Abstract
The number and areal extent of marine protected areas worldwide is rapidly increasing as a result of numerous national targets that aim to see up to 30% of their waters protected by 2030. Automated seabed classification algorithms are arising as faster and objective methods to generate benthic habitat maps to monitor these areas. However, no study has yet systematically compared their repeatability. Here we aim to address that problem by comparing the repeatability of maps derived from acoustic datasets collected on consecutive days using three automated seafloor classification algorithms: (1) Random Forest (RF), (2) K–Nearest Neighbour (KNN) and (3) K means (KMEANS). The most robust and repeatable approach is then used to evaluate the change in seafloor habitats between 2012 and 2015 within the Greater Haig Fras Marine Conservation Zone, Celtic Sea, UK. Our results demonstrate that only RF and KNN provide statistically repeatable maps, with 60.3% and 47.2% agreement between consecutive days. Additionally, this study suggests that in low-relief areas, bathymetric derivatives are non-essential input parameters, while backscatter textural features, in particular Grey Level Co-occurrence Matrices, are substantially more effective in the detection of different habitats. Habitat persistence in the test area between 2012 and 2015 was 48.8%, with swapping of habitats driving the changes in 38.2% of the area. Overall, this study highlights the importance of investigating the repeatability of automated seafloor classification methods before they can be fully used in the monitoring of benthic habitats.
Highlights
One of the consequences of the expanding human population is the increased exploitation of marine resources through industrial activities such as fishing and seabed mining
Different spatial scales exhibited different correlations, with the largest coefficients consistently associated with the 10-m scale and the lowest coefficients associated with the 2-m scale
We tested the repeatability of three different seafloor classification algorithms: Random Forest (RF) supervised algorithm, k-nearest neighbours (KNN) supervised algorithm, and k-means (KMEANS) unsupervised algorithm
Summary
One of the consequences of the expanding human population is the increased exploitation of marine resources through industrial activities such as fishing and seabed mining. A growing awareness of these problems is driving nations to develop strategies and resolutions—e.g., the World Heritage Convention, the International Coral Reef Initiative, the United Nations Convention on the Law of the Sea, The Convention on Biodiversity—to help manage resources and reduce human impacts [2]. These agreements often result in the establishment of Marine Protected Areas (MPAs) that aim to restrict human activity in order to protect natural resources. In order to effectively support decision makers in developing conservation and management plans for the growing number of protected areas, it is crucial to develop faster, repeatable, and objective methods to generate maps that accurately represent the seabed environment and the associated ecological patterns [6]
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