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Potential tree cover under current and future climate scenarios.

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This study maps potential tree cover using satellite data and machine learning to aid carbon neutrality goals. The findings help identify areas for forest restoration while highlighting uncertainties in human-altered landscapes.

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Area of Science:

  • Environmental science
  • Ecology
  • Remote sensing

Background:

  • Forests are crucial for global carbon neutrality goals.
  • Accurate maps of potential tree cover are needed for carbon storage and loss assessments.
  • Previous models had limitations in accuracy and spatial variance.

Purpose of the Study:

  • To develop a machine learning model for estimating tree cover carrying capacity.
  • To create high-resolution global maps of potential tree cover under current and future climate scenarios.
  • To improve the assessment of forest carbon storage potential and afforestation/reforestation opportunities.

Main Methods:

  • Integrated satellite-based tree cover observations.
  • Employed a machine learning framework to estimate tree cover carrying capacity.
  • Accounted for natural disturbances and climatic/soil conditions.

Main Results:

  • The model improved prediction accuracy and alignment with observed tree cover.
  • Reduced spatial variance in predictions, especially in areas lacking topographical variation.
  • Generated estimates of potential tree cover based on environmental factors.

Conclusions:

  • The tree cover carrying capacity provides a valuable estimate for afforestation/reforestation planning.
  • Further assessment is needed for land-use competition and ecological feasibility.
  • The model offers a foundational step in identifying suitable areas for forest restoration.