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Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran)

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  • Omidreza Mikaili

    (K. N. Toosi University of Technology)

  • Majid Rahimzadegan

    (K. N. Toosi University of Technology)

Abstract

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

Suggested Citation

  • Omidreza Mikaili & Majid Rahimzadegan, 2022. "Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2511-2529, April.
    • Handle: RePEc:spr:nathaz:v:111:y:2022:i:3:d:10.1007_s11069-021-05146-1
    DOI: 10.1007/s11069-021-05146-1
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    References listed on IDEAS

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    1. Ziqi Yan & Yapeng Zhang & Zuhao Zhou & Ning Han, 2017. "The spatio-temporal variability of droughts using the standardized precipitation index in Yunnan, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(2), pages 1023-1042, September.
    2. Robert V. Rohli & Nazla Bushra & Nina S. N. Lam & Lei Zou & Volodymyr Mihunov & Margaret A. Reams & Jennifer E. Argote, 2016. "Drought indices as drought predictors in the south-central USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1567-1582, September.
    3. Sanjay Jain & Ravish Keshri & Ajanta Goswami & Archana Sarkar, 2010. "Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(3), pages 643-656, September.
    4. Iman Khosravi & Yaser Jouybari-Moghaddam & Mohammad Reza Sarajian, 2017. "The comparison of NN, SVR, LSSVR and ANFIS at modeling meteorological and remotely sensed drought indices over the eastern district of Isfahan, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1507-1522, July.
    5. Mohammad Mokhtari & Robiah Adnan & Ibrahim Busu, 2013. "A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1249-1274, February.
    6. Dai, Meng & Huang, Shengzhi & Huang, Qiang & Leng, Guoyong & Guo, Yi & Wang, Lu & Fang, Wei & Li, Pei & Zheng, Xudong, 2020. "Assessing agricultural drought risk and its dynamic evolution characteristics," Agricultural Water Management, Elsevier, vol. 231(C).
    7. Watinee Thavorntam & Netnapid Tantemsapya & Leisa Armstrong, 2015. "A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 1453-1474, July.
    8. Iman Khosravi & Yaser Jouybari-Moghaddam & Mohammad Reza Sarajian, 2017. "Erratum to: The comparison of NN, SVR, LSSVR and ANFIS at modeling meteorological and remotely sensed drought indices over the eastern district of Isfahan, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1523-1523, July.
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    2. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2022. "Assessing and Predicting the Vulnerability to Agrometeorological Drought Using the Fuzzy-AHP and Second-order Markov Chain techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4403-4424, September.
    3. Abdol Rassoul Zarei & Marzieh Mokarram & Mohammad Reza Mahmoudi, 2023. "Comparison of the capability of the Meteorological and Remote Sensing Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 769-796, January.

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