Assessment of agricultural drought severity using multi-temporal remote sensing data in Lorestan region

Agricultural drought is a complex phenomenon with major impacts on ecosystems, biodiversity, and ecosystem services. This study examines drought severity in Lorestan, Iran, using spatial analysis of vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Landsat images from 2004, 2014, and 2024 were processed using TerrSet software to assess drought trends. The results show a clear increase in drought severity in Lorestan over the past 20 years, with fewer areas free from drought and more regions facing mild to moderate drought conditions. The analysis of results reveals that drought-free areas changed by approximately 14.7%, while areas under mild drought increased by 6.5% and moderate drought by 8.2%. These shifts show a strong correlation with observed land degradation (bare land + 8.1%, dense vegetation − 8.5%), confirming heightened ecosystem stress through integrated drought indices. This trend indicates a growing threat to crops, water resources, and ecosystem stability. The study demonstrates the effectiveness of using vegetation indices like NDVI, VCI, TCI, and VHI for monitoring drought patterns. These tools can help detect early warning signs, allowing farmers and policymakers to take timely action. To reduce the damaging effects of drought, improved water management, sustainable farming practices, and long-term adaptation strategies are urgently needed. By incorporating these monitoring techniques into regular assessments, authorities can better prepare for and respond to future droughts, protecting both agriculture and the environment.

• Publication year

  2025

• Venue

  Scientific Reports

• Publication date

  2025-05-27

• Fields of study

  Agricultural and Food Sciences, Medicine, Environmental Science

• Identifiers
  DOI 10.1038/s41598-025-03087-4PMID 40425636PMCID 12116739
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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