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Study on the Estimation of Forest Volume Based on Multi-Source Data.

Tao Hu1, Yuman Sun1, Weiwei Jia1

  • 1School of Forestry, Northeast Forestry University, Harbin 150040, China.

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|December 10, 2021
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Summary

Hybrid machine learning and ordinary Kriging methods significantly improve forest volume estimation accuracy. The random forest Kriging (RFK) model demonstrated the best predictive performance, offering an effective approach for remote sensing-based forest management.

Keywords:
artificial neural network (ANN)forest volumemulti-source remote sensing factorordinary Kriging (OK)random forest (RF)support vector regression (SVR)

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Area of Science:

  • Forestry Science
  • Remote Sensing
  • Machine Learning Applications

Background:

  • Accurate forest volume estimation is crucial for sustainable forest management.
  • Traditional methods often struggle with spatial variations and data integration challenges.
  • Multi-source remote sensing data offers potential for enhanced forest inventory.

Purpose of the Study:

  • To compare the prediction accuracy of machine learning (ML) and ordinary Kriging (OK) hybrid methods for forest volume models.
  • To evaluate the effectiveness of integrating remote sensing data with ground surveys for forest volume estimation.
  • To identify the optimal model for accurate and efficient forest volume inversion.

Main Methods:

  • Utilized data from the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system.
  • Extracted vegetation indices, texture features, terrain factors, and point cloud variables.
  • Developed and compared six forest volume estimation models: Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), and their hybrid forms with OK (RFK, SVRK, ANNK).

Main Results:

  • All six models showed prediction accuracies with R² values above 0.6.
  • Hybrid models consistently improved prediction accuracy compared to standalone ML models.
  • The Random Forest Kriging (RFK) hybrid model achieved the highest prediction accuracy (R² = 0.915).

Conclusions:

  • Machine learning methods combined with multi-source remote sensing data are effective for forest volume estimation.
  • Hybrid models integrating ML with ordinary Kriging significantly enhance forest volume estimation accuracy.
  • The RFK model provides a fast, effective method for remote sensing inversion of forest volume, aiding forest resource management.