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The northern foreland of the Central Gerecse Mountains is a complex landscape with a moderate relief and the presence of terrace remnants. To explore the hydrogeomorphic settings, the characteristics of the drainage network, several basin metrics, and geomorphometric maps were interpreted, which are the outputs of a semi-automated algorithm. Based on the results, the small catchments of the area were divided into two groups with diverse topographic characteristics and the presence of 7 terrace levels was revealed.

The current paper explores the suitability of geomorphons for the automatic extraction of drumlins. To calibrate the geomorphons to the size of drumlins, it is necessary to optimally define the maximum scale of mapping, i.e., the lookup distance parameter (L). Therefore, based on the concept of topographic grain, we introduce a new automated approach for identifying the specific threshold of L (13 cells) and assessing its potential to generate consistent and accurate results in drumlin extraction. Following an object‐based image analysis (OBIA) routine, a new method for mapping and detecting drumlins is proposed. The aggregated geomorphons map was employed both as a thematic layer for image segmentation and as a first criterion for the identification of drumlin candidates. The classification results were quantitatively compared with the reference data in order to evaluate the performance of the drumlin classification, by using five additional L values (3, 50, 100, 200, 400 cells). The evaluation revealed that the highest drumlin detection rate of 91.7% was reached at an L value of 13 cells (65 m), while the lowest value of 84.3% was reached at the default value (L‐3 cells). We conclude that the use of the automated procedure for the detection of the L value is useful in achieving a rapid computation of geomorphons, which leads to consistent and accurate results in drumlin extraction. A comparison with previous OBIA methods suggests that the proposed approach produced the most accurate extraction of drumlins. [ABSTRACT FROM AUTHOR]

Modern geomorphological analyses largely benefit from GIS tools developed for landform and landscape mapping. Semi-automated methods with the use of public domain elevation datasets ensure the mapping of large areas with relatively low time and cost requirements, leaving less space for subjectivity. For our analysis we chose the geomorphons approach, a robust cell-based method to identify landform elements at a broad range of scales. Based on the delineated landforms and auxiliary morphometric parameters it was possible to map the geomorphological landscapes occurring in Hungary with the supervised classification algorithm implemented in the GeoPAT toolset. The scientific output of the presented work is twofold: one aspect is the creation of an objective and quantifiable map of landforms and geomorphic landscapes of Hungary, while the successful application of the available methodology and the evaluation of SRTM1 model's applicability for geomorphological purposes are also significant results. [ABSTRACT FROM AUTHOR]