The larger extent of impacted area from CoMoNoD results largely coincides with two North-South-oriented depressions apparent in AUV bathymetry data. The western one of these two impact extensions reaches beyond the area imaged by OFOS. This results in the sharp cut-off of the grey lines visible towards the lower end of Figure 4. It is likely, that the impacted area extends beyond the surveyed area. This finding of extended blanketing along slopes is in line with other observations6.
The partly overlapping results of manual and automated image analysis indicate that the human annotators were likely influenced in their annotation decisions by the visual representation of the parameters determined by the CoMoNoD algorithms. Anyhow, mapping the extent of the blanketing requires an objective method that is not influenced by tiredness, distraction, the person currently annotating or else. This was apparent in the CoMoNoD results that correctly determined areas showing blanketing outside of the areas determined by manual annotation. Relying on the manual interpretation would have underestimated the plume impact at the seafloor.
While the blanketing extent could be assessed, this method cannot quantify the suspended plume. Other works on an acoustic and optical sensor array are being published elsewhere to report on that aspect of this plume (Halboom et al., in review).
Additionally, the presented method only allows to quantify macroscopic effects and does not provide information on impacts on meio-, macro- or in-fauna.
This study was limited by several technical failures that altered the monitoring capability as well as the intended scope of the plume mapping exercise8. It was planned to monitor the first industry-scale deep-sea mining activity using the Patania II vehicle. Due to technical issues, this could not be achieved and the small-scale dredge plume simulation was executed instead. As a result, the remobilized sediment amounts were much lower than previously planned.
In addition, it was initially planned to conduct the imaging using an AUV. Unfortunately, this AUV also suffered technical failure, resulting in no recorded image data. Using the OFOS was the backup solution and proved to be effective in creating the required data and results. Anyhow, an OFOS system cannot efficiently be deployed to monitor a contiguous area, resulting in excessive use of ship time to facilitate the mapping.
Due to the reduced amount of remobilized sediment, the plume simulation was undersized regarding the SLIC box design. The recorded blanketing of >1 mm, yet less than 1 cm, was outside of the initially planned scope of the low-cost sediment traps. It was also evident, that the corrugations were not steep or slippery enough to steer all particles to the bottom of the corrugation troughs. At the same time, the design using cheap corrugated sheet proved successful only in parts, as some sediment remained on top of the ridges of the corrugations. For a future design of low-cost blanketing quantification, we recommend to replace one of the corrugations with a saw tooth-style sheet that features straight slopes rather than the sigmoidal slopes of the corrugations. This will also ease the computation of redeposited sediment volumes. The corrugation from the other half of the SLIC boxes should be replaced by a flat checkerboard of black and white squares. Faint sediment blanketing can be assessed by inspecting color change. In addition, the checkerboard pattern enables highly-demanded in-situ camera calibration for increased accuracy for seafloor mosaicking or biomass measurements.
Manual live annotation for plume impacts can be achieved at real-time speed, in this case requiring 27 hours to complete. The results are affected by annotation bias though. Application of the CoMoNoD method resulted in objective blanketing maps with high efficiency. Computing the CoMoNoD results for individual images requires ca 15 minutes on a GPU compute cluster. The following map creation is an interactive process and achievable in less than 30 minutes.
To speed this process up even further, we are currently migrating the image analysis capability from the GPU cluster into a GPU compute-enabled camera system for edge computing. This will allow to compute – and acoustically transmit – nodule detection numbers to the sea surface live during deployments. This camera system should ideally be operated on an AUV but the technology can be deployed on other platforms as well. Similarly, the approach presented here can not only be applied to monitor mining impacts by plumes but can also be used to assess other seafloor plumes, e.g. created by benthic storms, landslides, submarine canyon downslope transport events. The prerequisite however is an extensive pre-disturbance image survey and the deployment of sediment traps for quantification.
Mapping the extent of plumes should be done by several AUVs. These platforms can effectively and efficiently monitor the spatial and temporal aspects of plume distribution. By operating turbidity meters and acoustic backscatter sensors on AUVs, the suspended plume can be mapped in 4D. By operating camera systems with GPU compute-capacity, seafloor blanketing can be measured in-situ. In combination with low-cost, or even disposable, sediment traps designed for image-based information retrieval, a tool set exists that can provide information on baselines, resource assessment and, most importantly, enables plume monitoring. Several AUVs should be operated in parallel to prevent critical technical failure by relying on a single platform and to further increase monitoring efficiency.
We recommend including extensive imaging with AUVs into the requirements for mining companies, both to enable baseline assessment as well as monitoring of active and past mining operations. Not only are images a valuable and credible information source for these use cases, but also inherently accessible and appealing to humans in fostering excitement and interest in ocean processes and ocean narratives. We hope, image data can also see and create an increased involvement in ocean governance.
This study adds another indication that mining-related impacts need to be quantified properly. Thorough reliability testing of all deep-sea mining monitoring methods must be achieved before mining activities may commence. A precautionary principle has to be applied in either case as all methods may suffer from unforeseen bias. Despite being a community standard, manual annotation did underestimate the impacted area by ca. fifty percent in this study. Future improvements in automated image analysis capacity may reveal an even larger impacted area with unknown effects to the deep-sea habitat.