This study by Romy Alana Lansbergen (2019) investigates the potential of drone-based multispectral imaging for mapping and monitoring coastal intertidal habitats. Conducted in Flø, Norway, the research aimed to identify macroalgae and abiotic features using high-resolution UAV imagery and ground-truth data. Five analytical methods were tested, including four vegetation indices (NDVI, NDWI, EVI, SABI) and a multinomial logistic regression model (Mlogit). While none of the methods yielded statistically significant results, the log-transformed EVI showed promise in distinguishing habitat classes. The study highlights challenges such as high data variance, limited observations, and spectral overlap among classes. It concludes that although simple algorithms offer a useful starting point, future work should explore machine learning approaches and refined classification schemes to improve accuracy and applicability in diverse coastal environments.