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<p>Understanding the spatiotemporal dynamics of vegetation in response to climatic variability is critical for ecosystem monitoring and sustainable land management, particularly in ecologically sensitive regions. Northern Iran, specifically Guilan Province, with its humid subtropical climate and complex topography, is highly vulnerable to hydroclimatic fluctuations. However, comprehensive assessments integrating multi-source satellite data remain limited. This study aims to investigate spatiotemporal vegetation-climate interactions in Guilan Province from 2020 to 2024 using an integrated analytical framework developed within Google Earth Engine (GEE). The objectives are to quantify interannual changes in vegetation greenness (NDVI and EVI), assess relationships with land surface temperature (LST) and precipitation, and develop a novel Vegetation–Climate Sensitivity Index (VCSI) to evaluate ecological vulnerability. Methodology employed multi-source satellite datasets: Landsat 8-derived NDVI and EVI, MODIS MOD11A2 LST, and CHIRPS precipitation data. All datasets were harmonized to 1&#xa0;km resolution and analyzed within GEE. The VCSI was constructed by integrating standardized vegetation indices with normalized precipitation and LST. Spatial autocorrelation analyses (Moran’s I, Getis-Ord Gi*) identified clustering patterns of vegetation-climate sensitivity. Results revealed marked interannual variability, with 2022 exhibiting the lowest NDVI/EVI and highest LST, indicating severe climatic stress, followed by partial ecosystem recovery in 2024. Strong positive correlations were found between vegetation indices and precipitation (<i>r</i> = 0.88–0.92), while negative correlations with LST (<i>r</i> = -0.62) confirmed thermal stress, particularly during summer (mean summer VCSI = 56.96). Lagged response analysis showed vegetation responded to precipitation with one- to two-month delays, highlighting soil moisture retention effects. The VCSI effectively captured spatial heterogeneity: high values (resilient zones) concentrated in forested highlands of the west and north, while low values (vulnerable zones) characterized agricultural lowlands and urbanized areas. Spatial autocorrelation confirmed strong clustering of ecosystem sensitivity (Global Moran’s I &gt; 0.93, <i>p</i> &lt; 0.001), with persistent hotspots in highland forests and coldspots in lowland anthropogenic landscapes, intensified during 2022 climatic stress. In conclusion, vegetation responses to climate variability in Guilan Province are spatially structured, temporally lagged, and highly sensitive to hydroclimatic drivers. The integrated GEE-based framework combining VCSI with spatial statistics provides a robust approach for mapping ecological vulnerability. Findings offer actionable insights for adaptive land-use planning and climate adaptation interventions in northern Iran and similar humid subtropical regions.</p>

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Mapping spatiotemporal variability of vegetation and climate sensitivity in northern Iran using google earth engine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月7日

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  • Mehrdad Mehrjou,
  • Hassan Ahmadi

摘要

Understanding the spatiotemporal dynamics of vegetation in response to climatic variability is critical for ecosystem monitoring and sustainable land management, particularly in ecologically sensitive regions. Northern Iran, specifically Guilan Province, with its humid subtropical climate and complex topography, is highly vulnerable to hydroclimatic fluctuations. However, comprehensive assessments integrating multi-source satellite data remain limited. This study aims to investigate spatiotemporal vegetation-climate interactions in Guilan Province from 2020 to 2024 using an integrated analytical framework developed within Google Earth Engine (GEE). The objectives are to quantify interannual changes in vegetation greenness (NDVI and EVI), assess relationships with land surface temperature (LST) and precipitation, and develop a novel Vegetation–Climate Sensitivity Index (VCSI) to evaluate ecological vulnerability. Methodology employed multi-source satellite datasets: Landsat 8-derived NDVI and EVI, MODIS MOD11A2 LST, and CHIRPS precipitation data. All datasets were harmonized to 1 km resolution and analyzed within GEE. The VCSI was constructed by integrating standardized vegetation indices with normalized precipitation and LST. Spatial autocorrelation analyses (Moran’s I, Getis-Ord Gi*) identified clustering patterns of vegetation-climate sensitivity. Results revealed marked interannual variability, with 2022 exhibiting the lowest NDVI/EVI and highest LST, indicating severe climatic stress, followed by partial ecosystem recovery in 2024. Strong positive correlations were found between vegetation indices and precipitation (r = 0.88–0.92), while negative correlations with LST (r = -0.62) confirmed thermal stress, particularly during summer (mean summer VCSI = 56.96). Lagged response analysis showed vegetation responded to precipitation with one- to two-month delays, highlighting soil moisture retention effects. The VCSI effectively captured spatial heterogeneity: high values (resilient zones) concentrated in forested highlands of the west and north, while low values (vulnerable zones) characterized agricultural lowlands and urbanized areas. Spatial autocorrelation confirmed strong clustering of ecosystem sensitivity (Global Moran’s I > 0.93, p < 0.001), with persistent hotspots in highland forests and coldspots in lowland anthropogenic landscapes, intensified during 2022 climatic stress. In conclusion, vegetation responses to climate variability in Guilan Province are spatially structured, temporally lagged, and highly sensitive to hydroclimatic drivers. The integrated GEE-based framework combining VCSI with spatial statistics provides a robust approach for mapping ecological vulnerability. Findings offer actionable insights for adaptive land-use planning and climate adaptation interventions in northern Iran and similar humid subtropical regions.