Multispectral drone sensors enable fine-scale ecological mapping, but added bands can inflate processing costs. We evaluated the MicaSense RedEdge-MX Red and Blue cameras (5 bands each) versus the Dual Camera System (10 bands) for vegetation mapping in two salt marsh sites in Aulac, New Brunswick, Canada (24 classes at the reference site; 15 at the restoration site). Pixel-based Random Forest (RF) classifications were used to compare validation accuracy, variable importance, and processing time for stitching and classification. Five-band maps achieved up to 95% validation accuracy; the 10-band configuration improved accuracy by ≤2%. Band contributions were site dependent: the near-infrared (NIR) band from the Red camera aided classification at the reference site, whereas additional red-edge bands in the Blue/Dual setups improved performance at the restoration site. However, stitching time rose sharply for the Blue and Dual systems, and RF classification time scaled with data volume and class complexity. Overall, the 5-band Red camera provided a cost-effective balance of accuracy and efficiency, offering practical guidance for sensor selection in drone-based salt marsh monitoring.