Standardized benthic imaging with small, modular ROVs: an open-source framework developed in temperate kelp forests

The ocean floor beneath temperate kelp forests is a world of immense biological importance, teeming with fish, invertebrates, and complex plant life that supports entire coastal ecosystems. Yet, studying this hidden realm is notoriously difficult. While the floating tops of kelp forests can be seen from satellites or airplanes, the life growing on the seafloor remains obscured by dark, murky water and the dense, vertical structure of the kelp itself. For decades, scientists have relied on human divers to swim these underwater gardens, counting species and mapping habitats. However, divers are limited by how long they can safely stay underwater, how deep they can go, and how much ground they can cover in a single day. To truly understand these fragile environments, researchers need a way to see more, for longer, and with greater consistency than human legs and lungs allow.

A team of researchers at the Seattle Aquarium has developed a practical, open-source solution to this problem: a small, modular underwater robot designed specifically to take standardized photographs of the seafloor in these challenging conditions. Rather than building a massive, expensive machine for deep-sea exploration, they adapted a compact, affordable robot to work in the shallow, turbulent waters of kelp forests. Over four years, they iterated through four different versions of the robot, refining its lights, power systems, and navigation tools to solve the specific problems of taking clear pictures in dark, moving water. Their work resulted in a complete, freely available framework that allows other scientists and conservationists to replicate these surveys, turning a complex engineering challenge into a repeatable method for monitoring the health of coastal ecosystems.

The core of the challenge lies in the environment itself. Kelp forests are often dark and filled with suspended particles that scatter light, making it hard to capture clear images. Furthermore, the kelp plants grow from the seafloor all the way to the surface, creating a dense forest that can easily tangle a robot's tether. To get a useful picture, the robot must hover at a perfectly consistent height above the ground. If it flies too high, the image is too dim and blurry; if it flies too low, the lights create uneven shadows or the robot stirs up sediment. The researchers found that simply holding a steady depth relative to the surface was not enough, because the seafloor itself rises and falls. They needed the robot to follow the contours of the bottom, maintaining a fixed distance from the ground regardless of whether it was flying over a flat sandy patch or a rocky ridge.

To achieve this, the team developed a specialized flight mode called "surftrak," which allows the robot to sense the distance to the seafloor and automatically adjust its height to stay at a precise target, usually less than a meter above the bottom. This system proved remarkably effective, keeping the robot within a very tight margin of error for the vast majority of its flight time. This consistency is crucial because it ensures that every photograph covers the exact same amount of seafloor, allowing scientists to count organisms and measure their coverage with mathematical precision across different sites and times. Without this automated altitude control, the data collected would be too variable to compare reliably.

Lighting was the next major hurdle. In the dark, murky waters of the Pacific Northwest, standard underwater lights were insufficient to capture the fine details needed to distinguish between different types of algae and small marine life. The researchers tested several lighting setups, eventually moving from simple battery-powered lights to a system where the robot was powered by a large battery bank on the surface ship. This allowed them to use much brighter, more powerful lights that could be controlled directly by the robot's computer. They also redesigned the placement of these lights to focus the beam exactly where the camera was looking, reducing the glare caused by particles in the water. The result was a system capable of taking high-resolution, sharp images even in water with very low visibility, revealing textures and species that would have been invisible in earlier, less sophisticated surveys.

Powering the robot for long survey sessions presented another set of trade-offs. Early versions of the robot relied on internal batteries, which limited flight time and required the robot to be brought back to the surface to swap batteries—a process that interrupted data collection and required restarting the entire system. The team eventually switched to a surface-supplied power system, where electricity is sent down the tether from the boat. This change dramatically extended the robot's endurance, allowing it to work for hours without interruption and handle the extra power demands of the bright lights and complex navigation sensors. While this surface power system required a larger initial investment in equipment, it proved essential for conducting the repeated, long-duration surveys needed for rigorous scientific monitoring.

Navigation in shallow, rocky waters is notoriously difficult for underwater robots. Traditional systems that use sound waves to locate the robot relative to a boat often struggle in these environments because the shallow depth and rocky terrain create "acoustic shadows" that block the signals. The researchers found that relying on a single navigation method was not enough. Instead, they combined data from multiple sensors: a device that measures the robot's speed over the bottom, a system that tracks its position relative to the boat, and a compass. By fusing these different streams of information, the robot could build a much more accurate picture of where it was and where it had traveled. This sensor fusion allowed them to reconstruct the exact path the robot took, which is vital for mapping the surveyed area and ensuring that the data can be placed on a map with high precision.

To make this technology accessible to non-experts, the team built two free computer programs that handle the heavy lifting of data management. One program guides the pilot through the dive, checking that all sensors are working before the robot leaves the boat and logging every detail of the flight. The other program takes the raw photos and video from the robot and automatically organizes them, corrects the lighting, and prepares them for analysis. These tools reduce the time and technical expertise required to turn a day of diving into a set of usable scientific data, making it possible for smaller organizations and conservation groups to adopt this method.

The researchers tested their system across dozens of survey days in various locations, including Elliott Bay and the San Juan Islands, covering a wide range of conditions from calm, clear days to windy, turbulent waters with currents up to three knots. They successfully mapped soft sediments, rocky reefs, and areas with dense kelp, demonstrating that the system works in the real-world environments where kelp forests actually exist. They found that while the robot could not penetrate the densest parts of the kelp canopy without getting tangled, it could effectively survey the seafloor beneath the kelp and in areas where the canopy was thinner. The system proved robust enough to handle the physical challenges of the environment, including the risk of the tether getting caught on kelp fronds near the surface.

This work does not replace human divers, who remain essential for observing certain behaviors and navigating the most complex parts of the kelp forest. Instead, the robot provides a complementary window into the underwater world, expanding the area that can be surveyed and allowing for consistent, repeatable monitoring over time. By providing a complete, open-source guide to the hardware, software, and methods used, the team has created a starting point for others to build upon. Their framework offers a practical path forward for expanding the monitoring of kelp forests and other shallow coastal ecosystems, ensuring that these vital habitats can be watched and protected with the clarity and consistency they deserve.