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

Abstract

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

Geomorphological Dating of Pleistocene Conglomerates in Central Slovenia Based on Spatial Analyses of Dolines Using LiDAR and Ground Penetrating Radar

“…New methods for automated doline detection and delineation as well as numerous tests of their reliability are lately subject to considerable expansion [5,7,8,28,29]. Additionally, more and more precise input data (LiDAR) for such researches has recently been made available, but issues such as understanding the concept of surface karstification and the pitfalls of new methods should be considered when trying to obtain results of the same quality as those expected from high-resolution data.…”

“…Despite this, in most studies the doline upper rim is simply treated as the uppermost closed contour [5,35]. Even though it is the most reasonable definition of the doline perimeter, the principle of "an abrupt change in the surface slope" [33] has seldom been used in practice due to its fieldwork requirements.…”

“…The increasingly available high-resolution LiDAR (Light Detection and Ranging) method for obtaining elevation data and creating digital elevation models (DEMs) is still in early stages of ability and reliability testing compared to traditional topographic and aerial surveying methods in modern karst geomorphology [5]. Researches applying high-resolution DEMs are mainly focused on automation of dolines detection and delineation [5][6][7][8], rather than on interpretations of the acquired geomorphological data [4,9].…”

Detecting Terrain Stoniness From Airborne Laser Scanning Data †

“…There are similarities with our study, e.g., application of several computational steps and tuning of critical parameters (e.g., the depression depth limit in [23]), although the horizontal micro-topology feature size is much larger than in our study (diameter of doline depressions is 10-200 m vs. 1.5-6 m diameter of stones in our study). The vertical height differences are at the same range, 0.5-1.5 m in our study and in [23,24], though. A similar study of [25] uses higher density LiDAR data with ρ = 30 m −2 to detect karst depressions of size 26 m and more.…”

“…Another application is detecting karst depressions, where slope histograms [23] and local sink depth [24] were used to detect karst depressions. There are similarities with our study, e.g., application of several computational steps and tuning of critical parameters (e.g., the depression depth limit in [23]), although the horizontal micro-topology feature size is much larger than in our study (diameter of doline depressions is 10-200 m vs. 1.5-6 m diameter of stones in our study). The vertical height differences are at the same range, 0.5-1.5 m in our study and in [23,24], though.…”