COMPILED BY

Michael Bevers, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins,

Colorado and Tara M. Barrett, U.S. Department of Agriculture, Pacific Northwest Research Station, Portland, Oregon.

Papers were provided by the authors. Authors are responsible for the content and accuracy. Opinions expressed may not

necessarily reflect the position of the U.S. Department of Agriculture.

The use of trade or firm names is for information only and does not imply endorsement by the U.S. Department of

Agriculture of any product or service.
Systems Analysis in 

Forest Resources:

Proceedings of the 

2003 Symposium

October 7-9, 2003

Stevenson, Washington

Compiled by

Michael Bevers and 

Tara M. Barrett

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report

PNW-GTR-656

October 2005
This page is a intentionally left blank.
ABSTRACT

Bevers, Michael; Barrett, Tara M., tech. comps. 2005. System analysis in forest resources: proceedings of the 2003 

symposium. Gen. Tech. Rep. PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station. 366 p. 

The 2003 symposium of systems analysis in forest resources brought together researchers and practitioners who apply

methods of optimization, simulation, management science, and systems analysis to forestry problems. This was the 10th

symposium in the series, with previous conferences held in 1975, 1985, 1988, 1991, 1993, 1994, 1997, 2000, and 2002.

The forty-two papers in these proceedings are organized into five application areas: (1) sustainability, criteria and indicators, and assessment; (2) techniques and decision support for forest planning; (3) forest assessment and planning case studies; (4) fire suppression, fire planning, and fuels management; (5) harvest scheduling; and (6) mill supply and forest

product markets.

KEYWORDS: Forest planning, forest management, forest modeling, operations research. 
PREFACE AND ACKNOWLEDGMENTS

The 2003 symposium on systems analysis in forest resources was the 10th symposium in this series. Originated as 

a conference of the Systems Analysis Working Group of the Society of American Foresters, the first meeting was held in

Athens, Georgia, in 1975. The second symposium was held again in Athens in 1985. The international success of these conferences has motivated additional meetings every 2 to 3 years since 1985, including systems analysis symposia in Asilomar,

California (1988 and 1994), Charleston, South Carolina (1991), Valdivia, Chile (1993), Traverse City, Michigan (1997),

Snowmass Village, Colorado (2000); and Punta de Tralca, Chile (2002). Future meetings are being planned in Brazil in

2005 and in the United States in 2006.

The 2003 symposium was made possible through the contributions of many people and organizations. Participating

organizations included three Society of American Foresters scientific groups (the management science and optimization 

(E-4) working group, the economics, policy, and law (E-1) working group, and the technology assessment and future 

analysis (E-5) working group), scientific divisions of the International Union of Forest Research Organizations, the College

of Forestry and Department of Statistics at Oregon State University, the Western Forestry and Conservation Association, 

the Forest Inventory and Analysis program at the Pacific Northwest Research Station, and the Natural Resource Assessment,

Ecology, and Management Science work unit at the Rocky Mountain Research Station, USDA Forest Service.

We thank our program committee: Doug Brodie, Peter Ince, Darek Nalle, Kevin Boston, Matt Turner, John Sessions, 

and Jeff Arthur. We were grateful for the assistance of Masha Konoshima, Jules Comeau, Rafael de la Torre, Paul Dunham,

Hamish Marshall, Glenn Christensen, Phil Lacey, and Demetrios Gatziolis. We appreciate the efforts of Robert Haight and

Mikael Rönnqvist, who provided the keynote address, Andy Gray, who led the field trip, and John Hof, Peter Ince, and

Dave Martell, who gave the general session talks. We thank Doug Brodie for his closing remarks. We recognize the session

moderators including Pete Bettinger, Joe Buongiorno, Larry Davis, Matt Pelkki, Alan Murray, Darius Adams, Jeremy Fried,

Mikael Rönnqvist, Marc McDill, Stephanie Snyder, and Marc Wiitala. Richard Zabel, Aimee Sanders, and Virginia

Hokkanen helped with logistics. The Skamania Lodge in Stevenson proved to be an excellent facility for this meeting. 

The continued support and enthusiasm of participants in this symposium series is very much appreciated.

Mike Bevers and Tara Barrett, Compilers
Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

CONTENTS

Sustainability, Criteria and Indicators, and Assessment 1

Decision-Support Systems for Forest Biodiversity: A Review 3

Sean N. Gordon, K. Norman Johnson, Keith M. Reynolds, Patrick Crist, and Nick Brown

Sustainable Forest Management: Control, Adaptive Management, Hierarchical Planning 7

Eldon A. Gunn

Methods for Projecting Large-Scale Area Changes for U.S. Land Uses and Land Covers: 

The Past and the Future 15

Ralph J. Alig

A Nonparametric Method for Defining and Using Biologically Based Targets in Forest Management 27

Kevin R. Gehringer

Sensitivity Analysis on Sustainable Forest Management Criteria and Indicators in Forest 

Development Planning: An Approach Using Multi-Criteria Optimization 39

Thomas Maness and Ross Farrell

Participatory Modeling and Analysis of Sustainable Forest Management: Experiences and 

Lessons Learned From Case Studies 49

Guillermo A. Mendoza and Ravi Prabhu

Techniques and Decision Support for Forest Planning 59

HATT: The Heuristic Algorithm Teaching Tool for Advanced Forest Planning Courses 61

Pete Bettinger

Spatial Sensitivity Analysis: An Application of HARVEST to a Spectrum Alternative 71

Larry A. Leefers, L. Jay Roberts, and Eric J. Gustafson

MAGIS eXpress: Spatial Modeling for Timber and Access Planning 79

Janet Sullivan, Greg Jones, Judy Troutwine, Kurt Krueger, Hans Zuuring, and Bruce Meneghin

Assessing Landscape Contiguity in Reserve Design 85

Alan T. Murray and Xiaolan Wu

Designing An Integrated Forest Planning System for the Forest Industry: An Application in Portugal 89

Rui Pedro Ribeiro, José G. Borges, Carla M. Pereira, Pedro M. Sousa and João  ́P. Lé

Estimation of Forest Parameters by Using Remote Sensing Single-Tree Detection and Field Plots 

With Tree Positions 97

Kenneth Olofsson, Jonas Bohlin, Tomas Lämås and Håkan Olsson

Modeling Infection and Spread of Heterobasidion annosum in Coniferous Forests in Europe 105

M. Thor, T. Möykkynen, J.E. Pratt, T.Pukkala, J.Rönnberg, C.G. Shaw III, J. Stenlid, G. Ståhl, 

and S. Woodward
Forest Assessment and Planning Case Studies 113

Integrating Spatial Objectives Into Forest Plans for Minnesota's National Forests 115

Howard M. Hoganson, Yu Wei, and Rickard H. Hokans

Landscape Changes From Managing Young Stands: A Fall Creek LSR Modeling Study 123

Brian McGinley, Allison Reger, Gary Marsh, and Kirk Lunstrum

Examination of Tradeoffs Between Timber Production and Spotted Owl Habitat in Coastal Oregon 131

Pete Bettinger and Jason Drake

Assessing Short- and Long-Term Thinning Opportunities on the Willamette National Forest 141

Bruce Meneghin, Allison Reger, and Neal Forrester

Generating a Forest Parcelization Map for Madison County, NY 147

Susan L. King and Brett J. Butler

Projecting Landscape Conditions in Southern Utah by Using VDDT 157

James Merzenich and Leonardo Frid

Fire Suppression, Fire Planning, and Fuel Management 165

Integrated Timber Harvest and Fire Management Planning 167

Mauricio A. Acuña, Cristian D. Palma, Andrés Weintraub, David L. Martell, and Wenbin Cui

A Systems Analysis Model for Wildland Fire Preparedness Planning 175

Marc R. Wiitala and Andrew E. Wilson

Optimizing Initial Attack Effectiveness by Using Performance Measures 183

Andrew G. Kirsch and Douglas B. Rideout

An Empirically Based Model for Estimating Wildfire Suppression Resource Response Times 189

Andrew E. Wilson and Marc R. Wiitala

Modeling Opportunities and Feasibility of Siting Wood-Fired Electrical Generating Facilities 

to Facilitate Landscape-Scale Fuel Treatment with FIA Biosum 195

Jeremy S. Fried, Glenn Christensen, Dale Weyermann, R. James Barbour, Roger Fight, 

Bruce Hiserote, and Guy Pinjuv

Spatial Optimization of Fuel Management Activities 205

Young-Hwan Kim and Pete Bettinger

Simulating Fuels Reduction Scenarios on a Wildland-Urban Interface in Northeastern Oregon 215

Alan A. Ager, R. James Barbour, and Jane L. Hayes

Stand Optimization and Harvest Operations 229

Estimating the Harvesting Potential for Highly Mechanized Harvesting Systems in Small-Scale

Forestry by Using a GIS Model 231

Peter Rauch

Meeting Order-Book Constraints by Adaptive Control of Bucking on Harvesters 239

Glen Murphy, Hamish Marshall, and Barbara Hock
Effects of Stochastic Stumpage Prices on Optimal Timber Rotations Determined 

by Dynamic Programming 249

Matthew H. Pelkki

Determining Thinning Regimes to Reach Stand Density Targets for Any-Aged Stand 

Management in the Blue Mountains of Eastern Oregon 255

David Graetz and Pete Bettinger

A Neuro-Dynamic Programming Approach to the Optimal Stand Management Problem 265

Eldon Gunn

Harvest Scheduling 273

Analysis and Evaluation of the R-Ratio Heuristic for Forest Scheduling 275

Silvana R. Nobre and Luiz C.E. Rodriguez

Improved Solution Techniques for Multiperiod Area-Based Harvest Scheduling Problems 285

Juan Pablo Vielma, Alan T. Murray, David Ryan, and Andres Weintraub

Extreme Policies Modeled Within the Landscape Management Policy Simulator (LAMPS) 291

Pete Bettinger and Marie Lennette

A Relational Data Model for the Generation of Large-Scale Forest Scheduling Problems 299

Silvana R. Nobre and Luiz C.E. Rodriguez

Short-Term Harvest Schedule Sensitivity to Future Stumpage Price Assumptions 307

Eric S. Cox

Decision Support in German Forestry by Using Industrial Simulation Software 315

Martin Hemm and Anne-Katrin Bruchner

Mill Supply and Forest Product Markets 323

Elasticities of Demand for International Forest Product Imports 325

J.A. Turner and J. Buongiorno

A Demand-Based Scenario Optimization Model for Supply of Raw Material to the Forest Industry 331

Daniel Hultqvist and Leif Olsson

Analysis of an Extensive Thinning Program on Eastern Oregon National Forests by Using 

a Dynamic Spatial Equilibrium Market Model With Endogenous Industry Capacity 341

Greg Latta and Darius Adams

The Potential of Arclogistics 3 Route for Solving Forestry Transportation Problems 351

Hamish Marshall and Kevin Boston

Using Spatial Indices to Estimate Wood Procurement Costs 357

Luc LeBel, Sylvain Théberge, Kim Lowell, and Vincent McCullough
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SUSTAINABILITY, 

CRITERIA AND 

INDICATORS, 

AND ASSESSMENT
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In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 S. Gordon is a Ph.D. student and K.N. Johnson a professor in the Department of Forest Resources, College of Forestry, Oregon State University,

Corvallis, Oregon 97331.

2 K. Reynolds is a Research Forester with theUSDA Forest Service PNW Research Station, Corvallis, Oregon 97331.

3 P. Crist and N. Brown are Science Applications Manager and Forestry Program Officer for NatureServe, 1101 Wilson Boulevard, Arlington, VA 22209.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

DECISION-SUPPORT SYSTEMS 

FOR FOREST BIODIVERSITY: A REVIEW

EXTENDED ABSTRACT

Sean N. Gordon1, K. Norman Johnson1, Keith M. Reynolds2, Patrick Crist3, and Nick Brown3

INTRODUCTION

Biodiversity is a major theme in ecosystem management

and the first criterion in the internationally-recognized set

of Montreal Protocol Criteria and Indicators. It has impacted

both public and private forest management in the United

States, primarily through the Endangered Species Act treatment of individual species, and more recently through voluntary forest certification standards. Biodiversity presents a

complex challenge for forest managers, from policymakers

to field foresters, due to its broad definition as “...diversity

of ecosystems, the diversity between species, and genetic

diversity in species” (Montreal Process 1998) and lack of 

a widely accepted operational definition.

Decision support systems are commonly defined as

computer applications designed to help managers deal with

complex problems. Previous reviews of DSS have focused

ABSTRACT

The primary objectives of this in-progress review are to (1) help potential decision support system (DSS) users find

systems which meet their needs and (2) help DSS designers and funders identify unmet DSS needs in the area of forest

biodiversity. Thirty systems clearly applicable to forest biodiversity issues were identified and reviewed against three themes:

(1) classes of forest biodiversity indicators used, (2) major forest influences addressed, and (3) abilities to tackle complex

political decisions. Preliminary results show few DSS model both coarse-scale forest and fine-scale species indicators. A

number of forest modeling tools evaluate the influences of fire and biological threats on forest ecosystems, but these systems

do not generally deal with related biodiversity effects. Only one system was found which attempts to integrate the influence

of climate change. Very few DSS appear to have capabilities explicitly designed to address the often value-based, political

nature of forest biodiversity decisions.

3

on national forest plans (Schuster, Leefers and Thompson

1993), ecosystem management (Mowrer 1997; Rauscher

1999), and biodiversity in county-level planning (Johnson

and Lachman 2001). This review focuses on forest biodiversity and attempts to address two basic questions:

1. What DSS exist that can help managers address forest 

biodiversity issues?

2. How well do these existing DSS cover the range of 

issues related to forest biodiversity?

METHODS

An initial inventory of available systems was developed

from previous DSS reviews (Schuster, Leefers and Thompson

1993; Mowrer 1997; Rauscher 1999; Johnson and Lachman

2001; Barrett 2001; Lee, Meneghin, Turner, Hoekstra 2003)

and the personal knowledge of the authors. We screened

this inventory to a shorter review list based on evidence of
design for or application to forest biodiversity issues. This

evidence typically consisted of a mention of a forest biodiversity application in the previous reviews, the DSS’ own

documentation or website.

A framework for reviewing the systems was developed

through a scoping exercise based on semi-structured interviews with eight national-level experts on forest biodiversity. Issues expressed by the experts were grouped into three

themes: (1) methods to characterize biodiversity, (2) influences on forest biodiversity, and (3) the often complex

political nature of decisions related to forest biodiversity

conservation. Themes 1 and 3 required further operational

definition. We chose to operationalize theme 1 by generalizing the Montreal Process biodiversity indicators into indicator classes. Theme 2 influences were used directly: silviculture, land use change, climate change, biological threats,

and fire. Theme 3 was operationalized by adapting three

themes identified as deficient in an earlier DSS review by

Mowrer (1997): interdisciplinary information integration,

decision support at multiple spatial scales, and facilitation

of social negotiation.

RESULTS AND DISCUSSION

Out of 114 systems in our current inventory, 30 systems have met our initial screening criteria and have been

reviewed (table 1). An additional 26 systems are still in 

the screening process, due to late discovery or difficulty in

locating current information. Of the 30 reviewed, only five

DSS appear to integrate capabilities for both forest and 

biodiveristy modeling. Ten systems focus on wildlife and

biodiversity modeling, 12 on forest modeling, and three 

are general purpose DSS. The full inventory of systems is

available on the project website (www.ncssf.org).

The Montreal biodiversity indicators can be roughly

split into those covering forest structure and management

and those emphasizing species-based measures. Forest

modeling systems tend to cover the former and wildlife/

biodiversity systems the latter. There appears to be potential

for more formal linkages between these types of systems.

The Willamette Basin Futures Analysis and the LANDIS

forest DSS stand out in that they have established explicit

links between forest growth and wildlife population modeling systems (PATCH and RAMAS, respectively).

While many of the combined forest-biodiversity systems

model the effects of silviculture and land use change, none

of these systems nor the biodiversity systems include tools

to address the influences of fire, biological threats (pest,

pathogens, invasive species) or climate change on biodiversity. The forest modeling systems frequently consider silviculture, fire, and biological threats, but they generally do

not include mechanisms to address the impacts of these

disturbances on non-tree organisms. LANDIS appears to 

be the only system with some designed capacity to model

climate change effects.

Integration of biophysical, economic, and social information is possible in many of the systems but is only actively

supported and structured in a few. NED and Restore both

enable users to input their relative values for biophysical,

economic and social goals and evaluate results accordingly.

Similarly, many of the systems can be used at different

scales but few provide coordinated products for decision

makers at multiple scales. Previous surveys identified communication and consensus building as top needs, but we

are finding these abilities difficult to judge. Some specific

examples worth mentioning include the following: LMS

integrates visualization tools at the stand and landscape

scales; EZ-IMPACT is designed to integrate individual values in group decision processes; and the Willamette Basin

Futures Analysis made extensive use of stakeholder groups

in setting up model assumptions and visualization techniques in presenting model results.

A prime rationale for decision support systems is to faciliate a diffusion of decision support capacity, for example

domain-specific knowledge (such as in forest growth simulators) or decision-aiding techniques (such as optimization).

However, four of the five systems in our survey that include

both forest and biodiversity modeling capabilities are large,

regional-scale assessment efforts. As such, they are more

prototypes than systems that could be easily transferred to

others. In the same vein, the LANDIS system, whose capabilities stood out in a few of the review categories, has been

designed more as a research tool rather than a system ready

for diffusion.

ACKNOWLEDGEMENTS 

AND FURTHER INFORMATION

This study is being sponsored by the National

Commission on Science for Sustainable Forestry. Our 

complete inventory of systems, criteria, and reviews is

available through the projects section of the NCSSF web

site: http://www.ncssf.org

4
LITERATURE CITED

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

SIMPPLLE, and VDDT. General Technical Report

RMRS-GTR-76-WWW. Ogden, UT : USDA Forest

Service, Rocky Mountain Forest and Range Experiment

Station. [http://www.fs.fed.us/rm/pubs/rmrs_gtr76.pdf] 

Johnson, P.; Lachman, B. 2001. Rapid scan of decision 

support system tools for land-use related decision 

making. Unpublished report on file with: NatureServe,

Arlington, VA.

Montreal Process Criteria and Indicators [Online]. 1998.

http://www.mpci.org/criteria_e.html [2003, November

12].

Lee, B.; Meneghin, B.; Turner, M.; Hoekstra, T. 2003. 

An evaluation of landscape dynamic simulation models.

Fort Collins, CO : USDA Forest Service, Inventory and

Monitoring Institute. [http://www.fs.fed.us/institute/

news_info/evaluation_LDSM.pdf]

Mowrer, H.T. (Tech. Ed.). 1997. Decision support systems 

for ecosystem management: an evaluation of existing

systems. Gen. Tech. Rep. RM-GTR-296. Fort Collins,

CO: USDA Forest Service, Rocky Mountain Forest and

Range Experiment Station.

5

Table 1—List of reviewed systems

System focus Abbreviated name Full name

Forestry & biodiversity CLAMS Coastal Landscape Analysis and Modeling System

Forestry & biodiversity LUCAS Land-Use Change and Analysis System

Forestry & biodiversity MRLAM Multi-Resource Land Allocation Model (Umpqua Land Exchange)

Forestry & biodiversity NED NED

Forestry & biodiversity WBAFA Willamette Basin Alternative Futures Analysis (PNW-ERC)

Biodiversity BMAS Biodiversity Management Area Selection

Biodiversity CAPS Conservation Assessment and Prioritization System

Biodiversity C-Plan C-Plan

Biodiversity MARXAN MARXAN / SPEXAN

Biodiversity PATCH Program to Assist in Tracking Critical Habitat

Biodiversity RAMAS RAMAS Biodiversity Refuge GAP Refuge GAP

Biodiversity ResNet & Surrogacy ResNet & Surrogacy

Biodiversity Restore Restore

Biodiversity Sites Sites/Site Selection Module

Forestry FVS Forest Vegetation Simulator

Forestry INFORMS Integrated Forest Resource Management System

Forestry LANDIS LANDIS

Forestry LANDSUM Landscape Successional Model

Forestry LMS Landscape Management System

Forestry RELM Regional Ecosystem and Land Management Decision Support System

Forestry RMLANDS Rocky Mountain Landscape Simulator

Forestry SIMPPLLE Simulating Patterns and Processes at Landscape Scales

Forestry Spectrum Spectrum

Forestry VDDT / TELSA Vegetation Dynamic Development Tool / Tool for Exploratory 

Landscape Scenario Analyses

Forestry Woodstock Woodstock, Spatial Woodstock & Stanley

General DEFINITE DEFINITE

General EMDS Ecosystem Management Decision Support

General EZ-IMPACT EZ-IMPACT
Rauscher, H.M. 1999. Ecosystem management decision 

support for federal forests in the United States: a review.

Forest Ecology and Management 114: 173-197.

Schuster, E.G.; Leefers, L.A.; Thompson, J.E. 1993.

A guide to computer-based analytical tools for implementing national forest plans. Gen. Tech. Rep. INT-296.

Fort Collins, CO: US Department of Agriculture, Forest

Service, Rocky Mountain Forest and Range Experiment

Station.

6
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 Eldon Gunn is a Professor of Industrial Engineering, Dalhousie University, Halifax, NS, Canada, B3J 2X4.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

SUSTAINABLE FOREST MANAGEMENT: CONTROL, 

ADAPTIVE MANAGEMENT, HIERARCHICAL PLANNING 

Eldon A. Gunn1

INTRODUCTION

Sustainable forest management (SFM) is often discussed

as adaptive management with respect to a system of criteria

and indicators (C&I). The image of a process of feedback

control is embedded in many of these discussions. This

paper attempts a brief review of some of the issues that this

image raises. 

The concept of criteria and indicators plays an important

role in most discussions of SFM. Duinker (2000) has a useful introduction and critique of C&I with a particular emphasis on the Canadian process. The C&I of the Montreal

Process (MPWG, 1998) have been summarized under the

Canadian Council of Forest Ministers (CCFM, 2000). In

the U.S., the Montreal Process has been less codified but

still influential in establishing definitions of SFM (see USFS,

2002). Certification processes have driven industry’s efforts

7

in SFM. The standard CAN/CSA Z809 A Sustainable

Forest Management System (CSA, 2003) is based closely

on the CCFM criteria. The Forest Stewardship Council certification (FSC, 2003) and the Sustainable Forestry Initiative

are not focused on criteria and indicators to the same extent

as CSA Z809, yet both have a strong flavour of this process.

Closely connected with C&I for SFM is the notion of adaptive management, usually with reference to Holling (1978)

and Walters (1986). Duinker and Trevisan (2003) discuss

the dependence of SFM on adaptive management.

This paper first considers C&I and adaptive management as feedback control, a notion ingrained in the minds

of many policy makers, but which, although present, is not

a key part of Holling (1978) or Walters (1986) thought

process. It then goes on to look at adaptive management 

as an approach to learning. Finally it looks at hierarchical

planning and tries to place some of the SFM and adaptive

ABSTRACT

This paper reviews sustainable forest management, adaptive management and hierarchical planning from the perspective 

of control systems. If sustainable forest management is portrayed as monitoring and responding to indicators of sustainability,

then it is a form of feedback control. Adaptive management is control aimed at learning the unknown dynamics of the system

being controlled. Hierarchical planning aims at controlling complex systems by appropriate decompositions that emphasize the

span of control of decision makers. The paper reviews some of the issues associated with these control systems and suggests

that a view of sustainable forest management as a value system rather than a control system may be more productive. To effectively protect or enhance future productivity, a forest manager has to continuously improve current harvesting practices

based on lessons learned from monitoring the effects of past practices—sometimes over long periods. Where possible, the

effects should be measured directly rather than indirectly, e.g., monitoring not only the soil disturbance but also its effect on

tree volume and quality. Such observations are likely to show that not all disturbance and soil changes from harvest operations are equal in their effects on site productivity and net value recovery, i.e., they can range from negative to insignificant

to positive.
management ideas in that context. Overall, the view of SFM

as a control process is not seen as productive. The more

hopeful perspective is as a value context for hierarchical

planning. 

ADAPTIVE MANAGEMENT

AS FEEDBACK CONTROL

Adaptive management is often interpreted as feedback

control (Figure 1). The idea is that by choosing appropriate

indicators and setting target levels, by monitoring the values of these indicators, and by taking management actions

whenever the indicators are not suitably close to the target,

it is possible to sustainably manage the forest. The concept

is like the thermostat that measures room temperature at

some appropriate spot in the room, and issues commands

to the heating/air conditioning system to make adjustments

to maintain the temperature at the desired target comfort

level. This “thermostat” idea is inherent in processes that

emphasize setting targets for indicators. However, it is

important to ask if the concept of target levels makes sense

and, further, is it possible to manage a forest to keep it at

the target level? 

The answers to the above questions are not obvious.

Davis and Barrett (1993) showed that, for various harvest

scenarios, including doing nothing, habitat quality for a

broad variety of species fluctuated strongly over the 200

year planning horizon with differing patterns of habitat

quality increase and decrease. Bevers and Kent (1994) and

Hof and Bevers (2000) have pointed out that sustainability

does not imply constancy, but rather implies stable repeating patterns. If this is the case, then there are no specific

levels to aim for.

Even if desirable indicator levels can be established, it

may be unrealistic to expect a feedback process to maintain

a stable system. The indicator state space is very large. For

example, the CCFM group the indicators under six criteria

with 22 main elements and 83 indicators (see table 1).

Restricting ourselves to vegetation management reduces

the indicator space, since many are not related to vegetation

management. However, this is more than offset by the fact

that many indicators involve multiple forest states. For

example, consider CCFM indicator 2.2.1 Percentage and

extent of area by forest type and age class. Choosing just

three forest types (softwood, mixedwood, hardwood) and 6

age classes (0-20, 21-40, 41-60, 61-80, 81-100, 100+), this

indicator implies 18 states to monitor. We are thus asking

our control system to maintain a very large number of indicators close to chosen target values

CONTROL SYSTEM DESIGN

The simplest control models are based on linear systems. Mathematically, a state representation linear system

is:

dx(t) = Ax(t) + Bu(t) + C x(t) Rn,u(t) Rm,

dt 

where x(t) is the state of the system and u(t) is the control

action. If this seems overly restrictive, recall that linear

systems that involve higher order derivatives can always 

be represented this way by introducing new variables. For

example, instead of just the position (x), we can have variables that refer to velocity (v) and acceleration with appropriate equations (dx(t) = v(t)). The control system designer

attempts to find a matrix G that gives a feedback control

u(t) = G( ̄x - x ̄(t)) in tracking a target input  ̄x.

To design controllers for dynamic systems (see Dorf

and Bishop (2001) or other standard texts for details), the

systems must be controllable and observable. Simply put,

controllability means that the system is capable of being

8

Man. Indicator Control Targets Indicator

Levels Forest

System

Measurement

Management Process

+

Figure 1—The Automatic Control (“Thermostat”) View of SFM.

Table 1—Number of criteria, elements and indicators

Criteria Main elements Indicators

1 – Biodiversity 3 8

2 – Ecosystem Health 

and Productivity 3 12

3 – Soil and Water 2 8

4 – Global Cycles 5 20

5 – Multiple Benefits 4 16

6 – Accepting Society’s 

Responsibility 5 19

Total 22 83

dt
steered from any initial state to any other state in a finite

amount of time. Observability means that, by observing the

system outputs and controls over a fixed period of time, it

is possible to calculate (uniquely) the initial state of the

system. Both of these have quite specific technical characterizations (Dorf and Bishop 2001) that are unlikely to be

satisfied in systems measured by “indicators”. The very

term “indicator” arises because the complexity of forest

systems is such that they are not observable and we only

have partial knowledge of the “state” of the forest at any

instant of time. 

Control system designers cope with uncertainty by

ensuring that the control system is stable. If the system is

perturbed by some sort of outside influence, the control

will bring it back to the desired state. If the elements of G

are not chosen properly, then a small perturbation in the

system state away from the desired  ̄x can result in large

oscillations in system response. For a given control matrix

G, there is no a priori guarantee that a system will have an

equilibrium; that if it has an equilibrium, that this equilibrium state is x ̄, and that this equilibrium is stable. The design

problem is to try to find a G that causes the system to have

a stable equilibrium at  ̄x with a reasonably fast response to

deviations. There exist linear systems that are impossible 

to control with simple position error feedback. To achieve

stable control, capable of tracking a target, one often needs

not just state information, but also information on the rate

of change and some sort of time average of the state. This

is referred to as PID control (Dorf and Bishop 2001). PID

control implies measurement processes that are capable of

measuring time rate of change and time averages of indicators. Information also needs to be more or less instantaneous. Systems that are stable with no time delay between

the acquisition of information and taking control actions

can be unstable when such delays exist. Few forest indicators can be measured with estimates of rate of change and

without substantial time delays. 

Assuming controllability and observability, designing

stable controllers for large dimensional systems is extremely

challenging unless there are very special conditions. These

usually amount to the existence of a few driving state variables for the system with the other states highly correlated

to the drivers. There is always debate about what constitutes a large system. However, systems of the size implied

by the C&I (80 indicators, many which are themselves

multidimensional) would be considered large in the extreme. 

Standard control theory establishes the control feedback

through appropriate choice of the elements of G along with

some sort of control actuator (B). Even if we assume the

system can be controlled stably, it is worth asking: “By

what mechanism can public SFM processes make such

design choices?”. Note also that the feedback regulator

operates with a fixed target  ̄x (or at least a target trajectory

x ̄(t)) and a fixed gain matrix G. As discussed, it is unclear

how one might set an  ̄x(t)). It is also hard to imagine public

processes that can resist fiddling with the  ̄x(t)) and G.

Putting all this together, it is highly questionable that a

process of setting indicator targets and responding to deviations from these targets to suggest new management actions

can result in stable systems. We must look beyond this if

the notion of C&I for SFM is to be seen as sensible. This

conclusion may be surprising, but this is not the first time

that a systems approach has overextended its legitimacy

(Andrews 2001). 

ADAPTIVE MANAGEMENT AS 

A SEARCH FOR INFORMATION

Walters (1986) recognizes natural resources problems 

as control problems and is well aware of problems raised

above. The discussion in Walters is based on parsimonious

models with few state variables. Nothing in Walters (1986)

suggests the simultaneous consideration of 80+ state indicators. Walters, together with C.S. Holling, developed a

process called adaptive environmental assessment, where 

a number of “key actors” participated in a model building

process aimed at policy assessment. The outcome of this

work was the recognition of important effects that “were

only clearly evident over large spatial and/or time scales”.

Uncertainty in the nature of stock dynamics was a major

issue. An emphasis on the macro level process dynamics,

aimed at senior managers, with a particular focus on uncertainty and risk, is characteristic of hierarchical planning. 

Walters discusses his modeling in the context of feedback control, but it is a context illuminated by a dynamic

programming point of view. The fundamental problem is to

find Jt

(x), the optimal expected return of the system over

the future (possibly infinite) given that the system is in state

x at the present time t. Jt(x) is referred to as the cost-to-go

function in dynamic programming. The fundamental equation is :

Jt(xt) = Max{Eξ[gt(xt,u,ξ)+Jt+1(xt+1(xt,u,ξ))]} u∈U(x)

where u ∈ Ut(xt) is the set of allowable control actions 

in state xt, ξ is the random realization, gt(xt,u,ξ) is the

reward and xt+1(xt,u,ξ) is the state that results from action

u this period. Fundamental to dynamic programming is the

notion of tradeoff of expected present returns E[gt(xt,u,ξ)]

9
versus expected future returns E[Jt

(xt+1(u,ξ)]. If a good

estimate of the cost-to-go function is available, all sequential decision problems are two stage problems of making

this tradeoff between present and future. Although dynamic

programming is an almost ideal framework for decision

analysis and optimization, it is worth noting that problems

where the state vector xt is of dimension higher than 3 are

computationally challenging.

SFM indicators are just that – indicators. They are

imperfect information on the actual state of the forest. The

observer has access to observations and has the capability

to observe how these observations change over time in

response to control actions. Bertsekas (2000) shows how,

by replacing the concept of state by the information vector,

one can reformulate problems with imperfect state information as a dynamic programming problem with perfect information. The information vector is the entire history of

observations and control actions. The question of making

an optimal decision for a given state is replaced with making an optimal decision for a given information vector.

Implicit in this approach is a Bayesian updating of the probability of a given state based on a given information vector.

However, unless the modeler can find a sufficient statistic

that can stand in place of the information vector, imperfect

information poses even more computational challenges. 

Walters focuses on situations where the major uncertainty is in the underlying model of stock reproduction 

and growth. The state is made up of two components: one

representing the measured state of the stock and another

representing the true underlying growth model with prior

probabilities on that state. If the control action is primarily

aimed at the optimal exploitation of the stock using the

current estimate of the underlying model, then we may

never detect situations where the actual model is different

from the estimate and a superior solution is possible. Walters

focuses on actively adaptive policies where actions are

taken with a primary purpose of learning about the system.

These ideas are closely related to what are often referred

to as bandit problems. (see Puterman, 1994, Berry and

Fristedt,1985). The classic one-armed bandit problem

involves a gambler with the choice of either not playing or

paying an amount c to pull a slot machine lever that pays

an amount 1 with probability q where q is unknown. By

playing, the gambler has the opportunity for Bayesian

updating the estimates of q. If the gambler concludes q is

unfavourable, he/she stops playing but then acquires no

further information about q. Multi-armed bandit problems

are a natural extension with several arms to pull, each with

their own characteristic q. Bandit problems have been

applied in project selection, and sequential clinical trials.

They are difficult to solve since the state space is not a

scalar but a density function on the q.

Duinker and Trevisan (2003) emphasize the contrast

between passive and active adaptive management. Walters

(1986) characterized passive adaptive management as the

learning that occurs about the underlying system model in

the course of implementing a policy. Duinker has used the

phrase of “staying on the wrong road long and smart enough

to know how and why it is wrong”. Active adaptive management is characterized as the deliberate use of management policies “that seek ... some ... balance between

learning and short term performance: actions that perturb

the system state and output in an informative manner may

require giving up immediate harvests, accepting the risk

that the system may not recover after some perturbations or

simply living with temporal variation that is uncomfortable

from a social and economic perspective” (Walters, 1986).

Duinker and Trevisan note that some view active adaptive

management as simply field trials or trial and error. The use

of field trials in forest science is uncontroversial, but what

Duinker and Trevissan, as well as Walters, mean by active

adaptive management is the use of alternative strategies

“employed at the large ecosystem level at a fully operational scale ... intended to examine the effects of management strategies on the entire system”(Duinker and Trevissan,

2003). In the sense of large scale gaming with simulation

models, a concept that dominates much of Walters (1986),

there is again little of controversy here. In the sense of

using actual ecosystems as experimental devices with the

potential for significant failures, active adaptive management may raise significant moral questions (Lee, 1999).

This is particularly troublesome when we consider the bandit problems. One pull of the lever tells you only that you

won or lost that time. It takes many pulls of the lever to

narrow estimates of q.

HIERARCHICAL PLANNING SYSTEMS

Hierarchical planning follows from the observation

(Anthony 1965) that there is a natural hierarchy in decision

problems and that this decision hierarchy often corresponds

to the management hierarchy: i) Strategic Decisions: defining the role and nature of the enterprise and the resources

that the enterprise will have available to it, ii) Tactical

Planning: making the most effective use of the resources

available to the enterprise, iii) Operational Control: detailed

scheduling of weekly and shift level activities to make the

system function. At each level, decisions are taken within

the goals and objectives of the decision makers at that level.

Decisions taken at one hierarchical level act as constraints

10
on the lower level decisions. In turn, the process of planning and operating the lower levels feeds back cost and

feasibility information on these constraints to the upper

level decision processes (Figure 2).

Anthony’s (1965) main observation is that decision

problems at each level differ in time horizon, management

level, source and detail of information and the uncertainty

and risk associated with the decision outcome. Table 2

indicates some characteristics of each type of decision

problem. At all levels, decision-making is a dynamic

process with information and plans constantly changing.

However, information feedforward and feedback enables

most organizations to function effectively. 

As a modeling approach, hierarchical planning systems

emphasize models that reflect the decision hierarchy and

the decision structure. This leads to:

Separate Models: Upper level models are based on

aggregate data, for long-term analysis. The upper level

models provide appropriate constraints for lower level,

shorter-term models. 

Rolling Planning Horizon Implementation: Plans 

are developed for multiple periods but only the immediate

decisions of that plan are explicitly implemented. 

Recognition of Uncertainty: The most uncertain data

is detailed information for time periods removed from the

current period. Aggregate models broadly attempt to optimize the performance of the enterprise over time. Detailed

scheduling models are run when more accurate information

is available as to data and system state becomes available.

Mirroring of Organizational Structure: Each model

is aimed at a specific level of management. The constraints

11

Strategic Decisions

Tactical Plans

Operations

Sensitvity

Information on

Resource

Constrains

Feasibility and

Cost

Implications

Constraints on

Available Resources

in Planning Period

Constraints on

Operational

Activities

Figure 2—Feedback process of Decision Hierarchy.

Table 2—Characteristics of hierarchical processes

Strategic analysis Tactical planning Operational control

Objective Resource Acquisition Resource Use Execution

Time Horizon Long Middle Short

Management Top Middle Low

Scope Broad Medium Narrow

Information External/internal External/internal Internal

Level of Detail Highly Aggregated Mod. Aggregated Very detailed

Uncertainty High Moderate Low

Risk High Moderate Low
provided from the upper level models correspond to the

type of constraints that management normally experience

on their own decisions. 

Dempster and others (1981) have observed “hierarchical

organizations, as well as hierarchical planning systems are

a response to the nature of the problem being solved and to

the need to reduce complexity and respond to uncertainty”.

Complexity and uncertainty are characteristic of forest management. Gunn (1991; 1996) focus on hierarchical planning

as a way of dealing with the uncertainty associated with the

long-term tactical issues that arise in forestry. In this context, rolling planning horizons amount to using the linear

programming or other long run tactical model as a way of

estimating the cost-to go function, thus recovering the twostage paradigm of dynamic programming. Gunn (1996)

shows that, if forest systems are large scale with sufficient

opportunities for recourse, the decision maker is justified

in treating them as deterministic, so long as the implementation involves only the first period. Simple mean value

estimates provides a near optimal assessment of the strategic choices being made. Others have showed similar results

in different contexts. Dempster and others (1981), studying

a job shop design/scheduling problem, showed that treating

processing time as deterministic is optimal in the design

problem as long as there are enough jobs. Sethi and Zhang

(1994) show, for manufacturing systems subject to failure

and repair, that treating machine availabilities as deterministic becomes optimal as the rate of failure/repair becomes

fast relative to the rate of growth in demand. 

Successful organizations typically have strategies for

what business they are in and where they want to go with

their business. This defines their goals and objectives, their

value structure within which hierarchical planning proceeds.

Planning models used in forest management are often

referred to as strategic, but if examined closely, will be seen

as tactical. The confusion arises because of the two roles 

of tactics in the decision hierarchy. The first role is to optimally use the resources provided through the strategic

design. For a given strategic design, this provides a performance assessment of that design (as in Gunn 1991;

Dempster and others 1981; Sethi and Zhang 1994). The

second role is the feedback role. This provides sensitivity

analysis on the resource constraints. Whether through shadow

cost information (Paredes and Brodie, 1989) or through

redeveloping tactical plans by explicitly changing constraints, tactical analysis is necessary to provide strategic

decision makers with tradeoffs between their overall goals

and constrained resources. For the strategic decision maker,

this is essential information.

SFM AS STRATEGY

AND VALUE CONTEXT

The “thermostat” model of adaptive forest management

and even the passive/active adaptive management discussions are clearly tactical in their main themes. They involve

making effective use of the resources of the forest enterprise, not with the acquisition of new resources. However

this paper would argue that the proper view of SFM is

inherently strategic; SFM defines the role and nature of 

the enterprise.

Mintzberg (1978) has studied the strategy formation

process extensively. See Gluek and Jauch, 1984 for extensive references to his work. At the heart of Mintzberg’s 

discussion is the idea that strategy is not a computation, it

involves the decision maker’s developing a concept of what

it is they want to do and how they want to do it. Strategy is

subjective, not objective. As Mintzberg (1978) observes,

some strategy is deliberate and some strategy is unintended,

what Mintzberg calls emergent. 

The SFM C&I are values that enter into the worldview

of a forest decision maker. By agreeing to use indicators 

of sustainability, the enterprise is giving expression to the

values and social framework within which it operates. To

some extent, these are values that can be seen as the “law

of the land”. In Canada, forests are a provincial responsibility and all provincial forest ministers are members of the

CCFM. Except for the four eastern provinces, most forest

land is public land, under the control of these ministers,

even when leased or licensed in some form to private enterprise. In another sense, SFM C&I can also be seen as reflecting a cost of doing business. In feudal times, one might

debate the necessity of paying workers. Today, paying

workers is a required cost of doing business. This has

evolved to recognition that employers are responsible for

standards of health and safety in the working environment

and continues to evolve to recognition that business is also

responsible for the maintenance of a healthy environment.

However strategy is about more than obeying the law and

dealing with the cost of doing business. Organizations define

themselves by how they go about their business and the

types of experiences they provide to their customers. The

Montreal Process and the CCFM C&I define criteria by

which governments and businesses that wish to make claims

of sustainability would want to be examined. An important

aspect of the C&I system is that it is directed at local level

indicators; indicators that are specific to the particular environment in which the organization works. Another important

point is the lack of prescribed levels or targets. As governments and enterprises attempt to position themselves with

12
respect to their practices of sustainability and environmental

stewardship, they need tactical models to explore and assess

their strategy and to provide the shadow costs on the

resource restrictions inherent in this strategy.

Models such as RELM (Church and others 1994),

Spectrum (Sleavin 1994) and DTRAN (Rose and Hoganson

1995) provide ways of assessing strategy as well as expressing the cost-to go function for the future. From a strategic

perspective, the RELM model in particular (but others

also) allow the decision makers to focus on defining the

resources available to the organization in terms of present

conditions and in terms of the desired future conditions.

The shadow-pricing framework of these models provides

the appropriate mechanisms to assign marginal benefits/costs

to modifying the ecosystem and other constraints. These

marginal benefits are in terms of the objectives chosen by

the analysts, which reflect the goals and objectives for the

decision makers to whom they report. This is implicit in

hierarchical planning; tactical models operate with the value

structure of the strategic decision maker. Economists such

as Arrow and others (2000) also argue for the use of marginal costs, but the value system there is the general theory

of economic equilibrium. 

No organization, public or private, is immune to the

strategic context that SFM places them in as they develop

their organizational strategy. The specific criteria, and the

local level indicators of sustainability that are chosen by

their own organization, or by bodies that either regulate 

or certify their organization, must be acknowledged and

addressed. Some of these are simple (“we will obey the

law”). Others are more complicated such as a strategy of

being recognized as good corporate/organizational citizens,

both for internal values and for marketing.

SUMMARY

By taking too literally the ideas of SFM as a control

process and as adaptive management, we run the risk of

exceeding the legitimacy of the systems approach (Andrews,

2001). By recognizing the role of the C&I of SFM as context, managers can proceed to develop their own strategy

within this context. Implementing strategy in systems of

the complexity found in forest management will require

well-conceived hierarchical planning approaches. 

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14
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 Ralph J. Alig is a Research Forester and Team Leader, USDA Forest Service, Pacific Northwest Research Station, 3200 Jefferson Way, Corvallis, 

OR 97331.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

METHODS FOR PROJECTING LARGE-SCALE AREA

CHANGES FOR U.S. LAND USES AND LAND COVERS: 

THE PAST AND THE FUTURE

Ralph J. Alig1

INTRODUCTION

Over the past 25 years, renewable resource assessments

have addressed demand, supply, and inventory of various

resources at large spatial and over long temporal scales in

increasingly sophisticated fashion, including simulation and

optimization analyses of area changes in land uses (e.g.,

conversion of forests to agriculture or developed uses) and

land covers (e.g., plantations vs. naturally regenerated

forests). This synthesis reviews related research over the

last quarter century since area projection modeling systems

replaced expert opinion approaches in the national Resources

Planning Act (RPA) assessments (e.g., Alig and Butler 2004,

Alig and others 2003a). 

It is important upfront to define what policy-relevant

questions that land use and land cover research have been

designed to answer, given the many demands for information about the current and future land base. The core research

ABSTRACT

Over the past 25 years, renewable resource assessments have addressed demand, supply, and inventory of various renewable resources in increasingly sophisticated fashion, including simulation and optimization analyses of area changes in land

uses (e.g., urbanization) and land covers (e.g., plantations vs. naturally regenerated forests). This synthesis reviews related

research over the more than two decades since area projection modeling systems replaced expert opinion approaches in the

national Resources Planning Act (RPA) assessments, as part of state of the art approaches for regional and national resources

assessments. Such models reflect that key land base changes such as afforestation and deforestation are driven by quite 

different socio-economic factors. Projections of area changes are important for a wide range of natural resource analyses,

including those for wildlife habitat, timber supply, global climate change, water, recreation, and others. The demand for

applications in global change analyses has increased recently, and the synthesis addresses information needs in such macro

assessments. Significant challenges in the research area in general include systematic integration of approaches and therefore findings across resource areas to support sustainability analyses. Another challenge is a unified view of future forest

conditions constructed at a scale that serves all of these uses adequately.

15

described here was designed to support periodic U.S. natural resource assessments mandated by the national Forest

and Rangeland Renewable Resources Planning Act (RPA)

of 1974, to support USDA Forest Service strategic planning and policy analyses. The RPA act requires that

decadal national assessments, with mid-decade updates,

include an analysis of present and anticipated uses; demand

for and supply of the renewable resources of forest, range,

and other associated lands; and an emphasis on pertinent

supply, demand, and price relationship trends. Land use

and land cover changes have important consequences for

the future availability of timber, wildlife habitat, and other

renewable resources and, therefore, are a critical component

of this analysis. 

The RPA act clearly defines the national assessment as

a forward-looking exercise in examining resource conditions and services. The first RPA Assessment was carried

out quickly around 1975 in response to the 1974 RPA
legislation. Over the past 25 years, assessment efforts have

addressed demand, supply, and inventory of various renewable resources in increasingly sophisticated fashion. Methods

developed through the RPA in many areas define the state

of the art approaches for regional and national resources

assessments. One area that remains a significant challenge

in the field in general is the systematic integration of

approaches and therefore findings across RPA resource

areas. 

The 2000 RPA assessment is the most recent one and

the context has broadened over time (USDA Forest Service

2001). Interest in sustainable management of the world’s

forest resources was heightened by the United Nations

Conference on Environment and Development in 1992.

Since that time, various countries have joined together to

discuss and attempt to reach consensus on ways to evaluate

progress toward the management of their forest resources.

The United States participates in the Montreal Process,

designed to use a set of criteria and indicators for the conservation and sustainable management of temperate and

boreal forests. The criteria provide a common framework

for describing, assessing, and evaluating a country’s progress

toward forest sustainability at the national level. The 2000

RPA assessment provides a broad array of information

about the Nation’s forests and rangelands, including the

current situation and prospective area changes over the

next 50 years. Such information can help shape perceptions

about whether we can sustain both increasing consumption

of forest products and forest resource conditions (Alig and

Haynes 2002). Related data illustrate the dynamics of our

Nation’s land base, and how adjustments are likely to continue in the future. The projections of land use and forest

cover changes also provide inputs into a larger system of

models that project timber resource conditions and harvests,

wildlife habitat, and other natural resource conditions

(USDA Forest Service 2001). Current debates about sustainability involve both physical notions of sustainability

and competing socioeconomic goals for public and private

land management. The land-base changes also indicate the

importance of viewing “sustainability” across the entire land

base and across sectors, in contrast to the current typical

sector approach, as in examining “sustainable forest management” (Alig and Haynes 2002). 

This paper focuses on land use changes that involve

forestry. Land use is the purpose to which land is put by

humans, e.g., protected areas, forestry for timber products,

plantations, row-crop agriculture, pastures, or human settlements. Land cover is the observed (bio)physical cover on

the Earth’s surface, e.g., oak-hickory forest. 

REVIEW OF MODELS

Over the last 25 years, land use projections at large scales

for RPA assessments have moved from an expert opinion

basis (e.g., Wall 1981) to systematic models. Substantial

population growth in the United States has been associated

with an increase in the rate of conversion of forest and

agricultural lands to residential, commercial, and industrial

uses (Alig and others 1983, USDA SCS 1989, USDA

NRCS 2001), increasing the importance of models that can

aid in assessing future land use scenarios. An example of

an improvement introduced by systematic approaches was

the elimination of possible double accounting of land use

changes when projections were done by sector (e.g., agriculture). With a total land base perspective and zero-sum

constraints built in, systematic approaches ensured that

land base totals would sum appropriately across sectors.

Availability of additional quantities and types of data

was a major contributor to the expanded number of models

developed over the last 25 years. A major boost was broad

coverage in data collection by the Natural Resources

Inventory (NRI) (e.g., USDA NRCS 2001) that provided a

national snapshot of land use for a particular year. This was

in contrast to the periodic forest inventories by state staggered across time in Forest Inventory and Analysis forest

inventories (e.g., Rosson 2001). The NRI covers the entire

nonfederal land base, with major land uses classified as

cropland, pastureland, forest land, and urban and developed.

I next look at three classes of land use models that 

utilized NRI and other types of land use data. This review

supplements and updates earlier ones by Alig and others

(1984) and Parks and Alig (1988). 

Classes of Land Use Models

Land use models characterize human and natural influences on landscapes. Theoretical land use models are

derived from rules that are assumed to govern human and

natural processes. Typically profit-maximization is assumed

as the goal of forest and agricultural commodity production.

Theoretical models explore the types of land-use patterns

that emerge from given sets of behavioral assumptions.

Empirical land-use models can provide a test of theoretical

models, using real world data to quantify model parameters

and see how consistent they are with underlying hypothesized behavioral relationships. Empirical models can be

used to predict how land use will change in response to

changes in economic conditions and policies. Three basic

types of empirical land-use models are econometric, mathematical programming, and simulation, each varying in

their relative strengths and spatial and temporal scales. 

16
Econometric Models—Econometric land use models

are based on statistical methods that are used to quantify

relationships between land uses and hypothesized determinants. Landowners’ profit maximization typically is the

theoretical basis for these models--landowners are assumed

to allocate land parcels to that use generating the highest

land rent or present value of future profits. Models are estimated with data describing land use decisions and profits

derived from alternative land uses. Additional variables

may be included to control for land-use regulations and

other factors that influence land use decisions. For example,

land-use policies often are used to mitigate potential negative impacts of urbanization. Econometric land use models

typically are estimated with sample plot data comprised of

a random sample of parcels or aggregate data such as county-level observations of land use (e.g., Alig 1986, Plantinga

1996, Wear and others 1996, Hardie and Parks 1997, Kline

and Alig 1999, Ahn and others 2000, Kline and Alig 2001).

With the advent of satellite imagery and geographical information systems (GIS), econometric land-use models have

been estimated using spatially-referenced plot or parcel-level

data (e.g., Bockstael 1996, Wear and Bolstad 1998, Irwin

and Geoghegan 2001, Kline and others 2001). Examples of

explanatory variables in such models are rents (or proxies)

for forestry, agriculture, and urban/developed uses. 

The principal advantage of econometric land use models

over other approaches is that they are based on the observed

or revealed landowner behavior (e.g., Stavins 1999). In

particular, they measure how landowners actually respond

to observed changes in economic and other decision variables. Econometric models may capture the combined

influence of several factors motivating land use decisions

that may otherwise be difficult to describe in an explicit

way. Because econometric models can be estimated with

highly disaggregated data, such as plot or county data, they

can generate land use projections at correspondingly fine

spatial scales. Although estimation based on historical data

is the strength of econometric models, it is also a weakness.

Econometric models may not always yield accurate predictions outside observed historical ranges. For example, it

may be difficult to use econometric models to predict consequences of a major land use policy change not within the

bounds of historical data. In addition, factors influencing

land use decisions that cannot be explicitly controlled for

in the model estimation will be carried along in predictions

of future land use changes. 

Findings from econometric studies indicate that drivers

for deforestation differ notably for those of afforestation

and reforestation activities. Major determinants for deforestation associated with conversion to urban and developed

uses in the United States are population and personal income

levels. The rate and extent of urbanization are typically

governed by such determinants that shift demand for urban

and developed uses. Revealed behavior by landowners

indicates that values for developed uses (e.g., deforestation

for residential purposes) generally dominate those for rural

uses such as forestry and agriculture (Alig and others 2004).

Within the rural land base, relative land rents between

forestry and agriculture affect deforestation (i.e., forest converted to agriculture), afforestation, and reforestation decisions. The significance of these findings for RPA Assessment

and global climate change analyses is that policy deliberations recognize that the growth in developed area is not

likely to be arrested, but rather may be diverted or relocated

by policies offering preferential tax assessment or other

traditional programs. 

Implications regarding future policy deliberations about

afforestation programs include focusing on incomes for

rural land enterprises. A series of econometric studies offer

insights about determinants of afforestation (e.g., Plantinga

and others 1999) and reforestation activities (e.g., Alig and

others 1990, Kline and others 2002). 

Most econometric models of land use developed to date

have been regional in nature, although Lubowski (2002)

recently developed a national land use model. His work

takes advantage of transition rates among major land uses,

as identified by the NRI survey (USDA NRCS 2001). Modeling transitions among land uses, in contrast to net area

changes, enhances analysis of land-based policies that 

can cause ripple effects across major uses. For example,

Lubowski (2002) finds that rising government subsidies for

agricultural crops restrained an increase in forest area in the

Mississippi Delta area by 10% from 1982 to 1997. This

approach is being tested in the 2005 RPA Assessment Update.

Mathematical Programming Models—Mathematical

programming models of land-based economic sectors are 

a second land-use modeling approach. Mathematical programming describes land use allocations using numerical

optimizing techniques to find the multi-market price and

quantity vectors that either maximize or minimize the value

of an objective function, resulting in an optimal land use

allocation (e.g., USDA SCS 1989, Adams and others 1996,

Alig and others 1998). Maximization or minimization of

the objective function can be subject to sets of constraints

that characterize the resource commodity production over

time, initial land and resource conditions, availability of

fixed resources, such as land, and policy constraints. Land

is modeled as an input resource that moves among different

sectors depending on relative land rents, in an optimization,

17
as contrasted to a positivistic approach (Alig and others

1984). This allows “what if” questions with respect to optimal land allocation to be investigated, based on selected or

prescribed objective functions such as economic efficiency.

Dynamic economic behavior pertaining to land use is investigated using intertemporal optimization by mathematical

programming models (e.g., Alig and others 1998). 

Advantages of the mathematical programming approach

include its theoretical basis of market equilibrium, whereas

econometric models can sometimes suffer from incorrect

specifications of economic variables or omitted variables

where data are not available to fully represent theoretical

constructs. Mathematical programming models are better

able to handle economic conditions outside of historical

ranges, which facilitate evaluating new policies. However,

such models may also be limited by data unavailability that

affects representation of responses to policies. Programming

models can incorporate a wealth of physical structural detail,

which is important when physical structure strongly influences the behavioral response, such as existing forest age

class structure and timber harvest behavior. Disadvantages

of mathematical programming land use models are their high

level of spatial aggregation, behavioral relationships are

determined to a greater extent by the researcher, either based

on assumed or revealed behavior, and greater difficulty in

calibrating to recent historical trends where appropriate.

Programming models based on intertemporal optimization

also are less able to incorporate feedback effects due to

biophysical processes and explicitly account for the spatial

distribution of decision variables. However, the latter limitation can also apply to econometric models in some broadscale applications. 

Other Simulation Models—Another spatially explicit

land use modeling approach is to simulate land use change

using spatial land use data and sets of decision rules. For

example, several studies have combined cellular automata

models and spatial land-use data (e.g., Clarke and Gaydos

1998, Wu 1998, Webster and Wu 1999a,b). Cellular automata

models generally consist of an action space, a set of initial

conditions, and a set of behavioral rules. In land use modeling, the action space is a grid of cells where the state of

each cell is one of a finite number of land uses, initial conditions are land uses described by a GIS base land-use layer,

to which are applied decision rules that specify how land

uses change over time. Decision rules typically are conditional on the initial land use as well as the land uses of 

surrounding cells. 

Wu (1998) develops a cellular automata model of the

transition from undeveloped to developed land in a province

in China. At each time step in the simulation, a development

suitability index is computed for each undeveloped cell.

The index is a weighted average of distances to urban features such as transportation hubs and the degree of development in the neighborhood of the cell. Based on their

suitability for development, cells are converted until an

aggregate (and exogenous) “demand” is satisfied. Using 

a related approach, Landis (1995) simulated land development in the San Francisco Bay Area. The spatial unit of

analysis is a Developable Land Unit—a polygon construct

with similar environmental (e.g., slope) and policy (e.g.,

zoning) attributes. Developable land units are identified

through the overlay of the corresponding GIS layers. Each

undeveloped unit is scored according to its profitability for

development, taking into account location-specific home

sales prices and construction costs. As in Wu (1998), developable land units are “converted” according to profitability

given exogenous demands for developed land.

The advantage of simulation methods is that they can

provide very detailed information about spatial patterns of

land use change and are very flexible with regard to the

types of rules that govern change. Although decision rules

adopted in some studies seem somewhat arbitrary, it is possible to specify rules consistent with empirical evidence.

For example, an updated version of the Landis (1995) model

incorporates a statistical model of urban land use change

(Landis and Zhang 1998). Another example is the transitionbased model by Theobald and Hobbs (1998), employing a

single-step Markov transition function. This is in contrast

to regression-based approaches that are used to understand

underlying historical relationships. The principal drawbacks

of simulation models relate to their data and computational

requirements. These models have mostly been developed

for individual urbanized areas. Such an approach is needed

for modeling land use change in urban areas due to the

importance of location and existing land use zoning in

determining land development. When considering changes

in the rural landscape, it may make sense to sacrifice spatial

detail for broader regional coverage. This is particularly

the case with econometric modeling since many important

economic factors, such as prices, often exhibit little spatial

variation.

Increasing Use of Spatial Analyses

With the increased interest in land use modeling, the

geographic scales at which investigators have been working

have expanded in both directions. More ecological assessments at finer scales have increased demand for land use

and land cover projections that are spatially explicit, including investigations of forest fragmentation (e.g., Butler and

others 2004) and ownership parcelization, i.e., an owner18
ship is subdivided into one or more ownerships. Spatial

modeling is growing in its use and application in the natural

resources because natural resource processes are spatially

linked. Economists increasingly face opportunities to collaborate with ecologists and other scientists in multi-disciplinary research involving landscape-level analyses of

socio-economic and ecological processes. 

Spatial statistics investigates the relationship between 

a subject of interest and the subjects around it to determine

if they are more related to each other than to other subjects

that are farther removed. Using remotely sensed data can

involve some degree of dependency between pixels, most

likely in the form of positive spatial autocorrelation. Such

dependence has potentially a dual impact on the analysis of

image data. It can be a source of nuisance and error, when

traditional statistical techniques involving assumption of

independence of sampling units are applied. On the other

hand, it can represent valuable information, which may be

exploited as an image characteristic. Diffusion of recent

advances in GIS technology and modeling/simulation tools

has increased the application of quantitative techniques in

investigating land use and land cover changes. Changes in

land use and land cover can result from the interplay of

complex factors; some causative factors (e.g., land owner

characteristics) cannot be measured cost-effectively over

large areas; and sophisticated techniques of data manipulation do not offset the adverse effects of unreliable input

data. Quality of basic data remains an issue, possibly

affecting reliability of modeled predictions.

Spatial land use models based on econometric estimation can be viewed as extensions of area-base models first

developed by economists about two decades ago (e.g., Alig

1986, Hardie and Parks 1997, Kline 2003). Area-base models

describe proportions or shares of land in forest agriculture,

urban, or other discrete use categories, within well defined

geographic areas (e.g., counties). 

The need to consider spatial relationships has grown

with increased populations across regional and national

landscapes. An example is the hypothesis that increasing

population density in forested areas will cause changes in

timber management, including likelihood of timber harvest.

More people in the woods will also affect fire suppression

efforts and costs. Private forestlands in the United States

face increasing pressures from growing populations, resulting in greater numbers of people living in closer proximity

to forests. What often is called the forest/urban interface is

characterized by expansion of residential and other developed land uses onto forested landscapes in a manner that

threatens forestlands as productive socioeconomic and ecological resources. Prevailing hypotheses suggest that such

forestlands can become less productive, because forest

owners reduce investments in forest management. Kline

and others (2003) developed empirical models describing

harvest, thinning, tree planting, and forest stocking in western Oregon, as functions of stand and site characteristics,

ownership, and building densities. They use the models to

examine the potential impacts of population growth and

urban expansion, as described by increasing building densities, on the likelihood that forest owners harvest, pre-commercial thin, plant trees following harvest, and maintain

forest stocking. Empirical results support the general conclusion that population growth and urban expansion are

correlated with reduced forest management and investment

on private forestlands in western Oregon. Results have

potential implications for both economic outputs and ecological conditions, as well as for wildfire risks at the forest/

urban interface.

LINKAGES TO OTHER MODELS 

IN RESOURCE ASSESSMENTS

Land use models have increasingly been linked to other

models in order to extend the power of both types of models. In the RPA context, the shift to land use models from

expert opinion approaches started around the time that timber supply and demand “gap” models were being replaced

in the early 1980s by equilibrium models. Feedbacks

among assessment models are important in that changes in

land uses and land covers affect the condition and characterizations of future forests, which in turn influence timber

market conditions. Projections of market conditions from

supply and demand models feed back into the area change

models, as timber prices from equilibrium models from the

timber assessment are one input into the RPA land use

models (Alig and Butler 2004, Haynes 2003). In general,

RPA specialists have a long history of exploring linkages

among models for different resource areas (Joyce and

others 1986). 

In the timber supply case, Alig and others (1984)

described potential linkages among models used to represent the following activities: land allocation, timber growth

and yield, timber harvest, and timber investment. In recent

years, additional modeling components have been added 

to model forest carbon, biodiversity, wildlife habitat, and

other forest-based goods and services. Linkages reflect both

spatial (e.g., interregional shifts in timber production) as

well as temporal changes. Examples of other linkages to

address in future work include land allocation decisions in

a particular region that leads to impacts in other regions or

19
nations, affecting timber harvest decisions in other countries. In view of timber imports into the United States, this

requires a broad view of sustainability in a multi-national

context. 

In addressing problems with complex biophysical, ecological, and economic aspects, testing linkages among modeling components is critical. Sensitivity analyses can reveal

critical linkages and information gaps and need for feedback loops. The holistic approach, with land use and land

cover changes as components, will vary depending on the

composition of the portfolio of policy-relevant questions 

to be addressed. Data availability and accuracy are prime

concerns in such complex modeling, and monitoring of

land use and land cover at relatively frequent intervals is

warranted. An example is monitoring any migration of tree

species over space and time due to global climate change,

for use in land cover modeling. 

PROPOSED NEXT-GENERATION

MODELS 

To help improve analyses in future RPA Assessments

and global change assessments, I propose assessment modules to explicitly project the condition of land in the United

States (Alig and Wear 2003) (figure 1). This is at the core

of what the national RPA Assessment seeks to accomplish.

Characterizations of the future land base, including forest

and aquatic ecosystems, are required to conduct all supply

and demand analysis. For example, the timber assessment

projects changes in timberland area and forest cover types,

along with age class distributions and other vegetation

information, in order to project growth and yield and the

market-clearing outputs and prices for timber products.

Examples of specific land condition attributes are plantations versus naturally regenerated forests, old versus young

forests, and interior versus fragmented forests. The public

has a direct interest in how forest conditions in their respective regions might change in the future. For example, public concerns in response to the Southern Forest Resource

Assessment (Wear and Greis 2002) were largely focused on

the anticipated future condition of forests—e.g., plantations

vs. naturally regenerated forests.

In addition to information needs in the timber assessment, the wildlife assessment relies on projections of forest

area, configuration, and condition. Salmon restoration

analyses require information on what is happening on the

entire land base, including urban areas where human activities may impact aquatic systems (e.g., fertilizer leakage

into streams). Similar data needs characterize assessment

activities in the areas of global climate change, water, and

recreation. Characterization of land condition will depend

on a comprehensive information needs assessment for

applications such as in the national RPA Assessments,

related global climate change assessments, and the U.S.

Climate Change Science Program’s plan (in process) for

land use and land cover research. Figure 1 largely represents current RPA linkages involving land use and land

cover modeling and opportunities exist to build in other

two-way flows of information between resource areas, such

as wildlife management that could influence vegetation

management.

A primary need in global change assessments is to test

models of drivers in land use and use them for projections

of land condition. Analysts working on global change have

a large body of land use theory, models, and empirical

results to draw upon from applications in other areas of

inquiry (e.g., RPA Assessment land use and land cover

analyses). They can also draw upon both econometric 

models of revealed behavior, as well as optimization models

(e.g., Adams and others 1996). Use of both modeling types

can be designed to be complementary, and can address

questions regarding land resource allocation and potential

for reallocation. In the global climate change context, modelers are closer to dealing with a closed system, but face

significant data gaps, such as consistent and comprehensive

time series of consistent land use and land cover data. 

One complication in past RPA assessments and global

climate change assessments has been the lack of a unified

view of future land conditions constructed at a scale that

serves all of these assessment areas adequately. Attaining

the ideal unification is a substantial undertaking, and this

could be aided upfront by an assessment of common information needs. An example of a possible case study in the

20

Figure 1—Schematic depicting conceptual framework for land condition

projection system.
land condition context would focus on methodologies that

model the evolution of land base conditions at the FIA plot

level, and then scale to aggregate levels using plot expansion factors (Prestemon and Wear 2000). Such analyses

could be updated on an annual basis as the continuous FIA

inventory is implemented. Access to plot locations would

be a critical element for the work to proceed along this track.

Modeling at more aggregate levels will also be considered,

as land markets are effectively analyzed at large enough

scales where prices are formed. Sufficiently large scales are

also necessary for addressing certain policy-relevant questions, such as whether land allocation in one region or

country leads to increased timber imports from other regions

or countries, e.g., land set asides for spotted owl and

increased timber imports from Canada (Wear and Murray

2003). Micro- and macro-scale analyses can be linked, such

as having city-scale or subregional analyses tied to larger

scale regional or national studies. 

A modeling system that can generate land base condition projections could provide for forest ecosystems a thorough and unified description of anticipated change in the

extent, structure, and condition of the nation’s forests at

useful regional and subregional scales. At the same time,

such a system could augment economic measures, useful

when investigating changes in land markets and analyzing

trends in land values. Land prices embody information on

relative valuations by different sectors of the economy. For

example, valuation of land currently in forest and agricultural uses in some areas is strongly influenced by trends in

developed areas (e.g., Shi and others 1997).

Global Change Assessments

Improvements for global climate change analyses will

require enhancements in climate change projections, production function information and ecological translations of

climate-change effects, land owner behavior modeling, and

socio-economic effect determination (e.g., equity). Key

feedbacks also warrant more attention, such as between

land use changes and climate change impacts. Uncertainty

in projecting climate change impacts on land use and land

cover include any carbon dioxide fertilization impact, interactions with air pollution on productivity under climate

change, and impacts of climate change on abiotic and biotic

disturbances (e.g., extreme events, insects, disease, fire

regimes). Several studies have employed linkage of different modeling systems to investigate how changes in forest

productivity may translate into impacts in the forest sector

under climate change (e.g., Joyce and others 1995, Alig and

others 2002). A key information gap appears to be empirical estimates of production function changes, including any

migration of tree species. Sensitivity analyses in a systems

analysis approach can aid in determining critical components, and linkages among those components (Sohngen and

Alig 2000). 

In reality, land use choices often involve a complex

interaction of factors, including the initial land endowment

of the owner, landowner characteristics, institutional influences (e.g., local zoning), and the economic and policy

context in which the land use choices are made. Changes 

in the area of any of the major land use classes relate to

demographic, economic, biophysical, and policy factors

(Alig 1986), so that the suite of factors need to be considered in global climate change assessments. Projections of

forest-land and timberland areas are based on projections

of relevant demographic and economic factors, which are

more likely to change in the future than biophysical factors.

Current policies can be frozen in place in an initial conditions run or baseline, so that we can examine where the

current policy trajectory (e.g., no U.S. action or implementation of policy for mitigating climate change) would lead,

and then examine sensitivity of projections to certain policyrelated assumptions. 

Risk and uncertainty considerations include changes 

in technology. Impacts from global warming in some cases

may be partially offset by technological changes, such as

genetic stock improvements to boost forest carbon sequestration efforts. Other technological changes may also allow

more output from input of land, especially in regional climates favorably affected regarding crop or forest production.

The net outcome can not be easily forecast. Some scenarios

may arise where forest use might be better able to compete

with other major rural land uses, such as agriculture (e.g.,

Alig and others 2002). 

Land use models based on historical data will not be as

well suited to simulate global climate change as mathematical programming models if future events are outside the

range of historical data. Changes in productivity for crops

and forests may change slowly, although some hypothesize

certain thresholds beyond which actions may accelerate.

Global climate change will also affect the relative risk of

land-based enterprises, given uncertainty of the magnitude,

duration, and deleterious nature of such events. 

Multidisciplinary research increasingly has striven to

examine the impacts of land use and land cover changes on

ecological conditions and processes brought about by timber

harvesting, land use change, or other human-caused disturbances. However, these efforts can be costly and time-consuming. Planning upfront is critical for effectively augmenting

land use and land cover analyses in both the general RPA

21
and specific global climate change applications. A streamlined and relatively inexpensive approach enabling managers and policymakers to anticipate, describe, and plan for

potential land use impacts on habitat and other goods and

services is desirable. 

Land Cover Monitoring and Projections

Another key part of land base changes is changes in

major forest cover types. Although detailed discussion of

them is outside the scope of this paper, I will describe some

examples of modeling issues for land cover monitoring and

modeling. Analogous to the snapshot of land-use information by USDA’s NRI, land cover modeling would benefit

from periodic nationwide estimates of changes in forest

cover. A start down that path is the National Land Cover

Data mapping project (an output of the Multi-Resolution

Land Characteristics Consortium). This project will produce an updated version of the National Land Cover map

for the year 2002, providing the means to conduct multitemporal analysis. 

A key goal of any forest cover monitoring program is to

understand the processes affecting changes in forest cover.

This would enable projections about future trends in forest

cover and tie them to the local demands, forest investment

(e.g., Alig and others 2003b), timber trade, and global issues

of concern such as global change and biodiversity. The

availability of modern technology of remote sensing and

GIS makes this task feasible. The models should include

the spatial component of the landscape and human activities.

These new GIS data layers could be combined in models 

to obtain various indices that most likely influence or are

influenced by human use of the land such as: (1) bio-physical and climatic factors, (2) fragmentation indices (e.g.,

perimeter/area ratios of forest area), (3) transportation networks (e.g., roads, rivers, railways), (4) population density

and its rate of growth, and (5) socio-economic and political

factors (e.g., land tenure, market availability, GNP/capita). 

Sustainability Assessments

Many users and interests rely on the common land base,

and myriad questions are asked about land conditions and

sustainability prospects. Numerous approaches have been

developed to address such questions, and land use planners,

geographers, natural resource managers, and others have

other information needs at spatial and temporal scales other

than the primary ones for RPA and global climate change

assessments. However, large-scale assessments provide a

context for smaller scale inquiries, and aid in addressing

policy-relevant questions. Other papers at this symposium

pertain to smaller scale inquiries on the common land base.

Regardless of scale, studies need to work toward common

assumptions (e.g., population growth), consistent definitions, and sufficient use of statistical guides. Studies at different scales can inform each other, and this prospect can

be enhanced if standard protocols are in place. 

The common land base is competed for by different

sectors of the economy, thereby complicating efforts to

view sustainability from a single sector, such as “forest

sustainability.” A broad view could foster resolution of

important forest and rangeland health issues through design

of integrated decision systems that involve ecological,

political, economic, and institutional factors. Focal areas

include ecological risk assessment, socio-economic modeling, and policy/decision analysis–particularly as they relate

to forest and rangeland productivity, fish and wildlife habitat, and human communities. Crucial to this endeavor is the

ability to bridge multiple disciplines, recognize multiple

values, and analytically evaluate risks and tradeoffs associated with various scenarios for improving the health of

forests, rangelands, and related natural resources. The ability

to articulate prospective outcomes for both short- and longterm time frames to policy makers and resource managers

is also pivotal.

ACKNOWLEDGEMENTS

This paper’s preparation benefited from helpful review

comments from Jennifer Swenson, Susan Stein, Susan King,

Dave Wear, and Richard Haynes. The RPA Assessment

components also drew upon earlier discussions with Dave

Wear. 

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25
This page is a intentionally left blank.
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 K.R. Gehringer is a Post-Doctoral Researcher with the Rural Technology Initiative, College of Forest Resources, Box 352100, University of Washington, 

Seattle, WA, 98195-2100.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

A NONPARAMETRIC METHOD FOR DEFINING 

AND USING BIOLOGICALLY BASED TARGETS 

IN FOREST MANAGEMENT

Kevin R. Gehringer1

INTRODUCTION

Forest policy increasingly relies on the use of biologically determined criteria such as trees per acre, stand density

indices, average tree sizes, species composition, or basal

area per acre to quantitatively define a set of desirable forest

conditions as a target or reference condition for forest management. Management objectives defined by a set of target

criteria may include desirable habitats, clean water, or aesthetically pleasing forests. Effective target criteria must be

representative of the desired forest conditions, must be associated with data that are readily obtained, must be easily

computed, and must be easy to use with an objective assessment procedure to determine whether the desired forest

conditions have been achieved for a particular forest management scenario.

Representative target criteria must recognize the inherent variability of forest ecosystems, whether managed or

unmanaged, and their multidimensional nature. These two

objectives may be achieved by using multiple, quantitative

ABSTRACT

Forest policy increasingly relies on the use of biologically determined criteria to quantitatively define desirable forest

conditions as targets for forest management. Nonparametric procedures for defining targets and performing assessments relative to those targets have been developed. The target definition and assessment procedures were applied to the problem of

defining targets for riparian zone forest management. Four target definition and assessment scenarios were considered: 1)

basal area per acre, 2) conifer basal area per acre, 3) trees per acre and quadratic mean diameter, and 4) trees per acre, quadratic mean diameter, and average tree height. Targets were defined using riparian stands having ages between 100 and 180

years. Assessments were performed using stands having a minimum age of 80 years and using acceptance levels of 95%,

90%, 80%, and 50%. Acceptance percentages computed for each scenario were all at least 75% of their respective acceptance levels, with the majority of values being at least 88% of their acceptance levels.

27

stand parameters and their distribution as the basis for

defining a desired forest conditions target. Using the distribution allows a neighborhood of acceptable values to be

used when specifying a target. Using multiple quantitative

parameters to describe the desired forest condition provides

a more detailed description than could be obtained using

any single parameter or parameter value, thereby increasing

the likelihood of actually achieving the desired conditions.

Recognizing the relationship between target definition

and assessment is also essential for the development of

effective target criteria and an objective assessment procedure. The target definition and assessment procedure are

linked through the distribution of parameters used to

describe the desired forest conditions. Consistent target

definition and assessment procedures must, therefore, be

based upon the underlying distribution of parameter values

describing the desired forest conditions.

Interest in the problem of defining targets for forest

management was motivated by the new forest practices
rules for riparian areas in Washington State. The new rules,

known as the Forests and Fish Rules (FFR), were established in 1999 based on the recommendations of the Forests

and Fish Report (FFR 1999; WFPB 2001). The primary

objectives of the FFR include providing support for harvestable levels of salmonids and the long-term viability of other

species, compliance with the Endangered Species Act, meeting or exceeding water quality standards defined by the

Clean Water Act, and maintaining the economic viability 

of the state’s forest industry (FFR 1999).

The biological and water quality objectives of the FFR

for western Washington are based in part on a desired

future condition (DFC) target for riparian forest stands

along potentially fish-bearing streams. Along these streams

three buffer zones are defined: a 50 foot no-harvest buffer

adjacent to the stream, an inner zone where timber harvest

is allowed subject to restrictions ensuring the development

of the DFC, and an outer zone where up to 20 trees per

acre must be left after harvest. The total buffer width is

determined by the site potential tree height and can vary

from 90 ft to 200 ft based on the site class. The inner zone

extends from the core zone to either 67% or 75% of the

total buffer width depending on stream size (FFR 1999;

Ehlert and Mader 2000; Fairweather 2001; WFPB 2001). 

Unmanaged, mature riparian forests were identified as

the DFC for western Washington under the FFR, where a

mature riparian forest stand was defined to have a reference

age of 140 years, the midpoint between 80 and 200 years

(FFR 1999; Fairweather 2001; WFPB 2001). The FFR

further specifies the DFC targets as site-class specific minimum conifer basal area per acre (CBA) limits. Commercial

harvest in the inner buffer zone is permitted only if the

post-harvest stand conditions for the combined inner and

core zones meet or exceed the minimum CBA target when

projected to an age of 140 years using a stand simulator

(FFR 1999; Fairweather 2001). Initial estimates of the minimum CBA values were obtained for each of five Douglasfir (Pseudotsuga menziesii) site classes based on data from

a sample of riparian stands in western Washington. The

data were supplied by the Forest Inventory and Analysis

(FIA) program, the forest industry, and the Olympic and

Mount Baker-Snoqualmie National Forests (Moffett and

others 1998; FFR 1999; Ehlert and Mader 2000; Fairweather

2001).

The selection of CBA to quantify DFC targets was

motivated by the desire for simplicity and the need to recognize the variability of riparian forest structures that provide adequate function for streams (FFR 1999; Ehlert and

Mader 2000; Fairweather 2001). The moderate to low stand

densities, relative to managed upland stands, and the generally larger diameter trees found in mature riparian forests

were some of the key structural features that the DFC targets

were intended to represent (Fairweather 2001). Further,

CBA was assumed to adequately describe the structural

characteristics of a mature riparian forest, and when the

target levels of CBA are present, the desired stream functions, in particular the production of large woody debris

and shade, are also assumed to be present (FFR 1999;

Ehlert and Mader 2000; Fairweather 2001). Assuming that

the structure of a mature riparian forest can provide adequate stream function may be reasonable. The use of minimum mean CBA values by site class as target criteria may,

however, oversimplify the problem of representing the

structure of a mature riparian forest: the targets may be too

restrictive or they may not adequately discriminate between

conditions that are desired and those that are undesirable. 

Given the potential importance of quantitative targets in

forest management, biologically and statistically consistent

target definition and assessment procedures are necessary.

Such procedures have been developed for use with multiple, coupled forest stand parameters using a minimal number of assumptions. A nonparametric approach was used to

specify the procedures since the actual parameter value distribution is unknown. The target definition and assessment

procedures do not make direct use of a reference age, as was

done in the FFR, other than for selecting data for a target. The

forest structure is emphasized, rather than a specific point

in time, since achieving a desired forest structure sooner

than the reference age may be possible and beneficial.

The target definition and assessment procedures and

implementations of them are described next. The procedures are then demonstrated by defining riparian management targets within the paradigm of the FFR for western

Washington. Finally, a brief discussion of some of the

potential benefits of using multiple parameters to define

targets and perform assessments is then provided.

METHODS

A forest stand may be described using quantitative values for a specified set of forest stand parameters, including

but not limited to: site index, slope, aspect, stand density,

average tree diameter, average height, basal area, volume,

species composition, distance to the nearest stream, or

measurements of the individual trees comprising the stand.

For a particular application the set of parameters used may

be large, possibly consisting of a tree list with spatial coordinates for the location of each tree and individual tree

measurements, or small, consisting only of stand density

28
and average diameter, volume, or basal area per acre. For

any specific set of parameters there exists a joint, or simultaneous, distribution of their quantitative values for some

collection of forest stands.

Let k ≥1 be the number of quantitative parameters used

to describe a forest stand, and let x = [x1, x2,..., xk]

T be the

vector of quantitative parameter values for a stand, where

each xj

, j = 1,2,...,k, represents one parameter value and

superscript T indicates the transpose of the vector. A collection of N forest stands may then be represented by a set of

parameter vectors xi

, i = 1,2,... N. The distribution of parameter vectors for this collection of forest stands is then

described by some unknown probability density function

(p.d.f.) f (x). 

Target definition and assessment

Given parameter vectors for a collection of forest stands,

a target region may be defined based on probabilities

derived from the unknown p.d.f. for their distribution. A

target defined in this way will be called a probability based

target. Such a target definition must take into account two

factors. First, the target should use the most likely parameter values, those with the largest p.d.f. values. The most

likely values then form the center of the target, which is

not necessarily near the mean value. Second, the extent of

the target should be defined using an acceptance region

derived from the p.d.f. for a desired probability of hitting

the target (Duda and Hart 1973; Mardia and others 1979;

Zar 1996). These objectives are met simultaneously by

choosing an acceptable level of error specifying the probability of not hitting the target, analogous to the selection of

an α – level in the classical statistical hypothesis testing

context (Mardia and others 1979; Zar 1996).

The natural way to define a probability based target is

to use the likelihood contours or level sets of the p.d.f. f (x).

Let p be the probability of not hitting the target, or the

probability of error. The probability of hitting the target is

then given by 1 – p, and a target having a (1 – p) x 100%

chance of being hit may then be defined as

T1–p = {x  f (x) ≥ c and ∫ f (y) dy = 1 – p},

where c ∈ [0, max f (x)] is a value defining a level set or

contour of the p.d.f. f (x), for p ∈ [0,1]. The first condition

in the target set definition, f (x) ≥ c, guarantees that the

most likely values from the domain of the p.d.f. f (x) are

used first. The second condition in the target set definition,

∫ f (y) dy = 1 – p, guarantees that the target set obtains the

desired acceptance level (1 – p) x 100%. The values of x

such that f (x) = c define the critical contour for the target

T1–p.

An assessment procedure consistent with this target definition may now be obtained. The procedure simply determines whether a parameter vector y is contained within the

target set for the desired acceptance level. If y ∈ T1–p, then

y is statistically indistinguishable from the target at the 

(1 – p) x 100% acceptance level and is considered acceptable. If y ∉ T1–p, then y is statistically different from the

target at the (1 – p) x 100% acceptance level and is considered unacceptable. An assessment of this type will be

called a probability based assessment.

Assuming that the unknown p.d.f. f (x) is continuous,

unimodal, and symmetric, the problem of identifying critical contours for the target T1–p is simplified. With these

assumptions the critical contours of f (x) are defined by

standardized distances from a central value xc, which could

be the mean, median or mode. Thus, to define a target only

a standardized critical distance dcrit from the central value

xc for a specified (1 – p) x 100% acceptance level needs to

be determined.

The critical distance dcrit, then, determines whether a

parameter vector y is indistinguishable from the distance

based target T d

1–p ={d  Pr(d , dcrit) = 1 – p}. The superscript d indicates that the (1 – p) x 100% target is defined

using the p.d.f. f d (d) based on a distance function d(x,xc)

and not on the contours of the p.d.f. of the actual distribution (Mardia and others 1979). A parameter vector y is,

then, acceptable relative to the target if its standardized distance dy from the central value xc is less than the critical

distance, dy <dcrit, for a distance function d(x,xc). An

observation is unacceptable otherwise.

Under these simplifying assumptions the mean, median,

and mode are coincident. In a more general setting, say

without the symmetry assumption, the three central values

would all be different. In this situation the mode, as the

most likely value, should be used as the central value in the

target definition and assessment procedures.

Implementation

Let X = {x1, x2, ...xM} represent a set of parameter vectors xi = [xi1, xi2, ...xik]T containing the values for the k forest parameters of interest for a collection of M forest

stands. The M parameter vectors in the set X are used to

represent the p.d.f. f (x) and, subsequently, to define a target T d

1–p. Let Y = {y1, x2, ...yN} represent a set of N observed

parameter vectors yj = {yj1, xj2, ...yjk} containing values

29

{yf (y)≥c}

{yf (y)≥c}
for the k forest parameters of interest that are to be assessed

relative to the target data set X. The objective is to determine which of the observation vectors y are indistinguishable from the target data set X for a (1 – p) × 100%

acceptance level and a level of error p, where 0 < p <1. Let

xc represent a central value, the mean, median or mode from

the target data set X, and let d(x, xc) be the distance function used to obtain standardized distances from the central

value xc for a vector x.

The k -dimensional empirical distribution was assumed

for the parameter vectors xi in the target data set X, and the

distance function

d(x, xc) = (x – xc)T S-1

xc(x – xc) ,

where S-1

xc is the inverse of the variation matrix Sxc= (Sij),

and

Sij = 1 ∑(xri – xc

i

)(xri – xc

j

)

M – 1 r =1

for i, j = 1,2,...,k was used to compute standardized distances from a central value. The critical distance dcrit for a

(1 – p) x 100% acceptance level was computed in three

steps. First, the central value xc and the variation matrix Sxc

were computed from the M parameter vectors xi in the target data set X. Second, the standardized distances xi

d = d(xi

,

xc) were computed for the M parameter vectors xi in the

target set X. Third, the index for the critical standardized

distance,

1, if p = 0

icrit = (1 – p)M, if 0 < p < 1,

M, if p = 1

was computed, and dcrit = x(i

d

crit), where x is the floor function, returning the largest integer less than or equal to x,

and x(i

d

) denotes the ith order statistic, x d(1) ≤ x d(2)... ≤ x d

(M) , for

the set of target distances (Mardia and others 1979; Serfling

1980; Zar 1996).

An assessment of the parameter vectors yj in the observation data set Y, relative to the target data set X, was performed in two steps. First, standardized distances from the

central value xc were computed for the observed parameter

vectors yj, yj

d = d(yj,, xc). Second, the observed distances

y d

j were compared to the critical distance dcrit. If y dj < dcrit,

then the observed parameter vector yj is statistically indistinguishable from the target data set for a (1 – p) x 100%

acceptance level, and is considered acceptable. A parameter

vector is considered unacceptable otherwise.

Application

Following the paradigm of the FFR, the target definition used here was based on mature riparian forest stands

having a midpoint age of 140 years and a minimum stand

age of 80 years, giving an approximate upper age boundary

of 200 years (FFR 1999; Ehlert and Mader 2000; Fairweather

2001; WFPB 2001). Four compatible target definition and

assessment scenarios were considered. The same sets of

stands were assigned to the target and observation data sets

for all scenarios, making the only differences among the

scenarios the parameter vector components used. The stand

parameters used were: basal area per acre (BA), conifer

basal are per acre (CBA), trees per acre (TPA), quadratic

mean diameter (QMD), and average tree height (H). The

parameter vectors used in each of the four scenarios are

listed in table 1.

Letting s = 1,2,3, or 4 be the assessment scenario number, define As = {a1,a2, ...aN} to be a set of N available

parameter vectors ai from a sample of mature riparian forest stands. Identify a subset As

target of As , Astarget As

, containing M of the available parameter vectors as the target

data set for each assessment scenario. The sets X = As

target

and Y = As then, define the target and observation data sets,

respectively, for each scenario. Assessments of the observation data set Y relative to its respective target data set X

were then made for each scenario. The modes of the target

data sets were used as the central values in all assessments.

Mode estimates were computed using the mean update

algorithm (Thompson 2000).

Assessments for each scenario were performed using

acceptance levels of 95%, 90%, 80%, and 50%. Acceptance

percentages were computed for each assessment scenario

and acceptance level as (Ns

accept /N) x 100%, where Nsaccept is

the number of acceptable observations for each acceptance

level and scenario s. Finally, as a measure of the performance of the assessment procedure, relative acceptance 

percentages, the ratio of the acceptance percentage to the

acceptance level, were computed. In the example, the target

data set and the observation data set were selected from

the same unknown distribution, that of a mature riparian

stand, so the empirical acceptance percentages and the

acceptance levels should be similar.

DATA DESCRIPTION

The mature riparian forest data used to define targets

for this analysis were obtained from the Forest Inventory

and Analysis (FIA) program of the U.S.D.A. Forest Service.

The data were collected by the Pacific Resource Inventory,

Monitoring, and Evaluation (PRIME) program of the FIA,

30

M

∩






and contain forest inventory data collected from all ownerships except national forest and reserved areas (Woudenberg

and Farrenkopf 1995). The FIA PRIME database was used

for this analysis since it was readily available and because

it was one of the data sets used in the original DFC analysis for the FFR (Moffett and others 1998; Fairweather 2001).

The FIA PRIME database was not restricted to unmanaged

stands, but data from this source were considered sufficient

for the purpose of demonstrating the target definition and

assessment procedures, and highlighting the benefits of

using multi-parameter targets within the paradigm established by the FFR.

The FIA PRIME data were collected using a stratified

sampling scheme with two levels: the plot and subplot.

Each plot has multiple subplots whose measurement data

are intended to be aggregated to estimate plot level parameters (Woudenberg and Farrenkopf 1995). The number of

subplots per plot varied over time due to changes in the

sampling protocols, but five subplots has been the standard

number since 1994 (Woudenberg and Farrenkopf 1995).

The data for the analysis consisted of subplots that met the

following four criteria: 1) Subplots were at least 80 years

of age as indicated by the FIA age class codes; 2) Subplots

were within 215 ft of a stream; 3) Subplots were classified

by the FIA as timberland; 4) Diameter at breast height (d.b.h)

and height measurements for each tree were both greater

than zero. These data selection criteria were largely motivated by a consideration of the original Forests and Fish

DFC analysis (Moffett and others 1998; Fairweather 2001).

These criteria yielded tree data from 127 subplots contained in 47 unique plots. The number of subplots obtained

for each plot varied from one to five with almost equal 

frequencies making an analysis at the plot level infeasible.

The selected subplots were all from plots having five subplots distributed over an area of approximately 6.67 acres.

Using the subplots directly for an analysis seemed reasonable and this is what was done. In doing so a subplot to

plot scale factor of five was used when computing areabased values from the subplot data. Using the subplot data

directly simply increases the observed variability in computed stand parameters. The subplot data still provide an

unbiased random sample of riparian forest stands, with the

caveat that subplots associated with the same plot are not

independent.

Tree data extracted from the PRIME database included

the d.b.h., height, species, and TPA represented by each

sampled tree. The data were originally in metric units and

standard conversion factors were used to obtain English

units for this analysis. After the data were extracted and

converted to English units, the individual tree data from

each subplot were filtered to remove trees having d.b.h.

values less than 4 inches to reduce the influence of very

small trees on stand density. The stand parameters of interest were computed using standard formulas and relationships. Numerical summaries of the stand parameters for the

127 riparian subplots appear in table 2, and a summary for

the 42 subplots having stand ages in the range of 100 to

180 years appears in table 3. 

For the assessment scenarios the observation and target

data sets, As and As

target,were defined to be the whole data

set and the subset of the riparian data having stand ages in

the range of 100 to 180 years, respectively. The statistical

summaries indicate that the target data sets and the observation data sets are in general agreement, and a visual

inspection of the data sets indicated that the target data

were well distributed throughout the range of the larger

observation data for each assessment scenario.

RESULTS

Acceptance percentages and relative acceptance percentages for the four assessment scenarios and the four

acceptance levels are presented in table 4. A strong correspondence between the acceptance level and the computed

acceptance percentages clearly exists. The acceptance percentages decrease as the acceptance levels decrease in all

cases for all of the target definition and assessment scenarios. Further, the computed acceptance percentages were all

at least 75% of their respective acceptance levels, with the

majority being at least 88%, as indicated by the relative

acceptance percentages.

DISCUSSION

The overall performance of the probability based target

definition and assessment procedures was quite good. Trends

in the acceptance percentage results are in strong agreement

with expectations; acceptance percentages decreased for

each of the four target definition and assessment scenarios

31

Table 1—Parameter vector descriptions for the four

target definition and assessment scenarios.

Parameter

Scenario (s) vector dimension (k) Parameter(s)

1 1 BA

2 1 CBA

3 2 TPA, QMD

4 3 TPA, QMD, H
as the acceptance levels decreased. Exceptions to the expected

trends occurred for the higher dimensional parameter vectors, which may be explained by the relatively small size 

of the target data sets, which contained only 42 points. The

small target data set limits the achievable resolution for

computing critical distances and probabilities: the procedures assume a continuous p.d.f., but the parameter vectors

are discrete points, providing only an approximation to

the distribution. These artifacts may be reduced by increasing the size of the target data set.

The behavior of the acceptance percentages and the high

degree of agreement between the acceptance levels and the

acceptance percentages would seem to indicate that any of

the four targets could be used to successfully define a target

for riparian forest management. A comparison of how well

each of the targets performed in terms of identifying riparian stands that would have met the desired forest condition:

a mature riparian forest with moderate to low stand densities and larger average tree sizes is warranted. The comparison is based on assessments at a 90% acceptance

level. The 90% acceptance level was used since the 95%

acceptance level may not be restrictive enough and the

lower acceptance levels may be too restrictive. Only the

CBA based basal area assessment results are presented here

for consistency with the FFR. Results for BA were similar.

The CBA based assessment results had an acceptance

percentage of 88.2%. A histogram of the CBA values from

the observation data set appears in figure 1 along with the

32

Table 2—Stand summary for the 127 riparian subplots defining the observation data sets in the four assessment scenarios.

Variable Mean Std. Dev. Minimum Median Maximum

BA (ft2 ac-1) 252.1 121.1 30.5 241.5 673.8

CBA (ft2 ac-1) 215.1 136.6 0.0 201.2 673.8

H (ft) 97.6 32.5 33.1 99.5 191.2

QMD (in) 19.9 8.9 6.0 17.8 62.3

TPA 180.5 170.1 10.6 135.0 1095.6

Table 3—Stand summary for the 42 riparian subplots defining the target

data sets in the four assessment scenarios.

Variable Mean Std. Dev. Minimum Median Maximum

BA (ft2 ac-1) 247.7 107.8 30.5 239.9 507.6

CBA (ft2 ac-1) 206.9 126.6 0.0 187.8 507.6

H (ft) 99.1 33.0 33.1 96.7 162.3

QMD (in) 18.7 6.9 7.7 16.8 37.2

TPA 190.0 166.6 10.6 141.1 753.5

Table 4—Acceptance percentages (relative acceptance percentages) for

the four target definition and assessment scenarios. 

Acceptance level

Parameter(s) 95% 90% 80% 50%

BA 93.7 (98.6) 86.6 (96.2) 76.4 (95.5) 37.8 (75.6)

CBA 94.5 (99.5) 88.2 (98.0) 66.9 (83.7) 47.2 (94.5)

TPA, QMD 92.9 (97.8) 84.3 (93.6) 75.6 (94.5) 53.5 (107.1)

TPA, QMD, H 90.6 (95.3) 81.1 (90.1) 71.7 (89.6) 43.3 (86.6)
acceptance region boundaries and the mean and mode for

the target CBA values, 215.1 ft2ac-1 and 136.3 ft2ac-1 respectively. CBA values between the acceptance region boundaries are considered acceptable. The acceptance region

clearly captures the most likely CBA values, rejecting only

the largest CBA values on the upper tail of the distribution.

Using this figure alone the performance of the target relative to the desired stand density and tree size criteria cannot be determined. The CBA assessment results are plotted

in figure 2 using the corresponding TPA and QMD values.

The unacceptable stands appear along the TPA-QMD selfthinning curve, where the highest basal area values are

generally found. They are also located near the central portion of the TPA and QMD distribution, along its edge. The

acceptable stands are distributed throughout the range of

TPA and QMD values with no apparent discrimination

between stands of high density and low density. In fact, the

highest density stands are considered acceptable under the

CBA assessment. This CBA assessment, therefore, failed to

identify stands that meet the desired conditions. This result

was anticipated, and it clearly demonstrates the difficulty

of targeting a desired forest condition using CBA, or BA,

as the sole parameter.

The TPA and QMD based assessment had an acceptance

percentage of 84.3%. The assessment results appear in figure 3, with the mean and mode vectors for the target TPA

and QMD being 190.0 TPA and 18.7 inches and 175.2 TPA

and 16.0 inches respectively. The acceptable stands for this

assessment are clustered about the center of the TPA-QMD

distribution, indicated by the mode. High density stands

with small tree sizes and low density stands with very large

tree sizes are identified as unacceptable relative to the target.

33

Figure 1—Histogram of CBA showing the 90% acceptance region (AR). The target mean and mode are also shown.
The TPA and QMD assessment, therefore, succeeded in

identifying stands meeting the desired conditions.

The TPA, QMD and H based assessment had an acceptance percentage of 81.1%. The assessment results appear 

in figure 4, with the mean and mode vectors for the target

TPA, QMD, and H being 190.0 TPA, 18.7 inches, and 99.1

ft and 1750.1 TPA, 15.3 inches, and 78.3 ft respectively. As

with the TPA and QMD assessment, the acceptable stands

are clustered about the center of the TPA-QMD-H distribution, and both high and low density stands have been identified as unacceptable relative to the target. The TPA, QMD,

and H assessment, therefore, also succeeded in identifying

stands meeting the desired conditions.

The assessment procedures make no value judgments;

they simply identify stands that are far from the target mode.

If, for example, the low density, large tree forest structures

are desirable, then a second tier assessment could be performed to accept them. If used in this way the primary

assessment identifies stands that are indistinguishable from

the target and stands that need further consideration, the

unacceptable stands. In a management context, the acceptable stands from a primary assessment could be used to

determine appropriate management strategies, whereas the

unacceptable stands could be used to identify management

strategies that need further investigation or refinement.

CONCLUSIONS

The uses of quantitatively defined targets and assessment procedures to identify desired conditions and to assess

management practices relative to the desired conditions in

forest management, or other areas of natural resources

34

200 400 600 800 1000 1200

10

20

30

40

70

90% assessment about the mode using CBA

Stand density (TPA)

QMD (in)

Acceptable 

Unacceptable

Figure 2—Plot of acceptable and unacceptable TPA and QMD values based on the CBA assessment and a 90% acceptance level.
management, are likely to increase in the future. Target

definition and assessment methods must allow for the variability inherent in natural systems and provide for flexibility in the attainment of the desired conditions. Further,

effective target definition and assessment methods must be

biologically and statistically consistent. Biological consistency is necessary to ensure that the defined targets are relevant, representative of actual conditions, and achievable.

Statistical consistency is necessary to ensure the correct

interpretation of inferences derived from the target definition and assessment procedures.

The nonparametric target definition and assessment 

procedures described automatically take into account the

inherent variability of a desired forest structure, as identified by a representative data set, and they may be used with

parameter vectors of any dimension. The procedures are

both statistically and biologically consistent. Statistical

consistency is obtained by using the underlying distribution

of parameter values as the basis for the target definition and

assessment procedures. Biological consistency is obtained

by using actual data for relevant parameters to define the

target and perform assessments. Methods like those presented here provide an effective conceptual framework that

may enable scientists and policy makers to focus on identifying the relevant biological issues, rather than setting

potentially arbitrary targets for management.

ACKNOWLEDGEMENTS

This work was funded by the Rural Technology Initiative

(RTI) at the University of Washington College of Forest

Resources and the Family Forest Foundation (FFF), Chehalis,

WA. I would like to thank Bruce Lippke and Larry Mason

35

200 400 600 800 1000 1200

10

20

30

40

70

90% assessment about the mode using TPA, QMD

Stand density (TPA)

QMD (in)

Acceptable 

Unacceptable

Target mean 

Target mode 

Figure—3 Plot of acceptable and unacceptable TPA and QMD values for a 90% acceptance level. The target mean and mode are also shown.
at RTI and Tom Fox and Steve Stinson at the FFF for providing the funding and the opportunity to work on this

problem. I would also like to thank Kevin Zobrist of RTI

for proof-reading a draft of this paper.

LITERATURE CITED

Duda, Richard O.; Hart, Peter E. 1973. Pattern 

Classification and Scene Analysis. John Wiley & Sons.

482 p.

Ehlert, Hans; Mader, Steve. 2000. Review of the 

Scientific Foundations of the Forests and Fish Plan.

Prepared for: Washington Forest Protection Association

724 Columbia Street, N.W., Suite 250 Olympia, WA

98501, by CH2M Hill 777 108th Avenue N.E. Bellevue,

WA 98004-5118 (425)453-5000. 180 p.

Fairweather, Stephen. 2001. Westside RMZs and the 

DFC Model: Documentation Of Their Conceptual and

Methodological Development. Prepared for RSAG – the

Riparian Scientific Advisory Group, and CMER – the

Cooperative Monitoring, Evaluation, and Research

Committee, Olympia, Washington. Mason, Bruce, &

Girard, Inc. Portland, Oregon. 41 p.

FFR. 1999. Forests and Fish Report, April 29, 1999. 

www.wa.gov/dnr/htdocs/fp/fpb/forests&fish.html. 179 p.

Mardia, K.V.; Kent, J.T.; Bibby, J.M. 1979. Multivariate 

Analysis. Academic Press. 521 p.

Matlab. 1999. Using Matlab. Matlab Version 5.3. The 

MathWorks.

36

200

400

600

800

1000

1200

20

40

60

80

20

40

60

80

100

120

140

160

180

200

Stand density (TPA)

90% assessment about the mode using TPA, QMD, H

QMD (in)

Average height (ft)

Acceptable 

Unacceptable

Target mean 

Target mode 

Figure 4—Plot of acceptable and unacceptable TPA, QMD, and H values for a 90% acceptance level. The target mean and mode, obscured by

observations appearing in front of it, are also shown.
Moffett, Jeff; Ludwig, Michelle; Lippke, Bruce. 1998. 

Technical Analysis for the Desired Future Condition

Work Group (DRAFT). 8 p.

Serfling, Robert J. 1980. Approximation Theorems of 

Mathematical Statistics. John Wiley & Sons, Inc. 371 p.

Thompson, James R. 2000. Simulation: A Modeler’s 

Approach. John Wiley & Sons. 297 p.

Washington Forest Practice Board (WFPB). 2001. 

Forest Practices Rule Book. Washington Department of

Natural Resources. Forest Practices Division. Olympia.

212 p.

Woudenberg, Sharon W.; Farrenkopf, Thomas O. 1995.

The Westwide Forest Inventory Data Base: User’s

Manual, USDA Forest Service, Intermountain Research

Station, INT GTR-317. 57 p.

Zar, Jerrold H. 1996. Biostatistical Analysis. Third 

Edition. Prentice Hall. 662 p.

37
This page is a intentionally left blank.
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 Thomas Maness is Associate Professor and Ross Farrell is Research Assistant at the Faculty of Forestry, University of British Columbia, Vancouver, BC.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

SENSITIVITY ANALYSIS ON SUSTAINABLE FOREST

MANAGEMENT CRITERIA AND INDICATORS IN FOREST

DEVELOPMENT PLANNING: AN APPROACH USING 

MULTI-CRITERIA OPTIMIZATION

Thomas Maness and Ross Farrell1

BACKGROUND

The vast majority of productive timberland in British

Columbia is public land. There are 37 timber supply areas

(TSA’s) and 34 tree farm licenses (TFL’s), which cover

about 90 million hectares of public land in British Columbia.

TFL’s are 25 year agreements with a company responsible

for designing and carrying out a management plan. TSA’s

are large forested areas, usually with multiple licensees.

Each licensee has rights to harvest a specific volume of

timber, or Annual Allowable Cut (AAC) every year. In the

past, the AAC has been determined by the Provincial Chief

Forester through a timber supply analysis that is conducted

every 5 years. Under proposed legislation in BC, licensees

in each TSA will be required to collaboratively conduct a

timber supply analysis at least once every 5 years. 

The purpose of the timber supply analysis has been to

project the volume of timber available for harvest. Advanced

forest level models based on simulation and heuristics are

ABSTRACT

This paper describes the use of a multi-objective optimization model for creating forest development plans in the East

Kootenay area of British Columbia. The planning model determines appropriate harvest levels and management treatments

on planning units to satisfy stakeholder objectives related to the criteria and indicators of sustainable forest management.

The model uses indicator targets and thresholds and determines the degree of goal satisfaction using fuzzy-sets. The paper

illustrates how the model can be used to conduct sensitivity analysis on the relative impact of the various criteria as an aid

in a participatory planning process. Results of a case study are shown to demonstrate use of the model in a sustainable forest

development planning context.

KEYWORDS: Multi-objective planning, fuzzy sets, goal programming.

39

used to project timber supply based on the inventory information, expected growth and management practices (Nelson

2003). Sensitivity analysis is performed to determine the

impact of various uncertainties that affect timber supply.

The sensitivity analysis is conducted by varying one variable at a time over a natural range, and noting the impact

on timber volume projections.

While this type of sensitivity analysis is useful, this

paper addresses three areas of concern from current modeling methods used in BC. First, economic outputs are measured solely in terms of timber volume. The changing timber

size and quality distribution have an important impact on

economic sustainability, and this is not considered. Second,

important ecological and social indicators are usually treated

as constraints rather than as opportunities, although Lui et

al. (2000) have made important contributions in this area

and forest models used in practice are evolving. Third, it is

well known that many indicators are clearly complementary

(such as ecological representation and old growth), some
are clearly competitive (such as visual quality and timber

supply), and in some the degree of complementarity is

unknown.

This paper describes a decision support system (DSS)

that was designed to create and evaluate management scenarios using a set of criteria and indicators for sustainable

forest management. We illustrate the use of the model in a

Timber Supply Area (TSA) in British Columbia, and we

give an example of how the model can be used to conduct

sensitivity analysis to determine the relative impact of SFM

indicators for use in a participatory planning process and

for investment planning.

The Planning Area

This Invermere Timber Supply Area (TSA) is located 

in the Rocky Mountain Trench at the headwaters of the

Columbia River. It is a highly scenic area and contains a

number of national and provincial parks, as well as a destination ski resort. The small communities in the region are

highly dependent on the forest industry. The TSA comprises 1,110,700 hectares located in the interior dry belt of

Southeastern British Columbia. It contains 6 biogeoclimatic zones. The timber harvesting land base consists of 22%

of the total area. Major reductions are taken for parks and

reserves (25%), non-productive forest land (31%), and

inoperable areas (16%). The forest types on the TSA range

from dry open stands of ponderosa pine (PP Zone) and

interior Douglas-fir (IDF) at low elevations to Englemann

spruce – subalpine fir (ESSF) at higher elevations. Lodgepole

pine commonly occurs in the montane spruce zone (MS)

due to the fire history in the region. There are 6 animal

species listed as threatened and 19 species listed as vulnerable that potentially occur in the TSA. Threatened species

include the southern population of woodland caribou and

Swainson’s hawk, endangered species include grizzly bear,

Rocky Mountain bighorn sheep, sandhill crane and bull

trout (MOF 2000).

Timber from the TSA supplies three sawmills. In addition wood chips are sold to a local pulp mill, and logs are

traded to an LVL plant located in another region of BC.

The current allowable annual cut was set in 1996 at 581,570

cubic meters (m3). The long term sustainable cut level is

projected to decline to 426,880 m3 over the next 3 decades

as older stands are harvested. The transition from an older

natural forest to younger managed stands leads to obvious

concern about the ecological, social and economic sustainability of the TSA.

The TSA consists of 34 Landscape Units (LU), based

roughly on watershed boundaries. Each LU consists of

approximately 12,000 polygons with an average size of 2

hectares, but a great deal of variation in size. This paper

concerns the chart area that was assigned to Slocan Forest

Products Company, which has one sawmill located in

Radium Hot Springs, BC.

The Modeling Approach

Models for multi-objective forest management planning

have been created using multi-objective linear programming and goal programming (Weintraub and Bare 1996,

Bare and Mendoza 1988, Mendoza and others 1987, Rustagi

and Bare 1987, Van Kooten 1995, Varma and others 2000).

These methods require the model users to fix a “weight” or

“priority” for each of the objectives. However, these objectives are often imprecise, conflicting and non-commensurable. Consequently, the weights are increasingly more

difficult to obtain as the number of objectives increase.

An alternative approach to multi-criteria forest planning

uses fuzzy set theory. Fuzzy constraints have been applied

to single objective harvest scheduling with non-declining

even flow constraints (Hof and others 1986; Bare and

Mendoza 1990), for carbon balancing in forestry and agriculture (Krcmar and others 2001) and for multi-criteria forest harvest planning (Mendoza and Sprouse, 1989).

Our decision support system is built around a decomposed linear programming model with a MINMAX optimization framework. The SFM indicators are modeled

using fuzzy membership functions in the tactical (master)

LP. Rather than having criteria weights, this approach uses

targets, thresholds and triggers. Targets are the desired outcomes for each goal. Thresholds are the minimum acceptable outcome for each goal. Triggers are the management

activities that occur on the ground to change the achievement levels of the goals. We call this optimization structure

the “3T Approach”.

Forest planning models typically have log volume (or

log value) maximization as the objective pertaining to timber. However, most manufacturing facilities perform best

with a mix of diameters, lengths, species and grades suited

to their technology and markets that they serve. To maximize the economic benefits, planning units that give the

correct mix of logs would be chosen for harvesting. Other

planning units may have higher values for other objectives.

For this reason, an operational LP is called as a subroutine

to determine the optimal manufacturing decisions and

return the marginal log values to the tactical LP.

Early manufacturing models that used LP with decomposition techniques are described in Mendoza and Bare

40
(1986), Eng and others (1986), and Maness and Adams

(1991). We use the Sawmill Production Control Model

(SPCM) developed by Maness and Norton (2002).

The forest planning and manufacturing models are

linked using a hierarchical planning (HP)2 framework

described by Paredes (1996). HP in our model refers to separating the planning problem into temporal contexts, and

integrating separate models for each one. This allows each

model to focus on the objectives that are important at that

level. Sequential HP models are described by Jamnick and

Robak (1996) and Ogweno (1994). These models operate

from the top down in one direction, so they do not find a

global optimum. To ensure global optimality our model is

linked through the use of shadow prices on logs.

METHODS

Creation of Planning Units

It is impractical to use individual forest polygons for tactical planning. They are too small, and there are too many

of them. Groups of polygons called cut blocks could not be

used either as these are not created until an area has actually

been scheduled for harvest. For this reason we created the

Stewardship Unit (SU), a homogeneous planning unit of

variable size, ranging from 0.02 hectares to 1,114 hectares

depending on the characteristics. The average SU is 180

hectares. The SU’s were created by manually amalgamating

contiguous polygons that had similar attributes. The GIS

dataset for the Invermere TSA was obtained from Interior

Reforestation Co. Ltd. in Cranbrook BC, in December 2002.

GIS data for the TSA is organized by polygon. The timber

type inventory data was obtained from the BC Ministry of

Sustainable Resource Management in February 2002.

Commercial Species

The following are the principal commercially important

tree species in the region.

• Lodgepole pine (Pinus contorta var. latifolia) – 40.7%

• White spruce (Picea glauca) / Englemann spruce

(Picea engelmannii) – 13.0%

• Douglas-fir (Pseudotsuga menziesii) – 28.7%

• Western larch – (Larix occidentalis) – 7.2%

• Subalpine fir (Abies lasiocarpa) – 4.3%

• Western redcedar (Thuja plicata) – 0.2%

Sawmill and Product Information

The lumber mill modeled in this study is Slocan’s

Radium Division sawmill located in Radium Hot Springs,

BC. The mill produces commodity dimension, J-Grade and

machine stress rated lumber products with 50% of their

production in the latter two categories. The mill has 180

employees, employs 60 contract loggers, and produces 150

million board feet per year. The mill also produces 71,000

bone dry units of wood chips annually.

Logs are trucked over public roads to the mill’s log

yard. About 30% of the mill’s logs are cut-to-length, the

balance are cross cut in 2 manual bucking lines. Primary

breakdown consists of 3 sawing lines: a high speed small

log canter, a canter twin and a carriage headrig for larger

high quality logs. Three large kilns provide adequate drying capacity.

Development of Criteria and Indicators

The choice of criteria and indicators (C&I) used for SFM

depends on the scale on which our judgments about sustainability are made. The broad principles for international

sustainable development were developed in the 1992 Earth

Summit (United Nations 1992), and followed up by the

Montreal Process (1995), and Canadian Council of Forest

Ministers (1997, 2000). While the Canadian Council of

Forest Ministers C&I provide a good framework of the

important principles for sustainable development, they fall

short of defining operational criteria for local or regional

management decisions (Reynolds 2001; Brang and others

2002). Effective criteria for management planning must be

practical and simple, and they must make common sense

(Bunnell 1997).

We conducted a comprehensive review of the SFM 

C&I published by the Canadian Council of Forest Ministers

(1997, 2000), and the C&I developed by a local study in

the West Kootenay region of BC (Robinson 2002). Indicators

selected for inclusion were required to meet operational

standards adapted from Bunnell (1997). Our study used the

4 criteria and 12 indicators listed in table 1. Targets and

thresholds were developed for each indicator based on

expert judgment. A full description of the indicators and

rationale for choosing them can be found in Maness (2003).

Mathematical Formulation

Tactical Planning Model

The tactical planning model is a multi-objective linear

programming model with a “fuzzy formulation”. We use a

MAXMIN formulation which maximizes the degree of satisfaction with the least satisfied indicator. The full mathematical formulation of the model can be found in Maness

and Farrell (2004).

41

2 A thorough review of HP techniques applied to forest management can be found in Weintraub and Davis (1996).
Operational Planning Model

The operation planning model used is a decomposed

crisp linear programming model that maximizes net revenue from sawmilling operations given a log distribution.

The model contains a bucking and sawing simulation

model that generates activities using column generation.

The mathematical formulation and full details of the model

can be found in Maness and Norton (2002).

Post-optimization analysis from the operational model

yields return-to-log (RTL) values for the logs input into the

sawmill. The RTL values change as a function of the species

and size of the logs that are delivered. Consequently, the

tactical and operational models were solved iteratively

using a hierarchical framework.

Linking the Models

Figure 1 shows a flowchart of the overall process for

developing a management plan. First the GIS polygons are

42

Table 1—Criteria and indicators chosen for the planning model. From Maness and Farrell (2004).

Indicator Measured variable Trigger

Criterion I – Biological richness and its associated vales are sustained

1. Full range of ecosystem Number of hectares of types deemed Indicator value can only be retained by

types are represented under-represented by experts preservation in current state

2. Ungulate winter range Number of hectares of unmanaged Indicator value can only be retained by

represented in UWR preservation in current state

unmanaged state

3. Old-growth forests are Percentage of area in required forest Indicator value is retained up to a 30% net 

represented types considered mature and old harvest removal

Criterion II – Forest productivity is sustained 

4. Annual removal of forest Annual harvests Harvest volumes are constrained by the 

products relative to the Annual Allowable Cut (AAC)

volume determined to 

be sustainable 

Criterion III – The flow of economic benefits from the forest is sustained

5. Net profitability is Revenue (RTL values based on Links between tactical and operational model.

sustained (proxy tax harvested log distribution)

revenues)

Cost (Based on harvest costs) Employment coefficients per cubic meter

6. Total employment in all Direct employment determined by 

forest sectors is sustained operation activities

7. The provincial government Harvest volumes determined by Stumpage payments to government

continues to receive tactical model

portion of benefits

Criterion IV – Forest management supports ongoing opportunities for quality of life benefits

Availability of recreation Number of recreation features Indicator value can only be retained by

opportunities – 3 types: by type preservation in current state

8. Fishing

9. Camping 

10. Hiking 

11. Visual quality of managed Visual quality objective (VQO) scores Current VQO and harvest treatment. Based on

landscape is acceptable for each stewardship unit from no guidelines by Picard and Sheppard 2001.

to stakeholders value (0) to maximum value (10)

12. Community watershed are Number of hectares of community Indicator value is retained up to a 25% net

sustained and protected watersheds harvest removal
aggregated into stewardship units (SU’s). Next, the forest

cover type data is used to generate a stand and stock table

for each SU. Taper curves are used to determine the tree

shape.

An expert is identified for each indicator used in the

model. Before running the model, the expert analyzes the

input data and the objectives of the stakeholders, and specifies the required target and threshold for each indicator.

This information is added to the database.

The DSS is solved using a hierarchical planning framework. Trees are bucked into woods length logs based on

return-to-log values (shadow prices from the operational

LP). The tactical model determines which areas will be

harvested with which treatments considering all of the criteria and indicators. The log information is passed to the

operational model, which runs the sawmill optimally based

on that log distribution. Return-to-log values are then

determined, and the tactical/operational sequence is reexecuted until a global optimum is reached. At this point

43

Figure 1—Flowchart of the overall solution

process. The process continues iteratively in 

two domains: a) temporal domain, b) stakeholder domain. From Maness and Farrell 

(2004).
the DSS is completed and the optimal scenario is prepared

for stakeholder review.

In practice, stakeholders would review the output to

determine if criteria have been sufficiently met. If not, targets and thresholds are reviewed, negotiated and modified,

and a new scenario is built as above. The process continues

until stakeholders are satisfied or all options have been

explored.

Harvesting treatments

The ten different management treatments considered by

the DSS are listed in table 2. Selection cuts operate at a 50%

retention level. The model assigns harvesting prescriptions

to stewardship units taking account of the targets, thresholds and triggers for each criteria and indicator. Each harvesting prescription has an associated harvesting cost based

on the harvest method (clear-cut or selection-cut) and the

net harvest volume. Selection-cuts are more expensive than

clear-cut systems and cost increases are incurred as net harvest volume decreases. Costs are also adjusted according 

to the likely harvesting system that would be used in each

stewardship unit, determined by analysis of terrain data

extracted from the GIS.

RESULTS & DISCUSSION

Results are organized in 2 sections. The first section

shows the results of the base case scenario, where the

model was used to prepare a 5 year forest development

plan for the 2 landscape units. The second section shows

the results of the sensitivity analysis, where an optimal

solution was found for each indicator separately, and the

effects on all other indicators was noted.

Base case targets, thresholds and results

The base case scenario was prepared for the first fiveyear planning period using the annual allowable cut to constrain harvest volumes from each of the landscape units.

The scenario required two iterations of the tactical and

operational LP’s to generate a global optimum solution

based on the updated RTL values and the indicator targets,

thresholds and triggers.

The RTL values for each iteration of the operational

model are shown in figure 2. Iteration 1 increased RTL

values for SED’s of nine inches and over, while SED’s less

than nine inches have decreased in value. This result is

intuitive as the tactical LP seeks to harvest SU’s that provide a more optimal log distribution, therefore increasing

volumes of the larger SED’s that generate greater profit.

The RTL values adjust to reflect the more balanced log 

distribution showing an increase for large SED’s and a

decrease for small SED’s.

The harvesting treatments prescribed by the global optimum solution to the tactical LP for each of the landscape

units are shown in table 2. Only one harvest treatment is

applied to LU 27. Approximately 14% of LU 27 is prescribed a 20% clear-cut and the remainder is preserved.

Approximately 98% of LU 29 is preserved with the remainder allocated to 5 different treatments. The largest treatment is a 100% clear-cut prescribed to 1% of the LU area.

Table 3 shows the targets and thresholds with the result

and satisfaction score for each of the 7 indicators that had

threshold below the target. The threshold percentages specify the minimum acceptable values as a percentage of the

target for each indicator. The criterion II indicator (forest

productivity is sustained) is modeled as a hard constraint

that ensures harvest volumes do not exceed the annual

allowable cut for each landscape unit. Consequently this

indicator has no target or threshold values. Four other indicators were deemed critical in the base case scenario and

were fixed in the base case run. This means their minimum

acceptable value is equal to the target for that indicator,

thereby ensuring that they are sustained in their current state

(in other words they were hard constraints also). These four

indicators were: ecosystem type representation, old-growth

forest representation, visual quality of the landscape, and

protection and sustainability of community watersheds.

However, for the sensitivity analysis in the next section all

indicators were considered variable.

44

Figure 2—Return-To-Log (RTL) values for SPF by Small End Diameter

(SED) for each iteration of the operational LP. From Maness and Farrell

(2004).
For illustration, the thresholds for ungulate winter range,

fishing, hiking, camping, employment and benefits to the

government were set at 90% of the target. The threshold

for net profitability was set at 75% of the target as it was

anticipated that this indicator would be the most difficult to

satisfy (at its target level) when balancing the needs of all

the other indicators.

Table 3 shows the achievement level and percent for

each of the 7 indicators. The achievement level is related

directly to the membership function. Since all membership

functions were linear in this study, the achievement level

represents the percentage achieved between the threshold

and the target. It is the actual fuzzy indicator used in the

model. The achievement percent reported in table 3 is the

percent of the target that was achieved (between zero and

the target).

Three indicators failed to achieve the target. Ungulate

winter range achieved a level of 0.95, while both hiking

and net profitability achieved the minimum level of 0.34.

Experience with the model has shown that using the

MAXMIN approach the achievement level will always be

equal for 2 or more indicators at the lowest level (the minimum). This represents the balance point. None of the indicators at the lowest level can be raised without decreasing

at least one of the others. This is an important feature of

the MAXMIN fuzzy approach—it identifies all the indicators at the balance point.

It is interesting to note that even though profit does 

not attain its target value, the other economic indicators

(employment and benefits to government) were fully satisfied. This occurs because both employment and government benefits are a function of harvest volumes rather than

a direct function of sawmill profitability. This is an important result because even though volume can be sustained,

45

Table 2—Management prescriptions considered by the DSS, and the amount of each prescription

selected in the base case.

Prescription Hectares Harvest Net harvest

ID Method treated intensity volume LU 27% LU 29%

1 Clear-cut 3% 100% 3% 0.00% 0.03%

2 Clear-cut 10% 100% 10% 0.00% 0.00%

3 Clear-cut 20% 100% 20% 13.64% 0.32%

4 Clear-cut 32.50% 100% 32.50% 0.00% 0.00%

5 Clear-cut 100% 100% 100% 0.00% 1.01%

6 Selection-cut 25% 50% 12.50% 0.00% 0.12%

7 Selection-cut 67.50% 50% 33.75% 0.00% 0.00%

8 Selection-cut 95% 50% 47.50% 0.00% 0.15%

9 Selection-cut 97.50% 50% 48.75% 0.00% 0.00%

10 Preserve 100% 0% 0% 86.36% 98.37%

Table 3—Threshold percentages, attainment levels and attainment percentages for indicators 

used in the model.

Indicator Threshold Attainment Attainment 

Indicator code percent level percent

2. Ungulate winter range UWR 90% 0.95 95%

5. Net profitability PFT 75% 0.34 84%

6. Employment in forest sectors EMPL 90% 1 100%

7. Benefits to governments BNFT 90% 1 100%

8. Availability of recreation (Fishing) FISH 90% 1 100%

9. Availability of recreation (Camping) CAMP 90% 0.34 93%

10. Availability of recreation (Hiking) HIKE 90% 1 100%
the decreasing timber quality over time is unable to sustain

profitability. This factor is often overlooked in long range

timber planning. In this case the high quality timber that would sustain profits is located in high recreation value

areas.

From these results we conclude that profitability and

hiking are the most critical indicators in the base case scenario, with ungulate winter range falling next in line. This

was checked out with the forest company and was found to

indeed be the case on these landscape units. If the model

was used in a participatory decision making context the

output of the base case scenario would be reviewed by all

stakeholders to determine if criteria have been sufficiently

met. If not, the targets and thresholds would be reviewed,

negotiated and modified, and a new scenario would be

defined. When reviewing this information the forest company had an important insight – they may decide to invest

to hiking trails as a way to improve their profitability. This

is an interesting outcome of using this type of model.

The scenario evaluation model can also be used to generate a number of alternative solutions with different target

and threshold settings. This allows decision makers to

explore a variety of options in the context of a “what-if”

analysis. 

Indicator Sensitivity Analysis

The way in which the indicators impact one another

was investigated by conducting a series of runs where the

threshold level for one indicator is set at the target, while

the thresholds for all others are relaxed at 50% of the tar-

get. This forces the model to achieve the target for the designated indicator and maximizes the minimum of the

remaining indicators. Eleven runs were made in total, one

for each indicator. Table 4 shows the results from the sensitivity analysis.

Each row represents the results from a single run with the

indicator listed in column 1 set at the target. The corresponding achievement level for each indicator is reported

under the appropriate column. Recall that when the achievement level equals 1.00 the indicator has attained the target

value.

The lowest achievement level score (0.51) was incurred

(by the hiking indicator) when profit is constrained to the

target. The next lowest achievement level is obtained when

the community watershed indicator is set at the target, in

which case both profit and hiking indicators are reduced to a satisfaction score of 0.72 (this identifies the two balancing indicators).

46Table 4—Indicator attainment levels determined by sensitivity analysis. From Maness and Farrell (2004).

Indicator ECO UWR OLD PFT EMPL BNFT FISH HIKE CAMP CWAT VIS MINIMUM1. ECO 1.00 0.98 0.94 0.94 1.00 1.00 1.00 0.94 1.00 0.94 0.98 0.94

2. UWR 1.00 1.00 0.94 0.94 1.00 1.00 1.00 0.94 1.00 0.94 0.96 0.94

3. OLD 0.97 0.98 1.00 0.90 1.00 1.00 1.00 0.90 1.00 0.93 0.95 0.90

5. PFT 0.97 0.98 0.91 1.00 1.00 1.00 1.00 0.51 1.00 0.94 0.95 0.51

6. EMPL 0.98 0.98 0.95 0.95 1.00 1.00 1.00 0.95 1.00 0.95 0.96 0.95

7. BNFT 0.98 0.98 0.95 0.95 1.00 1.00 1.00 0.95 1.00 0.95 0.96 0.95

8. FISH 0.95 1.00 1.00 0.98 0.98 0.95 1.00 0.95 1.00 0.95 0.96 0.95

9. HIKE 0.95 1.00 1.00 0.98 0.98 0.95 1.00 1.00 1.00 0.95 0.96 0.95

10. CAMP 0.95 1.00 1.00 0.98 0.98 0.95 1.00 0.95 1.00 0.95 0.96 0.95

11. CWAT 0.72 1.00 1.00 0.97 0.98 0.98 1.00 0.72 1.00 1.00 0.97 0.72

12. VIS 0.90 1.00 1.00 0.97 0.90 0.90 1.00 0.90 1.00 0.90 1.00 0.90
Decision makers can make inferences about the tradeoffs between specific indicators using this information. For

example when profit is maximized, the hiking indicator is

most affected, i.e. hiking incurs the lowest achievement

level. It is also evident that the community watershed indicator has the biggest single impact on profit levels when

this indicator alone is maximized. In this case profit drops

to an achievement level of 0.72.

CONCLUSION

There are many factors that must be considered when

generating sustainable forest development plans. Sensitivity

analysis is a key component to identify solutions that satisfy

a wide range of stakeholders. Using the DSS illustrated in

this paper, stakeholders and a panel of experts could collaborate in the decision making process to generate potential

development plans. Stakeholders would review the output

to determine if criteria have been sufficiently met, and if

not, the targets and thresholds could be reviewed, negotiated and modified, and a new scenario defined. The process

would continue until stakeholders are satisfied or all options

have been explored. In addition, forest products companies

would receive feedback on which areas they should invest

in to assure timber supply and future profits. Such investments may include those undertaken to satisfy non-timber

objectives, as has been illustrated in this paper. This type of

win-win investment is in the true spirit of sustainable forest

management.

Research and development on this model is ongoing in

both Northern and Southern British Columbia. Much work

remains to be done before the model reaches its full potential. Researchers are currently expanding the planning horizon to several rotations, refining the indicators, adding new

indicators, solving much larger areas, and adding road networks to the model.

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1 Guillermo A. Mendoza is an Associate Professor, University of Illinois, Urbana, IL 61801. 2 Ravi Prabhu is a Chief Scientist, Center for International 

Forestry Research, Bogor, Indonesia.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

PARTICIPATORY MODELING AND ANALYSIS 

OF SUSTAINABLE FOREST MANAGEMENT: EXPERIENCES 

AND LESSONS LEARNED FROM CASE STUDIES

Guillermo A. Mendoza1 and Ravi Prabhu2

INTRODUCTION

The concept of sustainability has become prominent in

almost all natural resource management situations. Its great

appeal has dominated discussions about resource utilization,

conservation, biodiversity, and many other resource development and management issues. Despite some lingering

differences and confusion about the meaning and essence

of sustainability, there is widespread acceptance that it is,

in principle, a noble goal that all resource management

must strive for. Consequently, the literature is now very

rich with reports and discussions about its meaning and

practice (Floyd et al 2001; AF&PA, 1995; Ferguson, 1996;

Munasinghe and Shearer 1996; Maser 1994). Over the last

few years, significant amount of effort, resources, and initiatives worldwide have been dedicated to the implementation of sustainable management of forests and other natural

resources (FSC, 1994; IUFRO 1997; Varma et al. 2000).

For example, one of the most common initiatives is the

ABSTRACT

Participatory approaches to natural resource management and development have become widely accepted as the most

effective instruments for achieving sustainable resource management particularly in the developing nations. This paper 

presents a participatory modeling framework that is consistent with participatory methods of assessing sustainable forest

management. Under this participatory modeling framework, a number of techniques have been developed aimed at: 1)

communicating the concept of sustainable forestry to local communities, 2) soliciting direct input and active participation of

local communities in the planning and decision-making process, and 3) seeking active involvement of local stakeholders in

the formulation of the models and in their implementation for generating strategies and action plans. These models include:

multi-criteria analysis, cognitive mapping, qualitative, and quantitative system dynamics. The models can be stand-alone

models, or they can be combined together to constitute a more robust and flexible planning framework. These models have

been applied to a number of case studies in the Philippines, Indonesia, Zimbabwe, and Ontario, Canada. Experiences and

lessons learned from a selected set of applications are described in the paper.

49

development of criteria and indicators for assessing and

monitoring sustainability (ITTO 1992; IUFRO 1997; Maser

1994; Mendoza and Prabhu 2000a,b). 

The practice of sustainable resource management has

also brought new paradigms in terms of how it is implemented at the field level. Traditionally, resource management has often been entrusted to experts and professionals

who exert enormous influence in how forests and other

resources are managed. Local communities and other

stakeholders have, in the past, been marginally involved in

the planning and decision-making process that ultimately

affect how the resources were managed. This traditional

management paradigm has, over the last decade, been

debunked or considered ineffective and unable to address

the challenges posed by sustainable resource management.

In its place, the paradigm of participatory or collaborative

management has been widely accepted as a more appropriate and effective paradigm for natural resource management
particularly in the developing nations. Consequently, there

now exists a large body of resource management and development literature describing and advocating the practice of

participatory approaches to resource management (Chambers

and Gujit 1995; Selener 1987; Saxena et al. 2002; Richards

et al. 1999).

This paper describes modeling approaches to support

the participatory or collaborative management paradigm.

Participatory approaches have been described in a significant body of literature and will not be rehashed here

(Chambers and Gujit, 1995; Selener 1987; Saxena et al.

2002). The paper however, will briefly describe the principle of participatory or collaborative modeling in general

and also describe how it has been applied in a number of

case studies.

THE NEED FOR PARTICIPATORY

MODELING

Participatory approaches described and reported in the

literature have taken many different forms with their glossary of terms, concepts, and analytical constructs. Some of

the more popular or better-known approaches include: participatory rural appraisal (Chambers and Gujit, 1995), participatory action research (Selener, 1997), communitybased resource management, co-management, joint forest

management, adaptive management, integrated resource

management, and other similar terms (Sarin, 1995; Misra

1997). While subtle differences exist among these methods,

they have fundamental similarities and commonalities in

terms of their general process and the nature of issues and

problems they are designed to address, which generally

include: multiple stakeholders and their multiple interests,

plurality of perspectives, and the empowerment of local

communities and stakeholders. Also common to these

approaches is the prerequisite for direct and active involvement of stakeholders in the planning, decision-making, and

actual management of the resources.

While these approaches have been widely accepted 

and promoted by many national and international agencies,

both governmental and non-governmental, they have also

received general criticisms from practitioners, management, and development scientists. Much of the criticisms

revolved around the apparent lack of rigor, structure, and

analytical framework provided by these approaches. On

one hand, the strength of these approaches lies on the highly

transparent and open-ended exploration of the issues, problems, and objectives that characterize the complex environment typical of many resource management situations. The

weakness, on the other hand, stems from an apparent lack

of a structured or systematic framework with which management strategies in general, and action plans in particular, can be evaluated for purposes of making decisions or

choices among competing management alternatives.

Resource management often includes many components

and stakeholders each of which has their own demand in

terms of resources, uses, goods, and services. To effectively

manage the resource, it is imperative that stakeholders’

concerns are addressed individually and collectively in a

manner consistent with the resource’s ability to meet these

demands without compromising its future productivity.

Hence, in one way, comprehensiveness that allows the

accommodation of multiple interests, uses, and products, is

desirable. On the other hand, choice of optimal alternatives

would almost certainly be impaired or impossible to identify

in light of the complexity surrounding the comprehensive

scope of resource management. Faced with such broad

scope, potentially large number of alternatives and their

intricate relationships, processes and impacts, it is almost

imperative to have a mechanism or framework for evaluation. Modeling, in general, offers such a framework.

Modeling, to be effective under a participatory management environment, must be transparent and within the reach

and grasp of local communities and stakeholders who often

are not familiar with, or do not have the experience and

technical know-how about, models. Historically, modeling

has been the exclusive domain of scientists and experts in

part because most models are complex and require some

expertise to formulate and develop. Moreover, traditional

models referred to as ‘hard systems models’ by Checkland

(1981), are generally very structured and formalized requiring sophisticated analytical constructs and mathematical

functions. Because of these restrictions, it is not advisable

or appropriate to use a complex modeling environment as

the platform for participatory modeling. On the contrary,

for modeling to be able to offer genuine analytical support

for participatory management, it must be simple and transparent for local stakeholders and at the same time it must

of be of sufficient rigor that enables more in-depth analysis

capable of accommodating the breadth and depth that characterizes the scope and complexity of natural resource

management. 

The type of analysis, insights, and decisions that can

reasonably be expected from a group, community of stakeholders, or under a participatory setting is worth noting. As

pointed out by Checkland (1981, 1988), most traditional

models are designed to ‘seek’ the ‘best’ or ‘optimal’ solution to a perceived problem. Clearly, the issues surrounding

sustainable forest management are too complex to presume

50
that an optimal solution can be identified, particularly when

there are other stakeholders involved. Under these conditions, models must be viewed as ‘problem structuring’ tools,

rather than ‘problem solving methods. Hence, results of

analysis and insights from participatory models are generally broad and tend to be strategic in nature rather than

operational or tactical. These models are generally designed

as tools to ‘understand’ the problem rather, make decisions

with respect to optimal ways of solving the problem

(Checkland, 1981).

PLURALISTIC AND QUALITATIVE 

SYSTEMS MODELS

As pointed out above, because of their rigid assumptions

and generally restrictive nature, traditional models do not

suitably match the type of modeling and participatory

process required in analyzing sustainable forest resource

management. However, current methodologies in participatory management are also inadequate because they are

inherently qualitative and do not offer any systematic

framework by which natural resource management strategies and alternatives can be analyzed. 

While traditional models on one hand, and current participatory approaches on the other hand, have their limitations when used as stand-alone methods, they nonetheless

have desirable features that can enhance sustainable management of forest resources. The systematic approach of

traditional models and the stakeholder-focus of participatory approaches, are notable strengths of the two methods

that can be combined to form a modeling framework suitable for analyzing sustainable forest management consistent with the principles of participatory management.

The need for more flexible and still rigorous methodology particularly for human-dominated systems, such as the

case of public natural resource management or communitybased forest management, have been advocated by a number of management practitioners and scientists. Checkland

(1984,1988) was perhaps the first to propose the use of

what he calls ‘soft systems methodology’ as an alternative

to traditional scientific management method, which he

referred to as following the ‘hard systems’ paradigm. He

characterized the scientific management paradigm as highly

mechanistic, reductionist, and functionalist in orientation.

Recognizing that these characteristics do not conform with

messy and ill-defined problems (e.g. complex human systems coupled or interacting with natural systems) he developed a soft systems methodology purposely designed to

accommodate the anomalies and externalities that arise

when dealing with human-centered systems. Following the

same school of thought, other authors advocated similar

approaches such as value-focused thinking (Keeney, 1992),

robust planning methodologies (Rosenhead, 1989), Strategic

Options Development (Eden, 1988, 1989), and qualitative

system dynamics (Coyle, 2000; Wolstenholme, 1999).

Mendoza and Prabhu (2003a) describe some of these

methodologies in the context of community-managed

forests. The following sections describe three general

methodologies and how they are used in the context of 

sustainable forest management.

Cognitive Mapping and Analysis

Cognitive maps are essentially loosely structured ideas

laid out purposely for understanding basic relationships and

dynamics of a system. The process begins with generation

of ideas or concepts with direct and active participation of

all stakeholders. This process is very similar to participatory rural appraisal techniques. However, cognitive mapping

goes beyond simple listing of essential ideas concepts.

These ideas are organized into a ‘map’ showing the relationships and interactions between and among these ideas.

These relationships are organized following a layout of

nodes and arrows (i.e. nodes represent concepts or ideas

and arrows denote the interactions or linkages between

these ideas).

Mendoza and Prabhu (2003b) describe an application 

of cognitive mapping on a community-managed forest in

Zimbabwe. In this case study, three groups representing

three villages were convened to assess the sustainable management of the Mafungautsi forest whose boundary encompasses the community forests managed by the villages. The

modeling process started with an open-ended discussion of

issues and factors affecting the management of their forests.

In addition to ‘listing’ these issues or factors, the villagers

were also asked to indicate the connections or relationships

between these factors using lines and arrows. The process

was facilitated by a team of local scientists who are familiar with the forest its history and ecology. Each idea or factor was discussed and debated. Often, original ideas were

revised, re-stated, or sharpened to make them more meaningful, central, and relevant to the overall objective of sustainable forest management. Moreover, each connection or

relationship denoted by arrows also went through group

scrutiny.

The cognitive map generated by the villagers served 

as an excellent learning and communication tool. Viewing

the map they themselves generated kindled the villagers’

awareness and appreciation of the extent and complexity of

managing their forest in a sustainable manner. They found

it instructive to see the factors and issues in a systems-oriented view instead of a simple ‘listing’ as is often done

51
using other participatory methods described in the published

literature such as participatory rural appraisal (Chambers

and Gujit, 1995), and participatory action research (Selener,

1997). For further or more in-depth analysis of the cognitive map, the methods described by Eden and Ackermann

(1998) were used. Specifically, three significant analytical

results were pursued. First, is the concept of ‘domain’ of a

factor. This reflects the extent of influence or tactical significance of a factor. This is determined from the cognitive

map by simply examining the number of nodes affected by,

or directly connected to, a given factor. Another significant

concept is the ‘centrality’ of an indicator. This reflects the

‘strategic significance’ of a factor, which can be determined

by simply examining the scope of influence of a factor

through its direct and indirect connections. Finally, the

third concept is the ‘criticality’ of a node, which is determined by examining the number of ‘critical nodes’ connected to a factor.

The three model insights described above seemed to be

quite meaningful and helpful for the three villages. They

quickly recognize that these three insights can serve as a

guide to them as they prepare action plans for their forests.

For example, they realize the significance of a ‘central or

strategic’ factor when it comes to focusing on those factors

that they can affect the most and can get the most favorable

impact. The ‘critical factors’ were also viewed as highly in

need of mediation or urgent attention. The tactically significant factors were also viewed as factors that they, individually or as a group, can start to influence positively for

more immediate impact.

Qualitative and quantitative system dynamics

The cognitive maps described are essentially a first

attempt to structure the essential elements or components

of a system. Clearly, the objective of developing a cognitive

map is just to lay the overall relationships of factors or 

elements of a system. For some applications, this may be

sufficient level of analysis, however, in some situations

particularly where there is more information, knowledge,

or experience about the different factors or elements, it

may be possible to structure the cognitive map as ‘influence diagrams’. In other words, the relationships are

described in terms of causalities between nodes connected

by an arrow. In this case, the concept of system dynamics

is appropriate (Forrester 1961, 1999). 

System dynamics is a general term associated to the

study of the dynamic behavior of a variety of complex 

systems (Coyle, 2000). Typically, influence diagrams using

nodes and directed arrows are used to denote this dynamic

behavior. In addition, the relationships sometimes referred

to as feedback loops or causality diagrams, are either positive or negative as shown in Figure 1. The diagram can

serve as ‘sense’ making device for the purpose of identifying dynamic causality relationships. The potential advantages of qualitative inference diagrams showing causal loops

was described by Wolstenholme (1999) as follows: “Causal loop

qualitative model enhances linear and ‘laundry list’ thinking by introducing circular causality and providing a medium by which people can externalize mental models and

assumptions and enrich these by sharing them. Furthermore, it facilitates inference of models of behavior by

assisting mental simulations of maps”.

Purnomo et al (2003) used a number of influence diagrams to examine the criteria and indicators of a communitymanaged forest in Indonesia. Figure 2 shows an example of

a causality loop diagram generated by a focus group representing two indigenous tribes living within a forest concession-area located in Kalimantan, Indonesia. In this study,

selected members and representatives from the two villagers

were asked to serve as a team that will examine the sustainability of their forests. Following the principles of participatory action research, the villagers were asked to

participate in a historical examination of their forests, and

in the process, generate a set of relevant indicators that

could be used to evaluate and monitor the sustainability of

their forests. During the group modeling process, mental

maps or cognitive maps were generated following participative or collaborative modeling procedures as described

by (Richardson and Anderson, 1995; Vennix, 1996). The

modeling process, debate, and iterative presentations of

causality maps eventually led to a group model. An example of this is shown in Figure 2. With the generated set of

indicators, the villagers, along with some scientists, were

asked to provide input with respect to: a) the current condition of the indicators, and b) projections about the desired

condition of the forest with respect to the set of indicators.

These projections are not meant to be accurate; they are

meant only to show crude comparative analysis and projections between the status quo and projected desired future

conditions. 

Mendoza and Prabhu (2003b) reported another case study

describing the application of qualitative system dynamics.

The case study involved a community-managed forest

located in Midland Province of Zimbabwe. In their study,

they used a computer-assisted decision support system

called Collaborative Vision Exploration Workbench or CoVIEW (http://www.cifor.cgiar.org/acm/pub/co-view.html).

This system is organized and structured following a system

dynamics framework as shown in Figure 3. As can be seen

from Figure 3, the system has the objective in the middle,

52
53

Figure 1—Components of causal loop diagram (Source: Purnomo et al. 2004.

Figure 2—Causal loop diagram of management of forest as perceived by its stakeholders (Source: Purnomo et al. 2004).
which is reflected as the main resource in Figure 3. The

attainment of the objective is dependent on both favorable

factors and unfavorable factors considered detrimental to

the objective. These factors are structured following the

SWOT (Strength, Weaknesses, Opportunities, and Threats)

concepts familiar in strategic management. Framing the

issues and concerns in this manner helped the villagers

identify internal (Strengths) as well external (Opportunities)

factors that contribute to the attainment of their objectives,

as well as internal (Weaknesses) and external (Threats) factors that undermine the attainment of their objective. The

indicators are the variables that reflect the status of the

resource being managed in general, or a given objective in

particular. Clearly, the qualitative system dynamics shown

in Figure 3 is not sufficiently ‘formalized’ to allow simulation in terms of specific scenarios based on alternative

strategies. At this level of formalization, more simplistic

analysis such as those described by Purnomo et al (2003)

can be used. Or, it may also be desirable to focus on those

aspects of the problem that are sufficiently understood

whose relationships can be quantified or formalized.

Based on the qualitative system dynamics described in

Figure 3, a simpler and quantifiable portion of the model

was examined in more detail. This allowed the development of a quantitative system model, which was then used

as the simulation model to evaluate different management

alternatives and strategies presented to, and analyzed by,

the villagers facilitated by a team as proposed by Vennix

(1996). 

The Co-View decision support system has a ‘Bridge’

component that allows the system dynamic diagram to be

converted to a system dynamics model that is transparent to

the stakeholders. The ‘transformed’ model then becomes

the vehicle with which stakeholders can ‘analyze’ impacts

of different scenarios using another component of Co-View

called ‘Power to Change’. The simulation afforded the villagers a chance to examine alternative management scenarios, both long term and short term.

Multi-criteria Analysis

Multi-criteria Analysis (MCA) is an umbrella approach

that encompasses a number of methods, both qualitative

and quantitative. As its name implies, MCA is a structured

framework by which management problems that involve a

number of criteria or objectives can be systematically evaluated and analyzed within the context of rational decisionmaking. Because of its capability to accommodate multiple

criteria and multiple decision makers or participants, it offers

a convenient framework for assessing sustainable forest

management. Mendoza and Prabhu (2000a,b) were one of

the first to recognize the potential of this approach in sustainable forest management. Since their seminal work, a

number of applications have been reported describing case

studies where MCA was used as the organizing framework

with which sustainable forest management were implemented (Varma et al, 2000). Perhaps the most significant

application environment under which MCA was applied is

the development of criteria and indicators for sustainable

forest management. One of the more recent application is

the use of MCA as a tool to evaluate sustainability as part

of the Provincial State of the Forest Report (SOFR). The

54

Figure 3—SWOT Elements Structured in a System Dynamics Framework.
Ontario Ministry of Natural Resources (OMNR) is required

to put together a report every five years documenting the

status or condition of its forest particularly with respect to

how sustainable the Ontario forest is being managed. It is 

a report that is meant to inform the general public of the

current condition of the forest and the trends evident from

repeated and consistent measurement of indicators. To provide a framework for such an assessment, MCA was examined as one possible approach. Mendoza (2002) describes

the concept and principles used in this assessment. The

results of the analysis are reported by BioForest Technologies,

Inc. (2003). In this analysis, MCA was used as an assessment tool to evaluate the relative significance of indicators

and their contributions to the overall sustainability of the

Ontario forests.

CONCLUSIONS

It is now widely accepted that participatory methods 

are the most effective approaches to achieve sustainable

resource management. Increasingly, local communities are

demanding more voice and influence in the manner public

forests are managed. This calls for more active and direct

participation from a number of stakeholders who are affecting, or affected by, the forest. In response to such changing

management paradigms, participatory methods have been

proposed. However, to date, while many of the existing

participatory methodologies are strong in terms of inviting

participation, they but are still lacking in terms of providing a structured framework by which debate about management alternatives and strategies can be sufficiently analyzed

and evaluated. While formal methods of analysis like many

traditional analytical models can offer such structured

framework, they are often too mechanical, overly simplistic

and rigid in their assumptions, and generally lack the flexibility and ‘robustness’ necessary for participatory analysis.

On the other hand, many of the recent participatory methods

like participatory rural appraisal, though they are inclusive

and generally meet requirements of participation, are often

lacking in rigor for more in-depth analysis. This paper proposed an alternative approach using participatory modeling

as a more suitable framework for analyzing more complex

problems like sustainable resource management within the

context of managing public forests. The participatory modeling framework proposed in this paper combines the

strength of participatory rural appraisal by taking advantage

of local knowledge through active participation of local

communities, and the analytical capabilities of structured

modeling. Three general models were presented for this

purpose: 1) cognitive mapping, 2) qualitative and quantitative system dynamics, and 3) multi-criteria analysis.

Experiences gained from a number of applications indicate that in general, a soft modeling paradigm is capable 

of enhancing both planning and the decision-making processes necessary to implement participatory management.

Its transparent nature allows for more inclusive participation of local stakeholders. Nothing is hidden beyond the

complexities of the model as is often the case for more

sophisticated models. This is one of the significant lessons

learned from the case studies; that is, transparency is critical

to build trust, confidence, and integrity of any planning

exercise. 

The models described in this paper are simple yet powerful in terms of ‘externalizing’ relevant aspects of sustainable forest management. Because of their relative simplicity,

they brought modeling within the comfortable reach of

local stakeholders. Unlike traditional models that historically have been in the exclusive domain of modeling experts,

the models described in this paper are simple enough for

local stakeholders to understand. Consequently, local stakeholders were able to confidently participate in the entire

process: from formulation to more in-depth analysis. This

was made possible also in part because of the adoption of

the group modeling process suggested by Vennix (1998),

particularly the emphasis on team facilitation.

Feedback received from many participants have generally been favorable with respect to the use and potential 

of the models described in this paper. Because the overall

process itself (not just certain stages) is participatory, the

participants often share ownership of the model, as well as

the insights, results, and decisions made stemming from the

application of the models. We believe this is in large part

because the participants have higher confidence in the analytical results generated because they understand the inner

workings of the model, which they themselves helped put

together. This increases the likelihood that policies developed, actions plans generated, and decisions made following the participatory modeling and analysis process will be

adopted.

LITERATURE CITED

AF&PA (American Forest & Paper Association). 1995. 

Sustainable forestry: principles and implementation

guidelines. Washington D.C.

BioForest Technologies, Inc. 2003. Multi-Criteria 

Analysis and Provincial State of the Forest Reporting

An Approach to Evaluating Forest Sustainability. Final

Report prepared for the Ontario Ministry of Natural

Resources. Ontario, Canada.

55
Chambers, R. and I. Guijt. 1995. PRA five years later – 

Where are we now? Forest, Trees and People. 26/27: 

4-14. Checkland, P.B. 1981. Systems Thinking Systems

Practise. Wiley. Chichester.

Checkland, P.B. 1981. Systems Thinking Systems 

Practise. Wiley. Chichester.

Checkland, P.B. 1984. Systems thinking in management: 

the development of soft systems 

Checkland, P.B. 1988. Soft systems methodology: 

overview. J. appl. Sys. Anal. 15: 27-30.

Coyle, G. 2000. Qualitative and quantitative modeling in 

system dynamics: some research questions. Systems

Dynamics Review. 16(3): 225-244.

Eden, C. 1988. Cognitive mapping: A review. European J. 

of Operational Research. 36: 1-13

Eden, C. 1989. Using cognitive mapping for strategic 

options development and analysis (SODA). In. Rational

Analysis of a Problematic World. (Ed). J. Rosenhead. J.

Wiley and Sons.

Eden, C. and F. Ackermann. 1998. Making Strategy: The 

Journey of Strategic Management. Sage Publications.

London.

Ferguson, I.S. 1996. Sustainable Forest Management. 

Oxford Press, Melbourne, Australia.

Floyd, D.W.; S.L. Vonhof, and H. Seyfang. 2001. Forest 

Sustainability: A Discussion Guide for professional

Resource Managers. Journal of Forestry. February.

99(2): 8-28.

Forrester, J.W. 1961. Industrial Dynamics. MIT Press.

Forrester, J.W. 1999. System Dynamics: The Foundation 

Under Systems Thinking. Sloan School of Management.

Massachusetts Institute of Technology. Cambridge,

Massachussetts.

FSC (Forestry Stewardship Council. 1994. Forestry 

Stewardship Principles and Criteria for Natural Forest

Management. Oaxaca, Mexico.

IITO (International Timber Trade Organization). 1992. 

Criteria for the measurement of sustainable tropical 

forest management. Yokohama, Japan. IUFRO

(International Union of Forestry Research Organization).

1997. Symposium on sustainable Management of Small

Scale Forestry. Murashima, Japan. September 8-13,

1997.

Keeney, R.L. 1992. Value-Focused Thinking: A Path to 

Creative Decision Making. Harvard University Press,

Cambridge, MA. USA.

Mendoza, G.A., Prabhu, R. 2000a. Multiple criteria decision making approaches to assessing Forest sustainability

using criteria and indicators: A Case Study. Forest

Ecology and Management. (131): 107-126.

Mendoza, G.A. and Prabhu, R. 2000b. Multiple criteria 

analysis for assessing criteria and indicators in sustainable forest management: A case study on participatory

decision making in a Kalimantan forest. Environmental

Management. 26(6): 659-673.

Mendoza, G.A. 2002. Evaluation Methodologies for 

Assessing the Sustainability of Forest Management in

Ontario. Report submitted to Ontario Ministry of

Natural Resources. Ontario, Canada.

Mendoza, G.A. and Prabhu, R. 2003a. Qualitative multicriteria approaches to assessing indicators of sustainable

forest resource management. Forest Ecology and

Management. 174: 329-343.

Mendoza, G.A. and Prabhu, R. 2003b. Enhancing participatory planning of community-managed forests using

problem structuring models and approaches: Experiences

from a case study. Department of Natural Resources

and Environmental Sciences. Working paper 2003-2.

University of Illinois. Urbana, Illinois.

Mendoza, G.A. and Prabhu, R. 2004. Combining participatory modeling and multicriteria analysis for community-based forest management. Forest Ecology and

Management. (In Press).

Munasinghe, M. and W. Shearer. 1996. Defining and 

measuring sustainability: the biogeophysical foundations.

Env. Dev. Economy. 1(4): 489-493.

56
Misra P.K., 1997. Potentialities of JFM: some reflections. 

Wastelands News 12(4): 32-33

Purnomo, H.; G. Mendoza; R. Prabhu. 2003. Developing

a collaborative model for community managed resources:

A qualitative soft systems approach. Department of

Natural Resources and Environmental Sciences.

Working paper 2003-1. University of Illinois. Urbana,

Illinois.

Purnomo, H.; Mendoza, G.A.; and R. Prabhu. 2004. A

Model for Collaborative Planning for CommunityManaged Resources based on Qualitative Soft Systems

Approach. Journal of Tropical Science. 16(1): 106-131.

Richards, M.; Davies, J.; and Cavendish, W. 1999. Can 

PRA methods be used to collect economic data? A nontimber forest product case study from Zimbabwe. PLA

Notes 36: 34-40.

Richardson, G.P. and D.F. Anderson. 1995. Teamwork in 

group model building. System Dynamics Review. 11(2).

Rosenhead, J. (ed.) 1989. Rational Analysis for a 

Problematic World: Structuring Methods for Complexity,

Uncertainty and Conflict. John Wiley & Sons Ltd.

England.

Sarin, M. 1995. Joint Forest Management in India: 

achievements. Unasylva 46(1): 30-36.

Saxena, K.G., Rao, K.S., Sen, K.K., Maikhuri R.K. and 

Semwal, R.L. 2002. Integrated Natural Resource

Management: Approaches and Lessons from the

Himalaya. Conservation Ecology. 5(2): 203-210.

Selener, D. 1997. Participatory Action Research and Social 

Change. The Cornell Participatory Action Research

Network. Cornell University. New York.

Varma, V.K., I. Ferguson, and I. Wild. 2000. Decision 

support system for sustainable forest management.

Forest Ecology and Management. 128: 49-55.

Vennix. 1996. Group Model Building: Facilitating Team 

Learning Using System Dynamics. Chichester.,

England. John Wiley and Sons.

Wolstenholme, E.F. 1999. Qualitative vs. quantitative 

modeling: the evolving balance. Journal of Operational

Research Society. 50: 422-428.

57
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TECHNIQUES AND 

DECISION SUPPORT

FOR FOREST

PLANNING
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Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

HATT: THE HEURISTIC ALGORITHM TEACHING 

TOOL FOR ADVANCED FOREST PLANNING COURSES

Pete Bettinger1

INTRODUCTION

Forest planning is evolving, and while the traditional

mathematical programming solution techniques (linear programming, integer programming, etc.) are still being used

to assist in the decision-making processes associated with

the scheduling of forest management activities, other nontraditional heuristic techniques are also being evaluated for

their usefulness. The reason for the exploration into nontraditional techniques is mainly due to the combinatorial

nature of today's forest management problems. Spatial constraints, integer decision variables, and complex physical 

or biological effects models are increasingly seen as critical

aspects of forest plans. The traditional mathematical programming solution techniques can locate optimal solutions

to a number of complex forest planning problems, but may

find solving increasingly complex problems difficult, if not

impossible. Therefore, heuristic programming techniques

are viewed as, perhaps, viable alternatives that allow the

development of forest plans that recognize spatial constraints and complex physical or biological relationships

between activities and the environment.

ABSTRACT

The Heuristic Algorithm Teaching Tool (HATT) is a Windows®-based computer program developed to support an

advanced forest planning course at the University of Georgia. The procedures available within HATT include basic implementations of Monte Carlo simulation, simulated annealing, threshold accepting, tabu search, and genetic algorithms. The

Model I forest planning problem that can be solved with HATT is relatively simple: achieve the highest even-flow of timber

harvest volume over a three-period planning horizon, using a unit restriction model for 1-period green-up concerns. The

number of management units that can be modeled is flexible, as are the yields used to describe forest conditions. The problem

size is only limited by a computer’s RAM. HATT was developed in Visual Basic, perhaps the easiest programming language

to learn. Students are encouraged to modify the program to enhance the heuristic search processes (e.g., add intensification

techniques), to model larger problems (e.g., those with more time periods), or different problems (e.g., maximize net present

value, maximize habitat, area restriction adjacency).

61

Most advanced forest management courses in U.S.

forestry schools emphasize an expansion on the usage of

traditional mathematical programming solution techniques.

Larger and more complex linear and integer models are

evaluated, and goal programming formulations are introduced. Few schools offer an examination of heuristic techniques. Those that do include the University of Georgia

(Advanced Forest Planning), Oregon State University

(Combinatorial Optimization), and tangentially, Ohio State

University (Special Topics in Quantitative Geography). One

of the major drawbacks of doing so is the limited availability of heuristic algorithm software. Given this, students

need a background in computer programming in order to

develop their own heuristics, since heuristic techniques are

generally based on computer program logic, and not explicit

formulations of equations representing an objective function

or constraints. In fact, most of the knowledge gained from

the use of heuristic techniques is through research projects

and applications to real world forest planning efforts (e.g.,

Sessions and others 1998, Bettinger and others 2003). This

paper introduces the Heuristic Algorithm Teaching Tool

(HATT, Bettinger 2003), a Visual Basic program that could

In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 P. Bettinger is an Associate Professor, Warnell School of Forest Resources, University of Georgia, Athens, GA 30602.
facilitate the integration of heuristic techniques into advanced

forest planning courses. HATT includes a suite of heuristics

in their basic implementation, and thus provides a framework of computer program logic that can be expanded upon

to solve more difficult planning problems or to generate

more efficient solutions.

SYSTEM REQUIREMENTS

The HATT model was developed within Microsoft Visual

Basic 6.0. In order to run the HATT executable program, a

computer with a Windows® 95/98/ME/NT/2000/XP operating system is required. Computers equipped with other

operating systems may also be able to run HATT, however,

this has not been tested. If Visual Basic is available on the

computer being used, students and teachers can modify the

HATT program. If Visual Basic is not available, yet users

desire to run the executable program, the installation package must be used to set up the program, since it will require

a few dynamic link library (.dll) files that are usually not

available on computers where Visual Basic has not been

installed. 

HATT requires two input data files, one describing the

condition of the landscape being modeled, and the other

describing the adjacency relationships among landscape

management units. The file that describes the landscape

condition is a comma-delimited ASCII file with the following format for each line of data:

management unit, area, potential volume1, potential 

volume2, potential volume3

Management unit is an integer, the remaining values are

real numbers. The potential volumes represent the timber

volume per unit area that is available for a clearcut management activity if the management unit is not clearcut in

any of the other time periods. The length of a time period

is an assumption made by the user. The yields should reflect

the appropriate volumes for each stand grown over the

length of the time periods.

The file that describes the adjacency information is also

a comma-delimited ASCII file, with the following format:

management unit, adjacent management unit

Each of these values are integers. Adjacency can be

defined as the user wishes, yet must be reflected in the data

contained in the adjacency data file. The three basic types

of adjacency relationships in forestry include management

units that share an edge, management units that share both

an edge and a point, and management units whose edges

are within some proximity of one another.

The size of problem that can be modeled with HATT

is only limited by the amount of RAM available on a computer, as all data are stored in arrays at run time.

PROGRAM STRUCTURE 

AND ALGORITHMS

HATT attempts to solve a forest management problem

where one desires to schedule the highest even-flow of timber volume over three time periods, yet no harvest areas

can be adjacent to each other during the same time period.

OBJECTIVE FUNCTION

The objective function consists of maximizing an evenflow harvest volume (i.e., scheduling harvest volume as

close as possible to a target harvest volume) over three time

periods. HATT attempts to do so by minimizing deviations

from a target harvest volume. Users must first decide what

the target harvest volume will be for the area. Each of the

HATT search algorithms then schedules harvests such that

the accumulated timber volume in each time period is as

close to the target as possible. Deviations from the target

are squared to prevent ambiguous solutions. For example,

one solution with a large deviation of harvest volume in

one time period (while keeping the other two periods close

to the target volume) may be seen as good as another solution with equal deviations in all three time periods (where

the sum of the deviations is the same as the solution with

one large deviation) if none of the deviations are raised to 

a power. The objective function, therefore, is to minimize

the squared deviations in periodic harvest volumes from a

target harvest volume:

Minimize

T n

∑ TVt – ∑ (ai vi,t xi,t)

2

[1] t=1

Where:

t = a time period

T = the total number of time periods

TVt = the target harvest volume during time period t

ai = the area of management unit i

vi,t = the volume per unit area of management unit i during

time period t

xi,t = a binary variable indicating whether (1) or not (0)

management unit i was clearcut during time period t

62



 


Constraints

Two constraints are explicitly recognized in the HATT

program. First, all decision variables are integers, and management units can only be harvest once during the three

time periods. 

T

∑ xi,t ≤ 1 ∀i [2] t=1

Second, adjacent timber harvests are prohibited from

being scheduled for harvest during the same time period.

This constraint utilizes the unit restriction model described

by Murray (1999) to prevent adjacent harvests within the

same time period.

xi,t + xj,t ≤ 1 ∀i,t,j ∈ Ni [3]

Where:

Ni = set of all management units adjacent to management

unit i

Heuristic solution algorithms

Five heuristic algorithms are available within HATT to

assist in developing forest plans. They are Monte Carlo

simulation, simulated annealing, threshold accepting, tabu

search, and genetic algorithms. Each of these is represented

in HATT using a very basic interpretation of their processes. HATT provides the structure for students to build upon

the basic interpretation, allowing incorporation of intensification or diversification processes that could lead to better

solutions, and other modifications that could allow one to

solve more complex problems. Of course, students will

need to understand how to develop programming logic in

Visual Basic to accomplish this.

Monte Carlo simulation—Within the Monte Carlo

(MC) simulation algorithm, a randomly generated solution is produced (fig. 1), and its objective function value

(solution value) calculated. If the solution value is better

than the best solution value, the solution becomes the best

solution and is reported at the end of a number of iterations

63

Figure 1—The basic Monte Carlo simulation process within HATT. Figure 2—A more detailed examination of the “develop a solution”

process within the Monte Carlo simulation process of HATT.
defined by the user. When a solution is being developed

(fig. 2), a set of unscheduled management units is defined.

This set represents all management units that have not yet

been scheduled, and does not include any management units

that are precluded from harvesting due to the incremental

harvests scheduled around it and the resulting adjacency

problems. For example, if a management unit has three

neighbors, and they are scheduled in periods 1, 2, and 3, the

management unit is precluded from harvest because doing

so would violate the adjacency constraint. Management

units are scheduled randomly, and once the unscheduled set

is empty, the process has completed an iteration (i.e., a forest plan has been developed). All that is required of the user

is to define a target harvest volume and the number of iterations to develop with this heuristic. See Clements and others

(1990), Nelson and Brodie (1990), Boston and Bettinger

(1999), and Bettinger and others (2002) for a more detailed

description of Monte Carlo simulation applications in

forestry.

Simulated annealing—Simulated annealing (SA) began

to be used in a widespread manner in the early 1980s

(Dowsland 1993), yet the ideas that form the basis for SA

were first published by Metropolis and others (1953) in an

algorithm to simulate the cooling of materials in a heat bath

– a process known as annealing. The SA search process

begins with users defining the initial annealing temperature,

the number of iterations to model at each temperature, and

the cooling rate of the temperature. As each activity is

scheduled, the process determines whether to change the

temperature (fig. 3). When the temperature gets below 10,

the process stops and the best solution is reported. During

the scheduling of activities (fig. 4), a management unit and

time period are selected at random, the adjacency constraint

64

Figure 3—The basic simulated annealing simulation process

within HATT.

Figure 4—A more detailed examination of the “schedule activities” process within

the simulated annealing process of HATT.
is assessed, and if needed, the simulated annealing criteria

assessed. As in other SA applications, if a randomly drawn

number is smaller than 

EXP(–(proposed solution value – best solution value) 

/ temperature) [4]

the proposed change to the solution is accepted. If the solution had resulted in the best solution found, the changes

would have been automatically accepted. If the change to

the solution is not acceptable, the process reverts to the

previous solution. The initial temperature must be high

(10,000,000 or so) for the problem at hand. Proposed solution values minus best solution values will be on the order

of 10,000,000 or more during the first several iterations,

given the objective function and inefficient solutions that

are developed initially, thus the test statistic will yield a

reasonable value (other than 0) only if the initial temperature is very high. See Bettinger and others (2002), Boston

and Bettinger (1999), Lockwood and Moore (1993), and

Öhman and Eriksson (2002) for a more detailed description

of simulated annealing applications in forestry.

Threshold accepting—Threshold accepting (TA) was

introduced by Dueck and Scheuer (1990), and requires

users to specify the target harvest volume, initial threshold

level, the number of iterations per threshold that will be

modeled, the change in the threshold, and the number of

unsuccessful iterations that may be attempted per threshold. As each activity is scheduled, the process determines

whether to change the threshold (fig. 5). When the threshold is equal to or less than 0, the process stops and the best

solution is reported. During the scheduling of activities

(fig. 6), a management unit and time period are selected at

65

Figure 5—The basic threshold accepting process within

HATT.

Figure 6—A more detailed examination of the “schedule activities” process within the

threshold accepting process of HATT.
random, the adjacency constraint is assessed, and if needed,

the threshold accepting criteria assessed. Here, the number

of unsuccessful choices (either due to adjacency violations

or failing the TA test) are tracked. If the number exceeds a

number specified by the user, the threshold is reduced and

the process continues. This aspect of TA prevents the search

process from wasting time with unacceptable changes to

solutions. See Bettinger and others (2002, 2003) for a more

detailed description of threshold accepting applications in

forestry.

Tabu search—The tabu search (TS) was introduced 

by Glover (1989, 1990) and within HATT is represented 

as a process requires that users define the target even-flow

volume level, a tabu state (the number of iterations each

management unit / time period choice will be tabu after

incorporation into a solution), and the total number of iterations to run the model. After the development of an initial

random solution (fig. 7), the process develops a neighborhood of choices that contain only those management unit /

time period changes that do not violate the adjacency constraint (fig. 8). The neighborhood consists of the potential

objective function values, holding everything else constant,

of changing the harvest timing of a single management

unit. In addition, the aspiration criteria is examined in the

development of a neighborhood. Here, if a choice is tabu,

yet may lead to a solution better than any other previous

solution, it can be chosen. Once a choice has been made,

the tabu states of all management unit / time period choices

are updated, and the process continues. See Bettinger and

others (1997, 1998, 2002), Boston and Bettinger (1999),

Brumelle and others (1998), Caro and others (2003), and

Richards and Gunn (2000) for a more detailed description

of tabu search applications in forestry.

66

Figure 7—The basic tabu search process within HATT.

Figure 8—A more detailed examination of the “schedule activities” process

within the tabu search process of HATT.
Genetic algorithm—The genetic algorithm (GA) search

process was initially described by Holland (1975), and

within HATT begins by developing a population of feasible

solutions (the actual number defined by the user) using a

Monte Carlo process. The objective function value (fitness)

of each solution is determined, then the search process

“evolves“ (fig. 9). In this search process, the very best

solution (parent) is selected from the population, and mated

with a randomly chosen partner (fig. 10). Each solution is

“split“ at a randomly chosen point, and two children are

created from the recombined portions of each parent. For

example, assume parent A had harvest timing values (time

periods of harvest) for 5 management units, listed as periods 1,2,0,2,3, and parent B had harvest timings lists as 2,0,

1,3,1. If the parents are split after the second management

unit value in these vectors, then recombined, child AB

would represent the harvest timings for the 5 management

units as 1,2,1,3,1, and child BA would represent them as

2,0,0,2,3. These new solutions may be mutated randomly

depending on the rate of mutation set by the user. Adjacency

constraint violations would also be examined. If either child

results in an infeasible solution, it is rejected. The child with

the highest fitness value, as measured by the objective

function value, is retained. It then replaces a randomly chosen parent in the population, and the search continues for a

number of iterations set by the user. The user, therefore must

define the target even-flow harvest volume, the population

size, the total iterations the model must run, and the mutation rate. See Boston and Bettinger (2002), Falcão and

Borges (2001), Lu and Eriksson (2000), and Mullen and

67

Figure 9—The basic genetic algorithm process within HATT. Figure 10—A more detailed examination of the “evolve” process within the genetic

algorithm process of HATT.
Butler (2000) for a more detailed description of genetic

algorithm applications in forestry.

PROGRAM APPLICATION IN ADVANCED

FOREST MANAGEMENT COURSES

The HATT program is available over the Internet at

www.forestry.uga.edu / Warnell / Bettinger / planning /

index.htm. Along with an executable version of HATT, the

actual Visual Basic code is available, as are two example

data sets, and a GIS database associated with the example

data sets. There is no user manual associated with this software - the only documentation is this paper. The HATT

code contains numerous comments (fig. 11), with the aspiration that people other than the author can follow the processing of information. However, the program was not

supported with grant or contract funds, so documentation may

seem incomplete to more savvy computer programmers.

Using the “west“ example data set, the five heuristics

were used to attempt to achieve an even-flow volume of

31,000 units per time period (a volume near the relaxed [no

adjacency constraint] linear programming solution to optimal even-flow). The time periods implied in this data set

are 10 years long, and the volumes are in thousand board

feet (MBF) per acre. The best results from 20 runs of each

of the five heuristic algorithms are noted in table 1.

DISCUSSION

A forest planning problem with an even-flow objective

and an adjacency constraint is not an easy problem to

solve, and some of the heuristic techniques, in their basic

implementation, do not seem to perform very well. However,

the intent of the HATT model was to demonstrate the basic

techniques, then encourage students to either (1) modify

the algorithms (with intensification or diversification

processes) to solve the problem faster and more efficiently,

or (2) explore variations in each algorithm's parameters to

locate those that seem to be the best for each problem, thus

examining the sensitivity of each algorithm to changes in

parameters. In many cases, the selection of appropriate

search parameters will require numerous trial runs.

As a test of the notion that some of the algorithms, in

their basic implementation, do not seem to perform well in

solving the even-flow, adjacency planning problem, the

adjacency file was altered to indicate that none of the management units had adjacent neighbors (e.g., the adjacency

data for unit was 1,0). This represents a relaxed version of

68

Figure 11—An example of the programming code documentation available within HATT.
the problem described above, yet continues to use integer

decision variables. The GA algorithm, for example, was

run for 10,000 iterations with a population size of 500, and

a mutation rate of 0.01. One solution we found had an

objective function value of 11,459.7 (volumes of 31,008.04,

31,045.15, and 30,903.27). The difficulty that the GA algorithm has with this problem is that the vector of harvest

timings for management units is arranged 1-n, yet the adjacency relationships are not as orderly. Management unit 1,

in fact, is adjacent to management unit 48. Thus this algorithm encounters numerous adjacency violations in the

children as parent solution vectors of harvest timings are

split and recombined.

Since the underlying Visual Basic code is available for

students to examine, the process of understanding how the

heuristic algorithms work is facilitated. This is a less desirable substitute for actually developing an algorithm from

scratch, but will hopefully engage those who are hesitant to

learn and use computer programming techniques. In addition, it may encourage students to modify the algorithms to

solve more complex planning problems, such as those with

area restriction adjacency constraints or complementary block

wildlife habitat goals (as in Bettinger and others 2002).

LITERATURE CITED

Bettinger, P. 2003. Heuristic Algorithm Teaching Tool 

(HATT). Warnell School of Forest Resources,

University of Georgia, Athens, GA.

Bettinger, P.; Graetz, D.; Boston, K.; Sessions, J.; 

Chung, W. 2002. Eight heuristic planning techniques

applied to three increasingly difficult wildlife planning

problems. Silva Fennica. 36: 561-584.

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69

Table 1—Values of the best single solution from 20 runs of each of the five heuristic 

algorithms.

Algorithm

Value MCa TSb TAc SAd GAe

Period 1 harvest 31,180.48 31,002.44 30,995.33 30,686.27 31,045.56

Period 2 harvest 31,532.88 31,002.31 30,991.07 30,734.55 30,596.48

Period 3 harvest 30,457.25 31,002.39 31,015.06 30,969.47 30,655.65

Obj. func. value 611,112 17 328 169,823 283,482

a Monte Carlo simulation: Iterations = 10,000.

b Tabu search: Tabu state = 150; Iterations = 10,000.

c Threshold accepting: Initial threshold = 10,000,000; Iterations per threshold = 1; threshold change = 1,000; 

Unsuccessful iterations per threshold = 1,000.

d Simulated annealing: Initial temperature = 11,000,000; iterations per temperature = 20; cooling rate = 0.98.

e Genetic algorithm: Population size = 1000; Iterations = 10,000; mutation rate = 0.01.
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70
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 L.A. Leefers and L.J. Roberts are Associate Professor of Forest Economics and Policy and Graduate Fellow, Department of Forestry, Michigan State 

University, East Lansing, MI 48824-1222. 2 E.J. Gustafson is Project Leader and Research Ecologist, North Central Research Station, Rhinelander,

WI 54501.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

SPATIAL SENSITIVITY ANALYSIS: AN APPLICATION 

OF HARVEST TO A SPECTRUM ALTERNATIVE

Larry A. Leefers1, L. Jay Roberts1, and Eric J. Gustafson2

INTRODUCTION

Spectrum and its FORPLAN predecessor are widely

used tools for developing harvest schedules (Hoekstra and

others 1987; Greer 1997). HARVEST has been used to simulate a number of spatial effects of different management

strategies over time. For example, Gustafson and Rasmussen

(2002) conducted simulation experiments on an 84,111-acre

(34,053-ha) portion of the Hoosier National Forest in southern Indiana using a baseline harvest target of 2,613 acres

(1,058 ha) per decade for eight decades. The simulations

focused on the interactions of adjacency constraints, spatial

dispersion, and size of harvest units. Harvest unit size was

the most important factor in reducing harvests below the

cutting targets. Smaller 4.45-acre (1.8 ha) harvest sizes significantly reduced the cutting levels achieved in comparison

71

to 44.5-acre (18 ha) cuts. Adjacency constraints had a significant, but smaller, effect on accomplishing harvest targets, and the harvest dispersion method (described below)

had only a negligible effect.

Planners on the Chequamegon-Nicolet National Forest

in northern Wisconsin created seven alternatives in their

recent forest plan revision process. Each alternative was

analyzed using Spectrum. As part of their species viability

analysis, HARVEST was used to calculate various spatial

statistics and to display maps of transformed landscapes

(Gustafson and Rasmussen 2002, Leefers and others 2003).

The purpose of this paper is to (1) describe HARVEST

spatial allocation options, stand cutting approaches, and

spatial analyses, (2) explain linkages between Spectrum

ABSTRACT

The Chequamegon-Nicolet National Forest (CNNF) in northern Wisconsin analyzed several forest plan alternatives

using Spectrum, a linear programming model developed by the USDA Forest Service. Spatial analyses of the Spectrum

results were assessed using HARVEST (Gustafson 1997; Gustafson and Rasmussen 2002), given the spatial restrictions 

of plan alternatives. Metrics related to interior forest habitat, edge habitat, and patch size were generated. For these alternatives, CNNF standards and guidelines were used in defining forest openings, harvest sizes, harvest dispersion, green up

intervals, and buffers. Sensitivity analyses are sometimes used in a linear-programming context to explore the implications

of changing constraints (acres harvested, areas protected) or model inputs (prices, costs, productivity, etc.), but often comparisons between alternatives dominate analyses. We examine one CNNF (Spectrum) alternative, but change a variety of

spatial assumptions in HARVEST to see their effects on selected spatial metrics, and their consistency with the Spectrum

harvest solution. Some assumptions yielding different spatial configurations are equally consistent with Spectrum results;

usually 92-95 percent of Spectrum results can be simulated with HARVEST. In other cases, assumptions lead to significant

deviations from the Spectrum solution and may require additional, more constrained Spectrum analyses that will yield more

satisfactory spatial results. 
and HARVEST, and (3) illustrate effects of changing

assumptions regarding edge creation and spatial allocation

options. One Spectrum-based forest plan alternative for the

Nicolet National Forest in northern Wisconsin is used for

all HARVEST simulations. The Nicolet National Forest has

approximately 600,000 acres (243,000 ha) of national forest

land within its larger proclamation boundary that includes

private lands as well.

HARVEST SPATIAL ALLOCATION

OPTIONS, STAND CUTTING APPROACHES,

AND SPATIAL ANALYSES

HARVEST is a spatial simulation model that requires

four input maps: stand age, forest type, management area,

and stand boundaries. These are raster-based maps that,

when combined, stratify an area for many common forest

management purposes (for example, harvesting). In our

case, the pixel or cell is 98.4 feet X 98.4 feet (30 m X 30

m) or .222 acres (900 m2). Details regarding HARVEST

parameters are presented in Gustafson and Rasmussen

(2002) and are available in the User’s Guide for Version

6.0 of HARVEST at the USDA Forest Service North Central

Research Station website (http://www.ncrs.fs.fed.us/4153/).

Spatial Allocation Options

HARVEST has three spatial allocation options: dispersed, clustered, and “oldest first.” Each of these is subject to management area, forest type, minimum age and

optional adjacency restrictions. For example, within a management area for a given forest type, stands that are old

enough to harvest cannot be cut because they are adjacent

to recent harvests. Different forest types typically have different minimum age restrictions based on their life history

and typical management practices for the species. Forest

stands, one of the HARVEST map inputs, are the focus in

applying harvests. HARVEST uses the chosen option to

select stands for cutting until the target acreage has been

harvested, or until no stands remain that satisfy restrictions.

The dispersed method selects stands independently of

each other (that is, randomly) to harvest for the designated

forest type(s) that are old enough to harvest within the

management area(s). If the harvest target acreage is not

met, then the next acceptable stand is selected randomly.

The clustered method chooses a focal stand for harvest.

Additional harvests are attempted in stands that are neighbors of its neighbors, potentially creating a halo of harvested

stands around the focal stand. Of course, nearby stands must

satisfy the same HARVEST restrictions as the focal stand.

If nearby stands are harvested and the target acreage has

not been exceeded, a new focal stand is randomly selected

and the process is repeated. “Oldest first” selects from the

stands within the management area of the designated forest

type that are old enough to harvest in order of decreasing

age.

Stand Cutting Approaches

Once a stand is selected for harvest, three cutting

approaches are available. The first approach, “stand filling,”

cuts all of the cells in the stand. The second approach is

controlled by mean, standard deviation of harvest size, and

maximum/minimum harvest size—these parameters are

used to randomly generate a harvest block size from a truncated normal distribution. Harvest blocks are generated

around a single pixel chosen randomly within the stand to

be harvested. Adjacent pixels are sequentially added to the

harvest block by randomly choosing from all adjacent pixels

within the stand. If the entire stand is cut before the target

block size has been reached, HARVEST will begin cutting

in an adjacent stand that also satisfies all restrictions. If no

such stand exists, the harvest process is truncated. The final

approach, group selection, chooses stands using the dispersed method, and then harvests small openings (groups of

pixels or patches) within those stands. The number of openings harvested in a stand during each entry is calculated by

HARVEST as a user-specified proportion of the size of the

stand. These small harvests are randomly placed within the

stand to mimic group selection.

Stands are selected for harvesting if the average stand

age is greater than or equal to the minimum harvest age for

the forest type. Initially all pixels within a stand are the same

age. However, because partial stands can be cut, pixels

within a stand can have different ages and average stand

age is calculated for the stand weighted by the area in each

age. Thus, HARVEST may encounter stands that are old

enough to cut, but pixels that are too young. However,

HARVEST cannot cut pixels within a stand that are younger

than the minimum harvest age—instead, older pixels within the stand are located and harvested.

HARVEST can simulate traditional rotation-based cutting, where stands are cut periodically at some specified

interval that mimics a linear programming Model I formulation (Johnson and Scheurman 1977). When the user

selects the re-entry feature associated with a dispersion

method, HARVEST ensures that re-entries occur automatically for the same stands, using the parameters set during

the initial entry. If re-entry is not specified, HARVEST has

more flexibility in choosing where future harvests occur—

this is similar to the Model II pooling concept in linear

programming (Johnson and Scheurman 1977). 

72
Spatial Analyses

HARVEST calculates a number of standard spatial statistics. Alternatively, map files can be imported into different GIS environments and other spatial statistics software

can be used for analysis. Standard analyses incorporated in

HARVEST include calculation of forest interior and edge

habitat, and patch analysis.

Interior and edge habitat are calculated for the forest

area based on openings, permanent or temporary due to

green up restrictions following harvests, and buffer size

around the openings. Area totals are calculated by summing the number of pixels in openings, buffers and interior

forests. The buffer size, a multiple of the pixel width, simulates the effects of proximity to the opening (or edge) on

plants and animals. Edge effects are the result of microclimatic and ecological differences between two adjacent

habitats. These coarse habitat characteristics are calculated

for the entire planning area.

Patches can be calculated for the entire planning area 

or for individual management areas. They can be defined

simply by age classes or by a combination of age classes

and forest types. For example, patches may be defined for

regenerating, young, mid-seral, mature, and late-seral age

classes. For more fine-scale patch details, forest type can

be added, too.

LINKING SPECTRUM AND HARVEST

HARVEST provides a simple, yet powerful tool for spatial analyses of strategic forest plan alternatives. One of its

main drawbacks is that for large problems, manual input

for creating the input data used to run HARVEST would be

very tedious and prone to error. We have largely addressed

this by developing Spec2Harv, a program for converting

Spectrum output to HARVEST input (Gustafson and others

2003).

Spec2Harv uses a Spectrum output file that identifies

how much harvesting occurs over time by analysis unit.

Spectrum models must be designed to correspond with

inputs required by HARVEST. Hence, Spectrum analysis

units must be linked to management areas, forest types, and

forest type ages—these correspond to three HARVEST

map inputs. 

By using an algorithm that recognizes the pattern of

harvests and crosswalk tables that allow the two models to

communicate, most inputs are automatically transferred

from Spectrum to HARVEST. Several management area

and forest type inputs are then completed and a HARVEST

script is generated. The script can be edited with a word

processor to facilitate sensitivity analysis. For example,

“dispersed” and can be changed to “clustered” for the

entire script and a new HARVEST simulation can be run.

SPATIAL SENSITIVITY ANALYSIS 

FOR AN ALTERNATIVE ON THE

NICOLET NATIONAL FOREST

Sensitivity analysis is commonly used in linear programming work to analyze the consequences of changing

assumptions, constraints, costs, and prices. In fact, linear

programming computer software is designed to show the

effects of changing certain parameters on the value of the

objective function. Sensitivity analysis may be even more

important for spatial problems because juxtaposition, proximity and other factors may be crucial to the acceptability

of management plans. For example, a feasible aspatial

Spectrum solution may call for 1,000 ac (400 ha) to be harvested. However, guidelines may specify that the cuts should

be 40 ac (16 ha) in size and not adjacent to other harvests.

The latter may not be spatially feasible due to the locations

of the proposed cuts; HARVEST can be used to examine

the spatial feasibility.

Method

Rather than comparing spatial simulation results for a

variety of harvest schedules generated using Spectrum, we

analyzed effects of changing assumptions regarding specification of buffer width, what constitutes an opening, and

how the cuts are spatially allocated for one forest plan

alternative on the Nicolet National Forest. These simulations are intended to illustrate the utility of HARVEST in

analyzing different standards and guidelines. For the purposes of this paper, only forest interior and edge habitat

were considered over the 10-decade planning horizon.

The base case for comparisons uses the “oldest first”

spatial allocation because forest managers indicated this

most closely mirrors their management when harvests are

involved. Most harvests were based on the following parameters: a truncated normal distribution pattern with a mean

harvest size of 25 acres (10 ha), a standard deviation of 15

acres (5.9 ha), a maximum harvest size of 40 acres (16 ha),

and a minimum harvest size of one acre (0.4 ha). For a

small number of management areas, larger mean harvest

sizes were used. 

Adjacency restrictions were used in all simulations; that

is, stands adjacent to recently harvested areas could not be

cut until two decades passed for the green up period. Private

lands within the forest boundaries were included in calculations for forest interior and edge habitat. The private lands

73
were classified based on 1992 Landsat Thematic Mapper

(TM) data which has a 98.4 foot (30 m) square resolution.

Forested cells on these lands were assumed to have closed

canopies for the 10-decade simulation.

HARVEST was used to calculate forest interior and

edge habitat at the current time and in decades 5 and 10 for

the base case. Three different distances for defining edge

effects were used: 98.4, 196.8, and 295.2 feet (30, 60, and

90 m). By examining these analytical results, we derive an

understanding of the effect of distance on edge effects and

habitat when only permanent and temporary openings are

considered. However, other features such as roads and water

may also create edge effects, so additional simulations

included roads and water as features influencing edge. The

cumulative results of these forest features help us understand

how habitat effects differ based on how we define buffers.

Most sensitivity analyses conducted in this study were

performed by analyzing one Spectrum-HARVEST simulation—one set of multi-period age maps. In those simulations, HARVEST was used to conduct analyses based on

changing assumptions on buffer width and forest features.

In other cases, such as the no-harvest buffers around roads,

new Spectrum-HARVEST analyses were required. These

analyses were facilitated by using a word processor to edit

the HARVEST script file which has all the parameter and

data inputs for HARVEST (for example, we changed

“Clustered” to “Dispersed”).

HARVEST simulates Spectrum harvest schedules, but

does not perfectly match the schedule due to spatial restrictions. So it is important to understand the effect of different

spatial parameters in HARVEST on the achievement of

Spectrum harvest targets. For purposes of this paper, the sensitivity of spatially allocating Spectrum results is explored

by changing the harvest dispersion method and by creating

no-cut buffers around roads. The former will provide insights

regarding “oldest first,” dispersed, and clustered harvesting,

whereas the latter will illustrate the effects of systematically

restricting harvests from more and more of the forest.

Results

Edge habitat was defined initially as area bordering temporary and permanent forest openings, and the edge width

was varied to examine its effects. Then edge habitat was

expanded to include areas around roads, and finally water

was also included. There is over a five-fold change in edge

habitat when the buffer is expanded to 295.2 feet (90 m)

and openings, roads, and water are assumed to create edge

when compared to a narrow buffer and openings only

(table 1). Moreover after existing openings, roads have a

much greater effect on edge than water. Roads were the

dominant feature affecting edge (table 2). Edge habitat

diminished over time due to the large current, or decade 0,

area influenced by recent harvests; relative to the current

condition, projected harvests will decline leading to less

edge in future decades (fig. 1). And, of course, forest interior

increased with the decreases in edge habitat by decade 10. 

As noted by Gustafson and Rasmussen (2002), the dispersion method also influences habitat. For this case study,

however, dispersion method did not have much influence

on area of edge habitat (table 3). “Oldest first” created the

least amount of edge, and dispersed and clustered approaches

were very similar in total, but distinct in detail. In fact, with

the narrow buffer of 295.2 feet (60 m), the dispersed

approach created only 1,000 acres (400 ha) more edge

habitat than the clustered approach. The clustered approach

was designed to concentrate harvests more and thereby

decrease overall edge, but the effect on edge does not appear

until the buffer width is increased to 590.4 feet (180 m).

Comparisons between dispersion methods are complicated due to the random placement of harvests. Moreover,

for the base case, or “oldest first,” approximately 95 percent of the 10-decade, 205,901-acre (83,361-ha) Spectrum

harvest target was accomplished by HARVEST. For the

dispersed and clustered approaches, 94 percent was achieved.

Overall, regardless of forest features, decreased edge habitat is due principally to lower Spectrum-projected harvest

levels in decade 10 relative to recent harvests which are

reflected in the current conditions (tables 1 and 2).

The preceding results treated creation of forest edge 

and interior habitat as effects determined by forest features,

buffer width, and dispersion method. However, we can also

restrict the location of harvesting by creating no-harvest

zones around certain features thereby reducing potential

harvest area. So another type of sensitivity analysis can

examine the effects of changing the size of the no-harvest

buffer on habitat. For these comparisons, the base case uses

the “oldest first” dispersion method and a buffer definition

for assessing habitat based on a 295.2 feet (90 m) buffer

around openings, roads, and water; the base case has a 

no-harvest buffer of 0 feet (0 m).

As the no-harvest buffer around roads expands from 0

to 98.4 to 196.8 to 393.6 feet (0 to 30 to 60 to 120 m), area

of edge habitat increases (table 4). In effect, harvests are

being pushed away from the extensive road network (which

is creating edge habitat) and into the forest interior thereby

increasing edge. Total forest area increases as well due to

74
75

Table 1—Effects of forest features and buffer width on area of 

edge habitat for current conditions on the Nicolet National 

Forest, in thousands of acres.

Forest features

Buffer width Openings Openings Openings, roads, 

(feet) only and roads and water

98.4 47 89 98

196.8 90 168 185

295.2 132 240 263

Table 2—Effects of forest features and buffer width on area of

edge habitat in decade 10 on the Nicolet National Forest, in

thousands of acres.

Forest features

Buffer width Openings Openings Openings, roads, 

(feet) only and roads and water

98.4 23 76 85

196.8 45 146 163

295.2 68 212 225a

a Base case.

Figure 1—Area of edge habitat over time on the Nicolet National Forest based on openings,

roads, and water features and buffer width.
lower harvest levels which allow current temporary openings to revert back to forest. As in the other sensitivity

analyses, these results are complicated by the relationship

between Spectrum and HARVEST. Due to the reduced area

available for harvesting, only 92 percent, 90 percent, and

85 percent of the Spectrum target could be achieved by

HARVEST for the 98.4, 196.8, and 393.6 feet (30, 60, and

120 m) no-harvest buffer, respectively. As a consequence,

the areas of edge habitat created with the 196.8 foot (60 m)

and 393.6 foot (90 m) no-harvest buffers were almost identical. This was due to the reduced harvesting in the latter

simulation. Hence, no-harvest buffers have a fairly dramatic

influence on the ability of HARVEST to satisfy Spectrum

targets in this situation because of the extent of the road

network. 

DISCUSSION

The application of HARVEST to Spectrum outputs was

pioneered by USDA Forest Service personnel at the North

Central Research Station and on the Chequamegon-Nicolet

National Forest (Leefers and others 2003). Spatial sensitivity

analyses in this paper highlight the importance of defining

edge in analyses. Edge and forest interior habitat effects are

greatly influenced by the definition. 

For any given buffer width and features included in

defining edge, the total area of forest interior habitat

increased over time. This is attributed in large part to initial

conditions which have more area of temporary openings

than succeeding periods. As the green up period returns

recent harvests to forest, new additions increase the area 

of forest interior more than it is diminished by future 

harvests.

Considering the effects of edge based solely on permanent and temporary openings provides the smallest amount

of edge habitat and largest amount of forest interior habitat.

Of course, these amounts vary over time and by buffer

width. Smaller buffer widths yield more forest interior

habitat and less edge because edge around an opening is

based on perimeter length and buffer width. If roads on the

Nicolet National Forest create edge, then their influences

are much more far reaching than those of openings due to

the extensive road system.

Though not presented in detail for this paper, specific

interactions of HARVEST and Spectrum are generated for

each Spectrum analysis unit, identifying where problems 

in allocating Spectrum harvests occurred. These results

may be used to redefine Spectrum constraints, if needed, to

better align model results. Managers may be satisfied with

allocating 95 percent of the Spectrum target spatially with

HARVEST, or they may require more consistency and new

Spectrum results. Diagnostic tables are available to facilitate this process.

Currently, HARVEST does not track changes in forest

type, but Spectrum does. As a result, both models are needed

to address conversion and succession. Modifications to

HARVEST may allow us to track forest type changes in

future versions.

76

Table 3—Effects of dispersion method and buffer width 

on area of edge habitat in decade 10 on the Nicolet 

National Forest, in thousands of acres.a

Dispersion method

Buffer Width

(feet) Oldest first Dispersed Clustered

295.2 225b 243 242

590.4 407 436 432

a Edge is based on openings, roads, and water features.

b Base case.

Table 4—Effects of no-harvest buffer width 

around roads on area of edge and forest interior

habitat in decade 10 on the Nicolet National 

Forest, in thousands of acres.a

Habitatb

No-harvest width

(feet) Edge Forest interior

0c 225 651

98.4 246 618

196.8 249 617

393.6 249 622

a Edge is based on oldest first dispersion method and openings, 

roads, and water features.

b Area of simulated openings varies slightly for each simulation.

c Base case.
It is clear that future harvest scheduling models will

increasingly be linked to spatial models. The SpectrumHARVEST approach provides one straightforward alternative. It does, however, require coordinated development of both models so that desired analyses can be completed

efficiently. 

LITERATURE CITED

Greer, Kendrick D. 1997. Spectrum: a decision support 

tool for ecosystem planning. Proceedings of the 1996

Society of American Foresters Convention, November

9-13, 1996, Albuquerque, NM. Bethesda, MD: Society

of American Foresters. pp. 283-288.

Gustafson, E.J. 1997. HARVEST: a timber harvest allocation model for simulating management alternatives.

Proceedings of the 1996 Society of American Foresters

Convention, November 9-13, 1996, Albuquerque, NM.

Bethesda, MD: Society of American Foresters. 

pp. 277-282.

Gustafson, Eric J.; Rasmussen, Luke V. 2002. Assessing 

the spatial implications of interactions among strategic

forest management options using a Windows-based 

harvest simulator. Computers and Electronics in

Agriculture. 33: 179-196.

Gustafson, Eric J.; Rasmussen, Luke V.; Leefers, L. A. 

2003. Spec2Harv, converting Spectrum output to HARVEST input, version 1.0a. Published on INTERNET

server, http://www.ncrs.fs.fed/4153/userguide.asp.

Hoekstra, Thomas W.; Dyer, A.A.; LeMaster, Dennis C. 

1987. FORPLAN: an evaluation of a forest planning

tool. Gen. Tech. Rep. RM-140. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky

Mountain Research Station. 164 p.

Johnson, K.N.; Scheurman, H.L. 1977. Techniques for 

prescribing optimal timber harvest and investment

under different objectives: Discussion and synthesis.

Forest Science Monograph 18. 31 p.

Leefers, Larry A.; Gustafson, Eric J.; Freeman, Phillip. 

2003. Linking temporal-optimization and spatial-simu

-

lation models for forest planning. In: Arthaud, G.J.;

Barrett, T.M.; eds. Systems Analysis in Forest Resources.

Netherlands: Klwer Publishing. pp. 165-173.

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In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 Janet Sullivan and Kurt Krueger are Information Technology Specialists, and 2 Greg Jones is a Research Forester with USDA Forest Service, PO Box 

8089 Missoula, MT. 3 Hans Zuuring is a Professor of Forestry and 4 Judy Troutwine is a GIS Programmer/Analyst at the School of Forestry, University of

Montana, Missoula, MT. 5 Bruce Meneghin is a GIS Planning Analyst, Inventory and Monitoring Institute, USDA Forest Service, Ft Collins, CO.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

MAGIS EXPRESS: SPATIAL MODELING 

FOR TIMBER AND ACCESS PLANNING

Janet Sullivan1, Greg Jones2, Judy Troutwine4,

Kurt Krueger1, Hans Zuuring3, Bruce Meneghin5

INTRODUCTION

Forest managers are increasingly in need of GIS-based

planning tools for developing projects that are both economically efficient and environmentally beneficial. Integrated

management, from the strategic level down to operational

planning, across multiple objectives and over the long-term,

is more cost effective than independent planning at various

stages (Aspinall and Pearson 2000, Bellamy and others

1999, Hahn and others 2001, Jakeman and Letcher 2003).

Projects including timber harvest in particular need to be

planned with strategic or tactical consideration of the transportation problem. Software is available to determine optimal rotation times and maximize economic benefit both at

the strategic level (Gustafson 1999) and at the tactical level

with commercial software packages available (Mowrer

1997), but which does not consider access costs. 

Conversely, operational-level planning software is

available for supply-chain or traffic flow problems, but

which assumes the user already knows which units are to

be harvested (Chung and Sessions 2002). If the problems

ABSTRACT

MAGIS eXpress is a modeling system for spatially-explicit analysis of timber harvest scheduling and access management. GIS (Geographic Information System) layers are imported and used as the basis for formulating harvest and access

models. Access issues that can be addressed include new road construction, existing road reconstruction, and road decommissioning. Vegetation growth is based on ‘vegetative pathway’ principles. Data are viewed, scenarios developed, and

results analyzed using state-of-the-art ArcGIS map input screens. A MAGIS eXpress solution includes the schedule of harvest activities and associated volumes, present net value, values predicted for individual treatment units, and the predicted

vegetation distribution, including standing volume. A sample problem is presented to illustrate MAGIS eXpress uses and

features. 

79

are considered together, a more complete picture of the

problem emerges: an in-depth analysis of scheduling alternatives that will improve efficiency and minimized adverse

environmental effects, leaving managers less vulnerable to

criticism about data and information used to develop projects. With increased pressure on public land managers to

provide economic and ecological justification for harvest

projects, the use of analytical tools has become critical for

efficient planning. Planning tools need to be flexible, fast,

easy-to-use, and address the relevant economic issues for

efficient planning.

We present here a software application: MAGIS eXpress,

which was developed to address this need. MAGIS eXpress

is an application for timber harvest scheduling which selects

harvest activities on user-defined treatment units, with access

and road ‘management’ considerations. MAGIS eXpress is

an explicit model of timber harvest and road access issues.

It addresses the need for incorporating access issues, including modeling of activities such as road maintenance or road

improvement to reduce pollution sources, and temporary

closure or permanent decommissioning of roads. 
MAGIS eXpress is an offshoot of the more robust ecological modeling tool, MAGIS, which, in addition to timber products, can incorporate non-timber outputs, including

wildlife habitat, sediment and water yield, fire risk indexes,

and other forest health issues. Economic benefit is the major

criteria for selecting harvest schedules and access, but other

resource values and environmental effects can be used as

constraints. 

Optimization is complimentary to other approaches that

include simulation modeling, and ‘blackboard’ applications

that make it easier to modify and collaborate on project

design, with or without simulation (Argent and Grayson

2003). A common theme is the need for spatially explicit

information, either within the model or in the solution;

MAGIS eXpress incorporates many GIS-related input

screens and solution displays.

Software Description and Features

MAGIS express is a PC-based, spatially explicit, timber

harvest and network access modeling system that allows

the user some flexibility in the ways that costs are accounted, and significant flexibility in defining treatment regimes

and rules for treatment options. The user creates all the

basic definitions, imports GIS data for a specific project

area, and runs scenarios that are customized for the specific

problem. Because it is spatially explicit, MAGIS eXpress

features many map-based interfaces for data entry, scenario

definition, and solution display that assist the user in setting up the planning problem in a meaningful manner.

The following seven custom GIS interfaces facilitate

validation and import of geospatial databases, user-assignment of planning area feature attribute values and model

specifications, assignment of user-selected scenario presets,

and viewing of scenario solution values: 

• Multiple, task-specific interactive maps. 

• Custom task management controls side-by-side with

the interactive maps.

• Custom, single- or multiple-feature, filtered selection

tools.

• Custom interactive tables and table editing tools for

single or multiple records.

• On-the-fly feedback of user decisions, both in map

and table displays and status bar displays of attribute

values.

• Customizable legends for user-defined categories for

nominal or numeric attribute values.

Interface designs are implemented via ESRI ArcGIS

ArcObjects as Microsoft Visual Basic standalone ActiveX

user controls or ArcMap VBA projects. The standalone

ActiveX controls are embedded in and managed by Microsoft

Visual FoxPro forms launched by the MAGIS VFP framework. 

Users can make decisions about treatment unit and 

road options using GIS-based queries and selection tools.

Solutions are fully displayed using both maps and tables.

Basic Operating needs

In its final configuration, MAGIS eXpress will have 

a dedicated solver incorporating both simulated annealing

and heuristic algorithms. Currently, it is functioning with a

commercial linear programming and mixed integer programming solver package which is launched from the

MAGIS eXpress application.

MAGIS eXpress runs in any PC-based Microsoft operating system. The GIS-based graphical interfaces rely on

ArcGIS capabilities and objects; ArcGIS must be installed

on the same computer. Any PC computer with the capability

to run ArcGIS can run MAGIS.

Model Parameters

A MAGIS eXpress model consists of four main components: the planning framework, project area, effects

functions, and scenarios. The planning framework is the

definitions of the parameters for the model, including

activity-costs, timber products, management regime definitions and rules for assignation to individual treatment units

as options, and the vegetation pathways. These pathways

consist of individual states linked either by succession or

by management activities; a stand exists in a given state

until it is changed by succession or management action into

a new state. Trajectories from state to state are determined

by habitat-type group, and length of time in a given state.

Selection of management actions can change the projected

state in particular ways. For example, a selective harvest

treatment could reduce both the density and dominant

species components of the state, setting the stand down 

a different pathway.

The Project Area data model consists of the specific

geographic area, represented as two GIS coverages: a polygon coverage and a road network coverage. Each coverage

needs to be attributed with specific information determined

by the definitions in the planning framework (for example,

the vegetation growth model has a set of pathways using

definitions of dominant species, size class and density: the

polygon coverage vegetation attributes have to match these

definitions.). Each treatment unit polygon has one or more

management options, in one or more time periods. Each

treatment unit with management options has ‘connections’

80
to the network (loading nodes). As units are selected for

harvest, traffic from the harvest is loaded onto the network.

If the loading point is on a ‘proposed’ road, or a road that

requires reconstruction before it can carry traffic, the road

options for those construction or reconstruction options are

selected as well. The model selects the least cost route to

the ‘exit’ or final demand node, and keeps track of the total

amount of traffic (of each type) by road segment and for

each period. Each coverage has specific criteria and attributes it needs to have before being used in a MAGIS model.

The effects functions are the items of interest, defined

by the user, that are to be calculated as part of the solution.

The types include: 1) harvest quantities, either as a total, or

by product, 2) net costs, either total or split out by type, 3)

net revenues, also split or lumped as the user sees fit, and

4) area control, which is the acreage of land in user-defined

conditions. These include acres of activity (groups), acres

by vegetative state characteristic (acres of large or v-large

size class, for example), and acres by management schedule. Length control functions report miles of road by activity type (again, user-selected criteria) for example, miles of

new construction, or miles of road decommissioning.

To define a scenario, the user selects one of two possible objective functions (maximize PNV or minimize total

cost), and sets constraints using any of the defined effects

functions. There is no explicit limit on the number of constraints that can be used; any of the cost, revenue, and area

control functions can be used. For example, a constraint

could be set for a specific number of acres in the entire

area to be in the saw size class. This could be used to control the amount of old growth or new growth, as the user

requires.

The scenario setup allows the user to create, solve, and

save any number of individual scenarios. Each scenario

consists of the objective function, constraints, and preselected decision variables. Constraints are limits (upper or

lower) placed on other defined effects functions (most effects

functions are available for this use). Preselected decision

variables are road network or treatment unit options that

the user either sets into or excludes from the solution. The

user may choose to set any number of constraints and decision variables.

Example Problem: Upper Belt Planning Area

This example problem will be used to schedule two

alternatives in addition to the No Action alternative using

MAGIS eXpress, based on the idea that a combination of

harvest and non-harvest treatments can be used to improve

forest health and reduce risk of catastrophic fire. 

Goals will be set based on forest vegetation stand-structure classes and economics. Analysis of the No Action simulation indicates the direction of the vegetation pattern

without management intervention. If desired, instead of

using the MAGIS eXpress vegetation simulation, one could

run a No Action simulation using SIMPPLLE (same GIS,

same pathways, but with disturbance processes factored in)

to determine a more realistic projection of vegetation patterns over time, and use the outcomes of the SIMPPLLE

projection to set the vegetation pattern constraints for the

MAGIS eXpress scenario.

The Upper Belt Planning area is an actual planning 

area on the Helena National Forest, in Montana. Timber is

mostly lodgepole pine or mixed lodgepole pine and Douglasfir. The vegetation description in the model includes a simple set of ‘pathways’ (total 109 records), with three species

groups; four size classes (Saw, Mix, Pole, and SeedSap);

and seven density classes (including the non-stocked category). The planning area is approximately 47000 acres of

mostly forested land, within Forest Service administrative

boundaries. There is an established road system with two

main exit points (north to one mill, south to a different mill).

For access, a harvest systems engineer designed an extensive system of proposed roads, to illustrate what could be

done if all areas were accessible by road. Activity-costs and

harvest specifications for three levels of harvest (a commercial thin, a more aggressive commercial ‘restoration’ thin,

and a regeneration harvest) and a flat rate for log prices

were entered in the model. Some areas are not considered

for harvest because they are too rocky, too steep, or both. 

No Action: This scenario is created by maximizing the

acres of No Action in period 5 and setting the road cost

constraint to zero. The results of the No Action scenario

suggest explicit parameters for developing alternatives.

81

Figure 1—Relative distribution of vegetation size classes for the No

Action scenario.
Results: In the No Action scenario, we see that, given

successional changes (but no disturbance processes are

modeled here) there is a predicted increase overall in large

size class, and a decrease in the early successional size

classes. If we assume that a mixture of large (saw), pole

and seedsap size classes is more desirable, and the ‘mix’

size class is less desirable from a fire risk standpoint, the

user would adopt this strategy. 

Scenario 1: The objective function is set to minimize

costs, and constraints are set on the relative mix of size

classes as follows: Large is to comprise 70% of the acres,

Seedsap and Pole comprise 15% of the acres each, and Mix

is set to 0% of the acres. These specific goals are to be

reached by the third decade of the planning horizon.

Results of Scenario 1: Vegetation constraints were met

within the time alloted, but with a total projected cost of

82

Figure 2—Relative distribution of vegetation size classes for Scenario 1.

Figure 3—Scenario 1 Schedule

of Treatments.
62.5 million dollars, with almost 30 miles of new road 

construction in each of the first two decades. This is not

acceptable because the cost is too high and there are too

many miles of road construction with negative environmental impacts, so there is a need to limit the miles of 

new road construction and still achieve (or come close to

achieving) the vegetation management goals.

Scenario 2: Minimize costs with the same vegetation

constraints, and allow no new road construction.

Results of Scenario 2: Vegetation constraints were not

able to be met with the road construction constraint (apparently many stands with the mix size class are inaccessible.)

However, some shift towards the ‘ideal’ goal is still possible.

Total projected cost is now 3.44 million dollars, with some

reconstruction costs. There has been a tradeoff for the ideal

vegetation pattern goal, for one with fewer economic (and

environmental) costs. One can analyze this new solution

and then explore additional modifications by entering new

constraints, or, using the vegetation management goals that

83

Figure 4—Scenario 2 Relative Distribution of Size Classes.

Figure 5—Scenario 2 Schedule

of Treatments.
are achievable, set up a new scenario that maximizes present net value (rather than minimizing costs) to achieve the

same vegetation patterns. There are now many possibilities

where predicted outcomes are calculated quickly and consistently, allowing the development of feasible alternatives

that are determined by different combinations of objectives

and constraints.

CONCLUSIONS

The example problem illustrates how a MAGIS eXpress

model can be used to efficiently schedule harvesting and

road access with vegetation management goals (not strictly

‘timber production’) and use constraints to modify scenarios to reach management objectives. MAGIS eXpress can

assist forest planners and timber sale designers in building

feasible, economically efficient alternatives that address

forest health, vegetation management, fuels treatment considerations and access problems, including road maintenance, removal, and new road construction.

LITERATURE CITED

Argent, R.M.; Grayson, R.B. 2003. A modeling shell 

for participatory assessment and management of natural

resources. Environmental Modelling & Software 18:

541–551

Aspinall, R.; Pearson, D. 2000. Integrated geographical 

assessment of environmental condition in water catchments: Linking landscape ecology, environmental modelling and GIS. Journal of Environmental Management

59: 299–319

Bellamy, J.A.; McDonald, G.T.; Syme, G.J.; 

Butterworth, J.E. 1999. Evaluating integrated resource

management. Society and Natural Resources 

12: 337–353.

Chung, W.; Sessions, J. 2002. NETWORK 2001 – 

Transportation planning under multiple objectives. In

Proceedings of The International Mountain Logging and

11th Pacific Northwest Skyline Symposium held Seattle,

WA, Dec. 10-12, 2001.

Gustafson, E.J.; Crow, T.R. 1999. HARVEST: linking 

timber harvesting strategies to landscape patterns. In

Spatial modeling of forest landscapes: approaches and

applications, Mladenoff, D.J. and W.L. Baker (eds.).

Cambridge University Press, Cambridge, England.

Hann, W.J.; Hamstrom, M.A.; Haynes, R.W.; Clifford, 

J.L.; Gravenmier, R.A. 2001. Costs and effectiveness

of multi-scale integrated management. Forest Ecology

and Management 153: 127-145.

Jakeman, A.J.; Letcher, R.A. 2003. Integrated assessment 

and modelling: features, principles and examples for

catchment management. Environmental Modelling &

Software 18: 491–501.

Mowrer, H.T. 1997. Decision support systems for ecosystem management: an evaluation of existing systems.

General technical report RM-GTR-296. Fort Collins,

Colo. USDA Forest Service, Rocky Mountain Forest

and Range Experiment Station. 154 p.

84
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 Alan Murray is Associate Professor in the Department of Geography at The Ohio State University. 2 Xiaolan Wu is a doctoral student in the Department 

of Geography at The Ohio State University.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

ASSESSING LANDSCAPE CONTIGUITY

IN RESERVE DESIGN

Alan T. Murray1, and Xiaolan Wu2

INTRODUCTION

There are many ways in which landscape characteristics

are sought to be summarized, particularly in this age where

spatial information is readily available through geographic

information system (GIS) technologies. Examples include

shape, compactness and contiguity in lands being acquired

for managed productivity, development or preservation.

Often what is of interest is a single measure reflecting a

two (or three) -dimensional pattern. Of course driving this

need for quantitative measures is the fact that spatial variation in patterns can be observed and visualized using GIS,

and as such relative comparison of alternative landscapes 

is critical in management planning.

In this paper we are interested in examining contiguity

and how it has been approached in the context of land use

planning. Contiguity is inherently a spatial property indicating whether specific parcels of land are mutually interconnected (Wright and others 1983). The notion of contiguity

has been an important consideration in nature reserve

design, and land use planning more generally (Nalle and

others 2002; Williams 2002).

ABSTRACT

Contiguity is a vital property of spatial structure in landscape design, particularly forest and habitat reserve planning.

While it is well recognized that contiguity represents the ability to travel from any point within a region to any other point

within that region without leaving the region, it is typical that associated landscapes in this context, natural or planned, are

not contiguous. As a result, contiguity measurement must account for relative degree of fragmentation in a landscape. This

paper proposes a measurement approach for assessing the relative degree of contiguity based on mathematical and spatial

theories. Empirical applications of this measure are provided to illustrate the advantages of the approach.

85

A number of measures of contiguity have been utilized

in the literature, though authors have been quick to recognize they are only proxy approaches. Much of the reason

for this is that contiguity has been narrowly defined as a

binary concept, where parcels of interest are either mutually interconnected or they are not (Wright and others 1983;

William 2002). Such a definition offers little help in characterizing a landscape that is fragmented to some degree.

This paper reviews approaches that have been used to

model contiguity in land use planning and nature reserve

selection. The spatial implications of existing approaches

are detailed. Given the limitations of existing approaches,

we develop a spatial measure for quantifying contiguity.

Empirical results are presented to illustrate the validity of

this measure. Finally, conclusions are provided.

APPROACHES FOR MODELING

CONTIGUITY

A range of approaches have been suggested in the literature for addressing contiguity concerns in spatial models.

Most can be characterized as implicit approaches designed
to encourage contiguous configurations of land for a

designed use. However, modeling approaches have been

explicitly structured to ensure contiguity in developed

plans.

One of the first attempts to characterize and model

spatial structure in the context of land use planning was the

work of Wright and others (1983). It was recognized that

“land acquisition has a continuous border or it has not”,

reflecting the binary nature of contiguity (Wright and others

1983). To model contiguity, the notion of internal, external

and open borders was introduced as a way to track formed

edges of acquired land. With this, it is possible to minimize

total perimeter of border and thereby promote a contiguous

collection of land. When edge/border length is minimized,

this most certainly will lead to a contiguous arrangement of

land. However, the minimum total edge length for a specified land area occurs when a circle is formed. As such, the

use of a border minimizing approach has a spatial bias

seeking a circle-like configuration of land. This is actually

a compactness approach.

Williams and ReVelle (1998) introduced mathematically

the concept of core and buffer in acquired lands in the context of nature reserve design. A core area represents land

that is an interior portion of the reserve, while a buffer area

is that land on the border (but still in the reserve). The idea

here is actually to represent border using buffer lands (as

they are by definition on the periphery), which is nothing

other than the concept of perimeter discussed previously.

Thus, the model developed in Williams and ReVelle (1998)

attempts to minimize total cost of acquired parcels such that

a specified number of core parcels is selected, but a parcel

cannot be counted as core unless it is surrounded by either

core or buffer parcels. Thus, boundary buffer parcels must

first be acquired before core parcels can result. When parcel costs are equivalent, such a minimization approach is

nothing other than the edge/border approach. As such, there

is a spatial bias to produce more circle-like arrangements

of land as this produces minimum cost.

Also focused on nature reserve design, Nalle and others

(2002) proposed a modeling approach that combined contiguity and compactness. What they proposed was an approach

that sought to minimize distance between acquired parcels

and maximize interior shared edges. The focus on maximizing interior shared edges represents their approach to

model contiguity. However, the minimum shared edge

length results in a circular shaped configuration of acquired

land parcels, which is a spatial bias reflecting compactness

rather than contiguity. While contiguity is promoted, it is

not explicitly being modeled.

Another line of work can be characterized as imposing

contiguity explicitly in the modeling framework. Cova and

Church (2000) constrain selected sites to be contiguous

using a rooted shortest path approach. The result is in fact

one contiguous collection of acquired land parcels. Williams

(2002) relaxes the stipulation of only one contiguous collection of land, but ensures that each resultant cluster be

constrained to be contiguous. This is achieved by representing the land parcels for potential acquisition as a planar

graph. With this, a primal/dual graph approach can be

structured to impose contiguity. As such, the approach of

Williams (2002) considers fragmented clusters (specified 

a priori) and imposes contiguity for each cluster.

In summary, existing approaches either use proxy measures to implicitly model contiguity or impose contiguity

explicitly. The implicit approaches detailed above do promote contiguity, but they have an unintended spatial bias

that produces compact land configurations. This may be

problematic in some land use planning situations, as compactness is not equivalent to contiguity. Alternatively, one

may want to optimize contiguity rather than strictly enforce

it completely as done in Cova and Church (2000) or Williams

(2002). The rationale for contiguity optimization is that

fragmentation is often an unavoidable outcome, but ensuring the greatest degree of contiguity in acquired lands is

desirable.

DEVELOPING A NEW MEASURE 

OF CONTIGUITY

In structuring a measurement approach for assessing

contiguity, there are a number of underlying properties that

such a measure should have. First, the measure should be

relatively consistent in order to allow comparative assessment of alternative land use patterns for a given region.

Second, the measure should be meaningful across various

landscapes. Finally, the measure should preserve conceived

notions of contiguity, varying in the range (0,1). As such,

complete contiguity would be indicated by a value of 1.

The proposed approach utilizes graph theory (see van

Langevelde and others 1998) and spatial interaction modeling (see Fotheringham and O’Kelly 1989). Figure 1 depicts

the basic representational transformations that we rely upon

in the quantification of contiguity. Figure 1a shows a raster

representation of a region, where the shaded cells represent

land parcels to be acquired. As such, we would like to be

able to assess the relative degree of contiguity in this spatial configuration. As a first step, figure 1b shows the associated contiguity graph for our land use pattern, where each

node represents a land parcel and an arc indicates a cluster

86
of nodes (defined when two parcels form a contiguous

cluster). If the graph is disconnected, complete contiguity

is not achieved. Using the contiguity graph, we can identify

a graph representing connected clusters using a minimum

spanning tree of the clusters based upon the underlying

grid structure (fig. 1a). This graph is shown in figure 1c.

Thus, what results to this point is the observed connectivity

(fig. 1b) and a measure of the relationship between independent clusters (fig. 1c). If we observe that complete contiguity of these parcels would result in the graph shown in

figure 1d, then an approach to assess relative contiguity

may be derived.

Consider the following notation:

i, j = indices of clusters (entire set denoted Ω);

ni = number of parcels in cluster i;

dij = minimum spanning tree distance from cluster i to 

cluster j;

r = distance decay parameter.

With the above notation, a contiguity index is proposed as

the following:

∑ni

(ni –1) + 1 ∑ ∑ ninj

i∈Ω i∈Ω j∈Ω

C = ∑ni

(

∑ni –1)

i∈Ω i∈Ω

2

The numerator reflects intra- and inter-cluster connectivity. The first component of the numerator accounts for

the observed number of intra-cluster arcs. The second 

component of the numerator accounts for potential spatial

interaction of pairs of clusters. Notice that the minimum

spanning tree distance between two clusters is raised to the

power r. This is a standard way of treating distance decay

in spatial interaction modeling (see Fotheringham and

O’Kelly, 1989). The denominator reflects the graph that

would result if the parcels where contiguous.

EMPIRICAL EVALUATION

In order to assess the relative merits of this measure of

contiguity, we now present a number of empirical examples

for comparison. The distance decay parameter utilized here

is r=2. Further, it is assumed that raster cell widths are 5 km.

Cell neighbors are those cells sharing a non-zero length

common boundary, which excludes diagonals.

Figure 2 depicts a range of spatial configurations of

acquired land use parcels. Indicated beneath each configuration is C, the relative contiguity. It is clear that different

configurations are more or less contiguous than others, and

the proposed measure appears to do an adequate job of

reflecting the spatial variation in contiguity.

87

Figure 1—Graphic representation of selected

land parcels.

2 2 i≠ j

(dij)

r
CONCLUSIONS

This paper has reviewed the concept of contiguity and

detailed how contiguity has been approached in land use

modeling. A measurement approach for assessing relative

contiguity was developed based upon graphic representation of land use parcels and concepts of spatial interaction.

Empirical results were presented to illustrate that the proposed approach provided measures that conform to visual

expectations of spatial variation. Further research is needed

to confirm the degree to which this measure is reliable. In

addition, further research is needed to assess capabilities

for integrating this measure in a land use planning model.

ACKNOWLEDGEMENTS

This research was supported by the National Science

Foundation (Geography and Regional Science Program and

the Decision, Risk, and Management Science Program)

under grant BCS-0114362.

LITERATURE CITED

Cova, T.; Church, R. 2000. “Contiguity constraints for 

single-region site search problems.” Geographical

Analysis 32: 306-329.

Fotheringham, A.S.; O’Kelly, M.E. 1989. Spatial 

Interaction Models: Formulations and Applications.

London. Kluwer Academic.

Nalle, D.; Arthur, J.; Sessions, J. 2002. “Designing compact and contiguous reserve networks with a hybrid

heuristic algorithm”. Forest Science 48: 59-68.

van Langevelde, F.; van der Knaap, W.; Claassen, G. 

1998. “Comparing connectivity in landscape networks.”

Environment and Planning B 25: 849-863.

Williams, J. 2002. “A zero-one programming model for 

contiguous land acquisition”. Geographical Analysis 

34: 330-349.

Williams, J.; ReVelle, C. 1998. “Reserve assemblage of 

critical areas: A zero-one programming approach.”

European Journal of Operational Research 104: 497-509.

Wright, J.; ReVelle, C.; Cohon, J. 1983. “A multiobjective integer programming model for the land acquisition

problem.” Regional Science and Urban Economics 13:

31-53.

88

Figure 2—

Relative 

contiguity of 

various land use

configurations.
In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 Metacortex, Rua Jau, no 44–B, 1300-314 Lisbon, Portugal. 2 Departamento de Engenharia Florestal, ISA, Tapada da Ajuda 1349-017 Lisbon, Portugal. 3 Link/INESC, Av Duque d’Ávila 23, 1000-138 Lisbon, Portugal. 4 Grupo Portucel Soporcel, Centro Fabril de Setúbal, Apartado 55, 2901-861 Setúbal,

Portugal.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

DESIGNING AN INTEGRATED FOREST

PLANNING SYSTEM FOR THE FOREST INDUSTRY: 

AN APPLICATION IN PORTUGAL

Rui Pedro Ribeiro1, José G. Borges2, Carla M. Pereira3, Pedro M. Sousa3 and João P. Lé4

INTRODUCTION

Research on vertically integrated forest industry management planning has traditionally focussed on the development of modeling techniques to address specific business

processes (e. g. Barros and Weintraub 1982, Gunn and Rai

1986, Gadow 1989, Burger and Jamnick 1991, Walters 1997,

Falcão and Borges 2002, Troncoso and Garrido 2004). For

modeling purposes, these business processes have generally been classified into three major groups: strategic or

long-term planning, tactical or medium-term planning and

operational planning. The forestry literature also reported

modeling efforts to address the interrelations and the tradeoffs between planning levels (e.g. Hoganson and Rose 1982,

Gunn and Rai 1986, Weintraub and Davis 1995). Epstein 

et al. (1999) and Carlsson and Ronnqvist (in press) further

focussed on modelling applications and information systems

(IS) used by forest industries to address business processes

ABSTRACT

Organizations currently face the challenge of managing the integration of very diverse Information Systems (IS) in their

Information and Communication Technology (ICT) infrastructure. Ineffective IS architectures may lead to poor planning.

An approach to architecture of an Integrated Forest Planning System (IFPS) that can best adapt both to the organizational

system and to the strategy of a forest industry is presented. The Zachman framework is used to enterprise architecture planning encompassing business modeling and data, applications and technology architectures. Emphasis is on the definition of

an IFPS that can facilitate the insertion, retrieval and update of data, models and technology by the industry organizational

system to address the complexity of its forest management decision processes and its planning goals. Results of an application to a major European pulp and paper group are presented. Results suggest that this approach contributed to define an

IFPS that may adequately support the vertically integrated forest industry management planning processes and objectives.

KEYWORDS: Forest industry, forest management, information systems, information and communication technology, 

enterprise architecture planning.

89

within their supply chain. Carlsson and Ronnqvist (in press)

pointed out that forestry has relatively few advanced IS as

compared to other industrial sectors. Moreover, to our knowledge, no study has addressed the definition of an enterprise

architecture plan that may provide business models and

data, applications and technologies architectures to support

effectively the vertically integrated forest industry management planning. 

Yet organizational systems bedevilled by redundant,

fragmented and inconsistent IS can hardly have an accurate

understanding of management problems. An effective datato-information strategy requires that the improvement of

modeling techniques and of corresponding software applications to address specific business processes in a forest

industry is framed by an ICT development plan. As

Strassmann (1997) pointed out, managing the integration 
of diverse IS within an organizational system ICT infrastructure is as important as the investment on new applications

and technology. An effective ICT architecture is key for

that integration and to good planning. It provides a guide

for the structure and place of information within an

organization (Davenport e Prusak 1997). It enables the

discovery and the elimination of redundancy in the business

processes reducing IS complexity (Cook 1996). It becomes

the bridge between business and technical domains (Young

2001) thus contributing to the alignment between the forest

industry IS and business management strategies. 

The approach to architecture of an Integrated Forest

Planning System (IFPS) discussed in this paper focuses on

the need to integrate business processes such as strategic,

tactical and operational planning and on the need to develop

an ICT infrastructure to effectively support them. Accordingly,

it addresses too forest industry organizational system issues.

The Zachman framework is used to enterprise architecture

planning encompassing business modeling and data, applications and technology architectures. Emphasis is on the

definition of an IFPS that can facilitate the insertion,

retrieval and update of data, models and technology by the

industry organizational system to address the complexity of

its forest management decision processes and its planning

goals. After describing the approach, results of an application to a major European pulp and paper group are discussed. Preliminary results suggest that this approach

contributed to define an IFPS that may adequately support

the vertically integrated forest industry management planning processes and objectives.

MATERIALS AND METHODS

The case study – the Portucel Soporcel Group

The Portucel Soporcel Group (PSG) is one of the five

largest European producers of uncoated woodfree paper

(http://www.portucelsoporcel.com/). It has a productive

capacity that exceeds 1 x 106 tonnes of paper and 1.2 x

106 tonnes of pulp and has an annual turnover of 1,000 

x 106 euros. The PSG is the largest Portuguese forestry

owner. It manages 1,5% of the Portugal’s land, 4,1% of 

the country’s forests and 15,6% of its eucalyptus forest.

Because of its characteristics, the Eucalyptus globulus

Labill is the preferred species for pulp and paper production. This vertically integrated forest industry manages

about 138 x 103 ha of property, of which the eucalyptus

plantations occupy 76%, and it is responsible for a diversity

of forestry assets spread over 172 local districts from North

to South of Portugal. The PSG also manages forest areas in

the Açores and in Argentina (about 400 ha each). The production is carried out at three mills in Portugal (Setúbal,

Figueira da Foz and Cacia) with 1950 employees. The most

important market for PSG is Europe (http://www.portucelsoporcel.com/).

A typical eucalyptus prescription for raw material supply encompasses a plantation that may leave a number of

trees per hectare ranging from 1,000 to about 1,700. A full

rotation may include up to 2 or 3 coppice cuts, each cut

being followed by a stool thinning that may leave an 

average number of shoots per stool ranging from 1 to 2.

Harvest ages range from 10 to 20. In Portugal, eucalyptus

maximum mean annual increment may reach up to 30

m3/ha/year at the age of 10 years in the first cycle, in the

northern coastal region. In other regions, average eucalyptus productivity may decrease to less than 8 m3/ha/year.

Productivity may increase up to 10% in the second cycle

and it declines afterwards. Thus, generally, the final harvest

occurs at the end of the third cutting cycle after which the

site is converted into a new eucalyptus stand. 

One of the Group’s priorities is its strategy of integration. This framework prompted the development of a project aiming at the definition of architecture for an Integrated

Forest Planning System that might effectively support

strategic, tactical and operational planning and project and

budget control business processes to manage eucalyptus

forests owned or rented by this vertically integrated forest

industry. The project was developed in 2002 over a period

of 3 months.

Enterprise architecture of an Integrated Forest

Planning System for the Portucel Soporcel Group

Spewak (1992) defined enterprise architecture (EA)

planning as the process of defining architectures for the use

of information in support of the business and the plan for

implementing those architectures. The concept of EA encompasses a set of basic components (Figure 1) that are present

in different EA frameworks (e.g. Zachman 1987, Macaulay

2004): Business Architecture (BA), Information Architecture

(IA), Application Architecture (AA) and Technological

Architecture (TA). An EA framework guides the architectural process from BA to TA. Specifically, for that purpose

the Zachman framework crosses the views of participants

involved in the planning, conception, building, using and

maintaining activities of an organization’s IS with the data,

applications, technology and people components of IS. The

reader is referred to Zachman (1987) for a detailed description of this framework.

The human component of information systems is prominent in the EA planning process. The project team involved

both consultants from the consortium Link/ Metacortex/ISA

90
and actors in PSG involved in forest management planning.

For example, all actors in PSG involved in the business

participated in the development of the BA. Both the information technology (IT) and the forest resources management departments were permanently involved in the EA

planning process. In this project over 60 all-day meetings

and workshops took place. The number of persons per

meeting ranged from 7 to 10. Some people were present 

in all meetings (e.g. project manager, forest management

planning director, IT director). Some people were present

in the meetings when business issues related to their expertise were addressed (e.g. financial area, forest inventory,

forest operations).

Business modeling is instrumental for BA, i.e. for 

the definition of PSG forest planning business strategies,

processes, and its functional requirements. It encompassed

three main stages. Firstly, the project team (PT) defined the

scope of business modeling. It was decided that the IFPS

should encompass strategic, tactical and operational planning and project and budget control business processes to

manage eucalyptus forests owned or rented by this vertically integrated forest industry. Secondly, the characterization

of current business processes involved the analysis of PSG

documentation, preliminary meetings and modeling workshops with PSG staff. The workshops further enabled the

definition of how PSG wants to carry out forest management planning in the future. Thirdly, the PT inventoried

central and departmental IS that support current PSG forest

planning processes. 

The IA is instrumental for identifying what PSG needs

to know to run its forest planning business processes. It

described the data’s logical aspects, as well as the management of data resources at a macro level. It encompassed

three stages. Firstly, workshops conducted a systematic

analysis of the future IFPS business processes to identify

both the information entities needed to support them and

the PSG information systems where these entities are managed. Secondly, entity-relationship (E-R) diagrams were

drawn to illustrate IA views from the perspective of IFPS

business processes. Finally, each entity was characterized

by its relevant attributes and an IFPS data dictionary was

organized. 

The AA is instrumental for identifying the applications

needed to manage information entities in order to fulfill

business processes requirements. It encompassed four stages.

Firstly, workshops conducted a systematic analysis of the

future IFPS business processes to identify the entities

manipulated by each activity. Secondly, cluster analysis of

the resulting CRUD matrices (Figure 2) grouped business

activities according to information entities they manipulate.

Thirdly, workshops used both the business processes knowledge base and the results of cluster analysis to define the

applications that might support each process, the way they

should interface with each other and to identify main data

repositories needed. Finally, each application was described

in detail. 

The TA is instrumental for providing information about

the technical foundation to support the BA, IA and AA. It

was conducted according to some general principles (e.g.

the priority of business needs; the importance of re-using

current PSG technology, of the geographical information

systems support, of the modular concept, of using hardware

and software open standards whenever possible, of using

ergonomic and intelligent human/machine interfaces, of

using 3-tier application architecture and of introducing

security mechanisms). TA selected and inventoried relevant

technological areas (e.g. network infrastructure, servers and

workstations, management information systems, decision

systems, helpdesk) and recommended technological solutions to support the IFPS. The EA process ended with the

definition of an implementation/migration plan.

RESULTS

The BA provided a knowledge base about current forest

planning at PSG (Figure 3). It encompasses two loosely

tied business processes: strategic and operational planning.

The former is based on a simulation procedure that does

not support systematic scenario generation and analysis.

The latter involves two stages that are managed by the GPS

central planning department and by its regional divisions,

respectively (Figure 3). The lack of integration between

these business processes prompted the definition of how

the PSG organizational system wants to manage its eucalyptus area in the future (Figures 4 and 5). It was decided

91

Figure 1—Enterprise Architecture Components. Source:

http://www.eacommunity.com.
92

Figure 2—Fragment of a CRUD matrix to identify and cluster business processes activities according to the way they manipulate information entities (C – create, R – read, U – update, D – delete). 

Figure 3—Current business processes. The symbol D identifies an information source. The symbol A identifies a business process activity.
93

Figure 4—Fragment of future business model detailing strategic, tactical and operational planning. The symbol D identifies an information source. The symbol A identifies a business process activity.

Figure 5—Fragment of future business model detailing work and budget monitoring. The symbol D identifies an information source. The symbol A identifies a business process activity.
that the future model should integrate and align all business

processes. Strategic or long-term planning will provide the

framework for all other business processes (Figure 4). It

will produce a 30/36 year-plan that complies with PSF

financial, self-supply and eucalyptus area expansion objectives. Tactical planning will produce a 3-year plan to comply with both long-term and other spatially detailed objectives.

Strategic and tactical plans will be compared within the

IFPS to check whether tactical objectives constrain substantially long-term objectives (Figure 5). If that is the

case, the planning process will be restarted and a new longterm scenario will be generated. If not, the first year of the

tactical plan will be used as input to operational planning

(Figure 4). PSG regional divisions will validate the proposed operational plan. If operational conditions as evaluated by regional offices constrain substantially tactical

objectives, tactical planning will have to be redone. If not,

the operational plan will be accepted and will provide the

basis for the forest planning budgeting. The budget approval

will determine the final acceptance of strategic, tactical and

operational plans. Both work and budget monitoring will

enable checking if proposed plans are being executed

(Figure 5). If not, the overall planning process may be

revised. 

The results of IA included documentation identifying

the informational entities needed to support the IFPS business processes and their relationships and an IFPS data

dictionary. The results of AA included documentation

defining the IS within the IFPS, the way they interface with

each other and with other PSG systems. At a macro level,

applications to include in the IFPS were strategic, tactical

and operational decision systems, project and budget monitoring systems, silviculture and growth and yield modelbase management systems and a planning control system.

The latter coordinates the overall IFPS functionality. The

results of the AA further included the detailed description

of the modular structure of each IS. For example, the structure of the strategic planning application encompasses a

module to extract the data needed for strategic planning

that is stored in IS outside the IFPS, a module to generate

strategic scenarios formulations and to search their optimal

solutions and a module to compare and analyze solutions

from each scenario (Figure 6).

The recommendations of TA to support the IFPS included

technological solutions to each relevant technological area.

The implementation/migration plan identified the impact 

94

Figure 6—Description of the modular structure of the strategic planning application and of its interfaces with other IFPS applications.
of the IFPS on current IS in the organization. It presented

business-oriented and technical-oriented implementation

sequences. The former took into account on-going organizational system changes. It further listed expected benefits

of the development of the IFPS and its critical success 

factors. 

In summary, the EA process was crucial to an accurate

understanding of how this vertically integrated forest

industry works, enabling business changes. They further

provided information needed to align business and IS 

management strategies.

SUMMARY

This paper presented results from a project to define an

Integrated Forest Planning System for a vertically integrated

forest industry. The focus was on the development of an

approach to architecture of an IFPS that might best adapt

both to the organizational system and to the strategy of a

major European forest industry group. This approach

encompassed the application of an Enterprise Architecture

Planning methodology to evolve an effective forest industry

data-to-information strategy. Business modeling and data,

applications and technological architectures were deemed

critical to a sustained and successful development and use

of the IFPS. Rather than developing applications to address

the needs of individual business processes in a fragmented

way, the proposed approach emphasizes the need to frame

this development by an overarching organizational ICT plan.

Results from an application of this approach to Portucel

Soporcel Group, a vertically integrated forest industry that

manages over 1 x 105 ha of eucalyptus forests in Portugal,

suggest that it contributed to define an IFPS that may adequately support its management planning processes and

objectives. Actually, the implementation/migration plan 

of this architecture project was accepted by PSG. At this

moment implementation has already completed the development of the strategic and tactical planning applications.

An interesting extension to this IFPS architecture is the

architecture of the whole forest industry supply chain. 

ACKNOWLEDGMENTS

This work was developed under the Project “Arquitectura

de Sistemas de Informação no âmbito do Sistema Integrado

de Planeamento Florestal” for Portucel Soporcel Group. 

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In: Bevers, Michael; Barrett, Tara M., comps. 2005. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report

PNW-GTR-656. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

1 K. Olofsson is a Researcher, 2 J. Bohlin is a Research Assistant, 3 T. Lämås is an Assistant Professor and 4 H. Olsson is a Professor at the Department of 

Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, S-901 83 Umeå, Sweden.

Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium

ESTIMATION OF FOREST PARAMETERS BY USING

REMOTE SENSING SINGLE-TREE DETECTION 

AND FIELD PLOTS WITH TREE POSITIONS

Kenneth Olofsson1, Jonas Bohlin2, Tomas Lämås3 and Håkan Olsson4

INTRODUCTION

The forest ecosystem is providing a wide set of goods

and services such as timber, forest fuel and recreation opportunities. Other objectives concerning forest functions are,

for example, the role of forests as a carbon sink, maintained biodiversity, and fire hazard reduction. Forest management is thereby a complex and intricate task. To provide

decision makers with appropriate information on the outcome of different management options, complex decision

support systems are being developed; see for example

CLAMS (http://www.fsl.orst.edu/clams; Ohmann and Gregory

2002; Spies and others 2002) and Heureka (http://heureka.

slu.se; Lämås and Eriksson 2003). In such systems, models

forecasting the forest ecosystem state and models predicting

the outcome of different goods and services are integrated.

ABSTRACT

Template matching, originally developed in Canada by Richard Pollock, is a method for single tree detection in high 

resolution (pixel size 0.3 m – 1 m) aerial digital imagery. The method was tested for Scandinavian conditions and further

developed. It is a potential method to be included in a concept for estimating forest parameters from aerial imagery, such 

as stem number, stem volume, and tree biomass. Template matching gives estimates of position, crown diameter and crown

shape for most trees in a stand. The concept proposed in this article includes a quick field method for measuring tree positions on field plots, which currently is under development at SLU. Another vital part is a method for rendering templates of

whole field plots (with radius about 10 m), whereby the field plots can be exactly located in the aerial image. Then the vector of field-surveyed forest parameters can be related to the vector of image derived features for the same plot. If the image

is divided into a grid, with approximately the same area as the field plots, the relations between image derived tree features

and field plot data could be used for estimating forest variables for all grid cells, or as correlated information in a sampling

scheme. In addition, maps of all trees visible from above can be created.

KEYWORDS: Forest inventory, image processing, template matching.

97

Quite naturally, the tree cover forms the base for timber

production but also for forest biodiversity, recreation, and

other phenomena. Consequently, the models describing the

tree cover and its development make up the core in forest

decision support systems. Detailed information of the initial state of the tree cover is thereby crucial not only for

timber production but also for forest management in a wider

setting. 

The objective of the Heureka Research Program is to

build computerized decision support systems for multi-purpose forestry. The models included depend on accurate and

large quantities of forest data. Therefore, development of

efficient data acquisition methods (Lämås and Eriksson

2003) is a vital part of the research program. The present

study is a part of that effort.
Most digital automated remote sensing methods have

been developed for satellite remote sensing imagery with

pixel sizes in the order of 10x10 m up to 30x30 m, for

example Landsat TM. Such imagery is created by averaging reflectances from sunlit canopy and ground, and shadowed canopy and ground, for several trees per pixel. In

addition, relative to airborne sensors, the view angle differences within the imagery are small because of the altitude

of the satellite sensor. These factors makes “Landsat-typeimagery” a relative simple case for computer based analysis, where pixel values are used in combination with field

plot data. However, the information content in reflectance

mean values from a group of trees is limited, and it is for

example difficult to tell if it is differences in tree size or in

stem numbers that makes one pixel darker than another.

Air photos do potentially contain more information than

satellite imagery. They are also often used as a visual background in GIS systems and are therefore often already available at forest companies, without any extra costs. Automated

analysis of aerial photos is however a complicated task,

with a “many pixels per tree” viewing situation, and large

variations in view angle within the imagery. Kleman (1987).

There are several approaches for detecting single trees

in digital aerial photos, such as segmentation (Gougeon,

1999; Erikson 2001), finding local maxima (Dralle 1997;

Rudemo 1999), edge detection techniques (Brandtberg and

Walter 1999) or template matching (Larsen 1999; Pollock

1996). The template matching approach was pioneered 

by Richard James Pollock (1996), University of British

Columbia, and compared to several other methods; it has

the advantage that view angle differences are handled in 

an integrated way. 

Here, we present a framework for estimation of forest

information, based on the template matching approach. 

The framework is based on (i) sample plot survey (ground

truth), (ii) detection of single trees in digital aerial orthophotos, and (iii) estimation of forest parameters for all trees,

such as stem number and stem volume. That is, the estimations include also non-detected trees. A case study concerning the second step of this approach is presented. The

template matching method is applied on four stands in a

conifer dominated forest in southern Sweden. It is shown

that most of the dominating trees can be detected using this

method. 

A FRAMEWORK FOR ESTIMATION OF

FOREST INFORMATION USING SINGLE

TREE DETECTION 

For estimation of forest variables on stand level or larger

areas, using single tree detection we propose the following

framework. The primary forest variables to be assessed,

such as stem height, stem diameter, age, and species, are

measured on sample plots in the field. The plot centers are

determined with an accuracy of a few meters, using GPS.

Using the true field measured tree positions in a local coordinate system, the estimated visual appearance of the field

plot in the digital image is rendered (fig. 1). The rendered

plot template is matched against the digital image, to obtain

the likely global position of the field plot in the image data.

If this procedure is to be applied in practice there is a need

for an instrument for quick field measurement of tree positions (in a local coordinate system) on sample plots. The

Dept of Forest Resource Management and Geomatics at

SLU are developing a system for such field measurements

on circular plots with a maximum radius of approximately

10 m. 

Single-tree detection is performed for the image data

over the whole forest area to be assessed, such as a stand 

or a real estate. The image is analyzed as grid cells, for

example of size 20 x 20 m. Based on the relative position,

size and shape of the detected trees in a grid cell, a number

of image derived features are computed. For the grid cells

where sample plot field data is present, the image derived

features are paired with the field measured forest data. This

paired information could be extrapolated to all the image

derived grid cells in a number of ways, for example, imputation using the kNN method (a non parametric imputation

method much used for forestry satellite remote sensing

Tomppo 1993), or estimation by regression or neural networks. The paired information could also be used in a sampling scheme, for example for post stratification of the field

98

Figure 1—Synthetic image of a field

plot generated from ground sampled

data. The position, size and type of

each tree in the ground survey, is 

used as input when generating the

ellipsoidal synthetic trees. 
sample. According to the framework outlined, it is possible

to obtain estimated parameters for all trees, such as stem

number and stem volume. That is, these include trees that

are not visible from above. In addition, the single tree detection method also provides the basis for drawing tree maps

and 3D-visualizations of all trees that are visible from

above.

Below a case study is presented in which the template

matching method is applied to detect single trees on four

stands in a conifer dominated forest in southern Sweden.

Estimation of totals for all trees (including non-detected

trees) using the approach outlined above will be presented

in forthcoming studies. 

CASE STUDY

Materials and Methods

The forest areas in the experiment are two pine stands,

labeled P1 and P2, and two spruce stands, labeled S1 and

S2, located at the Remningstorp estate in the south west of

Sweden (lat. 58° 30’ N, long. 13° 40’ E), table 1, table 2

and figure 2. 

Field Survey—The field survey was carried out in

November 2000. Stem diameters of all trees (≥ 0.05 m

stem diameter at 1.3 m above ground) within the areas

were measured and the tree species were registered. The

position of the centre of these tree stems was measured

(1.3 m above ground) relative to two reference points in

the nearest open area using a theodolite/EDM total station

(SOKKISHA SET 4). The positions of the reference points

were measured using kinematic GPS equipment. According

to the specifications for the GPS equipment, accuracies of

5 to 10 cm were possible to achieve. 

Aerial images—The aerial photos were acquired 25

September 2001 around 12:00. The flight height was 600

m. The camera used was a Hasselblad SWCE 61085, lens

Biogon 38 mm f/4.5, with a digital back. The images

received were True color (24 bit, tiff) RGB files, see table

99

Figure 2—Aerial images used in the study. Left image 143, middle image

160 and right image 175. The positions of the stands P1, P2, S1 and S2

are marked in the figure.

Table 1—The rectified aerial images used in the study and corresponding field stands. 

Resolution Sun Sun

Evaluation Width Height [meter / altitude azimuth

images Stands [pixels] [pixels] pixel] angle[°] angle[°] Time

Image 143 S2 3073 4149 0.147 29.19 18.40 11:53

Image 160 P1 and S1 3073 4054 0.154 29.67 14.71 12:06

Image 175 P2 3073 4375 0.136 29.77 13.85 12:09

Table 2—The relative positioning of the 

stands compared to the aerial images 

center. The X-axis is pointing to the east 

and the Y-axis is pointing to the north. 

Stand X [m] Y [m]

P1 -10.0 131.9

P2 -21.4 59.0

S1 -10.2 30.9

S2 -120.2 -22.3
1 and figure 2. The images were rectified to a geo-referenced system RT90, using objects in the photo with known

coordinates. 

Evaluation key—Visible tree crowns in the aerial images

were for validation purpose delineated manually using the

positions from the field survey. Small hidden and shaded

trees were not digitized. Polygons of the test areas were

chosen so that no visible trees outside the test areas would

interfere with the evaluation. 

Template Matching—Template matching is an image

processing technique where a library of 3-dimensional

model trees are cross correlated against any potential tree

position in the digital image (Pollock 1996). The tree positions and tree templates with the highest correlations are considered as likely trees. A three dimensional model accounts

for the viewing angle and the sun angle variations of aerial

images. The library trees are rendered with a different view

depending on where in the image the cross correlation is

performed. This makes it possible to detect trees in images

where the viewing angle difference from the nadir to the

image edge is large. The library trees are generated from

generalized ellipsoids of revolution: 

zn (x2 + y 2 )n/2

+ = 1 (1)

an bn

where x, y and z are the coordinates describing the surface

and a, b and n are the height, radius and shape parameters

of the ellipsoid. By changing the parameter n in equation 1

it is possible to create trees with different crown shapes.

The shape is conical if n = 1, corresponding to spruces, and

elliptical with n = 2, corresponding more to pine trees.

When n = ∞ the shape is cylindrical. The parameters a and

b influences the height and radius of the ellipsoid. Figure 3

shows ellipsoids with different parameters and viewing

angles.

By matching each template with the aerial image using

the correlation coefficient (Gonzalez and Wintz 1987), a

correlation map is produced. The local maxima in this correlation image correspond to a possible tree. If the correlation coefficients of these possible trees are above a chosen

threshold the item is considered to be a hit. If two or more

hits are close to each other the templates will cover each

other. To remove multiple hits that possibly originate from

the same tree the amount of coverage is measured, as suggested by Olofsson (2002), and compared to the first and

the second template area. If the coverage ratio is larger

than 70 % for at least one of the templates the trees are

considered to be candidates to the same position (fig. 4). 

All templates that are candidates to the same position

are added to a list. Only one of the template members of

this list is selected by the program. The program chooses

the member with the largest correlation as the selected hit.

When all multiple tree hits are removed, the template matching estimate of the tree positions and crown sizes and shapes

in the aerial image remain. Pixel coordinates are transformed to a geo-referenced system. 

The sunlit part of the canopy was delineated for the

trees in the evaluation key. To compare the template matching hits with the evaluation key the sunlit part of the templates were therefore transformed into polygons (fig. 5).

Two images are shown in figure 6, one with a near nadir

viewing angle and the other with an oblique view. The 

template hits are shown as polygons in the aerial images. 

Before the evaluation, the template matching system

was trained on aerial images near the evaluation areas. The

100

Figure 3—Generalized ellipsoids of revolution rendered with different

parameters and in different viewing angles. Camera distance is 1000 pixels, X is the distance off centre when rendering the image, for parameters

a, b and n, see equation 1. From the top left (a=50, b=50, n=1) (a=50,

b=50, n=2) (a=200, b=50, n=2), units (pixels, pixels, 1). Down left (a=50,

b=50, n=1, X = -500) (a=50, b=50, n=1, X=0) (a=50, b=50, n=1, X=

500), units (pixels, pixels, 1, pixels). 

Figure 4—To the left, two templates which partly

cover each other. To the right, the template masks.

The intersection of the template masks is marked

with grey. If the grey area ratio is larger than 70 %

the templates are considered to be candidates to

the same tree.
101

Figure 5—Templates (top) and the corresponding

polygons of the sunlit area (bottom).

Figure 6—Aerial images with template hits shown as polygons. Left:

near nadir viewing angle. Right: oblique viewing angle. 

Figure 7—Template matching results

for the pine stands. Left, stand P1, and

right, stand P2. Test area delineation as

a thin white lined polygon. Evaluation

trees as thick white lined polygons.

Template matching trees as thin white

lined polygons.

Table 3—Tree library used by the template matching method in the evaluation. 

Crown shape, radius and height correspond to parameters, n, b, and a in 

equation 1. The crown elevation is the height from the ground to the base of 

the generalized ellipsoid

Crown Crown Crown Crown

Name shape, n [1] radius, b [m] height, a [m] elevation [m]

Tree1 1.3 1.5 10.0 2.0

Tree2 1.3 2.5 10.0 2.0

Tree3 2 1.5 10.0 2.0

Tree4 2 2.5 10.0 2.0

Tree5 5 1.5 10.0 2.0

Tree6 5 2.5 10.0 2.0

Tree7 2 3.5 10.0 2.0
tree library with the best results from the training was chosen to be used in the template matching of the evaluation

stands (table 3). When evaluating the method the template

polygons with more than 50 % of its area inside the stand

polygon was used in the evaluation. If one of these template polygons covered a visible-tree polygon, it was 

considered to be a hit. If one template was connected to

several visible-tree polygons the one with the largest cover

area was chosen, thus giving only one tree connection for

each template. After this the tree polygons were searched

for multiple template connections. If there were several

templates for each tree the one with the largest cover area

was chosen. This way there was only one template connection for each tree. These hits were considered to be true.

All others were considered as false trees. 

RESULTS

The results of the case study are shown in figure 7 and

8 and in table 4. Around two thirds of the trees were detected.

The optical method used detects most of the visible trees,

around four fifths. As seen in table 4, the amount of hidden

trees are (202 – 164)/202 ≈ 19% for all stands. Area P2

with the largest number of hidden trees have an amount of

102

Figure 8-Template matching results for

the spruce stands. Left stand S1. Right

stand S2. Test area delineation as a

thin white lined polygon. Evaluation

trees as thick white lined polygons.

Template matching trees as thin white

lined polygons.

Table 4—The template matching results for the test areas, on average 79.3 % of the visible trees were found

Ground Template Results in percentage Results in percentage

Plot survey matching results of visible trees of all trees

Found Found False All Found False All

Visible All visible False All trees hits hits trees hits hits

trees trees trees hits hits [%] [%] [%] [%] [%] [%]

P1 26 28 26 7 33 100.0 26.9 126.9 92.9 25.0 117.9

P2 44 64 32 5 37 72.7 11.4 84.1 50.0 7.8 57.8

S1 36 48 23 9 32 63.9 25.0 88.9 47.9 18.8 66.7

S2 58 62 49 9 58 84.5 15.5 100.0 79.0 14.5 93.5

All plots 164 202 130 30 160 79.3 18.3 97.6 64.4 14.9 79.2
(64 - 44)/64 ≈ 31%. These trees cannot be detected by an

optical method and have to be estimated in another way.

That is, a method to estimate forest stand parameters needs

additional ground truth information to increase the accuracy. 

DISCUSSION

As the case study shows, the single tree detection software detects most of the visible trees in the spruce and pine

stands in southern Sweden. It also shows that on average

20% or a maximum of 30% of the trees might be hidden or

shaded. These trees cannot be detected by an optical method

and have to be estimated in another way. Moreover, many

primary forest variables, such as stem diameter, can never

be assessed directly in image data only. The most immediate gains with the suggested method is that the estimates of

parameters such as stem number and stem volume can be

derived for all trees in the stand, in addition to tree maps

for the dominating trees.

ACKNOWLEDGEMENTS

This study was financed by the the Kempe foundation

and the Heureka Research Program. The field data was

financed by the Hildur and Sven Wingquist foundation. We

would like to thank Dr Johan Holmgren for providing us

with the field data set. 

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