the original boundaries mapped by the FS

(figs. 8A, B); correspondence was generally less than 50 m. A similar comparison was made of locations along fences mapped with GPS receivers

versus those visible on orthophotos.

Figure 8—Verification of La Grande Ranger District vegetation stand boundary map by locating and digitizing obvious boundaries on orthophoto

quadrangles (A); close-up view of 6 of the 29 segments compared (B).

17
• Most of the map layers provided by the FS for the habitat database were

digitized from orthophotos. To test the accuracy of digitizing from orthophotos,

a comparison was made between six locations digitized from USGS orthophotos

and the same locations mapped with DGPS (fig. 9; see Rowland 1996). Mean

offset was 16 m (easting) and 9 m (northing).

Suggestions for Future

Accuracy Assessment

and Monitoring

The requirement for accuracy assessment of the database is not uniform and

depends on such factors as the probability that the data are erroneous, how often

a variable will be used in analyses, what assumptions or further estimates will be

based on the value recorded for a variable, the cost involved in verification, and

the consequences of using erroneous data. In essence, some risk assessment is

necessary. Although formal, quantitative verification and monitoring of the habitat

database should be conducted, a more informal, qualitative review also will help

eliminate errors from the database. Nugent (1995) lists several basic techniques

to follow for quality control.

One data set collected previously that could be used to verify portions of the

database is the soil survey and habitat type classification delineated on aerial

black-and-white photographs taken in 1955 (Burr 1960). Another data set that

may be used in verification of photointerpreted vegetation variables is the CIMS

information (see “Vegetation,” above). If the elk habitat categories are used extensively in future habitat analyses, a more accurate rectification of the Landsat scene

may be necessary. Recent work has shown that Landsat scenes rectified using

GPS receivers at ground control points (versus points identified on 7.5-minute

quadrangles) may be far more accurate (Cook and Pinder 1996).

Figure 9—Comparison of locations for points mapped individually with GPS, points visible on

orthophoto quadrangles, and road and fence layers in the habitat database.

18
To summarize:

• The accuracy of the Starkey habitat database is within the commonly used standards for error in GIS and likely is better described than most (Rowland 1996).

• Of the variables for which error rates are unknown, those related to vegetation

probably should be tested first, especially ecoclass and species in the understory

layer(s) that may provide forage for ungulates.

• Data collected in the macroplot studies in Northeast should be used for comparison

with database vegetation variables wherever such comparisons are valid. These

data have been collected twice and represent intensive field work at Starkey.

• A regular schedule of sampling and monitoring at random sites within the Starkey

Project area would provide periodic checks on the accuracy of the database.

Using the Habitat

Database—Sample

Applications

Road Densities and Elk

Habitat Effectiveness

One objective of the Starkey Project is to develop and validate an elk habitat effectiveness (HE) model for summer range. The road component of such a model is

being tested by evaluating elk locations in relation to road densities and traffic

frequencies within Starkey. Two road models are now used in forest planning in the

Blue Mountains (Edge and others 1990): the curve in Thomas and others (1988),

which in turn was derived from Lyon (1983); and the multifunction curve in Thomas

and others (1979), developed with pellet count data collected by Perry and Overly

(1977) in the Blue Mountains of Washington. Most road models present elk HE

(dependent variable) as a linear function of open road density.

To validate these road models, open road densities and distribution at Starkey were

first explored in several ways through the habitat database (Rowland 1997). One

method was to calculate the road density in a square mile (2.6 km2

) centered on

each of the 86,000 pixels in Main study area by using a moving window procedure

(“Average Pixels” routine) in UCELL5 (McGaughey 1995). (Roads in a 0.8-km buffer

surrounding Main were included in the database where this calculation was performed.) Any pixel with an open road present (defined as all road use categories

except ZERO or CLOSED; see table 4) was counted as a “hit” in this procedure.

Total “hits” were then multiplied by a constant for the average length of a road

segment in a single pixel within Starkey, generating a new map layer of pixel-bypixel road densities on a square mile basis. These density values were then joined

in categories (for example, between 2 and 3 miles [3.2 and 4.8 km] of open road per

square mile) and plotted as isopleths in UVIEW, providing a graphic depiction of

areas with very low, as well as high, open road densities (fig. 10).

This portrayal of road densities on a fine scale aided subsequent delineation of

analysis units, by spanning a range of road densities, for validating the road model

within Main study area. The 15 units, averaging 506 ha, were created as a spatial

database in Paradox with a series of queries on the UTM coordinates. Before unit

boundaries were drawn, however, Main was first subdivided by subwatersheds to

give the boundaries more biological credence and conform with current NF management and planning schemes. Thus, analysis units were delineated within the

three primary subwatersheds (that is, unit boundaries did not cross subwatershed

boundaries). Actual length of open roads in each unit was then calculated by overlaying a roads map with a vector (polygon) map of the analysis units. Further analysis to calculate proportion of elk use in each habitat analysis unit is underway.

19
Forage Allocation

Modeling

Predicting distributional relations and allocating forage among elk, mule deer, and

cattle is a primary goal of the Starkey Project (Rowland and others 1997). To meet

this goal, a forage allocation model was developed by using linear programming

(Johnson and others 1996) to test modeling assumptions and develop methods for

spatially displaying modeling results. The model was designed to track, on a monthly

time-step, forage quality and quantity in four generalized habitat types: riparian, grasslands, open-canopy forests (10 to 40 percent canopy cover), and closed-canopy

forests (greater than 40 percent canopy cover). These habitat types were derived

from the habitat database as follows. Riparian areas were any pixels within 90 m

of a class I stream. “Veg code,” or life form of vegetation, was used to derive the

grasslands and forested types, combined with canopy cover in vegetation layer 1

(see table 3, appendix 1) to distinguish between open- and closed-canopy forests.

Forage quality and quantity values were obtained from previously published studies

conducted within the SEFR or nearby sites and were assigned to the appropriate

habitat types (Johnson and others 1996).

Modeled distributions of ungulates within all of Meadow Creek and Main study

areas (fig. 6) were based on species-specific animal responses to five environmental variables in the habitat database: slope, distance to water, distance to roads,

distance from cover into forage, and distance from forage into cover. Variables were

Figure 10—Open road densities (in miles per square mile) for Main

study area, Starkey, calculated with an averaging procedure in

UCELL5 and displayed in density isopleths. Open roads include

all road use categories except “ZERO” or “CLOSED.”

20
divided into several categories (for example, three slope categories of 0-15, 16-35,

and greater than 35 percent); each category was then assigned a habitat preference

score from 0.0 to 1.0. Variable selection and scores were developed from published

studies (McInnis and others [1990] for cattle and Thomas and others [1979] for deer

and elk).

The habitat database was then queried to identify and create polygons that contained unique combinations of the various categories of the five environmental

variables. Habitat type was linked to those polygons, and the associated forage

and environmental data were used in the model to predict elk, mule deer, and

cattle distributions based on forage production and utilization constraints. Three

model variates were tested (see Johnson and others 1996 for details); in the “max

preference” model, species grazed in preferred habitats first (based on nonforage

variables) and then moved to less preferred habitats. Results from the “max preference” model were then linked with the DEM to illustrate areas where the three

species were predicted to forage in September (fig. 11A). Observed distributions

of the three ungulate species in September 1992 and 1993 also are displayed

(fig. 11B). The model assumes that all Main and Meadow Creek study areas are

available to all three species, which was not true. Radio-telemetered cattle were

excluded from Meadow Creek and a portion of Main. Consequently, comparisons

of observed and predicted distributions for cattle must allow for this discrepancy.

Elk

Deer

Cattle

Elk and deer

Elk and cattle

All 3 species

Figure 11—Predicted distribution of elk, mule deer, and

cattle within the Starkey Experimental Forest and Range,

using the “max preference”

model for September (A; see

text), and actual distribution

from the automated animal

telemetry system in September

1992-93 (B).

Elk

Deer

Cattle

Elk and deer

Elk and cattle

All 3 species

21
Deriving Variables of

Traffic Rate

How deer and elk are distributed in relation to roads having different rates of motorized traffic is a fundamental question on which the Starkey traffic study is based (see

earlier description of this study). To answer this question, the rate of traffic (number

of vehicles per unit time) is being summarized by season and year, for both Main and

Northeast, from 1989 to the present. Following is a description of how variables of

traffic rate were estimated for spring 1994, as an example of how data on traffic

frequency are brought into the habitat database for analysis in relation to ungulate

distributions.

Traffic counters, placed at nearly every road intersection at Starkey, provide summaries of traffic counts on 15-minute intervals, 24 hours a day, from spring through

fall each year. For spring 1994 (April 15 to June 13), we first assigned each traffic

counter to an associated section of road. Because counters are placed along roads

just after their intersection with other roads, each counter estimates the frequency

of traffic unique to an associated section of road.

After assigning counters to road sections, we used a moving window analysis to

explore and estimate differences in rate of traffic during day versus night. We did

this by (1) summing the frequency of traffic at each counter for two 12-hour periods,

one for day and one for night; (2) calculating the ratio of traffic counts for the daytime

12-hour period to the traffic counts for the nighttime 12-hour period; and (3) shifting

the daytime and nighttime periods successively 1 hour until all unique 12-hour periods

of the day were encompassed. The resulting ratios indicated the 12-hour period of

greatest difference in day versus night traffic frequency for each counter, as well as

across all counters. The 12-hour period exhibiting the strongest difference in day

versus night traffic across all counters was then identified and used to define daytime

and nighttime. Distributions of daytime estimates of traffic frequency were plotted for

all counters, and categories established based on obvious groupings or “breaks” in

the data.

These groupings were defined as intervals of traffic rate, with daytime variables

having five rates, based on a 12-hour interval: (5) high, 10 or more vehicles; (4)

moderate, 4 to less than 10 vehicles; (3) low, 1 to less than 4 vehicles; (2) very low,

more than zero but less than 1 vehicle; and (1) zero vehicles. Similar variables of

low, very low, and zero traffic rates were identified for nighttime periods. Daytime

and nighttime rates were then assigned to their respective traffic counters, and in

turn, to the roads associated with each counter.

We then measured the distance of each 30- by 30-m pixel to the nearest road of

each day and night rate, by using UTOOLS software (Ager and McGaughey 1997).

For each pixel, we derived eight variables related to traffic rate for spring 1994:

distance to nearest high, moderate, low, very low, and zero vehicle roads per

12-hour daytime period; and distance to nearest low, very low, and zero vehicle

roads per 12-hour nighttime period (fig. 5). These variables are being analyzed in

relation to deer and elk distributions for predefined seasons (fig. 12).

22
Figure 12–Elk and deer distribution in May 1994 in relation to roads in Starkey, 

categorized by rate of daytime traffic during spring 1994. Traffic rates, in number

of vehicles per 12 hours, are as follows: low–1 to less that 4 vehicles; moderate–

4 to less than 10 vehicles; and high–10 or more vehicles.

23

LEGEND

Deer locations

Elk locations

Game Fence

Low traffic rate

Moderate traffic rate

High traffic rate

0 2 4 6 Kilometers
Recommendations Users of the habitat database should be fully aware of variable definitions before

undertaking analyses. These are found in lookup tables for each resource (table 1).

For example, the variable “Total CC,” or total canopy cover, was derived from photointerpretation and represents all vegetation cover present in all layers, including grasslands; it does not represent only tree canopy closure. If only tree cover is needed for

analysis, the user needs to construct a query combining other vegetation variables,

such as “Canclos1” and “Size1.”

Documentation of information in a GIS, including the data source(s), personnel

involved in map creation, dates of creation, and methods used, is strongly recommended, primarily to guide future users and architects of the database. Without

adequate records, it may be impossible to re-create portions of the database if

necessary. In long-term research like the Starkey Project, new hypotheses will

be developed and tested; the habitat database must be dynamic and flexible to

accommodate future research. As new layers or variables are added to the database, their creation and construction should be described as in tables 8-11 (appendix

2), including any known or potential sources of error. Accuracy should be evaluated

for those variables that are most used in analysis. This may require field sampling

or sampling from aerial photographs or other sources.

Acknowledgments Jack Ward Thomas first broached the Starkey research, designed and wrote the

initial proposals for the traffic effects and intensive timber management studies,

and obtained Forest Service funding for the research. Larry D. Bryant was instrumental in designing and procuring the automated telemetry system for tracking

animals, overseeing construction and management of Starkey facilities, and in

coordinating timber studies and livestock use within Starkey. Many of the ideas

later put in practice in the creation of the Starkey habitat database were first suggested by Donavin Leckenby. Catherine Poppenwimer took over development of the

database for 2 years (1988-90) while Priscilla Coe was on leave. Leonard Erickson,

Jim Noyes, Catherine Poppenwimer, and La Grande Ranger District GIS personnel

worked many hours digitizing. Dennis Isaacson, Donavin Leckenby, Cecilia Noyes,

and Barbara Wales provided expertise in classification and accuracy assessment

of the 1991 Landsat scene. Nathan Armburst, Dale Borum, Richard Collins, Brett

Cooper, Ryan Kennedy, and Roanna Ruston collected GPS data in the field. Brian

Fischer (LGRD GIS coordinator), Anne Kramer (Wallowa-Whitman GIS coordinator),

and Art Kreger (Wallowa-Whitman soil scientist) maintain the vegetation, streams,

and soils portions, respectively, of the FS databases used in the Starkey Project.

Dave Motanic (Umatilla NF) photointerpreted vegetation characteristics at Starkey

for 2 years.

Richard Collins and Randall Nielsen (GREZ) were instrumental in establishing and

maintaining the roads portion of the database. Joan Vaughan provides networking

assistance in connecting to these databases for updates. Robert McGaughey, with

coauthor Alan Ager, developed the UTOOLS software used to create the rasterbased portion of the database. We appreciate additional information provided by

Brian Fischer, Glenn Fischer, Art Kreger, and Roanna Ruston, and editing by

Scott Findholt, Brian Fischer, John Kie, and Donavin Leckenby.

24
Equivalents 1 kilometer (km) = 0.62 mile

1 meter (m) = 1.09 yards

1 meter (m) = 3.28 feet

1 centimeter (cm) = 0.39 inch

1 square kilometer (km2

) = 0.39 square mile

1 square kilometer (km2

) = 247.1 acres

1 hectare (ha) = 2.47 acres

References Ager, Alan A.; McGaughey, Robert J. 1997. UTOOLS: microcomputer software

for spatial analysis and landscape visualization. Gen. Tech. Rep. PNW-GTR-397.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 15 p.

Bryant, Larry D.; Thomas, Jack W.; Rowland, Mary M. 1993. Techniques to

construct New Zealand elk-proof fence. Gen. Tech. Rep. PNW-GTR-313.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 17 p.

Burr, James A. 1960. Soil survey, Starkey Experimental Forest and Range, Union

and Umatilla Counties, Oregon. Unpublished report. On file with: Evelyn Bull,

Pacific Northwest Research Station, Forestry and Range Sciences Laboratory,

1401 Gekeler Lane, La Grande, OR 97850.

Butler, William I.; Stehn, Robert A.; Balogh, Gregory R. 1995. GIS for mapping

waterfowl density and distribution from aerial surveys. Wildlife Society Bulletin.

23(2): 140-147.

Cimon, Norman J.; Collins, Richard K.; Bryant, Larry D.; Thomas, Jack Ward.

1996. Automated loading of Starkey traffic count data into a relational database

and subsequent use of the database for data validation. Unpublished report.

On file with: Pacific Northwest Research Station, Forestry and Range Sciences

Laboratory, 1401 Gekeler Lane, La Grande, OR 97850.

Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G. 1993. A multivariate

model of female black bear habitat use for a geographic information system.

Journal of Wildlife Management. 57(3): 519-526.

Coe, Priscilla Kolb; Quigley, Thomas M. 1986. Application of a geographic

information system for the Oregon Range Evaluation Project. In: Proceedings

of the geographic information systems workshop; 1986 April 1-4; Atlanta, GA.

Falls Church, VA: American Society for Photogrammetry and Remote Sensing:

88-98.

Coleman, Brett; Bobowski, Benny. 1991. Methodology: Starkey Experimental

Forest vegetation studies, intensive timber management area. [6 p.]. Unpublished

paper. On file with: Pacific Northwest Research Station, Forestry and Range

Sciences Laboratory, 1401 Gekeler Lane, La Grande, OR 97850.

Cook, Allen E.; Pinder, John E., III. 1996. Relative accuracy of rectifications using

coordinates determined from maps and the global positioning system. Photogrammetric Engineering and Remote Sensing. 62(1): 73-77.

25
Corbley, Kevin P. 1996. One-meter satellites: expanding imagery for GIS. Supplement to Geo Info Systems. July: 28-33.

Dana, Peter H.; Fowler, Walt; Hindman, David. 1989. Automated animal-tracking

system: tracking elk with retransmitted loran-C. In: Proceedings of the 18th annual

technical symposium of the Wild Goose Association; 1989 October 29-November

1; Hyannis, MA. Bedford, MA: The Wild Goose Association: 53-61.

Donovan, Michael L.; Rabe, Dale L.; Olson, Charles E., Jr. 1987. Use of geographic information systems to develop habitat suitability models. Wildlife Society

Bulletin. 15: 574-579.

Duncan, Brean W.; Breininger, David R.; Schmalzer, Paul A.; Larson, Vickie L.

1995. Validating a Florida scrub jay habitat suitability model, using demography

data on Kennedy Space Center. Photogrammetric Engineering and Remote

Sensing. 61(11): 1361-1370.

Eastman, J. Ronald. 1992. IDRISI user’s guide. Version 4.0. Worcester, MA: Clark

University Graduate School of Geography. 178 p.

Edge, W. Daniel; Olson-Edge, Sally L.; Irwin, Larry L. 1990. Planning for wildlife

in National Forests: elk and mule deer habitats as an example. Wildlife Society

Bulletin. 18: 87-98.

Findholt, Scott L.; Johnson, Bruce K.; Bryant, Larry D.; Thomas, Jack W. 1996.

Corrections for position bias of a loran-C radio-telemetry system using DGPS.

Northwest Science. 70(3): 273-280.

Goodchild, Michael; Gopal, Sucharita, eds. 1992. Accuracy of spatial databases.

New York: Taylor and Francis Ltd. 290 p.

Hall, Frederick C. 1973. Plant communities of the Blue Mountains in eastern Oregon

and southeastern Washington. R6 Area Guide 3-1. Portland, OR: U.S. Department

of Agriculture, Forest Service, Pacific Northwest Region. 62 p.

Hitchcock, Mark; Ager, Alan. 1992. Microcomputer software for calculating an elk

habitat effectiveness index on Blue Mountain winter ranges. Gen. Tech. Rep.

PNW-GTR-301. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 13 p.

Johnson, Bruce K.; Ager, Alan; Crim, Sarah A. [and others]. 1996. Allocating

forage among wild and domestic ungulates—a new approach. In: Olson-Edge,

Sally L.; Edge, W. Daniel, eds. Proceedings of a symposium on sustaining

rangeland ecosystems; 1994 August 29-31; La Grande, OR. Spec. Rep. 953.

Corvallis, OR: Oregon State University Extension Service: 166-169.

Johnson, Bruce K.; Noyes, James H.; Thomas, Jack Ward; Bryant, Larry D.

1991. Overview of the Starkey Project: current measures of elk vulnerability. In:

Christensen, Alan G.; Lyon, L. Jack; Lonner, Terry N., comps. Proceedings of

elk vulnerability—a symposium; 1991 April 10-12; Bozeman, MT. Bozeman, MT:

Montana State University: 225-228.

Johnson, Charles G., Jr.; Simon, Steven A. 1987. Plant associations of the

Wallowa-Snake Province, Wallowa-Whitman National Forest. R6-ECOLTP-255A-86. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Region. 399 p. [plus appendices].

26
Johnson, Charles Grier, Jr.; Clausnitzer, Rodrick R. [1992]. Plant associations

of the Blue and Ochoco Mountains. R6-ERW-TP-036-92. [Portland, OR]: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Region,

Wallowa-Whitman National Forest. 16 p. [plus appendices].

Koeln, Gregory T.; Cowardin, Lewis M.; Strong, Laurence L. 1994. Geographic

information systems. In: Bookhout, Theodore A., ed. Research and management

techniques for wildlife and habitats. 5th ed. Bethesda, MD: The Wildlife Society:

540-566. Chapter 21.

Kvamme, K. 1988. Development and testing of quantitative models. In: Judge,

J.; Sebastian, L., eds. Quantifying the present and predicting the past: theory,

method, and application of archaeological predictive modeling. Denver, CO:

U.S. Department of the Interior, Bureau of Land Management: 325-428.

Leckenby, Donavin A.; Isaacson, Dennis L.; Thomas, Sylvan R. 1985. Landsat

application to elk habitat management in northeast Oregon. Wildlife Society

Bulletin. 13(2): 130-134.

Lyon, L. Jack. 1983. Road density models describing habitat effectiveness for elk.

Journal of Forestry. 81(9): 592-595.

McGaughey, Robert. 1995. UTOOLS. http://forsys.cfr.washington.edu/utools.html.

McInnis, M.L.; Quigley, T.M.; Vavra, M.; Sanderson, H.R. 1990. Predicting beef

cattle stocking rates and live weight gains on eastern Oregon rangelands:

descriptions of a model. Simulation: 137-145.

Mowrer, H. Todd; Czaplewski, Raymond L.; Hamre, R.H. 1996. Spatial accuracy

assessment in natural resources and environmental sciences: 2d international

symposium; 1996 May 21-23; Fort Collins, CO. Gen. Tech. Rep. RM-GTR-277.

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain

Forest and Range Experiment Station. 728 p.

Noyes, Cecilia; Coe, Priscilla; Wales, Barbara [and others]. 1994. Estimates of

canopy cover and defoliation from a Landsat scene for La Grande Ranger District.

19 p. [plus appendices]. Unpublished manuscript. On file with: Oregon Department

of Fish and Wildlife, Forestry and Range Sciences Laboratory, 1401 Gekeler Lane,

La Grande, OR 97850.

Nugent, Jeffrey L. 1995. Quality control techniques for a GIS database development

project. Photogrammetric Engineering and Remote Sensing. 61(5): 523-527.

Pacific Meridian Resources. [1997]. Contract information for landcover mapping

using Landsat TM imagery. [Place of publication unknown]: [Publisher unknown].

[Not paged].

Perry, Charles; Overly, Robert. 1977. Impact of roads on big game distribution in

portions of the Blue Mountains of Washington, 1972-1973. Bull. 11. [Olympia, WA]:

Washington Game Department, Environmental Management Division, Applied

Research Section. 38 p.

Rowland, Mary M. 1996. Accuracy of the Starkey habitat database. 23 p. [plus 4

figs.]. Unpublished report. On file with: Forestry and Range Sciences Laboratory,

1401 Gekeler Lane, La Grande, OR 97850.

27
Rowland, Mary M. 1997. Starkey elk/road model analyses. Part I—delineation of

road density grids. 12 p. Unpublished report. On file with: Forestry and Range

Sciences Laboratory, 1401 Gekeler Lane, La Grande, OR 97850.

Rowland, Mary M.; Bryant, Larry D.; Johnson, Bruce K. [and others]. 1997.

The Starkey Project: history, facilities, and data collection methods for ungulate

research. Gen. Tech. Rep. PNW-GTR-396. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 62 p.

Skidmore, A.K. 1989. A comparison of techniques for calculating gradient and

aspect from a gridded digital elevation model. International Journal of Geographical Systems. 3: 323-334.

Skovlin, Jon M. 1991. Fifty years of research progress: a historical document

on the Starkey Experimental Forest and Range. Gen. Tech. Rep. PNW-GTR-266.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 58 p.

Thomas, Jack Ward; Black, Hugh, Jr.; Scherzinger, Richard J.; Pedersen,

Richard J. 1979. Deer and elk. In: Thomas, Jack Ward, tech. ed. Wildlife habitats

in managed forests—the Blue Mountains of Oregon and Washington. Agric.

Handb. 553. Washington, DC: U.S. Department of Agriculture: 104-127.

Thomas, Jack Ward; Leckenby, Donavin A.; Henjum, Mark [and others]. 1988.

Habitat-effectiveness index for elk on Blue Mountain winter ranges. Gen. Tech.

Rep. PNW-GTR-218. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station. 28 p.

Thompson, Morris M. 1987. Maps for America. 3d ed. Reston, VA: U.S. Department

of the Interior, Geological Survey. 265 p.

Trimble Navigation, Ltd. 1992. Introduction to GPS and GIS. In: [Author unknown].

General reference for the GPS Pathfinder Professional System, version 2.X;

manual 18470-00. Sunnyvale, CA: Trimble Navigation, Ltd. Chapter 2.

U.S. Department of Agriculture, Forest Service. 1991. Tri-Forest data dictionary:

Umatilla National Forest, Wallowa-Whitman National Forest, Malheur National

Forest. [Place of publication unknown]: [U.S. Department of Agriculture, Forest

Service]. 1-1 to 90-17. [plus appendices A-1 to A-26].

U.S. Department of Agriculture, Forest Service. 1992. R-6 mapping standards.

[Portland, OR]: [Pacific Northwest Region]. 6 p.

U.S. Department of Agriculture, Forest Service. 1994. Current vegetation survey.

Version 1.4. Natural resource inventory. Portland, OR: Pacific Northwest Region.

82 p.

28
U.S. Department of the Interior, Bureau of Land Management. [1990]. Map

overlay statistical system. MOSS user’s manual. Denver, CO: Service Center.

[Irregular pagination].

U.S. Department of the Interior, Geological Survey. 1986. Standards for digital

elevation models. National mapping program technical instructions, open-filereport 86-004. [Place of publication unknown]: National Mapping Division.

[Irregular pagination].

Walsh, Stephen J.; Lightfoot, Dale R.; Butler, David R. 1987. Recognition

and assessment of error in geographic information systems. Photogrammetric

Engineering and Remote Sensing. 53(10): 1423-1430.

Wickman, Boyd E. 1992. Forest health in the Blue Mountains: the influence

of insects and disease. Gen. Tech. Rep. PNW-GTR-295. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station.

15 p. (Quigley, Thomas M., ed.; Forest health in the Blue Mountains: science

perspectives).

Wisdom, Michael J. 1998. Assessing life-stage importance and resource selection

for conservation of selected vertebrates. Moscow, ID: Univeristy of Idaho. 118 p.

Ph.D. dissertation.

29
Appendix 1

Variables in the

Starkey Habitat

Database, Their

Definitions, and Codes

Table 3—Variables in the raster (UTOOLS) portion of the Starkey habitat database, grouped by resource

in Paradox tablesa

Variable Variable Paradox table

acronymb Definition type or directory Units or codes

UTMGrid Universal Transverse Mercator Discrete All tables UTM coordinatesc

coordinate grid; identifies centerpoint of each 30- by 30-m pixel

IN-OUT Specifies whether pixel is inside Categorical, STKYAREAd IN or OUT

or outside the Starkey perimeter binary

fence

ElkFence Elk fence; pixels containing game- Categorical, STKYAREA 1, 0e

proof fence binary

CowFence Cattle fence; pixels containing Categorical, STKYAREA 0 or 2f

fences that enclose a cattle pasture multiple

DistEFnc Distance to nearest game-proof Continuous STKYAREA Meters

fence

NrstEFnc UTM coordinates of nearest pixel Continuous STKYAREA UTM coordinates

containing game-proof fence

ElkPast Elk pastures; areas separated Categorical, STKYAREA MAIN, NE, CAMPBELLg

by game-proof fence multiple HANDLING, EXCLOSURES

DistCFnc Distance to nearest cattle fence Continuous STKYAREA Meters

NrstCFnc UTM coordinates of nearest pixel Categorical, STKYAREA UTM coordinates

containing cattle fence multiple

CowPast Cattle pastures; separately fenced Categorical, STKYAREA SMITH-BALLY, HALFMOON,

cattle pastures multiple CAMPBELL, STRIP,g BEAR,

HORSE, NE

Salt1293 Salt sites located in 12/93 Categorical, STKYAREA 1, 0e

(incomplete) binary

Random 1,000 randomly selected pixels Categorical, STKYAREA 1 = point present; 0 = no point

binary

RoadSeg# Road segment number, created Categorical, STKYROADh Unique 12-digit number; first

with GIS multiple 7 digits are road number;

last five (incl. decimal point)

are milepost at beginning

of segment

BEG_DATE Beginning date; year that a given Discrete STKYROAD Year (last 2 digits)

set of attributes begins that apply

to a road segment

END_DATE Ending date; year that a given set Discrete STKYROAD Year (last 2 digits)

of attributes ends for a road segment

ROADNO FS road number; first part of RoadSeg# Categorical, STKYROAD Unique 7-digit number; first

variable multiple 2 digits are arterial route

number, next 2 are collector

number; last 3 are local

designation of road

30
Table 3—Variables in the raster (UTOOLS) portion of the Starkey habitat database, grouped by resource

in Paradox tablesa (continued)

Variable Variable Paradox table

acronymb Definition type or directory Units or codes

MP Milepost at the beginning of each Discrete STKYROAD 5-digit number, including

segment decimal point

LENGTH Length of road segment as calculated Continuous STKYROAD Miles

by GIS

TERMINI Beginning and ending points of each Discrete STKYROAD Road numbers to “end,”

road segment, usually at a road milepost, or different road

junction number

ROAD USE Major categories of road use in SEFR Categorical, STKYROAD Coded (table 4, appendix 1)

multiple

RDPRIOR Road priority; created to assign road Categorical, STKYROAD Coded (table 4, appendix 1)

use categories when >1 road segment multiple

occurs in the same pixel. Lowest

number is used.

DistOPEN Distance to nearest pixel containing Continuous STKYROAD Meters

a greendot or open road

NrstOPEN UTM coordinates of nearest pixel Categorical, STKYROAD UTM coordinates

containing an open or greendot road multiple

DistRSTR Distance to nearest pixel containing a Continuous STKYROAD Meters

restricted road (administrative, possible,

or N/A codes)

NrstRSTR UTM coordinates of nearest pixel Categorical, STKYROAD UTM coordinates

containing a restricted road multiple

DistCLSD Distance to nearest pixel containing Continuous STKYROAD Meters

a closed road (coded zero or closed)

NrstCLSD UTM coordinates of nearest pixel Categorical, STKYROAD UTM coordinates

containing a closed road multiple

FENCE Describes whether a given road Categorical, STKYROAD IN or OUT

segment is inside or outside the dichotomous

perimeter fence at Starkey

OPML Operational maintenance level of Categorical, STKYROAD Coded (table 4, appendix 1)

a road multiple

OBML Objective maintenance level of a Categorical, STKYROAD Coded (table 4, appendix 1)

road multiple

MILES “True” length of a road segment Continuous STKYROAD Nearest 0.01 mile

measured on the ground

DITCH Indicates whether a drainage ditch Categorical, STKYROAD Y = yes, N = no

abuts road segment dichotomous

ID Year of last road inventory Categorical, STKYROAD Last 2 digits of year, or N/A

for a given road segment multiple (road never inventoried)

SURF Type of road surfacing Categorical, STKYROAD Coded (table 4, appendix 1)

multiple

F_CL Functional classification of roads Categorical, STKYROAD Coded (table 4, appendix 1)

multiple

DV1 Design vehicle for alignment and Categorical, STKYROAD Coded (table 4, appendix 1)

strength multiple

DV2 Design vehicle for surfacing Categorical, STKYROAD Coded (table 4, appendix 1)

(smoothness) multiple

31
Table 3—Variables in the raster (UTOOLS) portion of the Starkey habitat database, grouped by resource

in Paradox tablesa (continued)

Variable Variable Paradox table

acronymb Definition type or directory Units or codes

CVH Critical design vehicle; type of Categorical, STKYROAD Coded (table 4, appendix 1)

vehicle that must be able to multiple

traverse the road on a limited basis

S_LVL Service level; significant traffic Categorical, STKYROAD Codedk

characteristics multiple

STRE Existing management strategy for Categorical, STKYROAD Coded (table 4, appendix 1)

a road multiple

STRF Future management strategy for Categorical, STKYROAD Same as above

a road multiple

CLASS Road classification; includes number Categorical, STKYROAD S = single lane; values for

of travel lanes followed by design dichotomous Starkey are S05, S10, S15,

speed (miles/hr) and S25

POSITION Position of road on the slope Categorical, STKYROAD Coded (table 4, appendix 1)

multiple

GRADE Average percentage of grade, Categorical, STKYROAD Coded (table 4, appendix 1)

categorized by risk factors multiple

WIDTH Road width Continuous STKYROAD Feet

ALIGNMNT Horizontal alignment of road Categorical, STKYROAD Coded (table 4, appendix 1)

multiple

Soils Soil types Categorical, STKYSOIL.DB Coded (table 5, appendix 1)

multiple

Elev Elevation; distance above sea level Continuous STKYTOPO.DB Meters

%Slope Percentage of slope Continuous STKYTOPO.DB Percent (0-∞)

Aspect Direction a slope faces; direction of Continuous STKYTOPO.DB Degrees (-1 to 359)m

maximum downward slope

Convex3 Convexity index (gulch index); Continuous STKYTOPO.DB Meters

topographic measure of terrain

surrounding a pixel

SINAspct Sine of aspect Continuous STKYTOPO.DB -1.00 to 1.00

COSAspct Cosine of aspect Continuous STKYTOPO.DB -1.00 to 1.00

#Towers Number of Ioran-c towers visible Categorical, STKYTOPO.DB 0-8

from a pixel multiple

StrmSeg# Stream segment number Categorical, STKYWATR.DB Unique 21-character identifier,

multiple including subwatershed,n

stream number, and milepost

StrmClas Stream class; subjective description Categorical, STKYWATR.DB Coded (table 6, appendix 1)

of the relative value of a stream based multiple

on its uses or on the potential of the

stream to affect beneficial uses of

streams lower in the watershed

Order Stream order; relative position of Categorical, STKYWATR.DB Coded (table 6, appendix 1)

stream segment in a watershed multiple

Flow Stream flow; proportion of year that Categorical, STKYWATR.DB Coded (table 6, appendix 1)

water flows or is ponded in a segment multiple

FlowAcc Accuracy of stream flow measurements Categorical, STKYWATR.DB Coded (table 6, appendix 1)

multiple

32
Table 3—Variables in the raster (UTOOLS) portion of the Starkey habitat database, grouped by resource

in Paradox tablesa (continued)

Variable Variable Paradox table

acronymb Definition type or directory Units or codes

WaterPt6 Water point; water sources other Categorical, STKYWATR.DB Developed and undeveloped

than streams multiple springs; pond, reservoir, or

well

Lansat91 Elk habitat categories Categorical, LANSAT91.DB 1=forage, 2=marginal cover,

multiple (in STKYPI) 3=satisfactory cover

Ecoclass Plant association Categorical, STKYEVG Coded (table 7, appendix 1)

multiple

STANDID Unique identifier of vegetation Categorical, STKYPIo 12-digit number-letter

stands larger than 5 acres multiple combination

Veg code Life form of vegetation Categorical, STKYPI Coded (table 7, appendix 1)

multiple

TOTAL CC % vegetated or % canopy closure Continuous STKYPI 0-100%

(cover); sum of cover in all

vegetation layers

#Layers Number of vegetation layers Discrete STKYPI 1, 2, or 3

LYRCODE1-3 Layer type of vegetation layers 1-3 Categorical, STKYPI Coded (table 7, appendix 1)

multiple

LYR1SPC1-3 Primary, secondary, and tertiary Categorical, STKYPI Codedn

plant species in layer 1 multiple

SIZE1-3 Average size class of trees Categorical STKYPI Coded (table 7, appendix 1)

in layers 1-3 multiple

CanClos1-3 % canopy closure of all species Continuous STKYPI 0-100%, in 5% increments

in layers 1-3

Crndia1-3 Average crown diameter of Continuous STKYPI Feet

vegetation in layers 1-3

LYR2SPC1-3 Primary, secondary, and tertiary Categorical, STKYPI Codedn

plant species in layer 2 multiple

LYR3SPC1-3 Primary, secondary, and tertiary Categorical, STKYPI Codedn

plant species in layer 3 multiple

MgmtActiv Timber harvest management activity Categorical, STKYPI Coded (table 7, appendix 1)

multiple

a Many of these variables also are accessible in the Arc/Info platform of the database, especially the roads and vegetation variables. Variable

names may be somewhat different, however.

b Name of field in the database tables. c Composed of 6-digit easting and 7-digit northing coordinates in the UTM grid system, North American Datum 1983, zone 11. d STKYAREA contains two tables, AREAAFTR.DB and AREAB4.DB; these correspond to time periods after and before the Campbell Flat

elk fence was constructed in September 1992.

e A “1” indicates habitat feature present in that pixel; a “0” indicates feature absent. f A “0” indicates no fence; a “2” indicates barbed wire fence.

g CAMPBELL is not an ElkPast option in the AREAB4.DB table, which applies to the time before the Campbell elk fence was erected; likewise,

STRIP is not a CowPast option in this table. In AREAB4.DB, CAMPBELL is a cattle pasture in Main, and STRIP is part of CAMPBELL.

h STKYROAD contains 12 tables, based on year. See table 1, main text. k C = interrupted by limited passing facilities, or slowed by road construction; D = flow is low or may be blocked by an activity, such that

2-way traffic is difficult and may require backing to pass.

m -1 = no slope or aspect (flat); 0 = north. n See Tri-Forest Data Dictionary (USDA Forest Service 1991; p. 32-1 to 32-15 for subwatershed codes; p. 13-51 to 13-55 for plant species

codes). For vegetation entered by layers (for example LYR1SPC1, 2, or 3), species are listed in decreasing order of canopy closure, and

each must comprise at least 20 percent of the canopy closure.

o STKYPI contains two tables, 1987PI.DB and 1993PI.DB, that correspond to the two sets of aerial photographs interpreted.

33
Table 4—Codes and descriptions for variables in the roads portion of the Starkey habitat databasea

Field or variable

nameb Code Description

ROAD USE:

Inside fence Zero Barricaded or otherwise known to have no traffic

Possible Not open to public and rarely used; not physically closed

Administrative Receives only administrative use

Green dot Open to public from May 1 until mid-December; moderate to heavy use

Outside fence Closed Closed to all traffic by gate, windfall, or barricade

Open Open to public; moderate to heavy use

N/A Not inventoried; no information available

RDPRIOR 1 Green dot

2 Open

3 Administrative

4 Possible

5 N/A

6 Zero

7 Closed

OPML, OBML 0 Obliterated, to be returned to full resource production; usually ripped,

seeded, and drainage removed

1 Closed; annual culvert-drainage check; clean as necessary

2 Maintain for high clearance vehicles; brush out, check drainage

3 Lowest level maintained for low clearance vehicles (passenger cars);

brush out, check drainage, blade as needed

SURF 2 Open crushed or high-quality pit run

3 Dense graded crushed rock

4 Dirty pit run

6 Native or less than 25% spot rocked

F_CL:

LTCC Long term (permanent road), constant service (open for use on a

continuous or recurrent basis each year); collector (connected to

public or forest arterial road)

LTCL Long term, constant service, local (connects terminal facilities, landings,

campgrounds, buildings, etc., with arterial, collector, or other local roads)

LTIL Long term, intermittent use (road closed for >1 year between periods of

use), local

ST Short term, temporary roads with limited life, to be returned to resource

production

DV1, DV2, CVH 7A Loader-yarder (width at least 3.7 m)

7B Standard log truck

7C Lowboy configuration

7D 14-m chip van

7E 8.5-m truck with trailer

7F High clearance = 25 cm

7G Low clearance = 10 cm

7H Not applicable

34
Table 4—Codes and descriptions for variables in the roads portion of the Starkey habitat databasea

(continued)

Field or variable

nameb Code Description

STRE, STRF OTPT Open to passenger car, public use (always use with maintenance levels 3,

4, and 5)

ACC Accept use (used mostly)

ELST Eliminate; all use eliminated

ELLT Eliminate; all use inactivated

ENC1 Encourage high clearance vehicle use; used mostly when a destination

sign is used or planned for use

ENC2 Encourage project use

DIS1 Discourage low clearance vehicle; rarely used on level 2 roads

DIS2 Discourage public use

DIS3 Discourage snowmobile use

DIS4 Discourage all use

PRO1 Prohibit dispersed recreation

PRO2 Prohibit public use

PRO3 Prohibit snowmobile use

POSITION 5 Draw bottom (within 46 m of drainage bottom)

4 Lower 1/3 of slope, but farther than 46 m from drainage bottom

2 Middle 1/3 of slope

1 Upper 1/3 of slope including ridge top

GRADE 5 Road grade exceeds 10%

4 Road grade is between 8 and 10%

3 Road grade is between 5 and 7%

1 Road grade is less than 5%

NA Road grade unknown (not inventoried)

ALIGNMNT E Excellent

G Good

F Fair

P Poor

C Primitive (alignment that is impassable by logging trucks)

a These codes also are found in the database table ROADCODE.DB. b Variable names defined in table 3, appendix 1.

35
Table 5—Codes and descriptions for the variable “SoilCode” in the

rasterized portion of the Starkey habitat databasea

Symbol Map unit name

1Ab Vitrandic Xerochepts-Typic Vitrixerands frigid-Aquic Udorthents

complex, 0-5% slopes

5A Typic Argixeroll-Mollic Palexeralf complex, 0-5% slopes

165C Getaway-Threecabin-Rock outcrop complex, 30-60% slopes

170A Syrupcreek-Limberjim complex, 0-15% slopes

170B Syrupcreek-Limberjim complex, 15-30% slopes

170C Syrupcreek-Limberjim complex, 30-60% slopes

171A Limberjim-Syrupcreek complex, 0-15% slopes

171B Limberjim-Syrupcreek complex, 15-30% slopes

171C Limberjim-Syrupcreek complex, 30-60% slopes

174A Syrupcreek-Lowerbluff complex, 0-15% slopes

174B Syrupcreek-Lowerbluff complex, 15-30% slopes

175A Klicker-Syrupcreek complex, 0-15% slopes

175B Klicker-Syrupcreek complex, 15-30% slopes

175C Klicker-Syrupcreek complex, 30-60% slopes

176C Klicker-Limberjim complex, 30-60% slopes; includes former

code 166C

181A Downeygulch-Lowerbluff complex, 2-15% slopes

181B Downeygulch-Lowerbluff complex, 15-30% slopes

182A Syrupcreek-Tamarak complex, 0-15% slopes

190A Anatone-Bocker-Fivebit complex, 0-15% slopes

190B Anatone-Bocker-Fivebit complex, 15-30% slopes

190C Anatone-Bocker-Fivebit complex, 30-60% slopes

191A Albee-Bocker complex, 2-15% slopes

191B Needham-Parsnip moist-Bocker complex, 15-30% slopes

192A Bunchpoint-Bocker complex, 2-15% slopes

192B Fivebit-Bocker-Kamela complex, 15-30% slopes; formerly 192C

195A Fivebit-Klicker-Anatone complex, 0-15% slopes

195B Klicker-Thirstygulch-Anatone complex, 15-30% slopes

195C Klicker-Thirstygulch-Anatone complex, 30-60% slopes

195D Klicker-Fivebit-Anatone-Rock complex, 60-90% slopes

196A Klicker-Fivebit-Kamela complex, 0-15% slopes

196B Klicker-Fivebit-Kamela complex, 15-30% slopes

196C Klicker-Fivebit-Kamela complex, 30-60% slopes

196D Klicker-Fivebit-Kamela complex, 60-90% slopes

197A Bocker-Anatone-Rock outcrop complex, 2-15% slopes

197B Bocker-Anatone-Rock outcrop complex, 15-30% slopes

197C Bocker-Imnaha south-Rock outcrop complex, 30-60% slopes

198A Snell-Bocker-Anatone complex, 0-15% slopes

a These codes also are found in the Paradox database SOILCODE.DB. b 1A and 5A are alluvium, meadows, or miscellaneous land types. All other codes are basalt,

andesite, hard rhyolite, or hard tuff.

36
Table 6—Codes and their descriptions for variables related to streamsa

Variable

nameb Code Description

StrmClas 1 Class I—perennial or intermittent stream segments that have one or more of the

following characteristics:

—used by sensitive, threatened, endangered, or large numbers of other (nonlisted) fish

—direct source of water for domestic use

—flow enough water to be a major contributor to the quantity of water in a class 1 stream

2 Class II—perennial or intermittent stream segments that have one or more of the following

characteristics:

—used by fish but does not satisfy class I criteria

—flow enough water to be a major contributor to the quantity of water in a class II stream

3 Class III—all other stream segments that do not meet higher class criteria, and are perennial or

have bankside riparian shrubs or both

4 Class IV—all other stream segments that are intermittent but do not meet higher class criteria

5 Category E—all stream segments that have ephemeral flow duration

Order 0 Ephemeral draw channels

1 Small, unbranched tributaries with a defined scour channel

2 Produced by the junction of two first-order streams

3 Produced by the junction of two second-order streams

4-6 As above

FlowAcc C Good information, obtained from flow designations shown on USGS 7.5-min quadrangles

D Guess, based on the geomorphic setting of the segment

Flow E Ephemeral; water flows only during and immediately after precipitation or snow melt

N Intermittent; no riparian vegetation. Water flow ceases during dry season

P Perennial; water flows yearlong in stream segment except in periods of extreme drought

a These codes also are found in the Paradox table WATRCODE.DB. b Variable names are explained in table 3, appendix 1.

37
Table 7—Codes and their descriptions for vegetation variables

Variable

namea Code Description

Size1-3 1b Seedlings—trees less than 1 inch d.b.h.c

2 Seedlings and saplings mixed

3 Saplings—trees 1.0 to 4.9 inches d.b.h.

4 Saplings and poles mixed

5 Poles—trees 5.0 to 8.9 inches d.b.h.

6 Poles and small trees mixed

7 Small trees—9.0 to 20.9 inches d.b.h.

8 Small trees and medium trees mixed

9 Medium trees—21.0 to 31.9 inches d.b.h.

10 Medium and large trees mixed (large trees—

32.0 to 47.9 inches d.b.h.)

VegCode CX Coniferous forest

GM Moist grassland in forest zone

GX Other grassland

MM Moist meadow

SM Shrubland

SX Other shrubland

LYRCODE1-3 H Only one herb layer in stand

S Only one shrub layer in stand

T Only one tree layer in stand

1 First tree layer in stand

2 Second tree layer in stand

3 Third tree layer in stand

MgmtActiv HCPH Regeneration harvest, clearcutting, patch

HITH Intensive harvest method and other changes,

thinning

HPRC Regeneration harvest, partial removal

HROS Regeneration harvest, removal cut, overstory

HSST Regeneration harvest, seed cut, seed tree

HSSW Regeneration harvest, seed cut, shelterwood

Ecoclass AB Buildings, structures, roads

CD-G1-11 Ponderosa pine-Douglas-fir-elksedge; A 29d

CD-G1-21 Douglas-fir-pinegrass; B 332

CD-S6-11 Ponderosa pine-Douglas-fir-snowberry-oceanspray;

A 30

CD-S6-22 Douglas-fir-common snowberry; B 358

CD-G1-21 Douglas-fir-pinegrass; B 332

CD-S6-34 Douglas-fir-spirea; B 352

CD-G1-11 Ponderosa pine-Douglas-fir-elksedge; A 29

CD-S7-11 Ponderosa pine-Douglas-fir-ninebark; A 31

CD-S6-11 Ponderosa pine-Douglas-fir-snowberry-oceanspray;

A 30

CJ-G1-11 Juniper-bunchgrass; A 16

CJ-S8-11 Juniper-stiff sage scabland; A 17

CL-S5-11 Lodgepole-big huckleberry; A 35

CL-S4-11 Lodgepole-grouse huckleberry; A 36

CL-G2 Lodgepole-pinegrass; C 77

CL-G2-11 Lodgepole-pinegrass-grouse huckleberry; A 34

CL-S4-16 Lodgepole-pinegrass-grouse huckleberry; C 79

CL-F2-11 Lodgepole-twinflower; B 305

38
Table 7—Codes and their descriptions for vegetation variables (continued)

Variable

namea Code Description

CP-G1-11 Ponderosa pine-wheatgrass; A 25

CP-G1-12 Ponderosa pine-fescue; A 26

CP-G1-31 Ponderosa pine-Idaho fescue; B 378

CP-S5-22 Ponderosa pine-common snowberry; B 372

CW-G1-12 Mixed conifer-pinegrass-ash soils; A 33

CW-G1-11 Mixed conifer-pinegrass-residual soil; A 32

CW-S3-21 Grand fir-spirea; A 315

CW-S2-11 White fir-big huckleberry; A 38

CW-S8-11 White fir-grouse huckleberry; A 39

CW-F3-11 White fir-twinflower-forb; A 37

GB-91-11 Bluegrass scabland; A 8

GB-49-11 Bunchgrass on shallow soil, gentle slopes; A 9

GB-49-12 Bunchgrass on deep soil, gentle slopes; A 10

GB-49-13 Bunchgrass on shallow soil, steep slopes; A 11

GB-49-14 Bunchgrass on deep soil, steep slopes; A 12

GB-59-11 Idaho fescue-prairie junegrass (ridgetops); B 33

MD Dry meadow; A 4

MM Moist meadow; A 5

MW Wet meadow; A 6

NR Rocky land with minimal vegetation potential

SD-19-11 Low sagebrush-bunchgrass; A 14

SD-91-11 Stiff sage scabland; A 13

WR Running water-streams, creeks, rivers, ditches

a Variable names are explained in table 3, appendix 1. b Codes are from the Integrated Resource Inventory (IRI); see USDA Forest Service 1991,

p. 13-48.

c Diameter at breast height. d Letters and numbers refer to reference in which the ecoclass is defined and corresponding

page number; A = Hall 1973; B = Johnson and Simon 1987; C = Johnson and Clausnitzer 1992.

39
Appendix 2

Documentation and

Creation of Variables,

Source Maps, and

Tables in the Starkey

Habitat Database

Table 8—Creation of variables in the UTOOLS portion of the Starkey habitat database

Variable

acronym Type Method of creation Sourcea

UTMGrid NAb UCELL5c ; data conversion of MOSS vector map NA

to Paradox table

ElkFence Based UCELL5; data conversion of MOSS vector map FENCGPS3

to Paradox table

DistEFnc Derivede UCELL5; spatial operations, buffer all pixels ElkFence

NrstEFnc Derived UCELL5; spatial operations, buffer all pixels ElkFence

ElkPast Base UCELL5; data conversion of MOSS vector ELKPAST

map to Paradox table

CowFence Base UCELL5; data conversion of MOSS vector FENCGPS3

map to Paradox table

DistCFnc Derived UCELL5; spatial operations, buffer all pixels CowFence

NrstCFnc Derived UCELL5; spatial operations, buffer all pixels CowFence

CowPast Base UCELL5; data conversion of MOSS vector map COWPAST

to Paradox table

Salt1293 Base UCELL5; data conversion of MOSS vector map SALTD93

to Paradox table

Random Derived UCELL5; create database of random pixels UTMGrid

RoadSeg# Base UCELL5; data conversion of MOSS vector map TRN2OBT, TRN2INT,

to Paradox table TRN2EXT

BEG_DATEf Attributeg Paradox join of rasterized MOSS road maps with STARKEY.DB

STKROADS.DB roads database

MP Derived Paradox join of rasterized MOSS road maps with STARKEY.DB

STKROADS.DB roads database

LENGTH Derived Paradox join of rasterized MOSS road maps with STARKEY.DB

STKROADS.DB roads database

ROAD USE Attribute Paradox join of rasterized MOSS road maps STARKEY.DB

with STKROADS.DB roads database

DistOPENh Derived UCELL5; spatial operations, buffer all pixels ROAD USE

using “green dot” and “open” roads

SoilCode Base UCELL5; data conversion of MOSS vector SOILS4

map to Paradox table

Elev Base UCELL5; data conversion of MOSS/MAPS DEMSTKWI

SPSS file to Paradox table

%Slope Derived UCELL5; spatial operations; slope Elev

Aspect Derived UCELL5; spatial operations; aspect Elev

SINAspct Derived UCELL5; spatial operations, sine of Aspect

aspect

COSAspct Derived UCELL5; spatial operations, cosine of aspect Aspect

Convex3 Derived UCELL5; spatial operations, convexity; search Elev

distance = 129 m; elevational differential

= 500 m

40
Table 8—Creation of variables in the UTOOLS portion of the Starkey habitat database (continued)

Variable

acronym Type Method of creation Sourcea

StrmSeg# Base UCELL5; data conversion of MOSS vector map STRSTK2

to Paradox table

StrmClas Attribute Paradox join of rasterized MOSS streams map WAWSTR.DB

with WAWSTR.DB streams database

Order Attribute Paradox join of rasterized MOSS streams map WAWSTR.DB

with WAWSTR.DB streams database

Flow Attribute Paradox join of rasterized MOSS streams map WAWSTR.DB

with WAWSTR.DB streams database

FlowAcc Attribute Paradox join of rasterized MOSS streams map WAWSTR.DB

with WAWSTR.DB streams database

WaterPt6 Base UCELL5; data conversion of MOSS vector map WATERPT4

to Paradox table

STANDID Base UCELL5; data conversion of MOSS vector map MRISTK3

to Paradox table

Lansat91 Base UCELL5; data conversion of MOSS/MAPS SYHAB91

SPSS file to Paradox table

Veg codei Attribute Paradox join of MRISTK3.DB with 1987PI.DB and 1987PI.DB, 1993PI.DB

1993PI.DB vegetation databases

Ecoclass Attribute Paradox join of MRISTK3.DB with EVG1984 EVG1984

a May be either an SPSS or MOSS file (vector layer), a spatial database (all file names with .DB extension), or another variable. b Not applicable; UTOOLS software is used to create “UTMGrid” whenever a vector map layer is rasterized. c UCELL5 is a UTOOLS program. d Base variables are obtained from original vector or raster map layers entered in a GIS. e Derived variables are those calculated from base variables or other derived variables, usually with an algorithm or other mathematical

formulation.

f The following road attribute variables were created in an identical manner to the “BEG_DATE” variable: END_DATE, ROADNO, TERMINI,

FENCE, OPML, OBML, MILES, DITCH, ID, SURF, F_CL, DV1, DV2, CVH, S_LVL, STRE, STRF, CLASS, POSITION, GRADE, WIDTH, and

ALIGNMNT. Values for the following variables should not change during the lifespan of the project: FENCE, MILES, DITCH, F_CL, S_LVL,

CLASS, POSITION, GRADE, WIDTH, and ALIGNMENT. Others may change as road attributes change.

g Attribute variables have values assigned to them from field inventory or other sources. h The following derived variables were created with similar buffering routines in UCELL5, and are derived from “ROAD USE:” NrstOPEN,

DistRSTR, Nrst RSTR, DistCLSD, and NrstCLSD.

i The following vegetation attribute variables were created identically to the variable “Veg code:” TOTAL CC, #Layers, LayerCode1-3,

LYR1SPC1-3, SIZE1-3, CanClos1-3, Crndia1-3, LYR2SPC1-3, and LYR3SPC1-3.

41
Table 9—Documentation of source maps used to create base variables in the Starkey habitat database

Map Source Archived Date of

layer map namea map nameb creation Producer Method of creation

Fences FENCGPS3 FENCES_ALL Summer 1995 Starkey and LGRD staff Fences walked or driven using

DGPS

Cattle

pastures COWPAST PASTURE_CATTL Nov. 1995 Starkey staff Derived from FENCGPS2

Elk

pastures ELKPAST PASTURE_ELK Nov. 1995 Starkey staff Derived from FENCGPS2

Stream

locations STRSTK2 STREAMS_ALL 1993-94 LGRD,c WAWd Hand-manuscripted streams and

and Starkey staff crenulations on topographic

maps,e then scanned at Umatilla

NF; map scale 1:24,000 or

1:15,840f

Water

points WATERPT4 WATER_POINTS 1996 Starkey staff Mapped with DGPS

Soils SOILS4 SOILS 1986-present WAW and Starkey staff Polygons mapped on aerial

photographs and transferred

to 7.5-min quadrangles

(1:24,000); scanned and edited

at Wallowa-Whitman NF

Elevation DEMSTKWI ELEV_DEM Unknown FS Geometronics Service DEM created by digitizing 20-ft

Center contour lines on 7.5-min

quadrangles (raster map) to

USGS standardsg

Roads TRN2OBT, TRN_GONE 1993 LGRD and Starkey staff Road locations recorded with

TRN2EXT, TRN_EXTERIOR DGPS while driving

TRN2INT TRN_INTERIOR

Elk habitat

categories SYHAB91 ELK_HAB_91 1992 LGRD and Starkey Unsupervised classification of

staff 1991 Landsat Thematic Mapper

scene

Vegetation

polygons MRISTK3 VEG_EVG_ID 1988-90 LGRD Stand boundaries delineated by

hand on aerial photographs

(~1:12,000), then scanned at

Umatilla NF at half quadrangles.

Edited in LTPlus (2.4-m cell

size). Combination of photointerpretation and field examination

Salt block

locations SALTD93 SALT 1993 Starkey staff DGPS; incomplete

a All source maps are in Arc/Info format. b Archived layers are stored in Arc/Info on the FS IBM RS6000 system. Associated tables in Oracle, also residing on the IBM, contain attribute

data corresponding with the following maps: STREAMS_ALL, SOILS, TRN_GONE, TRN_EXTERIOR, TRN_INTERIOR, and VEG_EVG_ID.

c La Grande Ranger District. d Wallowa-Whitman National Forest. e USGS 7.5-min quadrangle maps covering the SEFR are Bally Mountain, Sullivan Gulch, McIntyre Creek, and Marley Creek. These maps

were produced by the USGS from aerial photographs taken in 1960 and field checked in 1965.

f Most of the Starkey Experimental Forest and Range was recorded with 1:24,000-scale maps that were photo-enlarged to 1:15,840.

g Only the DEM was originally received in raster format; all other layers were vector maps that were subsequently rasterized.

42
Table 10—Documentation of source tables used to create attribute variables in the Starkey habitat

databasea

Database name Owner Contact Reference name

STARKEY.DB, .DBF W-W SOb Anne Kramer Transportation Management

System (TMS)c

WAWSTR.DB, .DBF W-W SO Anne Kramer Streamsc

STKSOILS.DB, .DBF W-W SO Anne Kramer, Art Kreger Soils or SRIDS

EVG1984.DB, .DBF LGRDd Brian Fischer Existing vegetation (EVG)c

1987PI.DB, .DBF Starkey Project Dave Motanic (Umatilla SO), Starkey photointerpretation,

Starkey staff 1987 photographs

1993PI.DB, .DBF Starkey Project Dave Motanic (Umatilla SO), Starkey photointerpretation,

Starkey staff 1993 photographs

a Local archive for these tables is the Starkey Project Micron at the Forestry and Range Sciences Laboratory in La Grande and the

USFS IBM RS6000 mainframe, with data on disk at the LGRD.

b Wallowa-Whitman Supervisor’s Office; data are stored either on the Data General System or the newer IBM. c Variables for these databases are described extensively in the Tri-Forest Data Dictionary (USDA Forest Service 1991). d La Grande Ranger District.

Table 11—Documentation of map layers stored in the Arc/Info portion of the Starkey habitat databasea

GIS layer Source map nameb Date of creation Producer Method and description

NE harvest unit SYRUP_TS 1993 Starkey staff Digitized from USGS orthophoto quadrangles

boundaries (ACTIVITY_NE) (1:24,000); hand-drawn boundaries

Relay towers TOWERS

(TELEM_TOWERS) Summer 1991 Starkey staff DGPS

Test sites for accuracy GPSSITES

assessment (CONTROL_GPS) Summer 1991 Starkey staff DGPS

Ownership boundaries LANDSTAT Summer 1991 WAWc Manuscripted and digitized from 1:24,000

(OWNERSHIP) USGS maps; edited in LTPlus

Kill sites for elk KILLSITE Spring 1994 Starkey staff Digitized onscreen using topography as

harvested 1989-93 (HUNT_KILLSITE) background; points are approximate sites

provided by hunters who marked them on

1:15,840 topographic maps

20-foot contours TOPOG 1988 Starkey staff Digitized from USGS acetate layers

(1:15,840) with STRINGS mapping system

100-foot contours TOPOG100 1988 Starkey staff Derived by reselecting every fifth contour

(ELEV_CONT_100) from"TOPOG" (1:15,840 scale)

20-foot contours inside TOPOGSTK Derived by overlaying TOPOG with ELKPAS

game-proof fence only (ELEV_CONT_20) 1988 Starkey staff (1:15,840 scale)

Centerpoints of 1988 Transferred to 1:24,000 orthophotos and

aerial photos PHOTOPTS 1991 Starkey staff digitized with STRINGS mapping system

Vegetation phenology PHENPTS 1996 Starkey staff DGPS; plots are located along 11 transects

MRIPLOTS in 3 habitat types

Vegetation plot locations (VEG_EVG_PLOT) 1988-90 Starkey staff Points mapped on aerial photographs and

transferred to 7.5-min orthophotos

a These layers are not stored in the UTOOLS platform. b If applicable, name of equivalent archive file on the IBM RS6000 follows in parentheses. c Wallowa-Whitman National Forest.

43
Appendix 3

Computer Software

Used

Various GIS software was used to develop the Starkey habitat database, primarily

UTOOLS, MOSS, and Arc/Info. A brief description of the software programs used

and their specific application to the Starkey habitat database follows.

Arc/Info: PC Arc/Info, with digitizing, editing, analysis (vector only), and displayplotting subsystems, has been the main editing and plotting system for Starkey maps.

The following maps were digitized or edited with this system: TOWERS, KILLSITE,

parts of WATERPT4, and parts of FENCGPS2 and its derivatives, COWPAST and

ELKPAST. The official GIS of the FS is now the workstation

version of Arc/Info (UNIX Arc/Info), which brings the first vector-based GIS to

FS staff since MOSS. This version of Arc/Info also has an extensive raster-based

analysis system, and can access map-based information stored in Oracle. Terminals

are now accessible to all Starkey staff, and training and data import have begun.

Arcview: A display-plotting system in the Windows environment. Arcview is used

for some analysis (proximity-type selections), but has become essential in producing

hardcopy maps that are stored in Arc/Info. It replaces the plotting subsystem of

Arc/Info. The UNIX Arcview system is virtually identical to the PC version.

IDRISI: A DOS-based, primarily raster processing system. IDRISI was used extensively for the analysis of a 1991 Landsat Thematic Mapper scene and subsequent

development of elk habitat classes (“Lansat91”) (see “Vegetation,” main text) based

on that scene.

LTPLUS: This program resides at the FS District and Supervisor Offices on UNIXbased machines and is used to edit scanned, GPS-collected, or manually digitized

maps before transporting them into MOSS for analysis and display.

MOSS: The Map Overlay and Statistical System resides on the USDA Forest Service

Data General computer and has been the unofficial GIS for the FS since about 1985.

It consists of data entry (digitizing), editing, analysis (both raster and vector), and

display-plotting subsystems. The MOSS import-export format used extensively in

this project is ASCII.

Paradox: Paradox for Windows is a database program that resides on all Starkey

Project PCs and is used for GIS and non-GIS-based databases. As a GIS tool,

Paradox is used in several ways. It is the main data storage and query device for

UTOOLS maps, in which each 30- by 30-m pixel in Starkey is stored as a Paradox

database record. The UTM coordinates of a pixel are the linking (key) field for each

record. The querying abilities of Paradox are then used as the selection device for

displaying map information and creating new maps.

On PCs that are linked to the network, we use Paradox for Windows to access,

display, and download into the UTOOLS system map-based information that is

stored in the FS Oracle database on the IBM system (La Grande Ranger District

and Wallowa-Whitman NF.

44
STRINGS: This program was connected to a large digitizing table and Tektronix display terminal. Starkey staff began digitizing maps in 1987 with this system. Completed

maps were digitally transferred to the Data General and imported into MOSS for analysis and display. Most of the maps digitized with the STRINGS setup were redigitized

later by using GPS technology and LTPlus or Arc/Info. An exception is the 20-ft contour map “TOPOG” and its derivations, “TOPOG100” and “TOPOGSTK,” which are

still used for display. A map showing the center points of the 1988 photographic series, “PHOTOPTS,” also was digitized in STRINGS. A description of this software can

be found in Coe and Quigley (1986).

UTOOLS: This is a DOS-based collection of software developed in-house by Forest

Service programmers to analyze and display raster maps (Ager and McGaughey

1997). It is being used extensively by Starkey staff to accomplish the main analysis

objectives of the animal-unit-equivalencies and road and traffic monitoring studies.

UTOOLS converts traditional GIS data into a spatial database in Paradox. The program imports existing digital maps and rasterizes them, if necessary, with UCELL5.

Each pixel becomes a single record in the database, with fields representing map

layers or attributes. The UVIEW program is used to display data in three dimensions;

one can view a landscape from any perspective. Other programs in UTOOLS derive

new topographic variables from elevation data (for example, convexity or slope) and

use buffering routines to create distance band variables.

VGA ERDAS: This DOS version of the ERDAS Image Processing programs was

used in the initial analysis of a raw 1991 Landsat Thematic Mapper image for

grouping spectral values into classes. It consists of complex classification routines

and superior display capabilities for images.

Glossary AATS: Automated Animal Telemetry System; the radio-telemetry system at Starkey,

comprised of radio collars on elk, mule deer, and cattle that receive loran-C navigational signals; seven remote microwave towers; a base station microwave tower; data

processing center; and signals received from distant loran-C stations. The collars are

paged in sequence every 20 seconds and automatically transmit their locations to a

microwave tower, which retransmits the signals to the base station computer system.

Accuracy (absolute): How closely locations of objects on a map match their true

geographic locations on the surface of the Earth (Corbley 1996).

Accuracy (relative): How closely distances between pairs of objects as determined

from measurements on a map match their true distances (Corbley 1996).

Buffer: GIS routine that creates an area surrounding a feature, such as a point

or line; the width of the buffer is generally predefined for the area of interest (for

example, 100 m).

Categorical variable: One whose values fall into one or more distinct categories;

that is, the data consist of names of categories, rather than quantitative measurements. Categorical variables may be binary (values coded either “1” or “0”); dichotomous (only two categories, but the values are not coded as above); or multiple

(several possible categories exist).

45
Circular error probable: CEP; a term used in describing accuracy of GPS locations,

meaning 50 percent of the collected points are within a circle of a specified radius on

a horizontal plane (Trimble 1992).

Classification: Computer-assisted interpretation of remotely sensed imagery; a

supervised classification is one in which the operators train the computer to look

for surface features with similar reflectance characteristics to a set of examples of

known interpretation within the image; in an unsupervised classification, a computer routine classifies by identifying typical patterns in the reflectance data. These

patterns are subsequently identified by ground truthing selected sites (Eastman 1992).

Continuous variable: One whose values fall along a continuum, such as distance

to a road or weights of calves; also called interval data. May include discrete, quantitative data that are treated as continuous.

Convexity: Quantitative measure of topography that describes the terrain surrounding

a given pixel; how exposed a location is in terms of surrounding terrain. Calculated

for the Starkey database as the difference between the averaged elevations of 9

pixels in a 3 by 3 matrix (search radius of about 129 m) and an imaginary plane

500 m above the home (center) pixel (Ager and McGaughey 1997). Values lower

than 500 indicate valley bottoms; values greater than 500 suggest ridge tops.

Datum: Reference system for correlating results of surveys; a horizontal datum is

used for position. Based on the surface of a particular earth spheroid projected

beneath the land, a datum is the basis from which flat maps are produced that

show coordinates from a curved earth. Most of the original source maps for

Starkey were in NAD27, but have been converted to NAD83.

DEM: digital elevation model; image that stores data that can be envisioned as

heights on a surface (Eastman 1992). A digitized map of elevation in standard cell

format (30 by 30 m), produced by the U.S. Geological Survey on a 1:24,000 scale,

is used to map elevations at the SEFR. A standard DEM has the following features:

elevations referenced in the UTM system, in meters or feet above mean sea level;

coverage based on the standard USGS 7.5-minute quadrangle; data ordered from

south to north in profiles ordered west to east; and a spacing of 30 m along and

between each profile (that is, a 30- by 30-m pixel size) (USGS 1986).

Digitizer: Term commonly used to refer to a device for encoding vector graphic

data (point, line, or polygon locations) into plane (X,Y) coordinates. Also applies to

scan digitizers that encode raster images (Eastman 1992). Converts images from

photographic or other hard material to a numerical dataset format.

Discrete variable: Quantitative variable that consists of discrete points along a

continuum, such as counts of vehicles at a traffic counter, or the number of plant

species in a pixel.

Easting: The X coordinate in the UTM grid system; distance in meters east or

west from the central meridian of the UTM zone (Koeln and others 1994).

GIS: geographic information system; system for the input, storage, retrieval, analysis, and display of interpreted geographic data (Eastman 1992).

46
GPS: global positioning system; calculates the range (distance) to a set of simultaneously viewable satellites to intersect a position according to a specified geodetic

referencing system (Eastman 1992). Uncorrected GPS locations are accurate to

about 100 m. Differentially corrected GPS (DGPS) uses readings from a base

station of known location to correct positions collected from a remote (rover) GPS

unit; these positions are accurate to about 5 m CEP (see “circular error probable”). If

about 180 positions are recorded and averaged after differential correction, accuracy

is increased to 2 m CEP. Newer GPS units are capable of even greater accuracy.

Key field: A field or group of fields in a table used to order records or maintain

referential integrity by linking tables; prevents duplicate records.

NAD: North American Datum; refers to the georeferencing system used for locations in the Starkey habitat database. The telemetry system uses the most current

datum for recording coordinates of animal locations (North American Datum 1983,

or NAD83), whereas the habitat maps were digitized mainly from published USGS

maps that used an older datum system (NAD27). Starkey staff have converted

all habitat maps to NAD83 with the NADCON-USNGS routine in Arc/Info. For the

Starkey area, the correction from NAD27 to NAD83 is +203 m north and -81 m west.

Northing: The Y coordinate in the UTM grid system; distance in meters north from

the equator (Koeln and others 1994).

Orthophoto quadrangle: Aerial photograph corrected for geometric distortions,

accurate to scale in both X and Y directions; can be used as a photographic “map.”

The orthophotoquads encompassing Starkey are Bally Mountain, Sullivan Gulch,

Marley Creek, and McIntyre Creek. They were produced from 1:80,000 blackand-white aerial photographs taken in August 1976. A new set of digital orthophotoquads also has been purchased for use at Starkey. The black-and-white

photographs, produced by the National Aerial Photography Program (NAPP), were

taken at 6100 m. Scale is about 1:40,000, with a pixel size of 1 by 1 m, providing

excellent resolution (2 m). These raster format maps likely will be used to locate

or compare positions of fixed features in the field, rather than used in analysis or

entered as a spatial layer with GIS.

Pixel: Contraction of the words “picture element.” Strictly refers to a small (usually

rectangular or square) portion of a raster display device, whereas a cell in a raster

data grid refers to a raw data value that governs how the pixel is displayed. “Cell”

and “pixel” often used interchangeably (Eastman 1992).

Raster: Describes a system of representing images, where the image is composed

of small, internally uniform cells arranged in a grid. Order of image storage is typically

by scanlines, progressing from left to right, and then from top to bottom (Eastman

1992).

Rectify: To make image data conform to a map projection, typically for a Landsat

scene or other remotely sensed product; eliminates effects of distortion and orientation of the camera.

47
RMSE: Root-mean-square error. Measure of the variability of measurements about

their true values; directly comparable to concept of standard deviation. Encompasses

both random and systematic error. Differences between sample measurements and

the “true value” are squared and summed, then divided by the number of measurements to obtain mean square deviation. The square root is taken to produce an error

term in the same units as the original measurement (Eastman 1992).

Scanline: Horizontal group (row) of image cells or pixels spanning the entire image

(Eastman 1992).

TM: thematic mapper; sensor used in newer Landsat satellites capable of digitally

recording intensity of natural radiation in 7 spectral bands. Provides better spatial

and spectral resolution than previous Landsat sensors. The resulting Landsat TM

scene is a digital map with 30- by 30-m cells. Two scenes have been acquired for

Starkey, 1988 and 1991.

UTM: Universal Transverse Mercator; a map projection system based on 60 eastwest zones, each 6 degrees wide in longitude. X, Y coordinates in the grid are

recorded in meters and are seven-digit numbers, increasing as one moves east

and north (Koeln and others 1994). The Starkey area is in UTM zone 11N.

Vector: Technically, any variable quantity that can be described as having magnitude and direction and that can be resolved into components; also, all graphic

data that can be ultimately decomposed into point locations described by absolute

coordinates, including lines, points, or polygons; systems that make use of vector

representations in data storage and analysis (Eastman 1992).

48
This page has been left blank intentionally.

Document continues on next page.
This page has been left blank intentionally.

Document continues on next page.
Rowland, Mary M.; Coe, Priscilla K.; Stussy, Rosemary J. [and others].

1998. The Starkey habitat database for ungulate research: construction,

documentation, and use. Gen. Tech. Rep. PNW-GTR-430. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research

Station. 48 p.

The Starkey Project, a large-scale, multidisciplinary research venture, began in 1987

in the Starkey Experimental Forest and Range in northeast Oregon. Researchers are

studying effects of forest management on interactions and habitat use of mule deer

(Odocoileus hemionus hemionus), elk (Cervus elaphus nelsoni), and cattle. A habitat

database was compiled, using GIS (geographic information systems), to examine relations of environmental variables to ungulate distribution and habitat use. The database

contains over 100 variables associated with vegetation, water, soils, roads, topography,

and structural features such as fences. We describe database construction and documentation of GIS layers from 1987 to 1997. Error estimates associated with each

variable or layer and sample applications of the database also are presented.

Keywords: Habitat database, GIS, spatial data, ungulate, cattle, elk, mule deer,

northeast Oregon, Starkey Project, accuracy assessment, Blue Mountains.

The Forest Service of the U.S. Department of

Agriculture is dedicated to the principle of multiple

use management of the Nation’s forest resources

for sustained yields of wood, water, forage, wildlife,

and recreation. Through forestry research, cooperation with the States and private forest owners, and

management of the National Forests and National

Grasslands, it strives—as directed by Congress—to

provide increasingly greater service to a growing Nation.

The United States Department of Agriculture (USDA)

prohibits discrimination in its programs on the basis

of race, color, national origin, sex, religion, age, disability,

political beliefs, and marital or familial status. (Not all

prohibited bases apply to all programs.) Persons with

disabilities who require alternative means of communication of program information (Braille, large print, audiotape,

etc.) should contact the USDA’s TARGET Center at

(202) 720-2600 (voice and TDD).

To file a complaint, write the Secretary of Agriculture,

U.S. Department of Agriculture, Washington, DC 20250,

or call (800) 245-6340 (voice), or (202) 720-1127 (TDD).

USDA is an equal employment opportunity employer.

Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, Oregon 97208-3890
U.S. Department of Agriculture

Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, OR 97208

Official Business

Penalty for Private Use, $300

do NOT detach label