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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93056

    Title: 運用機器學習回歸方法改善臺灣地區從Landsat-8與Himawari-8融合影像中的高時空地表溫度;A Machine Learning Regression Approach for Improving High-Spatiotemporal LST from Landsat and Himawari Fused Imagery in Taiwan
    Authors: 雅瓜納;Januar, Tri Wandi
    Contributors: 環境科技博士學位學程
    Keywords: 時空圖像融合;機器學習;支持向量回歸;地表溫度;Landsat-8;Himawari-8;Spatiotemporal image fusion;machine learning;support vector regression;Land surface temperature;Landsat-8;Himawari-8
    Date: 2023-08-17
    Issue Date: 2023-10-04 16:23:59 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 摘要
    This research aims to enhance the estimation of high-spatiotemporal Land Surface Temperature (LST) in Taiwan by leveraging the fusion of Landsat and Himawari satellite imagery. A machine learning regression approach namely Support Vector Regression (SVR) is employed to improve the accuracy of the LST estimation. The study utilizes the combination of Landsat and Himawari thermal infrared (TIR) images to estimate hourly timeseries LST data. Feature engineering is performed on emissivity, elevation, and vegetation indices (such as NDVI) to create combined features for regression analysis. Stratified random sampling is employed using a classification map obtained from feature engineering, resulting in a dataset of 7,000 samples divided into 80% training and 20% testing data. The SVR algorithm is applied to correct the fused LST, resulting in a significant improvement in accuracy, especially in mountainous areas. The results demonstrate that the fusion of Landsat and Himawari imagery, combined with machine learning regression, can effectively enhance the estimation of high-spatiotemporal LST in Taiwan. This research contributes to the understanding of surface temperature variations and offers opportunities for applications in climate modeling, environmental monitoring, and urban planning.
    Appears in Collections:[環境科技博士學位學程] 博碩士論文

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