Ecological monitoring and analysis of temporal changes in vegetation cover using NDVI index and remote sensing data (Case study: Golestan National Park)

Ghodsifar, A.; Milan, A.; Azizi, Z.

doi:10.22061/jrsgr.2026.13034.1122

Articles in Press

Document Type : Original Research Paper

Authors

¹ Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

² Faculty of Civil, Water and Environmental Engineering Shahid Beheshti University, Tehran, Iran

https://doi.org/10.22061/jrsgr.2026.13034.1122

Abstract

Background and Objectives: Monitoring the spatial and temporal changes in vegetation cover is an important tool for assessing the condition of natural ecosystems and planning the management of protected areas. Remote sensing data provide the capability for continuous vegetation monitoring and enable the assessment of changes across different spatial and temporal scales. Golestan National Park, owing to its high biodiversity and considerable extent of natural habitats, is one of the important areas in Iran for monitoring vegetation changes. This study aimed to investigate the spatial and temporal changes in vegetation cover in Golestan National Park during 2015–2025 and to assess their statistical relationships with precipitation, air temperature, and land surface temperature.
Methods: In this study, Landsat 8 and Landsat 9 images with a spatial resolution of 30 m were processed in the Google Earth Engine environment, and the Normalized Difference Vegetation Index (NDVI) was calculated for all images. After generating annual composites, the spatial mean of NDVI was extracted for each year from 2015 to 2025. NDVI maps for 2015, 2019, 2023, and 2025, as well as a difference map between 2015 and 2025, were also generated. In addition, annual cumulative precipitation was derived from CHIRPS data, annual mean air temperature from ERA5-Land, and annual mean land surface temperature (LST) from the MODIS MOD11A2 product. Descriptive statistics, the Mann–Kendall test, Sen’s slope estimator, linear regression, and Pearson and Spearman correlation coefficients were applied to analyze the 11-year time series.
Findings: The mean NDVI over the study period was 0.227, with a coefficient of variation of 11.56%. Comparison of 2015 and 2025 indicated an approximately 5.56% decrease in NDVI; however, the Mann–Kendall test did not confirm a statistically significant decreasing trend. Air temperature showed an increasing trend, whereas precipitation and land surface temperature exhibited decreasing trends; however, none of these trends was statistically significant at the 0.05 significance level. Among the relationships examined, the relationship between NDVI and air temperature was negative and statistically significant (Pearson: …). The relationship between NDVI and precipitation was positive (…), but did not reach statistical significance. The relationship between NDVI and LST was also negative and statistically non-significant (…). The NDVI difference map revealed the simultaneous occurrence of areas with relative increases and decreases in NDVI across different parts of the park.
Conclusion: The results showed that vegetation cover in Golestan National Park experienced interannual fluctuations and spatial variations during 2015–2025; however, based on the analyzed time series, there was insufficient statistical evidence to confirm a significant decreasing trend across the entire park. At the same time, the significant negative relationship between NDVI and air temperature indicated a statistical association between variations in these two variables at the annual scale, although this relationship alone does not imply causality. The NDVI maps and difference map can be used as preliminary monitoring tools for identifying areas experiencing changes and for prioritizing field investigations. Future studies are recommended to extend the length of the statistical record, use monthly or seasonal data, investigate the time-lagged relationship between precipitation and NDVI, and incorporate field observations and complementary spatial layers to provide a more accurate assessment of the factors influencing vegetation changes.

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