Characterizing the error distribution of lidar elevation data for North Carolina

References

• Adams , J.C. and Chandler , J.H. 2002 . Evaluation of LIDAR and medium scale photogrammetry for detecting soft-cliff coastal change . Photogrammetric Record , 17 : 405 – 418 .
   Web of Science ®Google Scholar
• American Society for Photogrammetry and Remote Sensing LIDAR Committee . 2004 . ASPRS guidelines: Vertical Accuracy Reporting for LIDAR Data , Bethesda, MD : American Society for Photogrammetry and Remote Sensing .
   Google Scholar
• Bonin , O. and Rousseaux , F. 2005 . Digital terrain model computation from contour lines: How to derive quality information from artifact analysis . Geoinformatica , 9 : 253 – 268 .
   Web of Science ®Google Scholar
• Bowen , Z.H. and Waltermire , R.G. 2002 . Evaluation of light detection and ranging (LIDAR) for measuring river corridor topography . Journal of the American Water Resources Association , 38 : 33 – 41 .
   Web of Science ®Google Scholar
• Cobby , D.M. , Mason , D.C. and Davenport , I.J. 2001 . Image processing of airborne laser altimetry data for improved river modeling . ISPRS Journal of Photogrammetry and Remote Sensing , 56 : 121 – 138 .
   Web of Science ®Google Scholar
• Federal Geographic Data Committee . 1998 . Geospatial positioning accuracy standards part 3: National standard for spatial data accuracy , 3 – 1998 . FGDC-STD-007 .
   Google Scholar
• Fisher , P. 1998 . Improving modeling of elevation error with geostatistics . GeoInformatica , 2 : 215 – 233 .
   Google Scholar
• Fowler , R. 2001 . “ Topographic LIDAR, digital elevation model technologies and applications ” . In The DEM Users Manual , Edited by: Maune , D. 207 – 236 . Bethesda, MD : American Society for Photogrammetry and Remote Sensing .
   Google Scholar
• Greenwalt , C. and Shultz , M. 1968 . Principles of Error Theory and Cartographic Applications , St. Louis, MO : Aeronautical Chart and Information Center .
   Google Scholar
• Han , S. , Lee , J. and Yu , K. 2007 . An approach for segmentation of airborne laser point clouds utilizing scan-line characteristics . ETRI Journal , 29 : 641 – 648 .
   Web of Science ®Google Scholar
• Hodgson , M.E. and Bresnahan , P. 2004 . Accuracy of airborne LIDAR-derived elevation: empirical assessment and error budget . Photogrammetric Engineering & Remote Sensing , 70 : 331 – 339 .
   Web of Science ®Google Scholar
• Hodgson , M.E. , Jensen , J.R. , Schmidt , L. , Schill , S. and Davis , B. 2003 . An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs . Remote Sensing of Environment , 84 : 295 – 308 .
   Web of Science ®Google Scholar
• Hodgson , M.E. , Jensen , J. , Raber , G. , Tulils , J. , Davis , B.A. , Thompson , G. and Schuckman , K. 2005 . An evaluation of LIDAR-derived elevations and terrain slope in leaf-off conditions . Photogrammetric Engineering and Remote Sensing , 71 : 817 – 823 .
   Web of Science ®Google Scholar
• Horne , B.K.P. 1981 . Hill shading and the reflectance map . Proceedings of the IEEE , 69 : 14 – 47 .
   Web of Science ®Google Scholar
• Krabill , W.B. , Abdalati , W. , Frederick , E.B. , Manizade , S.S. , Martin , C.F. , Sonntag , J.G. , Swift , R.N. , Thomas , R.H. and Yungel , J.G. 2002 . Aircraft laser altimetry measurement of elevation the Greenland ice sheet: technique and accuracy assessment . Journal of Geodynamics , 34 : 357 – 376 .
   Web of Science ®Google Scholar
• Lilliefors , H. 1967 . On the Kolmogorov-Smirnov test for normality with mean and variance unknown . Journal of the American Statistical Association , 62 : 399 – 402 .
   Web of Science ®Google Scholar
• Lohmann , P. , Koch , A. and Schaeffer , M. 2000 . Approaches to the filtering of laser scanner data . International Archives of Photogrammetry & Remote Sensing XXXIII , : 540 – 547 . Part B3
   Google Scholar
• López , C. 2000 . Improving the elevation accuracy of digital elevation models: A comparison of some error detection procedures . Transactions in GIS , 4 : 43 – 64 .
   Google Scholar
• Maling , D.H. 1989 . Measurements from Maps , New York : Pergamon Press .
   Google Scholar
• NDEP . 2003 . Proposed changes to the geospatial positioning accuracy standards. Part 3: national standard for spatial data accuracy. National Digital Elevation Program, proposal submitted to FGDC. ,
   Google Scholar
• NDEP . 2004 . Guidelines for Digital Elevation Data Version 1.0 , Reston, VA : National Digital Elevation Program .
   Google Scholar
• 2004 . “ North Carolina Flood Mapping Program ” . In Quality Control of Light Detection and Ranging (LIDAR) Elevation Data in North Carolina for Phase II of NCFMP , Raleigh, NC : North Carolina Flood Mapping Program .
   Google Scholar
• North Carolina Flood Mapping Program, 2006, Issues Papers of the North Carolina Cooperating Technical State Flood Mapping Program. Available online at (accessed 20 April 2010). http://www.ncfloodmaps.com/issue_papers.htm (http://www.ncfloodmaps.com/issue_papers.htm)
   Google Scholar
• Oksanen , J. and Sarjakoski , T. 2006 . Uncovering the statistical and spatial characteristics of fine toposcale DEM error . International Journal of Geographic Information Science , 20 : 345 – 369 .
   Web of Science ®Google Scholar
• Peng , M. and Shih , T. 2006 . Error assessment in two lidar-derived TIN datasets . Photogrammetric Engineering ad Remote Sensing , 72 : 933 – 947 .
   Web of Science ®Google Scholar
• Raber , G.T. , Jensen , J.R. , Schill , S.R. and Schuckman , K. 2002 . Creation of digital terrain models using an adaptive LIDAR vegetation point removal process . Photogrammetric Engineering & Remote Sensing , 68 : 1307 – 1315 .
   Web of Science ®Google Scholar
• Shan , J. and Sampath , A. 2005 . Urban DEM generation from raw LIDAR data: A labeling algorithm and its performance . Photogrammetric Engineering & Remote Sensing , 71 : 217 – 226 .
   Web of Science ®Google Scholar
• Shan , J. and Toth , C. , eds. 2008 . Topographic Laser Ranging and Scanning: Principles and Processing , Boca Raton, FL : CRC Press .
   Google Scholar
• Shapiro , S.S. and Wilk , M.B. 1965 . An analysis of variance test for normality (complete samples) . Biometrika , 52 : 591 – 599 .
   Web of Science ®Google Scholar
• Sithole , G. 2005 . Segmentation and classification of airborne laser scanner data. Publications on Geodesy 59 , Delft, , the Netherlands : NCG .
   Google Scholar
• Su , J. and Bork , E. 2006 . Influence of vegetation, slope and lidar sampling density on DEM accuracy . Photogrammetric Engineering and Remote Sensing , 72 : 1265 – 1274 .
   Web of Science ®Google Scholar
• US Bureau of the Budget . 1947 . United States National Map Accuracy Standards , Washington, DC, , US : Bureau of the Budget .
   Google Scholar
• US Geological Survey (USGS) . 1999 . Map accuracy standards. United States Geological Survey Fact Sheet FS– , : 171 – 99 .
   Google Scholar
• Zandbergen , P.A. 2008 . Positional accuracy of spatial data: non-normal distributions and a critique of the National Standard for Spatial Data Accuracy . Transactions in GIS , 12 : 103 – 130 .
   Google Scholar
• Zhang , K. , Chen , S.C. , Whitman , D. , Shyu , M.L. , Yan , J. and Zhang , C. 2004 . A progressive morphological filter for removing non-ground measurements from airborne LIDAR data . IEEE Transactions in Geoscience and Remote Sensing , 41 : 872 – 882 .
   Web of Science ®Google Scholar