Abstract: With increasing threats to forest resources, there is a growing demand for accurate, timely, and quantitative information on their status, trends, and sustainability. Satellite remote sensing provides an effective means of consistently monitoring large forest areas. Vegetation Indices (VIs) are commonly used to assess forest conditions, but their effectiveness remains a key issue. This study aimed to assess and map forest degradation status and trends in the Lagawa locality, West Kordofan State, Sudan, using the Soil-Adjusted Atmospheric Resistant Vegetation Index (SARVI), quantify the relationship between SARVI and the Normalized Difference Vegetation Index (NDVI), and compare the efficiency of both indices in detecting and monitoring changes in forest conditions. The study utilized four free-cloud images (TM 1988, TM 1998, TM 2008, and OLI 2018) that were processed using GEE to derive the indices. The study found significant forest degradation over time, with 63% of the area categorized as moderately to severely degraded. A strong, positive relationship between SARVI and NDVI (R² = 0.9085, P <0.001) was identified, indicating both are effective in detecting forest changes. Both indices proved effective, cost-efficient, and applicable for monitoring forest changes across Sudan's drylands. The study recommends applying similar methods in other arid regions.