In this article, we investigate the potential for detection and characterization of sinkholes under dense forest cover by using airborne laser scanning data. Laser pulse returns from the ground ...provide important data for the estimation of digital elevation model (DEM), which can be used for further processing. The main objectives of this study were to map and determine the geomorphometric characteristics of a large number of sinkholes and to investigate the correlations between geomorphology and vegetation in areas with such characteristics. The selected study area has very low anthropogenic influences and is particularly suitable for studying undisturbed karst sinkholes. The information extracted from this study regarding the shapes and depths of sinkholes show significant directionality for both orientation of sinkholes and their distribution over the area. Furthermore, significant differences in vegetation diversity and composition occur inside and outside the sinkholes, which indicates their presence has important ecological impacts.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The traditional approach for defining sinkholes characteristics is based on topographic maps and air photographs with derived digital terrain models. This method is sometimes not accurate, requiring ...costly, time consuming and potentially dangerous fieldwork. Investigations have shown that airborne scanning laser data (lidar) is useful in detection of karst depressions due to the high density of ground points that can be obtained. This is especially important under dense forest canopy, where classical photogrammetric methods do not allow ground points to be measured. The objective of this work was to map and determine geomorphometric characteristics of a large number of sinkholes located in a diverse karst terrain under a dense forest tree-canopy using lidar data. We tested an algorithm described in previous literature which uses only information from the DTM. It is based on water flow simulations on a surface (DTM) and incorporates four phases: (i) watershed delineation, (ii) confining of sinkholes, (iii) confining of higher rank sinkholes and (iv) extraction of non-karstic sinkholes. Sinkholes were confined by effluent level with cells below the effluent level designated as part of the sinkhole. In the third step sinkholes were ranked according to their location and size – first rank sinkholes are the smallest and are located within a larger sinkhole. Results are that the sinkhole fraction of 1st, 2nd, 3rd, 4th and 5th rank in the study area was 3.25 %, 4.26 %, 5.68 %, 3.65 % and 3.14 %, respectively. Sinkhole distribution shows a peculiar directionality in their spatial distribution, which seems to be significantly towards a northwest – southeast direction. It was not possible to compare results with ground-truth data due to very low accessibility, nevertheless a statistical and visual assessment of the results shows that lidar is a very effective technique to model sinkholes under dense canopy.
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