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

Abstract

This study aims to develop a robust framework for assessing desertification dynamics in the semi-arid landscapes of Andhra Pradesh, India, by integrating multi-temporal remote sensing data with advanced drought modeling. The primary objective is to evaluate the spatiotemporal progression of land degradation by analyzing vegetation response to drought stress over a 30-year period (1990–2020). The Standardized Precipitation Index (SPI) was calculated using RStudio at 3-, 6-, 9-, and 12 - months intervals based on rainfall data derived from CHIRPS satellite-based precipitation, to characterize drought intensity and frequency. Concurrently, Landsat imagery (TM, ETM+, and OLI/TIRS) was processed to generate Normalized Difference Vegetation Index (NDVI) time series to assess vegetation cover changes. A Desertification Status Map (DSM) was prepared by integrating SPI metrics with NDVI-based land cover classifications for the years 1990, 2000, 2010, and 2020. The DSM classified the landscape into four severity categories: Highly Safe (79.45 km²), Safe (248.54 km²), Degraded (320.39 km²), and Desertified Land (402.57 km²). Results highlight a significant increase in degraded and desertified areas, particularly in the western region and along the Hagari River, driven by prolonged drought, vegetation loss, and aeolian activity. Validation of the DSM using 120 ground truth points and high-resolution overlays achieved an overall accuracy of 87.5 % confirming classification reliability. The proposed framework offers a scalable tool for monitoring desertification and supports data-driven planning for sustainable land management, particularly in vulnerable semi-arid ecosystems affected by climate variability and anthropogenic pressures.