A Computational Sustainability Framework for Vegetation Degradation and Desertification Assessment in Arid Lands in Saudi Arabia

This paper proposes a Computational Sustainability Framework for assessing vegetation degradation and desertification in arid ecosystems. The framework integrates remote-sensing data, GIS modeling, and multi-criteria decision algorithms to analyze environmental conditions at scale. It consists of four computational phases: (1) data acquisition and preprocessing, (2) feature extraction and normalization, (3) multi-criteria decision modeling using the Analytical Hierarchy Process (AHP), and (4) visualization and validation. The framework was applied to two Saudi protected areas, Al Khunfah and Ḩorrah al-Ḥorra, using Sentinel-2, Landsat-8, and ancillary datasets. Results show that high degradation zones correspond to high BSI (>0.27) and low NDVI (<0.09) with surface temperatures exceeding 48 °C. The AHP model achieved a consistency ratio of 0.0957, confirming reliability, and identified three suitability classes covering approximately 4 000 km² (high), 26 000 km² (moderate), and 6 000 km² (low). The study demonstrates how computational integration of spatial data and decision algorithms can support large-scale environmental monitoring and restoration planning.