Title

Detection And Morphologic Analysis Of Potential Below-Canopy Cave Openings In The Karst Landscape Around The Maya Polity Of Caracol Using Airborne Lidar

Abstract

Locating caves can be difficult, as their entranceways are often obscured below vegetation. Recently, active remote-sensing technologies, in particular laser-based sensor systems (LiDARs), have demonstrated the ability to penetrate dense forest canopies to reveal the underlying ground topography. An airborne LiDAR system was used to generate a 1 m resolution, bare-earth digital elevation model (DEM) from an archaeologically- and speleologically-rich area of western Belize near the ancient Maya site of Caracol. Using a simple index to detect elevation gradients in the DEM, we identified depressions with at least a 10 m change within a circular area of no more than 25 m radius. Across 200 km 2 of the karst landscape, we located 61 depressions. Sixty of these had not been previously documented; the other was a cave opening known from a previous expedition. The morphologies of the depressions were characterized based on the LiDAR-derived DEM parameters, e.g., depth, opening area, and perimeter. We also investigated how the measurements change as a function of spatial resolution. Though there was a range of morphologies, most depressions were clustered around an average maximum depth of 21 m and average opening diameter of 15 m. Five depression sites in the general vicinity of the Caracol epicenter were visited; two of these were massive, with opening diameters of ∼50 m, two could not be explored for lack of climbing gear, and one site was a cave opening into several chambers with speleothems and Maya artifacts. Though further investigation is warranted to determine the archaeological and geological significance of the remaining depressions, the general methodology represents an important advancement in cave detection.

Publication Date

12-1-2011

Publication Title

Journal of Cave and Karst Studies

Volume

73

Issue

3

Number of Pages

187-196

Document Type

Review

Personal Identifier

scopus

DOI Link

https://doi.org/10.4311/2010EX0179R1

Socpus ID

84862290716 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84862290716

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