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Aeolian sand migration induced land degradation and desertification hotspots identification in the semi-arid rain shadow regions of Anantapur, India

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Abstract

This study presents a multi-temporal geospatial modeling approach to identify and map land degradation and desertification hotspots in the aeolian-dominated semi-arid regions of Bommanahal, Beluguppa, and Kanekal Mandals of Anantapur District, Andhra Pradesh, India. Landsat 4–5 TM (1990), Landsat 7 ETM+ (2000, 2010), and Landsat 8 OLI/TIRS (2020) datasets were processed through a standardized workflow comprising radiometric calibration, atmospheric correction, LST retrieval, and spectral index computation. Three diagnostic indices: Normalized Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI), and Normalized Difference Salinity Index (NDSI), were integrated with Land Surface Temperature (LST) to quantify vegetation stress, soil texture variability, and salinity conditions. Correlation and regression analyses were employed to evaluate the relationships between LST and index-derived DN values, after which stratified sample extraction and buffer-based zonal statistics were used to delineate surface degradation intensity. A composite hotspot map was generated using mask extraction and cell statistics to merge the most degraded pixel clusters, identifying approximately 192 km2 as severe degradation zones. Model performance was validated using ROC-AUC analysis, yielding an accuracy of 0.851. The study demonstrates a reproducible GIS workflow for semi-arid degradation assessment and provides a robust spatial framework for targeted land restoration, sustainable resource planning, and long-term environmental management in vulnerable dryland ecosystems.

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Data availability

The raw data is obtained from NRSC Bhuvan and USGS website (https://bhuvan.nrsc.gov.in/ and https://earthexplorer.usgs.gov/) which is available free of cost and the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

The authors are grateful to the Director of the CSIR-National Geophysical Research Institute for providing the necessary facilities to conduct this work and for granting us permission to publish this paper. The first author expresses sincere gratitude to the Science and Engineering Research Board – National Post-Doctoral Fellowship (SERB-NPDF) for their valuable support (Fellowship Ref. No. PDF/2023/000774) during the tenure at CSIR-NGRI, Hyderabad. The authors also thank Editor-in-Chief/Handling Editor and anonymous reviewers for their constructive comments and valuable suggestions, which greatly improved the quality of the manuscript. This work is funded by the Main Lab Project MLP-7014 of the CSIR–National Geophysical Research Institute [Grant No. MLP-7014-28 (AKP)]. The CSIR-NGRI Library reference number is NGRI/Lib/2025/Pub-128.

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During the preparation of this work, the author(s) used Chat Gpt to improve the language of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication’s content.

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Authors and Affiliations

  1. CSIR-National Geophysical Research Institute, Uppal Road, Hyderabad, Telangana, 500007, India

    Pradeep Kumar Badapalli & Sakram Gugulothu

  2. Department of Geology, Yogi Vemana University, Kadapa, Andhra Pradesh, 516005, India

    Anusha Boya Nakkala & Raghu Babu Kottala

  3. Department of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, 515001, India

    Padma Sree Pujari

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  1. Pradeep Kumar Badapalli
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  2. Anusha Boya Nakkala
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  3. Raghu Babu Kottala
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  5. Sakram Gugulothu
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Contributions

Dr. Pradeep Kumar Badapalli: Manuscript preparation, Methodology creation and Validation. Remote Sensing and GIS mapping work. Mrs. Anusha Boya Nakkala: Manuscript statistics Generation, and correction. Dr. Raghu Babu Kottala: Manuscript corrections, English expert. Dr. Padma Sree Pujari: Manuscript corrections, data collection. Dr. Sakram Gugulothu: Manuscript corrections, corresponding author and validation.

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Sakram Gugulothu.

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Badapalli, P.K., Nakkala, A.B., Kottala, R.B. et al. Aeolian sand migration induced land degradation and desertification hotspots identification in the semi-arid rain shadow regions of Anantapur, India.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31610-0

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  • DOI: https://doi.org/10.1038/s41598-025-31610-0

Keywords

  • Land degradation hotspots
  • Spectral indices
  • Correlation
  • Buffer zones
  • GIS and remote sensing analysis

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