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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡亞倫 | zh_TW |
| dc.contributor.advisor | Ya-Lun S. Tsai | en |
| dc.contributor.author | 吳欣穎 | zh_TW |
| dc.contributor.author | Hsin-Ying Wu | en |
| dc.date.accessioned | 2026-02-26T16:53:46Z | - |
| dc.date.available | 2026-02-27 | - |
| dc.date.copyright | 2026-02-26 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-01-30 | - |
| dc.identifier.citation | 交通部統計處（2025a）。主要漁港進出港艘數及客運概況。2025年8月7日。取自https://statis.motc.gov.tw/motc/Statistics/Display?Seq=174
交通部統計處（2025b）。國內商港吞吐量。2025年8月7日。取自https://statis.motc.gov.tw/motc/Statistics/Display?Seq=196 中央氣象署（2024）。中央氣象署產品說明文件警報及特報_天氣特報資訊_濃霧特報。 中央氣象署（2025）。112觀測年報。 中央氣象署（2023）。中央氣象署產品說明文件霧或低雲。 交通部民用航空局（2014）。臺灣地區春季大霧經驗預報之量化。 許秀妮（2015）。2001~2015年1-5月份期間桃園、馬祖北竿、馬祖南竿與金門機場霧日觀測資料統計分析。飛航天氣，24，18-35。 許秀妮、蔡宜君（2015）。2015 年 1-5 月臺灣金門、馬祖(南竿/北竿)與桃園機場起霧之天氣診斷分析。第九屆海峽兩岸航空氣象與飛行安全研討會。 Amani, M., Mahdavi, S., Bullock, T., & Beale, S. (2020). Automatic nighttime sea fog detection using GOES-16 imagery. Atmospheric Research, 238, 104712. Andersen, H., & Cermak, J. (2018). First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib. Atmospheric Measurement Techniques, 11(10), 5461-5470. Ball, L., & Tzanopoulos, J. (2020). Interplay between topography, fog and vegetation in the central South Arabian mountains revealed using a novel Landsat fog detection technique. Remote Sensing in Ecology and Conservation, 6(4), 498-513. Bari, D., Lasri, N., Souri, R., & Lguensat, R. (2023). 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101709 | - |
| dc.description.abstract | 霧的發生會使得水平能見度降低，並造成交通事故發生的機率增加，因此霧偵測對於提供準確與即時的氣象預報至關重要。使用人工目視與能見度儀為傳統霧偵測方法，然而，這些方法僅能取得單點之資訊。相較之下，衛星遙測可以提供大範圍的資料，但多數的繞極軌道衛星重訪週期為數天，無法進行時間連續的觀測。而地球同步衛星克服了以上缺點，不僅可取得空間連續的資料，且時間解析度高，數十分鐘即產生一筆觀測，在霧偵測上展現出極大潛力。但霧與低雲擁有相似的光譜特徵，使得兩者經常被誤分類。為了更精確地進行霧分類，本研究使用Himawari衛星資料，建立了一種根據光譜與紋理特徵進行分類的機器學習模型，來進行臺灣離島夜間的霧偵測。並且根據2016至2025年的霧偵測結果，分析霧事件長期時空變遷。結果顯示，本研究所建立的海霧模型偵測率（Probability of Detection，POD）達81.66%，誤報率（False Alarm Rate，FAR）為20.76%。相較於只有使用光譜與亮度溫度差異特徵，加入紋理特徵後，模型的POD提高了3.04%。此外，進一步使用遞迴特徵消除法進行特徵選擇，使得模型的POD提升4.82%。在霧時空變遷結果中顯示，夜間海霧頻率較高的範圍集中臺灣海峽北緯24度至25度左右，且澎湖與金門附近海域的海霧頻率高於馬祖區域，並且在10年以來，霧頻率在三個區域未有顯著變化。本研究證明了使用光譜與紋理特能有效提升霧偵測的精度。並且透過長期的霧事件偵測結果，洞察臺灣離島區域霧的時空分布與趨勢，為未來交通安全與規劃工作提供參考。 | zh_TW |
| dc.description.abstract | Fog is a phenomenon that reduces horizontal visibility, often leading to traffic accidents on land and at sea. Therefore, fog detection is crucial for providing accurate and real-time weather forecast guidance. Geostationary satellites have high temporal resolution and spatially continuous observations, showing great potential in fog detection. However, low clouds and fog are often misidentified because of their similar spectral features. To distinguish between fog and low clouds, this study develops a machine learning algorithm based on the spectral and textural characteristics, using Himawari data, for nighttime fog detection in Taiwan’s offshore islands. The research then analyzes spatiotemporal fog patterns and long-term trends based on the results of fog detection from 2016 to 2025. Results show that the probability of detection (POD) and the false alarm rate (FAR) of the model are 81.66% and 20.76%, respectively. Compared to using only spectral features, adding texture features improves POD by 3.04%. Furthermore, applying recursive feature elimination for feature selection increases POD by 4.82%. The spatial distribution of fog indicates that the highest fog occurrence is concentrated in the Taiwan Strait between 24°N and 25°N. Among the offshore islands regions, higher fog frequencies are identified around Penghu and Kinmen than around Matsu. Over the past decade, fog frequency in these regions shows no statistically significant trend. This study improves the accuracy of fog detection and reveals the spatiotemporal patterns of fog in Taiwan’s offshore islands, which supports relevant traffic planning. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-02-26T16:53:46Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-02-26T16:53:46Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 目次 iv 圖次 vi 表次 vii 第一章 緒論 1 1.1 研究背景 1 1.2 霧的分類 4 1.3 霧和雲的光譜特性 5 1.4 霧偵測方法 6 1.5 霧分類方法 9 1.6 研究目的 9 第二章 研究區域 11 第三章 研究方法與使用資料 14 3.1 整體研究流程 14 3.2 資料前處理 15 3.2.1 地球同步衛星 15 3.2.2 Himawari-8/9衛星資料 16 3.3 霧偵測模型建立 18 3.3.1 特徵提取 18 3.3.2 隨機森林模型 22 3.3.3 特徵重要性與特徵選擇 26 3.3.4 成果評估 27 3.4 時空變遷分析 28 3.4.1 Theil-Sen推估法 28 3.4.2 Mann-Kendall趨勢檢定 29 3.4.3 時空熱點分析 30 第四章 研究成果 32 4.1 不同特徵組合對海霧分類的影響 32 4.2 特徵重要性評估 33 4.3 時空變遷分析 36 4.3.1 臺灣海峽區域之霧空間分布與長期趨勢 36 4.3.2 各離島附近海域之霧空間分布與長期趨勢 37 4.3.3 時空熱點分析 39 第五章 成果討論 40 5.1 光譜與紋理特徵之分析 40 5.2 特徵選擇 42 5.3 霧空間分布與趨勢之探討 44 5.4 研究貢獻與實際應用 45 5.5 研究限制與未來工作 46 第六章 結論 49 參考文獻 50 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 霧事件 | - |
| dc.subject | 遙感探測 | - |
| dc.subject | 隨機森林 | - |
| dc.subject | 時間序列分析 | - |
| dc.subject | 灰階共生矩陣 | - |
| dc.subject | Fog identification | - |
| dc.subject | Remote sensing | - |
| dc.subject | Random Forest | - |
| dc.subject | Time series analysis | - |
| dc.subject | Gray-Level Co-occurrence Matrix | - |
| dc.title | 應用地球同步衛星資料於臺灣離島夜間海霧偵測與長期時空變遷分析 | zh_TW |
| dc.title | Nighttime Sea Fog Detection and Long-Term Spatiotemporal Analysis Over Taiwan’s Offshore Islands Using Geostationary Satellite Data | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 曾國欣;蔡慧萍;黃春嘉 | zh_TW |
| dc.contributor.oralexamcommittee | Kuo-Hsin Tseng;Hui-Ping Tsai;Chun-Jia Huang | en |
| dc.subject.keyword | 霧事件,遙感探測隨機森林時間序列分析灰階共生矩陣 | zh_TW |
| dc.subject.keyword | Fog identification,Remote sensingRandom ForestTime series analysisGray-Level Co-occurrence Matrix | en |
| dc.relation.page | 57 | - |
| dc.identifier.doi | 10.6342/NTU202600472 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2026-02-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2031-01-09 | - |
| 顯示於系所單位： | 土木工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf 此日期後於網路公開 2031-01-09 | 4.11 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。