This study explores the application of Particle Swarm Optimization (PSO) to enhance vegetation indices (VIs) for the assessment of woody vegetation in a semi-arid savannah ecosystem. By optimizing VIs, the research aims to improve the discrimination between vegetated and non-vegetated areas, facilitating a more accurate random forest classification for habitat quality assessment. The optimization process preserves minimum VI values across different sensors to maintain lower bounds of reflectance, ensuring ecologically valid signals are represented, particularly in low-vegetated areas. Results indicate that maximum VI values increase post-optimization, enhancing sensitivity to canopy vigor, stress, health, and presence. The study highlights the effectiveness of UAVderived indices, such as NDVI, NDRE, and SAVI, in capturing the dynamics of vegetation health and dryness, thereby contributing valuable insights into remote sensing methodologies for ecological monitoring.