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Spatially-explicit models of global tree density.

Henry B Glick1, Charlie Bettigole1, Daniel S Maynard1

  • 1Yale School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA.

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Summary
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This study presents the first global tree density models using over 420,000 forest plots. These spatially-explicit maps offer insights into natural resources and ecosystem services at large scales.

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Area of Science:

  • Ecology
  • Geospatial Analysis
  • Forestry

Background:

  • Remote sensing and geographic analysis are crucial for understanding natural resources, biodiversity, and ecosystem structure.
  • Existing studies often underutilize extensive forest inventory field data.
  • Global datasets are increasingly integrated into analytical frameworks.

Purpose of the Study:

  • To develop the first spatially-explicit models of global tree density.
  • To create precise estimates of tree numbers at global and biome scales.
  • To provide data for resource management, ecological modeling, and ecosystem service quantification.

Main Methods:

  • Integration of over 420,000 forest inventory field plots worldwide.
  • Application of advanced analytical strategies and collaborative scientific effort.
  • Development of spatially-explicit modeling techniques.

Main Results:

  • Creation of novel global tree density maps.
  • Precise estimation of tree numbers at global and biome scales.
  • Data products suitable for large-scale ecological and resource management applications.

Conclusions:

  • The developed spatial data products offer valuable insights for resource management and ecological modeling.
  • These models enhance the quantification of ecosystem services at larger scales.
  • The findings emphasize the importance of integrating field data with remote sensing for global ecological assessments.