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Assessing Meteorological Drought and Detecting LULC Dynamics at a Regional Scale Using SPI, NDVI, and Random Forest Methods

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Abstract

The current research explores the relationship between meteorological drought and land-cover changes locally via the normalized difference vegetation index (NDVI) and standard precipitation index (SPI). The historical time-series dataset of rainfall from 1981 to 2018 was used to compute the SPI, whereas the NDVI and land use/land cover (LULC) for the year 2016–2018 were calculated from the Sentinel-2 dataset of the studied region. The positive (r = 0.82, r = 0.79, and p = 0.001) and negative (r = 0.51 and p = 0.087) correlations were observed between NDVI and SPI data during 2016–2018. The 1 month scale of the SPI was positively correlated with NDVI. It was noticed that the maximum and minimum correlations occurred during the starting and end of the growing period, respectively. The multiple regression models were developed based on the correlation coefficients to predict the NDVI and investigate the relationship between the NDVI and SPI. The models have predicted accuracy (R2) of 0.68, 0.63, and 0.26 for the normal (2016), moderate (2017), and severe drought (2018) years, respectively. The drastic changes in an LULC were noticed during the regular and severe drought years.

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Acknowledgements

The authors would like to thank the Indian Meteorological Department (IMD), the Government of India, for providing the precipitation data. The authors would like to also thank ESA for providing the satellite data.

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Authors and Affiliations

  1. Department of Computer Science and Applications, Charotar University of Science and Technology, Changa, Gujarat, 388421, India

    Sandeep V. Gaikwad

  2. Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, MH, 411016, India

    Amol D. Vibhute

  3. Dr. Babasaheb Ambedkar Technological University, Lonere, MH, 402103, India

    Karbhari V. Kale

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  1. Sandeep V. Gaikwad
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Contributions

SVG: conceptualization, data curation, methodology, and writing—original draft. ADV: investigation, methodology, modeling, experiments, validation, and writing—review & final editing. KVK: supervision, technical advice, and scientific validation. All authors read and approved the final manuscript.

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Correspondence to Amol D. Vibhute.

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This article is part of the topical collection “Soft Computing and its Engineering Applications” guest edited by Kanubhai K. Patel, Deepak Garg, Atul Patel and Pawan Lingras.

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Gaikwad, S.V., Vibhute, A.D. & Kale, K.V. Assessing Meteorological Drought and Detecting LULC Dynamics at a Regional Scale Using SPI, NDVI, and Random Forest Methods. SN COMPUT. SCI. 3, 458 (2022). https://doi.org/10.1007/s42979-022-01361-0

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