Resumen

RODRIGUEZ-CASTILLO, José Francisco; FRAUSTO-MARTINEZ, Oscar; UHU-YAM, Wilbert David  y  COLIN-OLIVARES, Orlando. Morphometric characterisation of karst depressions: coastal zone of the north-eastern Yucatan peninsula, Mexico. Ecosistemas y recur. agropecuarios [online]. 2022, vol.9, n.2, e3216.  Epub 02-Jun-2023. ISSN 2007-901X.  https://doi.org/10.19136/era.a9n2.3216.

Studying the relief through morphological analysis allows the typification of forms, elements and structures of the karst relief that helps in the determination of the genesis, evolution, and geomorphological dynamics. The implementation of tools with new methods and technologies helps to develop more precise studies in areas with low altitudinal contrast. The aim of the present study was to analyze and classify karst depressions in the north-eastern coastal region of the Yucatan Peninsula. The karst relief was analyzed from two data sources: a) a LiDAR (light detection and ranging) data mosaic and b) an AsterGDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) data mosaic. These were coupled to generate altimetric inputs and base topographic models. Subsequently, contour lines were generated by differentiating depressions with a minimum depth of 1m; depressions were then processed and classified by applying an analysis of morphometric measurements, profile morphological typology and elongation index. A total of 10,672 depressions were identified, 67% of which are V-type depressions, 23% U-type and 10% box-type; with respect to the elongation index, 18% are sinkholes, 42% sinkholes-uvala, 13% uvala and 27% poljes, being the uvalas and poljes the most complex, due to the structural control of the Holbox fault system, with a main orientation of NE7°SW and secondary SE83°NW, which highlights the disjunctive structural character. Finally, an inventory of karst depressions is presented, serving as a basis for the management of the coastal karst territory.

Palabras llave : Aster; coupled models; LiDAR; remote sensing.