Badapalli (2025) Integrating multi-temporal remote sensing and advanced drought modeling to assess desertification dynamics in semi-arid Andhra Pradesh, India: A framework for sustainable Land management

Identification

• Journal: Remote Sensing Applications Society and Environment
• Year: 2025
• Date: 2025-07-07
• Authors: Pradeep Kumar Badapalli
• DOI: 10.1016/j.rsase.2025.101654

Research Groups

• CSIR–National Geophysical Research Institute (NGRI), Hyderabad, India.

Short Summary

The study develops a framework integrating multi-temporal remote sensing and drought modeling to assess desertification in semi-arid Andhra Pradesh, India, finding a significant increase in degraded and desertified land between 1990 and 2020.

Objective

• To evaluate the spatiotemporal progression of land degradation by analyzing vegetation response to drought stress over a 30-year period (1990–2020).

Study Configuration

• Spatial Scale: Semi-arid landscapes of Andhra Pradesh, India (with specific focus on the western region and the Hagari River).
• Temporal Scale: 30 years (1990–2020).

Methodology and Data

• Models used: Standardized Precipitation Index (SPI) for drought intensity/frequency; Normalized Difference Vegetation Index (NDVI) for vegetation cover changes; Desertification Status Map (DSM) for integration.
• Data sources: CHIRPS satellite-based precipitation data; Landsat imagery (TM, ETM+, and OLI/TIRS); 120 ground truth points for validation.

Main Results

• Land Classification: The Desertification Status Map (DSM) categorized the area into:
  • Highly Safe: $79.45\text{ km}^2$
  • Safe: $248.54\text{ km}^2$
  • Degraded: $320.39\text{ km}^2$
  • Desertified Land: $402.57\text{ km}^2$
• Spatial Trends: Significant increase in degraded and desertified areas, particularly in the western region and along the Hagari River, attributed to prolonged drought and aeolian activity.
• Accuracy: The DSM achieved an overall validation accuracy of 87.5%.

Contributions

• Proposes a scalable, data-driven framework for monitoring desertification dynamics that integrates climatic (SPI) and vegetative (NDVI) indices, providing a tool for sustainable land management in vulnerable semi-arid ecosystems.

Funding

• Not specified in the provided text.

Citation

@article{Badapalli2025Integrating,
  author = {Badapalli, Pradeep Kumar},
  title = {Integrating multi-temporal remote sensing and advanced drought modeling to assess desertification dynamics in semi-arid Andhra Pradesh, India: A framework for sustainable Land management},
  journal = {Remote Sensing Applications Society and Environment},
  year = {2025},
  doi = {10.1016/j.rsase.2025.101654},
  url = {https://doi.org/10.1016/j.rsase.2025.101654}
}