Habitat classification is an important ecological activity used to monitor environmental biodiversity. Current classification techniques rely heavily on human surveyors and are laborious, time consuming, expensive and subjective. In this paper, we approach habitat classification as an automatic image annotation problem. We have developed a novel method for annotating ground-taken photographs with the habitats present in them using random projection forests. For this purpose, we have collected and manually annotated a geo-referenced habitat image database with over 1000 ground photographs. We compare the use of two different types of input (blocks within images and the whole images) to classify habitats. We also compare our approach with a popular random forest implementation. Results show that our approach has a lower error rate and it is able to classify three habitats (Woodland and scrub, Grassland and marsh, and Miscellaneous) with a high recall.