Extracting rubber tree parameters and estimating carbon storage using airborne LiDAR

Abstract

Facing the dual challenges of global warming and carbon neutrality, forestry carbon sinks play a vital role in achieving carbon neutrality. Rubber plantations, in particular, offer significant ecological and economic co-benefits. However, the efficient and rapid acquisition of data on rubber plantations and the calculation of carbon stock remain key challenges in forestry carbon sink studies. Airborne LiDAR is a powerful tool for forest surveys, yet its inability to directly measure DBH remains a major limitation. This study seeks to address this issue. High-resolution point cloud data were collected, followed by noise removal and ground point classification. Four individual tree segmentation methods were compared, and a linear regression model based on crown diameter parameters was proposed to estimate DBH. The results indicate that the direct point cloud segmentation method achieved the highest accuracy in tree identification. The proposed linear regression model for DBH estimation effectively predicts DBH, enabling precise biomass estimation. The total biomass estimated in the study area was 592,770.57 kg (aboveground biomass: 550,336.17 kg, belowground biomass: 42,434.39 kg), with the corresponding total carbon stock estimated at 278,602.17 kg.