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E-grāmata: Landscape Analysis and Visualisation: Spatial Models for Natural Resource Management and Planning

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Landscape Analysis and Visualisation: Spatial Models for Natural Resource Management and PlanningPalielināt
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Michael Batty Centre for Advanced Spatial Analysis, University College London Landscapes, like cities, cut across disciplines and professions. This makes it especially difficult to provide an overall sense of how landscapes should be studied and researched. Ecology, aesthetics, economy and sociology combine with physiognomy and deep physical structure to confuse our - derstanding and the way we should react to the problems and potentials of landscapes. Nowhere are these dilemmas and paradoxes so clearly highlighted as in Australia where landscapes dominate and their relationship to cities is so fragile, yet so important to the sustainability of an entire nation, if not planet. This book presents a unique collection and synthesis of many of these perspectives perhaps it could only be produced in a land urb- ised in the tiniest of pockets, and yet so daunting with respect to the way non-populated landscapes dwarf its cities. Many travel to Australia to its cities and never see the landscapes but it is these that give the country its power and imagery. It is the landscapes that so impress on us the need to consider how our intervention, through activities ranging from resource exploitation and settled agriculture to climate change, poses one of the greatest crises facing the modern world. In this sense, Australia and its landscape provide a mirror through which we can glimpse the extent to which our intervention in the world threatens its very existence.
Contributors XXI
Abbreviations XXXI
Introduction 1
1 Understanding Landscapes through Knowledge Management Frameworks, Spatial Models, Decision Support Tools and Visualisation
3
1.1 Introduction
3
1.2 Part 1: Natural Resource Knowledge Management Frameworks and Tools
5
1.3 Part 2: Integrating the Ecology of Landscapes into Landscape Analysis and Visualisation
7
1.4 Part 3: Socioeconomic Dimensions to Landscapes
9
1.5 Part 4: Land Use Change and Scenario Modelling
11
1.6 Part 5: Landscape Visualisation
13
1.7 Future Challenges
15
Part 1: Natural Resource Knowledge Management Frameworks and Tools 17
2 Reading between the Lines: Knowledge for Natural Resource Management
19
2.1 Introduction
19
2.2 Knowledge Hierarchy
20
2.3 Timelag between Question and Answer
23
2.4 Organising the Questions
24
2.5 Integrating Disciplines
26
2.6 Conclusion
27
3 Improving the Use of Science in Evidence-based Policy: Some Victorian Experiences in Natural Resource Management
29
3.1 Context
29
3.1.1 Historical Perspective
30
3.1.2 The Policy Process: Towards Evidence-based Policy
31
3.1.3 Use of Science as Evidence in Policy
32
3.2 Some Victorian Experiences in Natural Resource Management
35
3.2.1 Survey of Policy Analysts
37
3.2.2 Market Research
38
3.2.3 Improving the Utility of Project Outputs
40
3.2.4 Observation of How Policy Decisions Are Made
40
3.3 Case Studies of Successful Science—Policy Influence
41
3.3.1 Sawlogs for Salinity
42
3.3.2 Salinity Investment Framework 3
42
3.3.3 Soil Health
43
3.3.4 Greenhouse in Agriculture
43
3.4 Discussion
44
3.4.1 Toward Better Use of Science in Evidence-based Policy
44
3.5 Conclusion
46
4 The Catchment Analysis Tool: Demonstrating the Benefits of Interconnected Biophysical Models
49
4.1 Introduction
50
4.2 Catchment Analysis Tool: Background and Description
51
4.2.1 The CAT Interface
54
4.2.2 CAT Input Data
56
4.2.3 The CAT Model Components
59
4.2.4 Model Calibration and Conceptualisation
61
4.3 Case Study
61
4.3.1 Hypothetical Case Study
61
4.3.2 Results and analysis
66
4.4 Validation and Model Improvement
68
4.5 Conclusion
69
5 The Application of a Simple Spatial Multi-Criteria Analysis Shell to Natural Resource Management Decision Making
73
5.1 Introduction
74
5.2 Multi-criteria Analysis
74
5.2.1 Spatial Applications
75
5.2.2 The Decision-making Process
77
5.3 The MCAS-S Approach
79
5.3.1 Design Principles
79
5.3.2 Key Functions
80
5.4 Applications
82
5.4.1 Prioritising Revegetation Investment
82
5.4.2 Assessing the Sustainability of Extensive Grazing
85
5.5 Future Trends
89
5.6 Conclusion
90
5.7 Future Research Directions
91
6 Platform for Environmental Modelling Support: a Grid Cell Data Infrastructure for Modellers
97
6.1 Introduction
98
6.2 Background
100
6.3 Methodology
102
6.4 Progress and Discussions
103
6.5 The PEMS Demonstrator Project
105
6.5.1 National Seasonal Crop Monitoring and Forecasting
105
6.5.2 Develop and Demonstrate a Market-based Approach to Environmental Policy on Private Land
108
6.5.3 Wildfire Planning: Consequence of Loss Modelling
109
6.5.4 Land Use Data, Modelling and Reporting
111
6.6 Conclusion
115
Part 2: Integrating the Ecology of Landscapes into Landscape Analysis and Visualisation 119
7 Looking at Landscapes for Biodiversity: Whose View Will Do?
121
7.1 Introduction
122
7.2 To be Human is to Err
122
7.3 What's Good for the Goose?
124
7.4 Consider the Lilies
127
7.5 Best is Bunkum
128
7.6 Varied Perspectives
129
7.6.1 Mapping and Modelling Terrain, Hydrological, Pedological and Geological Features and Climate
129
7.6.2 Vegetation Mapping Using Remotely Sensed Data, Including Vegetation Condition and Temporal Variability
130
7.6.3 Mapping and Modelling Movement
131
7.6.4 Integrating Multiple Perspectives
133
7.7 Conclusion
135
8 Native Vegetation Condition: Site to Regional Assessments
139
8.1 Introduction
140
8.2 Measuring Vegetation Condition at Sites
141
8.3 Measuring Vegetation Condition across Regions
142
8.4 Case Study: Vegetation Condition in the Murray Catchment, New South Wales
143
8.4.1 Study Area
143
8.4.2 Site Data Collection
144
8.4.3 Modelling from the Site to the Region
146
8.5 Results and Discussion for the Murray Catchment Case Study
149
8.6 Conclusion
152
8.7 Future Research Directions
153
9 Towards Adaptive Management of Native Vegetation in Regional Landscapes
159
9.1 Introduction
159
9.2 What Adaptive Management is and is not
161
9.2.1 Step i: Statement of Objectives, Constraints and Performance Measures
163
9.2.2 Step ii: Specification of Management Options
164
9.2.3 Step iii: System Modelling and Model Credibility
165
9.2.4 Step iv: Allocation, implementation and Monitoring — Closing the Loop
165
9.3 Managing and Monitoring Native Vegetation
167
9.3.1 An Example of a Formal Approach to Adaptive Management of Vegetation Condition
169
9.4 Research
175
9.5 Conclusion
176
9.6 Future Directions
177
Appendix
181
10 Revegetation and the Significance of Timelags in Provision of Habitat Resources for Birds
183
10.1 Introduction
184
10.2 Methodology
186
10.2.1 Model Description
186
10.3 Case Study
191
10.3.1 Results
192
10.3.2 Discussion
197
10.4 Caveats and Extensions
199
Appendices
204
11 The Application of Genetic Markers to Landscape Management
211
11.1 Introduction
212
11.1.1 The Need for Information on How Biota Occupies and Moves through Landscapes
212
11.1.2 A Spectrum of 'Genetics' in Landscape Management and Planning
213
11.1.3 Molecular Population Biology Supplies Information Essential for Landscape Planning and Management
213
11.2 Background
215
11.2.1 Three Levels of Analysis Assess Three Levels in Time and Space
215
11.2.2 Main Molecular Tools in Landscape Molecular Population Biology
217
11.3 Case Studies
220
11.3.1 Impacts of Habitat Fragmentation on Cunningham's Skinks
220
11.3.2 Dispersal and Gene Flow of Greater Gliders through Forest Fragmented by Pine Plantation
221
11.3.3 Catchments Catch All: Congruent Patterns in Diverse Invertebrate Fauna in Decaying Wood at a Landscape Scale
222
11.4 Future Trends
223
11.5 Conclusion
225
11.6 Future Research Directions
225
Appendix
231
12 Scenario Analysis with Performance Indicators: a Case Study for Forest Linkage Restoration
235
12.1 Introduction
236
12.2 Background
237
12.3 Linkage restoration
239
12.3.1 Indicator Rule 1: Site Recovery Capacity
240
12.3.2 Indicator Rule 2: Site Biodiversity Value
241
12.3.3 Indicator Rule 3: Landscape Linkage Qualities
242
12.3.4 Indicator Rule 4: Landscape Connectivity
242
12.4 Atherton Tablelands Case Study
243
12.4.1 Restoration scenarios
245
12.4.2 Scenario Evaluation
246
12.5 Conclusion
247
Part 3: Socioeconomic Dimensions to Landscapes 251
13 Strategic Spatial Governance: Deriving Social–Ecological Frameworks for Managing Landscapes and Regions
253
13.1 Introduction
254
13.2 A Potted History of Catchments for Resource Governance
254
13.3 Defining Regions for Resource Governance
256
13.3.1 Principle 1
256
13.3.2 Principle 2
257
13.3.3 Principle 3
259
13.4 Application of Principles to Spatial Analysis
259
13.4.1 Delineating Civic Regions from a Social Surface
260
13.4.2 Deriving a Hierarchy of Civic Regions
262
13.4.3 Deriving Ecoregions
264
13.4.4 Integrating Ecoregions and Civic Regions through Boundary Optimisation
265
13.4.5 Comparing the Performance of Regions
266
13.5 Conclusion: Past, Present and Future Resource Governance
269
13.6 Future Directions
270
14 Placing People at the Centre of Landscape Assessment
277
14.1 Introduction
277
14.2 Background
278
14.3 Methodology
279
14.3.1 Pressure–State–Response Model
279
14.3.2 Driving Forces–Pressure–State–Impact–Response Model
281
14.3.3 Millennium Ecosystem Assessment Framework
281
14.3.4 Indicator Selection
282
14.4 A Landscape Approach for Victoria
283
14.4.1 Definitions of Five Victoria Landscapes
284
14.4.2 The Role of Indicators
285
14.5 Case Study 1: Semi-arid Landscape
285
14.5.1 Overview
286
14.5.2 Employment Indicator
288
14.5.3 Index of Stream Condition Indicator
290
14.5.4 Land Use Diversity Indicator
291
14.5.5 Management Response
293
14.6 Case Study 2: Coastal Landscape
293
14.6.1 Overview
294
14.6.2 Visitors to Parks and Reserves Indicator
295
14.6.3 Ratio of Land Value to Production Value Indicator
296
14.6.4 Land Use Diversity Indicator
297
14.6.5 Policy Response
298
14.7 Overview of Results
299
14.8 Conclusion
299
14.9 Future Research Directions
300
15 The Social Landscapes of Rural Victoria
305
15.1 Introduction
305
15.2 A Narrative of Rural Transformation in Australia
306
15.2.1 International Agricultural Competition
306
15.2.2 Agricultural Restructuring
307
15.2.3 Amenity Values in the Rural Land Market
307
15.2.4 Indicators Derived from the Narrative
308
15.3 From Indicators to Social Landscapes
310
15.3.1 Factor Analysis Using the Principal Components Method
310
15.3.2 Creating a Geography of Amenity and Intensification
314
15.4 Five Social Landscapes
315
15.4.1 The Production Landscape
316
15.4.2 The Transitional Landscape
317
15.4.3 The Amenity Farming Landscape
318
15.4.4 The High Amenity Landscape
319
15.4.5 The Intensive Agriculture Landscape
319
15.5 Conclusion
322
15.6 Future Research Directions
323
16 A Decision Aiding System for Predicting People's Scenario Preferences
327
16.1 Introduction
327
16.2 Background
328
16.3 An Extra Step for the SDSS Discipline
329
16.4 Description of the Preference Prediction Software
331
16.4.1 Finding a Larger Set of Criteria
331
16.4.2 Finding Relationships between Criterion Scores and Overall Scenario Merit
331
16.4.3 The Underlying Assumption
333
16.5 An Urban Planning Case Study Application of the Preference Prediction Software
334
16.5.1 Assigning Criteria Scores to the Scenarios
335
16.5.2 Predicting Scenario Ratings for Overall Merit
336
16.5.3 Checking the Personal Characteristics of the Advisors
338
16.5.4 Predicting Scenario Merit Ratings on Behalf of Past Workshops
338
16.5.5 Exploring How Scenario Ratings Were Derived
339
16.5.6 Searching for Reasons behind Each Scenario Merit Rating
342
16.5.7 Predicting All Groups' Preferences Simultaneously
345
16.6 Future Trends
347
16.7 Conclusion
347
16.8 Future Research Directions
348
Part 4: Land Use Change and Scenario Modelling 351
17 Mapping and Modelling Land Use Change: an Application of the SLEUTH Model
353
17.1 Introduction
353
17.2 Methodology
355
17.3 Results and Discussion
358
17.4 Conclusion
364
18 Uncertainty in Landscape Models: Sources, Impacts and Decision Making
367
18.1 Introduction
368
18.2 Models, Variability and Sources of Uncertainty
369
18.2.1 Model Structure
370
18.2.2 Natural Variability, Temporal Resolution and Spatial Resolution
371
18.2.3 Taxonomic Scale and Data Collection
375
18.2.4 Summary on Models and Sources of Uncertainty
377
18.3 Model Uncertainty and Decision Making
377
18.4 Conclusion
381
19 Assessing Water Quality Impacts of Community Defined Land Use Change Scenarios for the Douglas Shire, Far North Queensland
383
19.1 Context and Case Study Location
384
19.2 Dialogue over Sustainable Future Landscapes and Seascapes
386
19.3 Methodology of an Application of a Social–Ecological Framework for Sustainable Landscape Planning
387
19.3.1 Stage I: Community Perceptions and Visions
387
19.3.2 Stage II: Community-driven Landscape Scenarios
389
19.3.3 Stage III: Modelling of Landscape Scenarios and Assessing Water Quality
389
19.4 Results and Discussion
391
19.4.1 Visions for the Douglas Shire Coastal Landscape
391
19.4.2 Spatially Explicit Land Use Change Scenarios
392
19.4.3 Inputs into SedNet for Water Quality Analysis and Model Results
399
19.5 Conclusion
401
20 Analysing Landscape Futures for Dryland Agricultural Areas: a Case Study in the Lower Murray Region of Southern Australia
407
20.1 Introduction
408
20.2 Futures Thinking and Scenario Analysis
409
20.3 The Lower Murray Landscape Futures study
411
20.3.1 Collaborative and Participatory Approach
412
20.3.2 Defining Targets, Scenarios and Policy Options
413
20.3.3 Landscape Futures Analysis
419
20.4 Results
425
20.5 Risk, Preference and Strategic Policy Adoption
429
20.6 Further Research
430
20.7 Application in Other Regions and Contexts
431
20.8 Conclusion
431
21 Applying the What If? Planning Support System for Better Understanding Urban Fringe Growth
435
21.1 Introduction
435
21.2 The What If? Planning Support System
436
21.2.1 Suitability Module
438
21.2.2 Demand Module
438
21.2.3 Allocation Module
438
21.3 Mitchell Shire Application of What If?
439
21.3.1 Input Data Layers
442
21.3.2 Land Suitability Analysis
445
21.3.3 Demographic Projections and Land Use Demand
447
21.3.4 Future Land Use Allocation Scenarios 2031
449
21.4 Future Work
451
21.5 Conclusion
451
Part 5: Landscape Visualisation 455
22 Understanding Place and Agreeing Purpose: the Role of Virtual Worlds
457
22.1 Introduction
457
22.2 Established Options for Understanding Place
459
22.3 Emerging Options
460
22.4 Development Methodology
461
22.4.1 SIEVE
461
22.4.2 Links to Decision Support Systems
463
22.4.3 Virtual Decision Environment
463
22.5 Conclusion
464
23 Geographic Landscape Visualisation in Planning Adaptation to Climate Change in Victoria, Australia
469
23.1 Introduction
470
23.2 Context of Visualisation and 'Sense of Place'
471
23.3 Climate Change Predictions and Impacts in South-eastern Australia
472
23.3.1 Climate Change and the Need for Ecological Connectivity
473
23.3.2 Biolink Zones in South-eastern Australia
474
23.3.3 Visualisation Tools for Explaining the Context of Biolinks
474
23.3.4 Visualisation of Environmental Change at a Site over Time
475
23.4 Realism behind Visualisation Technology
479
23.5 Realism at the Front End
480
23.6 Future Directions
483
23.7 Conclusion
484
24 Visualising Alternative Futures
489
24.1 Introduction
490
24.2 The Barwon Heads Peri-urban Development Visualisation Tool
491
24.3 The Central Business District of Melbourne What the City Might Be? Prototype
495
24.3.1 Marvellous Melbourne
495
24.3.2 Melbourne and the Removal of Significant Buildings
497
24.3.3 Prototype World
498
24.3.4 Initial Impressions
502
24.4 Visualising Proposed Landscapes: Sydney Rd, Brunswick
503
24.5 Usefulness of the Prototypes
505
24.6 Conclusion
505
25 Virtual Globes: the Next GIS?
509
25.1 Introduction
510
25.2 Methodology
511
25.3 Results
515
25.3.1 Hardware
515
25.3.2 Background Data
516
25.3.3 GIS Data Import
517
25.3.4 Display and Data Manipulation
519
25.3.5 Data Sharing
522
25.3.6 Openness and Customisation
522
25.3.7 Performance
522
25.4 Discussion
523
25.4.1 Applications
524
25.5 Conclusion
529
26 A Virtual Knowledge World for Natural Resource Management
533
26.1 Introduction
534
26.2 Virtual Worlds
535
26.3 NRM Virtual Knowledge World
536
26.4 Bet Bet Virtual Landscape
537
26.5 Victorian Virtual NRM Knowledge Arcade
544
26.6 Future Work
547
26.7 Conclusion
548
27 Computer Games for Interacting with a Rural Landscape
551
27.1 Introduction
552
27.2 Cognitive Science
554
27.3 Conversation Theory
555
27.4 Visualisations
555
27.4.1 Viewing Simulations
556
27.4.2 Mobile and Computer Games
557
27.5 Game Development
558
27.5.1 Trainz
559
27.5.2 Farcry
561
27.5.3 Unreal Tournament 2004
562
27.5.4 Second Life
563
27.6 The Bushfire Rescue Game
565
27.7 Conclusion
568
28 Automated Generation of Enhanced Virtual Environments for Collaborative Decision Making Via a Live Link to GIS
571
28.1 Introduction
572
28.2 Background
574
28.3 Methodology
576
28.4 Case Study and Discussion
580
28.5 Conclusion and Outlook
587
29 Land Use Decision Making in a Virtual Environment
591
29.1 Introduction
592
29.2 Rational Decision Making
593
29.2.1 Values, Attitudes and Behaviours
593
29.3 Methodology
594
29.3.1 Social: Agent-based Modelling
595
29.3.2 Environmental: Three-dimensional Visualisation
595
29.3.3 Economic: Experimental Economics
596
29.3.4 Experiment Design
597
29.4 Environmental and Economic Efficiency: Results and Discussion
600
29.4.1 Complexity
601
29.4.2 Visualisation
602
29.4.3 Social context (ABM)
604
29.4.4 Value Priorities
605
29.4.5 Experience
606
29.5 Conclusion
606
Index 609
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