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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Individual-Tree DBH Estimation from Airborne LiDAR Data Using MSFS-XGBoost.

Pengfei Li1, Yue Jia1

  • 1Yunnan Provincial Mapping Institute, Kunming 650034, China.

Sensors (Basel, Switzerland)
|May 13, 2026
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Summary

This study introduces a new framework using Multi-Stage Feature Selection (MSFS) and Extreme Gradient Boosting (XGBoost) to accurately estimate tree diameter at breast height (DBH) using airborne LiDAR data.

Keywords:
DBH estimationMulti-Stage Feature Selectionairborne LiDARmachine learning

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Area of Science:

  • Forestry
  • Remote Sensing
  • Ecological Analysis

Background:

  • Diameter at breast height (DBH) is crucial for forest inventory but field measurements are inefficient.
  • Airborne Light Detection and Ranging (LiDAR) offers an efficient alternative for DBH estimation.
  • LiDAR-derived features can be high-dimensional and redundant, potentially hindering model performance.

Purpose of the Study:

  • To develop an integrated framework for accurate individual-tree DBH estimation using airborne LiDAR data.
  • To address the challenge of high-dimensionality and redundancy in LiDAR-derived features.
  • To improve the efficiency and reliability of large-scale forest resource monitoring.

Main Methods:

  • An integrated framework combining Multi-Stage Feature Selection (MSFS) and Extreme Gradient Boosting (XGBoost) was proposed.
  • 104 LiDAR-derived and structural variables were used as predictors.
  • MSFS was employed to reduce feature redundancy and select an optimal subset for XGBoost model training.

Main Results:

  • The MSFS-XGBoost model achieved a high coefficient of determination (R² = 0.901) and low root mean square error (RMSE = 1.647 cm).
  • Compared to models using the original feature set, R² increased by 0.384 and RMSE decreased by 1.146 cm.
  • The proposed framework demonstrated superior performance in DBH estimation.

Conclusions:

  • The integrated MSFS-XGBoost framework effectively improves DBH estimation accuracy using airborne LiDAR data.
  • This approach provides a reliable method for individual-tree parameter estimation.
  • The findings support the use of this framework for large-scale forest resource monitoring.