Yurddaş et al. (2026) Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği

Identification

• Journal: Turkish Journal of Remote Sensing and GIS
• Year: 2026
• Date: 2026-09-24
• Authors: Kemal Yurddaş, Murat Karabulut
• DOI: 10.48123/rsgis.1858019

Research Groups

• Manisa Celal Bayar University, Demirci Vocational School, Department of Architecture and Urban Planning, Manisa, Turkey
• Kahramanmaraş Sütçü İmam University, Faculty of Humanities and Social Sciences, Department of Geography, Kahramanmaraş, Turkey

Short Summary

This study analyzed the spatiotemporal effects of meteorological drought on vegetation health in the Gediz Basin using SPI and NDVI. It found a strong seasonal variability in the drought-vegetation relationship, with the strongest correlation in summer (r ≈ 0.70), and observed significant declines in vegetation health during identified drought years.

Objective

• To analyze the spatiotemporal effects of meteorological drought on vegetation health using the Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) in the Gediz Basin.

Study Configuration

• Spatial Scale: Gediz Basin, Turkey (approximately 17,500 square kilometers).
• Temporal Scale:
  • SPI analysis: 1970–2023 (long-term monthly precipitation data).
  • NDVI data: 2000–2023 (MODIS satellite images).
  • Correlation analysis: Common period 2000–2023.

Methodology and Data

• Models used:
  • Standardized Precipitation Index (SPI)
  • Normalized Difference Vegetation Index (NDVI)
  • Pearson correlation analysis
  • Lagged (cross-correlation) analysis
• Data sources:
  • Monthly total precipitation data from four Turkish State Meteorological Service (MGM) stations (Manisa, Salihli, Akhisar, Gediz).
  • Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 NDVI data (250 meter spatial resolution, 16-day temporal resolution).

Main Results

• The relationship between meteorological drought and vegetation health exhibits significant seasonal variability.
• The strongest correlation (r ≈ 0.70) was observed in summer, indicating high vegetation sensitivity to precipitation deficits during this period.
• Correlations were weak in winter (r ≈ 0.05) and spring (r ≈ 0.02), suggesting limited direct influence of meteorological drought on vegetation during these seasons.
• Identified drought years (2004, 2008, and 2022) corresponded to notable declines in NDVI values, indicating reduced vegetation health.
• Vegetation response to drought is not uniform throughout the year, being strongly influenced by seasonal climatic conditions and other environmental factors such as irrigation.
• Lagged correlation analysis showed limited delayed response of NDVI to precipitation anomalies, with correlations rapidly decreasing beyond a 1-month lag.
• Comparisons between severe drought years (e.g., 2008) and wet years (e.g., 2009, 2010, 2021) confirmed widespread negative NDVI changes during drought and significant vegetation recovery during wet periods.

Contributions

• Provides a holistic analysis of meteorological drought and vegetation dynamics in the Gediz Basin by integrating long-term station-based SPI data (1970–2023) with satellite-based NDVI data (2000–2023).
• Highlights the significant seasonal variability and limited lagged effects in the drought-vegetation relationship, offering a more nuanced understanding compared to existing literature for the region.
• Contributes to a deeper understanding of drought-vegetation interactions at the basin scale, particularly in semi-arid agricultural regions.
• Offers a methodological framework for integrating remote sensing approaches into drought monitoring and water resource management strategies.

Funding

No explicit funding information was provided in the paper text.

Citation

@article{Yurddaş2026Meteorolojik,
  author = {Yurddaş, Kemal and Karabulut, Murat},
  title = {Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği},
  journal = {Turkish Journal of Remote Sensing and GIS},
  year = {2026},
  doi = {10.48123/rsgis.1858019},
  url = {https://doi.org/10.48123/rsgis.1858019}
}