Intertidal macroalgae play a pivotal role in marine ecosystems. Therefore, monitoring their distribution is crucial for resource management and ecosystem conservation. However, traditional observation methods are laborious and resource demanding. We aimed to develop an automatic classification methodology of macroalgae using high-resolution Unmanned Aerial Vehicles (UAV) imagery and Haralick Textural Features (HTFs). A supervised classifier was trained with labelled RGB orthoimages acquired in a rocky intertidal area at Galicia, Spain. These images were classified at both species and phylum levels, using a superpixel-based algorithm that was trained with labelled data. Results indicated a validation accuracy of 75% for species and 86% for phylums. However, achieving high accuracy at the species level proved challenging due to species similarity and superpixel complexity. Future work could improve the accuracy by refining the superpixels and expanding the labelled dataset. Our approach offers a useful tool for the quantification of heterogeneous macroalgae cover in rocky intertidal areas, with room for refinement to improve classification precision.