本研究調查多高度無人機(UAV)結構從動(SfM)攝影測量是否能從樹冠頂部到林下細節捕捉全面的森林結構。中心假設是超低飛行在地面上方提供對森林莖幹、下層枝條和地面植被的直接視覺存取，而這些在傳統高空調查中無法獲得。在台南平石公園的四次序列飛行中，採集121張照片，通過Agisoft Metashape處理，在四個高度層生成2.85億個3D點。結果顯示：高空樹冠(>50 m)產生2140萬個點，重投影誤差為0.310像素；中間樹冠(~30 m)產生最高密度(1.696億個點，0.319像素誤差)；亞樹冠(8-11 m)表現極差，僅420萬個點，誤差7.77像素，原因是葉面密度、風不穩定性和重複紋理；超低林下(2-3 m)交付8990萬個點，誤差最低(0.169像素)，成功捕捉單個莖幹、枝條和地面特徵。研究建立了一個垂直信息層級，其中高空飛行表徵樹冠宏觀結構和樹冠細節，超低飛行獨特地提供林下存取。雙高度策略(高空+超低)比四層更具成本效益。Align中檔平台(約美元2,500-3,000)展現了操作限制，但在調查設計考慮平台限制時成功生成了研究品質結果。研究結果確認多高度UAV SfM對詳細森林表徵在中等成本下可行，林下操作提供從高空視角無法存取的獨特信息，策略性高度選擇比在所有高度採集數據更重要。這種成本可及的方法為森林監測提供了昂貴LiDAR的實用替代方案。

This study investigates whether multi-altitude unmanned aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry can capture comprehensive forest structure from canopy crowns to understory detail. The central hypothesis is that ultra-low flights above ground level provide direct visual access to forest stems, lower branches, and ground vegetation inaccessible from conventional high-altitude surveys. Four sequential flights at Pingshi Park, Tainan, acquired 121 photographs processed through Agisoft Metashape to generate 285 million 3D points across four altitude layers. Results show: high-altitude canopy (>50 m) generated 21.4 million points with 0.310 pix reprojection error; intermediate canopy (~30 m) yielded the highest density (169.6 million points, 0.319 pix error); sub-canopy (8–11 m) severely underperformed with 4.4 million points and 7.77 pix error due to foliage density, wind instability, and repetitive texture; and ultra-low under-canopy (2–3 m) delivered 89.9 million points with the lowest error (0.169 pix), successfully capturing individual stems, branches, and ground features. The study establishes a vertical information hierarchy where high-altitude flights characterize canopy macrostructure, crown detail, and ultra-low flights uniquely provide understory access. A two-altitude strategy (high + ultra-low) is more cost-effective than four layers. The Align mid-range platform (~USD 2,500–3,000) exhibited operational limitations but successfully produced research-quality results when survey design accounted for platform constraints. The findings confirm that multi-altitude UAV SfM is viable for detailed forest characterization at intermediate cost, that under-canopy operations deliver unique information inaccessible from high-altitude perspectives, and that strategic altitude selection is more important than acquiring data at all altitudes. This cost-accessible approach offers a practical alternative to expensive LiDAR for forest monitoring.

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