Spatio-Temporal Dynamics of Vegetation Cover in East Azerbaijan Province, Iran: A 22-Year Analysis Using MODIS Data and Advanced Statistical Models

Document Type : Original Article

Authors

1 Department of Climatology, Faculty of Geography and Planning, University of Isfahan, Isfahan, Iran

2 Department of Civil Engineering, Faculty of Technology and Engineering, Ta.C., Islamic Azad University, Tabriz, Iran.

3 Department of Urban Planning, Faculty of Art and Architecture, University of Guilan, Rasht, Iran.

4 Department of Climatology, Faculty of Humanities, Sayyed Jamaleddin Asadabadi University, Asadabad, Iran.

10.22034/nawee.2025.556274.1176
Abstract
V
Objective: Vegetation cover is a critical indicator of regional ecosystem health and a key component of global climate regulation models. In vulnerable mountainous environments, characterized by high sensitivity to climatic and anthropogenic pressures, monitoring vegetation dynamics over time is essential for effective resource management.
Method: This study focuses on the spatio-temporal variations of vegetation cover in East Azerbaijan Province, a crucial mountainous region in northwestern Iran, over a 22-year period (2000–2022). To achieve this objective, statistical and geospatial methods, including the Normalized Difference Vegetation Index (NDVI), Kolmogorov–Smirnov test (KST), Geographically Weighted Regression (GWR), and Principal Component Analysis (PCA), were applied to MODIS satellite products.
Result: The findings reveal sustained but unstable vegetation dynamics in the region. The KST indicated that NDVI distributions were non-normal across all months, suggesting considerable ecological instability. Notably, the mean annual rate of NDVI change increased from 0.166% in the first decade (2000–2009) to 0.192% in the second period (2010–2022). The high NDVI variance (36.78%) confirms pronounced spatial heterogeneity across the province. Furthermore, a moderate positive correlation (45%) was observed between precipitation and vegetation cover, highlighting the dominant role of moisture availability. Finally, PCA identified three high-density vegetation groups, collectively explaining 93.49% of the total variance, while the GWR model demonstrated strong predictive capability for localized vegetation changes.
Conclusion: These results provide valuable quantitative evidence of ongoing ecological changes in this important mountainous region and offer essential insights for regional land-use planning and conservation strategies.

Keywords


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