簡易檢索 / 詳目顯示

研究生: 張佩歆
Chang, Pei-Hsin
論文名稱: 舊臺中市區都市空間結構與環境異質性於都市熱島效應影響之研究
The Impact of Urban Spatial Structures and Environmental Heterogeneity on Urban Heat Island Effect in Old Taichung City Area
指導教授: 張國楨
Chang, Kuo-Chen
口試委員: 張國楨
Chang, Kuo-Chen
雷祖強
Lei, Tsu-Chiang
陳俊愷
Chen, Chun-Kai
口試日期: 2024/06/30
學位類別: 碩士
Master
系所名稱: 地理學系
Department of Geography
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 100
中文關鍵詞: 都市熱島效應都市空間結構都市環境空間異質性
英文關鍵詞: urban heat island effect, urban spatial structure, urban environment, spatial heterogeneity
研究方法: 個案研究法觀察研究
DOI URL: http://doi.org/10.6345/NTNU202401021
論文種類: 學術論文
相關次數: 點閱:79下載:4
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 世界都市人口急遽攀升,且氣候變遷之影響越發嚴峻,都市熱島成為都市發展時首要欲解決的問題之一。近年來都市熱島相關研究中,討論都市內部的空間結構與地表溫度間關聯性的研究逐漸增加。舊臺中市於近20年間,溫度上升幅度明顯,都市熱島效應極可能為助長舊臺中市區溫度升高的原因之一。然而過往探討臺中市都市熱島之研究,多數仍停留於探討舊臺中市區與周圍其他行政區的土地利用型態與地表溫度間的關聯。故本研究欲彌補過往研究之缺口,選定舊臺中市八區作為研究區域,透過相關文獻回顧,得出除氣候條件外,地景特徵、土地利用類型及都市空間結構等為影響都市溫度的主要驅動變數。本研究透過使用2014年及2022年Landsat 8衛星影像資料,了解舊臺中市區都市熱島效應的影響變化,並以地理加權迴歸分析探討地表溫度影響因素對局部區域地表溫度高低的影響。本研究發現在舊臺中市內部,都市熱區多與工業區、建物過於密集有所關聯;而都市冷區則多與大量植被覆蓋、高樓層建築物搭配適當棟距、鄰近水體及大型開放空間有關;而都市中冷區消失則多因市地重劃開發導致大量植被覆蓋消失所導致。此外,空間結構及周圍環境間會相互影響,進而呈現出不同的高低溫聚集現象。本研究透過前述結果證實除土地利用與土地覆蓋外,都市立體空間結構及周圍環境亦為影響都市內部地表溫度高低之重要考量因素。藉由本研究提供於都市發展時,何為導致地表溫度變化需考量的因素,以作為未來都市規劃時之參考。

    The world's urban population is rising rapidly, and the impact of climate change is becoming more severe. Urban heat islands have become one of the primary problems to be solved in urban development. In recent years, among studies related to urban heat islands, there has been an increasing number of studies discussing the correlation between the spatial structure within cities and surface temperature. The temperature of old Taichung City has increased significantly in the past 20 years, and the urban heat island effect is likely to be one of the reasons for the increase in temperature in the old Taichung urban area. However, most of the previous studies on urban heat islands in Taichung City still focused on exploring the relationship between land use patterns and surface temperature in the old Taichung urban area and other surrounding administrative districts. Therefore, in order to fill the gap in previous research, this study selected the eight districts of old Taichung City as the research area. Through a review of relevant literature, it was concluded that in addition to climate conditions, landscape characteristics, land use types, and urban spatial structure are main driving factors that affect urban temperature. main driving.
    This study uses Landsat 8 satellite image data from 2014 and 2022 to understand the changes in the urban heat island effect in the old Taichung urban area, and uses geographically weighted regression analysis to explore the impact of surface temperature influencing factors on the surface temperature in local areas. This study found that within old Taichung City, urban hot areas were mostly associated with industrial areas and overly dense buildings; while urban cold areas were mostly associated with large amounts of vegetation, high-rise buildings with appropriate building spacing, proximity to water bodies, and large open spaces. The disappearance of cold areas in cities is mostly caused by the loss of a large amount of vegetation coverage caused by urban land rezoning and development. In addition, the spatial structure and the surrounding environment would interact with each other, thus showing different high and low temperature aggregating phenomena. Through the aforementioned results, this study confirms that in addition to land use and land cover, urban three-dimensional spatial structure and the surrounding environment are also important considerations that affect the surface temperature within the city. This study provides the factors that need to be considered in urban development and can serve as a reference for future urban planning.

    第一章 緒論 1 第一節 研究動機與目的 1 第二節 研究區域 4 第三節 研究限制 5 第二章 文獻回顧 6 第一節 都市熱島效應理論及相關研究 7 壹、都市熱島效應理論與應用 7 貳、都市熱島效應觀測方法 9 參、都市熱島強度及都市熱島效應影響範圍 10 肆、都市熱島效應與環境影響變數 13 第二節 舊臺中市都市發展與熱島效應 19 第三節 衛星影像地表溫度反演 22 第四節 空間資料相關性分析方法 27 第三章 研究方法與設計 30 第一節 研究資料取得與處理 32 壹、地表溫度反演資料 32 貳、地表溫度環境影響因素 34 參、資料空間單元轉換 38 第二節 都市熱島效應與地表溫度環境影響因素空間關聯性分析方法 39 壹、都市熱島效應範圍變化 39 貳、地表溫度與地表溫度影響變數空間關聯性分析 39 第四章 研究結果與討論 41 第一節 舊臺中市區都市熱島效應影響範圍及分布變化 41 壹、2014年與2022年地表溫度反演結果 41 貳、2014年與2022年都市地表溫度冷熱區變化 44 小結 47 第二節 地表溫度影響因素於舊臺中市區對地表溫度的影響程度及其空間差異 48 壹、地表溫度與地表溫度影響變數OLS迴歸分析結果 48 貳、地表溫度與地表溫度影響變數GWR分析結果 54 小結 64 第三節 舊臺中市區都市熱島效應與其都市空間結構及周圍環境之關聯性 65 壹、都市熱島效應與土地覆蓋之關聯 65 貳、都市熱島效應與都市立體空間結構之關聯 68 參、都市熱島效應與都市空間周圍環境之關聯 74 小結 81 第五章 結論與建議 82 第一節 研究結論 82 第二節 後續研究建議 84 第六章 參考文獻 85 壹、中文文獻 85 貳、英文文獻 87 參、網站資料 90 附錄一 2014及2022年各地表溫度自變數敘述統計資料 91 附錄二 2014年各地表溫度自變數數值空間分布 92 附錄三 2022年各地表溫度自變數數值空間分布 96 附錄四 2014及2022年各地表溫度影響變數偏迴歸係數 100

    中央氣象局(2023)。2023年3月氣候監測報告。臺北市:交通部中央氣象局。
    孔繁恩、詹進發、邵怡誠、李茂園、葉堃生、陳連晃(2014)。物件式分類法於高解析度航照影像萃取崩塌地之研究。航測及遙測學刊,18(4),267-281。
    何佳薇、周天穎、楊龍士(2011)。臺中地區土地利用變化於熱島效應之研究。航測及遙測學刊,16(2),139-149。
    吳宜郡(2020)。都市公園綠地微氣候調節服務之能力與流動(碩士論文)。國立臺灣大學園藝暨景觀學系。
    吳彩珠、林峰田、林森田、許元綸(2013)。宜蘭農地宅舍分布型態之變遷與其影響因素之探討。都市與計劃,40(1),31-57。
    呂毓倫(2008)。應用遙測衛星地表溫度資料探討都市熱島現象與社經空間發展之關係(碩士論文)。國立成功大學都市計劃研究所。
    李育寬(2020)。應用Landsat衛星資料探討大台北都會區都市熱島效應之時空分析(碩士論文)。國立中央大學大氣科學學系大氣物理碩士班。
    林子平(2021)。都市的夏天為什麼愈來愈熱?:圖解都市熱島現象與退燒策略。臺北市:商周出版。
    林子平、蔡沛淇、歐星妤與張洲滄(2023)。熱島效應緩解策略之風廊系統的指認與應用。土木水利,50(1),24-29。
    康亦陞、廖冠閔、徐逸祥(2023)。都市街道變遷與都市熱島效應之關聯-以原臺中市八區為例。土木水利,50(1),4-9。https://doi.org/10.6653/MoCICHE.202302_50(1).0002
    張晏菁、徐逸祥、林峰正(2019)。應用空間迴歸模式探討都市綠地、pm2.5與地表溫度之關聯。航測及遙測學刊,24(1),25-43。https://doi.org/10.6574/JPRS.201903_24(1).0003
    張嘉玲(2004)。台中市都市空間體系的建構與擴展(碩士論文)。國立成功大學建築研究所。
    莊明軒(2020)台中地區土地利用與都市熱島效應之時空變遷分析(碩士論文)。國立臺灣師範大學地理學系。
    陳莉、魏曉萍、王泰盛(2004)。監督式分類方法於遙測影像判釋之研究。農業工程學報,50(3),59-70。
    臺中市政府主計處(2021年3月)。臺中市人口統計通報,臺中市人口成長概況。檔號:第110-01-3 號。
    臺灣氣候變遷推估資訊與調適知識平台(2023)。臺中測站溫度觀測值年際變化。取自https://tccip.ncdr.nat.gov.tw/ds_01_station.aspx。
    潘國樑(2009)。遙測學大綱:遙測概念、原理與影像判釋技術(第二版)。臺北市:科技圖書。
    潘豐家(2015)。結合衛星資料與建物資訊解析台北市空間發展與都市熱島效應之鏈結(碩士論文)。國立中央大學遙測科技碩士學位學程。
    鄭師中(譯)(1988)。都市氣候學。臺北市:財團法人徐氏基金會。(Landsberg, H. E., 1981)
    蕭國鑫、劉治中、劉進金、何心瑜、黃英婷(2008)。高解析影像應用於土地利用分類之探討。航測及遙測學刊,13(4),261-271。
    謝雨生與鄭宜仲(1993)。多元迴歸分析的假定與實例檢討–多元線性重合現象的診斷與處理。農業推廣學報,(10),189-213。
    鍾馨葆與蘇瑛敏(2022)。高層建築配置與量體退縮對天空可視率與都市微氣候之影響。建築學報,121(技術專刊),23-52。
    羅子雯(2018)結合局部氣候分區及景觀生態指標之都市氣候地圖建置及應用(碩士論文)。國立成功大學建築研究所。 
    Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 2016, 1-8. https://doi.org/10.1155/2016/1480307
    Bokaie, M., Zarkesh, M. K., Arasteh, P. D., & Hosseini, A. (2016). Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustainable Cities and Society, 23, 94-104.
    Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298.
    Cao, J., Zhou, W., Zheng, Z., Ren, T., & Wang, W. (2021). Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature. Landscape and Urban Planning, 206, 103979.
    Cao, X., Onishi, A., Chen, J., & Imura, H. (2010). Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landscape and urban planning, 96(4), 224-231.
    Chen, Y.-C., Lin, T.-P., & Shih, W.-Y. (2017). Modeling the urban thermal environment distributions in Taipei Basin using Local Climate Zone (LCZ) 2017 Joint Urban Remote Sensing Event (JURSE).
    Gao, Y., Zhao, J., & Han, L. (2022). Exploring the spatial heterogeneity of urban heat island effect and its relationship to block morphology with the geographically weighted regression model. Sustainable Cities and Society, 76, 103431.
    Giridharan, R., Lau, S. S. Y., Ganesan, S., & Givoni, B. (2007). Urban design factors influencing heat island intensity in high-rise high-density environments of Hong Kong. Building and Environment, 42(10), 3669-3684. https://doi.org/10.1016/j.buildenv.2006.09.011
    Giridharan, R., Lau, S. S. Y., Ganesan, S., & Givoni, B. (2008). Lowering the outdoor temperature in high-rise high-density residential developments of coastal Hong Kong: The vegetation influence. Building and Environment, 43(10), 1583-1595. https://doi.org/10.1016/j.buildenv.2007.10.003
    Grigoraș, G., & Urițescu, B. (2018). Spatial hotspot analysis of Bucharest’s urban heat island (UHI) using modis data. Ann. Valahia Univ. Targoviste Geogr. Ser, 18, 14-22.
    Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., & Morabito, M. (2021). Thermal summer diurnal hot-spot analysis: The role of local urban features layers. Remote Sensing, 13(3), 538.
    Hou, H., Su, H., Yao, C., & Wang, Z. H. (2023). Spatiotemporal patterns of the impact of surface roughness and morphology on urban heat island. Sustainable Cities and Society, 92, 104513.
    Howard, L. (1818). The climate of London: deduced from meteorological observations, made at different places in the neighbourhood of the metropolis (Vol. 1). W. Phillips, George Yard, Lombard Street, sold also by J. and A. Arch, Cornhill; Baldwin, Cradock, and Joy, and W. Bent, Paternoster Row; and J. Hatchard, Picadilly.
    Kim, S. W., & Brown, R. D. (2021). Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review. Science of The Total Environment, 779, 146389.
    Lam, N. S.-N. (1983). Spatial interpolation methods: a review. The American Cartographer, 10(2), 129-150.
    Mirzaei, P. A., & Haghighat, F. (2010). Approaches to study Urban Heat Island – Abilities and limitations. Building and Environment, 45(10), 2192-2201. https://doi.org/10.1016/j.buildenv.2010.04.001
    National Oceanic and Atmospheric Administration (2023). Global Average Surface Temperature. Retrieved from https://www.climate.gov/media/15021.
    Nunez, M., & Oke, T. R. (1977). The energy balance of an urban canyon. Journal of Applied Meteorology and Climatology, 16(1), 11-19.
    Park, C., Ha, J., & Lee, S. (2017). Association between three-dimensional built environment and urban air temperature: Seasonal and temporal differences. Sustainability, 9(8), 1338.
    Petrov, A. (2012). One hundred years of dasymetric mapping: back to the origin. The Cartographic Journal, 49(3), 256-264.
    Pu, R., Gong, P., Michishita, R., & Sasagawa, T. (2006). Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote sensing of Environment, 104(2), 211-225.
    Rizwan, A. M., Dennis, L. Y., & Chunho, L. (2008). A review on the generation, determination and mitigation of Urban Heat Island. Journal of environmental sciences, 20(1), 120-128.
    Shih, W. Y. (2017). The cooling effect of green infrastructure on surrounding built environments in a sub-tropical climate: a case study in Taipei metropolis. Landscape research, 42(5), 558-573.
    Shih, W. Y., Ahmad, S., Chen, Y. C., Lin, T. P., & Mabon, L. (2020). Spatial relationship between land development pattern and intra-urban thermal variations in Taipei. Sustain Cities Soc, 62, 102415. https://doi.org/10.1016/j.scs.2020.102415
    Silverman, B. W. (2018). Density estimation for statistics and data analysis. Routledge.
    Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250-1265.
    Tzavali, A., John P. Paravantis, Giouli Mihalakakou, Αngeliki Fotiadi, & Stigka, E. (2015). Urban heat island intensity: A literature review. Fresenius Environmental Bulletin, 24(12b), 4537-4554.
    U.S. Geological Survey (2020). Landsat 8 OLI and TIRS Calibration Notices. Retrieved from https://www.usgs.gov/landsat-missions/landsat-8-oli-and-tirs-calibration-notices.
    Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370-384. https://doi.org/10.1016/s0034-4257(03)00079-8
    Wang, X., Cheng, H., Xi, J., Yang, G., & Zhao, Y. (2018). Relationship between park composition, vegetation characteristics and cool island effect. Sustainability, 10(3), 587.
    You, M., Lai, R., Lin, J., & Zhu, Z. (2021). Quantitative analysis of a spatial distribution and driving factors of the urban heat island effect: a case study of Fuzhou Central Area, China. International journal of environmental research and public health, 18(24), 13088.
    Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829-9852.
    Zhao, C., Jensen, J., Weng, Q., & Weaver, R. (2018). A geographically weighted regression analysis of the underlying factors related to the surface urban heat island phenomenon. Remote Sensing, 10(9), 1428.
    Zhi, Y., Shan, L., Ke, L., & Yang, R. (2020). Analysis of land surface temperature driving factors and spatial heterogeneity research based on geographically weighted regression model. Complexity, 2020, 1-9.
    環境資訊中心(2022年8月3日)。台北市的「涼區」在哪裡?學者分析:城市要逃離熱浪,光靠公園綠地還不夠。The News Lens關鍵評論網。https://www.thenewslens.com/article/170975。
    環境資訊中心(2022年10月14日)。台北市「熱島」如何退燒?成大提出首個經科學實證的都市降溫指標。The News Lens關鍵評論網。https://www.thenewslens.com/article/174725。

    下載圖示
    QR CODE