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

被引:67
作者
de Carvalho Junior, Osmar Abilio [1 ]
Guimaraes, Renato Fontes [1 ]
Montgomery, David R. [2 ]
Gillespie, Alan R. [2 ]
Trancoso Gomes, Roberto Arnaldo [1 ]
Martins, Eder de Souza [3 ]
Silva, Nilton Correia [4 ]
机构
[1] Univ Brasilia UnB, Dept Geog, BR-70910900 Brasilia, DF, Brazil
[2] Univ Washington, Dept Earth & Space Sci, Seattle, WA 98195 USA
[3] EMBRAPA Cerrados, BR-73310970 Planaltina, DF, Brazil
[4] Univ Brasilia UnB, Fac Engn Gama, BR-72444240 Gama, DF, Brazil
关键词
Karst; limestone; DEM analysis; GIS; remote sensing; Brazil; TOPOGRAPHY MISSION; SHUTTLE RADAR; EBRO RIVER; SINKHOLES; RESOLUTION; ALGORITHM; DRAINAGE; DELINEATION; BASINS; STATE;
D O I
10.3390/rs6010330
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 SAo 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 m(2) 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.
引用
收藏
页码:330 / 351
页数:22
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