Climate Change Research

Climate Change Research

Assessment of Remote Sensing Indices for Drought Monitoring Across Different Vegetation Types in Gorgan County

Document Type : Original Article

Authors
khalil ghorbani 1
Esmaeil Valizadeh 2
1 Professor, Department of Water Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 PhD student in the Department of Desert Region Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
10.30488/ccr.2025.517417.1281
Abstract
Drought is one of the most complex and challenging climatic hazards, which can also be monitored through satellite imagery. However, the diversity of vegetation types and their varying responses to drought conditions pose significant challenges in this context. Accordingly, the present study investigates the relationship between two meteorological drought indices Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) and three satellite based indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) across three vegetation covers: forest, rangeland, and cropland. The satellite indices were derived from MODIS sensor imagery using coding within the Google Earth Engine (GEE) platform for the period 2000–2023 (24 years). Meteorological drought indices (SPI and SPEI) were calculated annually using data from the Hashemabad meteorological station in Gorgan. The results indicate significant climatic changes in the region due to temperature rise, which has led SPEI an index that incorporates both precipitation and evapotranspiration to depict drier conditions than SPI, along with a significant declining trend. Furthermore, SPEI showed stronger correlations with the satellite based indices compared to SPI. Among the remote sensing indices, TCI exhibited the highest correlation, particularly with SPEI over rangeland areas, reaching a maximum correlation coefficient of 0.77. This finding suggests that TCI is a more suitable tool for drought monitoring. Additionally, the observed increasing trend of VCI in forest areas, despite the ongoing drought conditions in the region, appears to be a consequence of global warming, which has likely extended the vegetation greenness period. In conclusion, satellite-based indices exhibit variable behavior across different vegetation types in response to drought, and their application on an annual scale requires cautious interpretation tailored to the ecological characteristics of each land cover type.
Keywords
Meteorological drought Remote sensing Climate variability MODIS Google Earth Engine(GEE) Vegetation types

Subjects

1.      
2.    Abdolalizadeh, Z., Ghorbani, A., Mostafazadeh, R., & Moameri, M. (2020). Rangeland canopy cover estimation using Landsat oli data and vegetation indices in Sabalan rangelands, Iran.Arabian Journal of Geosciences, 13(6), 1-13. doi:10.1007/s12517-020-5150-1.
3.    Abramowits, M. and Stegun, I.A. (1965). Handbook of Mathematical Functions. Dover Publication, New York.
4.    Ahmad, M., Sinclair, C. Werritty, A. (1988). Log-logistic flood frequency analysis. Journal of Hydrology, 98(3-4): 205-224.
5.    Banimahd, S. A., khalili, D. (2012). Comparative analysis of SPI and SPEI meteorological drought indices using parametric and nonparametric correlation tests in selected sites of iran. First National Conference on Sustainable Development Strategies, Natural Disasters Institute Education institute of Mehr Arvand Ministry of the Interior. (In Farsi).
6.    Edwards, D.C. and McKee, T.B. (1997). Characteristics of 20th century drought in the United States at multiple time scales. Atmospheric Science.
7.    Ejaz, N., Bahrawi, J., Alghamdi, K. M., Rahman, K. U. & Shang, S. (2023). Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case S tudy from Al-Lith Watershed, Kingdom of Saudi Arabia, Remote Sensing, 15(4), 984.
8.    Ghorbani, Kh., Kalili, A., Alavipanah, S.K. and Nakhaezadeh, Gh. (2010). Comparative Study of the Meteorological Drought Indices (Spi and Siap) Using Data Mining Method (Case Study of Kermanshah Province). Journal of Water and Soil. 24(3): 417-426.
9.    Gidey, E., Dikinya, O., Sebego, R., Segosebe, E., & Zenebe, A. (2018). Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using vegetation health index (VHI) in raya and its environs, Northern Ethiopia. Environmental Systems Research, 7(1), 1-18. doi:10.1186/s40068-018-0115-z.
10.              Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th ed.). New York: McGraw-Hill.
11.              Hamzeh, S., et al. (2017). "Spatio-temporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran)." Journal of Spatial Analysis Environmental Hazards 4(3): 53-70.
12.              Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
13.              Khalili, R., Montaseri, H., Motaghi, H., & Jalili, M.B. (2021). Water quality assessment of the Talar river in Mazandaran province based on a combination of water quality indicators and multivariate modeling. Water and Soil Management and Modelling, 1(4), 30-47. doi: 10.22098/mmws.2021.9322.1033 [In Persian].
14.              Khalili, R., Zali, A., & Motaghi, H. (2021). Evaluation of heavy metals in water and sediments of Haraz river, using pollution load index (PLI) and geoaccumulation index (Igeo). Iranian Journal of Soil and Water Research, 52(4), 933-942. [In Persian].
15.              Kogan, F. N. (2001). Operational space technology for global vegetation assessment, Bulletin of the American Meteorological Society, 82(9), 1949– 1964.
16.              Lettenmaier, D. P., Wood, E. F., & Wallis, J. R. (1994). Hydro-climatological trends in the continental United States, 1948-88. Journal of Climate, 7(4), 586-607.
17.              Maxwell Marumbwa, F., Moses Azong Cho., & Paxie W Chirwa. (2020). An assessment of remote sensing-based drought index over different land cover types in southern Africa, International Journal of Remote Sensing, Volume 41, 2020 - Issue 19.
18.              McKee, T.B., Doesken, N.J. and Kleist, J. (1995). Drought monitoring with Multiple Time scales. In Proceeding of the Ninth Conference on Applied Climatology, Dallas, TX, American Meteorological Society. Pp: 233-236.
19.              McKee, T.B.N., Doesken, J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Eight Conf. On Applied Climatology. Anaheim, CA, Amer. Meteor. Soc. Pp: 179-184.
20.              Moussa, M.B., Merga, B.B., & Gemeda, D.O. (2022). Multiple indices-based assessment of agricultural drought: A case study in Gilgel Gibe Sub-basin, Southern Ethiopia.Theoretical and Applied Climatology, 148(1), 455-464. doi:10.1007/s00704-022-03962-4.
21.              Ntale, H.K. and Gan, T.Y. (2003). Drought Indices and their application to East Africa. Int. J. Climatology, 23(11): 1335-1357.
22.              Partal, T., & Kahya, E. (2006). Trend analysis in Turkish precipitation data. Hydrological                                                                                                                    Processes: An International Journal, 20(9), 2011-2026.
23.              Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., Xia, G., (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and forest meteorology, 248, 215-227.
24.              Thiel, H. (1950). A rank-invariant method of linear and polynomial regression analysis, Part 3. In Proceedings of Koninalijke Nederlandse Akademie van Weinenschatpen A (Vol. 53, pp. 1397-1412).
25.              Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389.
26.              Soleimani Sardo, M. and M. Zarei (2019). "Drought Monitoring Using MODIS Data and Its Comparison with SPI Meteorological Index in Short Periods (Case Study: Jaz_Murian basin)." Journal of Watershed Management Research 10(20): 250-261.
27.              Vicente-Serrano, S. M., S. Beguería and J. I. López-Moreno. 2010. A Multi–scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index– SPEI. Journal of Climate 23(7): 1696– 1718.
28.              Wackerly, Dennis; Mendenhall, William; Scheaffer, Richard L. (2008). Mathematical Statistics with Applications (7 ed.). Belmont, CA, USA: Thomson Higher Education. ISBN 0-495-38508-5.
29.              Wu, H., Soh, L.K., Samal, A. and Chen, X.H. 2008. Trend Analysis of Streamflow Drought Events in Nebraska. Water Resour Manage, 22: 145-164.
30.              Zhang, X., Friedl, M. A., Schaaf, C. B., & Strahler, A. H. (2013). Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10(7), 1133–1145.
31.              Zhao, X., Xia, H., Liu, B. & Jiao, W. (2022). Spatiotemporal comparison of drought in ShaanxiGansu–Ningxia from 2003 to 2020 using various drought indices in google earth engine, Remote Sensing, 14(7), 1570.
32.              Zou, Y., Xi, Y., Pan, J., Ahmad, M.I., Zhang, A., Zhang, C., Li, Y., & Zhang, H. (2022). Soy oil and SPI based-oleogels structuring with glycerol monolaurate by emulsion-t emplated approach: Preparation, characterization and potential application. Food Chemistry, 133767. doi:10.1016/j.foodchem.2022.133767.