This thesis explores the impact of pre-processing on multispectral images for environmental monitoring using a VTOL UAV equipped with hyper/multi-spectral payloads. The images are aligned with a homography matrix, standardized through radiance correction, and adjusted for environmental factors using reflectance correction. Despite initial concerns, image alignment performed well, and radiance correction provided consistent values.

Reflectance correction was effective within certain ranges but required adjustments for low reflectance. Detection algorithms, particularly EVI corrected for radiance, successfully identified vegetation, though further processing is needed to integrate multispectral and hyperspectral data. The findings underscore the importance of pre-processing in enhancing multispectral data quality, with future research needed to refine these techniques and improve the synergy between multispectral and hyperspectral imaging.