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 iran,tabriz

2 Ph.D. candidate in Climatology, Department of Geography and Planning, University of Isfahan, Isfahan, Iran

3 Master of Science in Regional Planning, Department of Urban Planning, University of Guilan, Rasht, Iran

4 5. Associate Professor of Climatology, Department of Climatology, Sayyed Jamaleddin Asadabadi University, Asadabad, Iran.

10.22034/nawee.2025.556274.1176
Abstract
Vegetation cover is a critical indicator of regional ecosystem health and a key component in 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. This study focuses on the spatio-temporal variations of vegetation cover in East Azerbaijan Province—a crucial mountainous nexus in northwestern Iran—over a 22-year period (2000–2022). To achieve this objective, statistical and geospatial models, 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.

The findings reveal a sustained yet unstable dynamic in the region's vegetation. The KST indicated that the NDVI distribution was not normal across all months, suggesting significant ecological instability. Crucially, the mean annual rate of NDVI change accelerated from 0.166% in the first decade (2000–2009) to 0.192% in the second decade (2010–2022). The high variance of NDVI (36.78%) confirms pronounced spatial heterogeneity across the province. Furthermore, a strong positive correlation of 45% was established between precipitation and vegetation cover, underscoring the dominant role of moisture availability. Finally, the PCA successfully identified three high-density vegetation groups, collectively explaining 93.49% of the total variance, and the GWR model demonstrated high predictive capability for localized changes. These results provide critical quantitative data on the accelerated ecological changes in this vital mountain region, offering essential input for regional land-use planning and conservation strategies.

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Articles in Press, Accepted Manuscript
Available Online from 22 December 2025