Seagrass meadows contribute to multiple ecosystem services in the marine environment. Despite their ecological importance and the services they provide, seagrass habitats globally have shown a major decline. To understand this loss, in this study, we demonstrate the potential of a multi-rotor and a multispectral sensor for spatial assessment and monitoring for seagrass meadows in Waitemata Harbour, Auckland, New Zealand. Initially, we used structure from motion photogrammetry to generate a high-spatial-resolution orthomosaic of 3.5 cm/pixel. Next, we compared seagrass reflectance from a handheld field spectroradiometer to the reflectance extracted from the RPAS multispectral orthomosaic. This reflectance information was then used to create a spectral index. After that, we used an object-based image analysis (OBIA) technique to segment the orthomosaic and perform a supervised classification. Consequently, an overall accuracy of 95% and a Kappa Coefficient of 0.81 was achieved. This high accuracy is attributed to the sensor's capability to capture high-resolution imagery beyond the visible spectrum as the ability to distinguish meadows from other land cover features was improved. Similarly, researchers elsewhere can gain valuable insights to observe local changes and identify drivers of change. The technique used in this research can directly improve our understanding of seagrass spatial dynamics for conservation and planning.