Ms. Hao Chen Profile

Hao Chen

at Institute of Solid State Physics

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Proceedings Article | 9 December 2015 Paper

A semi-automatic model for sinkhole identification in a karst area of Zhijin County, China

Hao Chen, Takashi Oguchi, Pan Wu

Proceedings Volume 9808, 98080Q (2015) https://doi.org/10.1117/12.2207433

KEYWORDS: Visualization, Performance modeling, Data modeling, Remote sensing, Geology, Spatial resolution, Visual analytics, Photography, Geographic information systems, Environmental sensing

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The objective of this study is to investigate the use of DEMs derived from ASTER and SRTM remote sensing images and topographic maps to detect and quantify natural sinkholes in a karst area in Zhijin county, southwest China. Two methodologies were implemented. The first is a semi-automatic approach which stepwise identifies the depression using DEMs: 1) DEM acquisition; 2) sink fill; 3) sink depth calculation using the difference between the original and sinkfree DEMs; and 4) elimination of the spurious sinkholes by the threshold values of morphometric parameters including TPI (topographic position index), geology, and land use. The second is the traditional visual interpretation of depressions based on the integrated analysis of the high-resolution aerial photographs and topographic maps. The threshold values of the depression area, shape, depth and TPI appropriate for distinguishing true depressions were abstained from the maximum overall accuracy generated by the comparison between the depression maps produced by the semi-automatic model or visual interpretation. The result shows that the best performance of the semi-automatic model for meso-scale karst depression delineation was using the DEM from the topographic maps with the thresholds area ⪆ 60 m², ellipticity ⪆ 0.2 and TPI ≤ 0. With these realistic thresholds, the accuracy of the semi-automatic model ranges from 0.78 to 0.95 for DEM resolutions from 3 to 75 m.

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