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Remote Sens. 2014, 6(1), 330-351;

Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil

Departamento de Geografia, Campus Universitário Darcy Ribeiro, Universidade de Brasília (UnB), Asa Norte, Brasília, DF 70910-900, Brazil
Department of Earth and Space Sciences, University of Washington, Seattle, WA 98195, USA
EMBRAPA Cerrados, Planaltina, DF 73310-970, Brazil
Faculdade de Engenharias do Gama, Universidade de Brasília (UnB), Gama, DF-72444-240, Brazil
Author to whom correspondence should be addressed.
Received: 10 November 2013 / Revised: 12 December 2013 / Accepted: 18 December 2013 / Published: 27 December 2013
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
PDF [1419 KB, uploaded 19 June 2014]


Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the São Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst depressions using morphometric attributes. The advantages and limitations of the applied methodology using different DEMs are examined by comparison with a sinkhole map generated from traditional geomorphological investigations based on visual interpretation of the high-resolution remote sensing images and field surveys. The threshold values of the depth, area size and circularity index appropriate for distinguishing dolines were identified from the maximum overall accuracy obtained by comparison with a true doline map. Our results indicate that the best performance of the proposed methodology for meso-scale karst feature detection was using ALOS/PRISM data with a threshold depth > 2 m; areas > 13,125 m2 and circularity indexes > 0.3 (overall accuracy of 0.53). The overall correct identification of around half of the true dolines suggests the potential to substantially improve doline identification using higher-resolution LiDAR-generated DEMs. View Full-Text
Keywords: Karst; limestone; DEM analysis; GIS; remote sensing; Brazil Karst; limestone; DEM analysis; GIS; remote sensing; Brazil
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

De Carvalho, O.A., Júnior; Guimarães, R.F.; Montgomery, D.R.; Gillespie, A.R.; Trancoso Gomes, R.A.; De Souza Martins, É.; Silva, N.C. Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil. Remote Sens. 2014, 6, 330-351.

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