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<p>Traditional assessments of ecological sensitivity rely on static variables, failing to capture the pronounced seasonal dynamics of temperate mountain ecosystems, which leads to a mismatch between management and actual risks during critical seasons. Taking Mount Tai, China, as a case study, we established a dynamic evaluation framework. We processed Sentinel-2 imagery (2018–2024) on the Google Earth Engine (GEE) to derive seasonal NDVI and NDWI as key biophysical proxies. These were combined with static factors (topography, land use) and integrated using an AHP model validated by Random Forest (RF). The results reveal a significant “summer–winter dual-core driving” mechanism. In summer, patterns are vegetation-dominated (NDVI &gt; 0.6), with high-sensitivity areas (HSA) accounting for 17.79% and clustered in the core forest zone. The winter pattern shifts to be controlled by hydrological-cryospheric factors, where HSA within the 0–50&#xa0;m water buffer reaches 37.56%. RF analysis confirmed the water factor’s dominance in the cold season. Furthermore, phenological analysis showed that the Start of Season (SOS) exhibits substantially higher interannual variability (<i>σ</i> = 17.3&#xa0;days) than the End of Season, describing spring as a transitional period with higher interannual variability in early-season vegetation growth. Compared to static assessments, the dynamic framework proved necessary by revealing a severe underestimation of risks around water bodies in winter. This study advances ecological sensitivity assessment from static pattern description to spatiotemporal process simulation. The proposed framework is highly repeatable and transferable, providing spatial decision support for seasonal adaptive management in temperate mountain scenic areas. </p>

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Seasonal differentiation mechanism of ecological sensitivity in temperate mountain scenic areas: a case study of Mount Tai, China

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Kuifeng Luan,
  • Wenhui He,
  • Jie Wang,
  • Songyang Wu,
  • Hang Xu,
  • Weidong Zhu,
  • Yahui Zhang,
  • Xinyi You

摘要

Traditional assessments of ecological sensitivity rely on static variables, failing to capture the pronounced seasonal dynamics of temperate mountain ecosystems, which leads to a mismatch between management and actual risks during critical seasons. Taking Mount Tai, China, as a case study, we established a dynamic evaluation framework. We processed Sentinel-2 imagery (2018–2024) on the Google Earth Engine (GEE) to derive seasonal NDVI and NDWI as key biophysical proxies. These were combined with static factors (topography, land use) and integrated using an AHP model validated by Random Forest (RF). The results reveal a significant “summer–winter dual-core driving” mechanism. In summer, patterns are vegetation-dominated (NDVI > 0.6), with high-sensitivity areas (HSA) accounting for 17.79% and clustered in the core forest zone. The winter pattern shifts to be controlled by hydrological-cryospheric factors, where HSA within the 0–50 m water buffer reaches 37.56%. RF analysis confirmed the water factor’s dominance in the cold season. Furthermore, phenological analysis showed that the Start of Season (SOS) exhibits substantially higher interannual variability (σ = 17.3 days) than the End of Season, describing spring as a transitional period with higher interannual variability in early-season vegetation growth. Compared to static assessments, the dynamic framework proved necessary by revealing a severe underestimation of risks around water bodies in winter. This study advances ecological sensitivity assessment from static pattern description to spatiotemporal process simulation. The proposed framework is highly repeatable and transferable, providing spatial decision support for seasonal adaptive management in temperate mountain scenic areas.