The advantage of lidar digital terrain models in doline morphometry compared to topographic map based datasets – Aggtelek karst (Hungary) as an example

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. Key words: doline morphometry, LiDAR, interpolation, slope histogram, sink point. Prednost lidarskega digitalnega modela reliefa za raziskavo morfometrije vrtac v primerjavi s podatkovno bazo topografskih kart − primer Agteleskega krasa (Madžarska) Morfometrija vrtac je bila vedno v srediscu kraskih geomorfoloskih raziskav. V zadnjem casu so pri raziskavah vrtac postale zelo razsirjene metode, ki temeljijo na digitalnem modelu reliefa (DMR). Lidarski podatki zagotavljajo visoko locljivostne DMR-je, razviti so bili avtomatski algoritmi za prepoznavanje vrtac. V tem prispevku smo na primeru Agteleskega krasa v severovzhodni Madžarski preizkusili razlicne podatkovne baze in algoritme za prepoznavanje vrtac. Primerjali smo tri podatkovne baze: "TOPO" vrtace so razmejene na klasicen nacin z zunanjo zaprto plastnico na topografski karti v merilu 1: 10.000, "KRIG" vrtace so v istem merilu s pomocjo kriginga samodejno pridobljene iz digitaliziranih plastnic DMR, in "LiDAR" vrtace so samodejno pridobljene iz DMR, ki je ustvarjen iz lidarskih podatkov. Najprej smo analizirali obcutljivost avtomatske metode parametra "mejne globine", ki predstavlja prag, pod katerim se depresijske oblike stejejo kot "napake" in so zapolnjene. V konkretnem primeru smo glede na obicajno velikost vrtace in locljivosti DMR ugotovili, da je optimalna globinska meja za LiDAR ca. 0,5 m in 1 m za KRIG. Pri vseh podatkovnih bazah so bile statisticne porazdelitve morfometrijskih lastnosti (logaritemska normalna porazdelitev za prostor in gama porazdelitev za globino) podobne, vendar metodologija, ki temelji na DMR privede do rezultatov, ki kažejo na vecje vrtace v primerjavi s klasicno metodo. Rezultati obmocij vrtac (in njihovih znacilnosti) so pokazali zelo visoke korelacije med podatkovnimi nizi. Pri globinah so bile korelacije manjse in najnižje zabeležene korelacije (srednje mocne) so bile med podatki razlicnih podatkovnih bazah. Histogrami naklona, izracunani iz lidarskih podatkov, so bili uporabljeni za združevanje vrtac, in ti grozdi razlikujejo vrtace glede na klasicno razmerje med globino in premerom. Na koncu smo ugotovili, da lahko v konkretnem primeru dobro dolocimo morfometricne lastnosti vrtac s klasicnimi topografskimi metodami. Podrobnejse rezultate o globinah in oblikah lahko dosežemo na podlagi lidarskih podatkov. Kljucne besede: morfometrija vrtac, LiDAR, interpolacija, histogram naklona, ponor.