Research on Intelligent Estimation Model of Carbon Stock of Rocky Desertification Plantations Based on LiDAR Point Cloud Data

This paper proposes an intelligent estimation model of carbon stock of rocky desertification plantations based on LiDAR point cloud data. The model uses an advanced point cloud segmentation algorithm to finely segment the forest data collected by LiDAR, so as to accurately distinguish the vegetation type and density. Through the processing and feature extraction of point cloud data, combined with the geographical characteristics of rocky desertification areas, this study designed a carbon stock estimation algorithm model suitable for rocky desertification plantations. The system model includes modules such as data acquisition, point cloud segmentation, and carbon stock estimation, which are optimized by combining machine learning methods. The simulation part verifies the accuracy of the model through a large amount of field measurement data, and accurately estimates the carbon stock of different rocky desertification levels through quantitative analysis. The results show that the error rate of the model in carbon stock estimation is less than 5%, which has high application potential and practicality. The research in this paper provides an effective technical means for the accurate assessment of carbon stocks in rocky desertification areas, which will help further promote the implementation of carbon sink mechanisms and the restoration of ecosystems.