Abstract
Abstract This study proposes a fully automated and objective technique to map marine landscapes in submarine canyons. The method is suitable for broad and regional scale mapping derived from sonar data using multivariate statistical analysis. The method is divided into two main parts: the terrain analysis and the multivariate statistical analysis. The first part aims to optimise the sonar data and comprises three steps 1) data resampling, 2) determination of length scale, and 3) multiple scale analysis. The second part covers the actual marine landscape classification and consists of 1) principal component analysis (PCA), 2) K-means clustering, and 3) cluster determination. In addition, a confidence map is presented based on cluster membership derived from cluster distance in attribute space. The technique was applied in the Lisbon–Setubal and Cascais Canyons offshore Portugal. The area was classified into 6 marine landscapes that represent the geomorphological features present in submarine canyons. The main findings from the study are 1) the transferability of a tool from geomorphometric analysis – Estimation of Scale Parameter (ESP) – to detect the length scale of potential patterns in bathymetric grids; 2) multiple scale terrain analysis allows an appropriate discrimination of local and broad scale geomorphic features in marine landscape mapping; 3) the method not only delineates geomorphic seafloor features but also points out properties that might influence biodiversity in a complex terrain.
Highlights
Over the past decade, the ongoing effort to develop an efficient and reliable method to map and study benthic habitats in various environments has promoted the advancement of classification techniques in the habitat mapping community (Brown et al, 2011)
Clustering The Principal Components (PC) resulting from the Principal Component Analysis (PCA) are used as attributes for clustering
Kmeans is an iterative procedure that starts with a random allocation of class centres
Summary
The ongoing effort to develop an efficient and reliable method to map and study benthic habitats in various environments has promoted the advancement of classification techniques in the habitat mapping community (Brown et al, 2011). Advances in sonar technology permit seafloor imaging with high resolution and wide coverage using a wide variety of instruments and systems of different frequencies and resolutions (Hayes and Gough, 2009; Hansen et al, 2011; Nakanishi and Hashimoto, 2011; Paull et al, 2013; Harris et al, 2014; Wynn et al, 2014). These data can be used to depict various seafloor geomorphic features and interpreted to provide potential habitats represented on a marine landscape map
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