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| Title: | 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 |
| Authors: | 馬慧
Ma, Hui |
| Contributors: | 詹進發
Jan, Jihn-Fa
馬慧
Ma, Hui |
| Keywords: | 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 |
| Date: | 2021 |
| Issue Date: | 2021-08-04 15:50:38 (UTC+8) |
| Abstract: | 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. 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. |
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三、網頁參考文獻
DJI (2020) DJI Matrice 300 RTK from DJI on the World Wide Web：https://www.dji.com/tw
Geoslam (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/ |
| Description: | 碩士
國立政治大學
地政學系
108257008 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108257008 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202101131 |
| Appears in Collections: | [地政學系] 學位論文
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