GIS Research of Karst Terrains in Hungary

Telbisz, Tamás; Mari, László; Albert, Gáspár

doi:10.1007/978-3-030-92960-2_9

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…In some studies with slightly different objectives, factors associated with precipitation or temperature were considered, as the meteoric water temperature influences carbonate rocks' dissolution [36,72]. Considering the small dimensions of the study area (Figure 1), using these additional factors would be unwarranted because there would be no significant spatial differentiation [43,72]. Regarding the chosen conditioning factors (Table 1), the lithostratigraphic units permit the evaluation of susceptibility to karstification of each unit based on lithology, facies identification (granulometry, texture, and qualitative porosity), stratonomic characteristics, and strata geometry.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Section: Data Collection and Pre-processingmentioning

confidence: 99%

“…Considering the availability of detailed geological knowledge at the local scale and many known cave entrances, the present study is expected to allow the construction of a model that spatially represents the underground karstification potential. Using GIS potentialities, a prototype of the endokarst potential map for the study area was produced with the primary objective of enhancing and better directing the speleological prospection that should precede environmental impact studies and the diversified tasks related to cave protection/conservation [39][40][41], scientific research/divulgation [42][43][44], territorial planning, and management in karst areas [45][46][47][48]. The focus of the present work is to contribute to a more efficient management of the karst territory under study.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Endokarst Potential in the Northern Sector of Santo António Plateau (Estremadura Limestone Massif, Central Portugal)

Reis,

Dimuccio,

Cunha

2023

Sustainability

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Karst is a peculiar natural landscape arising from high rock solubility and well-developed underground solutional channel porosity. It is unique for its surface relief (exokarst) and subsurface drainage, including cave systems (endokarst). In Portugal, karst areas mainly consist of marginal or low-density territories with great fragility and vulnerability and great geo-environmental richness that merits better policies and practices regarding their geo-conservation. Endokarst potential assessments can provide decision-makers and local authorities insight into present and future territorial management and planning. In this context, the main objective of this study was to produce a cartographic model to identify areas with a greater probability of containing karstic caves—i.e., a greater endokarst potential—in the northern sector of the Santo António Plateau (Estremadura Limestone Massif, Central Portugal). Geological, topographic, hydrogeological, and land cover data were collected, processed, and integrated into a spatial database using a Geographic Information System. The locations of known cave entrances in the study area were also identified from local public institutions and speleological team records. Subsequently, four conditioning factors were extracted from the data: lithostratigraphic units, fracture density, relief energy, and land cover. Using a multi-criteria decision-making analysis, each previously chosen conditioning factor and its respective classes were weighted using an analytic hierarchy process. The locations of known cave entrances served to evaluate the cartographic model built, with results showing an agreement of 81.9%. This prototype of the endokarst potential map for the study area may be used for strategic and operational environmental planning (at least on a local scale) to assist decision-makers, competent authorities, and local speleological teams. Its application may promote a more accurate and thoughtful definition of areas to be investigated, substantially reducing the time and costs associated with field prospecting.

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Section: Data Collection and Pre-processingmentioning

confidence: 99%