Landscape similarity, retrieval, and machine mapping of physiographic units

• Published in: Geomorphology (Amsterdam, Netherlands) Netherlands), 2014-09, Vol.221, p.104-112
• Main Authors: Jasiewicz, Jaroslaw, Netzel, Pawel, Stepinski, Tomasz F.
• Format: Article
• Language: English
• Subjects: Algorithms; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geomorphology, landform evolution; Landforms; Landscape search; Landscape similarity; Landscapes; Marine and continental quaternary; Mathematical analysis; Pattern recognition; Physiographic mapping; Searching; Similarity; Supervised classification; Surficial geology; Texture; Topography; Web application
• ISSN: 0169-555X; 1872-695X
• DOI: 10.1016/j.geomorph.2014.06.011

We introduce landscape similarity — a numerical measure that assesses affinity between two landscapes on the basis of similarity between the patterns of their constituent landform elements. Such a similarity function provides core technology for a landscape search engine — an algorithm that parses the topography of a study area and finds all places with landscapes broadly similar to a landscape template. A landscape search can yield answers to a query in real time, enabling a highly effective means to explore large topographic datasets. In turn, a landscape search facilitates auto-mapping of physiographic units within a study area. The country of Poland serves as a test bed for these novel concepts. The topography of Poland is given by a 30m resolution DEM. The geomorphons method is applied to this DEM to classify the topography into ten common types of landform elements. A local landscape is represented by a square tile cut out of a map of landform elements. A histogram of cell-pair features is used to succinctly encode the composition and texture of a pattern within a local landscape. The affinity between two local landscapes is assessed using the Wave-Hedges similarity function applied to the two corresponding histograms. For a landscape search the study area is organized into a lattice of local landscapes. During the search the algorithm calculates the similarity between each local landscape and a given query. Our landscape search for Poland is implemented as a GeoWeb application called TerraEx-Pl and is available at http://sil.uc.edu/. Given a sample, or a number of samples, from a target physiographic unit the landscape search delineates this unit using the principles of supervised machine learning. Repeating this procedure for all units yields a complete physiographic map. The application of this methodology to topographic data of Poland results in the delineation of nine physiographic units. The resultant map bears a close resemblance to a conventional physiographic map of Poland; differences can be attributed to geological and paleogeographical input used in drawing the conventional map but not utilized by the mapping algorithm. •We propose a method for quantitative assessment of similarity between landscapes.•Landscape search engine finds all similar landscapes in the study area.•Automatic delineation of physiographic units•Physiographic provinces in Poland are delineated and compared to reference map.•GeoWeb application of landscape search over