Title Page
ABSTRACT
국문 초록
Contents
NOMENCLATURE 21
CHAPTER 1. INTRODUCTION 22
1.1. Background 22
1.1.1. Adaptive Management Planning for Climate Change Adaptation 24
1.1.2. Smart Technology to Manage Disturbance 26
1.1.3. Fine Dust Pollution Impacts on Climate Change 28
1.1.4. Benefits of GI: Focused on Fine Dust Pollution 30
1.2. Importance and Significance of the Study 36
1.3. Purpose of the Study 39
1.4. Definition of Terms 41
1.5. Dissertation Outline 43
1.5.1. Research Design of the Dissertation 43
1.5.2. Research Boundary 46
CHAPTER 2. SMART LANDSCAPE MANAGEMENT PLANNING FOR CLIMATE CHANGE ADAPTATION: FOCUSED ON FINE DUST POLLUTION 48
2.1. Sustainable and Resilient Urban System Management Planning: Focused on Social-Ecological Vulnerability to Fine Dust Pollution 48
2.1.1. Background 48
2.1.2. Materials and Methods 51
2.1.3. Results 63
2.1.4. Discussion 74
2.1.5. Conclusion 78
2.2. Landscape and Ecosystem Health Management Planning using Smart Technology 81
2.2.1. Background 81
2.2.2. Materials and Methods 84
2.2.3. Results 97
2.2.4. Discussion 104
2.2.5. Conclusions 106
2.3. Landscape Management Planning using Smart Green Infrastructure: Validation of Smart Green Infrastructure Technology 108
2.3.1. Background 108
2.3.2. Materials and Methods 111
2.3.3. Results 125
2.3.4. Discussion 132
2.3.5. Conclusion 135
CHAPTER 3. CONCLUSION 137
3.1. Summary 137
3.2. Implication 140
3.2.1. Smart Technology in Landscape Management Offers a Means to Enhance Social and Ecological Functions 140
3.2.2. Smart Landscaping Management is a Long-Term Economical Solution 140
3.2.3. Smart Landscape Management Serves as the Landscaping for Carbon Neutrality 141
3.2.4. Measures to Improve Climate Resilience for Sustainable Social-Ecological System 142
3.3. Future Study 143
REFERENCES 145
SUPPLEMENTARY MATERIALS 170
A. Landscape and Ecosystem Health Management Planning using Smart Technology 170
B. Landscape Management Planning using Smart Green Infrastructure: Validation of Smart Green Infrastructure Technology 178
Table 1.1. Results of TF-IDF Analysis (Only Top 20) 32
Table 2.1. Resilience evaluation index for fine dust pollution 58
Table 2.2. Resilience evaluation index for sensitivity to fine dust pollution 59
Table 2.3. Results of the resilience evaluation index for fine dust pollution. The indicators were divided into exposure, sensitivity, and adaptive capacity factors 64
Table 2.4. Results of the landscape fragmentation analysis 73
Table 2.5. Average annual fine dust pollution absorption by trees 91
Table 2.6. Scenario settings 95
Table 2.7. The results of scenario analyses 103
Table 2.8. Smart GI proposed at the study site 114
Table 2.9. Smart GI maintenance and management costs 118
Table 2.10. Information of fine dust pollution measuring devices and actuator 121
Table 2.11. Comparison of the fine dust pollution reduction performance between GI types 1, 2, and 3 122
Table 2.12. Design of GI scenarios 124
Table 2.13. Initial creation cost by type of smart GI 125
Table 2.14. Annual maintenance and management costs per meter of GI type 1 125
Table 2.15. Annual maintenance and management costs per meter of GI type 2 126
Figure 1. Adaptive management cycle 25
Figure 1.2. Results of Centrality Analysis (Only Top 5) 33
Figure 1.3. Result of Network Analysis 34
Figure 1.4. Results of Topic Modeling Analysis 35
Figure 1.5. Research Flow 45
Figure 1.6. The Location of the Study Site. Suwon in the Republic of Korea was Selected as the Case Study: (a) The Republic of Korea and (b) A map of Suwon divided into Four... 47
Figure 2.1. The Location of the Study Site. Suwon in the Republic of Korea was Selected as the Case Study to Explore the Resilience of the Region to Fine Dust Pollution: (a)... 51
Figure 2.2. Research Flow 53
Figure 2.3. Sites with High Concentrations of Areas Exposed to Fine Dust Pollution Obtained from the Suwon District Boundary Data. The Darker the Color is, the More... 65
Figure 2.4. Sites with Long-Duration Exposure to Fine Dust Pollution. The Maps Show Suwon District Boundary Data, and the Darker the Color is, the More Exposed the Area is... 66
Figure 2.5. Areas with Long-Duration Exposure to Fine Dust Pollution. Roadside Green Spaces Play an Important Role in Reducing Exposure to Fine Dust Pollution 67
Figure 2.6. Intensity at which Disturbances can Affect the System. The Closer to Black an Area is, the Lower the Resilience to Exposure to Fine Dust Pollution is in that Area 67
Figure 2.7. Degree to Which Sociodemographic Conditions are Susceptible to Disturbances. Suwon District Boundary Data are Displayed, and the Darker the Color is,... 68
Figure 2.8. Degree to which Local Sociodemographic Conditions are Susceptible to Disturbances. The Closer to Black an Area is, the Lower Its Sensitivity Resilience to Fine... 69
Figure 2.9. Adaptive Capacity to Fine Dust Pollution. Areas with High Adaptability to Fine Dust Pollution are Areas with Well-Established and Vibrant Plants or Areas with Excellent... 70
Figure 2.10. Ability of the Local Systems and Infrastructure in the Study Area to Respond to Disturbances. The Closer to Black an Area is, the Higher the Adaptive... 71
Figure 2.11. Results of a Spatial Analysis to Identify Areas Vulnerable to Fine Dust Pollution in Suwon 85
Figure 2.12. Status of the Study Site: (a) Picture of the Study Site (Picture was taken on May 18, 2021); (b) 3D Image of the Study Site 86
Figure 2.13. Research Flow 87
Figure 2.14. Variation Graphs of Monthly Fine Dust Pollution Absorption by Tree Species: (a) Zelkova serrata; (b) Prunus yedoensis; (c) Pinus densiflora 92
Figure 2.15. 3D Landscape Images of (a) Street Trees Planted in Double Rows; (b) Street Trees Planted on the Road Median 94
Figure 2.16. 3D Landscape Images for Each Scenario: (a) Rep_only_Zelko; (b) Plant_more_Zelko; (c) Rep_only_Prun; (d) Plant_more_Prun; (e) Rep_only_Pinus; (f)... 96
Figure 2.17. Simulation Results of the Replace-Only Scenarios: (a) Fine dust pollution absorption of Rep_only_Zelko; (b) Species diversity of Rep_only_Zelko; (c) Fine dust... 99
Figure 2.18. Comparison of the Replace-only scenarios: (a) Fine dust pollution absorption; (b) Species diversity 99
Figure 2.19. Comparison of additional tree planting scenarios: (a) Fine dust pollution absorption amount; (b) Species diversity 101
Figure 2.20. Distribution Changes in Tree Species Over Time: (a) Plant_more_Prun; (b) Plant_more_Mix 101
Figure 2.21. Scenario Simulations Reflecting the Seasonal Changes in Fine Dust Pollution Absorption by Trees: (a) Replace-Only Scenarios; (b) Additional Tree Planting Scenarios 102
Figure 2.22. Status of the Study Site: (a) Study Site Location Map; (b) Picture of the Study Site (Picture was taken on May 18, 2021); Saekdong Children's Park, Paldal-gu, Suwon,... 112
Figure 2.23. Research Flow 113
Figure 2.24. Appearance Before and After GI Installation: (a) Before; (b) After 114
Figure 2.25. Smart GI Detailed Structure [GI type 2 (left); GI type 1 (right)] 115
Figure 2.26. Process for Installing GI types 1 and 2 116
Figure 2.27. Base Model for CFD Analysis: (a) Study Site; (b) Study Site Created using AutoCAD 117
Figure 2.28. Measurement of Fine Dust Pollution Reduction: (a) Location of the Fine Dust Pollution Measuring Devices; (b) Obtaining Information through the IoT System 121
Figure 2.29. Total Amount of Fine Dust Pollution Reduction Achieved by Smart GI Type in Regions A and B with Different Concentrations 128
Figure 2.30. Total Amount of Fine Dust Pollution Reduction Achieved by Smart GI Type in Regions A and B with Different Concentrations 128
Figure 2.31. Total Amount of Fine Dust Pollution Reduction Achieved by Scenario in Region A 130
Figure 2.32. Total Amount of Fine Dust Pollution Reduction Achieved by Scenario in Region B 130
Figure 2.33. Total Length of GI by Scenarios 131