Schoening, Timm , Bodur, Yasemin and Köser, Kevin (Submitted) Automated Image Analysis to Map the Extent of Deep-Sea Mining Plumes Reveals a Larger Impacted Area Compared to Manual Analysis. Scientific Reports . DOI 10.21203/rs.3.rs-1173923/v1.

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Abstract

Deep sea mining for poly-metallic nodules impacts the environment in many ways. A key potential hazard is the creation of a sediment plume from resuspending sediment during seabed mining. The resuspended matter disperses with currents but eventually resettles on the seabed. Resettling causes a blanketing of the seafloor environment, potentially causing harm to in-, epi- and hyperbenthic communities with possible cascading effects into food webs of deep sea habitats. Mapping the extent of such blanketing is thus an important factor in quantifying potential impacts of deep-sea mining.One technology that can assess seabed blanketing is optical imaging with cameras at square-kilometre scale. To efficiently analyse the resulting Terabytes of image data with minimized bias, automated image analysis is required. Moreover, effective quantitative monitoring of the blanketing requires ground truthing of the image data. Here, we present results from a camera-based monitoring of a deep-sea mining simulation combined with automated image analysis using the CoMoNoD method and low-cost seabed sediment traps for quantification of the blanketing thickness. We found that the impacted area was about 50 percent larger than previously determined by manual image annotation.

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