Advancing Carbon Stock Estimation and 3D Tree Modeling: Harnessing the Potential of Low-Cost Backpack LiDAR Technology

Abstract The maintenance of carbon stocks in the forest is fundamental to the global carbon cycle and is essential for informing climate change policy. Traditional methods for measuring carbon stocks have often been labor-intensive and expensive, involving measurements that include DBH, and tree height. However, LiDAR technology provides an efficient alternative for swiftly assessing forest vegetation structure. In this study, a low-cost backpack equipped with LiDAR technology was utilized to create three-dimensional tree models, enabling precise estimations of stem volume and carbon stock. By collecting data on DBH and vertical plant structure, the researchers could produce highly accurate three-dimensional models. The results indicated that the low-cost backpack LiDAR yielded DBH estimates very close to manual measurements, characterized by a linear regression equation of y = 1.0106x - 0.2911 and a coefficient of determination R2 = 0.99822. The results confirmed the high accuracy of DBH measurement using LiDAR, with RMSE in a value of 0.53 cm. This study unequivocally establishes that low-cost backpack LiDAR is an effective tool for measuring biomass and carbon stocks in forests, offering superior monitoring capabilities compared to conventional methods.