This paper presents a novel approach for Visual Servoing (VS) using a multispectral camera, where the number of data are more than three times that of a standard color camera. To meet real-time feasibility, the multispectral data captured by the camera are processed using dimensionality reduction techniques. Instead of relying on traditional approaches that select a subset of bands, the proposed method unlocks the full potential of a multispectral camera by pinpointing individual pixels that hold the richest information across all bands. While sacrificing spectral resolution for enhanced spatial resolution -crucial for precise robotic control in forested environments -this fusion process offers a powerful tool for robust and real-time VS in natural settings. Validated through simulations and real-world experiments, the proposed approach demonstrates its efficacy by leveraging the full spectral information of the camera while preserving spatial details.