整合多元光達資料推估公園樹木碳吸存量

連介浩(Lian Jiehao); 謝漢欽(Xie Hanqin); 張崑宗(Zhang Kunzong)

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整合多元光达资料推估公园树木碳吸存量

Integrating multiple LiDAR data to estimate carbon sequestration by park trees

连介浩(Lian Jiehao) ；谢汉钦(Xie Hanqin) ；张崑宗(Zhang Kunzong) , 　　导师：　　

繁体中文;英文

空载光达；光达；碳储量；都市林；树木调查； Airborne lidar ； lidar ； carbon storage ； urban forest ； tree survey

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摘要 │ 参考文献 (7)

摘要〈TOP〉

随着全球气候变迁和温室效应问题日益严重，1997年，联合国通过了《京都议定书》，将森林对碳吸存的贡献进行了量化。根据该协议，以1990年为基准年，对之後进行的造林、再造林和森林采伐导致的二氧化碳吸收或排放的净值可以被纳入减排计算中。此外，该协议规定了碳排放权的交易机制，同时强调了透过森林管理中的碳储量可以用於国内的二氧化碳减排。依据文献回顾，将森林调查数据与遥测技术结合已成为评估碳储量、碳库和碳吸收量的主要方法，2018年林务局的一项调查指出，若在平地造林可减少温室气体排放，并对微气候产生正面影响，因此树木碳吸存量计算有利於都市林生态服务效益的评估。本研究目的是以分散於富冈运动公园内87棵树木进行调查，透过光达点云软体评估无人机、架站式、SLAM三种光达点云资料萃取树木调查参数之准确度；运用胸径卷尺等传统方法量测每木胸径（DBH）、树高（H）及冠幅南北向及东西向平均直径（CW）参数，并与光达点云量测之参数值进行比较分析；以及运用软体估算试验区内公园树木碳吸存含量。本研究於2023年3月份运用UAV Lidar、Trimble X7及Slam Lidar对公园树木进行扫描，再将三种光达点云汇入Lidar360软体各自进行每木点云分割、自动化或半自动方法量测树高、胸径等参数，并与传统方法量测结果进行统计变异量分析。在属性量测方面，半自动方法皆优於自动分割方法，自动分割方法分割成功率低於半自动，精确度也普遍较低。在DBH方面UAV Lidar无法获取有效的树干点云，Trimble X7的Circle Fitting量测的RMSE可达0.94cm远优於SLAM的9.82cm。在H方面UAV的垂直测量工具，可取得RMSE 0.65m与Trimble X7的垂直测量工具的0.62m为最佳精度，皆优於SLAM的0.83m。在南北向及东西向平均CW方面，SLAM的冠层无法明确区分，难以手动及自动分割方法，进行量测，Trimble X7的手动量测可得到RMSE为0.69m优於UAV手动量测的1.03m。在树木碳吸存量计算方面，於Lidar360软体上运用smalian分段材积法於每棵树干每1m处进行分段分割计算材积量运用於点云分析软体进行分析计算富冈运动公园内87棵树木的总材积为8.56立方米，再运用国家清册C吸存量公式估算树木碳吸存量为14.31吨，而用实际量测方式得出的87颗树木量测值运用传统材积计算方式得出之总材积量为7.62立方米，运用国家清册C吸存量公式估算得出树木碳吸存量为12.19吨。依据以上研究结果提供了不同光达种类的精度评估及研究试区的碳吸存量估算，建议未来的研究可进一步探讨不同光达技术在城市森林调查中的综合应用效果，以提高城市森林调查的准确性和效率。

并列摘要〈TOP〉

As global climate change and greenhouse effect problems become increasingly serious, in 1997, the United Nations adopted the Kyoto Protocol, which quantified the contribution of forests to carbon sequestration. According to the agreement, with 1990 as the base year, the net value of carbon dioxide absorption or emissions caused by subsequent afforestation, reforestation and deforestation can be included in the calculation of emission reductions. In addition, the agreement stipulates a trading mechanism for carbon emission rights and emphasizes that carbon reserves in forest management can be used to reduce domestic carbon dioxide emissions. According to the literature review, combining forest survey data with remote sensing technology has become the main method to assess carbon stocks, carbon pools and carbon absorption. A 2018 Forest Service survey pointed out that afforestation on flat land can reduce greenhouse gas emissions and It has a positive impact on the microclimate, so the calculation of tree carbon sequestration is beneficial to the evaluation of urban forest ecological service benefits. The purpose of this study is to conduct a survey of 87 trees scattered in Tomioka Sports Park, and use LiDAR point cloud software to evaluate the accuracy of tree survey parameters extracted from UAV, stationary, and SLAM LiDAR point cloud data; Use traditional methods such as the diameter at breast height tape measure to measure the diameter at breast height (DBH) of each tree, tree height (H) and the north-south and east-west average diameter of the crown (CW), and compare and analyze the parameter values from LiDAR point cloud measurements; And use software to estimate the carbon sequestration content of park trees in the experimental area. This study used UAV Lidar, Trimble X7 and Slam Lidar to scan the park trees in March 2023, and then imported the three lidar point clouds into the Lidar360 software to segment each tree point cloud and measure the tree height using automated or semi-automatic methods. , diameter at breast height and other parameters, and conduct statistical variation analysis with the measurement results using traditional methods. In terms of attribute measurement, semi-automatic methods are better than automatic segmentation methods. The success rate of automatic segmentation methods is lower than that of semi-automatic methods, and the accuracy is generally lower. In terms of DBH, UAV Lidar cannot obtain effective tree trunk point clouds. The RMSE measured by Trimble X7's Circle Fitting can reach 0.94cm, which is much better than SLAM's 9.82cm. In terms of H, UAV's vertical measurement tool can achieve the best accuracy of RMSE 0.65m and Trimble X7's vertical measurement tool's 0.62m, which are both better than SLAM's 0.83m. In terms of average CW in the north-south and east-west directions, the canopy of SLAM cannot be clearly distinguished, making it difficult to measure using manual and automatic segmentation methods. The manual measurement of Trimble X7 can obtain an RMSE of 0.69m, which is better than the 1.03m of manual measurement by UAV. . In terms of calculating the carbon sequestration of trees, the smalian segmented volume method was used on the Lidar360 software to segment each tree trunk every 1m to calculate the volume. The point cloud analysis software was used to analyze and calculate the volume of 87 trees in Tomioka Sports Park. The total volume is 8.56 cubic meters. The carbon sequestration volume of trees is estimated to be 14.31 tons using the National Inventory C sequestration formula. The total volume of 87 trees measured using actual measurements is calculated using the traditional volume calculation method. It is 7.62 cubic meters. Using the national inventory C sequestration formula, it is estimated that the tree carbon sequestration is 12.19 tons. Based on the above research results, the accuracy assessment of different lidar types and the carbon sequestration estimate of the research area are provided. It is recommended that future research can further explore the comprehensive application effects of different lidar technologies in urban forest surveys to improve the effectiveness of urban forest surveys. Accuracy and efficiency.

参考文献 ( 7 ) 〈TOP〉

黄立信,张嘉伦,邱建华,王建文(2019)。地面光达点云资料处理最佳化之研究。测量工程，58
连结：
行政院公报(2013)，农业环保篇，第 019 卷第 124 期，24813
Blogger(2014)【固碳好帮手】一起来进行「树木调查」，计算树木的「固碳量」TRL: https://luodong-nec.blogspot.com/2014/05/blog-post_15.html，第四段第一行
Greenpeace 绿色和平(2020)全球与东亚各地陆续宣布碳中和目标，台湾别在对抗气候危机上落後，TRL: https://www.greenpeace.org/taiwan/update/22392/%E5%85%A8%E7%90%83%E8%88%87%E6%9D%B1%E4%BA%9E%E5%90%84%E5%9C%B0%E9%99%B8%E7%BA%8C%E5%AE%A3%E5%B8%83%E7%A2%B3%E4%B8%AD%E5%92%8C%E7%9B%AE%E6%A8%99%EF%BC%8C%E8%87%BA%E7%81%A3%E5%88%A5%E5%9C%A8%E5%B0%8D/，专题报导气候
IPCC(2017).2017 年中华民国国家温室气体排放清册报告.

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