Climate Change and Vegetation Cover in the Kahneh Watershed, Khorasan Razavi, Using Remote Sensing in the GEE Environment

Dastorani, Mostafa; Zarei, Mahdi; Qaderi, Malihe

doi:10.22077/jdcr.2025.8922.1118

Climate Change and Vegetation Cover in the Kahneh Watershed, Khorasan Razavi, Using Remote Sensing in the GEE Environment

Document Type : Original Article

Authors

Mostafa Dastorani ¹

Mahdi Zarei ²

Malihe Qaderi ³

¹ Assistant Professor, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University. Sabzevar, Iran.

² Associate Professor, Research Center for Geographical Sciences and Social Studies, Hakim Sabzevari University, Sabzevar, Iran.

³ Environmental Education Expert, Department of Environment of South Khorasan Province, Birjand, Iran.

https://doi.org/10.22077/jdcr.2025.8922.1118

Abstract

This study examines vegetation cover changes in the Kahneh watershed, over two periods: 2000–2012 and 2012–2024. As a semi-arid region, it is affected by climate change and precipitation fluctuations. The research assesses vegetation cover dynamics using NDVI, SPI, and monthly precipitation data within Google Earth Engine (GEE). NDVI data from satellite imagery was analyzed with SPI and monthly precipitation records. The study utilized GEE for large-scale remote sensing analysis. NDVI trends were examined across timeframes, and seasonal variations were assessed to determine the impact of precipitation fluctuations. SPI classified wet and dry years, linking precipitation anomalies with vegetation changes. NDVI values significantly increased in 2012–2024 compared to 2000–2012, indicating improved vegetation cover, especially in the later years. This improvement correlated with increased annual precipitation. However, SPI analysis revealed fewer wet years in 2012–2024, suggesting ongoing climate variability. Seasonal analysis showed vegetation cover improvements across all seasons, including dry periods, highlighting the positive effects of additional rainfall. The mean NDVI rose from 0.06 in 2000 to 0.10 in 2024, nearly doubling soil and vegetation improvement conditions. The study indicates that increased precipitation in 2012–2024 positively impacted vegetation cover in the Kahneh watershed. However, ongoing climate fluctuations and long-term climate change effects require further research. The study also highlights limitations in using only monthly data and emphasizes the need for integrating additional environmental factors for a more comprehensive analysis. This research provides insights into the effects of precipitation variability on semi-arid ecosystems, essential for sustainable land and water resource management.

Keywords

Climate

Remote sensing

GIS

Trend

Land Degradation

Subjects

Climate change

Bagherzadeh, A., Hoseini, A. V., & Totmaj, L. H. (2020). The effects of climate change on the normalized difference vegetation index (NDVI) in the Northeast of Iran. Modeling Earth Systems and Environment, 6, 671–683. https://doi.org/10.1007/s40808-020-00724-x

Doulabian, S., Toosi, A. S., Calbimonte, G. H., Tousi, E. G., & Alaghmand, S. (2021). Projected climate change impacts on soil erosion over Iran. Journal of Hydrology, 598, 126432. https://doi.org/10.1016/j.jhydrol.2021.126432

Farooghi, F., Ebrahimi, Z., & Rahime, M. (2022). Evaluation of Quaternary soil sensitivity with NDVI indices and soil moisture about nonparametric climate change. Climate Change Research, 3(9), 41–52.

Ghorbanian, A., Mohammadzadeh, A., & Jamali, S. (2022). Linear and non-linear vegetation trend analysis throughout Iran using two decades of MODIS NDVI imagery. Remote Sensing, 14(15), 3683. https://doi.org/10.3390/rs14153683

Golla, B. (2021). Agricultural production system in arid and semi-arid regions. International Journal of Agricultural Science and Food Technology, 7(2), 234–244. https://doi.org/10.17352/2455-815X.000113

Gong, Z., Ge, W., Guo, J., & Liu, J. (2024). Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities. ISPRS Journal of Photogrammetry and Remote Sensing, 217, 149–164. https://doi.org/10.1016/j.isprsjprs.2024.08.011

Hosseinizadeh, A., SeyedKaboli, H., Zareie, H., Akhondali, A., & Farjad, B. (2015). Impact of climate change on the severity, duration, and frequency of drought in a semi-arid agricultural basin. Geoenvironmental Disasters, 2, 1–9. https://doi.org/10.1186/s40677-015-0031-8

Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of the normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1

Huete, A. R. (2012). Vegetation indices, remote sensing, and forest monitoring. Geography Compass, 6(9), 513–532. https://doi.org/10.1111/j.1749-8198.2012.00507. x

Jabal, Z. K., Khayyun, T. S., & Alwan, I. A. (2022). Impact of climate change on crop productivity using MODIS-NDVI time series. Civil Engineering Journal, 8(6), 1136–1156. https://doi.org/10.28991/CEJ-2022-08-06-04

Kalma, J. D., & Franks, S. W. (2003). Rainfall in arid and semi-arid regions. In Understanding water in a dry environment (pp. 31–80). CRC Press.

Kong, D., Zhang, Q., Singh, V. P., & Shi, P. (2017). Seasonal vegetation response to climate change in the Northern Hemisphere (1982–2013). Global and Planetary Change, 148, 1–8. https://doi.org/10.1016/j.gloplacha.2016.10.020

Loukas, A., Vasiliades, L., & Tzabiras, J. (2008). Climate change effects on drought severity. Advances in Geosciences, 17, 23–29. https://doi.org/10.5194/adgeo-17-23-2008

Ma, S., Wang, L.-J., Jiang, J., Chu, L., & Zhang, J.-C. (2021). Threshold effect of ecosystem services in response to climate change and vegetation coverage change in the Qinghai-Tibet Plateau ecological shelter. Journal of Cleaner Production, 318, 128592. https://doi.org/10.1016/j.jclepro.2021.128592

Milewski, R., Schmid, T., Chabrillat, S., Jiménez, M., Escribano, P., Pelayo, M., & Ben-Dor, E. (2022). Analyses of the impact of soil conditions and soil degradation on vegetation vitality and crop productivity based on airborne hyperspectral VNIR–SWIR–TIR data in a semi-arid rainfed agricultural area (Camarena, Central Spain). Remote Sensing, 14(20), 5131. https://doi.org/10.3390/rs14205131

Owen, D., Davis, A. P., & Aydilek, A. H. (2021). Compost for permanent vegetation establishment and erosion control along highway embankments. Journal of Irrigation and Drainage Engineering, 147(8), 04021031. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001587

Prăvălie, R. (2021). Exploring the multiple land degradation pathways across the planet. Earth-Science Reviews, 220, 103689. https://doi.org/10.1016/j.earscirev.2021.103689

Ren, Y., Liu, J., Liu, S., Wang, Z., Liu, T., & Shalamzari, M. J. (2022). Effects of climate change on vegetation growth in the Yellow River Basin from 2000 to 2019. Remote Sensing, 14(3), 687. https://doi.org/10.3390/rs14030687

Trivedi, M. R., Berry, P. M., Morecroft, M. D., & Dawson, T. P. (2008). Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Global Change Biology, 14(5), 1089–1103. https://doi.org/10.1111/j.1365-2486.2008.01553.x

Wu, L., Ma, X., Dou, X., Zhu, J., & Zhao, C. (2021). Impacts of climate change on vegetation phenology and net primary productivity in arid Central Asia. Science of the Total Environment, 796, 149055. https://doi.org/10.1016/j.scitotenv.2021.149055

Xu, C., McDowell, N. G., Fisher, R. A., Wei, L., Sevanto, S., Christoffersen, B. O., Weng, E., & Middleton, R. S. (2019). Increasing impacts of extreme droughts on vegetation productivity under climate change. Nature Climate Change, 9(12), 948–953. https://doi.org/10.1038/s41558-019-0630-6

Ying, L., Wang, L., Huang, X., Rao, E., Xiao, Y., Zheng, H., Shen, Z., & Ouyang, Z. (2024). Climate change impairs the effects of vegetation improvement on soil erosion control in the Qinghai-Tibetan Plateau. Catena, 241, 108062. https://doi.org/10.1016/j.catena.2024.108062

Yue, W., Xu, J., Tan, W., & Xu, L. (2007). The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15), 3205–3226. https://doi.org/10.1080/01431160500306906

Zuo, D., Han, Y., Xu, Z., Li, P., Ban, C., Sun, W., Pang, B., Peng, D., Kan, G., & Zhang, R. (2021). Time-lag effects of climatic change and drought on vegetation dynamics in an alpine river basin of the Tibet Plateau, China. Journal of Hydrology, 600, 126532. https://doi.org/10.1016/j.jhydrol.2021.126532