GIS and Karstic Cave Monitoring

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Karstic, or limestone, caves are potentially an important source of information for paleoclimate data and provide perhaps the most accurate terrestrial climate information relative to all other data sources. Furthermore, they play an important role in the hydrologic cycle and act as global warming pollution traps. Monitoring, therefore, has become important of karst formation, including the number of speleothemes, which are stalactites and stalagmites, present in these caves and what happens to them over time.[1]

Furthermore, karst formations containing caves useful for scientists are often unknown. Areas that have many limestone mountains, for instance, require the use of remote sensing data and GIS as a way to predict likely regions where caves can be found. Input data of landforms, iron oxide-rich sediments, and elevation provide the possibility to use multi-component modeling to determine areas scientists should explore in order to find unknown caves.[2]

Flow chart of data processing, evaluation and input parameters for the multi-method GIS analysis of  karst. From Siart et al., 2009.

Flow chart of data processing, evaluation and input parameters for the multi-method GIS analysis of  karst. From Siart et al., 2009.

For paleoclimate estimates, knowing estimates of when material from caves eroded and cosmogenic history, along with data on speleothem growth rates, has been used together with GIS to create wider regional dating of karst formations relevant for understanding how climate likely affected regions over millions of years. In this case, GIS is also useful for estimating volume of cave area that are affected by erosional processes.[3] Water and gas exchanges, particularly CO2, are important processes that affect caves and modern and ancient climate and hydrologic conditions. Furthermore, many parts of a cave are difficult to access or unknown. Using a combination of 3D laser scanning and monitoring data present within caves, estimates of where in caves water and gas exchanges might be high or low could be created using interpolative methods.[4] These techniques have now allowed cave monitoring to become a rapidly growing area of study for understanding modern and ancient environmental change.

Resulting surface drainage network (in blue) from hydrological model for the area surrounding Altamira cave (hillshade base model for relief). From Elez et.al, 2013.

Resulting surface drainage network (in blue) from hydrological model for the area surrounding Altamira cave (hillshade base model for relief). From Elez et.al, 2013.

References

[1] For an example of karst cave monitoring, see:  Harley, Grant L., Jason S. Polk, Leslie A. North, and Philip P. Reeder. 2011. “Application of a Cave Inventory System to Stimulate Development of Management Strategies: The Case of West-Central Florida, USA.” Journal of Environmental Management 92 (10): 2547–57.

[2] For more information on multi-component methods for cave detection, see: Siart, Christoph, Olaf Bubenzer, and Bernhard Eitel. 2009. “Combining Digital Elevation Data (SRTM/ASTER), High Resolution Satellite Imagery (Quickbird) and GIS for Geomorphological Mapping: A Multi-Component Case Study on Mediterranean Karst in Central Crete.Geomorphology 112 (1-2): 106–21. doi:10.1016/j.geomorph.2009.05.010.

[3] For more on cave monitoring using karst development and cosmogenic dating using GIS, see: acoby, B.S.*, Peterson, E.W., Dogwiler, T., and Kostelnick, J.C., 2011, Estimating the timing of Cave Level Development with GIS: Speleogenesis and Evolution of Karst Aquifers, v. 11, 52-61.

[4] For more on monitoring water and gas exchanges in caves, see:  Elez, J., S. Cuezva, A. Fernandez-Cortes, E. Garcia-Anton, D. Benavente, J.C. Cañaveras, and S. Sanchez-Moral. 2013. “A GIS-Based Methodology to Quantitatively Define an Adjacent Protected Area in a Shallow Karst Cavity: The Case of Altamira Cave.” Journal of Environmental Management 118 (March): 122–34. doi:10.1016/j.jenvman.2013.01.020.

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