As discussed in the previous part of this review paper, Remote Sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review paper was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR), High-Frequency (HF) radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review paper provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.

Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.