United States

Department of

Agriculture

Forest Service

Northeastern

Research Station

General Technical

Report NE-297

Chetan Agarwal

Glen M. Green

J. Morgan Grove

Tom P. Evans

Charles M. Schweik

A Review and Assessment

of Land-Use Change Models:

Dynamics of Space, Time,

and Human Choice

Indiana University

Center for the

Study of Institutions,

Population, and

Environmental Change
Abstract

Land-use change models are used by researchers and professionals to explore the dynamics

and drivers of land-use/land-cover change and to inform policies affecting such change. A

broad array of models and modeling methods are available to researchers, and each type has

certain advantages and disadvantages depending on the objective of the research. This report

presents a review of different types of models as a means of exploring the functionality and

ability of different approaches. In this review, we try to explicitly incorporate human processes,

because of their centrality in land-use/land-cover change. We present a framework to

compare land-use change models in terms of scale (both spatial and temporal) and

complexity, and how well they incorporate space, time, and human decisionmaking. Initially,

we examined a summary set of 250 relevant citations and developed a bibliography of 136

papers. From these 136 papers a set of 19 land-use models were reviewed in detail as

representative of the broader set of models identified from the more comprehensive review of

literature. Using a tabular approach, we summarize and discuss the 19 models in terms of

dynamic (temporal) and spatial interactions, as well as human decisionmaking as defined by

the earlier framework. To eliminate the general confusion surrounding the term scale, we

evaluate each model with respect to pairs of analogous parameters of spatial, temporal, and

decisionmaking scales: (1) spatial resolution and extent, (2) time step and duration, and (3)

decisionmaking agent and domain. Although a wide range of spatial and temporal scales is

covered collectively by the models examined, we find most individual models occupy a much

more limited spatio-temporal niche. Many raster models we examined mirror the extent and

resolution of common remote-sensing data. The broadest-scale models are, in general, not

spatially explicit. We also find that models incorporating higher levels of human

decisionmaking are more centrally located with respect to spatial and temporal scales,

probably due to the lack of data availability at more extreme scales. Further, we examine the

social drivers of land-use change and methodological trends exemplified in the models we

reviewed. Finally, we conclude with some proposals for future directions in land-use modeling.

The Authors

CHETAN AGARWAL is forest policy analyst with Forest Trends in Washington, D.C.

GLEN M. GREEN is a research fellow with the Center for the Study of Institutions, Population,

and Environmental Change at Indiana University–Bloomington.

J. MORGAN GROVE is a research forester and social ecologist with the U.S. Department of

Agriculture Forest Service, Northeastern Research Station in Burlington, Vermont, and co–

principal investigator with the Baltimore Ecosystem Study, a Long-Term Ecological Research

Project of the National Science Foundation.

TOM P. EVANS is an assistant professor with the Department of Geography and associate

director with the Center for the Study of Institutions, Population, and Environmental Change at

Indiana University–Bloomington.

CHARLES M. SCHWEIK is an assistant professor with the Department of Natural Resource

Conservation and the Center for Public Policy and Administration at the University of

Massachusetts–Amherst.

Cover Photos

Upper left: Earth, taken during NASA Apollo 17’s return from the moon (Harrison Schmitt, 1972)

Upper right: A young man selling charcoal in southwest Madagascar near the town of

Andranovory (Glen Green, 1987)

Lower left: Secondary forests and pasture with the hills of Brown County State Park, Indiana,

in the background (Glen Green, 1999)

Lower right: Fisheye view of a young tree plantation in Monroe County, Indiana (Glen Green,

1999)
Contents

Introduction ........................................................................................................................................ 1

Methods ............................................................................................................................................... 2

Background................................................................................................................................... 2

Framework for Reviewing Human-Environmental Models .................................................. 2

Model Scale ................................................................................................................................... 2

Model Complexity ....................................................................................................................... 5

Application of the Framework .................................................................................................. 6

Identifying the List of Models ................................................................................................... 7

Models .................................................................................................................................................. 8

Discussion .........................................................................................................................................24

Trends in Temporal, Spatial, and Human Decisionmaking Complexity .........................24

Theoretical Trends ....................................................................................................................26

Methodological Trends.............................................................................................................31

Data and Data Integration .......................................................................................................32

Scale and Multiscale Approaches ...........................................................................................33

Future Directions in Land-Use Modeling ..............................................................................34

Conclusion .........................................................................................................................................36

Acknowledgments ............................................................................................................................36

Literature Cited ................................................................................................................................37

Additional References .....................................................................................................................39

Appendix............................................................................................................................................50

Glossary .............................................................................................................................................62
Figures and Tables

Figure 1.—Three-dimensional framework for reviewing land-use change models. .............. 2

Figure 2.—Hierarchical spatial scales in social-ecological contexts. ........................................ 3

Figure 3.—Spatial representation of a hierarchical approach to modeling urban

systems. ............................................................................................................................ 4

Figure 4.—Three-dimensional framework for reviewing and assessing land-use

change models. ............................................................................................................... 7

Figure 5.—Spatial and temporal characteristics of reviewed models. ...................................24

Figure 6.—Raster and vector characteristics of reviewed models. ........................................25

Figure 7.—Human decisionmaking complexity of reviewed models. ....................................26

Figure 8.—Traditional conceptual framework for ecosystem studies...................................26

Figure 9.—Conceptual framework for investigating human ecosystems. ............................27

Figure 10.—Nine-box representation of the interaction between the three dimensions

of space, time, and human decisionmaking with biophysical processes. .......30

Appendix figures: Graphical representations of spatial and temporal scales

under which the 19 models operate. ........................................................................50

Table 1.—Resolution and extent in the three dimensions of space, time,

and human decisionmaking. .......................................................................................... 6

Table 2.—Six levels of human decisionmaking complexity. ...................................................... 6

Table 3.—Summary statistics of model assessment. .................................................................. 9

Table 4.—In-depth overview of models reviewed. .....................................................................10

Table 5.—Spatial characteristics of each model. .......................................................................16

Table 6.—Temporal and human decisionmaking characteristics of each model. ...............19

Table 7.—Summary of model variables that characterize relevant human drivers............29
1

Introduction

Land-use change is a locally pervasive and globally

significant ecological trend. Vitousek (1994) notes that

“three of the well-documented global changes are

increasing concentrations of carbon dioxide in the

atmosphere; alterations in the biochemistry of the global

nitrogen cycle; and on-going land-use/land-cover

change.” In the United States, 121,000 km2

 of nonfederal

lands were converted to urban developments between

1982 and 1997 (Natural Resources Conservation Service

1999). Globally and over a longer period, nearly 1.2

million km2

 of forest and woodland and 5.6 million km2

of grassland and pasture have been converted to other

uses during the last 3 centuries, according to Ramankutty

and Foley (1999). During this same period, cropland has

increased by 12 million km2

. Humans have transformed

significant portions of the Earth’s land surface: 10 to 15

percent currently is dominated by agricultural rowcrop or

urban-industrial areas, and 6 to 8 percent is pasture

(Vitousek et al. 1997).

These land-use changes have important implications for

future changes in the Earth’s climate and, consequently,

great implications for subsequent land-use change. Thus,

a critical element of the Global Climate Change Program

of the U.S. Department of Agriculture’s (USDA’s) Forest

Service (FSGCRP) is to understand the interactions

between human activities and natural resources. In

particular, FSGCRP has identified three critical actions

for this program:

1. Identify and assess the likely effects of changes in forest

ecosystem structure and function on human

communities and society in response to global climate

change.

2. Identify and evaluate potential policy options for rural

and urban forestry in order to mitigate and adapt to

the effects of global climate change.

3. Identify and evaluate potential rural and urban forest

management activities in order to integrate risks

associated with global climate change.

In addition to these action items, attention has focused

on land-use change models. Land-use models need to be

built on good science and based on good data. Research

models should exhibit a high degree of scientific rigor and

contribute some original theoretical insights or technical

innovations. However, originality is less important in

policy models, and sometimes it is more desirable for a

model to be considered “tried and true.” Also important

to policy models is whether the model is transparent,

flexible, and includes key “policy variables.” This is not to

say that research models might not have significant policy

implications (as is the case with global climate models

developed during the past decade) nor is it to say that

policy models might not make original contributions to

the science of environmental modeling (Couclelis 2002).

Because of its applied mission, the FSGCRP needs to

focus on land-use models that are relevant to policy. This

does not mean that these land-use models are expected to

be “answer machines.” Rather, we expect that land-use

change models will be good enough to be taken seriously

in the policy process. King and Kraemer (1993) list three

roles a model must play in a policy context: A model

should clarify the issues in the debate; it must be able to

enforce a discipline of analysis and discourse among

stakeholders; and it must provide an interesting form of

“advice,” primarily in the form of what not to do — since

a politician is unlikely to do what a model suggests.

Further, the necessary properties for a good policy model

have been known since Lee (1973) wrote his “requiem”

for effective models: (1) transparency, (2) robustness, (3)

reasonable data needs, (4) appropriate spatio-temporal

resolution, and (5) inclusion of enough key policy

variables to allow for likely and significant policy

questions to be explored.

The National Integrated Ecosystem Modeling Project

(NIEMP:Eastwide) is a part of the USDA’s Global

Climate Change Program. Through this project, the

Forest Service intends to:

1. Inventory existing land-use change models through a

review of literature, websites, and professional contacts.

2. Evaluate the theoretical, empirical, and technical

linkages within and among land-use change models.

This report’s goal is to contribute to the

NIEMP:Eastwide modeling framework by identifying

appropriate models or proposing new modeling

requirements and directions for estimating spatial and

temporal variations in land-cover (vegetation cover) and

forest-management practices (i.e., biomass removal or

revegetation through forestry, agriculture, and fire, and

nutrient inputs through fertilizer practices).
2

Methods

Background

Models can be categorized in multiple ways: by the

subject matter of the models, by the modeling

techniques or methods used (from simple regression to

advanced dynamic programming), or by the actual uses

of the models. A review of models may focus on

techniques in conjunction with assessments of model

performance for particular criteria, such as scale (see,

for example, the review of deforestation models by

Lambin 1994). The FSGCRP evaluates models using

the following criteria:

1. Does the model identify and assess the likely effects

of changes in forest ecosystem structure and

function on human communities and society?

2. Does the model evaluate potential policy options for

rural and urban forestry?

3. Does the model evaluate potential rural and urban

forest management activities?

While this review indirectly covers these topics, we

developed an alternative analytical framework. As

Veldkamp and Fresco (1996a) note, land use “is

determined by the interaction in space and time of

biophysical factors (constraints) such as soils, climate,

topography, etc., and human factors like population,

technology, economic conditions, etc.” In this review, we

use all four factors that Veldkamp and Fresco (1996a)

identify in the construction of a new analytical framework

for categorizing and summarizing models of land-use

change dynamics.

Framework for Reviewing

Human-Environmental Models

To assess land-use change models, we propose a

framework based on three critical dimensions to

categorize and summarize models of humanenvironmental dynamics. Space and time are the first two

dimensions and provide a common setting in which all

biophysical and human processes operate. In other words,

models of biophysical and/or human processes operate in

a temporal context, a spatial context, or both. When

models incorporate human processes, our third

dimension — referred to as the human decisionmaking1

dimension — becomes important as well (Fig. 1). In

reviewing and comparing land-use change models along

these dimensions, two distinct and important attributes

must be considered: model scale and model complexity.

Model Scale

Social and ecological processes operate at different scales

(Allen and Hoekstra 1992; Ehleringer and Field 1993).

When we discuss the temporal scale of models, we talk in

terms of time step and duration. Time step is the smallest

temporal unit of analysis for change to occur for a specific

process in a model. In a model of forest dynamics, tree

height may change daily. This model would not consider

processes which act over shorter temporal units. Duration

is the length of time that the model is applied. For

instance, change in tree height might be modeled daily

over the course of its life from seedling to mature tree:

300 years. In this case, time step would be one day, and

duration would equal 300 years. When the duration of a

model is documented, it can be reported in several ways:

109,500 daily time steps, a period of 300 years, or a

calendar range from January 1, 1900, to January 1, 2200.

Spatial Resolution and Extent. When we discuss the

spatial scale of models, we employ the terms resolution

and extent. Resolution is the smallest geographic unit of

analysis for the model, such as the size of a cell in a raster

grid system. In a raster environment, grid cells typically

are square, arranged in a rectilinear grid, and uniform

across the modeled area, while a vector representation

typically has polygons of varying sizes, though the

smallest one may be considered the model’s resolution.

Extent describes the total geographic area to which the

model is applied. Consider a model of individual trees in

a 50-ha forested area. In this case, an adequate resolution

might be a 2x2 m cell (each cell is 4 m2

), and the model

extent would equal 50 ha.

Scale is a term fraught with confusion because it has

different meanings across disciplines, notably geography

vs. the other social sciences. Geographers define scale as 1

Words that are in italics are defined in the Glossary on

Page 49.

Time (X)

Space (Y)

Human

Decisionmaking 

(Z)

Biophysical 

Processes Only

Figure 1.—Three-dimensional framework for reviewing

land-use change models.
3

the ratio of length of a unit distance (scale bar) on a map

and the length of that same unit distance on the ground

in reality (Greenhood 1964). A large-scale map (e.g., a

map of a small town or neighborhood at 1:10,000)

usually shows more detail but covers less area. Small-scale

maps usually show less detail but cover more area, such as

a map of the United States at 1:12,000,000. Other social

scientists give opposite meanings to the terms large scale

and small scale. To them, a large-scale study means it

covers a large extent, and a small-scale study is a detailed

study covering a small area. By this definition, the word

“scale” can generally be dropped completely with no

change in the meaning of the sentence. The term scale

also is complicated by the change in geographic

technology as we move from hard-copy, analog data

(maps) to digital products (images and GIS coverages).

To avoid this confusion, we define two other terms, fine

scale and broad scale, which have more intuitive meaning.

Resolution and extent may be used to describe fine- or

broad-scale analyses. Fine-scale models encompass

geographically small areas of analysis (small extents) and

small cell sizes (and thus are large scale, to use the

geographer’s term), while broad-scale models encompass

larger spatial extents of analysis and cells with larger sizes

(and thus correspond to small-scale maps of geographers).

Figure 2 provides an example of analysis moving from

broad scales (A) to increasingly finer scales (E).

We use different terms to characterize temporal and

spatial scale. Temporal time step and duration are

analogous to spatial resolution and extent, respectively.

Resolution and extent often are used to describe both

temporal and spatial scales; however, we make these

distinctions more explicit so that readers will not be

confused by which scale we are referring to in any

particular discussion, and we think these careful

distinctions in scale terminology are important for further

dialog of land-use/land-cover modeling. We propose a

similar approach in describing scale of human

decisionmaking.

Agent and Domain. How does one discuss human

decisionmaking in terms of scale? The social sciences have

not yet described human decisionmaking in terms that are

as concise and widely accepted for modeling as time step/

duration or resolution/extent. As with space and time, we

propose an analogous approach that can be used to

articulate scales of human decisionmaking in similar

terms: “agent” and “domain.”

Figure 2.—Hierarchical spatial scales in social-ecological contexts.

D. Location

(LANDSAT image)

X

A. Globe

X

Y

Broad

Spatial

Scale

Fine

Spatial

Scale

X

Y

B. Continent

• Watershed or sub-watershed

• Village(s), City or cities

• Forest(s)

• Land adjacent to a river system

E. Field Site

Examples:

Y

X

Y

C. Region
4

Agent refers to the human actor or actors in the model

who are making decisions. The individual human is the

smallest single decisionmaking agent. However, there are

many land-use change models that capture

decisionmaking processes at broader scales of social

organization, such as household, neighborhood, county,

state or province, or nation. All of these can be considered

agents in models. Domain, on the other hand, refers to

the broadest social organization incorporated in the

model. Figure 2 illustrates agents (villages) and domain

(countries of the western hemisphere) for the study of

social ecosystems in a hierarchical approach. The agent

captures the concept of who makes decisions, and the

domain describes the specific institutional and geographic

context in which the agent acts. Representation of the

domain can be facilitated in a geographically explicit

model through the use of boundary maps or GIS layers

(Fig. 3).

For example, in a model of collaborative watershed

management by different forest landowners, a multiscale

approach would incorporate several levels of linked

resolutions and domains. At a broad scale, the domain

would be the collaborative arrangement among owners

Figure 3.—Spatial representation of a hierarchical approach to modeling urban systems.

Examples of hierarchically nested patch structure at three scales in the Central Arizona-Phoenix

(CAP; upper panels) and Baltimore Ecosystem Study (BES; lower panels) regions. At the broadest

scale (A, D), patches in the CAP study area include desert (mustard), agriculture (green), and urban

(blue); for the BES, patches are rural (green), urban (yellow), and aquatic (blue). B: The municipality

of Scottsdale, Ariz., showing major areas of urban-residential development (blue, lower portion) and

undeveloped open lands (tan, developable; brown, dedicated). C: Enlargement of rectangle in B

showing additional patch structure at a neighborhood scale (green, golf course/park; mustard,

undeveloped desert; red, vacant; pink, xeric residential; purple, mesic residential; yellow, asphalt). E:

Gwynns Falls watershed, Maryland, with residential (yellow), commercial/industrial (red), agricultural

(light green), institutional (medium green), and forest (dark green) patch types. F: Enlargement of

rectangle in E showing additional patch structure at a neighborhood scale (dark green, pervious

surface/canopy cover; light green, pervious surface/no canopy cover; yellow, impervious surface/

canopy cover; red, impervious surface/no canopy cover; blue, neighborhood boundaries; black circles,

abandoned lots). Panel A courtesy of CAP Historic Land Use Project (http://caplter.asu.edu/overview/

proposal/summary.html); panels D, E, and F courtesy of USDA Forest Service and BES LTER

(http://www.ecostudies.org/bes). Source: (Grimm et al. 2000)
5

(coincident with the watershed boundaries), the agent

would be the owners and the resolution their associated

parcel boundaries (the agent would be the collaborative

organization). At a finer scale, the owner would be the

domain, and the resolution would be the management

units or forest stands within each parcel (the agent being

the individual). In this example, we also might model

other agents, operating in one of the two domains (e.g.,

other parcels), such as neighboring landowners whose

parcel boundaries would also be depicted by the same

domain map. Institutionally, agents may overlap spatially.

For example, a landowner might receive financial

subsidies for planting trees in riparian buffer areas from

an agent of the Forest Service; receive extension advice

about wildlife habitat and management from an agent of

the Fish and Wildlife Service; and have his or her lands

inspected for nonpoint-source runoff by an agent from

the Environmental Protection Agency.

In our watershed example, also consider the role of other

types of forest landowners. For instance, the watershed

might include a state forester (agent = state) who writes

the forest management plan for the state forest (domain =

state boundary) and prescribes how often trees

(resolution) in different forest stands (extent) should be

harvested (time step) for a specific period (duration)

within state-owned property. In this case, the human

decisionmaking component of the model might include

the behavior of the forester within the organizational

context of the state-level natural resource agency.

Model Complexity

A second important and distinct attribute of humanenvironmental models is the approach to address the

complexity of time, space, and human decisionmaking

found in real-world situations. We propose that the

temporal, spatial, or human-decisionmaking (HDM)

complexity of any model can be represented with an

index, where low values signify simple components and

high values signify more complex behaviors and

interactions. Consider an index for temporal complexity of

models: A model that is low in temporal complexity may

be a model that has one or a few time steps and a short

duration. A model with a mid-range value for temporal

complexity is one which may use many time steps and a

longer duration. Models with a high value for temporal

complexity are ones that may incorporate a large number

of time steps, a long duration, and the capacity to handle

time lags or feedback responses among variables, or have

different time steps for different submodels.

Temporal Complexity. There are important interactions

possible between temporal complexity and human

decisionmaking. For instance, some human decisions are

made in short time intervals. The decision of which road

to take on the way to work is made daily (even though

many individuals do not self-consciously examine this

decision each day). Other decisions are made over longer

periods, such as once in a single growing season (e.g.,

which annual crop to plant). Still other decisions may be

made for several years at a time, such as investments made

in tractors or harvesting equipment. When the domain of

a decisionmaker changes, this change may also affect the

temporal dimension of decisions. For example, a forest

landowner might make a decision about cutting trees on

his or her land each year. If this land were transferred to a

state or national forest, the foresters may harvest only

once every 10 years.

The decisionmaking time horizon perceived by an actor

could also be divided into a short-run decisionmaking

period, and a long-run time horizon. Again using the

forest example, if a certain tree species covering a 100-ha

area matures in 100 years, there is a need for a harvest

plan that incorporates both the maturity period and the

extent of forest land that is available. In other words, at

least one level of actor needs to have an awareness of both

short and long time horizons and be able to communicate

with other actors operating at shorter time horizons.

Institutional memory and culture can often play that role.

Spatial Complexity. Spatial complexity represents the

extent to which a model is spatially explicit. There are two

broad types of spatially explicit models: spatially

representative and spatially interactive. A model that is

spatially representative can incorporate, produce, or

display data in two or, sometimes, three spatial

dimensions, such as northing, easting, and elevation, but

cannot model topological relationships and interactions

among geographic features (cells, points, lines, or

polygons). In these cases, the value of each cell might

change or remain the same from one point in time to

another, but the logic that makes the change is not

dependent on neighboring cells. By contrast, a spatially

interactive model is one that explicitly defines spatial

relationships and their interactions (e.g., among

neighboring units) over time. A model with a low value

for spatial complexity would be one with little or no

capacity to represent data spatially; a model with a

medium value for spatial complexity would be able to

fully represent data spatially; and a model with a high

value would be spatially interactive in two or three

dimensions.

The human decisionmaking sections of models vary in

terms of their theoretical precursors and simply may be

linked deterministically to a set of socioeconomic or

biological drivers, or they may be based on some game

theoretic or economic models. Table 1 presents the
6

equivalence among the three parameters — space, time,

and human decisionmaking — based on the earlier

discussion about resolution and extent.

Human Decisionmaking Complexity. Given the major

impact of human actions on land use and land cover, it is

essential that models of these processes illuminate factors

that affect human decisionmaking. Many theoretical

traditions inform the theories that researchers use when

modeling decisionmaking. Some researchers are

influenced by deterministic theories of decisionmaking

and do not attempt to understand how external factors

affect the internal calculation of benefits and costs: the

“dos” and “don’ts” that affect how individuals make

decisions. Others, who are drawing on game theoretical

or other theories of reasoning processes, make explicit

choices to model individual (or collective) decisions as the

result of various factors which combine to affect the

processes and outcomes of human reasoning.

What is an appropriate index to characterize complexity

in human decisionmaking? We use the term HDM

complexity to describe the capacity of a humanenvironmental model to handle human decisionmaking

processes. In Table 2, we present a classification scheme

for estimating HDM complexity using an index with

values from 1 to 6. A model with a low value (1) for

human decisionmaking complexity is a model that does not

include any human decisionmaking. By contrast, a model

with a high value (5 or 6) includes one or more types of

actors explicitly or can handle multiple agents interacting

across domains, such as those shown in Figures 2 and 3.

In essence, Figures 2 and 3 represent a hierarchical

approach to social systems where lower-level agents

interact to generate higher-level behaviors and where

higher-level domains affect the behavior of lower-level

agents (Grimm et al. 2000; Vogt et al. in press; Grove et

al. 2002).

Application of the Framework

The three dimensions of land-use change models (space,

time, and human decisionmaking) and two distinct

attributes for each dimension (scale and complexity)

provide the foundation for comparing and reviewing

land-use change models. Figure 4 shows the threedimensional framework with a few general model types,

including some that were represented in our review.

Modeling approaches vary in their placement along these

three dimensions of complexity because the location of a

land-use change model reflects its technical structure as

well as its sophistication and application.

Table 1.Resolution and extent in the three dimensions of space, time, and human decisionmaking 

Space Time Human decisionmaking 

Resolution or 

equivalent 

Resolution: smallest 

spatial unit of analysis 

Time step: shortest temporal 

unit of analysis 

Agent and decisionmaking 

time horizon 

Extent or 

equivalent 

Extent: total relevant 

geographical area 

Duration: total relevant 

period of time 

Jurisdictional domain and 

decisionmaking time horizon

Table 2.Six levels of human decisionmaking complexity 

Level 

1 No human decisionmaking — only biophysical variables in the model 

2 Human decisionmaking assumed to be related determinately to population size, change, 

or density 

3 Human decisionmaking seen as a probability function depending on socioeconomic 

and/or biophysical variables beyond population variables without feedback from the 

environment to the choice function 

4 Human decisionmaking seen as a probability function depending on socioeconomic 

and/or biophysical variables beyond population variables with feedback from the 

environment to the choice function 

5 One type of agent whose decisions are modeled overtly in regard to choices made about 

variables that affect other processes and outcomes 

6 Multiple types of agents whose decisions are modeled overtly in regard to choices made 

about variables that affect other processes and outcomes; the model might also be able to 

handle changes in the shape of domains as time steps are processed or interaction between 

decisionmaking agents at multiple human decisionmaking scales 
7

The following analysis characterizes existing land-use

models on each modeling dimension. Models are assigned

a level in the human decisionmaking dimension, and

their spatial and temporal dimensions are estimated as

well. We also document and compare models across

several other factors, including the model type, dependent

or explanatory variables, modules, and independent

variables.

Identifying the List of Models

Any project that purports to provide an overview of the

literature needs to provide the reader with some

information regarding how choices were made regarding

inclusion in the set to be reviewed. In our case, we

undertook literature and web searches as well as

consultations with experts.

We began our search for appropriate land-use/land-cover

change models by looking at a variety of databases. Key

word searches using “land cover,” “land use,” “change,”

“landscape,” “land*,” and “model*,” where * was a

wildcard, generated a large number of potential articles.

The databases that proved to be most productive were

Academic Search Elite and Web of Science. Both

databases provide abstract and full-text searches. Other

databases consulted, but not used as extensively, include

Carl Uncover, Worldcat, and IUCAT (the database for

Indiana University’s library collections). We also searched

for information on various web search engines. Some of

the appropriate web sites we found included

bibliographies with relevant citations.

These searches yielded 250 articles, which were compiled

into bibliographic lists. The lists then were examined by

looking at titles, key words, and abstracts to identify the

articles that appeared relevant. This preliminary

examination yielded a master bibliography of 136 articles,

chosen because they either assessed land-use models

directly or they discussed approaches and relevance of

models for land-use and land-cover change. Articles in the

master bibliography are included in Additional

References. We then checked the bibliographies of these

articles for other relevant works. Web of Science also

allowed us to search for articles cited in other articles.

*

Time (X)

Space (Y) 

Human

Decisionmaking 

(Z)

D

C

E

A

B

F

Key

A-Time series statistical models, STELLA

models with no human dimension

B- Time-series models with human decisionmaking explicitly modeled

C- Most traditional GIS situations

D- GIS modeling with an explicit temporal

component (e.g., cellular automata)

E- Econometric (regression) and game 

theoretic models

F- SWARM and SME (spatial modeling

environment)

The ultimate goal of human-environment 

dynamic modeling: high in all three *

dimensions

Low

High

High

DECISIONMAKING

COMPLEXIY

S

P

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TIA

L

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P

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XIT

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PLEXITY

High

Figure 4.—Three-dimensional framework for reviewing and assessing land-use change models.
8

Twelve models were selected by reading articles identified

through this process, with the selection criteria being

relevance and representativeness. A model was relevant if

it dealt with land-use issues directly. Models that focused

mainly on water quality, wildlife management, or urban

transportation systems were not reviewed. Seven other

models were chosen from recommendations received

from colleagues and experts, especially the Forest Service,

after also being reviewed for relevance and

representativeness.

Our criteria for representativeness included the following:

1. Emphasis on including diverse types of models. If

several models of a particular type had already been

reviewed, other applications of that model type were

excluded in favor of different model types. For

example, our search uncovered many spatial simulation

models, several of which were reviewed.

2. If there were numerous papers on one model (e.g., six

on the NELUP model), only the more representative

two or three were reviewed.

3. If there were several papers by one author covering two

or more models, a subset that looked most relevant was

reviewed.

Expert opinion helped locate some of the models we

reviewed. In addition to our literature and web searches,

we consulted with the program managers for the USFS

Southern and Northern Global Climate Change

Programs to identify other significant land-use change

models. In addition to the models they identified, the

program managers also identified science contacts who

were working in or familiar with the field of land-use

modeling. We followed up with these contacts in order to

(1) identify any additional relevant models that we had

not identified through our literature and web searches

and (2) evaluate whether or not our literature and web

searches were producing a comprehensive list. The

evaluation was accomplished by comparing our “contacts’

lists” with the land-use model list we had developed

through our literature and web searches. This 3-month

follow-up activity provided fewer and fewer “new

models,” so we shifted our efforts to the documentation

and analysis of the models already identified.

By the end of the exercise, we had covered a range of

model types. They included Markov models, logistic

function models, regression models, econometric models,

dynamic systems models, spatial simulation models, linear

planning models, nonlinear mathematical planning

models, mechanistic GIS models, and cellular automata

models. For further discussion, please refer to the

Discussion section.2

Models

Using the framework previously described, we reviewed

the following 19 land-use models for their spatial,

temporal, and human decisionmaking characteristics:

1. General Ecosystem Model (GEM) (Fitz et al. 1996)

2. Patuxent Landscape Model (PLM) (Voinov et al.

1999)

3. CLUE Model (Conversion of Land Use and Its

Effects) (Veldkamp and Fresco 1996a)

4. CLUE-CR (Conversion of Land Use and Its Effects –

Costa Rica) (Veldkamp and Fresco 1996b)

5. Area base model (Hardie and Parks 1997)

6. Univariate spatial models (Mertens and Lambin

1997)

7. Econometric (multinomial logit) model (Chomitz

and Gray 1996)

8. Spatial dynamic model (Gilruth et al. 1995)

9. Spatial Markov model (Wood et al. 1997)

10. CUF (California Urban Futures) (Landis 1995,

Landis et al. 1998)

11. LUCAS (Land Use Change Analysis System) (Berry

et al. 1996)

12. Simple log weights (Wear et al. 1998)

13. Logit model (Wear et al. 1999)

14. Dynamic model (Swallow et al. 1997)

15. NELUP (Natural Environment Research Council

[NERC]–Economic and Social Research Council

[ESRC]: NERC/ESRC Land Use Programme

[NELUP]) (O’Callaghan 1995)

2

We have tried to be thorough in our search for existing

land-use/land-cover change models. However, we would like

to know of any important models we may have missed in this

review. For this reason, we have posted the model references

to a web-based database we call the “Open Research System”

(at http://www.open-research.org). If you have a reference to

a model we missed, we encourage you to visit this site,

register with the system, and submit a reference to a model

publication using the submit publication form.
9

16. NELUP - Extension, (Oglethorpe and O’Callaghan

1995)

17. FASOM (Forest and Agriculture Sector Optimization

Model) (Adams et al. 1996)

18. CURBA (California Urban and Biodiversity Analysis

Model) (Landis et al. 1998)

19. Cellular automata model (Clarke et al. 1998,

Kirtland et al. 1994)

We summarize key variations in modeling approaches in

Table 3. All models were spatially representative. Of the

19 models, 15 (79 percent) could be classified as spatially

interactive rather than merely representative. The same

number of models were modular. Models that were not

modular were conceptually simple and/or included few

elements. Interestingly, a majority of the models did not

indicate if they were spatially explicit. Another

observation was the level of temporal complexity: some

models include multiple time steps, time lags, and

negative or positive feedback loops.

Tables 4, 5, and 6 provide a summary and assessment of

land-use change models. Table 4 gives basic information

about each model: type, modules, what the model

explains (dependent variables), independent variables, and

the strengths and weaknesses of each model. Table 5

describes the spatial characteristics of each model: spatial

representation or interaction, resolution, and extent. Table

6 details the temporal characteristics of each model: time

step and duration as well as the human decisionmaking

element’s complexity, jurisdictional domain, and temporal

range of decisionmaking. Definitions are provided in the

Glossary.

Table 3.Summary statistics of model assessment 

Review Criteria Number (percentage) of 

Models 

Model Numbers 

Spatial interaction 15 (79%) All but 5, 9, 12, 13 

Temporal complexity 6 (31%) 1, 2, 3, 4, 15, 16 

Human Decisionmaking – Level 1 3 1, 6, 9 

Human Decisionmaking – Level 2 2 12, 19 

Human Decisionmaking – Level 3 7 5, 7, 10, 11, 13, 17, 18 

Human Decisionmaking – Level 4 4 2, 3, 4, 8 

Human Decisionmaking – Level 5 2 14, 16 

Human Decisionmaking – Level 6 1 15 
10 Table 4.—In-depth overview of models reviewed 

Model Name/ Citation

Model Type Components/ 

Modules

What It Explains / Dependent Variable 

Other Variables Strengths Weaknesses 

Name of model, if any, and citation 

Technical, descriptive terms 

Different models, or submodels or modules, that work together 

Description of other sets of variables in the model 

1. General Ecosystem Model (GEM) (Fitz et al. 1996) 

Dynamic systems model 

14 Sectors (modules), e.g. hydrology macrophytes algae nutrients fire dead organic matter separate database for each 

sector 

Captures feedback among abiotic and biotic ecosystem components 

103 input parameters, in a set of linked databases, representing the modules, e.g., hydrology macrophytes algae nutrients fire dead organic matter 

Spatially dependent model, with 

feedback between units and across time 

Includes many sectors Modular, can add or drop 

sectors 

Can adapt resolution, extent, 

and time step to match the process being modeled 

Limited human decisionmaking 

2. Patuxent Landscape Model (PLM) (Voinov et al. 1999) 

Dynamic systems model 

Based on the GEM model (1, above), includes the following modules, with some modification: 1) hydrology 2) nutrients 3) macrophytes 4) economic model 

Predicts fundamental ecological processes and land-use patterns at the watershed level 

In addition to the GEM variables, it -adds dynamics in carbon-to-nutrient 

ratios 

-introduces differences between 

evergreen and deciduous plant communities 

-introduces impact of land 

management through fertilizing, planting, and harvesting of crops and trees 

In addition to the strengths of the GEM, the PLM incorporates several other variables that add to its applicability to assess the impacts of land management and best management practices 

Limited consideration of institutional factors 

3. CLUE Model (Conversion of Land Use and Its Effects) (Veldkamp and Fresco 1996a) 

Discrete finite state model 

1) Regional biophysical 

module 

2) Regional land-use 

objectives module 

3) Local land-use 

allocation module 

Predicts land cover in the future 

Biophysical drivers Land suitability for crops Temperature/Precipitation Effects of past land use (may explain 

both biophysical degradation and improvement of land, mainly for crops) 

Impact of pests, weeds, diseases Human Drivers: Population size and density Technology level Level of affluence Political Structures (through command 

and control, or fiscal mechanisms) 

Economic conditions Attitudes and values 

Covers a wide range of biophysical and human drivers at differing temporal and spatial scales 

Limited consideration of institutional and economic variables 
11

Table 4.—In-depth overview of models reviewed Model Name/ Citation

Model Type Components/ 

Modules

What It Explains / Dependent Variable 

Other Variables Strengths Weaknesses 

4. CLUE-CR (Conversion of Land Use and Its Effects – Costa Rica) (Veldkamp and Fresco 1996b) 

Discrete finite state model 

CLUE-CR an application 

of CLUE (3, above) 

Same modules 

Simulates top-down and bottom-up effects of land-use change in Costa Rica 

Same as CLUE (#3, above) Multiple scales - local, regional, 

and national 

Uses the outcome of a nested 

analysis, a set of 6x5 scaledependent land-use/land-cover linear regressions as model input, which is reproducible, unlike a specific calibration exercise 

Authors acknowledge limited consideration of institutional and economic factors 

5. Area base model (Hardie and Parks 1997) 

Area base model, using a modified multinomial logit model 

Single module Predicts land-use 

proportions at county level 

Land base - classified as farmland, 

forest, and urban/other uses 

County average farm revenue Crop costs per acre Standing timber prices Timber production costs Land quality (agricultural suitability) Population per acre Average per capita personal income Average age of farm owners Irrigation 

Uses publicly available data Incorporates economic (rent), 

and landowner characteristics (age, income) and population density 

Incorporates the impact of land 

heterogeneity 

Can account for sampling error 

in the county-level land-use proportions and for measurement error incurred by the use of county averages 

An extended dataset over 

longer time periods would improve the model's predictions 

Long-term forecasts run 

the risk of facing an increasing probability of structural change, calling for revised procedures 

6. Mertens and Lambin 1997 

Univariate spatial models 

Multiple univariate models, based on deforestation pattern in study area 

1) total study area 2) corridor pattern 3) island pattern 4) diffuse pattern 

Each model runs with all four independent variables separately 

Frequency of deforestation 

All four models run with all four independent variables: 

1) road proximity 

In all cases, a single variable 

model explains most of the variability in deforestation 

Does not model interaction between factors 

2) town proximity 3) forest-cover fragmentation 4) proximity to a forest/nonforest 

edge 

Presents a strategy for modeling 

deforestation by proposing a typology of deforestation patterns 
12Table 4.—In-depth overview of models reviewed 

Model Name/ Citation

Model Type Components/ 

Modules

What It Explains / Dependent Variable 

Other Variables Strengths Weaknesses 

7. Chomitz and Gray 1996 

Econometric (multinomial logit) model 

Single module, with multiple equations 

Predicts land use, 

aggregated in three classes: 

Natural vegetation Semi-subsistence 

agriculture 

Commercial farming 

Soil nitrogen Available phosphorus Slope pH Wetness Flood hazard Rainfall National land Forest reserve Distance to markets, based on 

impedance levels (relative costs of transport) 

Soil fertility 

Used spatially disaggregated 

information to calculate an integrated distance measure based on terrain and presence of roads 

Also, strong theoretical 

underpinning of Von Thünen’s 

model 

Strong assumptions that 

can be relaxed by alternate specifications 

Does not explicitly 

incorporate prices 

8. Gilruth et al. 1995 

Spatial dynamic model 

Several subroutines for different tasks 

Predicts sites used for shifting cultivation in terms of topography and proximity to population centers 

Site productivity (no. of fallow years) Ease of clearing Erosion hazard Site proximity Population, as function of village size 

Replicable Tries to mimic expansion of 

cultivation over time 

Long gap between data collection; does not include impact of landquality and socioeconomic variables 

9. Wood et al. 1997 

Spatial Markov model 

Temporal and spatial landuse change Markov models Land-use change Models under development Investigating Markov variations, which relax strict assumptions 

associated with the Markov approach 

Explicitly considers both spatial 

and temporal change 

Not strictly a weakness, this is a work in progress and, hence, has not yet included HDM factors 

10. CUF (California Urban Futures) (Landis 1995, Landis et al. 1998) 

Spatial simulation 

Explains land use in a metropolitan setting, in terms of demand (population growth) and supply of land (underdeveloped land available for redevelopment) 

Population growth, DLUs, and 

intermediate map layers with: 

Housing prices Zoning Slope Wetlands Distance to city center Distance to freeway or BART station Distance to sphere-of-influence 

boundaries 

Underlying theory of parcel 

allocation by population growth projections and price, and incorporation of incentives for intermediariesdevelopers, a great strength 

Large-scale GIS map layers with 

detailed information for each individual parcel in 14 counties provide high realism and precision 

Compresses long period 

(20 years) in a single model run 

Has no feedback of 

mismatch between demand and supply on price of developable land/housing stock 

Does not incorporate 

impact of interest rates, economic growth rates, etc. 

Population growth 

submodel 

Spatial database, various 

layers merged to project Developable Land Units (DLUs) 

Spatial allocation 

submodel 

Annexation-incorporation 

submodel 
13

Table 4.—In-depth overview of models reviewed Model Name/ Citation

Model Type Components/ 

Modules

What It Explains / Dependent Variable 

Other Variables Strengths Weaknesses 

11. LUCAS (Land-Use Change Analysis System) (Berry et al. 1996) 

Spatial stochastic model 

Socioeconomic module Landscape change module Impacts module 

Transition probability 

matrix (TMP) (of change in land cover) 

Module 2 simulates the 

landscape change 

Module 3 assesses the 

impact on species habitat 

Module 1 variables: Land cover type (vegetation) Slope Aspect Elevation Land ownership population density Distance to nearest road Distance to nearest economic market 

center 

Age of trees 

Module 2: Transition matrix and same 

as Module 1, to produce land-cover maps 

Module 3: Utilizes land-cover maps 

Model shows process (the 

TPM), output (new land-use map), and impact (on species habitat), all in one, which is rare and commendable 

Is modular and uses low-cost 

open source GIS software (GRASS) 

LUCAS tended to 

fragment the landscape for low-proportion land uses, due to the pixelbased independent-grid method 

Patch-based simulation 

would cause less fragmentation, but patch definition requirements often lead to their degeneration into one-cell patches 

12. Wear et al. 1998 

Simple log weights 

Single module Predicts area of 

timberland adjusted for population density 

Raw timberland Population density (per county) 

Simple and powerful indicator of forest sustainability, of the impact of human settlement decisions on one forest function --its role as timberland 

Limited consideration of human decisionmaking and other forest goods and services 

13. Wear et al. 1999 

Logit model Single module Predicts the probability 

of land being classified as potential timberland 

Population per square mile Site index Slope Two dummy variables defining  ease of access to a site 

Includes several biophysical variables 

Includes only basic human choice variables, e.g., population density 

14. Swallow et al. 1997 

Dynamic model 

Three components: 1) timber model 2) forage production 

function 

3) nontimber benefit 

function 

Simulates an optimal harvest sequence 

Present values of alternative possible states of the forest, using the three model components 

The long time horizon, and the annual checking of present values under alternate possible states of the forest makes it a useful forest management tool for maximizing multiple-use values 

Authors note that the optimal management pattern on any individual stand or set of stands requires specific analysis rather than dependence on rules of thumb 
14Table 4.—In-depth overview of models reviewed 

Model Name/ Citation

Model Type Components/ 

Modules

What It Explains / Dependent Variable 

Other Variables Strengths Weaknesses 

15. NELUP (O’Callaghan 1995) 

General 

systems framework 

Economic 

component uses a recursive linear planning model 

Regional agricultural 

economic model of land use at catchment levels 

Hydrological model Ecological model 

Explains patterns of agricultural and forestry land use under different scenarios 

Variable types include: Soil characteristics Meteorological data Parish census data Input/output farm data Species Land cover 

Uses land cover to link market 

forces, hydrology, and ecology in a biophysical model of land use 

Uses mostly publicly available 

data, especially in the economic model, which greatly aids transferability 

Limited institutional variables 

16. NELUP - Extension (Oglethorpe and O'Callaghan 1995) 

Linear planning model at farm level 

Four submodels for farm types 1) lowland and mainly 

arable 

2) lowland mainly grazing 

livestock 

3) dairy 4) hill 

Maximizes income Profit is the dependent 

variable 

Level of farm activity Gross margin per unit of farm activity Fixed resources, represented as physical 

constraints 

Detailed farm-level model, with 

extensive calibration 

Farmers shown as rational 

profit-maximizing beings, but also includes the impact of offfarm income 

Limited institutional variables 

17. FASOM (Forest and Agriculture Sector Optimization Model) (Adams et al. 1996) 

Dynamic, nonlinear, price endogenous, mathematical programming model 

Three submodels : 1) forest sector - transition 

timber supply model 

2) agricultural sector that is 

optimized with the forest sector submodel 

3) carbon sector for 

terrestrial carbon 

Allocation of land in the 

forest and agricultural sectors 

Objective function 

maximizes the discounted economic welfare of producers and consumers in the U.S. agriculture and forest sectors over a nine-decade time horizon 

Forest sector variable groups: demand functions for forest products timberland area, age-class dynamics grazing labor agricultural demand imports/exports 

Carbon sector variables: tree and ecosystem carbon Additional variables: land transfer variables 

Incorporates both agriculture 

and forest land uses 

Price of products and land is 

endogenous 

The model is dynamic, thus 

changes in one decade influence land-use change in the next decade 

Good for long-term policy 

impacts 

Broad scale means that land capability variations within regions are not taken into account production technology and costs 

Agricultural sector variables: water 
15

Table 4.—In-depth overview of models reviewed Model Name/ Citation

Model Type Components/ 

Modules

What It Explains / Dependent Variable 

Other Variables Strengths Weaknesses 

18. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al. 1998) 

Overlay of GIS layers with statistical urban growth projections 

Statistical model of urban 

growth 

Policy simulation and 

evaluation model 

Map and data layers of 

habitat types, biodiversity, and other natural factors 

The interaction among the probabilities of urbanization, its interaction with habitat type and extent, and, impacts of policy changes on the two 

Slope and elevation Location and types of roads Hydrographic features Jurisdictional boundaries Wetlands and flood zones Jurisdictional spheres of influence Various socioeconomic data Local growth policies Job growth Habitat type and extent maps 

Increases understanding of 

factors behind recent urbanization patterns 

Allows projection of future 

urban growth patterns, and of the impact of projected urban growth on habitat integrity and quality 

Human decisionmaking 

not explicitly considered 

Further, errors are likely 

from misclassification of data at grid level or misalignment of map feature boundaries 

Errors also possible from 

limitations in explaining historical urban growth patterns 

19. Clarke et al. 1998, Kirtland et al. 1994 

Cellular automata model 

Simulation module 

consists of complex rules

Digital dataset of 

biophysical and human factors 

Change in urban areas over time 

Extent of urban areas Elevation Slope Roads 

Allows each cell to act 

independently according to rules, analogous to city expansion as a result of hundreds of small decisions 

Fine-scale data, registered to a 

30 m UTM grid 

Does not unpack human 

decisions that lead to spread of built areas 

Does not yet include 

biological factors 
16Table 5.—Spatial characteristics of each model 

Spatial Complexity Spatial Scale Model 

Representation n Interactio Resolution Extent

Static. Represents data on a map and may portray variation as well 

Dynamic. Includes effect of variation on processes as well as feedback between neighboring units and location of parcel within the larger scale 

Raster or vector. The area of the basic unit of analysis. A grid if raster. 

Location and total area covered by model, e.g., grid area x no. of grids 

1. General Ecosystem model (GEM) (Fitz et al. 1996) 

Yes Yes

Feedback between units 

 Raster 

Entire model runs for each spatial unit Trial unit of 1 km can vary 2

A trial simulation for the Florida Everglades/ 

Big Cypress area 

Approx. 10,000 acres 

2. Patuxent Landscape Model (PLM) (Voinov et al. 1999) 

Yes Yes

Feedback between units 

Raster Hydrological model: 200 m and 1 km 

58905 cells (200 m) or 2352 cells (1 km2) 

The Patuxent watershed (Maryland, USA), 

covering 2353 km2

3. CLUE Model (Conversion of Land Use and Its Effects) (Veldkamp and Fresco 1996a) 

Yes Yes

Attributes of one grid unit affect 

land-use outcomes in another unit.

 Raster 

In the generic CLUE model, size determined by 

extent divided by grid scale neutral matrix of 23x23 cells 

Can be scaled up or down 

See next model, CLUE-CR, for an application 

4. CLUE-CR (Conversion of Land Use and Its Effects – Costa Rica) (Veldkamp and Fresco 1996b) 

Yes As above Raster

Run at local, regional, and national levels One grid unit = 0.1 degrees or 6 minutes 

(= 7.5x7.5 km = 56.25 km at the equator) 2

Multiple extents that correspond to different 

modules 

National: Costa Rica, 933 aggregate grid 

units 

Regional: 16 to 36 aggregate grids Local: 1 grid unit 

5. Area Base Model (Hardie and Parks 1997) 

Yes Relies on land heterogeneity to explain the coexistence of several land uses and the shift between them 

No Neither raster nor vector 

Data averaged at county level Average county area = 315,497 acres 

Five southeastern U.S. states - Florida, 

Georgia, South Carolina, North Carolina, Virginia 

= 147,423,760 acres 

6. Mertens and Lambin 1997 

Yes Yes

Status of pixel is dependent on other 

spatial factors 

 Raster 

80 m x 80 m (LANDSAT Pixel size) 

Southeast Cameroon. Area not specified, but is the overlap between two LANDSAT images 
17

Table 5.—Spatial characteristics of each model 

Spatial Complexity Spatial Scale Model 

Representation n Interactio Resolution Extent

7. Chomitz and Gray 1996 

Yes Yes

Spatial variation in several variables, 

influences other variables, e.g., wetness and roads and slope to assess impedance to markets 

Uses vector data only Central and South Belize, approx. 2/3 of the 

total area of 22,000 km2

8. Gilruth et al. 1995 Yes Dynamic, spatially explicit model Raster 

100 m x 100 m cells, in a 60x60 cell grid, 

resampled from 120x120 grid 

6 km2 area, representative of a 60 km2 Diafore 

watershed, in the Tougue district, Guinea 

9. Wood et al. 1997 Yes No Raster 

Cell size of 80 m (x 80m) 

One department, Velingara, in south-central Senegal 

10. CUF (California Urban Futures) (Landis 1995, Landis et al. 1998) 

Yes Yes Vector.

Individual sites, with property boundaries Model run at city and county levels 

Nine counties of the San Francisco Bay area (Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma) and five adjacent ones, (Santa Cruz, Sacramento, San Joaquin, Stanislaus, Yolo) 

11. LUCAS (Land-Use Change Analysis System) (Berry et al. 1996) 

Yes Tentatively Yes, if the transition 

probability for one pixel, affected by factors in another pixel 

Raster Each pixel in this example represents 

90 m x 90 m, and has an attached table with unique attributes 

Two watersheds, the Little Tennessee River basin in North Carolina and the Hoh River watershed on the Olympic Peninsula in Washington 

12. Wear et al. 1998 Yes 

Displays variations 

among counties 

No Neither

County-level aggregate data 

Southern states of the USA 

13. Wear et al. 1999 Yes 

Variations among 

counties 

No Vector.

Fine scale, forested plots in private ownership 

Five-county region around Charlottesville, Virginia - Albermarle, Fluvanna, Louisa, Greene, and Nelson 

14. Swallow et al. 1997 Yes Yes 

Takes interactions among stands into 

account 

Still conceptual, neither raster nor vector Model simulates multi-stand dynamics Stands can vary in size 

Multiple stands Case study uses a simplified ecosystem of two 

stands. 

15. NELUP (O’Callaghan 1995) 

Yes Incorporates variation 

Yes Variation affects neighboring units 

Raster Ecological model: 1-km2 units 

The main economic model treats the whole 

catchment as a macro farm, but accounts for land-use variation using the land-cover data 

River Tyne catchment in Northern England - 3000 km2
18Table 5.—Spatial characteristics of each model 

Spatial Complexity Spatial Scale Model 

Representation n Interactio Resolution Extent

16. NELUP - Extension (Oglethorpe and O'Callaghan 1995) 

Yes Developed four 

submodels to capture variation 

Yes Not in submodel itself, but in the 

total NELUP model 

Neither Farm level 

Multiple farms Trial runs for 10 and 14 farms, and will cover 

the entire catchment 

17. FASOM (Forest and Agriculture Sector Optimization Model) (Adams et al. 1996) 

Yes Divides USA in 11 

regions that may be represented on a map 

Yes. Model at subcontinental scale, and 

changes in inventory and prices in one region affect prices and inventory in other regions 

Vector Demand: one national region Supply: subnational region 

The entire USA, except Hawaii and Alaska 

18. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al. 1998) 

Yes Yes, as impact of changes in one cell 

- in terms of highway, growth policies, population and job growth, influences probability of urbanization of surrounding cells 

Raster One-hectare grid cell (100x100 m) 

County level in California Pilot study for Santa Cruz County Model datasets developed for nine counties 

19. Clarke et al. 1998, Kirtland et al. 1994 

Yes Yes

Each cell acts independently, but 

according to rules that take spatial properties of neighboring locations into account 

Raster Converted vector data, e.g., roads to raster Base data registered at 30 m x 30 m Model run at 1 km level 2

Initial run for 256 km2 region around San 

Francisco in central California, USA 
19

Table 6.—Temporal and human decisionmaking characteristics of each model 

Model Temporal Scale Human Decisionmaking 

Time Step Duration of Model Run Complexity Domain Temporal Range

Time period for one iteration of the model. Modules may have different time steps -a function of the particular process. 

Time step x number of runs A 6-point scale for human 

decisionmaking or human choice (rank and rationale) 

Jurisdictional domain Short-run decisionmaking period 

and longer-run decisionmaking horizon 

1. General Ecosystem Model (GEM) (Fitz et al. 1996) 

Initial simulation runs at 0.5-

day time step 

The time step can vary across 

modules to match the dynamics of particular sectors

Can match the cycle of process being modeled 

Level: 1 Not covered in core model 

Not really considered Not really considered, as there is 

no explicit socioeconomic component in the basic GEM model

2. Patuxent Landscape Model (PLM) (Voinov et al. 1999) 

Hydrological module: one-day 

time step 

Land-use map from the 

economic model imported at a one-year interval 

Experimental run compared 1990 land-use patterns with complete forest 

Level: 4 Incorporates human decisions 

as a function of economic and ecological spatial variables 

Predicts probabilities of landuse conversion as functions of 

predicted values in residential and alternative uses and the costs of conversion 

Maximizes rent as a function of the value in different uses and the costs of conversion, hence generally referring to private decisionmaking, but aggregated at the grid level 

Annual iteration to capture variations in land use 

3. CLUE Model (Conversion of Land Use and Its Effects) (Veldkamp and Fresco 1996a) 

One month to update model 

variables 

Changes in land-use types 

however, are made on decisions for each year 

Set by user Example scenario is for several 

decades 

Level: 4 It applies several human drivers. 

Incorporates collective decisionmaking levels, from local to national 

Considers the temporal range of decisionmaking explicitly, in determining, for example the time period for updating changes in land-use types as well as minimum economic age and rotation length of the 10 different land-use types 

4. CLUE-CR (Conversion of Land Use and Its Effects – Costa Rica) (Veldkamp and Fresco 1996b) 

One month One-month time step 

Run 252 times 

Level: 4 Human demographic drivers 

only 

As above, applied to Costa Rica As in CLUE, above 
20Table 6.—Temporal and human decisionmaking characteristics of each model 

Model Temporal Scale Human Decisionmaking 

Time Step Domain Temporal Range Duration of Model Run Complexity

5. Area Base Model (Hardie and Parks 1997) 

No Cross-sectional study with 1982 

data 

At second stage, 5-year time 

step as pooled 1987 data 

Mostly 1982 to 1987 data Level: 3 

Land-use proportions are 

modeled as dependent on rent from land as well as average age, income, and population density 

While decisionmaking is mostly at landowner level, the study explanatory variable, land-use proportions, is consistent with the county-level data 

Not considered in this crosssectional study 

6. Mertens and Lambin 1997 

13 years. Single time step 13 years, 1973-86 Level: 1 

Human decisionmaking not 

included directly 

Implicit in the inclusion of 

variable like distance from road and town 

Not considered Not considered 

7. Chomitz and Gray 1996 Human decisionmaking 

implicit in the inclusion of variables that impact rent, distance to market, soil quality 

Not considered Not considered Cross-sectional analysis, hence 

time period not applicable 

Most data collected between 1989 and 1992 

Level: 3 

8. Gilruth et al. 1995 

Two years Based on the average cultivation 

period in the exterior fields 

1953 to 1989 Level: 4 

Tries to predict location of 

shifting cultivation decisions on the basis of biophysical variables over time, with feedback 

Subwatershed, with a small 

enough scale to capture large clearing 

No attempt to model 

individual fields 

Model time step tries to mimic the estimated fallow period of two years; however, too long a period between the base and final year - 36 years 

9. Wood et al. 1997 Two steps: Four years (1973-

78) and 12 years (1978-90) 

Will add a third step by 

including 1985 

17 years (1973-90) Level: 1 

Not included 

Mostly at farm and field level, while model operates at department level 

Considered implicitly, in choice of time step, to capture agricultural land-use change over longer time periods, rather than the decisionmaking associated with each crop cycle 
21

Table 6.—Temporal and human decisionmaking characteristics of each model 

Model Temporal Scale Human Decisionmaking 

Time Step Domain Temporal Range Duration of Model Run Complexity

10. CUF (California Urban Futures) (Landis 1995, Landis et al. 1998) 

Same as duration? Not clear from reference 

whether annual data are collected 

1990-2010 Model takes 1990 base data and 

forecasts growth in 2010 

Level: 3. Human choice seen as 

determined by market price and other environmental and zoning constraints 

No feedback, excessive demand 

does not lead to increase in prices in the model 

Model operates at the level of individual parcels, which is the level at which decisions are usually taken 

Not considered explicitly Model run compresses 20 years 

into one run 

Such housing decisions are often 

made quickly 

Model uses base-year price data It may inadequately represent 

exogenous factors in later years of the model 

A shorter time step may be 

better 

11. LUCAS (Landuse Change Analysis System) (Berry et al. 1996) 

Five Years 

Human choice modeled via a 

probability function for landcover change, with basic socioeconomic determinants and no feedback 

Not considered explicitly Grid does not include 

ownership, though it is at fine enough scale to broadly reflect private decisionmaking in the USA 

Not considered explicitly 

Single time step 

15 years (1975-91) Level: 3 

12. Wear et al. 1998 Nine years 18 yrs - 1974,1983,1992 (two 

time steps or three observations in forest inventory years) 

Level: 2 Demographic drivers determine 

impact 

Not considered explicitly The impact of individual 

decisions is aggregated to county level 

Not considered explicitly 

13. Wear et al. 1999 No 

Cross-sectional, 1990s data 

Single run, no duration Level: 3 

Human choice included at 

basic determinant level (population density), along with the impact of several biophysical factors, on the probability of a certain land use 

Average population data at aggregate county block level, correlated with individual plots

Cross-sectional study, hence not considered explicitly 
22Table 6.—Temporal and human decisionmaking characteristics of each model 

Model Temporal Scale Human Decisionmaking 

Time Step Domain Temporal Range Duration of Model Run Complexity

14. Swallow et al. 1997 

One year Run of 250 years, sufficiently far 

into the future that heavy discounting makes a change in the time horizon inconsequential

Level: 5 A land management model The explanatory variable, 

optimal harvest rotations, provides a decision support tool 

Focused on multiple stands, which is the level at which decisions are usually made, particularly if there are multiple owners 

Land management decisions are 

usually made annually, or even more occasionally, especially for forests. 

The long time horizon, and the 

annual checking of present values under alternate possible states of the forest makes it a useful forest management tool 

15. NELUP (O’Callaghan 1995) 

Economic model uses annual 

data from parish-level records

The time step of the hydrologic 

model was not available 

Economic submodel tested with annual data, 1981-88 

Level: 6 The model overtly models 

choices of farmers, while actions of other actors are included in the form of technology or policy constraints 

The main economic model treats the whole catchment as a macro-farm, thus perhaps overestimating factor mobility 

The annual time step corresponds to time scale of broad agricultural decisionmaking 

16. NELUP - Extension (Oglethorpe and O'Callaghan 1995) 

Annual financial and cost data 10-year period, 1981-82 to 1991-

92 

Level: 5 This submodel includes choices 

of farmers 

When combined with the rest 

of the NELUP model, it would rank at 6 

Also tries to model their riskaverse behavior 

The model resolution perfectly matches the decisionmaking unit - the individual farm 

The annual time step corresponds to the time scale of broad agricultural decisionmaking 

17. FASOM (Forest and Agriculture Sector Optimization Model) (Adams et al. 1996) 

Decadal – 10 years 100 years, 1990-2089 

Policy analysis limited to 50 

years, 1990-2039 

Demand region: nation Factor decisionmaking is 

modeled at subnational regional level, with aggregation from individual landowner level rather than at farm level 

A decadal time step is consistent with the slow rate of changes in forest sector, but not the annual decisionmaking cycle in agriculture. To compensate, the agricultural objective function is weighted by a factor reflecting the harvest of agricultural products each year during a decade. 

Level: 3 Human choice seen as 

economic rational decisionmaking based on returns under alternative uses with limited feedback from the environment 

Accounts for changes in intertemporal and price 

complexity 
23

Table 6.—Temporal and human decisionmaking characteristics of each model 

Model Temporal Scale Human Decisionmaking 

Time Step Domain Temporal Range Duration of Model Run Complexity

18. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al. 1998) 

Not apparent 15 years, 1995 to 2010 

Projections made for the latter 

year 

Level: 3 Long enough model period to 

capture longer term shifts in urbanization and its determinants 

Human decisionmaking in the 

urbanization context implicitly a function of highway facilities, natural constraints, growth policies, and job and population growth 

Useful depiction of zoning 

policies 

Calculates impacts at 1-ha grid 

level, a bit broader than individual decisionmaking levels in the urban context 

Also includes the impacts of 

scale decisionmaking, i.e., county or subcounty-level zoning 

19. Clarke et al. 1998, Kirtland et al. 1994 

Annual Used linear interpolation to 

estimate annual changes between datasets 

Used about 90 years of data for validation to project urban growth 100 years from 1990 

Level: 2 While human decisions not 

explicitly modeled, their impact taken into account 

Operates at a broader scale of 1 km2, utilizing the aggregate 

impact of hundreds of human decisions that affect urbanization 

Annual time step appropriate to reflect aggregate changes in built area, as most buildings are ready in less than a year 
24

Discussion

Trends in Temporal, Spatial,

and Human Decisionmaking Complexity

Figure 5 shows the spatial representation and extent and

the temporal time step and duration of the models. This

type of diagram is constructed by plotting four values:

time step and duration on the x-axis and resolution and

extent on the y-axis. The plotted area for each model

represents the spatial and temporal scales under which the

model operates (colors of models aid the reader in

distinguishing them). The 19 models examined in the

report collectively cover a wide range of scales, from less

than a day to more than 100 years, and from less than 1

ha to more than 1 million km2

. Yet this range of scales is

not covered by one model. Clearly, models seem to be

associated with a particular spatio-temporal niche.

Temporal Complexity. Many models with separate

ecological modules operate at fine time steps, e.g., a day

or a month (except certain climate-focused models). This

fine temporal resolution allows these models to more

accurately represent rapid ecological changes with time in

certain biophysical spheres, e.g., hydrology. Models with

multiple time steps (e.g., models 1, 2, 3, 4, 15, 16) can

span both fine and coarse time steps and reflect the

temporal complexity of different socioeconomic and

biophysical sectors more effectively. Some of the more

complex models (3 and 4) also incorporate time lags and

take into account the time taken for different crops and

other land uses to provide economic returns as well as

provide a 2-year buffer against food shortages by carrying

over yield surpluses from previous years.

Spatial Complexity. More than half of the models

provide for spatial interaction and demonstrate the

advantages of spatially explicit models that move beyond

simple spatial representation. These models include the

impact of variations across space and time of different

biophysical and socioeconomic factors on land-use

change. Figure 6 depicts the 19 models as in Figure 5, and

displays which models use a raster, a vector, or neither

approach. The spatio-temporal footprint of the

LANDSAT datasets also is included.

14

12

Temporal Scale [days]

Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

100,000,000

10,000,000,000

1,000,000,000,000

100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

9

10 6

11

5 States

Pixel / Stand

County

Landsat scene

Tree

U.S.A

17

13

6

2

11

1 15

18

16

15

5

Farm

Spatial

Extent

Spatial 

Resolution

Time

Step

Duration

CrossSectional

CrossSectional

19

Figure 5.—Spatial and temporal characteristics of reviewed models. The numbers correspond to models listed

on pages 8-9.
25

Eleven of 19 models are raster based, four are vector

based, and four are classified as neither. That may change,

for example, if model 14 goes beyond the conceptual

stage. The mechanistic vector models (10 and 18) are

focused at city and county levels and provide the finest

spatial resolution. Their extent may be limited by

availability of data. Most of the raster models have spatial

resolutions that are larger than 30 to 80 m, broadly

mirroring the pixel size of common remote-sensing data

(e.g., LANDSAT TM and MSS). Likewise, the raster

models seem to have extents at or less than the area

covered by one LANDSAT scene (170 km x 185 km).

The model with the largest extent (neither a raster nor a

vector model) was the continental-scale FASOM model

(17), with a 100-year time horizon. This model is a good

example of a dynamic, mathematical programming model

that predicts allocation of land between agriculture and

forestry, and is spatially representative but not spatially

explicit.

Human Decisionmaking Complexity. Figure 7 (a

space–time–human-decisionmaking diagram) adds

human decisionmaking complexity to the graphical

representation of temporal and spatial scales. Models

incorporating higher levelsof human decisionmaking are

more centrally located with respect to spatial and

temporal scales, probably due to the lack of data

availability at more extreme scales. Each model’s human

decisionmaking level is listed in Table 3. Models at level 3

(7 of 19) include significant elements of human

decisionmaking beyond demographic drivers, but are

defined by the lack of feedback; thus, the CUF model

(10) allocates land based on cost, but does not factor in

feedback on prices. At level 4, models incorporate

feedback, but most do not overtly model a particular kind

of actor. Thus, the PLM, CLUE, and CLUE-CR models

(2, 3, and 4) have well-developed ecological sectors and

extensive human decisionmaking elements as well as

feedback among sectors, but do not explicitly model

different types of actors. Model 8 (a land-use model that

incorporates shifting cultivation decisions) is ranked at 4

based on its complexity in portraying human

decisionmaking. Models at levels 5 (models 14 and 16)

and 6 (model 15) explicitly model one or more kinds of

actors. Model 14 simulates harvest decisions and includes

both economic and noneconomic criteria (e.g., habitat for

wildlife). The NELUP model extension (model 16) is a

farm-level model that includes the impact of farming

decisions on changes in land-use intensity and land cover.

The general NELUP model (15) has ecological and

economic components and farming decisions, and can

serve as a decision-support tool to provide feedback on

the impact of collective-level policies (e.g., support prices

14

12

Temporal Scale [days]

Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

100,000,000

10,000,000,000

1,000,000,000,000

100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

9

10 6

11

5 States

Pixel / Stand

County

Landsat scene

Tree

U.S.A

17

13

6

2

11

1 15

18

16

15

5

Farm

Raster

Neither

Vector

MSS

Scene

TM

Scene

Total

Landsat 

Archive

CrossSectional

CrossSectional

Vector

Converted to

Raster

19

Figure 6.—Raster and vector characteristics of reviewed models. The numbers correspond to models listed on pages 8-9.
26

or conservation programs). These characteristics position

the NELUP model among the most detailed in terms of

model specification in a variety of sectors affecting landuse change. However, it should be noted that a highly

detailed model is not necessarily more suitable than a

model with less specificity. The utility of a land-use

change model can be measured primarily by its ability to

demonstrate emergent patterns in land-use change

processes and, secondarily, as a predictive tool.

Theoretical Trends

Social Drivers of Land-Use Change. A general

consensus has emerged from working groups focusing on

social drivers of global change, particularly as it relates to

land-use change. Building on the National Research

Council’s report (Stern et al. 1992:2–3; hereafter referred

to as the NRC report) on “Global Environmental

Change: Understanding Human Dimensions”, a LongTerm Ecological Research (LTER) Network working

group developed a report (Redman et al. 2000; hereafter

referred to as the LTER report) to the National Science

Foundation (NSF), “Toward a Unified Understanding of

Human Ecosystems: Integrating Social Science into LongTerm Ecological Research”. The LTER report articulates

core social science areas that need to be studied to

understand variations in human land-use, production,

and consumption patterns.

14

12

Temporal Scale [days]

Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

100,000,000

10,000,000,000

1,000,000,000,000

100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

9

10 6

11

5 States

Pixel / Stand

County/

watershed

Landsat scene

Tree

U.S.A

17

13

6

2

11

1 15

18

16

15

5

Farm

6

5

4

3

2

1

CrossSectional

CrossSectional

Human Decisionmaking Level

19

Figure 7.—Human decisionmaking complexity of reviewed models. The numbers correspond to models listed on pages 8-9.

Figure 8.—Traditional conceptual framework for ecosystem studies.

Ecosystem

Dynamics

Biogeophysical 

Drivers

Human 

Activities
27

Social 

Patterns and 

Processes

• Demography

• Technology

• Economy

• Institutions

• Culture

• Information

Ecological 

Patterns and 

Processes

• Primary

production

• Populations

• Organic matter

• Nutrients

• Disturbance

Interactions

• Land use

• Land cover

• Production

• Consumption

• Disposal

“Natural” 

Ecosystems

Human 

Systems

Political and Economic Conditions

Biogeographical Conditions

Figure 9.—Conceptual framework for investigating human ecosystems.

To further illustrate this need, we examine the simplified

model in Figure 8, which describes a general, traditional,

conceptual framework that many ecologists have used to

study ecosystems. Although this conceptual model is

powerful in its inclusion of both ecological and humanbased processes, important interactions and feedbacks

influencing long-term ecosystem dynamics are absent. An

activity such as land use, traditionally seen as a driver, also

can be viewed as the result of more fundamental social

and ecological patterns and processes. Incorporating

greater contributions from the social sciences with

existing biophysical/ecological models could greatly

enhance our understanding of global change in general,

and land-use change in particular.

In contrast to Figure 8, the LTER report proposes a more

dynamic framework that explicitly links what is often

divided into separate “natural” and human systems into a

more integrated model (Fig. 9).

Although disciplinary training and traditional modeling

often treat elements of human and ecological systems as

distinct, this framework emphasizes dynamic linkages by

focusing on the interactions at the interface of the human

and ecological components of any human ecosystem. The

LTER report defines the following interactions as the

specific activities that mediate between the human and

ecological elements of the broader human ecosystem:

• Land-use decisions

• Land cover and land-cover changes

• Production

• Consumption

• Disposal

While each activity can be examined independently, the

report acknowledges their strong interdependencies.

Though there might be other mediating activities, the

LTER report proposes that the activities listed above are a

good starting point since they already are identified by

both ecologists and social scientists as prominent and

relevant processes.

The next step is to develop a perspective on what

motivates these activities. To integrate the social,

behavioral, and economic aspects of human ecosystems,

the LTER report proposes a list of social patterns and

processes. We further propose that this list can be used as

a practical guide for modeling land-use change. These

processes include the following:

• Demography

• Technology

• Economy

• Political and social institutions

• Culturally determined attitudes, beliefs, and behavior

• Information and its flow
28

Aspects of the last three drivers on the list — institutions,

culture, and information will be difficult to integrate with

more familiar biological factors. Certain aspects of these

drivers (and the first three to a lesser extent) are

constrained by human perception of the driver and how it

is already integrated into the established system. In a

human ecosystem, all choices are not equally available to

everyone; choices are conditioned by human perceptions

and preconceptions as well as physical constraints.

To guide the development of land-use models that are

more inclusive of social patterns and processes, we see a

need for land-use model developers to consider one broad

question and three subsidiary questions.

How did the social-ecological system develop into its

current state, and how might it change in the future?

This question focuses on several critical aspects of the

broader system, such as the nature of feedback linkages,

rates of change, important system components, and the

specifics of resource use and production. Three subsidiary

questions also are important for land-use model

development:

• How have ecological processes influenced the social

patterns and processes that have emerged?

• How have social patterns and processes influenced

the use and management of resources?

• How are these interactions changing, and what

implications do these changes bring to the state of

the social-ecological system?

These questions can guide the development of an

integrated land-use model as researchers attempt to

characterize the fundamental aspects of system

composition, system trends, and system operation. Such

an integrated approach to modeling land-use change

might necessitate a collaborative venture among scientists

in different disciplines, each expanding from a traditional

viewpoint. For most social scientists, this would mean a

greater emphasis on the flow of matter and energy in

human ecosystems. For ecologists, issues surrounding

information flow and decisionmaking might take on

greater relevance.

Current Social Drivers in Land-Use Models. Relevant

human-driver variables from all land-use models reviewed

for this report are summarized in Table 7 and the

Appendix. These drivers can be examined in the context

of the social drivers identified by both the NRC and

LTER reports. While some aspects of social drivers (such

as demography, markets, institutions, and technology) are

included in several models, there is no clear and

systematic consideration of each type of driver (and the

relationships among them) in any one model. Certainly

not all drivers are equally important over time, space, and

at different scales. We propose that, similar to ecological

models of forest growth (that might include the relative

effects of nitrogen, water, and light availability and

changes in atmospheric carbon on different tree species),

there is a comparable need for land-use models that can

include the relative effects of different social drivers on

land-use change in the context of space, time, and scale.

This is particularly crucial for assessing alternative future

scenarios and relative impacts of different policy choices.

We believe it is crucial for developers of land-use models

to discuss and adopt a more comprehensive and

systematic approach to including social drivers of landuse change within the context of the NRC and LTER

reports and existing social science efforts.

Multidisciplinary Approaches. Land-cover change is a

complex process affected by many social and ecological

processes. The multidisciplinary nature of land-cover

change is widely recognized in both the social and natural

sciences, yet the institutional powers of the disciplines

remain strong and multidisciplinary science is still in its

infancy. The broad spatial and temporal scales of the

human dimension of land-cover change (that our

reviewed models cover) demand that models also cross

multiple disciplines. As the dimension becomes broader,

more disciplines may need to be incorporated. Any model

of land-cover change is probably limited by the person(s)

constructing it, in accordance with their disciplinary

limits in understanding and funding. Some of the models

we examined incorporated multiple disciplines; GEM,

PLM, and NELUP (models 1, 2, and 15) incorporated

many biophysical disciplines as well as social sciences and

fields of modeling methods. Other models, especially the

purely statistical ones, were more limited in scope. In

general, the higher the ability of the model to deal with

complexity, the more multidisciplinary the model is likely

to be.

Temporal and Spatial Synchrony and Asynchrony.

Human decisionmaking does not occur in a vacuum.

Rather, it takes place in a particular spatial and temporal

context, and, since decisionmaking about land use usually

concerns some biophysical processes, we must include

these processes in the discussion.

The spatial extent of human problems is sometimes

smaller and sometimes larger than key actors. Equivalence

between the spatial extent of a given biophysical process

and the jurisdictional domain of at least one

decisionmaking unit often can help actors make effective

decisions. A lack of equivalence can present potential

problems inhibiting the incorporation of all impacts of a
29

process in decisionmaking. In the real world, decisions are

made at multiple scales with feedback from one scale to

another. Also, actors at a finer scale may have evolved a

decisionmaking system at a broader scale, without

actually having an actor at that scale.

This problem of scale mismatch occurs when the physical

scale of an ecological system varies substantially from that

of at least one organized decisionmaking system that

regulates human actions related to that system. For

example, scale mismatch can occur when the physical

system is much larger than any human decisionmaking

system that affects it. Most global ecological problems are

characterized by this kind of scale mismatch and often are

characterized as externalities. For example, until an

international treaty or special regime is created, nationstates are smaller than the stratosphere, but actions taken

within all nation-states affect the level of greenhouse gases

contained in the stratosphere. Looking at atmospheric

ozone as a shield, substantial progress has been made in

developing a successful international regime to limit the

level of chlorofluorocarbons that can be emitted, as well

as providing a warning system for when ozone levels are

dangerously low (Sandler 1997). While stratospheric

ozone levels are still falling, measurable progress has been

made. In regard to global warming, various efforts to

achieve an international regime to limit greenhouse gas

emissions are under way, but such a regime is still a long

way from being realized (Young 1999).

Scale mismatch also can occur when the ecological system

is smaller (or a dramatically different geographic shape)

Table 7.—Summary of model variables that characterize relevant human drivers 

Human drivers or social 

patterns and preferences 

Model variables Model Numbersa

Population size 2, 3, 4, 10, 15, 18 

Population growth 2, 3, 4, 10, 18 

Population density 2, 3, 4, 5, 10, 11, 12, 13, 

18 

 Returns to land use (costs and prices) 2, 5, 10, 14b, 16, 17 

 Job growth 10, 18 

 Costs of conversion 2, 10 

 Rentc 2, 3, 5, 16 

Collective rule making Zoning 2, 10, 15, 18 

 Tenure 7, 11 

Relative geographical position to 

infrastructure: 

Distance from road 

2, 3, 4, 6, 10, 11, 18, 19 

Distance from town/market 6, 7, 10d, 11, 18 

Distance from village/settlement 8, 19 

Infrastructure/Accessibility 

Presence of irrigation 5 

 Generalized access variable 13 

 Village size 8e

 Silviculture 2, 15, 16, 17 

 Agriculture 2, 15, 16, 17 

 Technology level 3, 4, 17 

 Affluence 3, 4, 5 

 Human attitudes and values 3, 4 

 Food security 3, 4 

 Age 5 

a

Model 1 is not listed because it has no human driver. At the time of the review, model 9 was under construction. 

b

Model 14 includes wealth and substitution effects of harvesting decisions across stands; includes nontimber benefits, 

e.g., of providing forage and cover to wildlife. 

c

It is not clear if economic rent is a variable in model 18. 

Model 10 measures distance to both downtown San Francisco and the nearest sphere-of-influence boundary (as a 

proxy for infrastructure costs – water, drainage, electricity, etc.). 

d

e

Population is a proxy of village size in model 8. 
30

than any relevant decisionmaking regime (e.g.,

watershed management). Wilson et al. (1999)

analyze scale mismatches that occur in fisheries

when managers in a large fishery agency perceive

their task as managing a single large population of

fish, when, in fact, multiple, small, spatially discrete

populations actually characterize the fishery. If a

fishery is characterized by “metapopulations” where

local populations of fish are relatively discrete and

reproduce separately, then management of the

species at a broad scale may overlook the protection

of specific spawning grounds and allow rapid

extinction of local populations (Hanski and Gilpin

1997). The extinction of local populations can

adversely affect the spawning potential of the entire

population. Similarly, if urban areas are governed

only by large units of government, and

neighborhoods are not well organized, many

neighborhood-level functions are overlooked,

eventually leading to serious problems throughout

an urban area (McGinnis 1999).

At a regional scale, SO2

 emissions from midwestern

U.S. coal-burning power plants carry downwind and

cause high ozone levels (in the lower atmosphere) in

several states on the east coast. This has led to regional

initiatives, like the 34-state Ozone Transport Authority

Group trying to resolve the problem.

Furthermore, missing connections might arise if

potentially effective institutions exist at the appropriate

scales but decisionmaking linkages between scales are

ineffective. Decisions also might be based on information

aggregated at an inappropriate scale, even though it may

exist at the appropriate scale (Cleveland et al. 1996). An

example of the latter is the biennial national forest cover

analysis prepared by the Forest Survey of India. While

forest cover is assessed at the level of small local units, it is

aggregated and reported at the district level, which is a

larger administrative unit, rather than at the watershedbased forest division level, at which forests are managed.

When human decisions relate to processes that change

over time, there might be a temporal mismatch between

the time step and duration of biophysical processes and

the decisionmaking time horizons of the human actors.

For example, elected officials on 3- to 5-year terms have

short decisionmaking horizons and make decisions on

issues and processes that often have long-term biophysical

consequences, such as tree species with long rotations or

nuclear waste storage. (See earlier discussion on

decisionmaking time horizons.)

Humans usually use some form of discounting to

compare preferences over time. The discount rate may be

implicit or explicit. A farmer choosing between growing

an annual crop and planting trees that are harvestable in

30 years is comparing the flows of costs and benefits over

different periods. Since models do not have the luxury of

implicit comparisons, they usually use a discount rate to

compare such choices. Most models make such

comparisons by adjusting the value of money as a

function of time. However, linking biophysical and social

models by valuing social, economic, and environmental

systems with this single parameter involves many

assumptions and has been controversial (Lonergan and

Prudham 1994).

Figure 10 represents spatial and temporal interaction of

decisionmaking and biophysical processes in a nine-box

figure. The middle edge boxes (a, b, c, and d) represent

the four factors whose interaction determines land use —

time, space, human decisionmaking, and biophysical

processes. The corner boxes represent the results of the

interaction of the two adjacent headings. Thus, box ii

represents the temporal dimension of biophysical

processes (i.e., time step and duration), while box iii

represents the spatial dimension of human

decisionmaking. The center box represents the problems

of mismatches between decisionmaking and biophysical

processes in the temporal and spatial dimensions, as

discussed above. Land-use models and modeling tools or

Resolution and 

extent

(iv)

Space

(b)

Jurisdictional 

domain

(iii)

Biophysical

Processes

(d)

Temporal

Mismatch

(v)

Spatial 

mismatch

Human

Decisionmaking

(c)

Time step and

duration

(ii)

Time

(a)

Decisionmaking

time horizon

(i)

Figure 10.—Nine-box representation of the interaction between

the three dimensions of space, time, and human decisionmaking

with biophysical processes.
31

approaches may be viewed in terms of their sophistication

or technical ability in portraying processes in each of the

three dimensions of space, time, and human

decisionmaking.

Methodological Trends

The reviewed models employ a range of modeling

methods (Table 4). The CUF and CURBA models (10

and 18) both use a mechanistic GIS simulation,

combining layers of information with growth projections.

Both were noted for their detailed vector resolution. A

range of statistical/econometric models (5, 6, 7, 9, 12,

and 13) applied either raster or vector approaches (though

at least two used neither) using aggregated county-level

data, mostly without spatial complexity. Dynamic systems

models include the GEM and its application, the PLM (1

and 2). The NELUP model (15) also used a general

systems framework. Additionally, several other models (3,

4, 8, 11, 14, and 17) used dynamic approaches. Model 19

applied a cellular automata approach to analyze urban

expansion.

Systems Approach. Nonlinearities and spatial and

temporal lags are prevalent in many environmental

systems. When models of environmental systems ignore

the presence of nonlinearities and spatial and temporal

lags, their ability to produce insights into complex

human-environmental systems may be significantly

reduced.

Statistical approaches using historical or cross-sectional

data often are used to quantify the relationships among

the components of human-environmental systems. In this

case, rich datasets and elaborate statistical models are

often necessary to deal with multiple feedbacks among

system components and spatial and temporal lags. Model

results often are driven by data availability, the

convenience of estimation techniques, and statistical

criteria — none of which ensure that the fundamental

drivers of system change can be satisfactorily identified

(Leamer 1983). A statistical model can provide only

insight into the empirical relationships over a system’s

history or at a particular point in time, but is of limited

use for analyses of a system’s future development path

under alternative management schemes. In many cases,

those alternative management schemes may include

decisions that have not been chosen in the past, and their

effects are therefore not captured (represented) in the data

of the system’s history or present state.

Dynamic modeling is distinct from statistical modeling

because it builds into the representation of a phenomenon

those aspects of a system that we know actually exist (such

as the physical laws of material and energy conservation)

and that describe input-output relationships in industrial

and biological processes (Hannon and Ruth 1994, 1997).

Therefore, dynamic modeling starts with this advantage

over the purely statistical or empirical modeling scheme.

It does not rely on historical or cross-sectional data to

reveal those relationships. This advantage also allows

dynamic models to be used in more applications than

empirical models. Dynamic models often are more

transferable to new applications because the fundamental

concepts on which they are built are present in many

other systems.

To model and better understand nonlinear dynamic

systems, we must describe the main system components

and their interactions. System components can be

described by a set of state variables (stocks), such as the

capital stock in an economy or the amount of sediment

accumulated on a landscape. These state variables are

influenced by controls (flows), such as the annual

investment in capital or seasonal sediment fluxes. The

nature of the controls (size of the flows), in turn, may

depend on the stocks themselves and other parameters of

the system. Using this approach, models are constructed

by identifying, choosing, and specifying values and

relationships among stocks, flows, and parameters.

Many land-use change models focus on specific processes

affected by a defined set of variables. An alternative

approach is to examine land-use change as one

component of a socioecological system. In developing this

systems approach, one difficulty lies in deciding how to

incorporate model complexity. Researchers from the

social sciences may tend to add complexity on the social

side while generalizing components on the biophysical

side. Researchers in the natural sciences might do the

opposite. A multidisciplinary team must struggle to find a

compromise, making the model complex enough to

operate properly and produce reasonable behavior

without making any single part of the model overly

complex. Another difficult task is to incorporate scale

issues into this systems approach. A number of researchers

have developed models that provide great insight into

very complex systems (Voinov et al. 1998; Voinov et al.

1999). Many of these models operate at a set spatial scale,

but there might be important processes or relationships

that are not evident at that particular spatial scale.

Once a systems model has been constructed, what-if

scenarios can be explored more easily than with other

modeling approaches that are not systems oriented. In

particular, a systems approach can examine what

feedbacks exist in a socioecological system such as the

impact of increases or decreases in agricultural

productivity on the local market prices of those

agricultural goods. This scenario-testing ability has

proved valuable both to researchers and to policy experts
32

in elucidating important relationships in different

systems.

Modularity of Models. The multidisciplinary nature of

land-cover change modeling is paralleled by modularity in

the models themselves. Of the 19 models evaluated, all

but four were characterized by modular components.

Modularity might help facilitate modeling land-cover

change by assigning a particular disciplinary aspect of the

model to a separate module. We found the majority of the

modular models tended to consider multiple disciplines.

This was true for models with explicit biophysical and

social components, such as models 1, 2, 3, 4, and 15, and

for the largely biophysical models, which incorporate

multiple processes in a single model.

The complexity of a model also is related to model

modularity. Complex models typically involve the

interaction of multiple parameters, and their creation and

validation can be facilitated by utilizing multiple modular

components; for example, modularity allows different

processes to run at different time steps, different actors

can be modeled simultaneously in different modules, and

differences in their decisionmaking horizons can be

incorporated by varying the time step of different

modules.

Data and Data Integration

The recent explosion in data availability has enabled the

development of more rigorous models. More data can

enable more accurate calibration and validation of

models. Data for independent variables are used to

calibrate model runs and the time frame for data

availability often determines the time step for particular

modules. After calibration, models often are validated by

comparing outputs of variables being modeled, typically

land cover, with actual land-cover data. Model calibration

and validation are perhaps the most critical and laborintensive parts of model development.

Data often are differentiated by source and are either

primary or secondary. Primary data collection can be

tailored to specific requirements. If collected extensively

at a regional scale, the source is spread out by necessity

and the data must be broadly aggregated. If, on the other

hand, detailed information is collected intensively at a

high concentration, say 100-percent sampling, resource

considerations often lead to very localized coverage.

Secondary data, by definition, are limited to data already

available but often cover longer time scales and broader

spatial scales (e.g., U.S. census data are averaged for

census blocks, large subcity units). At least one reviewed

model (15) consciously restricted itself to publicly

available data so the model could be transferable to other

locations.

Another issue is data form and availability over time.

When data are sought covering the last several centuries,

data sources often are limited and highly aggregated.

Assessing older land-use changes may involve other

disciplines (e.g., archaeology) to understand land cover.

Thus, the forest transition in the United States, where

deforestation likely peaked around 1900, and Brazilian

deforestation, which shows no signs of peaking yet, are

almost always viewed in a different light primarily

because of data availability.

Satellite images offer an extensive source of land-cover

data collected remotely at a cost that is significantly lower

than the cost of manual collection. Several recent data

trends include higher spatial and spectral resolution and

higher frequency of acquisition with time. The number of

satellites providing imagery has increased dramatically

since the early use of satellite imagery. In the 1970s, the

primary platform for publicly available imagery was the

LANDSAT MSS instrument. Currently there is a variety

of platforms, each with different imaging characteristics,

including French SPOT panchromatic and multispectral

instruments, data from the Indian IRS family of satellites,

and radar imagery from the Japanese JERS satellite. Data

are available at increasingly finer resolutions as well. The

first satellite instrument used for public land-cover

mapping in the 1970s (LANDSAT MSS) provided image

data at a spatial resolution of 56 x 79 m. The current

LANDSAT 7 provides much finer resolution at 28.5 x

28.5 m. The private IKONOS satellite launched in 1999

provides 1-m panchromatic data and 4-m multispectral

data. Also, image data are available over an increasingly

wider spectral range, which includes data in optical,

thermal, and radar wavelengths.

Along with improved satellite data come complications in

their use for land-use modeling. Satellite imagery is

available from the early 1970s. Examining land-use

change prior to this period requires the use of other

remotely sensed data, such as aerial photography, or

ground-collected historical information. A variety of

methodological issues related to comparing land-use data

derived from different data sources complicate the study

of land-use change processes across long periods. Perhaps

more importantly, many land-use change processes are

time dependent. For example, timber harvests in many

areas of the United States are based on an approximate

40-year rotation cycle, while tropical subsistence rotations

may be much shorter (5 years). These temporal issues

have serious implications for land-use change analysis in

terms of identifying the relationship of these land-use

changes within the context of varying study durations.

The LANDSAT system provides an excellent source of

land-cover data over a long duration (1972 to the
33

present). However, between 1972 and 1982, only image

data from the LANDSAT MSS instrument was available,

but acquisition of MSS images was curtailed in 1992 after

which only Thematic Mapper (TM ) images were

available. The difference in resolution between the MSS

and TM instruments means that the more recent, finer

data will have to be resampled to a broader resolution for

comparison. There also is a question of data availability

outside the United States, where several areas have spotty

image availability. For example, from the mid-1980s to

the early 1990s, there is extremely limited availability for

West African LANDSAT TM scenes. This differential

availability of imagery is due to market-oriented policies

following the privatization of the LANDSAT system in

the mid-1980s.

Finally, we must consider questions of data migration and

reading-device obsolescence. Data formats are

proliferating, and data stored in older formats need to be

migrated to newer formats as older formats become

obsolete along with the accompanying hardware and

software. The ability to read diverse formats affects data

availability and might, in the extreme case, render

archives of little use. For example, the earliest LANDSAT

satellites included a higher-resolution instrument, the

Return Beam Vidicon (RBV), which at the time was

thought to be a superior instrument to the MSS.

However, these image data are stored on magnetic tape

format which current data providers no longer use; this

means the archives currently do not provide access to

RBV data.

Despite the above caveats, the LANDSAT TM is a useful

remotely sensed data source. It has global coverage, an

excellent dataset for the United States, and could

potentially map the entire world at 16-day intervals

(except for occurrences of cloud cover). Another broaderscale remote sensing source is the AVHRR with a lower

resolution of 1.1 km, but which provides daily data for

the entire globe. AVHRR applications include a famine

early-warning system (operational in a dozen African

countries) that maps agricultural production based on

land-cover parameters.

Aerial photographs, another form of remotely sensed

data, usually have a higher resolution than satellite images

and can provide detailed information on land cover. Some

counties in Indiana use aerial photos to determine landuse categories at extremely fine scales for property tax

assessment. Aerial photographs have been available in the

United States for more than half a century. However, they

have both geometric and radiometric distortions, which

make them not directly comparable with satellite images.

Aerial photographs often vary in scale and season of

acquisition. For example, aerial photos in Indiana were

acquired every 5 years alternately in summer and winter

and usually require manual interpretation, a skilled and

labor-intensive task with a declining supply of

interpreters.

Nearly all parameters used in land-cover change models

have a spatial dimension and much of the data can be

organized effectively using a Geographic Information

System (GIS). While some models might use parameters

that are spatial in nature, these parameters may not be

spatially explicit. For example, models 5, 12, 14, and 16

exhibit parameters neither as raster (grid cells) nor as

vector (points, lines, or polygons). In our survey, these

nonspatially explicit models may reflect unavailable data

at finer scales (see Fig. 6).

One of the strengths of GIS and spatial representation is

the ability to integrate data from disparate sources. For

example, population data collected as point data from

villages in a rural area can be used to create a surface of

land-use intensity by creating a weighted interpolation

surface modified by other community-level variables such

as the sex ratio, occupation of village residents, and

landholdings. This land-use intensity surface can be

integrated with a land-use map to explore the relationship

between land use and past land-use changes and to

predict future changes. These data transformations are

enabled by a GIS approach through the development of a

spatial representation of the factors affecting the land-use

system. There are varieties of sources of error associated

with these data transformations and researchers must

evaluate the contribution of these errors to the overall

error in the model. However, even with these errors,

spatial representation can allow explorations of

relationships between social and biophysical factors,

which would not be possible with nonspatially explicit

methods of research.

Scale and Multiscale Approaches

We considered scale in three dimensions in this

assessment. We also have demonstrated the broad

equivalence of spatial (resolution and extent) and

temporal (time step and duration) scales and their echoes

in temporal (decisionmaking horizon) and spatial

(jurisdictional domain) attributes of human

decisionmaking. Mertens and Lambin (1997) hint at the

importance of both resolution and extent when they

recognize the tradeoff between analysis at broad scales

(where the high level of aggregation of data may obscure

the variability of geographic situations, thus diluting

causal relationships) and fine scales (impractical, if there

were no possibility of generalizing over large areas).
34

One of the issues in broader-scale decisionmaking

modeling is that developing such a sector-level model

involves “huge complexities likely to arise while trying to

assess behavioural characteristics at the sector level”

(Oglethorpe and O’Callaghan 1995). Certainly, fine-scale

models have particular benefits. Oglethorpe and

O’Callaghan (1995) conclude that the farm-level model

allows them to project land-use patterns and management

practices arising as a result of agricultural market and

policy changes while demonstrating the short- and longterm consequences for the environment.

The resolution and extent of a model or its submodules

often are based on the extent of computing power

available and the scale at which certain biophysical

processes operate. Increasingly, there is recognition that

different land-use change drivers operate at different

scales and that interscale dynamics should be included in

land-use/cover change models (Veldkamp and Fresco

1996a).

The importance and challenge of scale and nested,

hierarchical approaches cannot be overestimated. The

physical, biological, and social sciences are struggling with

the issue of scale and these have implications for

appropriate frameworks for collecting and analyzing data

at different spatial and temporal scales. This issue infuses

many activities that influence modeling, from data

collection, to data analyses, to interpretation of results. In

a “human” spatial sense, scales of interest range from

individuals to groups or institutions of increasingly large

size until they encompass global networks.

In a similar fashion, understanding processes acting at

varying temporal scales is important to understand highfrequency processes as well as those operating over longer

periods. The importance of this challenge is more

pronounced when modelers consider integrated models.

For example, some social and ecological processes may be

associated with a particular scale, while other processes

might occur across multiple scales. Further, ecological and

social processes may not operate at the same scale and

linkages may have to be developed to connect across

scales. It is unknown whether theories that explain

processes at one scale can be used to explain processes at

other scales. To date, no land-use model combining social

and ecological processes has completed a multiscale

approach. Thus, fundamental research and modeling

paradigms may need to be rethought (Redman et al.

2000).

We will need to develop a number of capabilities for

multiscale approaches and models of land-use change.

These include the ability to identify the following

(Redman et al. 2000):

• Optimal scale(s) and resolution(s) for modeling

underlying social and ecological patterns and processes

of land-use change

• Time lags, nonlinear relationships, and defining events

that affect the responses among social and ecological

processes of land-use change

• Spatial characteristics of certain phenomena, such as

shape, adjacency, and matrix, and how they affect social

and ecological processes of land-use change

• Boundary conditions relative to space and time that

might affect social and ecological processes of land-use

change

• Broad-scale data to explain fine-scale behavior

(ecological inferences) and fine-scale data to explain

processes at other scales of land-use change

• Data associated with one unit of analysis that can be

dis- and reaggregated to another unit (e.g., from census

tracts to watersheds) to model land-use change

Future Directions in Land-Use Modeling

Many of the models reviewed in this report have been

under development for a long time. Models that have

evolved over a long period often have to accommodate

changes in mission and expansion into new substantive

areas important to the system being modeled but not

originally included in earlier versions of the model. For

example, the PLM (model 2) was originally designed as

an ecologically based model of the Patuxent watershed in

the Eastern United States. Subsequent functionality has

been added to the PLM to incorporate various socialbased inputs, including population growth, agricultural

policy, and land-use management. This new functionality

has expanded the domain of the model but the socialbased inputs might not always be optimally

accommodated by the modeling framework developed for

the original ecologically based components of the model.

This is not to detract from the considerable

accomplishments of the PLM or the spatial modeling

environment (SME) framework in which the PLM has

been implemented. However, developing models in this

fashion may lead to early design decisions which obstruct

the performance of future model components added to

the base model.

In another example, during a model-design workshop in

support of the FLORES model, the initial discussion

among the workshop participants was used to design the

overall framework for the model: the time step, spatial

unit of analysis, and how the model components would

interact. Certain compromises had to be made by each of
35

the workshop groups representing separate components of

the model.

Constraints. Availability of data for model validation

imposes serious constraints in considering variables for

inclusion. Models that use significant amounts of primary

data are constrained in extent or duration, or both. Some

model development approaches deliberately have

restricted themselves to publicly available data for spatial

replicability.

Another issue in the land-use modeling community is the

duplication of effort and sharing of models. We have

observed that several models addressing similar systems

often are developed independently. This has the advantage

of demonstrating unique approaches to the same research

questions and may produce better models by enacting

some form of competition between models. The

downside to multiple development is the considerable

documentation needed to allow model developers to

understand each other’s code such that supplanted code

may be cannibalized into other models. Issues of

intellectual property rights also need to be addressed.

Opportunities. In accordance with Moore’s Law, we have

witnessed incredible increases in raw computing power.

Desktop PCs now run models that would have required a

roomful of mainframe computers a decade ago. This

development is a great enabler and has contributed

immeasurably to expanding land-use modeling efforts.

More computing power gives models the ability to

expand their extents and durations and, at the same time,

make resolutions and time steps smaller.

Modeling tools also are getting better: they do more with

time and are more user-friendly. Development of

modeling tools allows us to build more sophisticated

models in all three dimensions. Various modeling

frameworks have been developed that provide model

developers with tools suited to address common aspects of

land-use systems. They are easier to learn and use than

writing code and often have graphical interfaces. For

example, STELLA (type A in Fig. 4) provides a format for

dynamic modeling that has a very intuitive graphic user

interface and can be used to develop simple student

models or complex research models. Another example is

the SWARM simulation package (type F in Figure 4),

developed at the Santa Fe Institute, which has been used

for modeling multiagent systems and the interactions

between the agents in those systems. A variety of other

development tools are available to researchers. Many of

these tools, such as STELLA, are commercially based,

while others, such as SWARM, are accessible under

various public licensing structures. Of course, many

models we reviewed (5, 13, 14, 15, and 16) still depend

on labor-intensive mathematical programming or

econometrics for their core modeling.

Open-Source Approaches. Models involving time, space,

and human decisionmaking can be incredibly complex

and depend upon knowledge from many disciplines.

Until now, most models have developed in isolation. This

is related to the fact that modelers have been funded

through grants or focused funds from a particular

organization with an interest in human-environmental

modeling. Even in the context of large interdisciplinary

research centers like the NSF networks cited previously,

efforts have been constrained by funds, staff, and

expertise.

In contrast to traditional approaches to model

development, recent advances in worldwide web

technology have created new opportunities for

collaboration in the development of humanenvironmental modeling. Recently, “open-source”

programming efforts have been used to solve complex

computing problems (see for example, Kiernan 1999;

Learmonth 1997; McHugh 1998; OSI 2001). Opensource programming is based on a collaborative licensing

agreement that enables people to freely download

program source code and utilize it on the condition that

they agree to provide their enhancements to the rest of the

programming community. There have been several

successful, complex programming endeavors using the

open-source concept, the most prominent being the

Linux computer operating system. However, some opensource endeavors have failed, but the Linux model has

shown that extremely complex problems can be tackled

through collaboration over the Internet and that this kind

of collaboration can produce robust results. The stability

of the Linux software program is due to “Linus’ Law”

(Linus Torvalds is the initial developer of Linux): “Given

enough eyeballs, all [problems] are shallow” (Raymond

1999). In other words, if we can get enough human eyes

(and brains) with various skills and expertise working

together, many problems, regardless of their complexity,

can be solved because some individual or a team of

individuals will come up with elegant solutions.

How is an open-source approach to computing connected

to human-environmental modeling? We propose that a

similar approach to the development of humanenvironmental models provides the basis for focusing

enough “eyeballs” on important human-environmental

problems (Schweik and Grove 2000). A similar argument

has been made for open source endeavors in other areas of

scientific research (Gezelter 1999). Initiating such an

open-source modeling effort will require several

components: (1) a web site to support modeling

collaboration (e.g., data and interactions among
36

individuals, such as bulletin boards and FAQs); (2) the

establishment of one or more modeling “kernels” (core

components of models using various technologies) that

are designed in a modular fashion and allow relatively

easy enhancements from participants; and (3) the

development of mechanisms for sharing model

enhancements that encourage participation and provide

incentives that are comparable and as valued as publishing

in peer-reviewed journals.

In 2000, we initiated a web site titled “Open Research

System” or ORS (open-research.org). The first step of this

effort was to develop a web-based metadatabase that

allows the open sharing of geographic and nonspatial

datasets and references to publications and reports. If a

reader knows of a model not covered in this review or in

the Appendix, he or she could visit this site, register, and

submit a publication reference to the system database.

This would allow other visitors to the site, through the

search facility, to find the model publication. The next

step of this project is to move toward extending the

design to allow the sharing of various types of land-cover

models in an open-source approach.

We recognize that the application of the open-source

programming concept to human-environmental modeling

might appear daunting and even radical. However, the

Linux example shows how extremely complex problems

can be solved when enough people work on them. Given

the complexities involved in modeling time, space, and

human decisionmaking, the open-source programming

concept might be a vital modeling approach for creative

solutions to difficult human-environmental modeling

problems.

Conclusion

Land-use/land-cover change is a widespread, accelerating,

and significant process. Land-use/land-cover change is

driven by human actions, and, in many cases, it also

drives changes that impact humans. Modeling these

changes is critical for formulating effective environmental

policies and management strategies. This report details

our efforts to inventory land-use change models through

a review of literature, websites, and professional contacts.

We examined in detail 19 of these land-use change

models and developed a framework that evaluated scale

and complexity in three critical dimensions—time, space,

and human choice or decisionmaking—to observe and

describe multiple models in a single synoptic view.

We advocate the use of the LTER report list of social

patterns and processes as a practical guide for

incorporating social processes in modeling land-use

change. This list includes: demography; technology;

economy; political and social institutions; culturally

determined attitudes, beliefs, and behavior; and

information and its flow. We also advocate a more

comprehensive and systematic approach to include social

drivers of land-use change within the context of the NRC

and LTER reports and existing social science efforts.

Finally, open-source modeling offers additional hope for

future modeling. There have been several very successful,

complex programming endeavors using the open-source

concept. These methods might spur the development of

land-use/land-cover modeling as well.

We would like to conclude with some thoughts about

land-use models and policy. Increasingly, the policy

community is interested in land-use models that are

relevant to their needs. To answer policy questions, policy

makers will have to begin to identify the key variables and

sectors that interest them, their scales of analysis, and the

scenarios they anticipate. At the same time, land-use

modelers should begin discussions with policy makers to

understand their needs. Given policy makers’ needs, landuse modelers will have to translate those needs with

particular attention to implicit and explicit temporal,

spatial, and human decisionmaking scale and complexity

and the interactions between scale and complexity.

Further, land-use modelers will need to consider the

relative significance of different drivers on land-use

change within the context of policy makers’ needs. Issues

of scale mismatch between physical and decisionmaking

systems, missing connections between levels of

decisionmaking and intertemporal preferences gain

additional importance in this context. There is the need

to provide a framework for collaboration and model

development. We propose a modular open source

approach and believe land-use change is a sufficiently

important and complex environmental issue that it

urgently needs the collective resource provided by such an

approach.

Acknowledgments

We appreciate the guidance provided by Elinor Ostrom

during the development of this report and thank John

Branigin, Laura Wisen, and Charlotte Hess at the

Workshop in Political Theory and Policy Analysis Library,

Indiana University, who conducted the literature search

for the project. Personnel at the Indiana University

Library provided all of the databases, except for the web

search engines. We gratefully acknowledge support from

the National Science Foundation and the Environmental

Protection Agency through their funding to the Center

for the Study of Institutions, Population, and

Environmental Change (NSF Grant SBR-9521918) and

the Baltimore Ecosystem Study (NSF Grant DEB9714835 and EPA Grant R-825792-01-0).
37

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APPENDIX:

Plots of human driver variables represented in the models

across space and time

A graphical representation of the temporal time step and duration and the spatial

resolution and extent of the models facilitates several observations. The plotted area for

each model represents the spatial and temporal scales under which the model operates.

The 19 models examined in the report (see list on pages 8-9) together cover a range of

scales from less than a day to more than 100 years and from less than 1 ha to more than 1

million km2

. Yet this range of scales is not covered by any one model. Clearly, models

seem to be associated with a particular spatio-temporal niche.

Spatial

Extent

Spatial

Resolution

Temporal

Time Step

Temporal

Duration
51

19

14

12

Temporal Scale [days]

Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

100,000,000

10,000,000,000

1,000,000,000,000

100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

9

10 6

11

5 States

Pixel / Stand

County/

watershed

Landsat scene

Tree

U.S.A

17

13

6

2

11

1 15

18

16

15

5

Farm

CrossSectional

CrossSectional

Human Drivers or Social Patterns 

and Preferences Model Variables

Population Size

19

14

12

Temporal Scale [days]

Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

100,000,000

10,000,000,000

1,000,000,000,000

100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

9

10 6

11

5 States

Pixel / Stand

County/

watershed

Landsat scene

Tree

U.S.A

17

13

6

2

11

1 15

18

16

15

5

Farm

CrossSectional

CrossSectional

Human Drivers or Social Patterns 

and Preferences Model Variables

Population Growth
52

19

14

12

Temporal Scale [days]

Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

100,000,000

10,000,000,000

1,000,000,000,000

100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

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Pixel / Stand

County/

watershed

Landsat scene

Tree

U.S.A

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Farm

CrossSectional

CrossSectional

Human Drivers or Social Patterns 

and Preferences Model Variables

Population Density

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Spatial Scale [m2]

Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

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10,000,000,000

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100,000,000,000,000

(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

7

9

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Pixel / Stand

County/

watershed

Landsat scene

Tree

U.S.A

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Farm

CrossSectional

CrossSectional

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and Preferences Model Variables

Returns to Land Use

(Costs and Prices)
53

19

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

1,000,000

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(1 km2)

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(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

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Pixel / Stand

County/

watershed

Landsat scene

Tree

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CrossSectional

CrossSectional

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Job Growth

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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

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(1 km2)

(100 km2)

(10,000 km2)

(100 x 106 km2)

(1 ha)

(1 x 106 km2)

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Landsat scene

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CrossSectional

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Costs of Conversion
54

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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100

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(1 km2)

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(1 ha)

(1 x 106 km2)

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Rent

Not 4

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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100

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(1 km2)

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(1 ha)

(1 x 106 km2)

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Landsat scene

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Collective Rule Making:

Zoning
55

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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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(1 km2)

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(1 ha)

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Landsat scene

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Collective Rule Making:

Tenure

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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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(1 ha)

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Infrastructure/Accessibility

Distance from Road
56

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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

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0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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(1 km2)

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(1 ha)

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Infrastructure/Accessibility

Distance from Town/Market

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

10,000

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(1 km2)

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(100 x 106 km2)

(1 ha)

(1 x 106 km2)

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Infrastructure/Accessibility

Distance from Village/Settlement
57

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

1

100

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(1 km2)

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(1 ha)

(1 x 106 km2)

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Infrastructure/Accessibility

Presence of Irrigation

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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(1 km2)

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(1 ha)

(1 x 106 km2)

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Infrastructure/Accessibility

Generalized Access Variable
58

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Plot Site Landscape Location Regional Global

Hourly Daily Monthly Yearly Decadal Centennial 

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8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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Village Size

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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

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0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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Silviculture and

Agriculture
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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

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0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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Technology Level

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Hourly Daily Monthly Yearly Decadal Centennial 

3 & 4

8

0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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Affluence
60

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Hourly Daily Monthly Yearly Decadal Centennial 

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Age

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0.1 1 10 100 1000 10,000 100,000

(1 week) (1 month) (1 year) (10 years) (100 years)

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Human Attitudes and Values

& Food Security
61

Glossary

Area base model: Allocates proportions of a given land

base to predefined land-use categories.

Complexity: In this paper we refer to three types of

complexity—spatial, temporal, and human

decisionmaking—all of which are defined in this

Glossary.

Conceptual model: Theoretical description of

socioeconomic and physical processes.

Control (or flow) variables: System elements that

represent the action or change in a state.

Discrete finite state model: Model that is discrete (space

represented as cells or blocks) and finite state (represents

an object as being in only a few, finite number of states or

conditions).

Duration: The length of time for which the model is

applied. The duration of a model’s results may be

reported as the number of time steps used (e.g., 100

annual times steps), the period of the model (100 years),

or the model dates (January 1, 1900, to January 1, 2000).

Dynamic systems model: Systems models that attempt

to capture changes in real or simulated time.

Extent: The total geographic area to which the model is

applied.

Human decisionmaking: How models incorporate

human elements. Human decisionmaking sections of

models vary in terms their theoretical precursors and may

be simply linked deterministically to a set of

socioeconomic or biological drivers, or may be based on

some game theoretic or economic models. Three

attributes of human decisionmaking that are important to

consider in thinking about diverse models of land-use

change are complexity, jurisdictional domain, and

temporal range.

Human decisionmaking (HDM) complexity: The

specificity and detailed consideration given in a model to

the decisions that humans make that affect land-use

change. For this exercise, we have developed a scale of

complexity that ranges from 1 to 6.

Jurisdictional domain: The spatial scope of human

decisionmaking. If desired, a jurisdictional domain may

be split up to reflect resolution, the decisionmaking

domain for a particular actor, and to reflect spatial extent,

in this case the total area over which the actor(s) has(have)

influence, or the jurisdictional range.

Linear planning model: Model that optimizes a linear

function subject to several linear constraints, expressed as

linear inequalities or equalities.

Markov model: A probabilistic modeling method where

model state outcomes rely strictly on previous model

states. With this modeling technique, cell conditional

probabilities are used to change cell states through a series

of iterative operations.

Resolution: The smallest spatial unit of analysis for the

model. For example, in a raster or grid representation of

the landscape, each unit or cell area is usually treated as a

constant size.

Spatial complexity: The presence of a spatial component

of a model or information. Spatial complexity may be

representative or interactive.

Spatial dynamic model: Models that are spatially explicit

and dynamic.

Spatial interaction: Models are based on topological

relationships. Topology is a mathematical procedure for

defining spatial relationships, usually as lists of features,

and using the concepts of connectivity, area definition,

and contiguity.

Spatial Markov model: Spatially explicit model that

carries over memory from one state to the next, but

usually from only the last state; e.g., the probability that

the system will be in a given state (land class) at some

time t2

, is deduced from the knowledge of its state at time

t

1

.

Spatial representation: Spatially representative models

are able to display data as maps but do not include

topology and spatial interactions.

Spatial stochastic model: Spatially explicit model that is

interactive and incorporates random changes to

determine transition probabilities from one land cover to

another.

State variables: Elements that make up the system for

which the model is being developed.

Temporal complexity: A model’s ability to handle a large

number of time steps, time lags, and feedback responses.

Time step: The smallest temporal unit of analysis of the

model variable.

von Thünen: German landowner Johann Heinrich von

Thünen developed a model that relates intensity and type

of land use to transportation costs and land rent. The

model (published in The Isolated State, 1826) involves a

homogenous plane within which exists an isolated city.

Land-use patterns around this city are a function of travel

cost to the city. Land uses producing goods with a

relatively high transportation cost (e.g., perishable or

heavy products) would be produced close to the city, and

land uses producing more durable goods (with lower

transportation costs) would be produced farther from the

city. This arrangement results in a series of different landuse zones around the city. Many authors have applied and

expanded upon von Thünen’s initial theory (e.g.,

Papageorgiou 1990).
Printed on Recycled Paper

Agarwal, Chetan; Green, Glen M.; Grove, J. Morgan; Evans, Tom P.; Schweik, Charles M.

2002. A review and assessment of land-use change models: dynamics of space,

time, and human choice. Gen. Tech. Rep. NE-297. Newtown Square, PA: U.S.

Department of Agriculture, Forest Service, Northeastern Research Station. 61 p.

A review of different types of land-use change models incorporating human processes.

Presents a framework to compare land-use change models in terms of scale (both spatial

and temporal) and complexity, and how well they incorporate space, time, and human

decisionmaking. Examines a summary set of 250 relevant citations and develops a

bibliography of 136 papers. From these papers, 19 land-use models are reviewed in

detail as representative of the broader set of models. Summarizes and discusses the 19

models in terms of dynamic (temporal) and spatial interactions, as well as human

decisionmaking. Many raster models examined mirror the extent and resolution of

remote-sensing data. The broadest-scale models generally are not spatially explicit.

Models incorporating higher levels of human decisionmaking are more centrally located

with respect to spatial and temporal scales, probably due to the lack of data availability at

more extreme scales. Examines the social drivers of land-use change and methodological

trends and concludes with some proposals for future directions in land-use modeling.

Keywords: cellular automata, dynamic model, GIS, human decisionmaking, humanenvironment, land cover, LUCC, model comparison, model modularity, spatial model

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Manuscript received for publication 9 July 2001
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