Delineation and Classification of Karst Depressions Using LiDAR: Fort Hood Military Installation, Texas

Shaw-Faulkner, Melinda G.; Stafford, Kevin W.; Bryant, Aaron W.

doi:10.5038/9780979542275.1157

Cited by 10 publications

(13 citation statements)

References 5 publications

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“…A particularly promising area for future study is the acquisition of the morphometric attributes from LiDAR (Light Detecting and Ranging) data, which has impressive accuracy for detecting sinkholes and estimating subsidence rates [65][66][67][68]. Another major advantage of utilizing laser scanning technology is its canopy penetration ability.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2013

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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 Sã o 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 OPEN ACCESSRemote Sens. 2014, 6 331 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2013

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“…However, remote sensing methods are unsuitable to real-time monitoring (Paine et al, 2009). Similarly, LiDAR (Light Detection and Ranging) technology is unsuitable to real-time monitoring, although it provides high density data which can be processed to produce a high resolution topographic map (Doctor and Young, 2013;Shaw-Faulkner et al, 2013).…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

Numerical Simulation of Karst Soil Cave Evolution

Liu¹,

Meng²,

Guan³

et al. 2015

Sinkholes and the Engineering and Environmental Impacts of Karst: Proceedings of the Fourteenth Multidisciplinary Conference

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arching effect in the overlying soil layer. By analyzing the plastic zone of the soil layer, it was found that, in rigid clay, arch roof collapse and tensile failure are the major events that lead eventually to the barrel-shaped or bottle-shaped forms of collapsed pits. In loose soil, shear failure of the arch toe is the major event that eventually leads to the taper-shaped or bowl-shaped form of collapsed pits. Generally speaking, stability and size of soil caves can be determined using the three variables of low shear stress area, equal settlement plane, and plastic zone discussed above. The numerical simulation of this study is valuable to the monitoring and assessment of sinkhole occurrence.

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“…Acquisitions of LiDAR data for the creation of highly accurate (sub-meter) elevation models that can be used for rapid, precise detection of sinkholes. (Daniel.et al, 2013;Shaw et al, 2013).…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Study on Early Recognition Methods of Cover-Collapse Sinkholes in China

Liu¹,

Meng²,

Pan³

2018

Proceedings of the 15th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst and Th

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Cover-collapse sinkholes pose a major geohazard occurring in mantled karst regions in the south of China in recent years. Cover deposits generally mask the subsurface development and propagation of the deformation to the topographic surface. Human security and land-use planning in sinkhole-prone areas need to be preceded by detailed investigations focused on identification of pre-existing sinkholes, subsurface dissolution, subsidence features, and groundwater condition. Thefore the abnormal, early hidden danger signs (ground, underground and hydrodynamic) of karst collapse are studied. Further, the corresponding identification techniques and methods are presented, such as surface surveying techniques (satellite remote sensing, the drone, etc.), underground detection technology (ground penetrating radar, microgravity, micro tremor, etc.), and monitoring of groundwater conditions. Finally, all kinds of techniques and methods can be combined to form a comprehensive system for identifying hidden dangers of karst collapse. This paper aims at identifying the early hidden dangers of karst collapse geological disasters. This has important theoretical, technical and social significance in order to minimize the impact of disasters on the people and the environment.

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Delineation and Classification of Karst Depressions Using LiDAR: Fort Hood Military Installation, Texas

Cited by 10 publications

References 5 publications

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

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

Numerical Simulation of Karst Soil Cave Evolution

Study on Early Recognition Methods of Cover-Collapse Sinkholes in China

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