Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning.

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.