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Updated: Sep 10, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate

Shashika Himandi Gardeye Lamahewage1, Chandi Witharana2,3, Rachel Riemann4

  • 1Department of Natural Resources and the Environment, College of Agriculture, Health and Natural Resources, University of Connecticut, Storrs, CT, 06269, USA. shashika_himandi.lamahewa@uconn.edu.

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|August 24, 2025
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Summary

Accurately estimating forest aboveground tree biomass (AGB) is crucial for carbon storage assessment. This study uses remote sensing data and the random forest algorithm to improve AGB prediction models, enhancing forest carbon monitoring efficiency.

Keywords:
Aboveground biomassForest carbonForest inventory and analysisLiDARMachine learningRandom forest regression

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Area of Science:

  • Forestry
  • Remote Sensing
  • Ecology

Background:

  • Aboveground tree biomass (AGB) is vital for forest carbon storage assessment.
  • Traditional Forest Inventory and Analysis (FIA) methods lack the sampling intensity for fine-resolution AGB estimates.
  • Remote sensing (RS) offers a more efficient approach to forest carbon monitoring.

Purpose of the Study:

  • To develop accurate predictive models for aboveground tree biomass (AGB) using the random forest (RF) algorithm.
  • To assess the effectiveness of various remote sensing data sources in AGB estimation.
  • To enhance the efficiency and accuracy of forest carbon monitoring for decision-making.

Main Methods:

  • Utilized 67 explanatory variables derived from three remote sensing data sources (LiDAR, aerial imagery, satellite images).
  • Developed nine random forest (RF) models for AGB prediction, each undergoing variable selection and hyperparameter tuning.
  • Evaluated model performance using metrics such as Root Mean Square Error (RMSE) and R-squared (R²).

Main Results:

  • The optimal RF model incorporated 28 explanatory variables, achieving an RMSE of 27.19 Mgha⁻¹ and an R² of 0.41.
  • Combining LiDAR data with aerial and satellite image metrics significantly improved AGB prediction accuracy.
  • The study demonstrated the potential of integrated RS data for large-area biomass mapping.

Conclusions:

  • Remote sensing data, particularly when combined (e.g., LiDAR with imagery), significantly enhances the accuracy of aboveground tree biomass (AGB) estimation.
  • The random forest algorithm provides a robust framework for developing predictive AGB models.
  • Improved AGB estimation using RS is crucial for effective carbon stock assessment and informed climate change decision-making.