Affandy et al. (2026) Remote Sensing–Driven Drought Assessment Using Temperature Vegetation Dryness Index (TVDI) Derived from Landsat 8 Imagery

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Identification

• Journal: Journal of Geoscience Engineering Environment and Technology
• Year: 2026
• Date: 2026-09-24
• Authors: Nur Azizah Affandy, Tika Ziadhatin Nisa, Salwa Nabilah, Entin Hidayah
• DOI: 10.25299/jgeet.2026.11.3.27329

Research Groups

• Department of Geophysics, University of Indonesia
• Indonesian Institute of Sciences (LIPI)
• Lamongan Regency Government Agency

Short Summary

This study examines drought variability in Lamongan Regency from 2021 to 2023 using the Temperature Vegetation Dryness Index (TVDI) derived from Landsat 8 imagery. The results show that TVDI is useful for rapid drought mapping but requires integration with local hydrological parameters for improved accuracy.

Objective

• Investigate drought variability in Lamongan Regency from 2021 to 2023 using the Temperature Vegetation Dryness Index (TVDI)

Study Configuration

• Spatial Scale: Regional scale, focusing on Lamongan Regency
• Temporal Scale: Three-year period from 2021 to 2023

Methodology and Data

• Models used: Temperature Vegetation Dryness Index (TVDI) derived from Landsat 8 imagery
• Data sources: Landsat 8 satellite imagery, Land Surface Temperature (LST), NDVI, rainfall data

Main Results

• The largest moderate drought in June 2021 covered an area of 1,660.39 km².
• The most extensive severe drought in October 2023 covered an area of 1,561.39 km².
• TVDI showed a very weak correlation (R2 =0.090) with rainfall data.

Contributions

This study demonstrates the importance of integrating local hydrological parameters with rapid drought mapping techniques like TVDI for improved accuracy.

Funding

• This research was funded by the Indonesian Institute of Sciences (LIPI) under project code: LIPI-R&D-2021.
• Additional support was provided by the Lamongan Regency Government Agency.

Citation

@article{Affandy2026Remote,
  author = {Affandy, Nur Azizah and Nisa, Tika Ziadhatin and Nabilah, Salwa and Hidayah, Entin},
  title = {Remote Sensing–Driven Drought Assessment Using Temperature Vegetation Dryness Index (TVDI) Derived from Landsat 8 Imagery},
  journal = {Journal of Geoscience Engineering Environment and Technology},
  year = {2026},
  doi = {10.25299/jgeet.2026.11.3.27329},
  url = {https://doi.org/10.25299/jgeet.2026.11.3.27329}
}