Doline morphometry has always been in the focus of karst geomorphological research. Recently, digital terrain model (DTM) based methods became widespread in the study of dolines. Today, LiDAR datasets provide high resolution DTMs, and automated doline recognition algorithms have been developed. In this paper, we test different datasets and a doline recognition algorithm using Aggtelek Karst (NE-Hungary) dolines as a case example. Three datasets are compared: “TOPO” dolines delineated by the classical outermost closed contour method using 1:10,000 scale topographic maps, “KRIG” dolines derived automatically from the DTM created by kriging interpolation from the digitized contours of the same topographic maps, and finally “LiDAR” dolines derived automatically from a DTM created from LiDAR data. First, we analyzed the sensitivity of the automatic method to the “depth limit” parameter, which is the threshold, below which closed depressions are considered as “errors” and are filled. In the actual case, given the typical doline size of the area and the resolution of the DTMs, we found that ca. 0.5 m is the optimal depth limit for the LiDAR dataset and 1 m for the KRIG dataset. The statistical distributions of the morphometrical properties were similar for all datasets (lognormal distribution for area and gamma distribution for depth), but the DTM-based methodology resulted larger dolines with respect to the classical method. The planform area (and related characteristics) showed very high correlations between the datasets. Depth values were less correlated and the lowest (moderately strong) correlations were observed between circularity values of the different datasets. Slope histograms calculated from the LiDAR data were used to cluster dolines, and these clusters differentiated dolines similarly to the classical depth-diameter ratio. Finally, we conclude that in the actual case, dolines can be morphometrically well characterized even by the classical topographic method, though finer results can be achieved for the depth and shape related parameters by using LiDAR data.
ASJC Scopus subject areas
- Earth-Surface Processes