United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-12

Rule-based Mapping 

of Fire-adapted Vegetation 

and Fire Regimes for the 

Monongahela National Forest

Melissa A. Thomas-Van Gundy

Gregory J. Nowacki

Thomas M. Schuler
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August 2007 Fax: (740)368-0152

Abstract

The use of prescribed fire is expected to increase as efforts to restore fire-dependent ecosystems 

gain momentum nationally. The documentation of historical fire regimes is essential for setting 

restoration objectives that include prescribed burning. To aid the Monongahela National Forest 

in this endeavor, a rule-based approach was employed in GIS to map fire-adapted vegetation 

and fire regimes. Spatial analyses and maps were generated using ArcMap 9.1 using the 

proclamation boundary of the Monongahela National Forest as our study area. Based on current 

knowledge of fire-vegetation-site relationships, we reviewed available data sets for relevancy 

in estimating fire regimes. Four themes were selected: landtype association, potential natural 

vegetation (primary and secondary), and current forest type. All themes were converted to 20 

m2

 grids. Selected features of each theme were scaled from 1 through 5 according to their 

relationship to fire, with 1 representing conditions most conducive to fire and 5 the least. Each 

theme was weighted to reflect its inferred effect on system fire adaptation. The resulting fireadaptation scores were then categorized into standard fire regime groups. Fire regime group V 

(200+ yrs fire frequency) was the most common, assigned to more than 510,000 ha, primarily 

in the Allegheny Mountains Section. Fire regime group I (low & mixed severity, 0-35 yrs) and III 

(low & mixed severity, 35 -200 yrs) were assigned to nearly 198,000 ha, primarily in the Ridge 

and Valley Section and one subsection within the Allegheny Mountains Section. The resultant 

maps are intended to identify fire-adapted systems for land management purposes. These 

systems likely will require active silviculture using fire and/or fire surrogates for their maintenance 

or restoration. The transparent rule-based procedure can be easily modified and, as such, 

possesses the flexibility for application to other ecosystems with similar spatial databases

The Authors

MELISSA A. THOMAS-VAN GUNDY, research forester, received a B.S. degree in pre-forestry 

from Davis and Elkins College and an M.S. degree in forest resources management from the 

State University of New York-College of Environmental Science and Forestry. She joined the 

Northern Research Station’s Timber and Watershed Laboratory at Parsons, West Virginia in 2006 

and is engaged in research on management and ecology of Appalachian forests.

GREGORY J. NOWACKI, regional ecologist, received a B.S. in forestry and M.S. in natural 

resources from the University of Wisconsin-Stevens Point, and a Ph.D. in forestry from The 

Pennsylvania State University. As regional ecologist for the USDA Forest Service-Eastern 

Region, he guides land managers in applying ecological principles to resource management 

including restoration and disturbance ecology, and ecological classification and mapping. 

THOMAS M. SCHULER, research forester, received a B.S.F. in forest management and a Ph.D. 

in forest biology from Purdue University, and an M.S. degree in silviculture from Colorado State 

University. He is currently engaged in research on the ecology and silviculture of Appalachian 

hardwood forests and the restoration of central Appalachian spruce forests at the Northern 

Research Station’s Timber and Watershed Laboratory at Parsons, West Virginia.

Manuscript received for publication 1 March 2007
1

INTRODUCTION

Increasing public concern over recurrent large wildfi res 

in the western United States spurred government action 

in the form of the National Fire Plan, President’s Healthy 

Forest Initiative, and Healthy Forest Restoration Act. 

These federal policies fully acknowledge the current 

wildfi re situation, the contributing factors leading to 

the problem (including 20th century fi re suppression 

and fuel build-up), and the need to reintroduce fi re 

into fi re-dependent systems. Collectively, this represents 

a philosophical shift in the way we view and manage 

wildfi re. Instead of a continuation of fi re suppression 

policies of the past, policies which some believe may 

have led to the current wildfi re problem, more balanced 

approaches are being sought, including embracing fi re as 

a management tool.

To adequately implement legislation, there is an 

immediate need for information about fi re and its effects 

(historical and current fi re regimes, fi re risk/hazard maps, 

wildland-urban interface, etc.). Considerable research 

has ensued to fi ll this void including one effort, spanning 

the conterminous United States conducted by Schmidt 

and others (2002). This multifaceted project produced 

maps of fi re regimes and condition classes at a very 

broad scale (1:2 million). This undertaking resulted in 

products that were suitable for regional and national level 

planning efforts. However, at this scale (1-km2 cell size) 

the products lacked the specifi city to meet management 

needs of individual national forests.

Although attention to restoring fi re to landscapes has 

focused predominantly on the western United States, 

there is ample evidence that public lands in the eastern 

United States are in similar need of restoration (Brose 

and others 2001, Abrams 2005). Information on fi re 

regimes and their departure from the natural/historical 

range is even more defi cient in this part of the country. 

Recognizing the needs of national forests, we followed 

the approach of Schmidt and others (2002), with 

appropriate modifi cations, to generate landscape-scale 

products for the Monongahela National Forest (hereafter 

referred to as the Monongahela). The stepwise procedure 

Schmidt and others (2002) employed proved particularly 

instructive and spurred us to use rule-based mapping in 

a GIS (Morgan and others 2001). Our original goal was 

management based: to generate a map showing the best 

locations to restore fi re to the landscape given historical 

and current conditions. Areas that have pyrogenic origins 

(potential natural vegetation) and still possess those 

characteristics in their current vegetation are considered 

the best candidates for prescribed burning. Those areas 

possessing mesophytic characteristics, either currently or 

in the past, are poor candidates for prescribed burning.

This publication explains our approach for selecting 

resource themes, assigning scores and weights, and 

generating maps and associated tabular data. The process 

used here to create fi re-adapted landscape and fi re-regime 

maps can be easily transferred to other locales where 

similar spatial databases exist.

STUDY AREA

The spatial extent of this study is approximately the 

proclamation boundary for the Monongahela, which 

covers about 710,000 ha. National Forest System lands 

comprise about 371,000 ha of this area; the remainder 

is held in state, private, or other federal ownership. 

The Monongahela’s noted biodiversity stems from 

its rugged topography, which in turn affects primary 

ecosystem drivers of temperature, solar radiation, and 
2

precipitation. Moreover, it is located in the central 

portion of the Appalachians where plant and animal 

species with southern and northern affi nities coexist. 

The Monongahela spans portions of two ecological 

sections with different geomorphologies and climates: 

the Allegheny Mountains and Northern Ridge and Valley 

(Cleland and others 2005a). Because of these differences, 

conditions allow for a wide range of vegetation types 

from wet, sub-alpine red spruce (Picea rubens Sarg.) 

through species-rich, mixed mesophtyic forests to dry, 

pine-oak barrens (McCay and others 1997).

Most of the Monongahela lies in the Allegheny 

Mountains Section (DeMeo and others 1995, Abrams 

and McCay 1996, McCay and others 1997). The 

Allegheny Mountains Section has a wet and cool climate, 

with 114 to 152 cm of precipitation per year (about 

20 percent as snow; 30 percent at higher elevations), 

an annual temperature of 4 to 12 o

C, and a growing 

season of 140 to 160 days. Küchler (1964) mapped this 

section as northeastern spruce-fi r, northern hardwoods, 

mixed mesophytic, and oak-hickory-pine. Braun (1950) 

classifi ed the area as part of the mixed mesophytic forest 

region; however others have noted that hemlock-white 

pine-northern hardwoods would be more appropriate 

(Abrams and McCay 1996). The vegetation of the 

Allegheny Mountains is strongly infl uenced by elevation 

and aspect and forms four broad bands or zones: oaks, 

mixed mesophytic, northern hardwoods, and red 

spruce. The lowest elevations (valleys and foothills) 

are dominated by oaks, which associate with sycamore 

(Platanus occidentalis L.), river birch (Betula nigra L.), 

and various mesophytes along riparian corridors and 

in fl oodplains. Upslope, the vegetation transitions into 

mixed mesophytic forests, which include yellow-poplar 

(Liriodendron tulipifera L.), basswood (Tilia americana

L.), white ash (Fraxinus americana L), sugar maple (Acer 

saccharum Marsh.) and northern red oak (Quercus rubra 

L.). The rich, mesic cove hardwoods are diagnostic of this 

group. The northern hardwood group is found on upper 

slopes and ridge tops and features sugar maple, yellow 

birch (Betula alleghaniensis Britt.), American beech (Fagus 

grandifolia Ehrh.), eastern hemlock (Tsuga canadensis

(L.) Carr.), and black cherry (Prunus serotina Ehrh). Red 

spruce forests occur at the highest elevations (above 1,000 

m) often mixing with American beech, yellow birch, and 

eastern hemlock.

Much of the Northern Ridge and Valley Section lies 

in the rain shadow of the Allegheny Mountains and 

supports vegetation refl ective of drier conditions (Abrams 

and McCay 1996, McCay and others 1997). Annual 

precipitation ranges from 76 to 102 cm and may be 

as high as 152 cm near the Allegheny Plateau. Annual 

temperature ranges from 4 to 14 °C and the growing 

season ranges from 120 to 180 days. Küchler (1964) 

mapped this section as Appalachian oak forest, oakhickory-pine forest, and some northern hardwoods forest. 

Braun (1950) classifi ed much of the area as oak-American 

chestnut (Castanea dentata (Marsh.) Borkh.), although 

oaks now dominate since chestnut blight effectively 

eliminated American chestnut as an overstory species. In 

general, northern red oak and white oak (Quercus alba L.) 

occur on productive mesic sites, often intermixed with 

eastern white pine (Pinus strobus L.) on side slopes. Scarlet 

(Q. coccinea Muenchh.) and black oak (Q. velutina Lam.) 

increase in representation on progressively drier sites. 

On the driest sites (e.g., shale barrens), pitch (P. rigida P. 

Mill.), Table Mountain (P. pungens Lam), or Virginia (P. 

virginiana Mill.) pines predominate, either in pure stands 

or mixed with scrub oak (Q. ilicifolia Wangenh.).

METHODS

Mapping Approach

We applied aspects of the nationwide approach of 

Schmidt and others (2002) to the landscape scale. 

Schmidt and others (2002) mapped historical fi re regimes 

at a regional scale by assigning rankings to combinations 

of biophysical and vegetation spatial data layers. We 

used their approach and substituted best available local 

data sources, making appropriate modifi cations based on 

these databases and local environment, and employing 

weighted averaging and rule-based mapping.

A geographic information system (GIS) was used to 

couple available spatial data with expert opinion on 

Appalachian disturbance regimes for graphical display 

and analysis. We used ArcMap 9.1 (Environmental 

Systems Research Institute, Redlands, CA) in a manner 

similar to Hiers and others (2003) for spatial analyses, 
3

Table 1.—Weighting values by input theme to produce the fire-adaptation 

landscape map 

Theme Weight

(percent)

Land type association (LTA) 20

Potential natural vegetation, primary association (PNV1) 30

Potential natural vegetation, secondary association (PNV2) 20

Current forest types - overstory (CFT) 30

Figure 1.—Schematic showing the 

stepwise procedure of rule-based 

mapping. Input themes (far left column) 

are converted from polygons to 20 m2

grids and scale values are assigned 

to each grid cell from the score values 

(rules) shown in Tables 5, 6, and 7. 

Weighting factors are then applied to 

these grids resulting in the weighted 

average overlay map (far right column).

applying weights and scale values to determine scores 

(weighted averaging) resulting in map generation.

Four input themes were selected: landtype associations 

(LTA), potential natural vegetation primary (PNV1) and 

secondary (PNV2), and current forest type (CFT) (Fig. 

1). Primary PNV plant associations are the dominant 

plant association for a given area, whereas secondary 

plant associations are less prevalent inclusions that 

provide additional ecological information within a map 

unit. All themes were converted to 20 m2

 grids and 

weights were assigned to each (Table 1) according to their 

estimated infl uence on fi re adaptation based on expert 

opinion of the authors. Figure 2 provides an example of 

how scale values (assigned from Tables 5, 6, and 7) and 

weights (Table 1) were applied to individual themes to 

generate a fi re-adaptation score for a given cell.

To determine the fi nal input themes and their weights, 

an analysis was made of the relationships among themes, 

weights, and outputs. This sensitivity analysis was an 

iterative process, where different theme weights were 

applied (from equal weighting to various combinations) 

and output maps compared and scrutinized (Fig. 3 and 

4 and Tables 2 and 3). Equal weighting (25 percent 

to all themes) gave the most conservative estimate of 

fi re adaptation (higher scores; less fi re), whereas higher 

weights to either CFT or PNV1 (60 percent weighting) 

returned more liberal estimates of fi re adaptation (lower 

scores; more fi re). The fi nal output (Fig. 2 and 5) was 

based on equal weighting of CFT and PNV1 (30 percent 

each), and equal weighting (20 percent each) of PNV2 

and LTA. Themes PNV1 and PNV2 make up 50 percent 

of the weighting because these themes cover all land in 

the study area in fi ner detail than the other themes.
4

Figure 2.—Example of weighted averaging for a point on the Monongahela. Square represents 1,000 m2 with the 

centroid displayed as an asterisk. Input values for landtype association (LTA), potential natural vegetation (primary and 

secondary; PNV1 and PNV2, respectively), and current forest type (CFT) are displayed. The table of values within the 

fi gure shows the steps made through the application of the assigned rules to determine the weighted average fi readaptation score. 

Input theme Scale value Weight % Score

LTA 5 20 1

PNV1 2 30 0.6

PNV2 5 20 1

CFT 2 30 0.6

Weighted 

average

3.2
5

Figure 3.—Comparison of fi readaptation score totals for different 

weighting schemes. See also Table 2.

Figure 4.—Comparison of fi readaptation score totals for input 

themes (unweighted, not averaged) 

and results of weighted averaging. 

See also Table 3.

0

100000

200000

300000

400000

500000

1 2 3 4&5

Fire-adaptation score

Area

(ha)

model weights

PNV1 emphasis

current forest type emphasis

equal weights

0

100000

200000

300000

400000

500000

600000

1 2 3 4&5

Fire-adaptation score

Area

(ha)

PNV1

PNV2

current forest type

LTA

model weights
6

Features in each of the input themes were scaled from 1 

through 5 in accordance to fi re-adaptation characteristics 

of the landscape (primarily vegetation-based) with 1 

representing characteristics most adapted to fi re and 5 

the least. Weights and rules (scale values) were applied to 

each cell in the GIS and the resulting weighted averages 

were rounded to whole numbers using the default 

rounding function to produce the fi re-adapted score for 

each cell. Scale values and weighting factors were based 

on literature and expert opinion of the authors. Null 

values were assigned a 5, thus being conservative in areas 

with little information. This GIS rule-based approach 

provides an effective way to locate and summarize fi readapted landscapes based on commonly held notions 

regarding fi re-vegetation-site relations.

The map and associated database applies but does not 

test these theories of fi re ecology. As research advances 

and our knowledge of fi re ecology improves, the map can 

easily be modifi ed to refl ect those changes. The stepwise 

logic behind the weighted averaging is illustrated in 

Figure 1.

Literature Review

The assignment of fi re-adapted values to LTA, CFT, 

PNV1, and PNV2 was based on review of literature on 

historical disturbance regimes, tree-life histories, and 

silvical characteristics (Table 4). We used the Central 

Appalachian Broadleaf Forest-Coniferous Forest-Meadow 

Province (M221) (Cleland and others 2005a) as the focus 

for literature review and reconstructing presettlement 

disturbance regimes.

Presettlement and Historical Disturbance Regimes

Presettlement disturbance regimes, and land-use 

alterations since, are well documented across the eastern 

United States (Frost 1998, Wade and others 2000, 

Brose and others 2001). The ecological role of fi re is 

particularly well established for oak and pine systems 

(Abrams 1992, 2001, 2005; Brose and others 2001).

Native American ignitions were prevalent prior to 

European settlement, considering the unfavorable 

seasonality (mainly during green leaf-out) and conditions 

(wet and rainy) associated with most lightning storms—

Table 2.—Results of sensitivity analysis. Fire-adaptation score totals after changing weights

Weighting scheme Area (ha) by score

1 2 3 4 and 5

Equal weights for current forest type and primary potential 

natural vegetation (LTA - 20%, PNV1- 30%, PNV2 20%, CFT 

- 30%)

2,100 98,513 99,147 510,521

Equal weights all themes (LTA - 25%, PNV1 - 25%, PNV2 

- 25%, CFT - 25%)

2,098 73,154 95,452 539,577

Current forest type emphasized (LTA - 14%, PNV1 - 13%, 

PNV2 - 13%, CFT - 60%)

7,656 105,661 130,264 466,701

Primary potential natural vegetation emphasized (LTA - 13%, 

PNV1 - 60%, PNV2 - 14%, CFT - 13%)

0 124,467 107,857 477,957

Table 3.—Results of sensitivity analysis. Area (ha) by fire-adaptation score from individual input themes 

compared to weighted average of all themes

Score PNV1 PNV2 LTA CFT Model weights

1 2,750 27,403 165,497 28,593 2,100

2 201,613 80,215 0 178,492 98,513

3 58,799 43,139 0 126,886 99,147

4&5 447,119 559,525 544,784 379,328 510,521
7

Figure 5.—Fire-adapted vegetation within the study area. Ecological divisions shown are subsections; 

section break between the Allegheny Mountains and Northern Ridge and Valley sections is the thicker line.
8

Table 4.—Information sources for presettlement and historical disturbance regimes for major forest types 

General disturbance regimes Brown 2000 

Brown and Smith 2000

Chapman and others 1982

Delcourt and Delcourt 1997, Delcourt and Delcourt 1998

DeVivo 1991

Frost 1998

Guyette and others 2002

Ison 2000

Maxwell 1910

Patterson and Sassaman 1988

Rentch and Hicks 2005

Russell 1983

Schmidt and others 2002

Seymour and others 2002

Wade and others 2000

Williams 1989

Red spruce and spruce-hardwood forests Chandler and others 1983

Gorman 2005

Hopkins 1899

Rentch and others 2007

Schuler and others 2002

White and others 1985

Northern hardwood forests Canham and Loucks 1984

Cleland and others 2005b

Fahey and Reiners 1981

Frelich and Lorimer 1991

Lorimer 1977

Lorimer and White 2003

Marks and Gardescu 1992

Nowacki and others 2004

Parshall and Foster 2002

Patterson and Backman 1988

Seischab and Orwig 1991

Wade and others 2000

Whitney 1986, Whitney 1987, Whitney 1990

Mixed mesophytic forests Abrams and others 1998

Davenport 2005 

Lorimer 1980 

McCarthy and others 2001

Schuler and Fajvan 1999

Oak and oak-pine forests Abrams 1992, Abrams 2001, Abrams 2003, Abrams 2005

Abrams and others 1995

Brose and Van Lear 2004

Brose and others 2001

Collins and Carson 2003

Croy and Frost 2005

Fryar 2004

Lorimer 1985, Lorimer 1989

Lorimer and White 2003

Randles and others 2002

Rentch and others 2003

Schuler and McClain 2003

Shumway and others 2001

Signell and others 2005

Spetich 2004 

Sutherland 1997

Van Lear 1991, Van Lear 1993

Waldrop and others 2002

Welch and Waldrop 2001
9

the primary natural ignition source in the East (Bratton 

and Meier 1998, Dey and Guyette 2000, Lorimer 2001). 

For instance, fossil pollen and charcoal particle records 

preserved in a North Carolina peat bog indicated that 

local, presumably Native-caused, fi res increased to 30 

percent of the charcoal record during the Woodland 

period (3,000 to 1,000 years BP). This corresponds with 

documented lifestyle changes toward agriculture and 

hunting-based habitat manipulation by Native Americans 

(Delcourt and Delcourt 1997). Delcourt and Delcourt 

(1997, 1998) speculate that Native American activities 

including burning would have heightened differences 

between vegetation types. In eastern Kentucky, pond 

fossil pollen and charcoal records and archaeological 

records of prehistoric sites near the pond demonstrate 

the central role anthropogenic fi res played in shaping 

forest composition (Ison 2000). Sediment core studies at 

Cliff Palace Pond track the changes in vegetation due to 

climate and human infl uence, including the increase in 

fi re-tolerant plant taxa even as a wetter climate developed 

(Ison 2000).

The earliest historical accounts of the landscape of 

Virginia (and some areas later to become West Virginia) 

are documented in Maxwell (1910). Maxwell recounts 

the 1671 Thomas Batts expedition where “...meadows 

and old fi elds...” indicative of Native habitation were 

reported in a valley near the New River, near the present 

Virginia - West Virginia border. The time that elapsed 

between Native depopulation (ca.1672) and European 

arrival (mid-1700s) probably greatly obscured the scope 

and magnitude of Native American impacts in West 

Virginia (Maxwell 1910). Some ancillary accounts do 

exist, however, such as small areas of sod and young, 

pole-sized forests found by early settlers (1753) in Tygart 

Valley (Maxwell 1910).

Red Spruce and Allied Forests

Red spruce occurs on high elevation (>1000 m) 

ridges and plateaus within the Allegheny Mountains 

Section, often mixing with yellow birch, sugar maple, 

American beech, black cherry, and eastern hemlock 

(Rentch and others 2007). These topographically 

exposed locations are driven largely by weather-related 

disturbances, particularly wind and ice-storm events. 

These disturbances affect forests in a variety of ways, 

from frequent single-tree mortality and occasional partial 

canopy removal to infrequent catastrophic blowdowns 

(White and others 1985). This disturbance regime of 

principally gap-phase dynamics is conducive to the life 

history of red spruce—a shade tolerant species that 

can persist in deep shade for long periods and quickly 

respond to canopy gaps.

The prevailing cool, foggy, and wet conditions greatly 

suppresses fi re, with stand replacement burns recurring 

at an estimated 650 years (Gorman 2005). Pre-European 

settlement fi re events probably were restricted to times 

of extended drought, especially in the presence of dead 

or dying timber due to insect infestations or blowdowns 

(Hopkins 1899). This forest type was greatly affected 

by late 19th /early 20th century logging and subsequent 

fi res, converting many stands to northern hardwoods 

(Schuler and others 2002).

Northern Hardwood Forests

Northern hardwoods occur on upper elevations of 

the Allegheny Mountains Section below 1000 m 

in elevation, forming a transition between mixed 

mesophytic at lower elevations and red spruce 

forests at the highest elevations. Diagnostic species, 

including yellow birch and black birch (Betula lenta

L.), sugar maple and red maple (Acer rubrum L.), and 

American beech, are well suited to the prevailing cool, 

damp climate of this zone. The inherently moist site 

conditions coupled with the fi re-retarding tendencies 

of the dominant species (dense understory shade; 

rapid decomposition of moisture-retaining leaves and 

woody debris) strongly inhibit fi re. The low incidence 

of presettlement fi re is fi rmly established in historical, 

dendroecological, and palaeoecological records (Lorimer 

1977, Fahey and Reiners 1981, Whitney 1987, Patterson 

and Backman 1988, Frelich and Lorimer 1991, Parshall 

and Foster 2002). Instead, wind and ice-related events 

are the primary disturbances of this forest type.

Throughout their range, northern hardwoods are 

renowned for their long-term stability and old-growth 

character driven by gap-phase dynamics (single-tree 

and small multiple-tree gap creation). This gap-based 

disturbance regime reinforces the long-term dominance 

of the species of this forest type. Occasionally, intense 
10

disturbance events (hurricane-force winds, rime icing) 

lead to partial or full canopy destruction. However, 

stand-replacing disturbances are exceedingly infrequent, 

with return intervals averaging more than 1,000 years 

(Lorimer 1977, Canham and Loucks 1984, Whitney 

1986, Whitney 1990, Seischab and Orwig 1991, Marks 

and Gardescu 1992). At this rate, several tree generations 

of gap-phase-origin can cycle between catastrophic 

episodes.

Mixed Mesophytic Hardwood Forests

The mixed mesophytic hardwood forests are found 

mainly in the Allegheny Mountains Section and are 

distinguished by a diverse mix of tree species found 

generally on moist, nutrient-rich sites. Common tree 

species include basswood, yellow-poplar, sugar maple, 

northern red oak, American beech, and white ash. 

Weather-based and disease-caused single and small 

multiple-tree gaps are the primary disturbance agents, 

although infrequent, low-intensity fi re is possible in 

stands embedded in fi re-prone landscapes (Davenport 

2005). Frequent canopy disturbance was confi rmed on a 

unique talus slope in the study area, with release events 

occurring every decade from 1870 to 1990 (Abrams and 

others 1998). Fire scars also were noted on some sugar 

maples in the study. On the Fernow Experimental Forest 

in West Virginia, partial canopy disturbance occurred 

approximately every 31 years between 1797 and 1983 

(Schuler and Fajvan 1999). This return interval fell 

within the range found in southwestern North Carolina 

of 30 to 40 years (Lorimer 1980).

Oak and Oak-Pine Forests

Oak forests dominate parts of the study area, especially 

in the Northern Ridge and Valley section. A variety of 

oak species occur, including white oak, chestnut oak 

(Quercus prinus L.), black oak, scarlet oak, and northern 

red oak. Hickories (Carya spp) and pines (Pinus spp) are 

common associates. Frequent, low-intensity surface fi res 

are considered the principal disturbance that historically 

maintained these forest types. Local fi re-scar studies have 

calculated fi re-return intervals of 8 years (Shumway and 

others 2001) and 7 to 15 years (Schuler and McClain 

2003). Fire scars on one old-growth white oak in Ohio 

showed a median fi re interval of 2 years between 1731 

and 1881 (McCarthy and others 2001). Large canopy 

disturbances (involving multiple trees) from various 

origins (wind, fi re, ice) occurred every 16 years in oakdominated old-growth stands in the central hardwoods 

region (Rentch and others 2003).

Oaks are well suited to frequent surface burning because 

they possess a number of fi re adaptations, including thick 

bark, high resistance to rotting after injury, deep rooting, 

vigorous sprouting abilities, and increased germination 

and survival on fi re-created seedbeds (Abrams 2000). 

Also, oak leaves dry quickly, resist decay, and curl to 

provide a fuel bed for surface fi res, as opposed to the 

leaves of northern hardwoods that decay faster and lie 

fl at (Van Lear 2004). Maple and beech litter often do 

not carry surface fi re on prescribed fi res in the study area, 

especially if allowed to decay and fl atten under winter 

snow packs (personal observation of the authors). Oak 

is a disturbance-dependant genus that prefers high light 

conditions to germinate and recruit to the overstory. 

Although they will regenerate in forested understories, 

oaks are often relegated to small seedlings that do poorly 

against shade-tolerant competitors (Lorimer 1985, 

Lorimer 1989). Oaks and hickories respond to fi re by 

sprouting from root collar buds located deeper in the 

soil than their shade-tolerant competitors (Brose and 

Van Lear 2004). The connection between oak and fi re is 

strong, based on multiple lines of evidence (paleoecology, 

historical records, tree-life histories, ecophysiological 

traits) (Abrams 1992). Without fi re, oak forests readily 

succeed to fi re-sensitive, shade-tolerant species on all but 

the most xeric of sites.

Input Themes

Landtype associations are ecological units that represent 

biogeoclimatic relations at the landscape scale (Cleland 

and others 1997). Landtype associations were digitally 

mapped by DeMeo and others (1995) for the study area 

prior to this study. We appended fi re-history descriptions 

to each LTA and assigned scale values of 1 or 5 (Table 5). 

Landtype associations with documented fi re events, drier 

site conditions, and pyrogenic species were assigned a 

value of 1, whereas LTAs having little or no documented 

fi re history, moister conditions, and mesophytic (fi reavoiding) species were assigned a value of 5. The majority 

of LTAs within the study area (about 443,200 ha) have 

an unknown fi re history. As such, many LTAs received 
11

Table 5.—Assigned scale values for landtype associations (LTA) 

Landtype association Scale value Fire history

Allegheny Front Foothills (Fore Knobs) 1 Frequent, low intensity

Allegheny Mountain System 1 Frequent, low intensity

Beaverlick-Brushy System 1 Frequent

Cave Mountain System 1 Frequent, low intensity

Germany Valley 1 Frequent, low intensity

North Fork Mountain/River Knobs 1 Frequent, low intensity

Allegheny Front Sideslopes 5 Unknown

Allegheny Plateau 5 Unknown

Allegheny Plateau Block, Red Spruce, Frigid Soils 5 Low occurrence

Allegheny Plateau Red Spruce - Frigid Soils 5 Low occurrence

Burner Mountain - Laurel Fork Virginia System 5 Unknown

Canaan Valley 5 Unknown

Cheat Mountain Backslopes 5 Unknown

Cheat River 5 Riparian, low frequency

Cheat River Hills 5 Unknown

Cheat-Shaver’s-Back Allegheny Mountain System 5 Low occurrence

Cloverlick System 5 Unknown

Deer Creek 5 Unknown

Dolly Sods 5 Low occurrence, high severity

Middle Mountain System 5 Unknown

Northern Allegheny Mountain 5 Unknown

Potomac Riparian 5 Riparian, low frequency

Slabcamp - Little Mountain System 5 Unknown

Spruce Knob System 5 Low occurrence

Tygart Valley River Riparian 5 Low occurrence, riparian

Upper Tygart Valley 5 Unknown

Water 5 None

a scale value of 5, which contributed to the conservative 

tendency of our results. Using Figure 2 as an example, 

the Slabcamp-Little Mountain LTA had a scale value of 5 

based on its unknown fi re history (Table 5).

Potential natural vegetation for land of all ownership 

types was taken from the Monongahela’s landtype 

inventory to form two input variables. Primary potential 

natural vegetation plant associations (PNV1) were 

assigned scale values based on the fi re adaptation of their 

component trees (Table 6). Secondary plant associations 

(PNV2) are composed of the same associations as PNV1 

and were assigned the same scale values as PNV1, but 

remained a separate theme in the model. As shown 

in Figure 2, if an area has a PNV2 assigned, it will be 

different than the area’s PNV1, by defi nition; however 

the same list of plant associations is used (Table 6).

Current forest types (CFT) on National Forest System 

(NFS) lands were assigned scale values based on the 

current stand-level forest type inventory (Table 7). There 

were about 28,400 stands in the database, representing 

data collected from 1950 (one stand) through the 

present. This database, maintained by the Monongahela 

for ongoing management, is the best approximation of 

current vegetation available for NFS lands. A continuum 

of value assignments (1 through 5) were based on the 

life histories and physiological traits of the dominant 

overstory trees (Burns and Honkala 1990a, Burns and 

Honkala 1990b, Brown and Smith 2000). Using Figure 

2 as an example, the CFT of the point is mixed oak and 

receives a scale value of 2 (Table 7).

For land in other ownerships, the 1993 West Virginia 

Gap Analysis data for Land Use/Land Cover (West 
12

Virginia University 2006a) was used to estimate CFT. 

These data include NFS lands as well, however we used 

only those polygons not in NFS ownership. Current 

land-cover types were mapped at the alliance level from 

a combination of LANDSAT imagery, FIA plot data, 

and aerial photos (Strager and Yuill 2002, West Virginia 

University 2006b).

Map Validation

Validation of the resultant fi re-adaptation map proved 

diffi cult, given that efforts were geared to identify and 

use the best available spatial data in its creation. What 

could be considered “the truth,” especially since multiple 

interacting variables infl uence fi re regimes? Solely 

concentrating on biophysical parameters as theme inputs 

enabled us to use meteorological data as an independent 

Table 7.—Assigned scale values for current forest type (CFT) 

Scale value Forest type

1 Open, Pitch pine, Red pine, Red pine-oak, Shrubland, Virginia pine, Virginia pinehardwoods, Woodland 

2 Bigtooth aspen, Birch, Black locust, Black oak/scarlet oak/hickory, Chestnut oak, Eastern 

white pine, Eastern white pine-hemlock, Hardwood/conifer forest, Mixed oaks, Northern red 

oak, Oak dominant forest, Oak-Eastern white pine, Quaking aspen, Scarlet Oak, Upland 

shrubs, White oak, Yellow-poplar/white oak/Northern red oak

3 Hemlock, Mixed upland hardwoods, Paper birch, White pine – northern red oak

4 Black cherry-white ash/yellow-poplar, Black walnut, Cove hardwood forest, Diverse/

mesophytic hardwood, Herbaceous wetland, Lowland shrubs, Mountain conifer forest, 

Norway spruce, Red maple (dry site), Red spruce-balsam fi r, Spruce, Sugar maple-beechyellow birch/red spruce, Tamarack

5 Barren land (mining, construction), Beech, Conifer plantation, Floodplain forest, Forested 

wetland, Intensive urban, Light intensity urban, Major power lines, Moderately intense 

urban, Mountain hardwood forest, Mountain hardwood/conifer forest, Pasture/grassland, 

Planted grassland, Red maple (wet site), River birch/sycamore, Row crop agriculture, Shrub 

wetland, Sugar maple, Sugar maple – beech/yellow-poplar, Sugar maple-basswood, Surface 

water, Unknown, White ash, White spruce-balsam fi r

Table 6.—Assigned scale values for plant associations (PNV1 and PNV2) 

Scale value Plant association

1 Dry pines (Virginia, pitch, and/or Table Mountain pine), Heathland (blueberry/mountain laurel), Rock 

with blueberry

2 Chestnut oak-red oak-red maple, Chestnut oak-white oak, Chestnut oak-white pine, Goldenrod/

sedges, Hypericum glades, Limestone glades, Mountain hairgrass/hayscented fern, Red oak, Red 

oak/mountain laurel, Scarlet oak-black oak, Shale barrens, White oak, White oak-Eastern red cedar, 

White oak-hickory, White oak-white pine

3 Spirea thicket, Sugar maple-red oak (dry phase), White oak-yellow poplar, White pine-hemlock, 

Yellow birch, Yellow birch/rhododendron, Yellow birch/rhododendron

4 Cotton grass-beaked rush, Hemlock cove, Hemlock-yellow birch cove, Red spruce, Red spruce/

balsam fi r, Red spruce/hay scented fern, Red spruce, American beech, Red spruce-Eastern hemlock, 

Red spruce-eastern hemlock/rhododendron, Sedges, Sphagnum bogs, Sugar maple-hemlock cove, 

Sugar maple-red oak, Sugar maple-red oak (moist phase), Sugar maple-red oak/blue cohosh, Sugar 

maple-red oak/New York fern

5 Alluvial riparian, American beech, American beech-basswood, Rock, Speckled alder/willow thickets, 

Sugar maple, Sugar maple-American beech, Sugar maple-basswood, Sugar maple-basswood/wood 

nettle, Sugar maple-blue cohosh, Sugar maple-yellow- poplar, Unknown, Water
13

check. Due to its strong control on fi re regimes, 

precipitation was selected as a prime validation measure. 

We used a PRISM-based average annual precipitation 

cover (1961-1990) for West Virginia provided by the 

Natural Resources Conservation Service’s National Water 

and Climate Center and the Spatial Climate Analysis 

Service at Oregon State University (USDA NRCS 2007). 

Precipitation classes ranged from somewhat dry to very 

wet, lending themselves well for fi re-adaptation score 

assignment. The full range of fi re-adaptation scores 

(1-5) were assigned to precipitation classes along this 

moisture (inferred fi re) gradient. The 14 pre-determined 

precipitation classes in the data set were assigned fi readaptation scores as follows: 1 = ≤86 cm precipitation 

classes; 2 = 87-102 cm precipitation classes; 3 = 103-112 

cm precipitation classes; 4 = 113-137 cm precipitation 

classes, and 5 = >138 cm precipitation classes (Fig. 

6). Fire-adaptation scores generated by the model and 

precipitation data were geospatially compared at a 20-m 

pixel resolution and a correspondence matrix and Kappa 

statistic produced (Monserud and Leemans 1992).

Fire-regime Map Creation

The fi re-adaptation scores generated by our rule-based 

approach do not directly correspond to nationally 

standardized fi re regime groups (USDA FS 2003). 

Based on literature review, our fi re-adaptation scores 

were cross-walked and assigned to the standardized fi re 

regime groups as follows: 1 (most fi re-adapted areas) = 

fi re regime group IV (replacement, 35-200 years); 2 = 

fi re regime group I (low and mixed severity, 0-35 years); 

3 (moderately fi re-adapted areas) = fi re regime group III 

(low and mixed severity, 35-200 years), and 4 and 5 (least 

fi re-adapted areas) = fi re regime group V (200+ years).

Fire regime group II (replacement, 0-35 years) represents 

highly pyrogenic ecosystems such as grass and shrub 

lands—systems that are rare in the central Appalachians. 

As such, no landscapes on the Monongahela were 

categorized as fi re regime group II. The fi re regime map 

was substantiated by comparing it to site specifi c stand 

dynamic studies within the study area.

RESULTS

A rule-based, multifactor approach was used to generate 

a fi re-adaptation map and database that spatially 

identifi es and quantifi es vegetation-fi re patterns of the 

Monongahela landscape (Fig. 4 and Table 8). Our fi nal 

output (Fig. 2 and 5) was based on equal weighting of 

CFT and PNV1 (30 percent), resulting in a distribution 

generally between the two extremes from the other 

weighting schemes described previously and displayed 

in Table 2. These two themes were given equal weight 

since they were consistent with the original intents of 

the mapping effort: to identify and map those areas 

where prescribed fi re could be best applied to perpetuate 

current forest types or restore historical (PNV) forest 

types. By combining and weighting themes to generate 

fi re-adaptation scores, the model has a moderating effect 

relative to using individual “unweighted” themes (Fig. 4 

and Table 3).

For validation, we compared our model-based fi readaptation scores to those generated by an independent 

factor, precipitation. A strong correspondence was found 

between the two spatial covers analyzed at the 20-m pixel 

resolution. The direct correspondence between the cells 

is 44 percent with a Kappa statistic of 0.17. As Table 9 

shows, this correspondence is based on exact agreement 

between the two maps (main diagonal of the table). 

When near correspondence is computed (secondary 

diagonals off the main diagonal, outlined area in Table 9) 

the correspondence increases to about 92 percent with a 

Kappa statistic of 0.88.

Since fi re-adaptation scores of 4 and 5 were combined 

when we assigned fi re regime groups, another analysis 

of correspondence was made involving the main 

diagonal (direct correspondence) and cells for fi readapted rankings of either 4 or 5 (bold numbers in 

Table 8.—Fire-adaptation scores as a result of the 

mapping from rule-based weighted averaging

Fire-adaptation score Area

(ha)

1 – most fi re adapted 2,100

2 98,513

3 – moderately fi re adapted/neutral 99,147

4 308,129

5 – least fi re adapted 202,392

Total 710,281
14

Figure 6.—Average annual precipitation data and assigned fi re-adaptation scores. Ecological divisions shown 

are subsections; section break between the Allegheny Mountains and Northern Ridge and Valley sections is the 

thicker line.
15

Table 10.—Fire-regime group as cross-walked by fire-adaptation scores 

Fire regime group Fire-adaptation

score

Area

(ha)

I - Low & mixed severity, 0-35 yr 2 98,513

II - Replacement, 0-35 yrs NA 0

III - Low & mixed severity, 35-200 yrs 3 99,147

IV - Replacement, 35-200 yrs 1 2,100

V - Any severity, 200+ years 4 & 5 510,521

Total 710,281

Table 10). With this comparison, there was a 75 percent 

correspondence and a Kappa statistic of 0.37, indicating 

fair agreement between the maps (Monserud and 

Leemans 1992). Although precipitation is only one of 

many factors affecting fi re regimes, this correspondence 

provided some assurance of model validity. It should be 

noted that absolute correspondence was not attainable 

due to scale differences between the two layers; i.e., our 

fi re-adaptation map shows smaller inclusions associated 

with topographic variation (slope, aspect, elevation). 

Given its higher resolution, we consider our map superior 

to the precipitation-based fi re adaptation layer that was 

used for evaluation.

The fi re-adaptation map showed that much of the 

study area was not strongly adapted to fi re (fi readaptation scores of 4 and 5). This was especially true 

of the Allegheny Mountains Section, except for its far 

southeastern portion. In total, landscapes least adapted 

to fi re comprised 72 percent of the study area (Table 8). 

Scattered within this pyrophobic matrix were small areas 

of moderately fi re-prone areas (fi re-adaptation score of 

3). These areas often refl ected the infl uence of aspect, 

which creates drier conditions more conducive to fi readapted vegetation. About 14 percent of the landscape 

was classifi ed in the two most fi re-adapted categories (1 

and 2), having strong spatial ties to the Ridge and Valley 

Section and the Eastern Allegheny Mountains and Valley 

subsection.

The fi re-regime map (Fig. 7) is a direct derivative of the 

fi re-adaptation map and represents a conversion of fi readaptation scores into standardized fi re regimes (Table 

10). As such, similar spatial distributions exist between 

the two maps, differing only in their categorization. 

Fire regime V dominated the study area landscape with 

510,500 ha (Table 10). Fire regime groups I and III, 

concentrated in the drier portions of the study area, 

Table 9.—Matrix produced from cell-by-cell comparison of average annual precipitation and 

our rule-based model. The grey highlighted area represents about 44 percent correspondence 

between the two layers with a Kappa statistic of 0.17. The outlined area represents about 

92 percent correspondence with a Kappa statistic of 0.88. Numbers in bold were used to 

calculate correspondence based on the fire-regime groups and show a correspondence of 75 

percent with a Kappa statistic of 0.37. 

Result from precipitation layer

Result from 

our model

1 2 3 4 5Total 

Number of cells

1 1,329 26,431 22,796 2,335 0 52,891

2 69,031 777,183 577,238 1,048,575 752 2,472,779

3 51,660 458,762 675,457 1,221,769 70,103 2,477,751

4 24,585 127,828 595,722 3,942,389 3,003,431 7,693,955

5 463 10,584 125,229 2,434,005 2,488,013 5,058,294

Total 147,068 1,400,788 1,996,442 8,649,073 5,562,299 17,755,670
16

Figure 7.—Fire-regime groups within the study area. Ecological divisions shown are subsections; section 

break between the Allegheny Mountains and Northern Ridge and Valley sections is the thicker line.
17

had similar areal extents at 98,500 and 99,100 ha, 

respectively. Fire regime group IV covered only 2,100 ha 

of the study area.

To validate our rule-based procedures and resultant fi reregime map, we compared the latter with four sites in 

the study area with known dendrochronological records. 

Three of the comparison sites are somewhat atypical 

of conditions within the Monongahela and were from 

remnant old-growth, talus slope forest, or extremely dry 

sites with unique vegetation. As such, these areas may not 

be wholly representative of general disturbance regimes 

or forest types of the study area (see Table 4 for other 

references). However, these are the only known published 

dendrochronological studies from the project area with 

which to compare our inferred fi re regimes.

Fire-return intervals were estimated for a site in the 

Ridge and Valley Province on North Fork Mountain 

in eastern West Virginia (Schuler and McClain 2003). 

The site, referred to as Pike Knob, is one of the driest 

areas of the state, being in the eastern rain shadow of the 

Allegheny Plateau. Vegetation includes a mixture of oakpine forest, northern hardwood forest, grass bald, and 

the southern-most stand of red pine. The area sampled 

lies within the transition of the oak-pine forest and grass 

bald communities, dominated by northern red oak. 

Before widespread, organized fi re suppression, fi re-return 

intervals were found to be between 7 and 15 years. Most 

of the area around Pike Knob was modeled as fi re regime 

III (low & mixed severity, 35-200 years) which is slightly 

longer than the fi re return interval documented by 

Schuler and McClain (2003) although the study site may 

extend into areas mapped as fi re regime I (low & mixed 

severity, 0-35 years) adjacent just west and south.

Abrams and others (1995) conducted a 

dendrochonological study of a presettlement-origin white 

pine-mixed oak forest on the Monongahela. Recruitment 

patterns and release dates were determined to describe 

the history of this study area. Fire scars were noted on 

tree cores; however, all releases were grouped together 

regardless of disturbance agent. All species and age classes 

showed a series of major or moderate releases at regular 

intervals of every 20 to 30 years. This disturbance regime 

was corroborated by recruitment periodicity of white 

pine and various oaks until 1900. The site is assigned 

to fi re regime group III (low & mixed severity, 35-200 

years) based on our map. Considering that disturbances 

(fi re, wind, ice storms, insects, and diseases) were not 

differentiated, fi re regime group III seems to be a 

reasonable fi t to the overall disturbance regime of the 

stand.

In contrast to these drier areas, a dendroecological 

analysis to determine recruitment patterns in an area of 

the Fernow Experimental Forest (Schuler and Fajvan 

1999) was compared to our GIS map of fi re regimes. 

In this mixed mesophytic forest, the standwide median 

canopy disturbance interval was found to be about 

31 years between 1797 and 1983. The study area was 

assigned a fi re regime group V (200+ years). No fi re scars 

were reported from the 170 stems studied. While canopy 

disturbance is shown in this area through the release of 

residual trees, fi re was not identifi ed as the disturbance 

mechanism. Although mesic portions of the study area 

may have been infl uenced by fi re in the past, we reiterate 

that our model was designed to identify areas of highest 

priority for reintroduction of prescribed fi re to restore 

fi re-adapted characteristics.

Dendroecological study of a unique talus slope forest 

of oak, maple, and basswood on the Monongahela 

documented a disturbance history of moderate releases 

every decade from 1870 to 1990 (Abrams and others 

1998). The site is a steep, high elevation, talus over a 

rich silt loam soil. Fire scars were noted on the boles 

of standing trees, although no fi re scars were reported 

specifi cally from the tree cores taken to reconstruct 

disturbance history. Our map shows the study area to 

be in fi re regime group V. The authors conclude that 

continuous recruitment of red oak (until 1940) can 

be explained by the complex interaction of climatic, 

edaphic, and disturbance factors particular to this site. 

Decadal canopy disturbance is shown through the release 

of residual trees. Fire is not the primary disturbance 

mechanism, though it may be one of many.
18

DISCUSSION

Vegetation-fi re relationships differ sharply over the 

Monongahela landscape, and larger study area, (Figs. 5 

and 7), generally aligning with broad-scale ecological 

units: 1) the pyrophobic Allegheny Mountains Section; 

and 2) the pyrophillic Northern Ridge and Valley 

Section. The only exception is the Eastern Allegheny 

Mountain and Valley subsection, which is more 

similar to the Northern Ridge and Valley Section. 

This reconfi rms local land managers’ and researchers’ 

suspicions that this area is transitional in nature based on 

climate, vegetation, and disturbance types. Armed with 

this information, section boundaries (Cleland and others 

2005a) should be evaluated and possibly moved based 

on the results of our analysis. Others have used different 

section boundaries to describe the study area (Abrams 

and McCay 1996); those boundaries seem to fi t better 

with results of our analyses.

Our fi re-regime map contradicts the coarse-scale historic 

natural fi re regime map of Schmidt and others (2002), 

which shows the Monongahela as a mix of fi re regime 

groups I (45 percent), III (46 percent), IV (5 percent), 

and V (4 percent). Their results suggest a landscape 

heavily infl uenced by fi re with more than 90 percent of 

the study area experiencing a fi re at least once per century 

and almost half experiencing a fi re at least every 35 years. 

In stark contrast, our analysis generated area percentages 

as follows: 14 percent (I), 14 percent (III), <1 percent 

(IV), and 72 percent (V). The differences between 

these two efforts are striking and primarily are due to 

differences in the spatial scale of databases. Schmidt and 

others (2002, Appendix G) used very broad potential 

natural vegetation units based on Küchler (1964, 

1975) to assign fi re regimes. As such, fi re characteristics 

were averaged over much larger geographic areas, 

causing higher fi re frequencies (lower return intervals) 

to be used in their categorization. Our efforts better 

represent local conditions by concentrating on standand landscape-level data. We focused on more detailed 

data to specifi cally identify the extent and location of 

areas where fi re could be most effectively reintroduced. 

We acknowledge that some of the mesic portions of 

the Monongahela may have been infl uenced by both 

prehistoric and historical fi res, but these areas would 

require greater investments of resources to perpetuate or 

restore fi re-adapted communities.

Historical fi re regimes mapped through LANDFIRE 

Rapid Assessment more closely align with our analysis. 

LANDFIRE Rapid Assessment projections estimate 

about 16 percent of the study area as fi re regime group 

I, 9 percent fi re regime group III, and 74 percent fi re 

regime group V (USDI GS 2006). LANDFIRE Rapid 

Assessment data are intended for use at the national and 

regional levels while LANDFIRE National Products 

(applicable at landscape levels) are being produced. 

The close correspondence between the two maps is 

encouraging, but not surprising as both models were 

created primarily based on vegetation data and expert 

opinion about successional pathways. The increased 

reliance on local data sets and expertise of our rule-based 

products should prove superior to previous efforts for onthe-ground planning of management activities.

The maps and associated data tables we developed allow 

for quick identifi cation of pyrogenic landscapes. Fire 

periodicity, as shown by fi re-regime groups (Fig. 7), 

allows managers to estimate the resources (personnel, 

time, funding) needed to plan and schedule prescribed 

burning over the long term. For example, to maintain 

the estimated 99,000 ha in fi re regime group I, all 

lands within this class should experience low and mixed 

severity burns at least once every 35 years. Assuming this 

is the management objective, about 2,800 ha of land 

in this class should be treated each year with prescribed 

fi re. Alternatively, more achievable goals (if resources 

are limiting) can be set by concentrating on smaller 

areas of the Monongahela with specifi c management 

scenarios. For example, fi re regime group I lands within 

an area designated for ecosystem restoration could be 

specifi cally targeted for prescribed burning. Within this 

context, the map could be queried for the total area and 

location of the fi re regime group I and annual estimates 

of prescribed burning could be calculated. Our spatial 

products also can be used to identify potential confl icts 

or hazards (e.g., proximity of fi re regime I lands to towns 

or other developments), however fi re-regime maps are 

not synonymous with fi re-hazard maps or rankings.
19

This fi rst effort focused on landscape and vegetation 

factors; however, management concerns and 

opportunities also could be added to the input themes 

and factored in the scoring or the results could be 

scaled up and bounded by features such as roads or 

streams to create effi cient landscape-scale blocks for 

prescribed burning (Hiers and others 2003). For instance, 

management could focus on reintroducing fi re in areas 

where fi re-adapted oaks are being aggressively replaced by 

shade-tolerant competitors such as maple – a common 

phenomenon on the Monongahela and throughout the 

East (Abrams 1992). In this scenario, plot data could 

be queried for stands having oak-dominated overstories 

and maple-dominated understories and used to identify 

and prioritize locations for restoration activities using 

prescribed fi re and/or fi re surrogates. Plot data also could 

be queried for the presence of mountain laurel (Kalmia 

latifolia L.), blueberry (Vaccinium spp.), or other fi reprone species of interest to determine prescribed fi re 

needs for ecological reasons and/or fuel reduction.

Application of rule-based mapping is not limited to 

determination of fi re-adapted landscapes. Other resource 

questions and concerns could be addressed by gathering 

information, forming expert panels, and incorporating 

all into a rule-based GIS map. Using an existing 

component of a readily available software package to 

create a rule-based, spatially referenced map allows for 

future fl exibility (Hiers and others 2003). The inputs 

can be easily modifi ed as spatial information is updated. 

Moreover, scale values and weighting factors can easily 

be changed and other input themes could be added if 

necessary.

ACKNOWLEDGMENTS

We thank Tom DeMeo, Linda Tracy, Linton Wright, and 

all the summer crews involved for the development of 

the Monongahela’s ecological classifi cation system. Amy 

Hessl (West Virginia University), Tom DeMeo (Portland, 

OR), and John Kabrick (Columbia, MO) provided 

helpful technical reviews. The original versions of the 

maps and database were created while the lead author was 

forest ecologist on the Monongahela National Forest, and 

she thanks the forest staff for support and encouragement 

during that time.

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Service of any product or service to the exclusion of others that may be suitable. 

Thomas-Van Gundy, Melissa A.; Nowacki, Gregory J.; Schuler, Thomas M. 2007. 

Rule-based mapping of fire-adapted vegetation and fire regimes for the 

Monongahela National Forest. Gen. Tech. Rep. NRS-12. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, Northern Research Station. 24 p.

A rule-based approach was employed in GIS to map fire-adapted vegetation and 

fire regimes within the proclamation boundary of the Monongahela National Forest. 

Spatial analyses and maps were generated using ArcMap 9.1. The resulting fireadaptation scores were then categorized into standard fire regime groups. Fire 

regime group V (200+ yrs) was the most common, assigned to more than 510,000 

ha, primarily in the Allegheny Mountains Section. Fire regime group I (low & mixed 

severity, 0-35 years) and fire regime group III (low & mixed severity, 35-200 yrs) were 

assigned to almost 198,000 ha, primarily in the Ridge and Valley Section and one 

subsection of the Allegheny Mountains Section. These systems will likely require 

active silviculture using fire and/or fire surrogates for their maintenance or restoration. 

The transparent rule-based procedure can be easily modified and, as such, 

possesses the flexibility for universal application to other ecosystems with similar 

spatial databases.

KEY WORDS: historical fire frequency, GIS, prescribed fire
www.nrs.fs.fed.us

Capitalizing on the strengths of existing science 

capacity in the Northeast and Midwest to attain a more 

integrated, cohesive, landscape-scale research program