Extracting rubber tree parameters and estimating carbon storage using airborne LiDAR.

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Forestry carbon sinks are crucial for carbon neutrality goals.
• Rubber plantations offer ecological and economic benefits.
• Accurate data acquisition and carbon stock calculation in rubber plantations are challenging.

Purpose of the Study:

• To address the limitation of airborne LiDAR in measuring diameter at breast height (DBH).
• To develop a method for precise biomass and carbon stock estimation in rubber plantations.

Main Methods:

• Collected high-resolution airborne LiDAR point cloud data.
• Processed data including noise removal and ground point classification.
• Compared four individual tree segmentation methods.
• Developed a linear regression model using crown diameter to estimate DBH.

Main Results:

• Direct point cloud segmentation achieved the highest accuracy in tree identification.
• The proposed linear regression model accurately estimated DBH.
• Total estimated biomass was 592,770.57 kg, with carbon stock at 278,602.17 kg.

Conclusions:

• Airborne LiDAR, combined with the proposed DBH estimation model, enables precise biomass and carbon stock assessment in rubber plantations.
• This approach enhances the efficiency of forestry carbon sink studies.
• The findings contribute to better management of rubber plantations for climate change mitigation.