Drones & ROV's
CSUCI’s new and developing Aerial and Aquatic Robotic Research team (AARR) is allowing environmental research to now be completed faster and easier than ever before. Through the use of UAVs, or more commonly known as drones, 3D maps can be created with only a single flight over the intended area. These maps produce great accuracy, and are being used for many different projects, including coastal cliff erosion, beach erosion, watershed monitoring, among other new projects that students will venture into in the future. The AARR team is focused on finding the most useful aerial equipment being released, ensuring all forms of applied research that have the potential to be improved with these new technologies have the best resources available to use. The AARR team is open to all new members willing to learn and who are passionate about science.
Student Research Highlights
Alumni Chris Wells
Monitoring MPA's with ROV's
Remotely operated vehicles (ROVs) offer a potentially more comprehensive, cheaper, and safer approach to observing and studying the vast marine environment. They may be particularly helpful across multiple depths and assessing multiple ocean health metrics around the world. Five platforms were tested to determine which was the most capable for conducting research on Santa Rosa Island, California to determine Marine Protected Area effectiveness. These platforms include three variations of OpenROV as well as two Marine Advanced Technology Education Center (MATE) platforms. Attributes of the platforms were tested to determine their functionality in the field. The OpenROV was used to explore the Carrington Point States Marine Reserve (SMR) on the northeast side of Santa Rosa Island. Locations outside of the SMR were also studied to compare data between MPAs and fished area. Camera equip OpenROVs were run along eight 20 m transects multiple times at various times of day to document the abundance of fish species. Data was also collected on factors that have the potential to influence fish abundance (percent rock cover, percent kelp canopy cover, time of day, etc.). For the analysis, multiple 2-way ANOVAs were used with the dependent variables as number of fish observed, species richness, or species density and independent variables as location (MPA vs. Non-MPA) and distance from dock (distance from MPA border). There was no statistically significant difference in fish abundance between transects, at any distance, inside the MPA compared to the Non-MPA (F= 0.872, df= 97, p= 0.459), nor significant difference in density for six of the seven fish species identified. However, species richness had a statistically significant difference across transects inside the MPA compared to transects in the Non-MPA (F= 2.778, df= 97, p= 0.046).