The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

Abstract

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

“…It can then estimate the canopy area, density, and tissue nitrogen content based on time and space scales, which are significant to observe changes in kelp. To provide a natural image of the kelp forest canopy, sUAS have sensors such as color, multispectral and hyperspectral tcameras [168].…”

“…Table 8 presents the different application of unmanned vehicles for aquaculture farm monitoring and management. GoPro camera and led lights [172] Assessment of the population/ stocks of wild scallops Gavia AUV Downward-pointing digital camera [169] Telegavia UAV a Point Grey Grasshopper 14S5C/M-C model with Sony ICZ285AL CCD [170] Teledyne Gavia AUV Nose cone camera, GeoSwath phase measuring bathymetric sonar, Marine Sonic side-scan sonar) [171] Monitoring of the growth environment at the farm site Customized ROV USB camera based on LIFI [165] Offshore kelp monitoring DJI Phantom 4 Pro 20 MP (1" CMOS sensor, 84 • FOV) color camera [168] Recognition of fish species Underwater drone (type not specified) 360-degree panoramic camera with two 235-degree fisheye lenses [174] Salmon protection Underwater laser drone Stereo camera system [177] Fish cage inspection BlueROV2 of BlueRobotics Camera [175] Observation of fish behavior Customized UAV Cameras with power LEDs and water quality sensors [56] Fish tracking AggieAir…”

A review of Google Earth Engine application in mapping aquaculture ponds

“…The result obtained from this study was the ability to identify the location of most aquaculture ponds situated in and the cumulative area obtained from 1990 to 2020 is 21997.90km2, while the holding area is 9613.66km 2 . Bell et al [15] studied offshore aquaculture farm monitoring using satellites and autonomous remote sensing platforms. However, GEE was utilised only for cloud cover analysis of Landsat data.…”

A Review of the Opportunities and Challenges for Using Remote Sensing for Management of Surface-Canopy Forming Kelps

“…However, the spectral bands of most multispectral sensors are too wide to leverage these subtle changes in reflectance and assess variations in canopy pigment concentration. The narrow and contiguous spectral bands measured by hyperspectral sensors are better suited for estimating physiological properties such as Chl:C ratios (Bell et al, 2015) and tissue nitrogen content (Bell et al, 2020b).…”

“…Canopy biomass can be assessed by determining the relationship between the remote estimates of canopy density and field estimates of canopy biomass (Stekoll et al, 2006;Cavanaugh et al, 2011). Remotely sensed estimates of canopy physiology require the development of spectral algorithms established through the comparison of laboratory measured pigment concentration of kelp tissue with field or laboratory measured spectral reflectance (Bell et al, 2015(Bell et al, , 2020b. These spectral algorithms are then validated by comparing physiological estimates from the imagery to measurements in the field or by direct comparison of image and field measured reflectance spectra.…”