Geomorphometric relief classification with the k-median method in the Silesian Upland, southern Poland

• Published in: Frontiers of earth science Vol. 14; no. 1; pp. 152 - 170
• Main Authors: SZYPUŁA, Bartłomiej, WIECZOREK, Małgorzata
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01-03-2020; Springer Nature B.V
• Subjects: Ambient noise; Background noise; Classification; Clusters; digital elevation model (DEM); Digital Elevation Models; Earth and Environmental Science; Earth Sciences; Geomorphology; geomorphometry; k-median method; Landforms; Morphometry; relief classifications; Research Article; Silesian Upland; geomorphometry; digital elevation model (DEM); relief classifications; median method; Silesian Upland
• ISSN: 2095-0195; 2095-0209
• DOI: 10.1007/s11707-019-0765-9

The aim of this study is geomorphometric relief classification of a temperate latitude upland area in Central Europe. The Silesian Upland represents diversified structural relief which contains a fan-shaped configuration of long thresholds and wide erosion depressions. A 20 m × 20 m digital elevation model (DEM) provided input data for the analysis. The k-median method was applied to examine morphometric variables of the relief. The aim of these activities was to identify clusters with objects of similar mathematical characteristics. These clusters were the basis of landform classification. Smaller numbers of clusters 4 transparently show hypsometric relationships. Key elements of the morphology of the area were clearly visible. The division into 6 clusters gives the best results-a detailed but clear image of the morphological diversity by distinguishing characteristic landform elements. The results for 8 clusters show significant background noise and are ambiguous, which makes them difficult to identify. Our research has confirmed that the k-median method is a useful tool for landform classifications. We determined optimal parameters of this method (filtering window size, DEM resolution, number of clusters, aspect influence).