Using Lidar Data to Analyse Sinkhole Characteristics Relevant for Understory Vegetation under Forest Cover—Case Study of a High Karst Area in the Dinaric Mountains

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.

[1]  M. Manunta,et al.  DInSAR measurements of ground deformation by sinkholes, mining subsidence, and landslides, Ebro River, Spain , 2009 .

[2]  M. Parise,et al.  Morphometric analysis of sinkholes in a karst coastal area of southern Apulia (Italy) , 2012, Environmental Earth Sciences.

[3]  Francesco Pirotti,et al.  A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study , 2013 .

[4]  R. Valentini,et al.  Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data , 2014 .

[5]  Francesco Pirotti,et al.  Analysis of full-waveform LiDAR data for forestry applications: a review of investigations and methods , 2011 .

[6]  Z. Bátori,et al.  IMPORTANCE OF KARST SINKHOLES IN PRESERVING RELICT, MOUNTAIN, AND WET-WOODLAND PLANT SPECIES UNDER SUB-MEDITERRANEAN CLIMATE: A CASE STUDY FROM SOUTHERN HUNGARY , 2012 .

[7]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[8]  E. Pebesma,et al.  Classes and Methods for Spatial Data , 2015 .

[9]  Z. Bátori,et al.  The conservation value of karst dolines for vascular plants in woodland habitats of Hungary: refugia and climate change , 2014 .

[10]  Z. Bátori,et al.  Ecological conditions, flora and vegetation of a large doline in the Mecsek Mountains (South Hungary) , 2011 .

[11]  N. Pfeifer,et al.  Spatial analysis of anthropogenic impact on karst geomorphology (Slovenia) , 2009 .

[12]  C. Denizman.  MORPHOMETRIC AND SPATIAL DISTRIBUTION PARAMETERS OF KARSTIC DEPRESSIONS, LOWER SUWANNEE RIVER BASIN, FLORIDA , 2003 .

[13]  T. Nagel,et al.  Disturbance, life history traits, and dynamics in an old-growth forest landscape of southeastern Europe. , 2014, Ecological applications : a publication of the Ecological Society of America.

[14]  O. Sass,et al.  Combining airborne and terrestrial laser scanning for quantifying erosion and deposition by a debris flow event , 2012 .

[15]  Guangqing Chi,et al.  Applied Spatial Data Analysis with R , 2015 .

[16]  J. Remondo,et al.  Sinkholes in the salt-bearing evaporite karst of the Ebro River valley upstream of Zaragoza city (NE Spain): Geomorphological mapping and analysis as a basis for risk management , 2009 .

[17]  Arlen F. Chase,et al.  DETECTION AND MORPHOLOGIC ANALYSIS OF POTENTIAL BELOW-CANOPY CAVE OPENINGS IN THE KARST LANDSCAPE AROUND THE MAYA POLITY OF CARACOL USING AIRBORNE LIDAR , 2011 .

[18]  P. Newby.  Photogrammetric Terminology: Second Edition , 2012 .

[19]  U. Vilhar,et al.  Water status and drought stress in experimental gaps in managed and semi-natural silver fir--beech forests , 2012, European Journal of Forest Research.

[20]  A. Kobler,et al.  Prediction of Forest Vegetation Shift due to Different Climate-Change Scenarios in Slovenia , 2011 .

[21]  S. Schumm.  EVOLUTION OF DRAINAGE SYSTEMS AND SLOPES IN BADLANDS AT PERTH AMBOY, NEW JERSEY , 1956 .

[22]  E. Foufoula‐Georgiou,et al.  Automatic geomorphic feature extraction from lidar in flat and engineered landscapes , 2011 .

[23]  Alberto Guarnieri,et al.  Vegetation filtering of waveform terrestrial laser scanner data for DTM production , 2013 .

[24]  M. Zupan,et al.  Influence of soil properties on silver fir (Abies alba Mill.) growth in the Dinaric Mountains , 2015 .

[25]  Alberto Guarnieri,et al.  State of the Art of Ground and Aerial Laser Scanning Technologies for High-Resolution Topography of the Earth Surface , 2013 .

[26]  M. Shamos.  Problems in computational geometry , 1975 .

[27]  P. Lavalle.  SOME ASPECTS OF LINEAR KARST DEPRESSION DEVELOPMENT IN SOUTH CENTRAL KENTUCKY , 1967 .

[28]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[29]  Ralph Dubayah,et al.  Validation of Vegetation Canopy Lidar sub-canopy topography measurements for a dense tropical forest , 2002 .

[30]  Kaiguang Zhao,et al.  Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues , 2010, Remote. Sens..

[31]  C. Romão,et al.  Interpretation manual of European Union habitats. , 1996 .

[32]  Čar Jože.  Structural bases for shaping of dolines , 2001 .

[33]  Alan R. Gillespie,et al.  Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil , 2013, Remote. Sens..

[34]  T. Telbisz,et al.  Doline morphometric analysis and karst morphology of Biokovo Mt (Croatia) based on field observations and digital terrain analysis. , 2009 .

[35]  A. NagelThomas,et al.  Simultaneous influence of canopy decline and deer herbivory on regeneration in a conifer–broadleaf forest , 2015 .

[36]  P. Younger,et al.  Subsidence hazard avoidance based on geomorphological mapping in the Ebro River valley mantled evaporite karst terrain (NE Spain) , 2005 .

[37]  P. Tarolli,et al.  Variations in multiscale curvature distribution and signatures of LiDAR DTM errors , 2013 .

[38]  B. Muys,et al.  Plant distribution-altitude and landform relationships in karstic sinkholes of Mediterranean region of Turkey. , 2010, Journal of environmental biology.

[39]  U. Vilhar,et al.  Gap evapotranspiration and drainage fluxes in a managed and a virgin dinaric silver fir–beech forest in Slovenia: a modelling study , 2005, European Journal of Forest Research.

[40]  N. Coops,et al.  Estimation of watershed-level distributed forest structure metrics relevant to hydrologic modeling using LiDAR and Landsat , 2013 .

[41]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[42]  M. Hill,et al.  Detrended correspondence analysis: An improved ordination technique , 2004, Vegetatio.

[43]  D. Doctor,et al.  An Evaluation of Automated GIS Tools for Delineating Karst Sinkholes and Closed Depressions from 1-Meter LiDAR-Derived Digital Elevation Data , 2013 .

[44]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[45]  E. Alexander,et al.  Karst database implementation in Minnesota: analysis of sinkhole distribution , 2005 .

[46]  R. Brinkmann,et al.  Using ALSM to map sinkholes in the urbanized covered karst of Pinellas County, Florida—1, methodological considerations , 2008 .

[47]  Tomaž Podobnikar,et al.  Algorithm for karst depression recognition using digital terrain Models , 2013 .

[48]  E. Alexander,et al.  Locating Sinkholes in LiDAR Coverage of a Glacio-Fluvial Karst, Winona County, MN , 2013 .

[49]  Paul W. Williams,et al.  Morphometric Analysis of Polygonal Karst in New Guinea , 1972 .

[50]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[51]  B. Surina,et al.  Phytosociology And Ecology Of The Dinaric Fir-Beech Forests (Omphalodofagetum) At The North-Western Part Of The Illyrian Floral Province (Nw Dinaric Alps) , 2013 .

[52]  M. Parise,et al.  A review on natural and human-induced geohazards and impacts in karst , 2014 .

[53]  S. Panno,et al.  Comparison of a new GIS-based technique and a manual method for determining sinkhole density: An example from Illinois' sinkhole plain , 2004 .

[54]  J. Ar,et al.  STRUCTURAL BASES FOR SHAPING OF DOLINES , 2002 .

[55]  S. Filin,et al.  Sinkhole characterization in the Dead Sea area using airborne laser scanning , 2011 .

[56]  C. Tölgyesi,et al.  A comparison of the vegetation of forested and non-forested solution dolines in Hungary: a preliminary study , 2014, Biologia.

[57]  Norbert Pfeifer,et al.  Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain☆ , 2007 .

[58]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[59]  Frédéric Bretar,et al.  Full-waveform topographic lidar : State-of-the-art , 2009 .

[60]  R. Bivand,et al.  Tools for Reading and Handling Spatial Objects , 2016 .

[61]  B. Höfle,et al.  Topographic airborne LiDAR in geomorphology: A technological perspective , 2011 .

[62]  Edzer Pebesma,et al.  Applied Spatial Data Analysis with R. Springer , 2008 .

[63]  Francesco Pirotti,et al.  Assessing a Template Matching Approach for Tree Height and Position Extraction from Lidar-Derived Canopy Height Models of Pinus Pinaster Stands , 2010 .

[64]  Jurij Diaci,et al.  Modelling drainage fluxes in managed and natural forests in the Dinaric karst: a model comparison study , 2010, European Journal of Forest Research.

[65]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .