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<p>Alpine ecosystems are among the most environmentally sensitive systems and are increasingly threatened by climate change. Responding to these changes, adaptive management strategies supported by robust scientific data are needed. Species distribution models (SDMs) offer a powerful framework for forecasting range shifts, but their reliability depends on the quality and ecological relevance of input variables. Remote sensing offers a cost-effective means of capturing the complex heterogeneity of alpine ecosystems, either by describing proxies for other environmental variables or by providing direct estimates of specific drivers. This review synthesizes findings from studies that integrated variables obtained through passive remote sensing (RS) into SDMs targeting alpine vegetation. We assess the types of RS data used, their ecological role in modeling, geographic and methodological trends, and whether RS integration improves model performance. Most studies report enhanced predictive accuracy – particularly when using RS data to capture snow persistence, vegetation structure, or land surface temperature – though the degree of improvement varies by species and modeling approach. We identify limitations, including the risk of overfitting and challenges in scaling RS data appropriately. The review highlights both the benefits and caveats of using RS in SDMs and outlines future directions for improving model robustness, transferability, and relevance to conservation. Our findings suggest that integrating RS into SDMs can significantly enhance their ecological realism and support more effective conservation planning in mountain environments.</p>

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High detail in high mountains: a systematic review of passive remote sensing data in alpine vegetation species distribution models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Costin Enache,
  • Angheluță Vădineanu,
  • Paulina Anastasiu,
  • Constantin Cazacu

摘要

Alpine ecosystems are among the most environmentally sensitive systems and are increasingly threatened by climate change. Responding to these changes, adaptive management strategies supported by robust scientific data are needed. Species distribution models (SDMs) offer a powerful framework for forecasting range shifts, but their reliability depends on the quality and ecological relevance of input variables. Remote sensing offers a cost-effective means of capturing the complex heterogeneity of alpine ecosystems, either by describing proxies for other environmental variables or by providing direct estimates of specific drivers. This review synthesizes findings from studies that integrated variables obtained through passive remote sensing (RS) into SDMs targeting alpine vegetation. We assess the types of RS data used, their ecological role in modeling, geographic and methodological trends, and whether RS integration improves model performance. Most studies report enhanced predictive accuracy – particularly when using RS data to capture snow persistence, vegetation structure, or land surface temperature – though the degree of improvement varies by species and modeling approach. We identify limitations, including the risk of overfitting and challenges in scaling RS data appropriately. The review highlights both the benefits and caveats of using RS in SDMs and outlines future directions for improving model robustness, transferability, and relevance to conservation. Our findings suggest that integrating RS into SDMs can significantly enhance their ecological realism and support more effective conservation planning in mountain environments.