Object-based classification of global undersea topography and geomorphological features from the SRTM30_PLUS data

Abstract

The analysis of undersea topography and geomorphological features provides necessary information to related disciplines and many applications. The development of an automated knowledge-based classification approach of undersea topography and geomorphological features is challenging due to their multi-scale nature. The aim of the study is to develop and evaluate an automated knowledge-based OBIA approach to: i) decompose the global undersea topography to multi-scale regions of distinct morphometric properties, and ii) assign the derived regions to characteristic geomorphological features. First, the global undersea topography was decomposed through the SRTM30_PLUS bathymetry data to the so-called morphometric objects of discrete morphometric properties and spatial scales defined by data-driven methods (local variance graphs and nested means) and multi-scale analysis. The derived morphometric objects were combined with additional relative topographic position information computed with a self-adaptive pattern recognition method (geomorphons), and auxiliary data and were assigned to characteristic undersea geomorphological feature classes through a knowledge base, developed from standard definitions. The decomposition of the SRTM30_PLUS data to morphometric objects was considered successful for the requirements of maximizing intra-object and inter-object heterogeneity, based on the near zero values of the Moran's I and the low values of the weighted variance index. The knowledge-based classification approach was tested for its transferability in six case studies of various tectonic settings and achieved the efficient extraction of 11 undersea geomorphological feature classes. The classification results for the six case studies were compared with the digital global seafloor geomorphic features map (GSFM). The 11 undersea feature classes and their producer's accuracies in respect to the GSFM relevant areas were Basin (95%), Continental Shelf (94.9%), Trough (88.4%), Plateau (78.9%), Continental Slope (76.4%), Trench (71.2%), Abyssal Hill (62.9%), Abyssal Plain (62.4%), Ridge (49.8%), Seamount (48.8%) and Continental Rise (25.4%). The knowledge-based OBIA classification approach was considered transferable since the percentages of spatial and thematic agreement between the most of the classified undersea feature classes and the GSFM exhibited low deviations across the six case studies.