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題名 應用 UAV 光達與移動式光達於立木調查之研究
Individual Tree Investigation Using UAV and Mobile LiDAR作者 馬慧
Ma, Hui貢獻者 詹進發
Jan, Jihn-Fa
馬慧
Ma, Hui關鍵詞 森林調查
立木調查
立木偵測
基本參數測計
空載光達
移動式光達
Forest inventory
Individual tree investigation
Individual tree detection
Basic parameters measurement
Airborne LiDAR
Mobile LiDAR日期 2021 上傳時間 4-Aug-2021 15:50:38 (UTC+8) 摘要 台灣位於熱帶與亞熱帶氣候交界處，地形起伏大，蘊藏豐富的植被資源，為保存珍貴的自然資源，應更重視森林之管理。進行森林的經營管理之前，需調查森林基本參數以了解森林資源的現況，而立木偵測又為森林資源調查的首要工作，多數的森林調查作業與其相關。過去多以現地測量的方式進行森林資源調查，隨著技術的進步，航空影像及衛星影像亦可運用於調查立木，然兩者均有其限制。高精度、高效率的光達系統可以在短時間內大量求得精確的三維資訊，其多重反射的特性可提供林地垂直結構的資訊，故本研究欲透過光達系統進行立木調查。然而，不同種類的光達系統各具優勢及限制，結合不同光達系統可以克服各自的限制，提供更完整的資訊，因此本研究除了分析空載光達及移動式光達進行立木調查之結果，亦探討結合空載光達及移動式光達應用於立木調查之可行性。成果顯示利用空載光達進行立木偵測之 recall 值為 0.67、precision 值為 0.76、F-score 值為 0.71;利用移動式光達進行立木偵測之 recall 值為 0.95、 precision 值為 0.77、F-score 值為 0.85;結合空載光達及移動式光達偵測立木之 recall 值為 0.81、precision 值為 0.79、F-score 值為 0.80。而利用空載光達獲取樹高之 RMSE 為 0.94 m，Bias 為 0.14 m;利用移動式光達獲取樹高之 RMSE 為 0.59 m，Bias 為 0.07 m;結合空載光達及移動式光達獲取樹高之 RMSE 值為 0.71 m，Bias 值為 0.31 m。此外，利用移動式光達得以同時獲取立木的胸高直徑，然而，受研究區域內立木生長情形以及樹種之影響，其 RMSE 值為 7.27 cm，Bias 值為 -4.17 cm。研究結果顯示空載光達與移動式光達皆得以應用於立木調查，而相較於僅使用空載光達或移動式光達，結合空載光達及移動式光達提供更精確的立木偵測及基本參數測計結果，有助於提升立木調查之精度。
Taiwan is located at the junction of tropical and subtropical climate zones, with undulating terrain and abundant vegetation resources. To preserve precious forest resources, more attention should be paid to forest management. Before conducting forest management, it is necessary to investigate the basic parameters of the forest to understand the current status of forest resources. Detection of individual tree is the primary task of forest resources inventory, and most forest investigation operations are related to it.In the past, forest resources inventory were mostly carried out by means of field surveys. With the advancement of technology, aerial and satellite images can also be used to investigate forest resources, but both have their limitations. LiDAR system has the advantages of high precision and high efficiency. It can obtain a large amount of accurate three-dimensional information in a short time. Its multiple reflection characteristics can provide information about the vertical structure of forest stands. Therefore, this study intends to investigate individual tree with LiDAR system. However, different types of LiDAR systems have their own advantages and limitations. Combining different LiDAR systems can overcome their respective limitations and provide more complete information. Therefore, in addition to analyzing the results of airborne LiDAR and mobile LiDAR, this study also explored the feasibility of combining airborne and mobile LiDAR systems for individual tree investigation.To detect individual trees using LiDAR, for the airborne LiDAR dataset, the values of recall, precision, and F-score are 0.67, 0.76, and 0.71, respectively. For the mobile LiDAR dataset, the values of recall, precision, and F-score are 0.95, 0.77, and 0.85, respectively. For the combined dataset, the values of recall, precision, and F-score are 0.81, 0.79, and 0.80, respectively.To estimate tree height using LiDAR, for the airborne LiDAR dataset, the values of RMSE and bias are 0.94 m and 0.14 m, respectively. For the mobile LiDAR dataset, the values of RMSE and bias are 0.59 m and 0.07 m, respectively. For the combined dataset, the values of RMSE and bias are 0.71 m and 0.31 m, respectively.Moreover, by using mobile LiDAR to investigate individual trees, we can measure tree height and DBH (diameter at breast height) at the same time. The results of DBH measurements are 7.27 cm and -4.17 cm for RMSE and bias, respectively. However, the accuracy of DBH estimation can be affected by forest stand structures and trees species.The results show that both airborne and mobile LiDAR can be used to investigate individual trees. 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J. and Dubayah, R., 2013, "A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing" Remote Sensing of Environment, 128:289-298.三、網頁參考文獻DJI (2020) DJI Matrice 300 RTK from DJI on the World Wide Web：https://www.dji.com/twGeoslam (2020) hand-held lidar from Geoslam on the World Wide Web：https://geoslam.com/lidar360 (2020) LiBackpack DGC50 from lidar360 on the World Wide Web：https://greenvalleyintl.com/Livox (2020) Livox Mid-40 from Livox on the World Wide Web：https://www.livoxtech.com/Renishaw (2020). 車載光達 from Renishaw on the World Wide Web：https://www.renishaw.com.tw/宏遠儀器(2020). 宏遠儀器有限公司官方網站：http://www.control-signal.com.tw/政治大學(2019). 政治大學官方網站：https://www.nccu.edu.tw/ 描述 碩士
國立政治大學
地政學系
108257008資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108257008 資料類型 thesis dc.contributor.advisor 詹進發 zh_TW dc.contributor.advisor Jan, Jihn-Fa en_US dc.contributor.author (Authors) 馬慧 zh_TW dc.contributor.author (Authors) Ma, Hui en_US dc.creator (作者) 馬慧 zh_TW dc.creator (作者) Ma, Hui en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 15:50:38 (UTC+8) - dc.date.available 4-Aug-2021 15:50:38 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 15:50:38 (UTC+8) - dc.identifier (Other Identifiers) G0108257008 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136535 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系 zh_TW dc.description (描述) 108257008 zh_TW dc.description.abstract (摘要) 台灣位於熱帶與亞熱帶氣候交界處，地形起伏大，蘊藏豐富的植被資源，為保存珍貴的自然資源，應更重視森林之管理。進行森林的經營管理之前，需調查森林基本參數以了解森林資源的現況，而立木偵測又為森林資源調查的首要工作，多數的森林調查作業與其相關。過去多以現地測量的方式進行森林資源調查，隨著技術的進步，航空影像及衛星影像亦可運用於調查立木，然兩者均有其限制。高精度、高效率的光達系統可以在短時間內大量求得精確的三維資訊，其多重反射的特性可提供林地垂直結構的資訊，故本研究欲透過光達系統進行立木調查。然而，不同種類的光達系統各具優勢及限制，結合不同光達系統可以克服各自的限制，提供更完整的資訊，因此本研究除了分析空載光達及移動式光達進行立木調查之結果，亦探討結合空載光達及移動式光達應用於立木調查之可行性。成果顯示利用空載光達進行立木偵測之 recall 值為 0.67、precision 值為 0.76、F-score 值為 0.71;利用移動式光達進行立木偵測之 recall 值為 0.95、 precision 值為 0.77、F-score 值為 0.85;結合空載光達及移動式光達偵測立木之 recall 值為 0.81、precision 值為 0.79、F-score 值為 0.80。而利用空載光達獲取樹高之 RMSE 為 0.94 m，Bias 為 0.14 m;利用移動式光達獲取樹高之 RMSE 為 0.59 m，Bias 為 0.07 m;結合空載光達及移動式光達獲取樹高之 RMSE 值為 0.71 m，Bias 值為 0.31 m。此外，利用移動式光達得以同時獲取立木的胸高直徑，然而，受研究區域內立木生長情形以及樹種之影響，其 RMSE 值為 7.27 cm，Bias 值為 -4.17 cm。研究結果顯示空載光達與移動式光達皆得以應用於立木調查，而相較於僅使用空載光達或移動式光達，結合空載光達及移動式光達提供更精確的立木偵測及基本參數測計結果，有助於提升立木調查之精度。 zh_TW dc.description.abstract (摘要) Taiwan is located at the junction of tropical and subtropical climate zones, with undulating terrain and abundant vegetation resources. To preserve precious forest resources, more attention should be paid to forest management. Before conducting forest management, it is necessary to investigate the basic parameters of the forest to understand the current status of forest resources. Detection of individual tree is the primary task of forest resources inventory, and most forest investigation operations are related to it.In the past, forest resources inventory were mostly carried out by means of field surveys. With the advancement of technology, aerial and satellite images can also be used to investigate forest resources, but both have their limitations. LiDAR system has the advantages of high precision and high efficiency. It can obtain a large amount of accurate three-dimensional information in a short time. Its multiple reflection characteristics can provide information about the vertical structure of forest stands. Therefore, this study intends to investigate individual tree with LiDAR system. However, different types of LiDAR systems have their own advantages and limitations. Combining different LiDAR systems can overcome their respective limitations and provide more complete information. Therefore, in addition to analyzing the results of airborne LiDAR and mobile LiDAR, this study also explored the feasibility of combining airborne and mobile LiDAR systems for individual tree investigation.To detect individual trees using LiDAR, for the airborne LiDAR dataset, the values of recall, precision, and F-score are 0.67, 0.76, and 0.71, respectively. For the mobile LiDAR dataset, the values of recall, precision, and F-score are 0.95, 0.77, and 0.85, respectively. For the combined dataset, the values of recall, precision, and F-score are 0.81, 0.79, and 0.80, respectively.To estimate tree height using LiDAR, for the airborne LiDAR dataset, the values of RMSE and bias are 0.94 m and 0.14 m, respectively. For the mobile LiDAR dataset, the values of RMSE and bias are 0.59 m and 0.07 m, respectively. For the combined dataset, the values of RMSE and bias are 0.71 m and 0.31 m, respectively.Moreover, by using mobile LiDAR to investigate individual trees, we can measure tree height and DBH (diameter at breast height) at the same time. The results of DBH measurements are 7.27 cm and -4.17 cm for RMSE and bias, respectively. However, the accuracy of DBH estimation can be affected by forest stand structures and trees species.The results show that both airborne and mobile LiDAR can be used to investigate individual trees. As compared to using only airborne LiDAR, combining airborne and mobile LiDAR dataset can provide better accuracy for tree detection and basic parameters measurements, and thereby improve the accuracy of individual tree investigation. en_US dc.description.tableofcontents 謝誌 I摘要 IIIAbstract V目錄 VII圖目錄 IX表目錄 XI第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 4第三節 研究架構 5第二章 文獻回顧 7第一節 光達技術及原理 7第二節 光達技術應用於林業調查 15第三節 光達技術偵測立木之方法 28第三章 研究方法 35第一節 研究區域 35第二節 研究材料與設備 36第三節 研究流程 41第四節 研究方法及理論基礎 44第四章 成果與分析 55第一節 立木分割成果 55第二節 立木基本參數成果 73第五章 結論與建議 85第一節 結論 85第二節 建議 87參考文獻 88 zh_TW dc.format.extent 3904755 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108257008 en_US dc.subject (關鍵詞) 森林調查 zh_TW dc.subject (關鍵詞) 立木調查 zh_TW dc.subject (關鍵詞) 立木偵測 zh_TW dc.subject (關鍵詞) 基本參數測計 zh_TW dc.subject (關鍵詞) 空載光達 zh_TW dc.subject (關鍵詞) 移動式光達 zh_TW dc.subject (關鍵詞) Forest inventory en_US dc.subject (關鍵詞) Individual tree investigation en_US dc.subject (關鍵詞) Individual tree detection en_US dc.subject (關鍵詞) Basic parameters measurement en_US dc.subject (關鍵詞) Airborne LiDAR en_US dc.subject (關鍵詞) Mobile LiDAR en_US dc.title (題名) 應用 UAV 光達與移動式光達於立木調查之研究 zh_TW dc.title (題名) Individual Tree Investigation Using UAV and Mobile LiDAR en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文參考文獻江采薇，2006，「融合光達及高解析影像建立三維植生覆蓋模型」，中央大學土木工程研究所碩士論文：桃園。何心瑜、史天元，2005，「空載光達作業流程及品質管理之研究」，國立交通大學土木工程學系碩士論文：新竹。李崇誠，2018，「空載光達應用於亞熱帶森林之數值高程模型與立木密度資料之產製」，國立成功大學測量及空間資訊學系博士論文：台南。李鈺禪，2020，「應用光譜水體指數結合數學形態學於光學影像進行河道變遷偵測之研究」，國立政治大學地政學系碩士論文：台北。林莉萍，2013，「應用空載光達資料估計森林樹冠高度模型及葉面積指數」，國立成功大學測量及空間資訊學系碩士論文：台南。邱宜珊，2020，「利用PSInSAR觀察原住民部落之地表形變-以尖石鄉秀巒村為例」，國立政治大學地政學系碩士論文：台北。施瑩瑄，2005，「應用空載光達獲取林分高之研究」，國立臺灣大學森林環境暨資源學系碩士論文：台北。耿翊，2018，「利用衛星影像評估莫蘭蒂颱風對金門本島地景變遷及森林恢復之影響」，國立屏東科技大學森林系碩士學位論文：屏東。馬淑敏，2006，「應用高解析遙測影像進行立木偵測及樹冠描繪」，國立中央大學土木工程研究所碩士論文：桃園。張瑞文，2007，「應用光達資料於溪頭柳杉人工林分調查之研究」，國立台灣大學生物資源暨農學院森林環境暨資源學系碩士論文：台北。郭韋侖，2020，「三維建模結合虛擬實境應用於文化資產保存之研究」，國立政治大學地政學系碩士論文：台北。陳永寬、詹進發、葉堃生、鄭祈全、施瑩瑄，2005，「應用地面雷射掃描儀測算樹高之研究」，『航測及遙測學刊』，10(4)：327-336。黃世涵，2011，「以最小二乘平面套合法進行空載與車載光達點雲套合」，國立交通大學土木工程學系碩士論文：新竹。黃俊翔，2012，「空載光達於內插方法與不同地形類別精度分析」，國立成功大學地球科學系碩士論文：台南。楊孟學、吳銘志、劉進金，2016，「多光譜影像與空載光達於山崩植生復育監測之研究」，『航測及遙測學刊』，20(3)：171-181。葉日嫈，2015，「空載光達對立木樹冠及林分競爭解釋能力之探討」，國立屏東科技大學森林系碩士學位論文：屏東。解宇陽、王彬、姚揚、楊瑯、高媛、張志明、林露湘，2020，「基於無人機激光雷達遙感的亞熱帶常綠闊葉林群落垂直結構分析」，『生態學報』，40(3)：940-951。詹進發，2005，「空載雷射掃描資料於推估樹冠高度之應用」，『航測及遙測學刊』，10(1)：1-14。劉囿維，2010，「應用空載光達資料產製數值高程模型之品質評估」，國立成功大學測量及空間資訊學系碩士論文：台南。劉魯霞、龐勇，2014，「機載激光雷達和地基激光雷達林業應用現狀」，『世界林業研究』，27(1)：49-56。蕭子淳，2019，「地面雷射掃描系統於樣區尺度之立木測計」，國立屏東科技大學森林系碩士學位論文：屏東。蕭淳伊，2008，「應用空載光達資料與遙測影像推估樹林分佈及體積」，國立成功大學測量及空間資訊學系碩士論文：台南。賴君怡，2008，「整合空載光達資料與多光譜影像建立樹木模型-以成大校園為例」，國立成功大學測量及空間資訊學系碩士論文：台南。魏浚紘，2014，「應用光達技術於人工林之經營與監測」，國立屏東科技大學生物資源研究所博士學位論文：屏東。魏浚紘、吳守從、彭炳勳、陳朝圳，2010，「應用空載光達建立阿里山地區柳杉人工林之林分材積式」，『地理學報』，59：67-80。魏浚紘、陳朝圳、彭炳勳、李崇誠、陳建璋，2013，「地面光達應用於林木競爭之研究」，『航測及遙測學刊』，16(4)：279-287。羅詔元、陳良健，2009，「使用空載光達資料萃取森林區立木樹冠」，『航測及遙測學刊』，14(3)：201-210。二、外文參考文獻Aldred, A. 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