利用遙感和機器學習技術量化城市熱島現象

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姓名

陳德帆(Tran Duy Phien) 查詢紙本館藏

畢業系所

環境科技博士學位學程

論文名稱

利用遙感和機器學習技術量化城市熱島現象
(Quantification of urban heat island phenomenon with remote sensing and machine learning techniques)

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至系統瀏覽論文 (2026-1-1以後開放)

摘要(中)

背景
城市熱島（UHI）是研究城市熱環境中不可或缺的主題，近年來越來越受到關注。然而，對於影響地表熱島（SUHI）空間變異的基本機制和驅動因素的研究，以及台灣這樣複雜地形區域中SUHI與樹冠層熱島（CLHI）之間的比較尚未有報告。因此，本研究旨在實現三個主要目標：（1）利用MODIS的地表溫度（LST）數據，探索選定的十一個台灣城市的SUHI強度（SUHII）及其相關決定因素的日夜、季節和空間特性；（2）利用機器學習（ML）模型，在2003年至2020年間，生成一個具有1公里分辨率的台灣月均空氣溫度（Ta）數據集（涵蓋四個衛星過境時間和每日均值）；以及（3）對SUHII和CLHI強度（CLHII）之間的時空模式進行比較分析。
方法論
這項工作經歷了四個主要的處理階段，包括城市區域的定義（第一階段）、SUHI特性和驅動因素的調查（第二階段）、使用ML算法估算Ta（第三階段）以及比較SUHI和CLHI（第四階段），以實現研究目標。更具體地，在第一階段中，使用隨機森林算法生成的ISA地圖首先利用來自LULC地圖和移動窗口技術的建成區域建立。接著，本研究使用ISA百分比、水類別和海拔數據來定義不同的區域。在第二階段中，使用MODIS LST數據和精煉的城市區域，計算了十一個台灣城市的UHI強度。接著，採用皮爾遜相關方法和逐步多元迴歸來研究與SUHII時空模式相關的因素。在第三階段，首先為台灣開發了月均Ta數據集（每日平均和四個過境時間），涵蓋了2003年至2020年的時期。這些數據集的空間分辨率為1公里，利用整合一系列變量的ML模型進行開發，包括現場測量、遙測資料和輔助信息。在第四階段，首先利用第三階段創建的Ta數據集計算了CLHI。然後，本研究比較了SUHII和CLHII的三個方面：（1）在日、季節或年度尺度上的強度差異；（2）利用SPAtial EFficiency（SPAEF）的空間分布差異；以及（3）利用皮爾遜相關分析研究SUHII和CLHII之間的關係。
結果
這項調查的主要發現包括：
(1) 這項研究針對地表熱島強度（SUHII）及其相關決定因素的日夜、季節和空間特性進行了探討，發現SUHII在白天通常比晚上更高。此外，台灣北部城市的SUHII強度比南部城市高。SUHII在季節間有顯著波動，白天的變異性比晚上更大。白天的SUHII空間分佈受到歸一化差異潛熱指數（NDLI）、植被活動、建築密度（BI）和人為排放等因素的顯著影響。相反，夜間的SUHII與BI、夜間光信號和植被活動密切相關。這項研究的結果顯示，考慮的因素在白天SUHII變化中佔有更大比例，暗示夜間SUHII的潛在機制更為複雜。
(2) 就使用ML模型估算Ta而言，極端梯度提升（XGB）迴歸器表現優於其他兩個模型，呈現最高水準的準確度。該模型通過遞歸特徵消除（RFE）識別了八個關鍵變量。方差重要性分析的結果凸顯了夜間地表溫度（LST）做為最關鍵的預測因子，其次是白天LST和月份。由此產生的月均Ta數據集在5倍交叉驗證（5-CV）設置中表現良好（R2為0.986，RMSE為0.639°C，MAE為0.477°C）。值得注意的是，XGB模型在所有四個季節中始終表現出色，並在不同時間範圍（包括月份、年份和子集）下展現了顯著的準確性。此外，本研究的XGB模型在5-CV中取得了優秀的表現，分別為0.981（0.57）、0.980（0.60）、0.986（0.46）和0.986（0.45°C）在10:30、13:30、22:30和01:30時。
(3) 在比較兩種類型的城市熱島時，本研究觀察到全城市的年平均SUHII白天高於夜晚，但CLHII則呈現相反的模式。從季節性的角度來看，夜間SUHII-CLHII差異呈現出較弱的季節性變化。相反地，白天SUHII-CLHII差異觀察到顯著的季節變化，分別在溫暖季節具有較高值，在寒冷季節則具有較低值。至於空間分佈比較，夜間SUHI與CLHI相對相似（高SPAEF），但白天則有較大的差異（低SPAEF）。此外，相關性分析顯示各城市之間的SUHII與CLHII存在顯著且正向的相關性。
結論
本研究的發現為台灣的城市熱島提供了重要信息，例如SUHI的時空模式及其驅動因素，以及CLHI和SUHII之間強度和空間分佈差異的時空變化。因此，這項工作為發展SUHI預測和熱輻射緩解策略奠定了科學基礎。此外，本研究指出，具有高準確性的月均Ta數據集（四個過境時間和每日平均）可以成為環境研究和各種應用的重要因素。

摘要(英)

Background
The urban heat island (UHI), an essential topic in studying urban thermal environments, has drawn increasing attention in recent years. However, research on underlying mechanisms and drivers influencing the spatial variation of surface UHI (SUHI) and the comparisons between SUHI and canopy layer heat island (CLHI) for a complex topographic region like Taiwan have yet to be reported. Therefore, this study aims to achieve three primary objectives: (1) to explore the diurnal, seasonal, and spatial characteristics of SUHI intensity (SUHII) and its associated determinants across eleven chosen Taiwanese cities using land surface temperature (LST) data from MODIS; (2) to generate a monthly air temperature (Ta) dataset (at four overpass times and daily mean) with a resolution of 1 km employing machine learning (ML) models covering Taiwan from 2003 to 2020; and (3) to perform a comparative analysis of the spatiotemporal patterns between the SUHII and CLHI intensity (CLHII).
Methodology
This work underwent four main stages of processing, including the definition of the urban zones (stage 1), investigation of characteristics and driving factors of SUHI (stage 2), Ta estimation using ML algorithms (stage 3), and comparison between SUHI and CLHI (stage 4), to fulfill the research objectives. More specifically, in stage 1, the ISA map generated using the Random Forest algorithm was first created using the built-up area from the LULC map and the moving window technique. Next, this study used ISA percentage, water class, and elevation data to define different zones. In stage 2, UHI intensity was computed for eleven Taiwanese cities using the MODIS LST data and the refined urban zones. Next, Pearson′s correlation method and stepwise multiple regression were utilized to investigate the factors associated with the SUHII spatiotemporal patterns. In stage 3, monthly Ta datasets (daily average and four overpass times) were first developed for Taiwan, covering the period from 2003 to 2020. These datasets were developed with a spatial resolution of 1 km, utilizing ML models that integrated a range of variables, including in situ measurements, remotely sensed data, and auxiliary information. In stage 4, first, the Ta datasets created in stage 3 were used to compute the CLHI. Then, this study compared SUHII and CLHII in three aspects: (1) the differences in intensities at diurnal, seasonal, or annual scales; (2) differences in spatial distribution using the SPAtial EFficiency (SPAEF) and (3) the relationships between SUHII and CLHII employing Pearson′s correlation analysis.
Results
The main findings of this investigation include:
(1) As for the diurnal, seasonal, and spatial aspects of SUHII and its related determinants, this work′s findings reveal that SUHII tends to be greater during the daytime than at nighttime. Furthermore, northern cities of Taiwan exhibit higher SUHII intensities than southern cities. The SUHII experiences notable seasonal fluctuations, with more significant variability observed during the daytime than at nighttime. The daytime spatial distribution of SUHII is significantly influenced by factors such as the Normalized Difference Latent Heat Index (NDLI), vegetative activity, built-up intensity (BI), and human-induced emission. Conversely, nighttime SUHII is closely associated with BI, nighttime light signal, and vegetative activity. The findings from this research indicate that the factors considered explain a more significant portion of the daytime SUHII variation compared to nighttime, indicating the more complicated underlying mechanism for nighttime SUHII.
(2) As for the Ta estimation using ML models, the Extreme Gradient Boosting (XGB) regressor outperformed the other two models, demonstrating the highest level of accuracy. The model identified eight key variables through Recursive Feature Elimination (RFE). Results from variance importance analysis highlighted nighttime LST as the most critical predictor, subsequently daytime LST and month. The resultant monthly Ta dataset performed satisfactorily in a 5-fold cross-validation (5-CV) setting (R2 of 0.986, RMSE of 0.639 °C, and MAE of 0.477 °C). Notably, the XGB model consistently excelled across all four seasons and demonstrated remarkable accuracy over various time frames, including months, years, and subsets. Furthermore, this study′s XGB model yielded outstanding 5-CV performances, with R2 (MAE) of 0.981 (0.57), 0.980 (0.60), 0.986 (0.46), and 0.986 (0.45°C) at 10:30, 13:30, 22:30, and 01:30, respectively.
(3) As for the comparison of two kinds of UHIs, while this study observed a higher annual SUHII in the day than at night over all cities, an opposite pattern was found for CLHII. From a seasonal perspective, the nighttime SUHII-CLHII difference shows weak seasonal variation. In contrast, significant seasonal variations were observed for the daytime SUHII-CLHII difference, with high values for warm and low values for cold seasons, respectively. As for spatial distribution comparison, the SUHIs are relatively similar to CLHIs at night (high SPAEF) but more different in the daytime (low SPAEF). Besides, correlation analysis shows significant and positive correlations between SUHII and CLHII across cities.
Conclusion
This study′s findings provide crucial information on urban heat islands in Taiwan, such as the spatiotemporal patterns of SUHI and its driving forces and the spatiotemporal variations of the difference between CLHI and SUHII in terms of intensities and spatial distribution. Thus, this work formed a scientific basis for developing SUHI prediction and heat mitigation strategies. Also, this study suggests that the monthly Ta datasets (four overpass times and daily mean) with high accuracy can serve as a crucial factor in environmental research and diverse applications.

關鍵字(中)

★ 地表城市熱島（SUHI）
★ 樹冠層熱島（CLHI）
★ 地表溫度（LST）
★ 空氣溫度（Ta）
★ 機器學習（ML）

關鍵字(英)

★ Surface Urban Heat Island (SUHI)
★ Canopy Layer Heat Island (CLHI)
★ Land Surface Temperature (LST)
★ Air Temperature (Ta)
★ Machine Learning (ML)

論文目次

Chinese Abstract i
English Abstract iv
Acknowledgments viii
Table of Contents x
List of Figures xv
List of Tables xx
Explanation of Symbols xxi
CHAPTER I: INTRODUCTION 1
1.1. Background and motivation 1
1.1.1. Urban Heat Island (UHI) 1
1.1.2. Air temperature (Ta) estimation 5
1.2. Identification of research gap 8
1.3. Research objectives 11
1.4. Thesis structure 12
CHAPTER II: STUDY AREA AND MATERIALS 14
2.1. Study area 14
2.1.1. Location and selected cities 14
2.1.2. Topographical feature and climate regime 16
2.2. Materials 17
2.2.1. Meteorological data 18
2.2.2. Remotely sensed data from MODIS products 20
2.2.3. Nighttime light 23
2.2.4. Digital Elevation Model (DEM) 23
2.2.5. FLDAS and ERA5 23
2.2.6. Other auxiliary data for Ta estimation 24
2.2.7. LULC classification 25
CHAPTER III: METHODOLOGY 27
3.1. Research flowchart 27
3.2. Ta estimation using ML algorithms 29
3.2.1. Variable selection 29
3.2.2. Spatial averaging 31
3.2.3. Model selection 32
3.2.4. Model validation 35
3.2.5. Variance importance analysis 37
3.2.6. Generation of air temperature maps 37
3.3. Computing of UHI intensities 38
3.3.1. Different methods for calculating SUHII 38
3.3.2. Demarcation of urban and rural regions 39
3.3.3. Computing diurnal and seasonal SUHI and CLHI intensities 42
3.3.4. Computing urban-rural contrast of potential driving forces 45
3.4. Comparisons of the spatial distributions of SUHI and CLHI 45
3.5. Relationship analysis 47
3.5.1. Pearson′s correlation analysis 47
3.5.2. Stepwise multiple regression analysis 47
CHAPTER IV: SPATIO-TEMPORAL PATTERNS AND UNDERLYING FACTORS INFLUENCING SUHI 48
4.1. Seasonal and diurnal patterns of SUHII 48
4.1.1. Diurnal patterns of SUHII 49
4.1.2. Seasonal patterns of SUHII 50
4.2. Relationships between SUHII and influencing factors 54
4.2.1. Impacts of each contributing factor to SUHII 54
4.2.2. Combined effects of factors controlling SUHII 55
4.3. Discussion 57
4.3.1. Seasonal and diurnal cycles of SUHII in Taiwan 57
4.3.2. The possible driving mechanisms of SUHII′s spatial variations 61
CHAPTER V: AIR TEMPERATURE ESTIMATION 66
5.1. Model performance 66
5.1.1. Analyzing six datasets using three different algorithms 66
5.1.2. Features selection 67
5.1.3. Scatter plot and histogram displaying the residuals 68
5.1.4. Variation in model performance across seasons 69
5.1.5. Model performance evaluated during the training and testing phases 70
5.1.6. Performance of the model across different months over the years 71
5.1.7. Comparison between predicted Ta and Ta from FLDAS and ERA5 73
5.1.8. Geographical distribution of station errors 74
5.1.9. Model performance for all four overpass times 75
5.2. The importance of predicted variables 77
5.3. Geographical pattern of estimated Ta 78
5.4. Discussion on Ta Estimation 80
5.4.1. Comparison with other Ta estimation studies 80
5.4.2. Comparison with Ta from FLDAS and ERA5 datasets 83
CHAPTER VI: COMPARISON OF SUHI AND CLHI 84
6.1. Spatiotemporal pattern of UHI intensity 84
6.2. UHI differences in intensities 86
6.2.1. Diurnal variations of SUHII and CLHII 86
6.2.2. Seasonal variations of SUHII, CLHII, ΔSUHII, ΔCLHII, and SUHII-CLHII difference 88
6.3. UHI differences in spatial distribution using SPAEF 92
6.4. Relationships between SUHII and CLHII 94
CHAPTER VII: CONCLUSIONS 97
7.1. Summary and conclusions 97
7.1.1. Spatio-temporal characteristics and associated determinants of SUHI 97
7.1.2. Ta estimation using ML models 98
7.1.3. Comparison of SUHI and CLHI 99
7.2. Limitations and further research 100
Appendix A 104
References 113

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指導教授

劉說安(Yuei-An Liou)

審核日期

2024-5-20

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