Analyzing the Effects of Meteorological Drought on Vegetation Dynamics in the Golestan Province

Document Type : Research Paper

Authors

1 Department of Forest Science and Engineering, Faculty of of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

2 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

3 Department of Physical Geography, Faculty of earth science, Shahid Beheshty University, Tehran, Iran.

10.22126/ges.2024.10786.2762

Abstract

The characteristics of drought as one of the environmental events may affect climate changes in the future. The purpose of this study is to evaluate the effect of meteorological drought on vegetation dynamics from 2001 to 2021 in the Golestan province, northern Iran, using remote sensing satellite images. First, using MATLAB software, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated in time scales of 3, 6, 9, and 12 months for the Golestan province with a statistical period of 20 years and zoned in ArcGIS software with IDW (Inverse Distance Weighting) Interpolation Method. In the next step, we obtained the maximum value of the Enhanced Vegetation Index (EVI) per month from the MODI3Q1 product of the MODIS spectrometer. Also, using Tersest software, the correlation and line slope of EVI index changes were calculated based on SPEI index changes. The results of correlation analysis showed that in 54.19% of the area of Golestan province, 6-month SPEI changes have the maximum correlation with the EVI index compared to SPEI in other periods. As a result, the highest impact of drought on vegetation in most parts of the study area is related to the 6-month SPEI in May. The results of vegetation sensitivity to meteorological drought showed that in the south of Golestan province, which is covered with dense forest conditions and includes the eastern slopes of Alborz spread in the form of a strip from the west to the east of the province, the vegetation has relatively low sensitivity to the phenomenon of drought due to suitable climatic conditions and the relative humidity of the air. The research results can be used in long-term planning for sustainable natural resources management.
 
Extended Abstract
1-Introduction
Drought is a complex and natural phenomenon that occurs frequently or intermittently in any climate. The global air temperature is constantly changing under the influence of climate change, and the spatio-temporal pattern of rainfall and land use is expected to change significantly in the coming decades. The location of Iran in dry and desert areas has caused the amount of rainfall in some periods to be lower than the long-term annual average. Even though Golestan province is located in the north of Iran and is affected by the humidity of the Caspian Sea, limited research has been done in the field of drought monitoring and management in Golestan province. Satellite data provides the possibility of identifying the damaged vegetation in all types of uses and assessing the severity of the damage. In this research, using remote sensing technology and satellite images, the impact of meteorological drought on the vegetation index has been evaluated in Golestan province from 2001 to 2021.
 
2-Materials and Methods
In this study, the standardized precipitation-evaporation and transpiration index (SPEI) was calculated for 20 years in Golestan province, northern Iran. In different time scales, this index uses the simple relationship of water balance, that is, the difference between precipitation and potential evaporation and transpiration based on Thornthwaite's approach (Thornthwaite, 1948). In areas with non-dense vegetation, the complex combination of soil type, atmospheric effects, and vegetation reduces the possibility of extracting reliable information from satellite data. Therefore, efforts have always been made to provide vegetation indicators that can reduce the adverse effects of factors such as soil. In this research, the slope of the effect line between SPEI in different periods (3, 6, 9, and 12 months) was calculated as the independent variable, and the Enhanced Vegetation Index (EVI) as the dependent variable. This calculation was based on the linear regression equations of Chatfield (2016). In general, a negative slope indicates an inverse relationship, a positive slope indicates a direct relationship, and the value of the slope indicates the degree of dependence of the variables. The slope of the effect along with the value of R2, which indicates the accuracy of linear regression, was obtained using the Earth Trend Modeler (ETM) tool set in TerrSet software.
 
3- Results and Discussion
Based on the obtained results, the months of March, April, and May have the most validity for evaluating the vegetation index. In other words, these 3 months have the highest EVI index, and as a result, the data from these 3 months were used to investigate the effect of meteorological drought on vegetation. In the study of Afzali Kardamehle and Behzadi (2023), the greatest impact of drought on vegetation in Golestan province was found in April and September. According to the results of March, for the 12-month SPEI, 29.34% of Golestan province correlated. This distribution includes the north-east of the province, i.e. Marave Tepe city and the border with Turkmenistan, and a narrow strip in the south-west of the province. In the 9-month SPEI, 28.46% of Golestan province had correlation, which includes the east (Kalaleh city), and parts of the west (Agh Ghala city). In the 6-month SPEI, 11.01% of Golestan province correlated. These areas include the center of the province from Agh Ghala to Azadshahr. In the 3-month SPEI, 31.19% of Golestan province had a correlation, which consists of the southwest of the province from Galikesh to Azadshahr. According to the results of April, for the 12-month SPEI, 14.57% of Golestan province correlated. These areas include the north of Agh Ghala city and Marave Tepe city. In the 9-month SPEI, 25.52% of Golestan province correlated. This area covers the northeast of the province, which includes Golestan National Park to Gonbad-e Kavus city. In the 6-month SPEI, 34.01% of Golestan province had correlation, which consists of the entire north of the province. In the 3-month SPEI, 25.89% of Golestan province had a correlation, which is in the south of the province and includes a narrow area (Kordkuy to Galiksh and Gonbad-e Kavus to Agh Ghala counties). According to the results of May, for the 12-month SPEI, a small part of Golestan province (8.34%) had a correlation, which includes Aliabad city. In the 9-month SPEI, 15.58% of Golestan province had a correlation, which consists of the southwest of the province from Gorgan to Kordkuy. In the 6-month SPEI, 54.19% of Golestan province had correlation, which includes the entire east of the province from Marave Tepe and Goli Dag and parts from Agh Ghala and Gorgan to Kordkuy in the west of the province.
 
4- Conclusion
Golestan province is located in several important complications, including the Alborz mountain, the Caspian Sea, and the desert of Turkmenistan, it has non-uniform and diverse climatic conditions, which have led to different vegetation conditions in it, and the results of this study confirm it. Also, anthropogenic factors are effective, which requires more extensive research in this field. The results of this research investigating the effects of meteorological drought on vegetation indicators in Golestan province can be used in the management of vegetation and water resources of the province.

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Afzali Kardamehle, P., & Behzadi, S. (2023). Comparison of Drought in Golestan And Semnan Provinces By Satellite Data with Vegetation Condition Index (VCI) Measurement, 3rd National Conference on Water Resource Management Strategies and Environmental Challenges, Tarbiat Debir Shahid Rajaee University, Tehran, Iran. https://civilica.com/doc/ 1810344 (In Persian)
Ahmad, M.I., Sinclair, C.D., & Werritty, A. (1988). Log-logistic flood frequency analysis. Journal of Hydrology, 98(3-4), 205-224. doi: 10.1016/0022-1694(88)90015-7.
Alizadeh, P., Kamkar, B., Shataee, S., &Kazemi Posht Masari, H. (2020). Assessment of Soybean yield using changes meteorological and satellite-based drought indices in the west of Golestan province. Crop Production, 12 (3), 69-84. doi: 10.22069/ejcp.2019.15743.2171 (In Persian)
Aslanpanjeh, B., Arzani, H., Tavili, A., Keshtkar, H.R., & Khalighi Sigaroodi, S.H., (2023). Investigating the Impact of Climatic Drought Changes on Vegetation Indicators (Case study: Eshtehard city, Alborz province). Integrated Watershed Management, 3(4), 18- 32. doi: 10.22034/iwm.2023.2010428.1103 (In Persian)
Bagheri, S., Heydari Alamdarloo, E., Khosravi, H., & Abolhasani, A. (2021). The effect of meteorological drought on vegetation dynamics in Iran. Rangeland, 15(4), 622-637. dor: 20.1001.1.20080891.1400.15.4.4.5 (In Persian)
Chatfield, C. (2003). The Analysis of Time Series: An Introduction, Sixth Edition (6th ed.). Chapman and Hall/CRC. doi: 10.4324/9780203491683
Choi, M., Jacobs, J.M., Anderson, M.C. & Bosch, D.D. (2013). Evaluation of drought indices via remotely sensed data with hydrological variables. Journal of Hydrology, 476, 265-273. doi: 10.1016/j.jhydrol.2012.10.042
Eivazi, M., & Mosaedi, A. (2011). Monitoring and Spatial Analysis of Meteorological Drought in Golestan Province using Geostatistical Methods. Journal of Range and Watershed Management, 64(1), 65-78. dor: 20.1001.1.20087713.1398.11.3.18.7 (In Persian)
Eshghizadeh, M. (2023). Assessment of the effect of meteorological drought on the performance of vegetation in erosion control projects using Landsat satellite images. Quarterly of Geographical Data, 32(126), 93-113. doi: 10.22131/sepehr.2023.1971473.2921 (In Persian)
Hashemi, A., Yazdanpanah, H., & Momeni, M. (2025). The effect of climatic variables on vegetation indices (Case study: Orange orchards in Hassan Abad, Darab County. Journal of Applied Research in Geographical Sciences, 24 (75), 254-272. doi: 10.61186/jgs.24.75.17 (In Persian)
Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X. & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213.‏ doi: 10.1016/S0034-4257(02)00096-2
Javed, T., Li, Y., Feng, K., Ayantobo, O.O., Ahmad, S., Chen, X., Rashid, S., & Suon, S. (2021). Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China. Environmental Science and Pollution Research, 28, 3644-3659. doi: 10.1007/s11356-020-10769-1
Kefayati, N., Ghorbani, K., & Abdollahzade G.H. (2021). Regional leveling of drought vulnerability in Golestan province. Journal of Spatial Analysis Environmental Hazards, 8 (2), 15-32. doi: 10.52547/jsaeh.8.2.15
Khorshid Doust A, M., Panahi, A., Khorramabadi, F., & Imanipour, H. (2022). The effect of climatic parameters on plant distribution in central Iran. Journal of Spatial Analysis Environmental Hazards, 9 (2), 73-86. dor: 20.1001.1.24237892.1401.9.2.5.3 (In Persian)
Khosravi, H., Haydari, E., Shekoohizadegan, S. &Zareie, S. (2017). Assessment the effect of drought on vegetation in desert area using Landsat data. The Egyptian Journal of Remote Sensing and Space Science, 20, S3- S12. doi: 10.1016/j.ejrs.2016.11.007
Kocaaslan, S., Musaoğlu, N., & Karamzadeh, S. (2021) Evaluating drought events by timefrequency analysis: A case study in Aegean region of Turkey. IEEE Access, 9, 125032-125041. doi: 10.1109/ACCESS.2021.3110816
Kong, D., Miao, C., Duan, Q., Lei, X., & Li, H. (2018). Vegetation-Climate Interactions on the Loess Plateau: A Nonlinear Granger Causality Analysis. Journal of Geophysical Research: Atmospheres, 123(19), 11068-11079. doi: 10.1029/2018JD029036
Lee Rodgers, J., & Nicewander, W.A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59-66. doi: 10.1080/00031305.1988.10475524
Li, K., Tong, Z., Liu, X., Zhang, J., & Tong, S. (2020). Quantitative assessment and driving force analysis of vegetation drought risk to climate change: Methodology and application in Northeast China. Agricultural and Forest Meteorology, 282, 107865. doi: 10.1016/j. agrformet.2019.107865
Liu, Z., Yao, Z., Huang, H., Wu, S. & Liu, G. (2014). Land use and climate changes and their impacts on runoff in the Yarlung Zangbo river basin, China. Land Degradation & Development, 25(3), 203- 215. doi: 10.1002/ldr.1159
Measho, S., Chen, B., Trisurat, Y., Pellikka, P., Guo, L., Arunyawat, S. Tuankrua.V., Ogbazghi. W., & Yemane, T. (2019). Spatiotemporal analysis of vegetation dynamics as a response to climate variability and drought patterns in the semiarid region. Eritrea. Remote Sensing, 11(6), 724. doi: 10.3390/rs11060724
Mosaedi, A., Khalili Zade, M., & Mohammadi, A. (2008). Drought monitoring in Golestan Province. Journal of Agricultural Sciences and Natural Resources, 51(2), 176-182. https://sid.ir/paper/9659/en (In Persian)
Mousavi, S., & Gholami-Borujeni, F. (2020). Investigation of Drought Indices in Golestan, Mazandaran, and Guilan Provinces during a 10-Year Period (2009-2019). Journal Health Res Commun., 6 (2), 69-79. dor: 20.1001.1.24236772.1399.6.2.7.8 (In Persian)
Muradyan, V., Tepanosyan, G., & Asmaryan, S. (2019). Relationships between NDVI and climatic factors in mountain ecosystems: A case study of Armenia. Remote Sensing Applications: Society and Environment, 14 (158-169). doi: 10.20944/preprints202208.0432.v1
Nejadrekabi, M., Eslamian, S. & Zareian, M. J. (2022) Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province. International Journal of Environmental Science and Technology, 19 (7), 6573-6594. doi: 10.1007/s13762-021-03852-8
Pakdel, M., G.Mahmoodlu, M., Jandaghi, N., Fathabadi, A., & Nick Ghojogh, Y. (2023). Extraction effect of deep and semi-deep wells on water table decline and groundwater qaulity parameters in Gorgan Plain. Iranian Journal of Geology, 16(64), 65-84. dor: 20.1001.1.17357128.1401. 16.64.5.8 (In Persian)
Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y. Wang. K., Xu. L. & Xia, G. (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology, 248, 215-227. doi: 10.1016/j.agrformet.2017.10.001
Rimkus, E., Stonevicius, E., Kilpys, J., Maciulyte, V., & Valiukas, D. (2017). Drought identification in the eastern Baltic region using NDVI. Earth system dynamics, 8(3) ,627-637. doi: 10.5194/ esd-8-627-2017
Shamsipour, A., & Rodgar Safari, V. (2020). Investigating the Consequences of Climate Change with a Focus on Spatial analysis of drought severity in Golestan Province using Statistical and Remote sensing indices. Climate Change Research, 1 (3), 65-76. doi: 10.30488/ccr.2020. 246770. 1022 (In Persian)
Siasar, H., Mohammadrezapour, O., & Khodamorad Pour, M. (2024). Drought Monitoring using MODIS Sensor Data and Comparison with SPI Meteorological Index in Short-term Periods (Case study: Golestan province). Geography and Development, 22 (74), 166-186. doi: 10.22111/gdij.2024.8175 (In Persian)
Singh, A.C., Mantel, J.H., & Thomas, B.W. (1994). Time series EBLUPs for small areas using survey data. Survey Methodology, 20, 33–43. https://www150.statcan.gc.ca/n1/en/pub/12-001-x/1994001/article/14434-eng.pdf?st=fG-36mhn
Thornthwaite, C.W. (1948). An approach toward a rational classification of climate. Geographical review, 38(1), 55–94. doi: 10.2307/210739
Vicente-Serrano, S.M., Beguería, S. & López-Moreno, J.I. (2010). A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23, 1696–1718. doi: 10.1175/2009JCLI2909.1
West, H., Quinn, N. & Horswell, M. (2019). Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment, 232(111291), 1-14. doi: 10.1016/j.rse.2019.111291
Zareabyaneh, H., GHobaeisoogh, M., & Mosaedi, A. (2015). Drought Monitoring Based on Standardized Precipitation Evaoptranspiration Index (SPEI) Under the Effect of Climate Change. Journal of Water and Soil, 29(2), 374-392. doi: 10.22067/jsw.v0i0.36472 (In Persian)