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 Povzetek UDK 551.435.82:528.8.044.6(439) Tamás Telbisz, Tamás Látos, Márton Deák, Balázs Székely, Zsófia Koma & Tibor Standovár: Prednost lidarskega digitalnega modela reliefa za raziskavo morfometrije vrtač v primerjavi s podatkovno bazo topografskih kart − primer Agteleškega krasa (Madžarska) Morfometrija vrtač je bila vedno v središču kraških geomorfoloških raziskav. V zadnjem času so pri raziskavah vrtač postale zelo razširjene metode, ki temeljijo na digitalnem modelu reliefa (DMR). Lidarski podatki zagotavljajo visoko ločljivostne DMR-je, razviti so bili avtomatski algoritmi za prepoznavanje vrtač. V tem prispevku smo na primeru Agteleškega krasa v severovzhodni Madžarski preizkusili različne podatkovne baze in algoritme za prepoznavanje vrtač. Primerjali smo tri podatkovne baze: "TOPO" vrtače so razmejene na klasičen način z zunanjo zaprto plastnico na topografski karti v merilu 1: 10.000, "KRIG" vrtače so v istem merilu s pomočjo kriginga samodejno pridobljene iz digitaliziranih plastnic DMR, in "Li-DAR" vrtače so samodejno pridobljene iz DMR, ki je ustvarjen iz lidarskih podatkov. Najprej smo analizirali občutljivost avtomatske metode parametra "mejne globine", ki predstavlja prag, pod katerim se depresijske oblike štejejo kot "napake" in so zapolnjene. V konkretnem primeru smo glede na običajno velikost vrtače in ločljivosti DMR ugotovili, da je optimalna

The sentence in page 11, lines 14-21 (Studies about … full spectra) refers to the number of spectral bands that were used for classification. Spectral indices are based on 2-3 bands in general whereas full spectra is more than 100 bands in general.

Satellite images are important information sources of land cover analysis or land cover change monitoring. We used the sensors of four different spacecraft: TM, ETM+, OLI and ALI. We classified the study area using the Maximum Likelihood algorithm and used segmentation techniques for training area selection. We validated the results of all sensors to reveal which one produced the most accurate data. According to our study Landsat 8’s OLI performed the best (96.9%) followed by TM on Landsat 5 (96.2%) and ALI on EO-1 (94.8%) while Landsat 7’s ETM+ had the worst accuracy (86.3%).

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