Assessing the Trend of Spatio-temporal Drought Changes Using Remote Sensing Time Series Data in Central Khuzestan Province

Document Type : Original Article


University of Kashan



Extended Abstract
Introduction: As a dreadful natural disaster caused by a severe reduction in precipitation rate, drought brings about, compared with other natural disasters, far-reaching spatial and temporal consequences, incurring severe damages. On the other hand, in late the 20th century, drought monitoring approaches underwent a paradigm shift, and advances in remote sensing and earth observation technologies allowed observations and monitoring of key drought-related variables over larger temporal and spatial scales than what the then conventional methods had already made possible.
 There are different remote sensing indices used to assess drought, including the PDI index which has been developed based on the spectral patterns of soil moisture changes in the NIR-Red space using the red and near-infrared bands of the ETM+ sensor.
Therefore, as Khuzestan province is suffering from drought consequences, including but not limited to dust storms and economic difficulties, this study sought to identify the spatial and temporal trends of drought in the center of Khuzestan province.
Materials and Methods: The study area is located in southwestern Iran and the center of Khuzestan province at 31° 0ʹ 17ʺ to 31° 43ʹ 69ʺ N latitudes and 48° 35ʹ 51ʺ to 49° 32ʹ 2ʺ E longitudes covering an area of 7635/36 km2. To conduct the study, some twenty ETM+ remote sensing images of level-1 data taken from 1999 to 2018, (path/row 168/35) were collected from the United States Geological Survey website.
After gathering the required data, some 411 random points were selected on the collected images, the pixel values of red and near-infrared bands were extracted and plotted against each other, and the slope of the best-fitted line, known as the soil line, was obtained. Then, the PDI drought index values were calculated using the slope and the values of the aforementioned bands. Finally, by applying a natural break classification method, different degrees were separated, and the drought’s trend of spatial and temporal changes was identified using Mann-Kendall's seasonal trend test at different significance levels.
Results: The results of the spatial trend analysis of drought suggested that the trend was significant only in low drought and non-drought conditions. For the non-drought conditions, the probability of spatial changes was lower than the confidence level at 5% and 10% significant levels, indicating the significance of the conditions at these two levels, and thus, rejecting the null hypothesis at merely the 1% level.
On the other hand, as the low drought conditions showed significant spatial changes only at the 10% significant level, the null hypothesis is rejected at the 1% and 5% levels. However, moderate and severe drought conditions revealed no trends in terms of spatial changes due to the higher probability values ​​of 0.28 and 0.3, respectively, which were higher than the determined significance levels. Moreover, the results of temporal trend analysis indicated no trend for the non-drought conditions, considering the fact that the null hypothesis was rejected at all significant levels. On the contrary, in the moderate drought conditions, a temporal trend was confirmed at all significant levels with the probability rate of 0.006 which was lower than all the assigned levels. Also, a temporal trend was found at low and severe drought conditions at 5% and 10% significance levels with a probability rate of 0.023 and 0.014.
Discussion and Conclusion: The spatial analysis of the drought trend suggested that only the area with non-drought conditions had a significant increasing trend, which could be justified by the increase in the area of ​​irrigated land around water bodies in the area, especially around the Karun River in the west of the study area. The reduction in the area of land in the northeast of the study area with the low-drought conditions could be attributed to the rangeland degradation containing low and moderately dense vegetation.
Moreover, the status of drought conditions in some sandy areas has changed from low in 1999 to moderate in 2018 due to vegetation destruction. The decreasing and increasing trends in areas of land with moderate and severe drought conditions, respectively, indicated the worsening of the drought conditions in the study area. Taking the changes in the drought index into account, it could be said that merely the areas with non-drought conditions remained unaffected by any significant increase or decrease in drought conditions, considering the fact that such areas are mainly wetlands, irrigated farms, and fish farms (that are naturally wet).
However, the trend of the drought index value was found to be (highly) significant for other drought conditions, especially for moderate-drought conditions, indicating an increase in the severity of the drought conditions during the studied years.
The frequent occurrence of dust storms in Khuzestan in recent years suggests that the results of this study correspond to the current reality of the region. In fact, it could be argued that during the last decade, the exacerbation of climate change and drought conditions on the one hand, and the development of construction projects and excessive extraction of water resources, on the other hand, have led to the dryness of many wetlands and wet areas, thus creating small deserts which are regarded as the main sources of dust storms in Khuzestan province within the past few years.
Moreover, according to the findings of recent studies, desertification and drought trends have been increasing in recent years, indicating a great increase in the significance level of desertification in number 3 and 4 desertification centers in the east and southeast of Ahwaz, and a significant increase in the severity of drought conditions. This study proved the efficacy and applicability of the PDI drought index in drought monitoring.


  1. Alijani, B., Mahmoudi, P. and Doust, M. K., 2015. Statistical Analysis of Climatic Histories of Desertification in Iran, Geographic Space 15, 1-18.

    1. Amani, M., Salehi, B., Mahdavi, S., Masjedi, A. and Dehnavi, S., 2017. Temperature-Vegetation-soil Moisture Dryness Index (TVMDI). Remote Sensing of Environment 197, 1-14.
    2. Assareh, A. and Amiri, E., 2015. The frequency of droughts in the Rivers Watershed and wetlands of Khuzestan province on The Basis of different parameters. Journal of wetland ecobiology 7, 91-102.
    3. Behrang Manesh, M., Khosravi, H., Azarnivand, H. and Alfonso, S., 2020. Quantifying The Trend Of Vegetation Changes Using Remote Sensing (Case Study: Fars Province). Journal of Plant Ecosystem Conversation 7, 295-318.
    4. Bernstein, L. S., Jin. X., Gregor, B. and Adler-Golden, S. M., 2012. Quick atmospheric correction code: algorithm description and recent upgrades. Optical Engineering 51, 111719-1-111719-11.
    5. Chen, Y., Sun, L., Liu, K. and Pei, Z., 2019. "Drought monitoring using MODIS derived perpendicular drought indexes". 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Istanbul. Turkey.
    6. Ebrahimi Khusfi, M., Darvishzade, R., Matkan, A. and Ashourloo, D., 2010. Drought Assessment in Arid Regions Using Vegetation Indices - a Case Study of "Shirkooh of Yazd" in Central Iran. Enviromental Science 7, 59-72.
    7. Feng, H., Wang, L., Tian, J., Tian, J., Wang, J., Meng, Q., Xiong, Y. and Sheng, L. T., 2013. "The Analysis of Soil Line Accuracy Affected Drought Monitoring Accuracy". 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne. Australia.
    8. Ghulam, A., Qin, Q. and Zhan, Z., 2007a. Designing of the perpendicular drought index. Environmental Geology 52, 1045-1052.
    9. Ghulam, A., Qin, Q., Teyip, T. and Li, Z.L., 2007b. Modified perpendicular drought index (MPDI): a real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing 62, 150-164.
    10. Ghulam, A., Qin, Q., Kusky, T. M. and Li, Z-L., 2008. A re-examination of perpendicular drought indices. International Journal of Remote Sensing 29, 6037-6044.
    11. Golmehr, E., Mortazavi, M. and Parmoon, Gh., 2015. "Investigating the impact of drought on underground water resources and agricultural development in Khuzestan". The Second International Conference on Sustainable City Architecture and Culture. Tehran. Iran.
    12. Hashem Geloogerdi, S., Vali, A. and Sharifi, M. R., 2022. Investigation of Desertification Trend in the Center of Khuzestan province Using Remote Sensing Time Series Data. Iranian Journal of Soil and Water Research 52, 2743-2857
    13. Heidarian, P., Joudaki, M., Darvishi Khatoni, J. and Shahbazi, R. (2015). Recognized Dust Sources in Khuzestan Province: Ministry of Industry, Mine and Trade Geological Survey of Iran South West Regional Center.
    14. Helsel, D. R. and Frans, L. M., 2006. Regional Kendall Test for Trend. Environmental Science and Technology, 40, 4066–4073.
    16. Lahijanzadeh, A. R., Jafarzadeh Haghighifard, N., Khaksar, E. and Karimi, S., 2016. "Dust in Khuzestan province, challenges and solutions". First International Dust Conference. Ahwaz. Iran.
    17. Li, Y., Wen, Y., Lai, H. and Zhao, Q., 2020. Drought response analysis based on cross wavelet transform and mutual entropy. Alexandria Engineering Journal 59, 1223–1231.
    18. Liu, Y. and Yue, H., 2019. Remote Sensing Monitoring of Soil Moisture in the Daliuta Coal Mine Based on SPOT 5/6 and Worldview-2. Open Geosciences 11, 866- 876.
    19. Liang, L., Qiu, S., Yan, J., Shi, Y. and Geng, D., 2021. VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China. International Journal of Environmental Research and Public Health 18, 7967.
    20. Matkan, A. A., Darvishzade, R., Hosseiniasl, M., Ebrahimi Khusfi, M. and Ebrahimi Khusfi, Z., 2011. Drought Segmentation In Arid Regions Using Knowledge-Base Algorithms At Gis Environment (Case Study: Sheitoor, Yazd). Journal of Climate Research 2, 103-116.
    21. Nie, Y., Tan, Y., Deng, Y. and Yi, J., 2020. Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images. remote sensing 12, 2587.
    22. Palmer, W.C., 1965. Meteorological drought. In: US Weather Bureau Research Paper 45, 1-58.
    23. Park, Y. J., Lim, B. J. and Sung, J. H., 2018. Appraisal of drought characteristics of representative drought indices using meteorological variables. KSCE Journal of Civil Engineering 22, 2002-2009.
    24. Poussina, Ch., Massota, A., Ginzlerb, Ch., Weberb, D., Chatenoux, B., Lacroix, P., Piller, Th., Nguyen, L. and Giuliani, G., 2021. Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs. Big Earth Data 5, 445-475.
    25. Qin, Q., Ghulam, A., Zhu, L., Wang, L., Li, J. and Nan, P., 2008. Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China. International Journal of Remote Sensing 29, 1983-1995.
    26. Richardson, A. j. and Wiegand, C. L., 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43, 1541-1552.
    27. Roozbahani, M, Khazaei, K. and Hajeb, M., 2015. "Agricultural drought monitoring using MODIS sensor image series through Mann-Kendall test". The first scientific research conference of new horizons in geography and planning sciences, architecture and urban planning of Iran. Tehran. Iran.
    28. Shahabfara, A., Ghulamb, A. and Eitzingera, J., 2012. International Journal of Applied Earth Observation and Geoinformation 18, 119-127.
    29. Tao, L., Ryu, D., Western, A. and Boyd, D., 2021. A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data. remote sensing 13, 122.
    30. United Nations Office for Disaster Risk Reduction 2021. GAR Special Report on Drought 2021. Geneva.
    31. Varghese, D., Radulović, M., Stojković, S. and Crnojević, V., 2021. Reviewing the Potential of Sentinel-2 in Assessing the Drought. remote sensing 13, 3355.
    32. Wang, Y., Zhang, J., Tong, S. and Guo, E., 2017. Monitoring the trends of aeolian desertified lands based on time-series remote sensing data in the Horqin Sandy Land, China. Catena 157, 289-298.
    33. West, H., Quinn, N. and Horswell, M., 2019. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment 232, 111291.
    34. Yang, X., Qin, Q., Yao, Y. and Zhao, S., 2011. Comparison and application of PDI and MPDI for drought monitoring in Inner Mongolia.

    36. Zormand tarzjani, S., 2014. Drought Monitoring Using Remote Sensing and Climatic Indicesin Khorasan Razavi Province. M.Sc. thesis, Isfahan University of Technology. 109 pp