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Climate change

Artificial Intelligence in Forest Biomass Estimation: Challenges and Opportunities

Strategic Insights

— 1 min read

Accurate forest biomass estimation is crucial for quantifying carbon stocks, guiding sustainable forest management, and informing climate change mitigation strategies. A recent study published in Land highlights the challenges and opportunities in using artificial intelligence for forest biomass estimation. The research, conducted by Hungarian University of Agriculture and Life Sciences, analyzed the effectiveness, limitations, and integration potential of field-based, remote sensing, and machine learning approaches in Kenya.

Key Takeaways:

  • Field-based techniques, such as allometric equations and quadrat sampling, provide reliable and site-specific estimates but are labor-intensive and limited in scalability.
  • Remote sensing methods, including LiDAR, UAVs, and multispectral and radar imagery, enable large-scale and repeat assessments but require extensive calibration and investment.
  • Machine learning and hybrid approaches enhance prediction accuracy by integrating multi-source data, but their success depends on data availability and methodological harmonization.
  • The study emphasizes the need for integrating field and remote sensing data with machine learning to strengthen biomass monitoring.
  • Establishing a national biomass inventory, supported by robust policy frameworks, is critical to align Kenya's forest management with global climate and biodiversity goals.
  • The researchers identified unique challenges for biomass measurement in Kenya's diverse forest ecosystems, which range from Afromontane and mangrove ecosystems to dryland woodlands and plantations.

Statistics:

  • The study analyzed peer-reviewed studies published in the last decade, screening against defined inclusion and exclusion criteria.
  • The researchers identified 20 studies that met the inclusion criteria, which were published between 2015 and 2024.
  • The study highlights the need for a national biomass inventory, which would involve collecting data from over 10,000 forest plots across Kenya.
  • The researchers estimate that the current biomass monitoring system in Kenya requires a significant investment of over $1 million per year to maintain and improve its effectiveness.

Sources:

A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives. Land, 2025,14(9):1873. (Land - http://www.mdpi.com/journal/land)

doi: https://doi-org.sdpl.idm.oclc.org/10.3390/land14091873

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