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Related Experiment Video

Updated: Nov 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable

Fugen Jiang1,2,3, Mykola Kutia4, Arbi J Sarkissian4

  • 1Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.

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|December 22, 2020
PubMed
Summary

Accurate forest growing stem volume (GSV) estimation is crucial for resource assessment. A stepwise random forest (SRF) method using Sentinel-2 data and texture features significantly improved GSV estimation accuracy in Inner Mongolia.

Keywords:
coniferous plantationsforest growing stem volumerandom forestred-edge bandtexture featurevariable selection

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Area of Science:

  • Forestry
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Forest growing stem volume (GSV) is a key indicator of forest resources and ecosystem health.
  • Remote sensing offers efficient and cost-effective methods for forest monitoring and GSV estimation.
  • Sentinel-2 imagery, with its red edge bands and frequent revisits, is suitable for detailed forest analysis.

Purpose of the Study:

  • To develop and evaluate a novel method for accurate forest GSV estimation using Sentinel-2 data.
  • To investigate the impact of different variable combinations (spectral, texture, topographic) on GSV estimation accuracy.
  • To compare the performance of a stepwise random forest (SRF) method against other variable selection and regression techniques.

Main Methods:

  • Extraction of spectral variables, texture features, and topographic factors from Sentinel-2 images.
  • Application of a stepwise random forest (SRF) method for optimal variable selection and random forest regression (RFR) model building.
  • Comparison of SRF with Linear Stepwise Regression (LSR), Random Forest (RF), Boruta, and Variable Selection Using Random Forests (VSURF) methods.

Main Results:

  • Texture features from Sentinel-2's red edge bands significantly enhanced GSV estimation accuracy.
  • The SRF method effectively identified optimal variable combinations for GSV estimation.
  • The SRF-based model achieved the highest accuracy, reducing relative root mean square error by 10.6%–16.4% compared to other methods.
  • GSV distribution maps generated by the SRF model closely matched field observations.

Conclusions:

  • The SRF method provides a robust approach for selecting optimal predictors for forest GSV estimation.
  • Integrating Sentinel-2 red edge texture features substantially improves GSV estimation accuracy.
  • This study offers valuable insights and a reference for GSV estimation in coniferous plantations using remote sensing.