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研究生： 范志強
Pham Chi Cuong
論文名稱： 結合遺傳演算法與生物氣候指數在優化胡志明市及未來情境下的熱脆弱性之評估
Integrating Genetic Algorithms and Bioclimatic Indices for Optimal Heat Vulnerability Assessment in Ho Chi Minh City and Future Scenarios
指導教授： 林唐煌
Tang-Huang Lin
口試委員:
學位類別： 碩士
Master
系所名稱： 太空及遙測研究中心 - 遙測科技碩士學位學程
Master of Science Program in Remote Sensing Science and Technology
論文出版年： 2025
畢業學年度： 113
語文別： 英文
論文頁數： 143
中文關鍵詞： 熱脆弱性指數 、通用熱氣候指數 、主成分分析 、遺傳演算法 、PLUS 模型 、局部氣候區
外文關鍵詞： Heat Vulnerability Index, Universal Thermal Climate Index, Principal Component Analysis, Genetic Algorithm, PLUS model, Local Climate Zone
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    • 隨著胡志明市 (Ho Chi Minh City, HCMC) 的都市化，造成綠地面積的減少和地表的增溫，加劇了居民暴露於都市熱污染的程度，對不同區塊的社會群體形成了不等的影響與風險.。為正確地評估不同區塊的熱脆弱性，本研究提出了一個綜合性的多尺度框架，用於評估、模擬和優化都市區塊的熱脆弱性，其中通用熱氣候指數 (Universal Thermal Climate Index, UTCI) 使用 ERA5 再分析 資料 和經過驗證的衛星衍生溫度數據, 透過 迴歸模型進行估計，達到了高準確度 (R² = 0.96)。並藉由暴露度 (UTCI)、敏感性和適應能力等指標的整合，構建了一個基於主成分分析 (Principal Component Analysis, PCA) 的熱脆弱性指數 (Heat Vulnerability Index, HVI)，進行胡志明全市各空間區塊熱風險的分析。為了評估都市不同土地利用形態的作用，胡志明市劃分為六個 局部氣候區 (Local Climate Zones, LCZs)，其中LCZ 3 (緊密低層建築區) 成為熱脆弱性最重要的貢獻者，在隨機森林模型中佔預測重要性的 83% 以上。在2024年，超過 60% 的居民生活在 HVI 高或最高風險區域，且這一比例將隨著都市的發展而顯著地上升。使用區塊生成土地利用模擬 (PLUS) 模型進行的模擬結果顯示，到 2034 年LCZ 3 將增加 17.8%，特別是在快速發展的地區，例如守德區和第七郡。為了減輕這種熱脆弱性，本研究應用了遺傳演算法 (Genetic Algorithm, GA) 來優化發展計畫下的 LCZ 空間配置。GA 模型成功地將整體適應度值降低了約 12%，並將 HVI 熱點的數量從六個減半至三個。從空間分布的預估結果呈現出高風險和最高風險區域平均減少了 5 平方公里，同時全市低 HVI 區域淨增加了 50 平方公里。此一研究結果對於後續胡志明市的發展策略提供了一個明確的參考訊息，可大幅地降低都會區熱脆弱性與暴露風險。

    As Ho Chi Minh City (HCMC) continues to urbanize, rising land surface temperatures and reduction of green spaces have intensified residents’ exposure to urban heat stress, posing diverse risks across socio-spatial groups. This study introduces a comprehensive, multi-scale framework for assessing, simulating, and optimizing urban heat vulnerability. The Universal Thermal Climate Index (UTCI) was estimated using a regression model based on ERA5 reanalysis and validated satellite-derived temperature data, achieving high accuracy (R² = 0.96). A Principal Component Analysis (PCA)-based Heat Vulnerability Index (HVI) was then constructed by integrating indicators of exposure (UTCI), sensitivity, and adaptive capacity, enabling spatial mapping of risk across the city. To assess the role of urban morphology, HCMC was classified into six Local Climate Zones (LCZs). Among them, LCZ 3 (Compact Low-Rise) emerged as the most significant contributor to heat vulnerability, accounting for more than 83% of predictive importance in the Random Forest model. Currently, over 60% of residents live in areas with high or highest HVI, and this proportion is projected to rise significantly by 2034. Simulations using the Patch-generating Land Use Simulation (PLUS) model project a 17.8% increase in LCZ 3 by 2034, particularly in rapidly growing districts such as Thu Duc and District 7. To mitigate this heat vulnerability, A Genetic Algorithm (GA) was applied to optimize LCZ spatial configurations under development plans. The GA model successfully reduced the overall fitness value by approximately 12% and halved the number of HVI hotspots from six to three. Spatially, this translated into a 5 km² reduction in high and highest-risk zones and a net gain of 50 km² in low-HVI areas across the city. These findings provide a potential strategy for guiding urban growth in ways that reduce heat vulnerability while supporting continued development.

    摘要 i Abstract ii Acknowledgment iii Table of Contents iv List of Abbreviations ix List of Tables x List of Figures xi CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Problem Statement and Research Significance 3 1.3 Objectives 5 CHAPTER 2 LITERATURE REVIEW 6 2.1 Heat Vulnerability Index (HVI) 6 2.1.1 Development and Theoretical Foundation of HVI 6 2.1.2 Key Components: Exposure, Sensitivity, and Adaptive Capacity 7 2.1.3 Applications of HVI in Developed vs Developing Countries 8 2.1.4 Universal Thermal Climate Index (UTCI) 9 2.2 Urban Form and Local Climate Zones (LCZs) 11 2.2.1 LCZ Framework and Classification 11 2.2.2 Applications of LCZ in Heat Vulnerability Studies 13 2.3 Patch-generating Land Use Simulation (PLUS) 15 2.3.1 The PLUS Model Definition and Its Advantages 15 2.3.2 Integrating Scenario Planning and LCZ Projections with the PLUS Model 16 2.4 Genetic Algorithms in Urban Planning 16 2.4.1 GAs Applications in Land Use Planning and Urban Thermal Mitigation 17 2.4.2 Integrating Heat Vulnerability into GA-Based Urban Planning 18 CHAPTER 3 METHODOLOGY 20 3.1 Study Area 20 3.1.1 Geographic Location 20 3.1.2 Natural Conditions 21 3.1.3 Socioeconomic Characteristics 22 3.2 Dataset 23 3.2.1 Remote Sensing Data 23 3.2.2 Meteorological and Climate Data 24 3.2.3 Ancillary Geospatial and Socio-Economic Data 25 3.3 Methods 27 3.3.1 Research Flow 27 3.3.2 Image processing 28 3.3.3 Heat Vulnerability Index Construction 29 3.3.3.1 Retrieval of Land Surface Temperature (LST) 29 3.3.3.2 UTCI Calculation 33 3.3.3.3 Nighttime Light Intensity (NL) 35 3.3.3.4 Medical Facility Density (MD) 36 3.3.3.5 Modified Normalized Difference Water Index (MNDWI) 37 3.3.3.6 Enhanced Normalized Difference Impervious Surface Index (ENDISI) 38 3.3.3.7 Population Density (PD) 39 3.3.3.8 Road Density 39 3.3.3.9 Compute Heat Vulnerability Index (HVI) using PCA 41 3.3.4 LCZ Classification 43 3.3.4.1 LCZ Classification Workflow 43 3.3.4.2 LCZ Quality Control Process 45 3.3.4.3 Spatial alignment using Fishnet Grids 47 3.3.5 Simulating Future Local Climate Zones (LCZs) Using the PLUS Model 48 3.3.5.1 Data Preprocessing 48 3.3.5.2 Land expansion analysis strategy (LEAS) 50 3.3.5.3 CA based on multi-type random patch seeds (CARS) 50 3.3.5.4 Simulating LCZ 2034 52 3.3.5.5 Policy-Based Scenario Simulation Using Development Zones in PLUS 53 3.3.6 Predicting HVI in 2034 Using LCZ-Based Random Forest Regression 55 3.3.7 Optimizing the Heat Vulnerability Index (HVI) Using Genetic Algorithms 56 3.3.7.1 Initial Setup and Simulation 57 3.3.7.2 Genetic Algorithms Optimization and Convergence 59 CHAPTER 4 RESULTS AND DISCUSSION 61 4.1 Results 61 4.1.1 Estimation and Validation of the Bioclimatic Index (UTCI) 61 4.1.1.1 Validation of ERA5 Meteorological Variables Using Ground-Based Weather Station Data 61 4.1.1.2 Calculation of Universal Thermal Climate Index (UTCI) 63 4.1.1.3 Spatial and Temporal Analysis of UTCI Patterns During the 2024 Dry Season 66 4.1.2 Development of the Heat Vulnerability Index (HVI) 68 4.1.2.1 Integration of Multi-Source Socio-Environmental Indicators 68 4.1.2.2 Principal Component Analysis (PCA): Eigenvalues and Factor Loadings 70 4.1.2.3 Spatial Distribution of HVI in 2024 72 4.1.3 Local Climate Zone (LCZ) Classification and Scenario Simulation 74 4.1.3.1 LCZ Classification Accuracy and Quality Control 74 4.1.3.2 LCZ Distribution and Transition from 2014 to 2024 76 4.1.3.3 LCZ Transition Simulation to 2019 and 2024 Using the PLUS Model 81 4.1.3.4 Simulate LCZ 2034 Using CARS 85 4.1.4 Projected HVI Under LCZ-Based Urban Change 88 4.1.4.1 Association Between Local Climate Zones (LCZs) and Heat Vulnerability Index (HVI) 88 4.1.4.2 Predicting Heat Vulnerability Index (HVI) in 2034 using LCZ-Based Random Forest Regression 90 4.1.5 Optimization of Heat Vulnerability Using Genetic Algorithms (GAs) 93 4.1.5.1 GA Convergence and Fitness Function Evolution Across Generations 95 4.1.5.2 Comparison of Optimized vs Initial HVI Distributions 98 4.1.5.3 Optimization scenarios for city development in 2034 100 4.2 Discussion 106 4.2.1 UTCI Estimation and Validation: Strengths, Uncertainties, and Future Enhancements 106 4.2.2 Constructing the Heat Vulnerability Index (HVI): Dimensions, Drivers, and Data Challenges 107 4.2.3 Local Climate Zone (LCZ) Mapping and PLUS Simulation: Simplifications, and Refinement 108 4.2.4 Linking LCZ to HVI Using Machine Learning: Model Efficacy and Scalability 109 4.2.5 Spatial Optimization Using Genetic Algorithms: Reducing Vulnerability under Development Scenarios 110 CHAPTER 5 CONCLUSIONS 112 REFERENCES 115 APPENDIXES 124

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