台灣森林面積共約219萬公頃，森林覆蓋率高達60%。巨木探索能增加大眾對於台灣山林的保育意識，且為植物地理學上至關重要的議題，但目前台灣對巨木的探勘整體而言仍較為貧乏。現行之巨木探勘係利用空載光達(Airborne Laser Scanner, ALS)具有穿透森林樹冠層並產生多個回波的特性，使用點雲資料產製樹冠高度模型(Canopy Height Model, CHM)，並從中搜尋巨木。由於CHM的數值受懸崖、斜坡地及樹木傾斜等因素影響，無法直接使用CHM自動化尋找巨木，而是將CHM數值高於樹高閾值的區域分類為疑似巨木點位，再由專業人員人工檢視疑似巨木點位的光達點雲資料，確認巨木存在與否，並選取最適合之角度繪製最佳視覺化的巨木剖面圖用於展示。
然而重複檢視光達資料為高度勞力之工作，因此本研究提出一種結合群眾外包(Crowdsourcing)概念與自動化資料處理之巨木探勘方法，探詢借重社會大眾的力量協助巨木探勘之可行性。本研究光達資料為內政部地政司與經濟部中央地質調查局所提供，總共取得180個1/5000個圖幅資料，總面積約為1265 km2。本研究定義樹高高於65 m的樹木為巨木，並篩選CHM大於65 m的資料為疑似巨木點位，採用最小二乘法(Least Squares Method)與法向量法(Normal Vector Method)自動化產製疑似巨木點位之最佳視覺化的巨木剖面圖，以節省人工繪製所耗費之專業人力。本研究以國立成功大學測量及空間資訊學系之學生為受試群眾，依年級區分為三個群組，群組I由大二學生組成，共43人；群組II由大三學生組成，共53人；群組III由大四以上學生組成，共20人，以自架網站將疑似巨木點位之最佳視覺化的巨木剖面圖分配給各群組，使其判識巨木之存在與否，並與專業人員依現行方法檢視點雲資料判識之參考資料相比較。結果顯示自動化產製與專業人員人工繪製之最佳視覺化的巨木剖面圖的相關係數(R2)為0.95，兩者具有高度一致性。受試者使用自動化產製疑似巨木點位之最佳視覺化的巨木剖面圖判識成果之Accuracy、Recall、Precision精度指標分別為，群組I：80%、73%、17%；群組II：83%、69%、19%；群組III：75%、81%，15%，三者之Accuracy均高於75%。另一方面，運用羅吉斯迴歸方法，針對樹高、平均坡度、圖面高程差、地面點厚度等因素，分析其對於群眾使用剖面圖判識巨木的影響。結果顯示當平均坡度、圖面高程差與地面點厚度越低，樹高越高時，有利於群眾正確的從剖面圖中辨識巨木。

Airborne laser scanner(ALS) has the characteristic of penetrating the forest canopy, so the canopy height model (CHM) produced by ALS can be used to search for giant trees. In this research, giant trees were defined as trees with a height higher than 65 m, and locations higher than 65 m in the CHM are used as suspected giant tree locations. Since the value of CHM will be affected by cliffs, slopes and the inclination of trees, professionals must use the original point cloud data to confirm whether the giant tree exist. The giant tree point cloud profile is saved to record its appearance after confirmation. However, it takes a lot of human resource and time to generate the visualization profile of giant trees. Therefore, this research looks for an automatic way to generate the visualization profile. This research selected least-squares method and normal vector method to determine the visualization profile, and produce it automatically. The results showed that the correlation coefficient (R2) between the manual and the automatic visualization profile reached 0.95.
Crowdsourcing has gradually emerged in recent years. Under this model, human resources can be obtained through the Internet. According to our experience, there are numerous suspected locations. Repeated confirmation in ALS data is labor-intensive work. Therefore, this research assigns the visualization profile of the suspected giant tree locations to the three different groups. Finally, the average Accuracy, Recall, and Precision of three groups are: group I: 80%, 73%, 17%; group II: 83%, 69%, 19%; group III: 75%, 81%, 15%, the Accuracy of the three groups is higher than 75%.

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