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Related Experiment Video

Updated: Dec 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms.

Yingchang Li1, Mingyang Li2, Chao Li1

  • 1Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.

Scientific Reports
|June 21, 2020
PubMed
Summary

Estimating forest aboveground biomass (AGB) using Landsat 8 and Sentinel-1A data with the XGBoost algorithm offers improved accuracy for subtropical forests. This remote sensing approach enhances carbon cycle studies and climate change research.

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Area of Science:

  • Ecology
  • Remote Sensing
  • Forestry

Background:

  • Forest aboveground biomass (AGB) is crucial for understanding the global carbon cycle and climate change.
  • Remote sensing provides an effective method for regional-scale AGB estimation.
  • Subtropical forests in Hunan Province, China, require accurate biomass assessment.

Purpose of the Study:

  • To estimate AGB in subtropical forests using remote sensing data and advanced algorithms.
  • To compare the performance of Linear Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for AGB estimation.
  • To evaluate the synergy of Landsat 8 and Sentinel-1A data for improved biomass estimation.

Main Methods:

  • Utilized Landsat 8 Operational Land Imager and Sentinel-1A satellite data.
  • Employed China's National Forest Continuous Inventory data for ground-truthing.
  • Applied and compared LR, RF, and XGBoost algorithms for AGB estimation.

Main Results:

  • The combination of Landsat 8 and Sentinel-1A data with the XGBoost model yielded the best AGB estimation results.
  • XGBoost and RF algorithms significantly improved AGB estimation compared to the LR method.
  • Parameter optimization had a more substantial impact on XGBoost performance than on RF.
  • The XGBoost model effectively reduced AGB overestimation and underestimation issues.

Conclusions:

  • The XGBoost model, utilizing synergistic remote sensing data, is a highly effective method for subtropical forest AGB estimation.
  • This research offers a novel remote sensing-based approach for forest biomass assessment.
  • Findings contribute to more accurate carbon cycle and climate change modeling.