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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林寶秀(Bau Show Lin) | |
dc.contributor.author | Yi-Chun Wu | en |
dc.contributor.author | 吳宜郡 | zh_TW |
dc.date.accessioned | 2021-06-15T12:39:07Z | - |
dc.date.available | 2022-07-01 | |
dc.date.copyright | 2020-08-24 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-18 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50398 | - |
dc.description.abstract | 都市中公園綠地為生態系統服務的主要來源,隨著氣候變遷及都市的擴張、熱島效應加劇,影響都市居民的舒適度及生活品質。公園綠地透過植被的遮蔽和蒸發散作用調節都市微氣候、產生局部的冷卻效應,稱為都市冷島(Urban cooling island),其效應受到公園內部環境(公園面積、形狀、地表覆蓋型等)以及外部環境條件(建成區、其他綠地等)的影響,其作用及降溫效果有待釐清。 遙測(Remote sensing)具全面性觀測之優點,能獲得大範圍的同步資料,至今使用遙測的相關研究大多以表面溫度來探討綠地的冷島效應,惟測量到的是植被的表面溫度,並非植被下方的表面溫度;空氣溫度是探討微氣候的重要條件,其反映了受加熱後的地表與周遭空氣混合後的結果,因此更適合描述公園的降溫能力和流動情形,許多研究使用遙測資料結合空間統計方法推估氣溫,例如地理加權迴歸(Geographically weighted regression),可使用多個自變項與距離進行加權,並配合高解析度的遙測資料,能夠更適當地反映當地的微氣候,有助於釐清公園冷島效應。 因此本研究之目的為: 一、 使用地理加權迴歸及遙測影像資料推估氣溫 二、探討內外環境特性對冷島強度的影響 三、探討內外環境特性對冷島延伸距離及範圍的影響 本研究以台北盆地的62個公園綠地為研究地點,使用Landsat-8遙測影像判斷植被、水體及建成區等地表覆蓋類型並推估地表溫度。而在空氣溫度的推估方面,結合地理加權迴歸,使用現有氣象測站觀測之空氣溫度及遙測資料集,建立其迴歸關係式以推估研究範圍之空氣溫度,並將空氣溫度及表面溫度圖像化、繪製成等溫線圖,將公園綠地內外環境屬性量化,最後以推估的氣溫為應變項、公園內外環境屬性為應變項進行統計分析。 研究結果顯示,都市公園最大可降溫達1.5°C、近山都市公園最大可降溫達1.8°C;都市公園平均降溫距離介於84-472公尺、近山都市公園平均降溫距離介於155-406公尺;都市公園平均降溫範圍15.8公頃;近山都市公園平均降溫範圍19.6公頃。進一部探討環境屬性的影響:公園面積越大、綠覆面積越大,公園的降溫強度越大、降溫距離越遠,因此降溫範圍也越大。而公園的周長面積比越小,意即公園形狀越規則,降溫強度較大、降溫距離越遠,因此降溫範圍也越大。而在外部環境屬性的方面,外部綠覆面積總和與外部平均綠覆面積對降溫強度、距離即範圍都呈現正相關,而外部綠地距離越遠,越能延伸公園降溫效果。 | zh_TW |
dc.description.abstract | Parks and green lands is main source of Ecosystem in urban area. With climate change and urban expansion goes severe, affecting comfort and living quality of city habitat. Furthermore, Urban park meditate urban micro-climate by canopy direct shelter and evapotranspiration, leading local ‘’cooling island.’’ This effect associate with interior and outer condition in parks and need to clarify. Remote sensing can measure and collect data simultaneously in a large area, thus nowadays there are lots amount of researches using it to get land surface temperature (LST), which helping understand cooling island effect. However, remote sensing measuring the temperature upper canopy rather the surface temperature under canopy. Air temperature is suitable to describe cooling capacity and flow of urban parks. Many researsher start to combine spatial statistics approach, such as Geographically weighted regression (GWR) to estimate the air temperature. To conclude, our purpose are: 1. using GWR and Remote sensing data to estimate local air temperature. 2. Discuss the cooling intensity influenced by interior and outer characteristic of parks 3. Discuss the cooling extent and area influenced by interior and outer characteristic of parks Study area included 62 parks in Taipei Basin, we use Landsat-8 image as material, which identifying vegetation, water body and built area then calculate LST. First, to estimate the air temperature, we use ground meteorological station data and remote sensing data as input in GWR to establish regression model. Second, mapping air temperature, discriminating cooling intensity, extent and area. Third, qualifying interior and outer characteristics of parks. Finally, using cooling indicators as dependent variables and park characteristics as independent variables. The results indicate: cooling intensity maximum of urban parks is 1.5°C, cooling intensity maximum of urban parks which near mountain is 1.8°C; cooling extent of urban parks is average 84-472 meter, cooling extent of urban parks which near mountain is average 155-406 meter; cooling area of urban parks is average 15.8 ha, cooling area of urban parks which near mountain is average 19.6 ha. Otherwise, the larger the park area and canopy is, the more cooling intensity is. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T12:39:07Z (GMT). No. of bitstreams: 1 U0001-1108202013311900.pdf: 3117807 bytes, checksum: 6e25c20567b7db34bd285f54466d2548 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 摘要 vii Abstract ix 目錄 xi 圖目錄 xiv 表目錄 xv 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 一、 使用空間統計方法及遙測影像資料推估空氣溫度 3 二、 探討都市公園綠地環境特性對冷島強度之影響 3 三、 探討都市公園綠地環境特性對冷島延伸之影響 3 第三節 研究流程 4 第二章 文獻回顧 5 第一節 生態系統服務 5 一、 服務類型 5 二、 生態系統能力及流動 6 第二節 都市微氣候 9 一、 都市微氣候 9 二、 都市熱島效應 9 第三節 都市公園綠地及冷島效應 11 一、 都市公園綠地 11 二、 冷島效應定義 13 三、 冷島效應影響因子 16 四、 量測方法 19 第四節 空氣溫度推估 21 一、 溫度–植被指數 21 二、 能量守恆 21 三、 統計分析 21 第五節 地理加權迴歸 24 第三章 研究方法 27 第一節 研究架構 27 一、 研究架構及假設 27 二、 操作流程 30 第二節 研究地點 31 第三節 氣溫推估 32 一、 衛星影像及地面氣象測站 32 二、 模型變項 34 三、 地理加權迴歸 38 四、 解釋能力驗證 40 第四節 公園降溫指標 42 一、 降溫強度 43 二、 降溫距離 43 三、 降溫範圍 43 第五節 公園環境屬性量化 46 一、 內部環境屬性 46 二、 外部環境屬性 47 第六節 假設檢定 48 一、 公園區位對降溫效果之影響探討 48 二、 公園內部屬性對降溫效果之影響檢定 48 三、 公園外部屬性對降溫效果之影響檢定 48 四、 公園內部及外部屬性對降溫效果之影響檢定 48 第七節 研究限制 50 一、 氣象測站資料 50 二、 公園降溫服務之能力與流動 50 第四章 研究結果 51 第一節 氣溫推估 51 一、 地理加權迴歸模型 51 二、 氣溫推估結果 53 第二節 公園環境屬性 54 一、 公園內部環境屬性 54 二、 公園外部環境屬性 58 第三節 公園環境屬性與降溫效果 61 一、 公園降溫的測量結果 61 二、 降溫強度與降溫範圍的關係 64 三、 公園區位 64 四、 公園內部環境屬性對降溫效果之影響 67 五、 公園外部環境屬性對降溫效果之影響 73 六、 公園內部及外部環境屬性對降溫效果之影響 78 第五章 結論與建議 81 第一節 結論與討論 81 一、 公園區位 81 二、 公園內部環境屬性對降溫效果的影響 82 三、 公園外部環境屬性對降溫效果的影響 83 四、 公園內外部環境屬性對公園降溫的影響 84 第二節 建議 85 一、 公園規劃設計 85 二、 後續研究建議 85 參考文獻 87 | |
dc.language.iso | zh-TW | |
dc.title | 都市公園綠地微氣候調節服務之能力與流動 | zh_TW |
dc.title | Capacity and Flow of Microclimate Regulating Services of Urban Parks | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林晏州(Yann-Jou Lin),張俊彥(Chun-Yen Chang),侯錦雄(Jing-Shoung Hou),謝正義(Cheng-I Hsieh) | |
dc.subject.keyword | 微氣候,調節服務,都市冷島效應,公園環境屬性, | zh_TW |
dc.subject.keyword | micro-climate,regulation service,cooling island effect,park composition, | en |
dc.relation.page | 91 | |
dc.identifier.doi | 10.6342/NTU202002931 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-19 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 園藝暨景觀學系 | zh_TW |
顯示於系所單位: | 園藝暨景觀學系 |
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