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Understanding spatiotemporal patterns of global forest NPP using a data-driven method based on GEE.

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Summary
This summary is machine-generated.

Global forest productivity (NPP) is influenced by climate. This study categorizes forests by NPP levels, revealing distinct responses to climate patterns like El Niño-Southern Oscillation (ENSO).

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Area of Science:

  • Ecology
  • Climate Science
  • Remote Sensing

Background:

  • Global forest net primary productivity (NPP) is crucial for understanding climate-terrestrial carbon cycle interactions.
  • Traditional analyses often generalize forest productivity based on geography or climate types.

Purpose of the Study:

  • To investigate global forest NPP dynamics using remote sensing and climate data.
  • To categorize forest regions by NPP levels for a novel analytical approach.

Main Methods:

  • Utilized Google Earth Engine (GEE) for processing remote sensing and climate datasets.
  • Employed the self-organizing map (SOM) method to categorize global forests into nine NPP levels.
  • Analyzed eight relative factors influencing NPP across these categories.

Main Results:

  • Forest NPP is more strongly influenced by climate than by tree characteristics (height, density, leaf type).
  • Precipitation showed a weak/negative correlation with vegetation greenness in high-NPP forests, but lack of water reduced productivity in low-NPP forests.
  • Different NPP categories exhibited varied responses to El Niño-Southern Oscillation (ENSO) events, indicating synchronization between climate oscillations and forest growth.

Conclusions:

  • Forest NPP patterns are significantly modulated by climate, with distinct responses across different productivity levels.
  • The synchronization between climate oscillations (e.g., ENSO) and forest growth conditions varies with NPP levels.
  • This novel categorization approach offers a new perspective on global forest NPP variations.