The Impact of Digital Elevation Model Preprocessing and Detection Methods on Karst Depression Mapping in Densely Forested Dinaric Mountains

Abstract

Karst landscapes have an abundance of enclosed depressions. Many studies have detected depressions and have calculated geomorphometric characteristics with computer techniques. These outcomes are somewhat determined by the methods and data used. We aim to highlight the applicability of high-resolution relief laser scanning data in geomorphological studies of karst depressions. We set two goals: geomorphometrically to characterize depressions in different karst plateaus and to examine the influence of data preprocessing and detection methods on the results. The study was performed in three areas of the Slovene Dinaric Karst using the following steps: preprocessing digital elevation models (DEMs), enclosed depression detection, calculating geomorphometric characteristics, and comparing the characteristics of selected areas. We discovered that different combinations of methods influenced the number and geomorphometric characteristics of depressions. The range of detected depressions in the three areas were 442–491, 364–403, and 366–504, and the share of the depressions’ area confirmed with all the approaches was 23%, 29%, and 47%, which resulted in different geomorphometric properties. Comparisons between the study areas were also influenced by the methods, which was confirmed by the Mann–Whitney test. We concluded that preprocessing of high-resolution relief data and the detection methods in karst environments significantly impact analyses and must be taken into account when interpreting geomorphometric results.

Mapping karst depressions and sinkholes in anthropogenically influenced areas

“…Kim & Boo (2019) propose a method to detect sinkholes using geometric characteristics from a DTM applying logistic regression. In the Slovenian Karst, two semi-automatic methods were tested using high-resolution DTMs (Ciglič et al 2022). The authors practice the ll method.…”

Revealing functional responses in habitat selection of rocky features and rugged terrain by Eurasian lynx (Lynx lynx) using LiDAR data

“…This is mainly due to the lack of detailed GIS layers containing information on such features across larger areas. Until the development of advanced remote sensing techniques, tasks, such as field mapping, digitising relief features, and the quantification of vegetation availability, were time consuming and costly, which resulted in low accuracy and poor data quality (Čonč et al 2022;Ciglič et al 2022;Sergeyev et al 2023). High-resolution data, such as satellite images or LiDAR (Light Detection and Ranging)-based digital terrain models (hereafter DTM), combined with various (semi-)automatic methods for detecting and delineating relief and vegetation features, now enable no-contact, low-cost, and accurate mapping of large, remote, and densely forested areas (Bailey et al 2018;Oeser et al 2020;Ciglič et al 2022;Čonč et al 2022).…”

Enclosed Karst Depression Identification and Analysis for the Pumped Storage Power Station Reservoir Construction Using DEM

“…The enclosed karst depression with natural negative terrain has good potential in the water reservoir, tailing reservoir, and technology infrastructure for its good stability and less excavation [1][2][3]. Many researchers have studied the development, evolution, genesis, and classification of enclosed karst depression [4,5], especially in recent years, some automatic detection methods and technologies on different types of karst depressions have been proposed [6][7][8][9][10][11][12][13][14][15][16][17][18], but there is a lack of research on the engineering utilization of enclosed karst depressions [19,20].…”