United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report

RMRS-GTR-76-WWW

June 2001

Models of Vegetative

Change for Landscape

Planning: A Comparison

of FETM, LANDSUM,

SIMPPLLE, and VDDT

T. M. Barrett
Rocky Mountain Research Station

324 25th Street

Ogden, UT 84401

Abstract ________________________________________

Barrett, T. M. 2001. Models of vegetation change for landscape planning: a comparison of

FETM, LANDSUM, SIMPPLLE, and VDDT. Gen. Tech. Rep. RMRS-GTR-76-WWW. Ogden,

UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.

Landscape assessment and planning often depend on the ability to predict change of vegetation.

This report compares four modeling systems (FETM, LANDSUM, SIMPPLLE, and VDDT) that can

be used to understand changes resulting from succession, natural disturbance, and management

activities. The four models may be useful for regional or local assessments in National Forest planning

rules. Although these models are limited in their ability to support site-specific decisionmaking, they

can prove helpful in understanding and comparing strongly differentiated alternatives. They can also

be used to make sure that interdisciplinary planning teams share a common base of knowledge. Since

all four models are still evolving, increased validation and documentation should develop over time.

This report includes a comparison of the models in terms of purpose, data requirements, use, and

outputs. It is intended to provide planners with a general introduction to the use of these models

within a landscape assessment process.

Keywords: National Forest planning, spatial modeling, succession modeling, vegetation dynamics, simulation, computer models

The Author ______________________________________

T. M. Barrett is an Assistant Professor, Natural Resources Integrated Planning, School

of Forestry, The University of Montana, Missoula.
Contents________________________________________

Page

Background ............................................................................................................... 1

Models of Vegetative Change for Landscapes ...................................................... 1

Modeling Systems Versus Models ..................................................................... 2

Vegetation Classification Systems ..................................................................... 2

Pathways for Vegetation Development .............................................................. 2

Spatial Versus Nonspatial Models ...................................................................... 2

Uncertainty in Models ......................................................................................... 3

FETM, LANDSUM, SIMPPLLE and VDDT: A Brief Description................................ 4

Relationship to National Forest Planning ............................................................... 5

Criteria for Comparing Models ............................................................................... 5

Model Purpose and Structure ............................................................................. 6

Model Use .......................................................................................................... 6

Model Validation ................................................................................................. 6

Detailed Comparison and Discussion ....................................................................... 6

Classification Systems and Processes .................................................................. 6

Description of Simulations ..................................................................................... 7

Model Inputs and Outputs ...................................................................................... 8

FETM Inputs ....................................................................................................... 8

LANDSUM Inputs ............................................................................................... 8

SIMPPLLE Inputs ............................................................................................... 9

VDDT Inputs ....................................................................................................... 9

Use of Models ........................................................................................................ 9

Validation of Models ............................................................................................ 10

Strengths and Weaknesses ................................................................................. 11

Recommendations for Future Development and Research ................................. 12

Conclusion .............................................................................................................. 13

Acknowledgments ................................................................................................... 13

References .............................................................................................................. 13

Appendix ................................................................................................................. 14
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 1

Numerous computer models have been developedto predict vegetative change for landscapes. This

project compares four modeling systems (FETM,

LANDSUM, SIMPPLLE, and VDDT) by looking at

their formulation, data structure, capabilities, and

potential applications. It is anticipated that those

considering using these models might be working

within the National Forest planning process. For that

purpose, this report discusses strengths and weaknesses of using these models under a variety of possible planning situations. However, no recommendation is made for use of a particular model. Developers

provided materials for comparing their modeling

systems.

Background ____________________

Models of Vegetative Change for

Landscapes

Vegetation changes over time as a result of a combination of natural processes (for example, succession,

growth, fire, windthrow, insect and disease cycles,

climatic variation) and human-induced processes and

activities (for example, timber harvest, grazing, road

building, fire suppression, and introduction of exotic

species). Over time, these processes result in changing

patterns of vegetation within forest landscapes (fig. 1).

In recent years, interest has increased in understanding

Models of Vegetative Change for

Landscape Planning: A Comparison

of FETM, LANDSUM, SIMPPLLE,

and VDDT

T. M. Barrett

Figure 1—Landscape level vegetation change: canopy cover for Elk Creek, MT, watershed

classified from air photos (data from Columbia River Basin Assessment collection).

Canopy cover

0 - 25%

26 - 50%

51 - 75%

76 - 100% 2 0 2 4 Kilometers

N

1938 1988
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

the range of natural variation in vegetation in North

American landscapes prior to European settlement.

Although it is generally recognized that restoring

landscapes to pre-European vegetation conditions is

not always possible or desirable, moving toward that

goal may sometimes help to conserve biodiversity and

restore ecological processes. Predicting landscape vegetation change under a variety of natural processes and

human-induced actions allows us to understand the

future consequences of current decisions.

Understanding past vegetation change can involve

the use of tree-ring analysis, pollen counts, historic

photographs, inventories, surveyor records, and other

documents. Predicting how vegetation will change in

the future requires the use of some type of modeling

process, and a wide variety of models have been

developed for this purpose. Because of the variation

and complexity of these models, some basic terminology is introduced that will be used throughout this

report.

Modeling Systems Versus Models—A program

that predicts future vegetation for a particular landscape is a model. A program that has been designed so

that it can be easily modified to predict future vegetation for a number of different landscapes is a modeling

system. All four of the programs discussed in this

report are best described as modeling systems; however, for simplicity, this report will usually refer to

them as models.

None of the models discussed here are fully mature;

they are still experimental and continue to be changed

as they are applied to new landscapes and planning

situations. This creates some problems in describing

and comparing the four systems. In general, the discussion applies to the model versions as of fall 1999,

and past or future developments are indicated as such.

Vegetation Classification Systems—In forested

landscapes, individual trees or plants are the most

likely candidates for discrete entity, but they exist in

quantities too great for practical use in forest planning. Instead, planners generally use land areas of

more or less homogenous vegetation. Vegetation in

these areas is classified by continuous variables (for

example, canopy cover, leaf-area index, or basal area),

or by categorical variables (for example, structural

stage or species type), or by a mixture of the two.

Landscape vegetation classification systems vary

widely, depending on the intended use of the conceptual model.

Classification systems for forest vegetation may be

based on (1) potential vegetation, (2) current vegetation, (3) structure, and/or (4) other concepts (O’Hara

and others 1996). Potential vegetation is the vegetation that will develop over long time periods in the

absence of disturbance, and acts as an indicator of

the site. In the Rocky Mountain States, land classes

supporting unique potential vegetation types are conventionally referred to as “habitat types” (Pfister and

Arno 1990). Current or existing vegetation is based on

what is presently there; cover type is often used to

describe the predominant species mix. Structure classes

are related to the three dimensional distribution of

vegetation. Structure classes are sometimes conceptualized as developmental stages (for example, Oliver

and Larson 1990), but an absence of stand history data

results in structural classification that is usually based

on what is there at a particular time. Structure classification commonly uses items such as tree diameter,

canopy closure, or canopy layering. All four of the

models (FETM, LANDSUM, SIMPPLLE, and VDDT)

predict change in classified vegetation, although the

classification systems differ.

“Classified vegetation system” is used here in a

broad sense. FETM, for example, uses fuel condition

classes, which are vegetation-based classes that are

assigned attributes for fuel loading, stand characteristics (such as height to crown base), and emissionrelated factors. LANDSUM uses topographic or biophysical groups to determine which classes of

vegetation are eligible for particular pathways. In

general forest planning, vegetation classification is

often combined with physical site characteristics (soil,

elevation, aspect) and management concerns (road

access, visibility, recreation opportunity) to form land

classes (Davis and others 2000). Vegetation and site as

measured by potential vegetation are the primary

classification emphases for the FETM, LANDSUM,

SIMPPLLE, and VDDT models.

Pathways for Vegetation Development—Changes

in classified vegetation can be thought of in terms of

pathways of vegetation development. If we observed a

patch of forest following a stand-replacement event,

we might conceptualize the development of structure

classes as stages in a pathway, where times shown

represent the transition time required before the patch

is converted from one class to another (fig. 2).

After a stand-replacing disturbance event, the “old

forest” type might transfer back to the seedling class.

If the process is conceived as linear and transition

times are deterministic, there is a 1-to-1 relationship

between the current class and a future class.

Spatial Versus Nonspatial Models—Nonspatial

models predict the change in land area for vegetation

classes. FETM and VDDT are nonspatial models.

Spatial models track the specific places that are in

those vegetation classes. LANDSUM and SIMPPLLE

are spatial models. VDDT has also been developed into

a spatial model called TELSA.

“Spatial” in this report refers to whether the model

has a real-time GIS component, rather than to whether

the results can be spatially mapped. If, for example, a

nonspatial model uses initial vegetation classes that
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 3

can be linked to a GIS theme, it is often possible to also

link the output of predicted vegetation to a GIS theme.

If the model incorporates a 1-to-1 relationship of current vegetation types to future vegetation types, this

linkage is straightforward; otherwise, more complex

assignment is required, and multiple spatial assignments can be made for a single model simulation. Even

if a model is spatial, pixels or polygons may not be

influenced by spatial context, in other words, the

current state of the pixels or polygons around them.

SIMPPLLE uses spatial context, while LANDSUM

does not.

Units of land are generally modeled either as raster

cells (pixels) or as polygons. Classified vegetation

mapped from air photos is typically delineated by

polygons. Classified vegetation mapped from remote

sensing data often originates as pixels, and must be

processed into polygons. SIMPPLLE uses polygons.

CRBSUM, an earlier version of LANDSUM, modeled

with raster cells, while the current version of

LANDSUM uses raster cells that contain polygon

identifiers. TELSA, the spatial version of VDDT, uses

polygon identifiers.

Uncertainty in Models—Deterministic models

give the same result every time they are run with the

same set of inputs. Stochastic models include at least

one random component, and generate different results

with the same set of inputs. The distinction is not as

clear as it might seem: systematically altering the

parameters of a model allows prediction under variation, regardless of whether the alteration occurs manually in a deterministic model or automatically in a

stochastic model. However, manual alteration will

take more time, limiting the amount of sensitivity

analysis that can be done. Most stochastic models also

include deterministic components for some processes.

Vegetation change is often conceptualized as having

multiple alternative pathways (fig. 3), where fire,

climate, insects, disease, harvest, prescribed burning,

or other factors influence the particular pathway that

a patch follows. Multiple pathways may still be deterministic: for example, one could assume that 2 percent

of a vegetation class always experiences fire. If stochastic, the model might include either probabilistic

transition times or probabilistic branching, or both.

Pathways, whether they are deterministic or stochastic, single or multiple, can be described using either

diagrams (fig. 3) or transition matrices (fig. 4). Transition times may be directly applied to individual land

classes that contain an age attribute (as is done in

LANDSUM), or they may be indirectly represented by

assuming a certain proportion of the class experiences

transition (as is done in FETM).

One advantage of stochastic models is that most

natural processes that affect vegetation change are

stochastic. Therefore, users may have greater confidence in stochastic models of those processes. That is,

users may distrust the use of a deterministic model to

Seedling Sapling Young forest Old forest ... 10 yrs 20 yrs 70 yrs

Figure 2—Structural stage development represented as a pathway.

Seedling

Young-forest

multistory

Young forest

single-story

Old forest

single-story

growth

Stand-replacing disturbance

Stand-replacing disturbance

growth

growth

ingrowth

Figure 3—Multiple pathways for changes in structural stage.
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

represent a process they know is stochastic. Despite

this advantage, stochastic models also have limitations:

• The probabilities and possible outcomes of stochastic processes may not be well known or may

vary widely over time and space.

• The user may have difficulty processing and comprehending the information for a large number of

possible futures.

Many stochastic models use post-processors to simplify the large amount of information that is generated. FETM, LANDSUM, SIMPPLLE, and VDDT

are all stochastic models with some deterministic

components.

FETM, LANDSUM, SIMPPLLE and

VDDT: A Brief Description ________

FETM is the Fire Emissions Tradeoff Model; version 3.3 is described in this report. FETM was developed to examine tradeoffs between prescribed fire and

other fires in the context of emissions. The processes

modeled are succession, harvest, fuel treatment, and

fire; the classification system used is fuel condition

classes. In addition to those condition classes, outputs

include fire emission for four weather classes. FETM

is stochastic and nonspatial, and it was first applied to

a 485,000 hectare landscape in northeastern Oregon.

The initial developer was the engineering firm CH2M

Hill (1998), and the current Forest Service contact is

Ken Snell of Region 6.

LANDSUM is the LANDscape SUccession Model.

LANDSUM and its predecessor CRBSUM were developed as research tools to investigate landscape fire

succession modeling, but the models can be used as

management tools. The processes modeled are

succession, harvest, disease, and fire; the classification system used is structural stages and cover types

within potential vegetation types. In addition to classified vegetation, outputs can include summaries of

land area affected by processes by year, harvest area,

and if accompanied by a user-supplied volume table,

harvest volume. LANDSUM is stochastic and spatial.

The earlier raster version of the model, CRBSUM, was

applied to very large landscapes as part of the Interior

Columbia River Basin Assessment. The first application of the polygon-based LANDSUM model was applied to nested landscapes of 89,000 ha and 23,000 ha

within central Idaho. A number of people within the

Forest Service have worked on the model, but the

primary developer is Robert Keane of the Fire Sciences Laboratory, Rocky Mountain Research Station,

in Missoula, MT.

SIMPPLLE is taken from SIMulating vegetative

Patterns and Processes at Landscape scaLES.

SIMPPLLE was designed as a management tool to

understand how processes and vegetation interact to

affect landscape change. The processes modeled are

succession, harvest, disease, insects, and fire; the

classification system used is current species, potential

vegetation, density, and structure. In addition to classified vegetation, outputs include maps and charts

of processes. SIMPPLLE is stochastic and spatial

(polygon-based). Two variants, the Upper Clark Fork

and the Headwaters of the Missouri, have been used in

assessments of landscapes of 3,000 to 30,000 hectares.

The primary developer is Jimmie Chew of the Rocky

Mountain Research Station.

VDDT is the Vegetation Dynamic Development

Tool. VDDT was developed to facilitate improved

understanding of vegetation change through the use

of successional pathway modeling. The processes modeled could include succession, harvest, disease, insects,

Classes after transition

Seedling Young forest

single-story

Young forest

multistory

Old forest

single-story

Seedling <no change> growth

Young forest

single-story

stand-replacing

disturbance <no change> ingrowth growth

Young forest

multistory

stand-replacing

disturbance <no change> growth

Old forest

single-story

stand-replacing

disturbance <no change>

Initial classes

Figure 4—Multiple pathways represented as a transition matrix.

Probabilities (or proportions) would be assigned to each cell.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 5

and fire; the classification system used is cover type

and structural stage. The primary output is classified

vegetation. VDDT was developed to support the modeling effort in the Interior Columbia River Basin

Ecosystem Management Project. A spatial version of

VDDT is known as TELSA (Tool for Exploratory Landscape Scenario Analyses). The chief developers of

VDDT are Sarah Beukema and Werner Kurz of Essa

Technologies (Vancouver, BC), and the Forest Service

contact is Jim Merzenich of the Pacific Northwest

Region.

Relationship to National Forest Planning

All four models predict future conditions. Thus, one

criterion that could be used to evaluate them would be

the accuracy of predictions. However, if the models are

to be used in practical planning, other criteria also

become important. In this section, the elements of a

proposed Forest Service Planning Rule (USDA Forest

Service 2000) that relate to vegetative modeling are

discussed. In the following section, some criteria are

suggested that could correspond with the possible use

of the models under the proposed Rule.

The Rule contained a number of changes from the

current planning regulations. Ecological sustainability would have been the first priority for planning.

Planning would occur at multiple stages:

• Regional ecosystem assessments

• Local analyses

• Land and resource management plans (LRMPs)

• Site-specific projects

Only the latter two steps were envisioned as

decisionmaking, and thus subject to judicial review

and NEPA with its required formal public participation. However, increased collaboration was emphasized for all planning, with input from scientists,

tribes and other agencies, and the general public. By

the guiding principles, plans and planning should be

understandable. Plans should promote a shared vision

of desired conditions, and planners should engage

people in the collaborative development of “landscape

goals” (USDA Forest Service 2000).

Models that predict future conditions could be used

at any of the four proposed stages. For example, broadscale assessments can “describe historic conditions,

current status, and future trends of ecological, social,

and/or economic conditions, their relationship to sustainability, and the principal factors contributing to

those conditions and trends” (USDA Forest Service

2000). Local analyses provide descriptions of current

and likely future conditions for smaller areas of land.

Decisions in land and resource management plans

“may include, but are not limited to, the desired

watershed and ecological conditions and aquatic and

terrestrial habitat characteristics” (USDA Forest Service 2000).

Forest planning is often complicated by the difficulty of integrating local, regional, and national planning, as it is not always clear which level should occur

first or at which level decisions should be made. With

the proposed Rule, the spatial scale of planning was

flexible to accommodate different issues and regions;

the exception to this was the LRMPs, the National

Forest Plan revisions, which are required by the enabling National Forest Management Act.

The proposed Rule did not specify what must be

included in broad-scale assessments and local analyses, leaving instead a flexible process where data and

analysis would be chosen relative to the issue. No

particular analysis or analytical tools were required,

and many of the requirements of the previous rules

had been dropped. However, the proposed Rule specified that the “best available science” be considered in

planning and suggested the use of peer review “where

appropriate.” In addition, it stated that the responsible official should “acknowledge incomplete or unavailable information, scientific uncertainty, and the

variability inherent in complex systems” (USDA Forest Service 2000).

Criteria for Comparing Models

The Forest Service’s need for landscape assessment

is unlikely to be met with a single model. A model that

is appropriate for use in local analysis may be inappropriate for a regional assessment. A model useful for

estimating risk and uncertainty of management options may not be best suited for collaborative planning.

A model that simulates fire accurately may not simulate harvest effectively. Models cannot simultaneously

be based on minimum analytical requirements and

the best available scientific information.

In a general sense, the four vegetation simulation

models could be used:

• To understand and predict the dynamics of vegetation-related change within landscapes

• To communicate these to other members of planning teams, to other landowners and agencies,

and to the general public

• To compare alternative outcomes for landscapes

• To aid in developing a consensus on desired future

condition, and on the actions necessary to reach

those desired conditions

• To meet legal and institutional requirements for

planning and management

As FETM, LANDSUM, SIMPPLLE and VDDT could

conceivably be used within a variety of planning situations, a fair number of comparison criteria could be

used to assess their usefulness.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

Model Purpose and Structure—Criteria related

to the intended purpose of the model include the

factors already discussed:

• The development objective

• Vegetation classification system used

• Processes modeled

• Treatment of uncertainty

• Incorporation of spatial factors

• Possible inputs and outputs

• Incorporation of management choices

Model Use—In addition, the type of planning situation the model was designed for will influence:

• Difficulty of gathering and formatting input data

• Ease of formulating different scenarios

• Compatibility with other modeling systems

• Documentation and training

• User interface

Vegetation change is site-specific, and frequently

pathways and processes must be adjusted for individual landscapes. In addition, users will place more

confidence in projections they helped develop, regardless of actual accuracy. Therefore, ease of model

customization may be an important criterion:

• Adaptability of vegetation classification system

• Adaptability of pathways, probabilities, and transition times

Both ease of use and customization may be dependent on software and hardware platforms:

• Operating systems and tested/supported platforms

• Programming language

• Other software requirements

• Required storage space

• Required memory

It is possible that a model intended for small landscapes would not be equally well suited to modeling

large river basins. In general, trying to limit the

complexity of analysis means that models for larger

land areas must have lower resolution than models for

smaller land areas. Some traits that might be important in choosing a model include:

• Maximum number of vegetation classes

• Maximum number of polygons or cells (if spatial)

• Intended minimum mapping unit and landscape

size

• Planning horizon (timeframe) and planning

periods (time steps)

• Flexibility of the above

• Run times for various sized problems

Model Validation—The models all predict future

vegetation. Although it is not the only area of importance, accuracy should still be a concern. Even when

the primary purpose is to serve as a centerpiece for

discussions on desired future conditions, inaccuracy of

projections can undermine the confidence of the planners and the public. Some traits that users might look

for in models include:

• Size, source, and quality of the data set used to

build pathways and projections

• Validation against independent data sets

• Comparison against other means of projection

User confidence might also be enhanced by:

• Explanation and documentation of model assumptions, with references where appropriate

• Sensitivity analysis showing how the model behaves when default parameters are varied

• Peer review of model structure, results, and

applications

The following section presents a detailed discussion

of the four models in relation to these criteria.

Detailed Comparison and

Discussion _____________________

Classification Systems and Processes

VDDT was developed to be a modeling system that

would easily allow users to develop the relationships

between vegetation classes and processes (Beukema

and Kurz 1998). Users work with a combination of

input boxes and graphic displays of processes (fig. 5)

to develop the pathways for a potential vegetation

type.

VDDT was also used to develop the pathways used

in CRBSUM, the precursor to LANDSUM (Keane and

others 1996), so the two systems are extremely similar

in concept. Both VDDT and CRBSUM were developed

as part of the Interior Columbia River Basin Project

(USDA and USDI 1997), which assessed an area of

over 80 million hectares. CRBSUM and LANDSUM

classify by structure type and cover type within potential vegetation type.

The Interior Columbia River Basin Project used 126

cover types, 25 potential vegetation types and 7 structural stages for forest, and 29 potential vegetation

types and 8 structural types for range. Vegetation

classes, processes, pathways, and probabilities were

developed from a series of workshops with “experts,”

which probably resulted in some classes and processes

being better represented than others (Keane and others 1996). Stage and others (1995) compared the experts’ projections with the Forest Vegetation Simulation model and concluded that pathways differed, and

CRBSUM assumed faster growth, but transition times

were reasonable. When CRBSUM was adapted into

the polygon-based LANDSUM for a landscape in the

Salmon National Forest, it was found that the pathways
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 7

developed for the Interior Columbia River Basin needed

to be altered for the higher resolution landscape simulations (Keane and others 1997).

The most recent version of SIMPPLLE uses a

graphical interface similar to VDDT’s to represent

structural stages and processes. SIMPPLLE’s classification scheme has typically used habitat type groups,

predominant species classes (cover type), structure

classes (size and layering), and canopy closure (density)

classes. A simple exercise in the Swan Valley included

14 cover classes, 13 structure classes, and 5 canopy

closure classes. Like VDDT and LANDSUM, the classification system is designed to be flexible with the

application. Most applications have been in western

Montana. SIMPPLLE’s processes were developed by

Jimmie Chew of the Rocky Mountain Research Station

with some input from other resource professionals.

The typical classification system used in SIMPPLLE

is less extensive but more detailed than that of VDDT/

LANDSUM. For example, the Swan Valley SIMPPLLE

exercise used both a Larch/Douglas-fir/Ponderosa

pine and a Larch/Douglas-fir/Grand fir type. The LANDSUM model would have used a single Larch cover type

to represent both. VDDT/LANDSUM and SIMPPLLE

both use potential vegetation type, cover type, and

structural stage to classify vegetation, and SIMPPLLE

also uses canopy density.

FETM classifies vegetation into “fuel condition

classes” (FCCs). The recent application of FETM to the

1.2 million acre Grande Ronde River Basin contained

206 different FCCs. These FCCs cannot be edited or

deleted, but users may add additional classes. In the

Grande Ronde project, fuel condition classes were

created by stratifying the landscape by vegetation

type (cover), age class (to represent structure), loading

class (low, medium, high), and an activity class. Users

develop a transition matrix to represent the development of vegetation; the transition matrix allows the

user to specify what percentage of each FCC develops

into other FCCs in response to a process. The transitions are deterministic. The developers caution that it

would be inappropriate to use the original transition

matrix for other study areas (CH2M Hill 1998).

The processes modeled by the four systems vary

(table 1). FETM, for instance, has a small number of

processes that it models, but it allows six different

levels of prescribed fire treatment, lets the user set the

weather condition for prescribed fire, and computes

fire emissions for four different weather classes.

Description of Simulations

LANDSUM, although it has a large number of processes modeled, has a fairly simple structure. It starts

at year one, and works through each polygon in the

coverage. Each polygon, depending on its potential

vegetation type and its current cover type and structural stage, is eligible for a given set of processes. Each

possible process (including simple succession) has a

probability associated with it, with the total sum of

probabilities for a polygon summing to one. A random

number is drawn from a uniform probability distribution to determine which process occurs. The polygon’s

cover type and structural stage is updated. The program moves on to the next polygon. After all polygons

have been updated, the program moves on to the next

year and starts again with the first polygon. VDDT is

essentially the same as LANDSUM, but the basic

modeling elements are the vegetation classes instead

of the polygons.

LANDSUM’s modeling of processes, with the exception of fire, is essentially nonspatial; one should get

similar results by running VDDT for each initial

vegetation class, and then mapping the results to

Figure 5—Example screen from the VDDT model.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

polygons. Or, in other words, the polygons in any

initial class (= unique combination of potential vegetation class, current cover class, and structure class) are

all equally likely to develop into a future class, regardless of their location within the landscape. LANDSUM

version 2 includes spatial simulation of fire.

SIMPPLLE has a modeling process similar to

LANDSUM, but also uses (1) the polygon’s spatial

context and (2) the polygon’s past processes to adjust

process probabilities. With spatial context, SIMPPLLE

uses the current state of each neighboring polygon to

adjust the probability of insect and disease processes

in the next time period, where neighbors are those

polygons that share a border to the polygon under

consideration. For the past process adjustment,

SIMPPLLE uses the state of the polygon one time

period ago to affect the projection of the next time

period’s state. SIMPPLLE allows two deterministic

options: turning off disturbances to get natural succession only, or using the process with the highest probability (Stalling 1999).

Within a single time step, SIMPPLLE’s modeling

process for disease and insect outbreak limits the

influence of neighboring polygons to those that are

immediate neighbors. The fire process spreads from

polygon to polygon within a single time step, with the

fire spread being influenced by vegetation, elevation,

and suppression assumptions. The default process

probabilities, and the use of neighboring polygons

and past history to adjust probabilities, are not well

documented.

FETM’s modeling process starts with the usersupplied initial vegetation classes (FCCs) and the

acres in each class. Six transition tables, one for each

possible treatment (succession, fire, prescribed fire,

mechanical treatment, thinning, regeneration harvest), specify the proportion of each FCC that will

transfer to one or more FCCs annually. Thus, vegetation development is influenced both by the proportions

of land area assigned to the six different transition

tables and by the transition matrix contents. If one

specifies a 2 percent level of prescribed fire, and the

prescribed fire transition table contains transitions

for 25 FCCs, 2 percent of each of those 25 FCCs will use

the prescribed fire transition matrix.

The amount of land assigned to the FETM’s fire

transition matrix is stochastic, and influenced by

weather data and fire history data. The percentage of

land assigned to the prescribed fire transition table is

set deterministically at up to 6 different levels. Modifications are currently being made to the model to

allow more flexible implementation of treatments.

Model Inputs and Outputs

FETM Inputs (CH2M Hill 1998)—

• Fuel condition classes for the landscape characterized by fuel loading by time lag fuels, stand

characteristics (height to base of ladder fuels,

total height, height to base of live crown, stand

density), and emission factors

• Transition matrices for each process (fire, succession, other treatments)

• Personal Computer Historical Analysis (PCHA)

data files

• Interagency Initial Attack Assessment (IIAA) data

files. Both the PCHA and IIAA database files are

used as part of the National Fire Management

Analysis System process, and are derived from

national databases containing historical fire

incidents

LANDSUM Inputs (from Keane and others 1996;

Keane and others 1997)—

• Polygon GIS theme with attributes for each

polygon

➣ Potential vegetation

➣ Cover type

➣ Structural stage

Table 1—Processes modeled in recent applications.

Process VDDT/LANDSUM SIMPPLLE FETM

Thinning X X X

Regeneration harvest X X X

Mechanical fuel treatment X X X

Prescribed fire X X X

Fire X X X

Natural succession X X X

Root disease X X

Mountain pine beetle X X

Western spruce budworm X X

Other X X

Total processes/treatments ~250 ~20 ~6
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 9

• ASCII data files for

➣ Driver file (contains input and output file

names)

➣ Simulation file (number of time steps)

➣ Successional file (pathways)

➣ Scenario file (management assumptions)

➣ Volume file (optional, if harvest volumes are

desired)

SIMPPLLE Inputs (Jimmie Chew, personal

communication)—

• Polygon GIS theme containing attributes for

➣ Cover type

➣ Potential vegetation groups

➣ Structure class

➣ Density class

• Pathways and probabilities for processes

• Scenario assumptions such as fire suppression

activities, weather conditions, and management

treatments

VDDT Inputs (Beukema and Kurz 1998)—

• Five ASCII text files listing codes

➣ COVER.TXT and COVERC.TXT (long and

short descriptions of the vegetation cover types)

➣ STRUCTUR.TXT (description of structure

types)

➣ DISTCODE.TXT and DISTGRP.TXT (description of disturbances and disturbance groups)

As the user builds the model, additional ASCII files

are generated that control model runs, including files

describing pathways, probabilities, and probability

multipliers.

Outputs are similarly variable (table 2). FETM and

VDDT, as nonspatial models, do not directly produce

maps, although VDDT has been developed into the

spatial model TELSA. FETM is the only model that

can directly produce a calculation of smoke emissions,

but both SIMPPLLE and LANDSUM have been used

to estimate emissions by matching vegetation classes

to FCCs.

Use of Models

All of the models were built with the intention of

being adaptable to other landscapes. Anyone considering using these models on a new landscape should be

cautioned that a substantial investment of time would

be required for adaptation. The learning curve for

software use will most likely be outweighed by the

problems of formulating the initial vegetation classes,

determining land area in those classes (if a nonspatial

model) or developing a GIS theme of the classified

vegetation (if a spatial model), specifying the development pathways for each combination of a class and a

process, setting appropriate probabilities or proportions, and gathering other necessary input data. The

difficulty of this modeling effort will be determined by

the availability of information, the correspondence of

default processes and probabilities to the sample landscape, and the degree of accuracy (or user confidence)

required.

I believe that adapting these models to new landscapes would require input from both local managers

and the model developers. Adapting FETM to a landscape would probably require input from people knowledgeable in ecology and disturbance processes (to

develop the transition matrix) and fire management

(to develop the emissions-related data), along with

some help from the developers. Adapting SIMPPLLE

to a landscape would probably require input from

people knowledgeable in ecology and disturbance processes (to review the pathways and processes) and

some help from the developer. Adapting LANDSUM to

a landscape would probably require input from people

knowledgeable in ecology and disturbance processes

(to review pathways and processes) and substantial

help from the developer. Adapting VDDT to a landscape would probably require input from people knowledgeable in ecology and disturbance processes (to

review pathways and processes) and a small amount

Table 2—Outputs from the models (X = yes; O = in development).

Outputs FETM LANDSUM SIMPPLLE VDDT

Area in vegetation classes at a particular time XXXX

Maps of classified vegetation at a particular time X X

Area affected by processes at a particular time XXXX

Maps of processes at a particular time X X

Graphs of classified vegetation by time XXXX

Graphs of processes by time X X X

Auto averaging of outputs for multiple simulations X O

Harvest volume X X

Emissions (smoke) X O O
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

of help from the developer. An additional option might

be to contract for help from a group such as the Fire

Modeling Institute, which provides training and assistance with projects.

All of the models, as with all software developed by

the Federal government, are available without cost.

However, support and training are not necessarily

free. The VDDT model has a user guide (Beukema and

Kurz 1998) and a set of training exercises, and was

developed by a consulting company, ESSA Technologies. The FETM model has a user guide and was

developed by a consulting company (CH2M Hill). For

both VDDT and FETM, Forest Service researchers

serve as the primary contacts for the models. The other

two models, SIMPPLLE and LANDSUM, are the products of Forest Service researchers, and user’s guides

do not exist. There is a booklet for SIMPPLLE describing possible output, some additional information in a

dissertation (Chew 1995), and a Web site at http://

www.fs.fed.us/rm/missoula/4151/SIMPPLLE/

index.htm. LANDSUM does not have documentation

per se, but there is some for the earlier version CRBSUM

(Keane and others 1996). SIMPPLLE has also been

used twice for a module of the Continuing Education

for Ecosystem Management course, a joint program

between the University of Idaho and the University of

Montana. All of the models have some additional

documentation in the form of conference proceedings

or Forest Service publications (see References).

Hardware and software requirements vary (table 3).

Computing power continues to be less of a problem

each year, as cost per computation unit decreases, and

should not be a problem for most landscapes. For

example, Keane and others (1996) reported CRBSUM

run times of 30 to 50 hours for 822,000 pixels, representing 200 million acres simulated for 100 years, but

LANDSUM can now simulate 1,000 years for a 100,000

ha landscape in about 1 to 2 hours (Keane, personal

communication). As one would expect, the nonspatial

models run faster. FETM reports a 1-minute run time

for 205 vegetation classes, 100 years of simulation,

and 30 iterations on a Pentium II with WinNT.

Model run time should be related to both scope

(extent) and resolution (detail). Most models should

not be scale independent. For example, the probability

of a fire occurrence in a landscape should be related to

Table 3—Hardware and software requirements.

Requirements FETM SIMPPLLE LANDSUM VDDT

Machine PC Workstation (PC in development) PC or Workstation PC

Operating system Win95 / WinNT3.1+ UNIX Win95 Win95 / WinNT3.51+

Recommended memory Pentium 166, 40+MB RAM 128 MB RAM, 90 MB disk space Pentium 100, 32+ MB, 100 MB disk space Pentium, 8+MB RAM

Recommended storage 50 MB Depends on landscape Depends on landscape 5 MB

Other recommendations CD for installation ArcView Good editor mouse

Other software needed MS Access for database files

the size of the minimum mapping unit used. Probabilities should also always be expressed in relation to

time. For any given vegetation class, the probability of

spruce budworm occurring within 1 year should not be

equal to the probability of spruce budworm occurring

within 10 years.

Because probabilities are related to temporal and

spatial resolution, users should stay with the resolution used by developers. Temporal resolution is 1 year

in VDDT, FETM, and LANDSUM, and 10 years for

SIMPPLLE. Spatial resolution is not clearly specified

for any process for any of the models, and is not

applicable to VDDT and FETM since the user must

input their own pathways and probabilities. For the

spatial models, the landscape applications have been

based on available vegetation coverages typically developed from air photo or remote sensing imagery

using scales from 1:12,000 to 1:24,000. The spatial and

temporal extent is more amenable to mutability for

different user needs, as the primary effect of increasing the number of time periods, vegetation classes, or

polygons is an increase in run time. Only extremely

detailed models might run into software limitations;

for example, VDDT allows up to 480 classes within a

potential vegetation type and 1,000 time steps.

Validation of Models

Modelers often separate verification, or the internal

validity of a model, from validation, the process of

checking a model’s accuracy against a set of data

independent from that used to develop it. Verification

often includes some sensitivity analysis, or looking at

how model outputs change in response to alterations

of model inputs or parameters. Calibration is the

process of altering parameters to increase accuracy for

local conditions. Because these four models are intended to be used by people other than their developers, it is also important to consider user confidence, or

the subjective belief in the model’s accuracy. User

confidence may be enhanced by verification or validation, but it is also possible that it has little relation to

actual accuracy.

In the Forest Service’s proposed planning process,

regional and local assessments will not directly result

in decisions and so will not be subject to NEPA (USDA
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 11

Forest Service 2000). However, the assessment process will include collaboration and public involvement

(USDA Forest Service 2000). Thus, if FETM,

LANDSUM, SIMPPLLE, or VDDT are to be used in

the assessment process, confidence in the model by

both the users and by the public is an important

concern. Each Forest Service region may wish to more

clearly define the role of landscape assessment (and

vegetation projection within landscape assessment)

so that an appropriate amount of effort is spent on

validation and calibration.

Validation is not an easy process for any of these

models. Both of the spatial models, LANDSUM and

SIMPPLLE, specifically state that they are not intended to be capable of accurately predicting where

and when a process occurs. Thus, an appropriate

validation for these models would be to test nonspatial

results for landscapes. For example, was the land area

affected by mountain pine beetles in the years 1990 to

2000 within the range model predictions? Unfortunately, historical vegetation input data are difficult to

obtain. The occurrence of historical disturbance processes is also difficult, if not impossible, to obtain.

Historical data also are often not in accessible electronic formats.

CRBSUM and SIMPPLLE have both had some attempts at verification. For CRBSUM, Keane and others (1996) provide a discussion of a sensitivity analysis, a validation that projected a historical data set

forward to current conditions, and model limitations.

The validation showed poor results, which the authors

believe was the result of problems with the historical

data set. Results for CRBSUM cannot be reasonably

extrapolated to fine-scale applications of LANDSUM,

as was shown with a hierarchical comparison discussed in Keane and others (1997). A sensitivity analysis of SIMPPLLE was the subject of a master’s thesis

(Stalling 1999), and the original model was the subject

of a dissertation (Chew 1995). Neither paper documents model assumptions or attempts validation.

Strengths and Weaknesses

The two spatial models, LANDSUM and SIMPPLLE,

are intended to be used to understand the range of

possible future vegetative conditions and disturbances,

rather than to portray the future landscape that will

occur. Potential users should be careful how they use

maps in this situation because maps tend to convey the

idea that particular conditions occur in particular

places. Developers and users of these models need to

continue to stress that any individual future landscape depicted is representative, and that the probabilities and pathways have not been validated. Thematic maps showing predicted probabilities may be

more apt than maps of individual future landscapes;

predicted probability maps should also be careful to

state the timeframe associated with the prediction. If

the user is careful in how results from the models are

presented, the models may prove to be useful tools in

communicating the dynamics of vegetation change.

Although all of the models simulate vegetation

change for landscapes, they are not direct substitutions for one another. FETM demonstrates emission

tradeoffs between fire and the timing and intensity of

treatments. VDDT is helpful for developing an understanding of vegetation pathways, with an interface

useful for easy alterations; LANDSUM and FETM do

not have this capacity. The only overlap in these

models is between LANDSUM and SIMPPLLE (in the

capacity to model and display future landscapes) and

between SIMPPLLE and VDDT (in the graphical

interface used to show pathways).

The two approaches for putting in development

pathways, graphic (fig. 3) or by transition matrix (fig. 4),

have different advantages. The graphic pathways used

by SIMPPLLE and VDDT allow easy visualization of

state changes, but can also be used to create very

complicated multiple-loop pathways. The transition

matrices make it easier to see what proportion of a

class moves into another, but do not allow easy understanding of how an initial class may develop through

time.

In using general forest planning models, often only

a tiny fraction of the time involved is spent in analysis.

Almost all of the work involves delineation of the

current landscape, developing future vegetation pathways, and checking over the resulting predictions for

reasonableness. There are possible ways to shorten

this process: developing standard classification systems and maintaining regional “libraries” of predictions for the standardized classes. As it is, users of

FETM, LANDSUM, SIMPPLLE, and VDDT are likely

to be faced with the choice of either using models

developed for other areas or spending considerable

amounts of time adapting the models for new areas.

The proposed Forest Service planning rules suggested multiple components of National Forest planning (regional ecosystem assessments, local analyses,

land and resource management plans [LRMPs], and

site-specific projects). The four models discussed here

may be unsuited to creating LRMPs and site-specific

projects, because they are not intended to directly

support decisionmaking and because they lack validation. Unlike many decision support systems (Mowrer

1997) these models lack the ability to realistically

constrain management activities by such concerns as

road networks, topography, or budgets. The four models do not have the ability to predict what vegetation

will occur in particular places at particular times, even

in the short term. Succession/growth is the only process
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

backed by a substantial amount of research, and even

it can be unpredictable.

The purpose of regional assessments and local analyses is, as yet, undefined. The four models have the

potential to be helpful, but we can’t know precisely

what they can contribute without understanding the

purpose of the regional or local assessment process.

Forest managers already know that disease, fire, insects, harvesting, and growth affect the composition of

the forest landscape. Thus, a model that confirms this

will happen is unlikely to lead to new insights. Perhaps it would be useful to know how much land will be

affected by these processes, or to know which land will

be affected, or to know how vegetation will change as

a result. But the latter item is an assumption in all

four of these models, and the former items require an

understanding of the accuracy of the models.

The strengths of these models lie in the comparison

of strongly differentiated alternatives, rather than in

prediction. If planners (or the public) are choosing

between alternatives of no management and aggressive treatment, these models may still capture the

general consequences well enough to aid in

decisionmaking.

It might be useful for managers, before using the

models, to speculate on how they might alter decisions

based on model outcomes. It may be helpful to provide

them with a few simple results from previous uses of

the particular model. Modeling is time consuming and

expensive. If other concerns constrain their ability to

make choices, projecting future vegetation may not be

a worthwhile use of resources.

Recommendations for Future

Development and Research

In general, documentation for the models could be

improved. VDDT is the only model with both a user’s

manual and a tutorial data set; it also needs less

documentation than the others because it is a framework with no built-in data. If the Forest Service is

interested in developing these models to the point

where they can be easily adapted to new areas, it must

support documentation and training with adequate

funding and personnel time. Given current trends,

transferring SIMPPLLE to a PC-based platform would

increase accessibility; this development is already

underway.

If the models had been adapted to the same sample

landscapes, potential users would have a better basis

for comparison. At least two efforts are underway to do

this. One is the development of a Landscape Fire

Succession modeling Web site (contact: Dr. Robert

Keane, Fire Sciences Laboratory, Rocky Mountain

Research Station, Missoula, MT). The other is the

adaption and comparison of FETM, VDDT/TELSA,

and SIMPPLLE (with MAGIS) for a number of sample

landscapes (contact: David Weise, Pacific Southwest

Research Station, Riverside, CA).

Model builders should be careful that the inclusion

of stochastic elements leads to improved understanding. Where modelers choose to include stochastic elements, they should make it as easy as possible for

users to make sense of the large number of sample

landscapes that will result. Conducting multiple runs

and summarizing the results should be automated.

The emissions projection capability of FETM, the

user interface for pathway development of VDDT, and

the polygon-projection capabilities of SIMPPLLE and

LANDSUM are not incompatible. They could be combined into a single model. Choices would have to be

made regarding the vegetation classification system,

the time resolution (annual versus 10-year), and the

inclusion of spatial context or stand history. This could

create a model that was unwieldy and overly complex

and would require substantial initial investment and

cooperation. However, in the long run it might save on

development costs compared to supporting four individual models.

Regardless of whether models are combined, it would

be helpful if Forest Service Regional Offices and Research Stations cooperated on developing common

databases on the occurrence of disturbance processes.

It would also be helpful if model developers documented their assumptions. Otherwise, each new model

of vegetation change must start from scratch, from

literature reviews to compiling “expert opinion.” In

addition, it is important for modelers to collaborate

with researchers in other disciplines to ensure that the

best state of knowledge is incorporated in model

assumptions.

If the Region continues to support the development

and use of vegetation change models like these four, it

would be helpful to have an organized way for users to

share their experiences. In particular, it will be important to explore effective methods of synthesizing results and communicating with the public. Developers

should not bear responsibility for coordinating this

effort. A simple Web site or discussion list could

improve communication without much cost.

SIMPPLLE has been used in combination with the

tactical planning model MAGIS. Developers of models

of vegetation change need to continue to explore such

avenues of cooperation. Otherwise, the Forest Service

may find there is no integration between regional

assessments and forest plans, and no integration between local analysis and project plans.

The proliferation of vegetation classification systems has created substantial difficulties. In some

cases, standardized classification systems would help

to alleviate these problems. However, it should be

recognized that research on vegetation development
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 13

that is tailored to a particular classification scheme

may be limited in its potential development. To avoid

this problem, researchers should be encouraged to

relate processes to continuous variables instead of

categorical variables where possible. For example, it

would be better if data collected on root disease were

related to a density index (LAI, basal area, SDI, and so

forth) instead of density classes.

The primary structural differences between

SIMPPLLE and LANDSUM is (1) the inclusion of

spatial context (in other words, the interaction among

polygons), and (2) the use of stand history. If a landscape with good data on past processes can be found,

the effect on accuracy of these two features can be

tested.

We have little knowledge about how the public

relates to GIS polygon maps. It might be useful to work

with social scientists to assess the relative effectiveness of graphs, polygon maps, and 3-D computer

visualizations for communicating various concepts

about vegetation change. The data may not exist to

support extensive accuracy assessment for these models. Nonetheless, it would be possible to do more than

has been done. Accuracy assessment should be directed at assembling independent data sets for validation, rather than toward extensive sensitivity analysis

of these particular models. If past experience is a

guide, all four of these models will be obsolete within

a few years. However, historical data sets would remain useful for all the models yet to come.

Conclusion_____________________

FETM, LANDSUM, SIMPPLLE, and VDDT, can be

used in the general process of understanding vegetation change for forest landscapes. The four models only

partially overlap, as FETM and VDDT are specialized

toward fuel modeling and pathway development, respectively. Although the models might be useful in

either regional or local assessments, users must first

define the purpose of vegetation projection within the

planning process to make informed choices with regard to these models.

If the primary purpose is to better understand the

future development of particular forest ecosystems,

better documentation, verification, and validation is

necessary. If the primary purpose is to educate nonprofessionals about the general dynamics of forest

ecosystems, better user interface, documentation, and

research on effective communication techniques would

be advisable. If the primary purpose is to lay the

foundation for forest or project planning, the models

need to be compatible with decisionmaking methods.

In spite of the need for future development and

research, all four of these models can be used at

present to make sure that interdisciplinary planning

teams share a common base of knowledge. As with all

assessment and planning, the true value of these

models lies not directly in output, but in what people

learn during the modeling process.

Acknowledgments ______________

Thanks to Jimmie D. Chew, J. Greg Jones, Robert E.

Keane, James Merzenich, Robert D. Pfister, Jane

Kapler-Smith, and Ken Snell for providing helpful

feedback and reviews.

References _____________________

Beukema, S. J.; Kurz, W. A. 1998. Vegetation dynamics development tool: user’s guide (v. 3.0). Available from: ESSA Technologies Ltd., #300-1765 West 8th Avenue, Vancouver, BC V6J 5C6.

104 p.

CH2M Hill. 1998. Fire emissions tradeoff model (FETM) ver. 3.3

user’s guide. CH2M Hill contract 53-82FT-7-06. Portland, OR:

U.S. Department of Agriculture, Forest Service, Pacific Northwest Region. 52 p.

Chew, J. D. 1995. Development of a system for simulating vegetative patterns and processes at landscape scales. Missoula: The

University of Montana, Mansfield Library. Dissertation. 182 p.

Davis, L. S.; Johnson, K. N.; Bettinger, P.; Howard, T. 2000. Forest

management: to sustain ecological, economic, and social values.

New York: McGraw-Hill. 804 p.

Keane, R. E.; Long, D. G.; Basford, D.; Levesque, B. A. 1997.

Simulating vegetation dynamics across multiple scales to assess

alternative management strategies: proceedings for GIS ’97;

1997 February 17–20; Vancouver, BC. Fort Collins, CO: GIS

World Books. 640 p.

Keane, R. E.; Long, D. G.; Menakis, J. P.; Hann, W. J.; Bevins, C. D.

1996. Simulating coarse-scale dynamics using the Columbia

River Basin Succession Model—CRBSUM. Gen. Tech. Rep. INTGTR-340. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Research Station. 50 p.

Mowrer, H. Todd. 1997. Decision support systems for ecosystem

management: an evaluation of existing systems. Gen. Tech. Rep.

RM-GTR-296. Fort Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Research Station. 154 p.

O’Hara, K. L.; Latham, P. A.; Hessburg, P.; Smith, B. G. 1996. A

structural classification for Inland Northwest forest vegetation.

Western Journal of Applied Forestry. 11(3): 97–102.

Oliver, C. D.; Larson, B. C. 1990. Forest stand dynamics. New York:

McGraw-Hill. 467 p.

Pfister, R. D.; Arno, S. F. Classifying forest habitat types based on

potential climax vegetation. Forest Science. 26: 52–70.

Stage, A. R.; Hatch, C. R.; Rice, T. M.; Renner, D. L.; Korol, J. 1995.

Calibrating a forest succession model with a single-tree growth

model: an exercise in meta-modelling. In: Skovsgaard, J. P.;

Burkhart, H. E., eds. Recent advances in forest mensuration and

growth and yield research: proceedings from 3 sessions of Subject

Group S4.01 ‘Mensuration, growth and yield’ at the 20th World

Congress of IUFRO; 1995 August 6–12; Tampere, Finland.

Horsholm, Denmark: Ministry of Environment and Energy, Danish Forest and Landscape Research Institute: 194–203.

Stalling, C. M. 1999. A sensitivity analysis of the SIMPPLLE model

on Lubrecht Experimental Forest, Western Montana. Missoula:

The University of Montana, Mansfield Library. Thesis. 137 p.

USDA Forest Service. 2000. National Forest System land resource

management planning, final rule. 36 CFR Parts 217 and 219.

Federal Register 65:218:67514-67581. Withdrawn 4/2001.

USDA Forest Service; USDI Bureau of Land Management. 1997.

Upper Columbia River Basin draft environmental impact statement. Boise, ID: ICBEMP. 1191 p.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001

FETM Ken Snell, Air Management Leader, Region 6 Office, P.O. Box 3623, Portland,

OR 97208-3623. Email: ksnell@fs.fed.us

LANDSUM Robert Keane, Research Ecologist, Fire

Sciences Lab, Rocky Mountain Research

Station, P.O. Box 8089, Missoula, MT

59807. Email: rkeane@fs.fed.us

SIMPPLLE Jimmie Chew, Forester, Forestry Sciences Lab, Rocky Mountain Research

Station, P.O. Box 8089, Missoula, MT

59807. Email: jchew@fs.fed.us

VDDT James Merzenich, Planning Regional

Analyst, Region 6 Office, P.O. Box 3623,

Portland, OR 97208-3623. Email:

jmerzenich@fs.fed.us

Appendix: Developer Contacts _____________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-76-WWW. 2001 15

ROCKY MOUNTAIN RESEARCH STATION

RMRS

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its

programs and activities on the basis of race, color, national origin, sex, religion,

age, disability, political beliefs, sexual orientation, or marital or family status. (Not

all prohibited bases apply to all programs.) Persons with disabilities who require

alternative means for communication of program information (Braille, large print,

audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice

and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights,

Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington,

DC 20250-9410 or call (202) 720-5964 (voice or TDD). USDA is an equal

opportunity provider and employer.

The Rocky Mountain Research Station develops scientific information

and technology to improve management, protection, and use of the

forests and rangelands. Research is designed to meet the needs of

National Forest managers, Federal and State agencies, public and

private organizations, academic institutions, industry, and individuals.

Studies accelerate solutions to problems involving ecosystems,

range, forests, water, recreation, fire, resource inventory, land reclamation, community sustainability, forest engineering technology, multiple

use economics, wildlife and fish habitat, and forest insects and diseases. Studies are conducted cooperatively, and applications may be

found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center,

2150 Centre Avenue, Building A, Fort Collins, CO 80526