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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林寶秀 | zh_TW |
| dc.contributor.advisor | Bau-Show Lin | en |
| dc.contributor.author | 蔡雨樺 | zh_TW |
| dc.contributor.author | Yu-Hua Tsai | en |
| dc.date.accessioned | 2024-03-21T16:33:42Z | - |
| dc.date.available | 2026-02-16 | - |
| dc.date.copyright | 2024-03-21 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-02-18 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92311 | - |
| dc.description.abstract | 早期都市規劃與開發，大多強調人工基礎設施的建設。面對全球氣候變遷，綠色基礎設施(green infrastructure, GI)對於都市熱島現象舒緩的功效受到重視。本研究以大臺北地區為研究地點，探討綠色基礎設施對都市熱島現象舒緩之效益。目的為將都市環境在都市規劃的尺度下，運用土地利用與土地覆蓋分析分類並說明定義研究範圍的都市綠色基礎設施，以探討不同類型GI在空間中的各項特徵與降溫效果之關係。
本研究以TWD_1997_121分帶座標系統之五千分之一圖幅框為樣區大小(2.5km*2.8km)，各樣區面積約為700公頃，共計45樣區，使用Landsat 8與Landsat 9衛星影像抓取綠地，配合土地覆蓋與土地使用分區，將研究地點的綠色基礎設施分為自然與人造2大類，再細分自然類型包含自然森林、自然水體；人造類型包含人造水體、農畜牧地、公園綠地、堤外綠地、街道綠化以及建築綠地等，共計8類；在溫度方面，以Landsat 8與Landsat 9遙測資料推算出地表溫度、NDVI、NDBI、MNDWI加上內政部高程資料，再配合119個地面氣象測站的氣溫資料，包含89個迴歸點資料與30個驗證點資料，利用地理加權迴歸分析，將地表溫度推算為空氣溫度。利用景觀指數將各樣區的綠色基礎設施進行量化，作為自變項，氣溫為應變項進行統計分析。 研究結果顯示在臺北盆地的綠色基礎設施的類型當中，以自然生成的自然水體類型的降溫效果最佳，其次是森林類型；綠色基礎設施的面積越大、聚集度越高，連結度越好，降溫效益越好。研究結果可提供未來都市規劃對於綠色基礎設施有個量化的說明與定義，並提出綠色基礎設施最佳的配置。 | zh_TW |
| dc.description.abstract | In the early stages of urban planning and development, the focus was largely on the construction of artificial infrastructure. With the advent of global climate change, there is an increasing emphasis on the efficacy of Green Infrastructure (GI) in alleviating the urban heat island phenomenon. This study is conducted in the Greater Taipei area, aiming to explore the benefits of green infrastructure in mitigating the urban heat island effect. The objective is to categorize and define urban green infrastructure within the urban planning scale, utilizing land use and land cover analysis. The study seeks to examine the relationships between different types of GI, their spatial characteristics, and the cooling effects, by delving into the urban environment at the scale of urban planning.
This study uses the 1/5000 frame of the TWD_1997_121 zonal coordinate system as the sample area (2.5km*2.8km). The area of each sample area is approximately 700 hectares, with a total of 45 sample areas. Landsat 8 and Landsat 9 are used. Satellite images captured green spaces, combined with land cover and land use zoning, and divided the green infrastructure at the study site into two categories: natural and man-made. The natural types were further subdivided into natural forests and natural water bodies; the man-made types included man-made water bodies, agriculture and animal husbandry. There are 8 categories in total, including land, park green space, green space outside the embankment, street greening and building green space. In terms of temperature, the surface temperature, NDVI, NDBI, MNDWI plus the elevation data of the Ministry of Interior are used to calculate the surface temperature, NDVI, NDBI, and MNDWI from Landsat 8 and Landsat 9 telemetry data, and then cooperate with The temperature data of 119 ground meteorological stations includes 89 regression point data and 30 verification point data. Geographically weighted regression analysis is used to calculate surface temperature into air temperature. The landscape index was used to quantify the green infrastructure in each sample area, and the temperature was used as an independent variable and the temperature was used as a strain term for statistical analysis. The research findings indicate that among the types of green infrastructure in the Taipei Basin, the cooling effect is most significant in the naturally generated natural water bodies category, followed by the forest category. The cooling benefits are enhanced with larger areas, higher aggregation, and better connectivity of green infrastructure. These results can contribute to providing a quantitative understanding and definition of green infrastructure for future urban planning, offering insights into the optimal configuration of green infrastructure in urban settings. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-21T16:33:42Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-03-21T16:33:42Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘 要 i
Abstract iii 目 次 v 圖 次 viii 表 次 ix 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 第三節 研究流程 4 第二章 文獻回顧 5 第一節 土地利用、土地覆蓋與都市氣候 5 一、 土地使用與土地覆蓋 5 二、 地表溫度與地表輻射 6 三、 都市熱島效應 7 第二節 綠色基礎設施 9 一、 綠色基礎設施定義 9 二、 綠色基礎設施服務類型 10 三、 綠色基礎設施組成 11 第三節 遙測影像 14 第四節 空氣溫度推估 17 一、 統計分析 17 二、 溫度-植被指數 18 三、 地理加權迴歸 18 第五節 空間特徵 21 第三章 研究方法 22 第一節 研究架構與內容 22 一、 研究流程 22 二、 研究假設 24 三、 研究變項操作性定義 25 第二節 研究設計 27 一、 操作流程 27 二、 研究範圍 28 三、 數據取得 30 第三節 綠色基礎設施分類 33 一、 自然生成 33 二、 人工建置 33 第四節 氣溫推估 35 一、 模型變項 35 二、 地理加權回歸模型設定 45 第五節 綠色基礎設施空間指標 48 一、 景觀面積 48 二、 景觀面積比例 48 三、 塊區數量 48 四、 塊區密度 49 五、 聚集度指數 49 六、 景觀形狀指數 50 七、 Shannon多樣性指數 50 第六節 假設檢定 51 一、 都市綠色基礎設施空間特徵對降溫效果之影響檢定 51 二、 都市綠色基礎設施不同類的空間特徵對降溫效果之影響檢定 51 第七節 研究限制 52 一、 綠地分類精細度 52 二、 氣象測站資料 52 第四章 研究結果 53 第一節 氣溫推估 53 一、 地理加權迴歸模型 模型預測能力 53 二、 氣溫推估結果 55 第二節 綠色基礎設施組成與空間分布 57 第三節 綠色基礎設施空間屬性 59 第四節 綠色基處設施各類型空間特徵與溫度計算結果 60 一、 森林 60 二、 自然水體 61 三、 人造水體 62 四、 農地 63 五、 公園綠地 64 六、 堤外綠地 65 七、 街道綠化 66 八、 建地綠化 67 九、 小結 68 第五節 綠色基礎設施空間屬性與降溫效果 69 一、 樣區綠色基礎設施空間特徵與降溫效果 69 二、 各類別綠色基礎設施空間特徵與降溫效果 70 三、 綜合各類綠色基礎設施空間特徵與降溫效果 76 第五章 結論與建議 78 第一節 結論 78 一、 綠色基礎設施類型 78 二、 綠色基礎設施空間特徵 79 第二節 建議 80 一、 都市規劃建議 80 二、 後續研究建議 80 參考文獻 82 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 都市規劃 | zh_TW |
| dc.subject | 地理加權迴歸 | zh_TW |
| dc.subject | 景觀指數 | zh_TW |
| dc.subject | 人工綠色基礎設施 | zh_TW |
| dc.subject | 自然綠色基礎設施 | zh_TW |
| dc.subject | Natural green infrastructure | en |
| dc.subject | Artificial green infrastructure | en |
| dc.subject | Landscape metrics | en |
| dc.subject | Geographically weighted regression | en |
| dc.subject | Urban planning | en |
| dc.title | 探討都市綠色基礎設施對都市熱島效應減緩之效益研究 | zh_TW |
| dc.title | The effect of urban green infrastructure on the urban heat island mitigation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 謝正義 | zh_TW |
| dc.contributor.coadvisor | Cheng-I Hsieh | en |
| dc.contributor.oralexamcommittee | 林晏州;張俊彥;歐聖榮 | zh_TW |
| dc.contributor.oralexamcommittee | Yann-Jou Lin;Chun-Yen Chang;Sheng-Jung Ou | en |
| dc.subject.keyword | 自然綠色基礎設施,人工綠色基礎設施,景觀指數,地理加權迴歸,都市規劃, | zh_TW |
| dc.subject.keyword | Natural green infrastructure,Artificial green infrastructure,Landscape metrics,Geographically weighted regression,Urban planning, | en |
| dc.relation.page | 92 | - |
| dc.identifier.doi | 10.6342/NTU202400705 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-02-18 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 園藝暨景觀學系 | - |
| dc.date.embargo-lift | 2026-02-16 | - |
| 顯示於系所單位： | 園藝暨景觀學系 | |
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|---|---|---|---|
| ntu-112-1.pdf | 9.64 MB | Adobe PDF | 檢視/開啟 |
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