Data Collection / Aerial / LiDAR / Government / Remote Sensing / Surveying & Mapping Software / Underground

Sinkhole ID

A portion of the base map combined with the new 2012 Bedrock Geology of the Evansville Quadrangle, by Joseph A. Devera and Jane Johnshoy Domier with ISGS.

High-resolution LiDAR technology provides improved elevation data along with a detailed inventory of karst features in the Sinkhole Plain of Illinois.

Situated in southwestern Illinois, principally within Monroe, Randolph and St. Clair Counties, the Illinois Sinkhole Plain contains the highest concentration of karst features within the state. Although 70 percent of the region is dominated by agricultural land use, St. Louis - East St. Louis, Ill., which ranks as the 19th largest metropolitan area in the United States, is located at the northern portion of the plain. It is estimated the Sinkhole Plain contains approximately 10,000 sinkhole features, making them a distinctive landscape feature within the region.

Sinkholes begin with the collapse of unconsolidated sediments into a bedrock crevice or cavity. Because the entire region is underlain by highly erodible limestone, geologic conditions are optimum for sinkhole creation. Over time, the cavity propagates to the surface, the overlying sediments collapse into the cavity, and erosional processes produce the bowl-shaped depression known as a sinkhole. Because sinkholes serve as a direct conduit to the underlying bedrock aquifer in the region, there is a high potential for groundwater contamination within the Sinkhole Plain.

Despite the potential health and safety hazard of sinkholes, no exhaustive inventory has been conducted within the Sinkhole Plain to determine their precise numbers and geographic locations. A common approach used to inventory sinkhole features is tabulation of the closed depression contours on USGS 7.5-minute (1:24,000 scale) topographic quadrangle maps. However, the 10- and 20-foot contour intervals commonly used for the USGS topographic maps in the region are too coarse to identify shallow sinkholes, resulting in an underestimation by as much as 30 percent. One contributing factor is that the most well-developed and deepest sinkholes are concealed by dense forest and woodland cover and are not easily detected using aerial mapping photography; therefore, many sinkhole features are not symbolized on USGS topographic maps. USGS one-third arc-second (10 x 10 meter, approximately 32 x 32 feet ground spatial resolution) digital elevation model (DEM) gridded data created from the topographic contours also provide a view of the karst landscape, but the sinkhole features lack detail, and many are missing altogether.

A digital terrain model produced from Merrick & Company 0.5 meter ground spatial resolution LiDAR using the ground point cloud data. The densest groupings of sinkholes, some of which reach 50 feet in depth, delineate the pathway of a subsurface cave system.

Outdated, low-resolution elevation data coupled with the need for a detailed inventory of karst features in the Sinkhole Plain of Illinois provided the rationale for using high-resolution LiDAR technology. In early 2012, the Illinois State Geological Survey (ISGS), a division of the Prairie Research Institute at the University of Illinois at Urbana-Champaign, contracted Merrick & Company to collect LiDAR data that would aid in developing a comprehensive understanding of the region.

LiDAR data is a proven tool increasing the functionality and effectiveness of terrain models. The capabilities of LiDAR technology would fulfill the needs of differing science groups within ISGS and would also meet the latest USGS specifications as described in the recently published LiDAR Base Specification Version 1.0.*

The high-resolution dataset for this project was collected using airborne LiDAR. The collection area encompassed 1,778 square miles, which included a buffer area of 1,500 feet outward from the outside perimeter of the three-county boundary. The nominal point spacing (NPS) of the LiDAR point cloud for the large project area was 1.0 meter.

Two localized focus areas were selected within the project areas as “high density LiDAR” test cases. LiDAR data for these two sites was captured at a NPS of 0.5 meter, which translates to a point density of 4 points per square meter. Site 1, the “Sinkhole Plain,” in Monroe County, has an area of 136 square miles, and is dominated by dense forest and woodland cover. Site 2, East St. Louis, a 74 square mile area in northwest St. Clair County, is mostly urbanized along with a portion of the Mississippi River floodplain and mixture of land cover types on the adjoining upland.

Using its Leica ALS50-II+ multiple-pulse scanning system, Merrick captured the LiDAR point cloud to meet a fundamental vertical accuracy (FVA) of 24.5 centimeters (0.80 feet) at the 95-percent confidence level (12.5 centimeters [0.41 foot] RMSEZ). Supplemental vertical accuracy (SVA) and consolidated vertical accuracy (CVA) both met 36.3 centimeters (1.19 foot) (95th percentile). National Digital Elevation Program/ASPRS LiDAR guidelines were used to validate these accuracies.

A digital terrain model produced from Merrick & Company 0.5 meter ground spatial resolution LiDAR using the ground point cloud data. The densest groupings of sinkholes, some of which reach 50 feet in depth, delineate the pathway of a subsurface cave system.

The team surveyed approximately 100 checkpoints in support of the LiDAR acquisition. These checkpoints were used to validate the FVA. Additionally, the team surveyed a minimum of 80 land cover class checkpoints, which were used to validate the SVA and CVA requirements. A minimum of 20 checkpoints for each of the following land cover classes were collected:

  • Tall weeds and crops
  • Brush lands and low trees
  • Forested areas fully covered by trees
  • Urban areas with dense artificial structures

Once the coverage and quality check of the LiDAR data were confirmed, the team proceeded with the post-processing of the laser, IMU (inertial measurement unit) and AGPS (airborne GPS) data and, subsequently, the boresight (or calibration) task. Once the boresight passed rigorous QC, Merrick began the classification (filtering) of the LiDAR point cloud. The LiDAR was classified to ASPRS LAS 1.2 standards (see sidebar, LiDAR Classification). Classes 9 and 10 were defined by incorporating the hydro-flattening break lines that were captured in accordance with USGS NGP LiDAR Base Specification Version 1.0.

All deliverables were prepared in real-world coordinates defined in the Illinois State Plane Coordinate System (SPCS), West Zone; North American Datum of 1983, adjusted to the National Spatial Reference System of 2007 (NAD 83/NSRS 2007); North American Vertical Datum of 1988 (NAVD 88), using GEOID09 for converting ellipsoidal heights to orthometric heights; and units presented as U.S. Survey Foot.

A classified LiDAR point cloud, rotated in 3D to provide a perspective view. The ground is interpolated to a tinned surface to appear solid, and all above-ground features are rendered as points.

Soon after the LiDAR data collections for the three-county Metro East Illinois region became available within the ISGS, the bare earth LiDAR elevation data was used to conduct an inventory of karst features. The same deliverables became invaluable in the preparation of new topographic base maps to support the Illinois component of the 2012 USGS STATEMAP program. The classified LAS LiDAR point cloud was used to create digital terrain model (DTM) and shaded relief hillshade model data for several USGS 7.5-minute topographic quadrangles. ISGS used the DTM to generate new topographic contours for each quadrangle since the existing USGS topographic contour data was nearly 45 years old. Having access to current data was especially important since the geologic mapping projects are within areas where geomorphic processes are actively modifying the physical landscape. The new LiDAR topographic base data were then used to produce final geologic maps for submission to the USGS.

The LiDAR data collected and processed under this effort is critical to producing accurate, realistic terrain for the modeling community. It provides truer surface features at actual locations and their physical attributes, which are used to generate detailed information that many groups need to fulfill specific informational/governmental requirements.

The LiDAR data deliverables also meet the statewide Illinois Height Modernization Program (ILHMP) requirements for enhanced elevation data. ILHMP coordinates collection, archiving and public distribution of the Illinois LiDAR elevation data funded by the Illinois Department of Transportation, in addition to federal State Planning and Research funding from the U.S. Federal Highway Administration.


*http://pubs.usgs.gov/tm/11b4/.

 

LiDAR Classifications

  • Class 0 = Created, never classified
  • Class 1 = Unclassified
  • Class 2 = Ground
  • Class 3 = Low Vegetation
  • Class 4 = Medium Vegetation
  • Class 5 = High Vegetation
  • Class 6 = Building
  • Class 7 = Low point (noise)
  • Class 8 = Model Key-point
  • Class 9 = Water
  • Class 10 = Ignored Ground (Breakline Proximity)

Deliverables

LiDAR Products (by tile)

  • Classified (all-return) Point Cloud
  • First-return LiDAR points in ASCII (x,y,z,i) format
  • Classified bare-earth (ground) LiDAR points in ASCII (x,y,z,i) format
  • FGDC compliant metadata (project/product level) in XML format

DEM (Raster Grid) Products (by tile)

  • Bare-earth (ground) LiDAR surface in 3.28' cell size 32-bit ERDAS IMG format
  • Bare-earth (ground) LiDAR surface in 3.28' cell size 32-bit Esri raster grid format
  • First-return LiDAR surface in 3.28' cell size 32-bit ERDAS IMG format
  • First-return LiDAR surface in 3.28' cell size 32-bit Esri raster grid format
  • Bare-earth (ground) LiDAR surface in 3.28' cell size in ASCII format (x,y,z)
  • First-return LiDAR surface in 3.28' cell size in ASCII format (x,y,z)
  • FGDC compliant metadata (project/product level) in XML format

Hydro-Flattening Breakline Products

  • Terrain Dataset (geodatabase) with “hydro-flattening” breaklines as polygon and line feature classes
  • “Hydro-flattening” breaklines in Esri PolylineZ and PolygonZ shapefile format
  • FGDC compliant metadata (project/product level) in XML format
     

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