森林碳儲量之空間化估算為碳收支核算與氣候變遷減緩的重要基礎。本研究以臺灣高屏地區森林為對象，整合地面調查、空載光達、Landsat 8、Sentinel-1 與地形資料，建構結合樣區空間對位、多源遙測及堆疊泛化集成學習之地上部碳儲存密度（Aboveground Carbon Density, AGCD）估算架構。
為降低森林環境下 GNSS 定位不確定性造成之樣區與遙測資料錯位，本研究建立以林分結構相似性為核心之移動窗口空間對位方法。搜尋半徑敏感性分析顯示 20 公尺後結果趨穩，因此以 30 公尺為主要搜尋範圍、20 公尺作跨半徑收斂檢核；223 個樣區經對位與品質篩選後保留 177 個。主要以未參與對位之光學、SAR 與地形特徵評估對位效果，平均驗證 R2 由 0.27 提升至 0.30，測試 R2 由 0.15 提升至 0.18，且七種演算法測試結果皆改善；光達特徵之補充比較亦由 0.58 提升至 0.64。
於相同空間對位位置整合光學、SAR 與地形特徵後，七種演算法平均驗證 R2 由 0.64 提升至 0.71。以七種基學習器之折外預測建構堆疊集成模型，CatBoost 元學習器之十折平均測試 R2達 0.78，進一步選定之主模型於驗證集與外部測試集之 R2 分別為 0.89 與 0.79，顯示其具良好之預測表現與泛化能力。兩階段 SHAP 近似溯源顯示光達貢獻 75.5%，P90 為最重要單一變數。QRF75 雖降低高值樣本之 MAE 與 RMSE，但加深低估 Bias 並降低其他樣本精度，故未納入最終主模型。
主模型產製之 2018 與 2023 年 30 公尺 AGCD 圖估算全區碳儲量分別為 52.97 與 55.65 百萬 Mg C，十折相對不確定性平均低於 6%。2023 年 AGCD 與屏東縣國土功能分區整合後，國土保育地區第一類與第二類之平均 AGCD 分別為 119.14 與 109.88 Mg C ha⁻¹，顯示空間連續碳密度資訊可支援高碳儲存區保全、土地利用碳風險辨識與增匯潛力初步篩選。此外，本研究採用 BEF = 1.4，可能使 AGCD 絕對值偏高，因此在解讀碳儲存量結果時需將此因素納入考量。

This study developed an aboveground carbon density (AGCD) estimation framework for the Kaohsiung–Pingtung region of Taiwan by integrating ground inventory, airborne LiDAR, Landsat 8, Sentinel-1 SAR, terrain data, plot spatial alignment, and stacking ensemble learning. A moving-window alignment based on stand-structure similarity used 30 m as the main search extent and 20 m for cross-radius convergence after sensitivity analysis showed stabilization beyond 20 m. After quality filtering, 177 of 223 plots were retained. Alignment was primarily evaluated with optical, SAR, and terrain features not used in matching: average validation R² increased from 0.27 to 0.30 and external-test R² from 0.15 to 0.18, with all seven algorithms improving on the test set. A supplementary LiDAR-only comparison increased validation R² from 0.58 to 0.64. Multi-source integration further raised validation R² to 0.71. The CatBoost stacking model achieved a ten-fold average test R² of 0.78, and the selected main model achieved validation and external-test R² values of 0.89 and 0.79. LiDAR contributed 75.5% in two-stage approximate SHAP attribution, with P90 as the most influential variable. The 2018 and 2023 maps estimated 52.97 and 55.65 million Mg C, respectively, with mean relative uncertainty below 6%. The 2023 map also revealed clear AGCD differences among Pingtung County national land functional zones, demonstrating the planning value of spatially explicit carbon information.

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