United States

Department of 

Agriculture

Forest Service

Pacific Northwest

Research Station

Research Note

PNW-RN-552

September 2006

Roads in Landscape

Modeling: A Case Study 

of a Road Data Layer 

and Use in the Interior

Northwest Landscape

Analysis System

Marti Aitken and Jane L. Hayes1

Abstract

Roads are important ecological features of forest landscapes, but their cause-andeffect relationships with other ecosystem components are only recently becoming included in integrated landscape analyses. Simulation models can help us to

understand how forested landscapes respond over time to disturbance and socioeconomic factors, and potentially to address the important role roads play in these

processes. The inclusion of roads as static or as process components of the landscape modeling, however, presents numerous challenges owing to a general lack of

adequate road data and threshold effect information. Roads have been included in

several recent landscape analysis efforts in the Pacific Northwest, but not as dynamic

components. The Interior Northwest Landscape Analysis System (INLAS) developed a framework for simulation modeling of succession, disturbance, and management activities at the subbasin level. Roads were included in the INLAS project

as a static landscape feature. We describe the data, analysis, and applications of

road data in the INLAS project. Using the INLAS effort as a case study, we examine the challenges of incorporating roads into multidisciplinary landscape-level

analyses. With an emphasis on data requirements, we identify practical and logistic barriers to dynamic modeling of road interactions, and propose a strategy for

future studies.

Keywords: Road ecology, landscape analysis, simulation modeling, 

NETDISTANCE.

1 Marti Aitken is an ecologist, Umatilla National Forest, 3502 Highway 30, La Grande, OR 97850; and

Jane L. Hayes is a research biological scientist, Pacific Northwest Research Station, Forestry and Range

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

1
2

RESEARCH NOTE PNW-RN-552

Introduction

The importance of roads in the landscape is receiving considerable attention by 

ecologists, particularly in the last decade (e.g., Forman 1998, 2000; Forman and

Alexander 1998; Forman et al. 2002; Gelbard and Belnap 2003; Gucinski et al.

2001; Lugo and Gucinski 2000; Trombulak and Frissell 2000; Ritters and

Wickham 2003). In these recent reviews, the direct and indirect impacts of roads

on terrestrial and aquatic ecosystems have been discussed extensively and include

transformation of the physical and chemical environment in both the immediate

and extended area; increased mortality of animals and alteration of behaviors and

habitats; and modification of plant communities, especially through the spread of

exotic species. Development of road networks on the rural landscape has been

largely influenced by socioeconomic considerations (Jaarsma 1997). Rural roads

provide increased access for recreation, commodities extraction, and management

activities. Roads also provide access for wildland fire suppression and serve as

firebreaks (e.g., Gucinski et al. 2001). It has been widely acknowledged that there

are ecological and economic tradeoffs associated with roads and that the true picture of those tradeoffs is best viewed at the landscape level (e.g., Gucinski et al.

2001, Lugo and Gucinski 2000). 

That said, an important issue is how to appropriately depict roads in a landscape to accurately assess these tradeoffs. As tangible features in the landscape,

roads can be considered as a current condition, much as a plant community or 

current vegetation structure can be modeled as a persistent fixed feature or state

variable. However, roads also serve as disturbance agents and vectors, and thus are

part of dynamic ecological processes. In some instances, roads may cause disturbance (e.g., soil erosion or habitat fragmentation) or facilitate disturbance (e.g., act

as conduits for spread of weeds and other disturbance agents); in other instances,

they may inhibit disturbance (e.g., creating a fuel break that stops progression of 

a fire). However, including roads in landscape-level analyses presents many challenges owing to a frequent lack of adequate attribute and effects data describing

types of roads and their impacts, as well as a lack of accepted modeling methodology.

Methods have been developed to incorporate roads information beyond relatively simple road-density analyses in landscape-level analyses; however, none of

these efforts explicitly addresses roads as a spatially and temporally dynamic entity

within the study landscape. When roads are included in landscape-level analyses

(e.g., Kline et al. 2003), typically data sources and methodologies are sparingly
3

described, limiting opportunities to fully evaluate and build on these past efforts.

In this paper, we describe many challenges of incorporating roads in multidisciplinary landscape-level analyses, and discuss the treatment of roads as a static feature

in an example from a landscape analysis project at the subbasin level in northeastern Oregon (Interior Northwest Landscape Analysis System [INLAS]). We provide

details of the road data, analysis, and application in this case study. Focusing on

data gaps, we identify practical and logistic barriers to dynamic modeling of road

interactions, and propose a strategy for future studies.

Roads in Landscape-Level Analyses

A number of recent landscape simulation efforts in the Pacific Northwest have 

attempted to incorporate roads in the assessment of forest management effects 

on landscape-level ecological processes. For the most part, these efforts have 

considered roads as persistent components in their examination of forest vegetation

change and management issues at various landscape scales. At the broadest scale

(around 58 million ha), the Interior Columbia Basin Ecosystem Management

Project (ICBEMP; Keane et al. 1996) developed an ecosystem-based strategy 

for the management of U.S. Department of Agriculture Forest Service and the

Department of Interior Bureau of Land Management lands within the basin. The

ICBEMP established a framework for the assessment and management of the

basin’s ecological, bio-physical, social, and economic conditions. Roads data were

compiled from National Forest Service and Bureau of Land Management databases

and represented over 200 000 km of roads on these public lands. In this study, projections indicated 30 to 50 percent more kilometers of roads may not have been

inventoried (Lee et al. 1997). Based on road density analyses, ICBEMP demonstrated significant influences of roads on aquatic and terrestrial ecosystem components in the basin (Hann et al. 1997, 2003; Lee et al. 1997; Wisdom et al. 2000). 

The Coastal Landscape Analysis and Modeling Study (CLAMS; Spies et al.

2002) considered a smaller area (over 2 million ha) from Oregon’s coast up the

west slope of the Cascades including the fertile Willamette Valley. The CLAMS was

designed to assess effects of land management policies in this relatively populous

multiowner province. Roads data were obtained from 1:24,000 U.S. Geological

Survey (USGS) digital linear grid data and treated as a fixed feature. These data

were used in a variety of analyses of the area including the influence of roads in

debris flows (Miller and Burnett, n.d.) and assessment of human modification of

forest habitats (Wimberly and Ohmann 2004). 

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
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RESEARCH NOTE PNW-RN-552

At still a finer scale, the Applegate River Watershed Forest Simulation Project

encompassed approximately 250 000 ha of forest in southwestern Oregon. The

objective of this project was to develop a model that included stochastic disturbance events to aid in evaluating potential effects of policies and management

practices over time (Graetz 2000). Roads were included in the form of equivalent

roaded acres (McGurk and Fong 1995), a measure used by some Forest Service

units to track cumulative watershed effects or overall disturbance impacts at the

planning watershed level 1215 to 4000 ha (Menning et al. 1996, Sessions et al.

1996). Equivalent roaded acre values were used to account for alteration of vegetation caused by management activities and fire that could result in increasing

waterflows and sediment production (Bettinger et al. 2004, Graetz 2000).

Using a 6000-ha watershed (hydrologic unit code 5 [HUC5]) of the goals with

aquatic habitat quality objectives. In this heuristic spatial model, decision choices

included silvicultural prescriptions and logging systems as well as road management by modifications of standards (e.g., standard rock to road obliteration). In a

subsequent study, Bettinger et al. (1998b) used data from this scheduling model to

compare predictions of equivalent clearcut areas and other indices of aquatic habitat quality along with timber harvest volume over 100 years. Equivalent clearcut

area (ECA) values, like equivalent roaded acre values are used to assess cumulative effects of land management activities (including roads) on stream habitats and

fish populations by aggregating these effects and assigning a single measurement

per watershed. Bettinger et al. (1998b) found ECA was a poor surrogate for stream

sediment and temperature levels. (For a review of these two approaches, see Ager

and Clifton 2005.) 

Most recently, the INLAS project created an integrated framework for modeling landscape change (Barbour et al. 2004, in press a) that applied new and existing simulation models at the subbasin scale (approximately 180 000 ha) focusing

on four contiguous HUC5 watersheds of the Upper Grande Ronde River. The purpose was to examine the long-term (≥60 years) relative effects of succession, disturbance, and resource management on terrestrial and aquatic conditions and thus

enable decisionmakers to evaluate the short- and long-term risks and tradeoffs of

policies and management actions (reviewed in Hayes et al. 2004). The importance

of and need to include roads in this integrated landscape simulation effort was

identified early in the development of INLAS. Unfortunately, efforts to address

roads in the landscape in the INLAS study area assumed the road network was

constant, and ultimately was limited to a static view.
5

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System

To our knowledge, no integrated landscape analysis project considering both

upslope interaction, as well as aquatic impacts, has included roads as a dynamic,

spatially and temporally explicit feature. Yet, dynamic modeling could facilitate 

a myriad of insights. For example, resource managers could evaluate the effects 

of road modifications, such as changes in recreational impacts, economic consequences, dispersal of invasive species, and fire control, as well as aquatic efforts.

Given the potential importance of roads in landscape-level analyses, clearly more

effort will be needed to evaluate and account for the effects of roads in landscape-level processes. Greater discourse regarding the many challenges of incorporating roads and better documentation regarding data sources and how roads

were examined will be necessary for significant advancement in how we deal 

with roads in multidisciplinary research. 

Roads in the INLAS Study Area 

The INLAS study area consists of four hydrologc unit code 5 (HUC5) watersheds 

within the Upper Grande Ronde watershed on the eastern slope of northeast

Oregon’s Blue Mountains, a predominantly rural area (fig. 1). With elevations

ranging from 820 to over 2130 m, this complex topography of deeply dissected

drainages supports numerous vegetation types from bunchgrass communities to

mixed conifer and subalpine fir (Abies lasiocarpa (Hook.) Nutt.). Decades of fire

suppression and timber harvesting have influenced the current forest structure and

composition in this disturbance-prone ecosystem (e.g., Jaindl and Quigley 1996,

Starr et al. 2001). Outbreaks of native and nonnative forest insects over the past

two to three decades have resulted in substantial, both widespread and patchy, tree

mortality in and adjacent to the project area (Filip et al. 1996, Gast et al. 1991). In

addition, wildfires burned about 8100 ha within and an additional 24 300 ha immediately adjacent to the project area during the last decade. Consequently, highly

heterogeneous fuel loads exist throughout the project area. 

Almost 70 percent of the lands within the study area are federally owned (table

1). Although no significant population centers occur within the study area, several

towns (>750 people) within the region may derive benefits from the study area

(table 2). Commodity extraction has primarily been through the timber and 

a few nontimber industries (e.g., mushrooms); however, other locally important

commodities (e.g., fire wood, huckleberries) also exist. Diverse recreational opportunities include camping, hiking, fishing, snowmobiling, hunting, and skiing.

Roads facilitate all of these activities. 
RESEARCH NOTE PNW-RN-552

6

Figure 1—Land ownership in the Interior Northwest Landscape Analysis System (INLAS) project area.

HUC5 = hydrologic unit code 5.
7

Approximately 4071 km of roads exist within the study area, including 102 

km of interstate and state highways (fig. 2). Most (3217 km) roads consist of

native material (“dirt” or “improved dirt”) and aggregate material (“gravel”). Of

these local roads, 1592 km (49 percent) are closed to vehicular traffic during at

least part of the year, but may continue to provide recreational access (e.g., allterrain vehicles, snowmobiles, and equestrian and pedestrian traffic). Estimated 

use throughout the project area averages less than five vehicles per week annually

with wide seasonal fluctuations (LeBold 2004). 

Road density over the entire study area (2.28 km/km2) exceeds both the

national average (1.21 km/km2) and the suggested maximum for maintaining 

large predator populations (0.6 km/km2) (Forman and Alexander 1998). Roads in

this landscape can create resource management challenges. For example, directly

and indirectly roads pose significant threats to the status and abundance of some

salmonid species (Baxter et al. 1999, Rieman et al. 1997) and to the viability of

rare plant populations (Croft 2001). The study area provides habitat or potential

habitat for 3 salmonids, 3 terrestrial vertebrates, 15 insects, and 1 plant species that

Table 1—Land administration within the Interior 

Northwest Landscape Analysis project area

Landowner or administrator Area

Forest Service 122 115 ha

Private 53 551 ha

Confederated tribes 1 373 ha

State of Oregon 885 ha

Bureau of Land Management 479 ha

Source: U.S. Department of Agriculture, Forest Service 2004. 

Table 2—Population centers (>750 people) 

within the region of the study area 

Population center Population

Baker City 9,860

Elgin 1,654

Enterprise 1,895

Joseph 1,054

La Grande 12,327

Pilot Rock 1,532

Pendleton 16,354

Union 1,962

Source: U.S. Census Bureau 2002.

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
8

RESEARCH NOTE PNW-RN-552

Figure 2—Transportation network in the Interior Northwest Landscape Analysis System (INLAS) project area.
9

Table 3—Attributes of National Forest System roads data as defined by the agencya

Attribute Categories

Administrative organization 061606—La Grande Ranger District

(The unit where the route 

segment physically resides)

Access and travel PLC–primary roads system suitable for passenger car clearance

management strategy

SHC–secondary road system suitable for high clearance

SLC–secondary road system suitable for high-clearance vehicles

OTHR- other ATM status, road not maintained

Critical traffic 4WD–four-wheel drive

(The vehicle, normally the Bus

largest [by weight, size, or Bus (40)–40 passenger tour bus

unique configuration] whose Car–passenger car

limited use on the road is Flatbed H15 truck (tandem axle bob-tail)

necessary to complete the Garbage–garbage truck

planned activity) LOGT–logging truck

LOWBOY–lowboy tractor trailer

MTRHOME–motorhome

OHH–off-highway haul vehicle

PICKUP–pickup truck, high-clearance vehicle

S4WD–short base four-wheel drive 

SEMI–semi truck (tractor trailer)

SERVICE–service vehicle

TRLR–car or truck with camper-boat trailer

TRUCK–straight truck (H-load)

YARDER–yarder-cable logging equipment

Design traffic 4WD–four wheel drive

Bus

Bus (40)–40-passenger tour bus

Car–passenger car

Flatbed–H15 truck (tandem axle bob-tail)

Garbage–garbage truck

LOGT–logging truck

LOWBOY–lowboy tractor trailer

MTRHOME–motorhome

OHH–off-highway haul vehicle

PICKUP–pickup truck, high-clearance vehicle

S4WD–short base four-wheel drive 

SEMI–semi truck (tractor trailer)

SERVICE–service vehicle

TRLR–car or truck with camper-boat trailer

TRUCK–straight truck (H-load)

YARDER–yarder-cable logging equipment

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
RESEARCH NOTE PNW-RN-552

Table 3—Attributes of National Forest System roads data as defined by the agencya (continued)

Attribute Categories

Functional class Arterial–service to large land areas and usually connects with 

(Character of service other arterial roads or public highways

provided) Collector–provides service to smaller land areas than arterial

roads, and usually connects forest roads to local roads

Local–connects terminal facilities with forest collector roads

ID Road ID

Jurisdiction BLM–Bureau of Land Management 

(The legal right to control or C–county

regulate the use of a FS–Forest Service

transportation facility. This L–local

requires authority, but not OFS–other Forest Service

necessarily ownership) P–private

S–state

Objective maintenance level Basic custodial care (CLOSED)

(Maintenance level High-clearance vehicles

considering future road Suitable for passenger cars

management objectives, Moderate degree of user comfort

traffic needs, budget High degree of user comfort

constraints, and 

environmental concerns)

Lanes Single- or double-lane road

Operational maintenance Basic custodial care (CLOSED)

level High-clearance vehicles

(Maintenance level currently Suitable for passenger cars

assigned to the road Moderate degree of user comfort

considering today’s needs, High degree of user comfort

road condition, budget 

constraints, and 

environmental concerns)

Primary maintenance C–county

(Agency or party having CU–commercial user

primary [largest share] FS–Forest Service

financial responsibility for P–private

maintenance) S–state

Route status Decommissioned–road surface has been significantly altered 

(Current physical state of to deter vehicle use 

being of the route segment) Existing–road surface exists

Surface type Asphalt

(The wearing course) Aggregate/gravel

Bituminous

Improved native material

Native material

Paved

Unknown

10
are federally listed as threatened, and 40 terrestrial vertebrate (Wisdom et al. 2000)

and 9 plant species (USFWS 2002) that are of conservation concern. With over 65

exotic plant species documented within the Blue Mountains (Harrod et al. 1996),

the role of roads in facilitating nonnative plant invasions (Gelbard and Belnap

2003, Parendes and Jones 2000) raises additional concerns. 

INLAS Roads Data Sources 

The INLAS modules used existing roads data from the Wallowa-Whitman National 

Forest geographical information system (GIS) database, with one exception. What

follows is a brief outline of the roads data compiled for these analyses. The exception was the land use analysis module, which used data obtained from the Bureau 

of Land Management ground transportation layer and included only paved roads

(Kline et al., in press); these data and analyses are not included here. For purposes

of this discussion, we distinguish roads as either on-forest (within the Forest

Service boundary) or off-forest (outside the Forest Service boundary).

On-forest roads originated from cartographic feature files. In 1992, the cartographic feature files were transferred to a GIS database and aligned to digital

orthophoto quadrangles. Roads not visible on digital orthophoto quadrangles 

were field recorded with class 2 and class 3 global positioning system equipment.

Attributes (e.g., type and use) were then added to the line data and calibrated to

odometer measurements. These data came from numerous sources including USGS

Table 3—Attributes of National Forest System roads data as defined by the agencya (continued)

Attribute Categories

System C–county

(Network of travel ways I–interstate highway

serving a common need or NFSR–National Forest System road

purpose, managed by an OF–other federal agency

entity with the authority to P–private

finance, build, operate and SH–state highway

maintain the routes) US–U.S. highway

Traffic service level Free-flowing mixed traffic 

(Description of the road’s Congested during heavy traffic

significant traffic Flow interrupted–use limited

characteristics and operating Slow flow or may be blocked

conditions)

a This table does not include fields that are the same for the entire study area (such as congressional district) or that relate 

solely to database management.

11

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
7.5 minute quadrangle hardcopy maps, internal hardcopy road logs, reports, historical documents, and computer-aided design (CAD) files. Attribute collection and

application followed Forest Service core data management specifications. Attributes

are summarized in table 3. As is typical of Forest Service roads data (Gucinski et

al. 2001), these data emphasize the transportation aspect of roads (vs. impacts to

natural resources). 

Off-forest roads were also based on cartographic feature files but generally did

not receive the extensive alignment and correction processes of the on-forest roads,

as they are outside the agency’s jurisdiction. For similar reasons, and with few

exceptions (e.g., state and federal highways), these roads lacked attribute data collected for the on-forest roads (see table 3). These roads occurred on state, county,

industrial, and nonindustrial private, and tribal lands. 

All spatial data obtained from the Wallowa-Whitman National Forest were

mapped at 1:24,000, comply with National Map Accuracy Standards (precision 

and accuracy ± 12.192 m) to the extent possible, and were assumed to be complete. The INLAS roads database was derived from a forest-wide coverage that

was updated in August 2003. 

The database consisted of 7,051 records, each representing a road segment and

associated attributes (fig. 2). A segment is defined as the portion of road between

an intersection and either an endpoint or another intersection. Together these segments form a transportation network. Record completeness (columns with data)

varied tremendously within the data set. For example, no records were complete,

and 17 percent of the records were missing more than half of the attribute data

(table 4). Furthermore, records with missing data were not necessarily missing the

same data. These variations in the composite database occurred as the result of

Table 4—Number of records/segments by number 

of data fields containing an attribute

Number of attributes Number of records/segments

1 1,205

5 1

6 2

7 15

8 30

9 2

10 7

11 40

12 313

13 5,436

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RESEARCH NOTE PNW-RN-552
aggregating records from different projects with different objectives, funding, and

staffing. As the agency’s organizational emphasis continues to change, databases

with comparable variation may become more common. Although many nonforest

administrative entities maintain databases of roads within their jurisdiction, no

common standards exist. No additional data were collected specifically for the

INLAS project. Although we cannot know how many uninventoried roads may

exist in the study area, Bate et al. (in press) estimated the percentage of uninventoried roads within this area to be 20 percent, which is significantly lower than

reported for the ICBEMP (Lee et al. 1997) mentioned earlier. 

The INLAS project assumed private roads were open and accessible. Closed

(access controlled with signage, gate or barricade) and decommissioned (physically altered to prevent use) roads were included in the analysis with the assumption that closed roads could be reopened. Closed roads were considered closed

regardless of duration of closure (continuous, temporary, or seasonal). Although

construction of new roads in the study area was considered unlikely at the time,

the project included closed and decomissioned roads to allow for some flexibility

in the roads network. 

Application of Roads Data in INLAS Modules

The detailed road GIS data layer described above was used in the INLAS study in

two specific ways: (1) to help determine the feasibility of forest product utilization

throughout the study area based on road access and (2) to account for the influence

of roads on wildlife habitat conditions. 

To support the forest product utilization and wildlife habitat components of

INLAS, we needed to be able to calculate the shortest distance between specified

points within the project area by using the existing transportation network. The

analyses were done in Arc/INFO2 and Arc/GRID with NETDISTANCE (Hatfield

2002), an Arc Macro Language script that calculates the shortest distance from any

point or polygon centroid (“approximate center”) on or near any specified linear

network (e.g., transportation, streams, etc.), and then the distance along that network to another point on that network (a network center). This program allowed us

to calculate (1) the distance from the centroid of each of the 27,557 mapped vegetation stands (polygon centroids) to the nearest road (part of a linear network), and

(2) the shortest distance within the transportation network from that point to a

2 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.

13

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
specified end point such as a town (“network center”). Unlike other Arc/INFO program functions, such as COSTDISTANCE, which returns a cost (e.g., time) on a

case-by-case basis, NETDISTANCE returns distances within a network and can

evaluate multiple cases, such as our large data set. Although NETDISTANCE was

developed specifically for INLAS, the tool can be applied to any network and may

have great utility to those working with stream networks.

The forest product utilization component of INLAS examined the forest product potential as part of planning ecological restoration treatments of forested landscapes. Barbour et al. (in press b) developed a composite utilization index based 

on the concept that the probability that harvest activities will occur on a site is 

a function of the site accessibility, treatment implementation cost, and removed

material value. They employed NETDISTANCE and the roads data for two purposes: first, to estimate potential physical access to vegetation stands for harvesting;

and second, to compute mileage from those accessible vegetation stands to an

existing mill in Elgin, Oregon (table 2). Any stand for which the minimum distance to an open or closed road was no more than 90 m was considered accessible

for harvest operations. Mileage values were used to calculate approximate hauling

costs for economic evaluation of potential harvesting prescriptions (Barbour et al.,

in press b). 

The wildlife component of INLAS used roads data to evaluate the influence 

of roads on certain habitat conditions (Bate et al., in press). Snags and logs provide

critical habitat for many terrestrial vertebrates and invertebrates. The existence 

of a road, open or closed, was an important factor for modeling snag density in

wildlife analyses (Bate et al., in press). Studies have shown that snag and log densities decrease as distance to roads decreases (Hann et al. 1997, Reed et al. 1996,

Trombulak and Frissell 2000). Bate et al. (in press) created two distance variables

to evaluate relationships of roads with snag/log densities. As with the utilization

component, distances were calculated by using NETDISTANCE from each stand

or plot to the nearest open or closed road. Bate et al. (in press) also used distances

to categorize each stand or plot according to the adjacent road status: (1) primary

road (open seasonally or year round), (2) secondary (gated, controlled, or closed),

or (3) no adjacent road. NETDISTANCE was also used to compute a mileage 

estimate from each sample stand or plot to the nearest network center. In this case,

network centers were the towns of La Grande, Pilot Rock, and Elgin (table 2). 

Note that the shortest distance analyses, such as used here, assume the shortest

route is the optimal route. In reality, the shortest route may not be optimal. For

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RESEARCH NOTE PNW-RN-552
example, topographic constraints (e.g., a sheer cliff, river, etc.) between the polygon centroid and the road may render a route undesirable. Within the transportation or road network, engineering and ownership constraints may prohibit certain

traffic. Also, a slightly longer route may have higher travel speeds and thus be

preferable owing to shorter travel time. To develop an analytical tool that yielded

optimal NETDISTANCE calculations would require the consistent availability of

attribute and use data on which to build reasonable assumptions about ideal travel

routes, and these data were unavailable.

Challenges for Future Landscape Analysis Efforts 

Roads data developed for INLAS contributed to forest product utilization and 

wildlife habitat components. Nevertheless, despite the high quality of the data (i.e.,

an estimated 80 percent of the roads were represented), those contributions were

based on a snapshot of the INLAS roads data. Unfortunately, snapshots are the case

to date for other landscape modeling efforts as well. Although theoretically possible, a dynamic modeling effort required data that are not presently available. A

dynamic method of modeling roads may allow better understanding of the impacts

of roads on ecosystem processes and on management activities and resource goals.

For example, the ability to simulate road construction could have contributed substantially to projections of forest product utilization, aligning it with prescribed

treatments and in keeping with nontimber resource management. However, simulating ecological impacts of road treatments requires not only the ability to add,

modify, and remove roads on the landscape, but also to assign accurate parameters

for effects. These assignments pose potentially significant challenges, given existing data and research gaps. These knowledge gaps occur primarily in the availability of spatially explicit baseline attribute and use data and of data documenting

resource effects, especially threshold levels (Lugo and Gucinski 2000, Miller 

et al. 1996). 

In the INLAS project, spatial data were collected at appropriate and comparable scales for most analysis modules, and were detailed and current. However, this

may not be the case for all projects, and supplementary data acquisition can be 

a major task. Numerous sources for roads data exist (e.g., commercial data, U.S.

Census Bureau TIGER data, etc.), but these data may have been collected at a scale

inappropriate for the analysis. Within interdisciplinary analyses the appropriate

scale may vary among disciplines. For example, the vegetation and other biophysical components of INLAS used local data, whereas the land use analysis component

15

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
of INLAS was carried out on a regional scale reflecting the potentially substantial

influence of regional factors on forest and rangeland development. In addition to

scale issues, data from these different sources do not necessarily follow standard

mapping guidelines or provide adequate metadata. Consequently, quality cannot

always be determined (e.g., data age, spatial accuracy) and in some instances,

existing data may be outdated. For example, USGS digital line data (vector) are

available for our study area but are derived from data that are 20 or more years

old. Hawbaker and Radeloff (2004) compared available sources of digital roads

data and concluded that road density is substantially underestimated and that fragmentation may be significantly underestimated as well. In the case of the INLAS

project, other data sources lacked representation of minor roads that make up the

majority of the road network and often play a significant ecological role in many

forest and range landscapes. 

In addition to the spatial representation, complete and pertinent attribute data

are also needed. Incomplete attribute data may result in the elimination of minor

roads and underestimation of road density (e.g., ICBEMP, Keane et al. 1996). Even

when data sets are complete, the attribute data typically reflect the interests of the

collecting entity, which may be insignificant or unsuitable for ecological analysis.

For example, attributes of TIGER data (U.S. Census Bureau 2002) consist primarily of addresses, which may not be useful for ecological analysis of rural landscape. Similarly, the preponderance of data collected for forest roads inventories

relates to transportation (Gucinski et al. 2001) and engineering needs, not ecological modeling efforts. 

Given the opportunity to collect additional data, a project like INLAS might

consider traffic count data. Strategically placed counters can yield valuable information about when roads are used (e.g., do traffic peaks occur during hunting seasons, during spring mushroom harvests, etc.) and by what types of traffic (e.g., a

logging truck, a passenger car, or an all-terrain vehicle). Limited traffic count data

existed for the study area, and these data were nearly two decades old. More recent

and extensive traffic count data exist for the Starkey Experimental Forest and

Range (Rowland et al. 1997, Wisdom et al. 2005), a 10 285-ha research facility

located entirely within the INLAS project area. Traffic within the facility is controlled for research purposes, and it is not necessarily appropriate to extrapolate

numbers throughout the project area. Road use data could contribute to and connect multiple components of INLAS. For example, traffic can influence elk ranges

16

RESEARCH NOTE PNW-RN-552
and movement (e.g., Rowland et al. 2005). Traffic data may be used to model elk

movement patterns, which may influence herbivory, and subsequently vegetation

patterns. Road use data could also enable more effective use of existing road modeling tools such as ROADMOD (Anderson and MacDonald 1998), which provides

spatially-explicit estimates of road surface erosion based on the upslope road

drainage area and the road gradient. They suggest that additional data such as road

surface characteristics and road use would likely increase the model’s predictive

accuracy. Because most roads in the study are constructed from “native materials,”

these data are likely to be very important (Miller et al. 1996). 

In addition to the data described above, there is a need for “threshold data”

that would identify the point(s) at which the effect(s) from roads become detectable

for the resource in question (Lugo and Gucinski 2000, Miller et al. 1996). Threshold

data for populations or species are complex because, over the landscape, they vary

temporally and by resource. Lugo and Gucinski (2000) pointed to the challenges 

of evaluating effects of roads with regard to fragmentation, because the road types

and use patterns represent different barriers to different species, and the fragment

size threshold for a normally functioning population or community is likely to 

differ. For example, elk and deer respond differently to roads, and these responses

differ with the level and type of traffic, but the precise levels of disturbance that

elicit responses are unknown (Wisdom et al. 2005). Ideally, threshold data would

not only be collected for functioning roads, but for closed and decommissioned

roads as well. Trombulak and Frissell (2000) found ecological benefits from the

selective removal, relocation, or remediation of roads may also have thresholds.

Road effects often continue over an extended period and impact resources in many

ways, such as through the continued harvest of snags by woodcutters along roads

closed to vehicular traffic (e.g., Bate et al., in press). Thus, the threshold at which

an effect from a management activity occurs (such as a road closure) may be 

spatially and temporally deferred. 

Although some resource impacts data exist and the number of studies is

increasing, these data are often specific to a species and locality. In watershed 

contexts, road effects are rarely distinguished from other landscape disturbances

such as logging and the site preparation that accompanies road construction.

Consequently, the ability to model the effects of roads on the landscape over time

is limited. Relatively few studies of road manipulations have considered multiple

ecological or resource impacts or been carried out over sufficient timeframes to

provide temporal “threshold data” for these activities. Currently, the general lack

17

Roads in Landscape Modeling: A Case Study of a Road Data Layer and Use in the Interior Northwest Landscape Analysis System
of research and information regarding the ecological impacts of roads ultimately

limits the relevance of roads data in landscape-level analyses. 

Although collection of new data exceeded the scope of INLAS and road data

requirements for future efforts may be daunting, projects with similar data issues

may consider implementing a stratified sampling approach. For example, the study

area could be divided into areas based on predominant types of use. Roads could

be stratified by surface type and functional class for traffic count data collection.

Subsequent stratifications might include topographic qualities (i.e., slope, aspect,

elevation), ownership, and surficial geology.

Given these data, one approach toward dynamic modeling of roads in landscape analyses may be to treat roads as both a static condition (a “state”) and a

process (a “transition”) occurring on the landscape. This would allow incorporation

into existing and accepted landscape modeling software, such as the Vegetation

Dynamics Development Tool (VDDT; Beukema et al. 2003) and the Tool for

Explanatory Landscape Scenario Analysis (TELSA; Kurz et al. 2000), which were

backbone tools for several INLAS modules including vegetation (Hemstrom et al.,

in press), herbivory (Vavra et al., in press), wildlife (Wales et al., in press), and

riparian (Wondzell et al., in press). VDDT is a nonspatial model used to build and

test state and transition models for a set of environmental strata. TELSA allows

assignment of state transition probabilities, assignment of contagions to polygons,

preferential flow between polygons, and neighborhood checks. With this flexibility, it may be possible to model a variety of processes such as changes in road use,

the spread of noxious weeds along the road corridor and into adjacent vegetation

stands, and changes in traffic flow, by modeling roads as both a state and a process

(or transition). Linear road data could be used to generate road polygons identifying both the road and the “impact zone.” Application of these tools to “linear” features builds on the approach taken by Wondzell at al. (in press) in modeling stream

and riparian habitat. However, unlike the stream and riparian modeling where disturbance typically propagates in one direction (downstream), disturbances associated with roads are at least bi-directional. Incorporating these data with other existing resource data may significantly increase the data set size. For example, with

INLAS data, incorporation of preliminary road polygon data with the vegetation

data (27,557 features) more than tripled the number of polygons for analysis. We

estimate the increase in polygons would double the already considerable processing time for TELSA analysis. 

18

RESEARCH NOTE PNW-RN-552
Considering the complexity of the roads resources, multidisciplinary projects

such as INLAS may be better able to incorporate roads data by the early and continued commitment of resources specifically for acquisition of necessary data and

analyses. The development of a dynamic road ecology component for similar landscape analyses might require the following skills: a working knowledge of GIS

applications including network modeling; first-hand knowledge of the study area;

knowledge of engineering, ecology, policy, and socioeconomics; and skills in

developing relationships among research disciplines. These considerations, along

with the realization that this will not be a simple effort, should be in place before

the project start.

Acknowledgments 

Financial support for this project was received from the USDA Forest Service, 

Pacific Northwest Research Station, Managing Disturbance Regimes and Focused

Sciences Delivery Programs. We thank Sue Cothren, and Brian Fischer for roads

data, and technical expertise. We thank Chuck LeBold for information regarding

road use in the Upper Grande Ronde watershed, and David Hatfield for analytical

support. We thank Mary Rowland, Miles Hemstrom, and three anonymous reviewers for helpful comments. 

English Equivalents

When you know: Multiply by: To find:

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Square meters (m2) 1.20 Square yards

Hectares (ha) 2.47 Acres

Kilograms per hectare (kg/ha) .893 Pounds per acre

Liters (L) 1.057 Quarts

Celsius (°C) 1.8 and add 32 Fahrenheit

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