在氣候變遷與都市化交互作用下，都市熱島效應已成為當前關鍵的氣候風險，土地利用變化為影響地區氣候之關鍵因素。評估都市發展土地利用變化產生之氣候狀況，有助於都市計畫通盤檢討等長期規劃程序中，提供調適策略研擬所需之區位評估資訊。然而，既有國內都市尺度研究多以歷史時序或橫斷面分析為主，較難反映未來都市擴張對熱環境之影響。在溫度指標上，多以地表溫度(LST)為指標，然易受地表材質影響，相較空氣溫度常高估人體實際近地熱感受。此外，既有研究常將建成地視為單一分類，忽略住商、工業等不同開發型態的熱環境差異。近一步而言，都市尺度熱環境模擬工具(如WRF、CFD等)在環境型態參數需求複雜、敏感且運算耗時，與土地利用變遷模型整合模擬仍具限制，使其於未來分析應用受限。
爰此，本研究以臺南市為實證地區，整合多層感知器(MLP)之馬可夫鍊細胞自動機(Cellular Automata Markov Model)與InVEST Urban cooling model之方法架構，探討未來土地利用變遷之空間分布，其對近地空氣溫度分布之影響，以及未來高溫與增溫空間群聚之區位。透過MLP-CA-MA模擬2036年與2050年發展情境下之土地利用變遷，而後利用資料需求相對具可操作性之InVEST模型，以局部氣候分區 (Local Climate Zone, LCZ)概念細分建成地型態，結合三維建物資料、土地使用分區管制與氣溫資料進行參數設定，估算近地空氣溫度，最後透過空間自相關分析辨識未來高溫與增溫之空間聚集特性，輔以規劃討論。
研究結果顯示，2036與2050年土地利用持續朝建成用地擴張，其以住商與工業用地增加最為顯著，多沿既有建成使用外緣。空氣溫度模擬結果顯示，隨建成用地擴張，2036年增溫以局部熱點為主，2050年則轉為帶狀連續分布。空間自相關顯示，高溫與增溫現象皆具有顯著正向空間自相關，高溫群聚長期穩定分布於臺南西南部之都市核心，其反映既有都市核心與產業發展地區之熱累積狀態。增溫群聚則明顯於都市外圍(永康、仁德、安南)、南科周邊地區(新市、善化)以及若干都市計畫地區之次級發展節點及鄉村地區(佳里與新營等)新增，呈現未來土地變遷所造成之新熱壓力。隨著年期增長，增溫群聚面積在非都市地區比例持續上升，顯示未來高溫並非僅侷限於都市地區，地方發展若持續建成開發，鄉村地區可能形成新升溫壓力區，亦有關注與討論之必要性。
綜合而言，高溫空間由既有都市發展核心區向周邊擴展，呈現群聚強化與空間連續化之趨勢，並由局部熱點轉為區域帶狀之空間問題，需由整體城鄉發展加以回應。本研究所建構之整合分析架構，可作為都市計畫通盤檢討與國土規劃中辨識未來高溫聚集區與調適優先區位之依據，支援降溫策略配置與開發管制決策，以提升面對氣候變遷之調適能力。

This study aims to assess the spatial distribution of high-temperature hazards under future land-use change scenarios and to provide spatial planning implications for climate adaptation. Under the combined effects of climate change and urbanization, land-use change has become an important factor influencing local thermal environments. However, previous studies have mainly focused on historical land-use change or land surface temperature, while fewer studies have integrated future land-use scenarios, near-surface air temperature, and spatial clustering analysis.
Taking Tainan City as the study area, this research develops an integrated framework combining land-use change simulation, urban cooling service assessment, and spatial autocorrelation analysis. A Cellular Automata–Markov model integrated with a Multi-Layer Perceptron was used to simulate exploratory land-use scenarios for 2036 and 2050. The InVEST Urban Cooling Model was then applied to estimate near-surface air temperature. The concept of Local Climate Zones was adopted to refine built-up land categories, and Global Moran’s I and Local Indicators of Spatial Association were used to identify high-temperature and warming clusters.
The results show that built-up land is expected to continue expanding, especially residential-commercial and industrial land. High-temperature clusters remain concentrated in the existing urban core and industrial areas, while warming clusters gradually extend toward urban fringe areas, the Southern Taiwan Science Park, and several secondary town and rural nodes. By 2050, warming areas shift from localized hotspots to more continuous belt-like and areal patterns.
In conclusion, future land-use change may intensify and spatially extend high-temperature hazards. The proposed framework can support the identification of heat adaptation priority areas and provide references for urban planning, national spatial planning, rural area planning, and climate adaptation strategies.

英文文獻
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