Automated delineation of karst sinkholes from LiDAR-derived digital elevation models Qiusheng Wu a, ⁎, Chengbin Deng a , Zuoqi Chen b a Department of Geography, Binghamton University, State University of New York, Binghamton, NY 13902, United States b Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China abstract article info Article history: Received 12 June 2015 Received in revised form 4 May 2016 Accepted 5 May 2016 Available online 07 May 2016 Sinkhole mapping is critical for understanding hydrological processes and mitigating geological hazards in karst landscapes. Current methods for identifying sinkholes are primarily based on visual interpretation of low- resolution topographic maps and aerial photographs with subsequent field verification, which is labor- intensive and time-consuming. The increasing availability of high-resolution LiDAR-derived digital elevation data allows for an entirely new level of detailed delineation and analyses of small-scale geomorphologic features and landscape structures at fine scales. In this paper, we present a localized contour tree method for automated extraction of sinkholes in karst landscapes. One significant advantage of our automated approach for sinkhole ex- traction is that it may reduce inconsistencies and alleviate repeatability concerns associated with visual interpre- tation methods. In addition, the proposed method has contributed to improving the sinkhole inventory in several ways: (1) detection of non-inventoried sinkholes; (2) identification of previously inventoried sinkholes that have been filled; (3) delineation of sinkhole boundaries; and (4) characterization of sinkhole morphometric proper- ties. We applied the method to Fillmore County in southeastern Minnesota, USA, and identified three times as many sinkholes as the existing database for the same area. The results suggest that previous visual interpretation method might significantly underestimate the number of potential sinkholes in the region. Our method holds great potential for creating and updating sinkhole inventory databases at a regional scale in a timely manner. © 2016 Elsevier B.V. All rights reserved. Keywords: Sinkhole Karst Closed depressions LiDAR Contour tree Minnesota 1. Introduction Sinkholes are closed depressions in the Earth's surface with internal drainage caused by subsurface dissolution of soluble bedrock in karst landscapes (Miao et al., 2013). Sudden sinkhole collapse and gradual ground subsidence phenomenon may cause severe damage to human properties and affect water quality in underlying carbonate acquirers (Shaban and Darwich, 2011; Rahimi and Alexander, 2013; Galve et al., 2015; Taheri et al., 2015). Consequently, sinkhole inventory mapping is critical for understanding hydrological processes and mitigating geo- logical hazards in karst areas. The reliability of sinkhole susceptibility and hazard maps and the effectiveness of mitigation activities largely rely on the representativeness, completeness, and accuracy of the sinkhole inventories on which they are based (Al-Kouri et al., 2013; Gutiérrez et al., 2014). In the last few decades, a number of institutions and associations in several states of the United States have developed sinkhole or other karst feature databases mostly integrated in Geo- graphical Information Systems (GIS), including Kentucky (Dinger et al., 2007; Shukunobe, 2012; Zhu et al., 2014), Minnesota (Gao et al., 2002; Larson, 2009; Rahimi and Alexander, 2013), Missouri (Mukherjee, 2012; Nwokebuihe et al., 2014), and Florida (Montane, 2001; Seale et al., 2008; Vacher et al., 2008). However, most previous methods for mapping sinkholes were pri- marily based on visual interpretation of low-resolution topographic maps (e.g. U.S. Geological Survey 1:24,000 scale topographic maps) and aerial photographs with subsequent field verification, which are labor-intensive and time-consuming. Moreover, complete field verifica- tion of each sinkhole is often impractical, thus the reliability of manually digitized sinkhole data by even the same worker may be questionable (Doctor and Young, 2013). Last but not least, some previous studies (Witthuhn and Alexander, 1995; Rahimi and Alexander, 2013; Zhu et al., 2014) found that sinkholes might be changing fast due to natural or anthropogenic causes such as urban development and agricultural expansion. Therefore, we need to automate mapping of sinkholes to up- date the sinkhole inventory regularly. In recent decades, the advent of airborne Light Detection and Ranging (LiDAR) and Interferometric Synthetic Aperture Radar (InSAR) remote sensing technologies have produced large volumes of highly accurate and densely sampled topo- graphical measurements. The increasing availability of high-resolution digital elevation data derived from LiDAR and InSAR technologies allows for an entirely new level of detailed delineation and analyses of small- scale geomorphologic features and landscape structures at fine scales (Berardino et al., 2002; Lindsay and Creed, 2006; Gutiérrez et al., 2011; Huang et al., 2014; Wu et al., 2014; Galve et al., 2015). Geomorphology 266 (2016) 1–10 ⁎ Corresponding author. E-mail address: wqs@binghamton.edu (Q. Wu). http://dx.doi.org/10.1016/j.geomorph.2016.05.006 0169-555X/© 2016 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Geomorphology journal homepage: www.elsevier.com/locate/geomorph