Abstract
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.
Highlights
Karst depressions cause damage both in rural areas through the loss of arable land and in urban areas due to damage to buildings, roads, and water supply systems [1,2]
The reference map was built from the visual interpretation of ALOS/PRISM image (2.5 m); Google
Our analysis shows that in our study area, the ASTER-GDEM is susceptible to noise, leading to inaccurate results and visual anomalies and artifacts that represent barriers to its effective utilization for doline detection (Figure 6)
Summary
Karst depressions cause damage both in rural areas through the loss of arable land and in urban areas due to damage to buildings, roads, and water supply systems [1,2]. Problems caused by karst depressions have motivated many studies on their identification and spatial distribution using remote sensing data [3,4]. Endokarst environments are typically characterized by open conduits with low capacity for storage and rapid groundwater flow. This intimate relationship between surface water and groundwater defines a system of interconnected caves and superficial features. Due to such relationships, the locations of karst aquifers or preferential flowpaths for groundwater have been inferred by the positions of fracture sets or doline alignments apparent on aerial photographs and satellite images [9,10,11]. Remote-sensing data are used as inputs in GIS models for detecting and monitoring areas vulnerable to groundwater pollution in karst terrain [12,13]
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