Compact-Morphology-based poly-metallic Nodule Delineation

Abstract

1Poly-metallic nodules are a marine resource considered for deep sea mining. Assessing nodule abundance is of interest for mining companies and to monitor potential environmental impact. Optical seafloor imaging allows quantifying poly-metallic nodule abundance at spatial scales from centimetres to square kilometres. Towed cameras and diving robots acquire high-resolution imagery that allow detecting individual nodules and measure their sizes. Spatial abundance statistics can be computed from these size measurements, providing e.g. seafloor coverage in percent and the nodule size distribution. Detecting nodules requires segmentation of nodule pixels from pixels showing sediment background. Semi-supervised pattern recognition has been proposed to automate this task. Existing nodule segmentation algorithms employ machine learning that trains a classifier to segment the nodules in a high-dimensional feature space. Here, a rapid nodule segmentation algorithm is presented. It omits computation-intense feature-based classification and employs image processing only. It exploits a nodule compactness heuristic to delineate individual nodules. Complex machine learning methods are avoided to keep the algorithm simple and fast. The algorithm has successfully been applied to different image datasets. These data sets were acquired by different cameras, camera platforms and in varying illumination conditions. Their successful analysis shows the broad applicability of the proposed method.Poly-metallic nodules (PMNs -also referred to as manganese nodules) are a marine mineral resource. They contain relevant concentrations of Copper, Nickel and Cobalt 1 . Most PMN research and resource assessment focusses on the deep abyssal plains in the eastern Pacific Ocean. Economically interesting areas were found within the Clarion Clipperton Zone (CCZ) where several countries are exploring PMN claims. These are license areas provided to the countries through the International Seabed Authority.International research projects are investigating aspects of nodule mining (Ecological Aspects of Deep-Sea Mining: https://jpio-miningimpact.geomar.de; Managing Impacts of Deep-Sea Resource Exploitation: https://www.eu-midas.net/). Current foci of these research projects lie on environmental aspects: e.g. epifauna associated with PMNs, mining-induced habitat disturbances and the resilience of the ecosystem to cope with this anthropogenic pressure 2,3 . Quantitative measurements of PMN abundance are required for various applications. Individual nodule sizes (in cm 2 ) provide basic measurements that can be aggregated to create descriptive spatial statistics. Examples are PMN density (nodules/m 2 ) and seafloor coverage (in %) that data can related to environmental parameters, like faunal densities or chemical gradients.Quantitative nodule information is traditionally provided by two methods. First, by physical sea floor sampling, e.g. using box cores. Second, by hydro-acoustic sensors, e.g. Multi-beam Echo Sounder (MBES) or Side Scan Sonar (SSS). Physi...

Monitoring mosaic biotopes in a marine conservation zone by autonomous underwater vehicle

“…() assessed ecological variation over a manganese nodule occurrence gradient in the Clarion‐Clipperton Zone (northeast Pacific), partitioning their data by seafloor nodule coverage with an automated detection technique (Schoening et al. ); and Simon‐Lledó et al. () assessed the long‐term impact of simulated deep‐sea mining in the Peru Basin (southeast Pacific) by segregating their data on proximity to 26‐year‐old seabed plough marks.…”

“…), the characterization of manganese nodule fields (Schoening et al. ), and the identification and coverage estimation of kelp forests (Marzinelli et al. ), corals, and macroalgae (Monk et al.…”

Ecology of a polymetallic nodule occurrence gradient: Implications for deep‐sea mining

“…The CoMoNoD algorithm calculates the size of each nodule (i.e., seafloor exposed area) detected in an image, enabling the calculation of descriptive nodule statistics. Note that it is currently not possible to directly relate the image‐based assessments of seabed nodule cover with those made by direct sampling methods (Schoening et al ; Gazis et al ). Megafauna specimens were identified to the lowest taxonomic level possible, and their physical dimension was measured, using BIIGLE 2.0 (Langenkämper et al ).…”

Understanding Mn-nodule distribution and related deep-sea mining impacts using AUV-based hydroacoustic sensing and optical observations

“…The data used in this work is available in Pangaea. This includes MBES data Greinert (2016), optical imagery Greinert et al (2017) and image-derived nodule abundances Schoening (2017a, b).…”

“…Photos were automatically 30 analyzed for the Mn-nodule coverage on the seafloor (percent coverage) and nodule size distribution (different quantiles in cm2). The 'Compact Morphology Nodule Delineation' algorithm was used for this task Schoening et al (2017). This nodule delineation method is based on color differences between the nodules and the sediment background.…”