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Using LiDAR to Map Sinkholes (EPAN09)


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Using LiDAR to Map Sinkholes in Jefferson County WV,

John Young, U.S. Geological Survey

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Using LiDAR to Map Sinkholes (EPAN09)

  1. 1. Using LiDAR to map sinkholes in Jefferson County, West Virginia John Young, USGS Leetown Science Center Kearneysville, WV
  2. 2. “ I read the news today, oh boy Four thousand holes in Blackburn, Lancashire And though the holes were rather small They had to count them all…” A Day in the Life The Beatles
  3. 3. Project Objectives <ul><li>* Part of a USGS study of water availability and threats to water supply of the Leetown Science Center (Kozar et al. 2007) </li></ul><ul><li>Acquire LiDAR data to allow for modeling of fine scale surface features </li></ul><ul><li>Generate fine-scale digital elevation models from LiDAR data </li></ul><ul><li>Use topographic analysis, aerial photography, and statistical analysis to map sink holes and potential groundwater recharge zones from fine-scale surface models </li></ul>
  4. 4. What is LiDAR? <ul><li>LiDAR = Li ght D etection A nd R anging (aka “airborne laser scanning”) </li></ul><ul><li>Laser pulses sent from aircraft in dense scanning pattern (0.5-2 meters apart) </li></ul><ul><li>Return of laser pulse recorded at aircraft </li></ul><ul><li>Time to return, speed of light, and altitude of plane used to compute surface height (z) of each pulse with high accuracy </li></ul><ul><li>Ground position of each laser pulse computed using differential GPS (x,y) </li></ul>
  5. 5. What is LiDAR? Image courtesy:
  6. 6. LiDAR returns: First (top of canopy, roofs), Last (ground surface) LiDAR returns (overlaid on aerial photograph)
  7. 7. Data Acquisition <ul><li>Acquired LiDAR data by partnering with USDA-NRCS </li></ul><ul><ul><li>Conducted accuracy assessment of LiDAR data acquisition in exchange for access to data </li></ul></ul><ul><li>LiDAR flight of Jefferson County WV, by Sanborn, Inc. in April 2005 (leaf off) </li></ul><ul><li>LiDAR data delivered as first return , last return , and “ bare earth ” (vegetation and buildings removed) </li></ul><ul><ul><li>81 tiles (1/16 th quad) </li></ul></ul><ul><ul><li>< 1.0 meter point spacing </li></ul></ul>
  8. 8. <ul><li>USGS QA/QC Campaign: </li></ul><ul><li>38 stations established throughout county in variety of land surface types </li></ul><ul><li>Survey-grade GPS used to collect surface height data </li></ul><ul><li>GPS surface heights compared to mean LiDAR return height within 2 meters of GPS points </li></ul><ul><li>All but 1 checkpoint were within ± 0.15 meter RMSE (< FEMA spec) </li></ul>
  9. 9. Problem: Find method to locate surface sinks, even under forest canopy Color Aerial Photo, 0.6 meter pixel resolution, 2003
  10. 10. Data: LiDAR, acquired Spring 2005, delivered Fall 2005 Raw (last return) data gridded to 2m surface
  11. 11. Progressive Curvature Filtering <ul><li>Evans and Hudak (2007) proposed a method for processing LiDAR data to find ground surface in forests of the interior western U.S. </li></ul><ul><li>Method uses an adaptive, iterative filter that considers scale of variation </li></ul><ul><ul><li>Fits “thin-plate splines with tension” at multiple scales to examine local curvature and determine which points to filter out </li></ul></ul><ul><li>Effective at removing vegetation </li></ul>
  12. 12. Data processing: Progressive Curvature Filter (Evans and Hudak, 2006) PCF filtered data gridded to 2m surface Raw (unflitered) last return data, gridded to 2m
  13. 13. Data processing: a modification of McNab’s (1989) “Terrain Shape Index” TSI = DEMgrid – focalmean(DEMgrid, annulus, 1, 5) Focal cell higher than surrounding cells = convex Focal cell lower than surrounding cells = concave * Graphic after F. Biasi (TNC)
  14. 14. Data processing: Landform analysis Landform shape in 10 m window, red = concave, blue = convex
  15. 15. Data processing: Landform assessment Find compact bowl features, possible sinkholes
  16. 16. Sinkhole = yes! Field Verification
  17. 17. Another view…
  18. 18. Field validation results 94 sites mapped, 55 visited on ground Sink (throat) found: 16.4% Probable sink (no throat): 43.6% Depression: 25.5% Not a sink: 14.5%
  19. 20. Other applications of LiDAR <ul><li>Site analysis </li></ul><ul><li>Geologic structure </li></ul><ul><li>Fault line tracing </li></ul><ul><li>Hydrology / floodplain assessments </li></ul><ul><li>Vegetation height/structure </li></ul><ul><li>Slope analysis </li></ul><ul><li>?? </li></ul>
  20. 24. Conclusions <ul><li>PCF filtered LiDAR data provided a high resolution “bare earth” elevation surface </li></ul><ul><li>Converting elevation values into a landform shape index was effective at highlighting depression features </li></ul><ul><li>Caveat: </li></ul><ul><ul><li>Depression ≠ sinkhole </li></ul></ul><ul><ul><li>(but it’s a good place to start looking!) </li></ul></ul>For additional info, contact John Young (
  21. 25. Acknowledgements <ul><li>Jared Beard , USDA-NRCS soil scientist, Moorefield, WV (collaborator) </li></ul><ul><li>John Jones , USGS Geography division, Reston, VA (collaborator) </li></ul><ul><li>Bob Glover , USGS Geography division, Reston, VA (GPS survey) </li></ul><ul><li>Jeffrey Evans , USFS, Moscow, Idaho (filtering algorithms) </li></ul><ul><li>Kenny Legleiter , (formerly of) USDA-NRCS, Ft. Worth, TX (LiDAR flight contracting) </li></ul><ul><li>Data acquired by Sanborn, Inc. under contract to USACE/USDA-NRCS </li></ul>