Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration

Abstract

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 m² 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:

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  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.
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  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.
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  Root Mean Square Error (RMSE) values for carbon sequestration using LiDAR measurements were 33.492 kgCO₂e, 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.