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<p>The Amazon Basin, a critical carbon sink, is increasingly vulnerable to climate change, yet the drivers of Leaf Area Index (LAI) variability remains poorly characterized. This study integrates MODIS-derived LAI data (2001–2022) with meteorological records to assess how temperature anomalies, precipitation extremes, and aridity shape canopy dynamics, offering guidance for adaptive forest management. Results reveal a nonlinear relationship between LAI and temperature, with LAI peaking at ~ 25.3&#xa0;°C, beyond which LAI declines, indicating heat stress-induced canopy suppression. Precipitation positively influences LAI, with seasonal variability exerting a stronger effect than annual means, emphasizing the role of short-term hydrological fluctuations in maintaining forest productivity. The aridity index explains 23% of LAI variability, underscoring its role as a key constraint on vegetation growth. Additional factors, including water vapor pressure (R² = 0.26) and elevation (R² = 0.27), further shape LAI dynamics, reflecting the role of atmospheric moisture and topographic gradients. Analysis of temporal trends show a post-2018 decline in high-LAI regions, with partial recovery by 2022, suggesting increasing climate-driven instability in Amazonian forest structure. These findings enhance understanding of climate-vegetation interactions in tropical forests, providing valuable insights for climate-adaptive management, ecosystem monitoring, and climate model refinement. Given the Amazon Basin’s role in global atmospheric circulation, sustained LAI monitoring is essential for tracking vegetation responses and ensuring long-term forest stability.</p>

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Climatic drivers of leaf area index dynamics in the Amazon basin: insights from remote sensing

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Md Shamim Reza Saimun,
  • Md Rezaul Karim

摘要

The Amazon Basin, a critical carbon sink, is increasingly vulnerable to climate change, yet the drivers of Leaf Area Index (LAI) variability remains poorly characterized. This study integrates MODIS-derived LAI data (2001–2022) with meteorological records to assess how temperature anomalies, precipitation extremes, and aridity shape canopy dynamics, offering guidance for adaptive forest management. Results reveal a nonlinear relationship between LAI and temperature, with LAI peaking at ~ 25.3 °C, beyond which LAI declines, indicating heat stress-induced canopy suppression. Precipitation positively influences LAI, with seasonal variability exerting a stronger effect than annual means, emphasizing the role of short-term hydrological fluctuations in maintaining forest productivity. The aridity index explains 23% of LAI variability, underscoring its role as a key constraint on vegetation growth. Additional factors, including water vapor pressure (R² = 0.26) and elevation (R² = 0.27), further shape LAI dynamics, reflecting the role of atmospheric moisture and topographic gradients. Analysis of temporal trends show a post-2018 decline in high-LAI regions, with partial recovery by 2022, suggesting increasing climate-driven instability in Amazonian forest structure. These findings enhance understanding of climate-vegetation interactions in tropical forests, providing valuable insights for climate-adaptive management, ecosystem monitoring, and climate model refinement. Given the Amazon Basin’s role in global atmospheric circulation, sustained LAI monitoring is essential for tracking vegetation responses and ensuring long-term forest stability.