Tantrairatn Suradet, Pichitkul Auraluck, Petcharat Nutchanan, Karaked Pawarut, Ariyarit Atthaphon
School of Mechanical Engineering, Institute of Engineering, Suranaree University of Technology, 111, MahaWitthayalai Rd, Suranari, Mueang Nakhon Ratchasima District, Nakhon Ratchasima 30000, Thailand.
Institute of Research and Development, Suranaree University of Technology, Maha Witthayalai Rd, Suranari, Mueang Nakhon Ratchasima District, Nakhon Ratchasima 30000, Thailand.
MethodsX. 2025 Feb 18;14:103237. doi: 10.1016/j.mex.2025.103237. eCollection 2025 Jun.
Carbon credits play a crucial role in mitigating climate change by incentivizing reductions in greenhouse gas emissions and providing a measurable way to balance carbon dioxide output, fostering sustainable environmental practices. However, conventional methods of measuring carbon credits are often time-consuming and lack accuracy. This research examines carbon credit measurement in a 40 × 40 rubber forest, evaluating the effectiveness of LiDAR technology in measuring Tree Height (TH) and Diameter at Breast Height (DBH) using a dataset of 100 samples. The method is as follows:•Three measurement methods were compared: conventional techniques using diameter tape and hypsometers, manual LiDAR measurements, and automated measurements using 3D Forest Inventory software with the CloudCompare plugin.•The Mean Absolute Percentage Error (MAPE) for carbon sequestration was 4.276 % for manual LiDAR measurements and 6.901 % for the 3D Forest Inventory method.•Root Mean Square Error (RMSE) values for carbon sequestration using LiDAR measurements were 33.492 kgCOe, whereas RMSE values for the 3D Forest Inventory method were significantly higher. This indicates that manual LiDAR measurements are more accurate and consistent, while the higher RMSE in the 3D Forest Inventory method reflects greater variability and potential estimation errors. The findings suggest that LiDAR technology, particularly manual measurements, provides a reliable and efficient alternative for carbon sequestration assessments in forest management.
碳信用额度通过激励减少温室气体排放并提供一种可衡量的方式来平衡二氧化碳排放量，从而在缓解气候变化方面发挥着至关重要的作用，促进了可持续的环境实践。然而，传统的碳信用额度测量方法通常耗时且缺乏准确性。本研究考察了40×40橡胶林中的碳信用额度测量，使用100个样本的数据集评估了激光雷达技术在测量树高（TH）和胸径（DBH）方面的有效性。方法如下： •比较了三种测量方法：使用围尺和测高仪的传统技术、手动激光雷达测量以及使用带有CloudCompare插件的3D森林资源清查软件的自动测量。 •手动激光雷达测量的碳固存平均绝对百分比误差（MAPE）为4.276%，3D森林资源清查方法的为6.901%。 •使用激光雷达测量的碳固存均方根误差（RMSE）值为33.492 kgCOe，而3D森林资源清查方法的RMSE值显著更高。这表明手动激光雷达测量更准确且一致，而3D森林资源清查方法中较高的RMSE反映出更大的变异性和潜在估计误差。研究结果表明，激光雷达技术，特别是手动测量，为森林管理中的碳固存评估提供了一种可靠且高效的替代方法。
Environ Monit Assess. 2023-5-16
Carbon Balance Manag. 2017-12
Oecologia. 2011-10-28
Ying Yong Sheng Tai Xue Bao. 2014-11
J Imaging. 2024-12-31
Environ Sci Pollut Res Int. 2023-4
Annu Rev Psychol. 2023-1-18
Zotero 插件
