Long-term Vegetation Cover Dynamics Assessment Using Remote Sensing in Semi-Arid Regions of Western Iran (Eyvan County, Ilam Province)

Document Type : Research Paper

Authors

1 Master's Student, Department of Rangeland and Watershed Management, Faculty of Agriculture, University of Ilam, Ilam, Iran.

2 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.

3 Assistant Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.

10.29252/aridbiom.2026.3994

Abstract

Vegetation is a fundamental component of natural ecosystems and plays a crucial role in soil conservation, carbon sequestration, and climate change. It serves as an important indicator for assessing ecosystem functionality and health. Proper vegetation management requires an understanding of its current status and long-term trends. Given the lack of historical vegetation data, remote sensing technology plays a critical role in assessing long-term vegetation dynamics. This study investigates vegetation cover changes in Ivan County, Ilam Province, over a 24-year period (2000–2024) using multi-temporal Landsat satellite imagery across five distinct time intervals (2000, 2005, 2010, 2015, 2020, and 2024). In this regard, different vegetation cover classes (bare, poor, moderate, good, and excellent) were first extracted and analyzed based on NDVI index data. Furthermore, vegetation cover changes (increased, unchanged, and decreased) were assessed for two 10-year periods (2000-2010 and 2015-2024) using image differencing and change thresholding methods. To assess the significance of observed change trends, the Mann-Kendall test was employed. The results revealed that the poor vegetation class represented the most extensive vegetation cover type in the study area, exhibiting a declining trend throughout the study period. Specifically, its coverage decreased from 88.18% in 2000 to 39.86% in 2020. Conversely, areas with moderate and good vegetation cover demonstrated increasing trends, rising from 10.74% and 1% in 2000 to 41.09% and 11.29% in 2024, respectively. The Mann-Kendall test results indicated that areas with poor vegetation cover exhibited a statistically significant decreasing trend, while those with moderate and good cover showed statistically significant increasing trends. However, areas with excellent vegetation cover and bare lands displayed no significant trends. According to the image differencing results, the majority of the study area (94.55%) remained unchanged during the 2000-2024 study period. The findings of this study demonstrate the high efficacy of remote sensing tools, particularly vegetation indices, for long-term vegetation monitoring. Furthermore, the results suggest that since the observed increase in moderate, good, and excellent vegetation classes primarily resulted from expanded irrigated lands and conversion of croplands to orchards; rather than improvement of forest or rangeland conditions conservation management programs should be implemented to enhance rangeland quality and prevent conversion of natural resources to agricultural uses, and promote natural revegetation to replace areas with poor vegetation cover.

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References

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Journal of Arid Biome
Volume 15, Issue 1
March 2025
Pages 85-101

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