本研究主要使用Landsat 8影像反演地表溫度，並蒐集影響溫度的地形、土地利用、氣溶膠濃度、天氣等相關因子，且經過相關性分析篩選相關性高的因子以作為本研究預測模型之變數，若可以準確的預測未來規劃區的地表溫度，規劃者即可提前知道規劃是否可以符合期待。
預測模型方面，以2015年、2016年與雙年度2015年及2016年作為樣本年，2019年作為預測年，並選定估計迴歸係數最常使用的最小平方法 (ordinary least squares, OLS) 傳統線性迴歸得出之誤差項作相依性檢定及配適度檢定，以檢定空間落遲模型(spatial lag model, SLM)以及空間誤差模型(spatial error model, SEM)何者模型表現較好，結果為SEM較SLM表現好。因此本研究將OLS及SEM預測2019年的結果作比較，得出2015年資料作三個園區預測之模型皆以OLS模型誤差較低，均方根誤差(root-mean-square error, RMSE)值為1.93至2.59度之間，SEM則為2.10至3.13度之間。以2016年的資料預測后里園區與外埔園區以SEM誤差較低，葫蘆墩園區以OLS誤差較低，OLS的RMSE在5.63至6.82度之間，SEM則為5.46至7.92度之間。以雙年度資料預測后里園區與葫蘆墩園區以OLS誤差較低，外埔園區則以SEM誤差較低，OLS的RMSE在2.43至6.07度之間，SEM則為2.24至6.77度之間。
將衛星反演溫度圖與迴歸模型預測溫度圖做差值比較，並比對衛星影像，發現預測結果誤差較低的地方多為植被覆蓋，誤差值較高的通常為裸土與建物等人造舖面。
本研究結果發現氣象因子造成預測溫度誤差較大，後續研究不建議當作預測因子，未來可以加入夏季月份，以探討冬季因子與夏季因子之差異，並統整因子，進一步探討資料連續性以及增加年度資料量是否影響預測地表溫度的精準度。

This study mainly used Landsat 8 image to retrieve surface temperature and collected relevant factors concerning topography, land use, aerosol concentration and weather conditions. These fac-tors were selected with correlation analysis to keep highly relevant factors for prediction model in this study. If the surface temperature of the planned area can be accurately predicted in the future, plan-ners can know in advance whether the plan can meet expectations.
The most commonly used traditional linear regression in geo-spatial prediction, ordinary least squares (OLS), was selected to test which model, spatial lag model (SLM) or spatial error model (SEM), performs better in spatial dependency and goodness of fit. The re-sult showed that SEM performs better than SLM. Therefore, this study compared the prediction results of OLS and SEM in 2019, and concluded that when using the data from 2015, the OLS model had lower error. The root-mean-square error (RMSE) of OLS is be-tween 1.93 and 2.59, and that of SEM is between 2.10 and 3.13. With the data from 2016 and that from both 2015 and 2016, it is not fixed which model is better. The RMSE of OLS is between 3.34 and 6.82, and that of SEM is between 3.70 and 7.92.
The prediction results in Houli and Waipu Park with the data from 2016 and the SEM model had lower error; while in Huludun Park, the results with the OLS model had lower error. The RMSE of OLS was between 5.63 and 6.82, and the RMSE of SEM was be-tween 5.46 and 7.92. With the data from both 2015 and 2016, the prediction results of OLS model in Houli Park and Huludun Park had lower error, while that of SEM model in Waipu Park had lower error. The RMSE of OLS is between 2.43 and 6.07, and the RMSE of SEM is between 2.24 and 6.77.
Compared the difference between the retrieved temperature maps from the satellite images and the predicted ones from the re-gression models, this study found that the land cover with lower prediction error was mostly vegetation cover, and that with higher error was usually artificial land cover such as bare soil and buildings.
The results of this study found that the meteorological factors cause large errors in forecasting temperature, so these factors were not recommended as predictive factors for future study. In the future, summer months can be added to explore the difference between win-ter and summer factors. These factors can also be integrated to fur-ther explore the continuity of data and explore if the increase in the amount of annual data will affect the accuracy or not.

第一章、 緒論 10
第一節 研究動機及目的 10
第二節 研究範圍 11
第三節 研究限制 14
第四節 研究流程 15
第二章、 文獻探討 17
第一節 環境溫度觀測法 17
第二節 地表溫度反演 20
第三節 地表溫度影響因子 22
第四節 小結 27
第三章、 研究方法 29
第一節 衛星影像 29
第二節 溫度反演 31
第三節 研究因子 39
第四節 建構模型 50
第五節 小結 54
第四章、 實證結果與分析 55
第一節 全區地表溫度反演 55
第二節 統計迴歸分析 57
第三節 花博園區地表溫度預測 73
第五章、 結論及建議 90

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