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 t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Slovenian Academy of Sciences and Arts
2016-07-01
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| Series: | Acta Carsologica |
| Subjects: | |
| Online Access: | https://ojs.zrc-sazu.si/carsologica/article/view/4138 |
| Summary: | 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 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 "LiDAR" 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 globinska meja za LiDAR ca. 0,5 m in 1 m za KRIG. Pri vseh podatkovnih bazah so bile statistične 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 večje vrtače v primerjavi s klasično metodo. Rezultati območij vrtač (in njihovih značilnosti) so pokazali zelo visoke korelacije med podatkovnimi nizi. Pri globinah so bile korelacije manjše in najnižje zabeležene korelacije (srednje močne) so bile med podatki različnih podatkovnih bazah. Histogrami naklona, izračunani iz lidarskih podatkov, so bili uporabljeni za združevanje vrtač, in ti grozdi razlikujejo vrtače glede na klasično razmerje med globino in premerom. Na koncu smo ugotovili, da lahko v konkretnem primeru dobro določimo morfometrične lastnosti vrtač s klasičnimi topografskimi metodami. Podrobnejše rezultate o globinah in oblikah lahko dosežemo na podlagi lidarskih podatkov.
Ključne besede: morfometrija vrtač, LiDAR, interpolacija, histogram naklona, ponor. |
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| ISSN: | 0583-6050 1580-2612 |