Developing Efficient Procedures for Automated Sinkhole Extraction from Lidar DEMs

Miao, Xin; Qiu, Xiaomin; Wu, Shuo-sheng; Luo, Jun; Gouzie, Douglas; Xie, Hongjie

doi:10.14358/pers.79.6.545

Cited by 42 publications

(20 citation statements)

References 16 publications

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“…Their spatial distribution and histogram are illustrated in Figure 11. Melt pond fraction of each aerial photo-covered area is mostly less than 0.10 (or 10%), which is compatible with in-situ observations in this marginal ice zone (Xie et al, 2013). The minimum fraction is 0.01 ± 0.0014, maximum fraction is 0.29 ± 0.04, and average fraction is 0.06 ± 0.006.…”

Section: Melt Ponds Statisticssupporting

confidence: 87%

See 1 more Smart Citation

Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs

Miao

Xie

Ackley

et al. 2015

Cold Regions Science and Technology

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Section: Melt Ponds Statisticssupporting

confidence: 87%

“…raster vs vector method. Since the raster method requires time-consuming rasterization and morphological operations (Miao et al, 2013), we adopt a more straight-forward vector-based polygon neighborhood analysis algorithm that is programmed through ArcGIS 10.1/Python.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs

Miao

Xie

Ackley

et al. 2015

Cold Regions Science and Technology

Self Cite

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“…The number of sinkhole-like depressions can be reduced by using an automated procedure. For example, Miao et al (2013) illustrated one such procedure by using a random forest algorithm that uses shape and depth parameters to extract circular-or elliptical-shaped sinkholes from depressions. In our study area, we found that although most sinkholes have a circular or elliptical shapes, some sinkholes have more complicated shapes and may potentially be excluded by an automated procedure.…”

Section: Resultsmentioning

confidence: 99%

Improved Karst Sinkhole Mapping in Kentucky Using Lidar Techniques: A Pilot Study in Floyds

Zhu¹,

Taylor²,

Currens³

et al. 2014

JCKS

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The existing sinkhole database for Kentucky is based on low-resolution topographic maps created more than fifty years ago. LiDAR (Light Detection and Ranging) is a relatively recent technique that rapidly and accurately measures features on earth's surface in high-resolution. To test the feasibility of using LiDAR to map sinkholes in Kentucky, we have developed a method of processing LiDAR data to identify sinkholes and tested the method in portions of the Floyds Fork watershed in central Kentucky. The method consisted of four steps, creating a high-resolution digital elevation model (DEM) from LiDAR data, extracting surface depression features from the DEM, inspecting the depression features for probable sinkholes, and verifying the probable sinkholes in the field. A total of 1,683 probable sinkholes were identified in the study area, compared to 383 previously mapped for the same area. We field-checked 121 randomly-selected probable sinkholes and confirmed that 106 of them were karst sinkholes. This method increased the number of sinkholes by a factor of four with a success rate between 80% and 93% for the study area, demonstrating that the LiDAR sinkhole-mapping method is reliable and efficient. This method identified approximately 55% of the previously mapped sinkholes, and approximately 98% of the missed sinkholes appeared to be filled or covered for urban development and agriculture purposes. The next step is to extend this method to provide high-resolution sinkhole maps for other karst areas in Kentucky where LiDAR data become available.

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“…Sinkholes are natural depressions in the Earth's surface that primarily form in places where carbonate rocks are dissolved from water and overlying soil particles are carried away underground, causing the surface to subside gradually or collapse suddenly [1][2][3]. Sinkholes serve as a major connection between surface water and groundwater by collecting precipitation and draining it internally into the subsurface [3].…”

Section: Introductionmentioning

confidence: 99%

“…Both sinkhole sudden collapse and gradual subsidence may cause substantial damage to infrastructure assets [2]. In rural areas, sinkholes can cause the loss of arable land.…”

Section: Introductionmentioning

confidence: 99%

Karst Sinkhole Detecting and Mapping Using Airborne LiDAR - A Conceptual Framework

et al. 2019

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Sinkholes cause subsidence and collapse problems for many transportation infrastructure assets. Subsequently, transportation infrastructure management agencies dedicate a considerable amount of time and money to detect and map sinkholes as part of their asset management programs. Traditionally, sinkholes are detected through area reconnaissance, which includes visual inspection of a site to locate existing sinkholes or device inspection of a site to locate potential sinkholes or previously filled sinkholes. Another method for sinkhole detection is through a review of maps such as geological maps. These methods are expensive, time-consuming, and labor-intensive. Recent advances in remote sensing, especially airborne light detection and ranging (LiDAR), allows for the examination of the change in the Earth's surface elevation accurately and rapidly. The focus of this study is to develop a conceptual framework for sinkhole detection and mapping with airborne LiDAR. This conceptual framework lays the foundation for the future application of airborne LiDAR for sinkhole detection and mapping.

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Developing Efficient Procedures for Automated Sinkhole Extraction from Lidar DEMs

Cited by 42 publications

References 16 publications

Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs

Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs

Improved Karst Sinkhole Mapping in Kentucky Using Lidar Techniques: A Pilot Study in Floyds

Karst Sinkhole Detecting and Mapping Using Airborne LiDAR - A Conceptual Framework

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