Part III
Synthesis and Prospect
© 2008 by Taylor & Francis Group, LLC
163
9
Synthesis, Comparative
Analysis, and Prospect
Michael J. Hill and Richard J. Aspinall
CONTENTS
9.1 Introduction 163
9.2 A Summation of the Chapters 164
9.3 Overall Messages 169
9.3.1 Realizing the Full Potential from Remote Sensing 169
9.3.2 Application of Integrated Methods 171
9.3.3 Placing Analysis in Context 173
9.4 Conclusions 174
References 176
9.1 INTRODUCTION
This book has examined the issue of integrated analysis of spatial structure and
spatiotemporal processes related to land use change in terrestrial coupled human
environment systems. The consequences of land use change have been to transform
alargeproportionofthelandsurfaceoftheEarth,andthesechangesareinuenc-
ing the global carbon cycle, regional climate, water quality and distribution, and
biodiversity through habitat loss.
1
The book presents a number of case studies that
include urban, wilderness, wet tropical forest, and arid desert-like environments,
human population densities from high to very low, and those that demonstrate both
direct and indirect human inuences (Table 9.1). The case studies include several that
focusonanalysisofclearingoftropicalforestenvironments(Chapters4,5,6,and7;
Table 9.1). These are environments of high signicance to global carbon stocks, bio-

Methodological
approach(es)
Geographical-system
context
1. Aspinall Technical overview of
dynamics, scale,
accuracy, uncertainty,
pattern and process
Models—conceptual, GIS,
RS, CA, MAS, simulation,
statistical, empirical,
visualization,
space-time scaling
International research
frameworks—GLP,
bio-complexity, etc.
2. Hill Transformation of
spatiotemporal data
and relationships to
simple indexes
Integration of time-series
analysis, spatial analysis,
numerical and
heuristic methods
Savanna and grassland
biomes/livestock
as agents
3. Byron/Lesslie Social surveys of
attitudes to natural
resource management

Amazonian tropical
forest
8. Fragkias and
Seto
Urban expansion/
megacities
Multiple logistic regression,
pseudo-Bayesian model
averaging to get predicted
probabilities of change
Cities of Pearl River
Delta—Shenshen,
Guangzhou, and
Foshan, China
© 2008 by Taylor & Francis Group, LLC
Synthesis, Comparative Analysis, and Prospect 165
vulnerability,andadaptabilityofcoupledhumanandnaturalsystems;scaleissues;
and accuracy and uncertainty issues. Approaches incorporating all of these consider-
ationscanbegroupedunderthegeneralheadingof“models,”butthisincludesadiver-
sity of types from conceptual through to empirical (Table 9.1). A broader context for
incorporating the different elements of case studies of land use change is to consider
a range of integrating frameworks (Figure 9.1). These include the analytical struc-
ture for the Global Land Project
7
; the Human Ecosystem Model
8
;theverygeneral
frameworkofexplicitfocusonlinkagesbetweenthedynamicsofhumanandnatural
systems of the U.S. National Science Foundation Biocomplexity in the Environment
program ( and, with a view

InChapter3,asocialsurveyapproachisusedtodevelopunderstandingofthe
attitudes and perceptions of rural communities. The work introduces important
methodologicalissuessurroundingrelationshipsbetweenpointsurveydataand
spatially explicit biophysical features of a landscape. The analysis looks at relation
-
shipsbetweenanswerstosurveyquestionsaboutlandmanagementpracticesand
undesirable landscape features based on a distance analysis. This immediately intro-
duces interesting questions about how humans perceive their spatial environment
andhowtheirspatialsensitivitiesandawarenessdiffer.Italsorequiressomeatten-
tiontothelegacyeffectsofhistoricalexperienceandviewsofthelandscape,rooted
inhistoricparadigms,andtheimpactofaspatialviews,norms,andmediaissues
© 2008 by Taylor & Francis Group, LLC
166 Land Use Change: Science, Policy and Management
D
e
c
i
s
i
o
n
M
a
k
i
n
g
E
c
o

What predictable differences might the
changes causes?
How should the landscape be changed?
Data Information Cultural knowledge
Representation Models
Process Models
Evaluation Models
Change Models
Impact Models
Decision Models


Biogeochemistry
Biodiversity
Water
Air
Soil









Land Use &
Management
Population
Social/Economic

T3.1
$&
T3.2
#!


T3.3
'%%
T2.3
T1.1
T1.2
T1.3
Natural Cultural
Socio
–
economic
Social
Institutions
Social
Cycles
Social
Order
" 
Natural System
Human System
(a)
(b)
(c)
(d)
FIGURE 9.1 (See color insert following p. 132.) A variety of integrating frameworks that seek interdisciplinary denition and focus on key ques-

-
tionandregeneration.Socialandeconomicprocessesareimportant,asislocaland
regional context, and deforestation follows a temporally explicit trajectory described
byasigmoidalcurve.Patternsarelinkedtoprocessatdifferentscales:national,
regional, and local. Although distances to roads and towns, proximate factors in
the terminology of Geist and Lambin,
11,12
are the best predictors at the national
level, deforestation and regeneration occur at local hot spots at a regional level.
However,atalocallevelmoreexplicitrelationshipsareobtainedwithaccessibility
andsoiltype,sincedeforestationratesarestronglyrelatedtoaspatialmetric—
forest edge density. This provides an example of spatially explicit analysis deriving
a metric with direct meaning in relation to deforestation potential associated with
accessibility. Hence, spatial analysis and calculation of pattern metrics can be used
to generate indicators of likelihood of deforestation at scales in which pattern and
process are directly connected.
This theme of pattern metrics as descriptors or indicators of landscape processes
is continued further in Chapter 6. Here, changes in pattern metrics are analyzed for
landscape patches through a number of time steps using remotely sensed imagery.
In particular the interspersion-juxtaposition index and mean patch size index pro
-
vide measures of fragmentation. These indices give temporal proles for different
land cover classes such as forest, savanna, and rice agriculture. The analysis in the
chapter combines the denition of landscape change in terms of pattern metrics,
forexample,uctuationsthroughtimeintheinterspersionoflanduse/landcover
classes,withassignmentofmeaningtothechangesinpatternmetrics,forexample,
reductioninforestinterspersionasforestpresencedeclineswiththespreadofrice
agriculture.Explanationofregionaldifferencesisbasedonuseofcontextualinfor
-
mation,forexample,proximitytoareasofmilitaryinstabilitydeterringagricultural

-
lishes the spatial skeleton, that is the foundation for the nal fragmentation pattern.
The three chapters that deal with spatial analysis of tropical deforestation and
fragmentation provide a powerful case for combination of spatial analysis and
metrication of spatial patterns in disturbed landscapes; analysis of changes in spatial
metricsthroughtimeasindicatorsofparticularprocessesandparticulartrajecto
-
ries in landscape structure to which economic and biophysical functionality can be
ascribed; and application of detailed analysis of socio/econo-political contexts in
ordertoexplainevolutionofspatialpatternsandregionaldifferencesinpatterns
described by spatial metrics.
ThenalcasestudyinChapter8addressestheissueofrapidurbantransforma
-
tion. A decision theory framework is presented that uses policies, economic data,
aneconomicmodel,andanoptimizationprocedurethatminimizesanobjective
function to produce probability maps of predicted urban expansion. There is no
best or true outcome; the output is a probability surface. The expected rates of
growth for the global megacities—there were already 19 cities with populations in
excess of 10 million in 2000—introduces an urgency to development of predictive
models for planning due to expanded need for energy, sanitation, transport, educa
-
tion, emergency management, health and safety, and clean air and water. Capture of
social, psychological, and economic drivers within a complex spatial context is even
more important. Many cities are already “landscapes of fate” that have most of the
undesirable properties described later in this chapter, and depend upon wealth gener
-
ationandgentricationofpooreroruglierareasforsignicanttransformationback
to a more “desirable” landscape. Therefore, the modeling described in Chapter 8 is of
paramount importance in providing spatially explicit probabilities indicating areas
© 2008 by Taylor & Francis Group, LLC

dictional, political, and psychological units and inuences, there needs to
be concerted and integrated application of a variety of numerical, heuristic,
spatial, and temporal methods to derive the highest levels of understanding
andquanticationofdependencybetweenpatternsandprocesses.
4. Theanalysisfromnumber3shouldbeplacedinapragmaticcontextthrough
closer relations between science with management and policy related to
land use and land use change.
9.3.1 REALIZING THE FULL POTENTIAL FROM REMOTE SENSING
TheimplementationplanfortheGlobalEarthObservationSystemofSystems
(GEOSS)
15
hasdenedninekeytargetsfordeliveryofsocietalbenetoverthenext
10 years, including improving the management and protection of terrestrial and
coastal ecosystems; supporting sustainable agriculture and combating desertica
-
tion; and understanding, monitoring, and conserving biodiversity. A large number
of observational requirements have been dened for ecosystems, biodiversity assess
-
ment,andagriculturalmonitoring.Theseincludeparticularpropertiesassociated
with land use change such as burned areas, land degradation, species distribution,
alien species, extent and location of ecosystems and habitat types, fragmentation
of ecosystems and community composition, cultivation and clearing, and grazing
© 2008 by Taylor & Francis Group, LLC
studyoftheseinteractions(Figure9.2).Simplyput,fourofthemajormessagesfrom
170 Land Use Change: Science, Policy and Management
Land use
Human control
Dynamic land transitions
Land cover
Biophysical control

Clean water
Waste recycling
Food/fiber/fuel
Recreation
Social systems
Population
Social/economic structure
Political/institutional regimes
Culture
Technology
Ecological systems
Biogeochemistry
Biodiversity
Air
Water
Soil
FIGURE 9.2 A diagram of the major elements of the coupled human environment system (after GLP, 2005;
Figure 2) augmented with key enabling technologies, methods and data, and paradigms for analysis of coupled
human environment systems and delivery of societal benets.
© 2008 by Taylor & Francis Group, LLC
Synthesis, Comparative Analysis, and Prospect 171
impacts. An improvement in the quality and coverage of observations of the land
surfacefromremotesensingisneededtorealizetheserequirements.
All of the case studies here that address tropical rain forest clearance (in South
America, Asia, and Africa) depend to a large degree on remote sensing as a primary
sourceofdataforbasicchangedetection.CrewsinChapter6discussestheprob
-
lems with multiplicative errors when using images from multiple dates, even with
high accuracies for individual classications. The increasing availability of very
high-resolution imagery from space (down to 60 cm pixel resolution) means that

andtheproductsofthissystemwouldprovideabench-
mark level of reliable, spectrally comprehensive, temporal and spatial coverage with
explicit quantitative uncertainty estimates.
This improved information content in remote sensing addresses the need
forbettercaptureofsystemdynamicsintimeandspaceinthefuture.However,
historical analysis can be greatly enhanced just by comprehensive analysis of global
Landsat MSS and Landsat TM/ETM archives. The Australian government sup
-
portedaninnovativeprogramtoanalyzemorethan30yearsofLandsatdatafor
Australia in order to monitor and measure land cover change to support a national
carbon accounting system.
19,20,21
A large archive exists for the United States, for
example, but a full-time series analysis of this has yet to be undertaken (research
has commenced to examine forest disturbance using these data but only at a regional
scale; Sam Goward, personal communication). The preceding is not intended to over-
© 2008 by Taylor & Francis Group, LLC
thebasicsciencequestionsoutlinedbyAspinallinChapter1.Manyaccuracyand
172 Land Use Change: Science, Policy and Management
emphasize the importance of remote sensing in integrated analysis, merely to high-
light the critical role it can and should play.
The important biophysical processes are described in detail by many mod
-
elsofvaryingcomplexity.
22,23
Often these models, developed for site-based
application, are difcult to supply with spatially explicit parameters and inputs,
resultingininsufcientinformationtoconstrainmodelparametersandprovide
effective model predictions.
24

histories and ne scale land statistics
30
), and increased ability to consider the spatial
organization of populations, leading to development of spatially explicit data sets
containingsurveyinformationonattitudesandbehaviorswithconcomitantcon
-
cerns for condentiality.
31
In addition observation of urban ecosystems is explicitly
consideredwith,forexample,theBaltimoreandPhoenixsitesintheU.S.LongTerm
Ecological Research network.
32
Integrationofallthisinformationandunderstandingofdynamicsmaybepro-
videdbylandusechangemodels
33
that range from cellular automata types,
34
to
statistical or simulation models,
33
agent-based models,
35
and integrated ecological
and economic modeling.
36
These models must accommodate dynamics across
scales, represent the driving forces, capture spatial interactions and neighborhood
effects, capture the temporal dynamics, and then be capable of integration across
disciplinary domains.
33

However, the analysis of the coupled human
environmentsystemmustalwaysbeplacedincontext.Thetrade-offbattlebetween
anthropocentricandenvirocentricviewsofthefutureoftheearthsystemwillbea
majorbattlegroundinthe21stcentury.Socontextmatters.
9.3.3 PLACING ANALYSIS IN CONTEXT
Therequirementfordeliveryofsocietalbenetthathasbeenemphasizedinmany
forums
7,15
has placed an imperative on the integration and harmonization of analysis,
modeling, and decision making and linkage of science to management and policy
making by society. Emphasis is placed on the context for problems and whether they
areimportant(orperceivedasimportant).Thisoftendependsoncommunicationof
issuesinalanguageorframeworkthatimpactsdirectlyonsocietymembers.
Althoughnotwithoutcontroversy,thesimplesocietalmodelofLuhmann
41
pro-
vides one way to conceptualize the problem (Figure 9.3). Luhmann views society as
a centerless set of “function systems” that constrain both what can be communicated
and how it is communicated. He labels economy, law, science, politics, religion,
andeducationasthemostimportantfunctionsystemsincontemporarysociety.
42
These function systems have different time intervals for external communication,
rangingfromdailydiscourseinscienceandreligion,monthlycourtprocessesin
law,quarterlyteachingandreportingcyclesineconomicsandeducation,toannual
electioncyclesinpolitics(averageoveralllevelsofgovernment).Informationfrom
onefunctionsystemonlybecomesactiveinanotherfunctionsystemwhenitis
translatedintothecodeofthatfunctionsystem.Hence,environmentalinforma
-
tiondoesnothaveanimpactoneconomicorpoliticalprocessuntilitistranslated
intothecodeoftheeconomicorpoliticalfunctionsystem.Globalclimatechange

43
9.4 CONCLUSIONS
This book documents the development of analysis of land use change through pre-
sentation of a range of case studies, placed in context through review of land use
science, integrative methods, and frameworks for addressing complexity and inter
-
relationships. An abiding theme that lingers subliminally throughout this book is the
time-limited decision space of Potschin and Haines-Young
10
and the trajectories of
Steinitz and colleagues.
9
By these means human decisions lead to “landscapes of fate”
(perhaps a completely urbanized world), and human aspirations crave “landscapes of
desire” (perhaps a fairytale land). The challenge is to use the sophisticated science
marriedtosocialandsoftsystemsparadigmstoweaveapathtoalandscapethat
preserves the full dimensions of human desire and aspiration while retaining a fully
functional earth system.
Data Information Cultural Knowledge
Politics
Societal
Sub
–
systems
Active
–
translated
into code of
function
subsystem

Positive properties
e.g., Aesthetically pleasing
Pastoral
Exciting
Productive
Multi-functional
Uplifting
Relaxing
Balanced ecosystem
Remote
sensing for
change
detection
Policy context,
importance,
impact, effect
of changes
Landscape
of desire
Current landscape
Management
New
landscape
Negative properties
e.g., Ugly
Industrial
Boring
Artificial
Uni-functional
Depressing

of the Royal Society B 361, 195–210, 2006.
3. Soares-Filho, B. S. et al. Modelling conservation in the Amazon basin.
Nature 440,
520–523, 2006.
4. Silva, J. F. et al. Spatial heterogeneity, land use and conservation in the cerrado region
of Brazil.
Journal of Biogeography 33,536–548,2006.
5. Defries,R.S.,Asner,G.P.,andHoughton,R.Trade-offsinlandusedecisions:Towards
a framework for assessing multiple ecosystem responses to land-use change. In:
Defries, R. S., Houghton, R., and Asner, G. P., eds.,
Ecosystems and Land Use Change.
Geophysical Monograph Series 153, American Geophysical Union, 2004.
6. Asner, G. P. et al. Ecosystem structure along bioclimatic gradients in Hawaii from
imaging spectroscopy.
Remote Sensing of Environment 96,497–508,2005.
7. GLP.
Global Land Project. Science Plan and Implementation Strategy. IGBP Report
No. 53/IHDP Report No. 19. IGBP Secretariat, Stockholm, 64 pp, 2005.
8. Machlis, G. E., Force, J. E., and Burch, W. R. The human ecosystem, Part I: The human
ecosystemasanorganizingconceptinecosystemmanagement.
Society and Natural
Resources 10,347–367,1997.
9. Steinitz,C.etal.
Alternative Futures for Changing Landscapes: The Upper San Pedro
River Basin in Arizona and Sonora.IslandPress,Washington,D.C.,202pp,2003.
10. Potschin,M.,andHaines-Young,R.“Rio+10”,sustainabilityscienceandLandscape
Ecology.
Landscape and Urban Planning 75,162–174,2006.
11. Geist, H., and Lambin, E. F. Proximate causes and underlying driving forces of tropical
deforestation.

Technical Report No. 28, Australian Greenhouse Ofce, Canberra,
2001.
20. Furby, S. Land cover change: Specication for remote sensing analysis.
National
Carbon
Accounting System, Technical Report No. 9. Australian Greenhouse Ofce,
Canberra, 2002.
© 2008 by Taylor & Francis Group, LLC
Synthesis, Comparative Analysis, and Prospect 177
21. Furby,S.,andWoodgate,P.W.,eds.Remotesensinganalysisoflandcoverchange-pilot
testing of techniques. National Carbon Accounting System,
Technical Report No. 16,
Australian Greenhouse
Ofce, Canberra, 2002.
22. Plummer, S. E. Perspectives on combining ecological process models and remotely
sensed data.
Ecological Modelling 129, 169–186, 2000.
23. Nightingale,J.M.,Phinn,S.R.,andHeld,A.A.Ecosystemprocessmodelsatmultiple
scales for mapping tropical forest productivity.
Progress in Physical Geography 28,
1–41, 2004.
24. Barrett, D. J. et al. Prospects for improving savanna carbon models using multiple con
-
straints model-data assimilation methods.
Australian Journal of Botany 55, 689–714,
2005.
25. Todling, R. Estimation theory and atmospheric data assimilation In: Kasibhatla, P. et al.,
eds.,
Inverse Methods in Global Biogeochemical Cycles. American Geophysical
Unions, Washington, D.C., 49–65, 2000.

24, 383–400, 2000.
35. Barreteau,O.,andBousquet,F.SHADOC:Amulti-agentmodeltotackleviabilityof
irrigated systems.
Annals of Operations Research 94,139–162,2000.
36. Vionov,A.etal.Patuxentlandscapemodel:integratedecologicaleconomicmodelling
of a watershed.
Environmental Modelling and Software 14,473–491,1999.
37. Hill, P., Cresswell, H., and Hubbard, L. Spatial prioritisation of NRM investment in the
West Hume area (Murray CMA region). Technical Report No. 2006, CSIRO Water for
aHealthyCountryNationalResearchFlagship,Canberra,2006.
38. Lesslie, R., Barson, M., and Smith, J. Land use information for integrated natural
resources management—a coordinated national mapping program for Australia.
Journal of Land Use Science 1, 45–62, 2006.
39. Thackway, R., and Lesslie, R. Vegetation assets, states and transitions (VAST): Account
-
ing for vegetation condition in the Australian landscape. BRS Technical report, Bureau
of Rural Sciences, Canberra, 2005.
40. Thackway, R., and Lesslie, R. Reporting vegetation condition using the vegetation,
assets, states and transitions (VAST) framework.
Ecological Management and
Restoration 7, s53–s62, 2006.
© 2008 by Taylor & Francis Group, LLC
178 Land Use Change: Science, Policy and Management
41. Luhmann, N.
Ecological Communication. Translation Bednarz, J. Jr. University of
Chicago Press, Chicago, 187 pp, 1989.
42. Grant, W. E., Peterson, T. R., and Peterson, M. J. Quantitative modelling of coupled
natural/human systems: simulation of societal constraints on environmental action
drawingonLuhmann’ssocialtheory.
Ecological Modelling 158, 143–165, 2002.