[Research on living tree volume forecast based on PSO embedding SVM]

Area of Science:

• Forestry Science
• Computational Modeling
• Remote Sensing Technology

Background:

• Traditional living tree volume estimation requires destructive sampling, leading to significant forest resource loss.
• Hundreds of thousands of trees are felled annually in China for volume modeling, highlighting the need for sustainable alternatives.

Purpose of the Study:

• To develop and validate a non-destructive method for accurate living tree volume measurement.
• To establish an intelligent prediction model for tree volume using advanced algorithms.

Main Methods:

• Utilized photoelectric theodolites and manual measurements for tree diameter and height data acquisition.
• Developed a novel software for tree volume and height calculation.
• Implemented a nonlinear intelligent living tree volume prediction model using Particle Swarm Optimization based on Support Vector Machines (PSO-SVM).

Main Results:

• The PSO-SVM model achieved a complex correlation coefficient (R2) of 0.91, outperforming the classic Spurr binary volume model by 2%.
• Mean absolute error rates were reduced by 0.44% compared to the Spurr model.
• The model demonstrated high prediction accuracy, fast learning speed, and adaptability with a small sample size requirement.

Conclusions:

• The proposed non-destructive method and PSO-SVM model offer a highly accurate and efficient alternative for living tree volume estimation.
• The PSO-SVM model exhibits superior performance and desirable characteristics, indicating significant potential for widespread application in forestry.