博碩士論文 106621018 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:31 、訪客IP:3.210.201.170
姓名 李育寬(Yu-Kuan Li)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 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
(Spatiotemporal Analysis of Urban Heat Island using Landsat Data: A Case Study of Taipei Metropolitan Area)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 隨著科技的發展,近年來科學家們嘗試使用高空間解析度的衛星資料探討都市熱島效應,本研究嘗試在大台北都會區中整合並擴展Landsat系列衛星的都市熱島研究方法。使用土地利用與地表型態變遷(Land Use and Land Cover, LULC)、地表溫度(Land Surface Temperature, LST)、都市熱島影響區域(Urban Heat Island area, UHI area)、都市熱島環境評估(Urban Thermal Field Variance Index, UTFVI)探討大台北都會區過去25年的都市變遷以及都市熱島影響,並測試三組遙測指數: 常態化差異建地指數(Normalized Difference Built-up Index, NDBI)、常態化差異潛熱指數 (Normalized Difference Latent heat Index, NDLI) 、常態化差異植生指數 (Normalized Difference Vegetation Index, NDVI)與地表溫度進行分析,其中NDLI為國際上新的遙測指數,本研究特別針對大台北都會區的不同土地利用型態適用範圍進行測試和整理。在都會區和市中心的比較上,本研究也進行了時序比較,尤其是在2016年的極高溫事件運用LST搭配地表觀測站計算兩者差異性測試,並整理大台北都會區及不同土地利用型態的都市熱島強度(Urban heat island intensity, ∆UHI)。
研究顯示,市中心雖然經綠地規劃植被(Vegetation)面積近幾年上升約2 %,但是市中心仍有擴張的情況,以致整個都會區的建地(Built-up)面積比例上升約2.18%,整個都會區的植被面積比例有下降約0.5%;在大台北都會區發展衍生的都市熱島增強影響下,可以發現近幾年都會區溫度上升約3°C,且市中心的溫度上升程度更為嚴重約上升4°C;而都市熱島影響範圍的測試中,近幾年越往市中心集中且擴張14% (0.5*SD)面積、11.5%(1*SD),且在UTFVI評估下,市中心的Excellent等級範圍面積下降約20%,Worst等級範圍面積上升約16%。在2016個案中大台北都會區整體都市熱島強度為4.6°C,其中建地的∆UHI最高為4.8°C,遠高於測站間所測得的∆UHI為3 °C。
在遙測指數的應用上,分別於夏季以及冬季進行測試,發現相關性在夏季時會優於冬季且市中心會優於都會區,其中夏季市中心的LST與NDBI有著高度的正相關性約0.87,NDLI有著高度的負相關性約-0.84;NDVI在冬季市中心呈現低度的負相關性約 -0.51,在夏季市中心才呈現較高度的負相關性 -0.75。在NDLI的運用中: 1. 夏季時NDLI在水體(Water Body)的負相關性均高於NDVI,適用於淡水出海口以及翡翠水庫區域的NDLI水體監測;2而台北市和新北市間的都市規劃差異及3.山區和都市地區間的環境保育發展評估上,也提供未來更為廣泛的應用和測試於台灣與國際都市熱島效應研究中。
摘要(英) The use of high-resolution satellite data for analyzing the Urban Heat Island effects (UHI) is becoming typical in the recent years due to advancement in the satellite technology. The study integrates and expands the earlier used Landsat series of urban heat island research methods for the Taipei metropolitan area. The Land Use and Land Cover (LULC), Land Surface Temperature (LST), Urban Heat Island area (UHI area), and Urban Thermal Field Variance Index (UTFVI) are used to explore the urban changes and analyze the effects of urban heat islands in the Taipei metropolitan area for the past 25 years. It also tests three sets of telemetry indices: Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI), and Normalized Difference Vegetation Index (NDVI) along with analyzing surface temperature for the same region. However, among all indices, NDLI is a new international telemetry index. The study specifically aims to differentiate LULC of the Taipei metropolitan area. A time-series comparison is done between the metropolitan and the urban area of Taipei for the recorded highest temperature year of 2016. The LST and the surface station based observational data are compared for both UHI and non-UHI area of the Taipei Metropolitan.
Results show that the vegetation area of the urban area is increased by about 2% in the recent years along with the expanding urban area. As a result, the proportion of the Built-up area of the Taipei metropolitan is increased by about 2.18%. The proportion of the vegetation area is decreased by about 0.5% under the influence of the increased urban heat island caused by the recent developments in the Taipei Metropolitan. It is found that the temperature of the metropolitan is gradually increased by about 3°C in recent years, and the temperature rise in the urban area is also increased even more (about 4°C). The area of influence in the urban heat island is more concentrated in the urban and expands by 14% (0.5*SD)和11.5% (1*SD) . The UTFVI assessment reveals that the area of the Excellent level in the urban is decreased by about 20%, whereas the area of the Worst level is increased by about 16%. In the year 2016, the overall urban heat island intensity of the Taipei Metropolitan was 4.6°C. The highest urban heat island intensity (ΔUHI) is formed on the construction site which is 4.8°C, it is much higher than the ΔUHI measured between the stations (3°C).
The indices are tested for both summer and winter seasons, which reveals that their correlation is better in summer than in winter especially for the urban area. The LST and NDBI in the summer have a high positive correlation of about 0.87, whereas NDLI has a high negative correlation of about -0.84 in the urban area. The NDVI presents a low negative correlation of about -0.51 in the winter and a high negative correlation of -0.75 in the summer of urban area. The application of NDLI found in the present study are: 1. The NDLI is more suitable for monitoring the water bodies like Tamsui River and Feicui Reservoir area, because of its negative correlation for the Water Bodies in summer than NDVI. 2. The NDLI is capable to show the difference in the urban greenery between Taipei and New Taipei city, 3. The NDLI shows the difference of nature between the urban and mountains areas, which can be further analyzed in detail in the future researches.
關鍵字(中) ★ 都市熱島效應
★ 地表溫度
★ 土地利用與地表型態
★ 常態化差異建地指數
★ 常態化差異潛熱指數
★ 常態化差異植生指數
關鍵字(英) ★ Urban Heat Island
★ Land Surface Temperature
★ Land Use and Land Cover
★ Normalized Difference Built-up Index
★ Normalized Difference Latent heat Index
★ Normalized Difference Vegetation Index
論文目次 摘要 i
Abstract iii
致謝 v
目 錄 vi
表目錄 ix
圖目錄 xi
第一章 緒論 1
1-1前言 1
1-2文獻回顧 2
1-3研究目的 4
1-4研究試區介紹 5
第二章 研究方法 6
2-1資料處理流程 6
2-2 Landsat系列衛星介紹 7
2-3遙測參數介紹 8
2-3-1地表溫度(Land Surface Temperature) 8
2-3-2 UHI area和UTFVI(Urban Thermal Field Variance Index) 10
2-3-3常態化差異建地指數(Normalized Difference Built-up Index) 12
2-3-4常態化差異潛熱指數(Normalized Difference Latent heat Index) 12
2-3-5常態化差異植生指數(Normalized Difference Vegetation Index) 13
2-4 Fmask 濾雲工具介紹 14
2-5地面測站介紹 15
第三章 結果與討論 16
3-1 Landsat系列衛星反演的土地利用型態 16
3-1-1 土地利用型態種類定義 16
3-1-2 土地利用型態分類工具 17
3-1-3 土地利用型態準確度分析 17
3-1-4 土地利用型態年差異分析 19
3-2 大台北都會區遙測指數時序變化 20
3-2-1 LST年變化分析 20
3-2-2 UHI area年變化分析 20
3-2-3 UTFVI環境指數分布分析 22
3-3 都市熱島效應下2016年高溫個案分析23
3-3-1 綜觀天氣介紹 23
3-3-2 觀測資料分析 23
3-3-3 不同土地利用型態的都市熱島強度 26
3-4 加入NDLI遙測指數於大台北都會區初步測試 28
3-4-1 LST與NDBI、NDLI、NDVI相關性分析 28
3-4-2土地利用型態與NDVI遙測值範圍整理 28
3-4-3土地利用型態與NDLI遙測值範圍初估 29
3-4-4大台北都會區適用NDLI參數區域介紹 30
3-5結果統整 32
第四章 結論與未來展望 35
參考文獻 36
附表 41
附圖 50
參考文獻 行政院 交通部中央氣象局及環境保護署
台北市政府 工務局、都市發展局
新北市政府 城鄉發展局
國立中央大學 大氣科學學系 太空及遙測研究中心
National Aeronautics and Space Administration (NASA)
United States Geological Survey (USGS)
Intergovernmental Panel on Climate Change (IPCC)
Special Report: Climate Change and Land (IPCC,2019)
李魁鵬,台灣四大都會區都市熱島之研究,國立成功大學博士論文,1999。
張子瑩、劉說安等人,利用Landsat 資訊反演大氣溫度以評估熱島之強度,航測及遙測學刊,第10 期,第4 卷,pp385-392,2005。
孫振義,運用遙測技術於都市熱島效應之研究,國立成功大學博士論文,2008。
嚴綾,應用衛星資料探討大台北地區都市熱島效應之時空分布,國立中央大學碩士論文,2012
王暐晴,利用WRF/Urban Canopy Model模擬探討台灣北部都市地區之熱島效應。國立中央大學碩士論文,2016。
林秉毅,不同土地利用資料對午後熱對流模擬的影響。國立中央大學
碩士論文,2017。
Cheng, F. Y., Y.C. Hsu., P. L. Lin., and T. H. Lin.,2013: Investigation of the Effect of Different Land Use and Land Cover Patterns on Mesoscale Meteorological Simulations in the Taiwan area. J. Appl. Meteor. And Climatol., 52, 570-587.
Chen., F., H. Kuasaka., R. Bornstein., J. Ching., C. S. B. Grimmond., S. Grossman-Clarke., T. Loridan., K.W. Manning., A. Martilli., S. Miao., D. Sailor., F. P. Salamanca., H. Taha., M. Tewari., X. Wang., A. A. Wyszogrodzki., C. Zhang. 2011: The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental
Problems. Int. J. Climatol., 31, 273-288.
Deng, T., S. Wang., X. Bai., Y. Tian., L. Wu., J. Xiao., F. Chen., Q, Qian., 2018: Relationship among land surface temperature and LUCC, NDVI in typical karst area. SCIENTIFIC REPORTS. 8-641.
Griend.,V.D., A. A., M. Owe., 1993: On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int. Journal of Remote Sens. 14:6, 1119-1131.

Hsu, C.-H., F.-Y. Cheng., 2016. Classification of weather patterns to study the influence of meteorological characteristics PM2.5 concentrations in Yunlin County, Taiwan. Atmospheric Environment, 144, 397-408.
Jiménez-Muñoz , J.C., C. Mattar., J. Barichivich, A. S-A, K. Takahashi., Y. Malhi., J.A. Sobrino., G.Schrie., 2016: Record-breaking warming and
extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130.
Koralegedara, S.B., C-Y Lin., Y.-F. Sheng., C.-H. Kuo., 2016: Estimation of
anthropogenic heat emissions in urban Taiwan and their spatial patterns. Environmental Pollution. 215, 84-95.
Landis, J.R., 1977: The measurement of observer agreement for categorical data. Biometrics. Vol.33,157-174.
Lin., C.-Y., C.J. Su., H. Kusaka., Y. Akimoto., Y.-F. Sheng., J.C. Huang, H.H. Hsu., 2016: Impact of an improved WRF urban canopy model on diurnal air temperature simulation over northern Taiwan. Atmos. Chem. Phys.,16, 1809-1822.
Liou., Y. -A., A.K. Nguyen., M.H. Li., 2017: Assessing spatiotemporal eco-environmental vulnerability by Landsat data. Ecological Indicators.80,52-65.
Liou, Y. -A.*, Mai Son Le, and Hwa Chien, 2018: Normalized Difference Latent Heat Index for Remote Sensing of Land Surface Energy Fluxes, IEEE Transactions on Geoscience and Remote Sensing.
Liu, l., Y. Zhang., 2011: Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sens. 3, 1535-1552.
Liu, H.L., Y-S, Shen., 2014: The Impact of Green Space Changes on Air Pollution and Microclimates: A Case Study of the Taipei Metropolitan Area. Sustainability.
Oke, T. R., 1973: City Size And The Urban Heat Island. Atmospheric Environment Pergamon Press. Vol. 7, pp. 769-779.
Oke., T. R.,1982: The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society. 108, pp. 1-24.
Oke, T.R., G. Mills, A. Christen, J.A. Voogt, 2017: Urban Climates.
Cambridge University Press, Cambridge.
Perez, R., R. Seals., A. Zelenka., 1997: Comparing satellite remote sensing and ground network measurements for the production of site/time specific irradiance data. Solar Energy. 60(2), 89-96.

Qiu, S., Z. Zhu., B. He., 2019: Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery. Remote Sensing of Environment. 231. 111205
Ren, Y.,G. Ren., 2011: A Remote-Sensing Method of Selecting Reference Stations for Evaluating Urbanization Effect on Surface Air Temperature Trends. Journal of Climate. 24(13), 3179-3189.
Zha, Y., J. Gao., S. Ni., 2003: Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing. 24, 583-594.
Zhu, Z., C.E. Woodcock., 2012: Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118. 83-94.
Zhu, Z., S. Wang., C.E. Woodcock., 2015: Improvement and expansion of
theFmask algorithm: cloud, cloud shadow, and snow detection for
Landsats 4–7, 8, and Sentinel 2 images. Remote Sensing of Environment.
指導教授 劉說安 嚴明鉦(Yuei-An Liou Ming-Cheng Yen) 審核日期 2020-1-16
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明