Paper
31 October 2005 Detection of karst depression by aster image in the Bambui Group, Brazil
Renato Fontes Guimarães, Osmar Abílio de Carvalho Júnior, Eder de Souza Martins, Ana Paula Ferreira de Carvalho, Roberto Arnaldo Trancoso Gomes
Author Affiliations +
Abstract
Karst is a characteristic geological feature of areas comprised of limestone. Due to the solubility of these rocks in water, exhibit an extreme heterogeneity of hydraulic conductivities. The characterizing features of karst aquifers are the open conduits, which provide low resistance pathways for ground water flow. Overall cave orientation is largely controlled by hydraulic gradient, joint patterns and other tectonic features, such as faulting and folding. The karst depressions may form on the surface by subsurface actions (dissolution and collapse). Thus, the depressions often show regularity of pattern or alignments, frequently in association with structurally guided cave systems below. The present work aims at to detect depressions zone, as dolines and uvalas in the limestone of the Bambui Group (Central Brazil) using ASTER and ASTERDEM images. A photogeological study, carried out on aster image allowed us to elaborate geomorphological map of dolines. Some guidance to detect dolines can be associated with fracture permeability dominated by nearly vertical joints and joint swarm is provided by fracture trace mapping from remote sensing. Commonly, dolines can be identified on the image and DEM as topographic depressions, which very often contain water or moist vegetation. The methodology allowed determining a doline distribution pattern what is important to environmental planning.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Renato Fontes Guimarães, Osmar Abílio de Carvalho Júnior, Eder de Souza Martins, Ana Paula Ferreira de Carvalho, and Roberto Arnaldo Trancoso Gomes "Detection of karst depression by aster image in the Bambui Group, Brazil", Proc. SPIE 5983, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V, 59831H (31 October 2005); https://doi.org/10.1117/12.627741
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Cited by 6 scholarly publications.
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KEYWORDS
Image filtering

Remote sensing

Vegetation

Image processing

Digital filtering

Data modeling

Digital imaging

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