LiDAR Data Gave Us 2’ Contours, Now What…?

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LiDAR Data Gave Us 2’ Contours, Now What…?

  1. 1. LiDAR Data Gave Us 2’ Contours, Now What…?
  2. 2.  1mb = 1,000 kb 1 gb = 1,000 mb 1 tb = 1,000 gb 2 Terrabytes = 1,428,571 3.5” floppy disks ◦ Today’s cost around $750.00 Stacked on top of one another = 2.5 miles high! ◦ And at the time, they cost $1.00+ MN GIS/LIS Fall Workshops 11/01/2012
  3. 3. LiDAR Derived DEM Cell Size: 1 meter sq Vertical Error: 15 cm1 1.5 million points / sq mile2.5mi USGS Standard DEM Cell Size: 30 meter sq Vertical Error: “Equal to or better than 15 meters”2 1600 points / sq mile 1 Varies based on project specifications 2 http://edc.usgs.gov/guides/dem.html MN GIS/LIS Fall Workshops 11/01/2012
  4. 4.  LiDAR datasets tend to be very large ◦ LAS Format  All Returns – 4 Million points ~ 55 mb / square mile  Bare Earth – 3 million points ~ 45 mb / square mile ◦ ASCII Format  All Points – 4 million points ~ 75 mb / square mile  Bare Earth – 3 million points ~ 73 mb / square mile ◦ Grid Format  5 mb / square mile in integer format  11.2 mb / square mile in floating point format OC has over 300GB in raw data!!! Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  5. 5.  Water Resources Water Quality ◦ Floodplain mapping Watershed modeling ◦ Storm water management ◦ Drainage basin Wetland reconstruction delineation Land cover/land use ◦ Shoreline erosion mapping Geology Forestry ◦ Sinkhole identification ◦ Geologic/geomorphic Forest characterization mapping Fire fuel mapping Transportation Fish and Wildlife ◦ Road and culvert design ◦ Cut and fill estimation Management ◦ Archaeological site Drainage and water identification control Agriculture Walk-in Accessibility ◦ Erosion control structure design Habitat Management ◦ Soils mapping Emergency ◦ Precision farming Management Debris removal Hazard Mitigation Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  6. 6.  Need licenses: ◦ ArcEditor/ArcInfo ◦ 3D Analyst ◦ Spatial Analyst AP Framework ArcHydro Extension Data in same projection
  7. 7.  3D Analyst Extension ◦ Manages 3D data ◦ Generates surfaces for use in ArcHydro Spatial Analyst Extension performs the analyses
  8. 8.  LAS files (Common LiDAR Data Exchange) ◦ Stores a variety of point information  Number of returns  Return Number  Intensity  Classification  X,Y, Z values  Scan Direction  Scan Angle Rank  GPS Time Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  9. 9.  Intensity = amount of energy reflected for each return Different surfaces reflect differently based on wavelength of laser Example at 1064nm (NIR), water absorbs, vegetation highly reflective Can be used to build black and white near-IR images 11/01/2012 Courtesy of MN GIS/LIS Fall Workshops Slide courtesy of USGS
  10. 10.  Single Return Multiple returns Waveform Returns Courtesy of MN GIS/LIS Fall Workshops 11/01/2012 Slide courtesy of USGS
  11. 11.  Single Return 1st return Multiple returns 2nd return Waveform 3rd return Returns 4th return Courtesy of MN GIS/LIS Fall Workshops 11/01/2012 Slide courtesy of USGS
  12. 12. ReturnsSingle ReturnMultiplereturnsWaveformReturns Courtesy of MN GIS/LIS Fall Workshops 11/01/2012 Slide courtesy of USGS
  13. 13.  Classification – Points can be classified to reflect their ground condition Class Definition 0 Created, Never Classified 1 Unclassified 2 Ground 3 Low Vegetation 4 Medium Vegetation 5 High Vegetation 6 Building 7 Low Point (noise) 8 Model Key-Point (mass point) 9 Water 12 Overlap MN GIS/LIS Fall Workshops 11/01/2012
  14. 14. Land & Water (LCD)
  15. 15. Lidar in Red GPS Topo in Green Co urt es y of Sa ukField was contour strip cropped so vegetation is not uniform which may Coaccount for some of the variability along the cross section. un ty
  16. 16. Lidar in Red GPS Topo in GreenThe same cross section with lidar shifted down vertically 3”. Lidar may giveabsolute elevations slightly higher than referenced vertical datum, but relativeelevations for similar land cover provide good results. Courtesy of Sauk County
  17. 17. Mowed meadow and lawn land cover provided lidar elevations thatmatched absolute reference vertical datum elevations.Marsh (heavy vegetation, unmowed, not pastured) land cover provided lidarelevations higher than absolute reference vertical datum elevations.There is a lidar elevation shift when going from one land cover type toanother, but most sites are typically of one land cover so Sauk Countyhasn’t considered it a significant issue. Courtesy of Sauk County
  18. 18. *#SCBM2 Maple Creek Shots within 0.02’ relative to control.
  19. 19. 50’ x 50’ Square Open Ground = 52 LIDAR Points (notice building point removal)
  20. 20. 50’ x 50’ Square Woods = 16 LIDAR Points
  21. 21. An 11.3 Acre Selection = 13,634 Points
  22. 22.  Great Planning tool Engineering may require survey. Shapefile limitation Land Use limitation Data maintenance
  23. 23.  Lidar data can be visualized a number of ways ◦ POINTS ◦ TERRAINS (ESRI) ◦ GRIDS (DEM or DSM) ◦ TINS ◦ CONTOURS MN GIS/LIS Fall Workshops 11/01/2012
  24. 24.  Create File Geodatabase Convert LiDAR to multipoint feature class Make a Terrain Export rasters (i.e. DEM, Grid) for analysis
  25. 25.  Here’s our process:1. Need 3D Analyst or Spatial Analyst for LIDAR Processing.2. In ArcCatalog, right-clickNEWCreate a file geodatabase.3. In Arc Catalog, right-click the named file geodata baseCreate a feature dataset and import County coordinate system for horizontal projection.4. Choose vertical projection of LIDAR data (NAVD 88 for us)5. In ArcCatalog Use 3D-Analyst ToolsConversionFrom File”ASCII 3d to Feature Class” tool to select tiles to process (careful over 6 tiles.)6. In ArcCatalog, right-click the Feature DatasetNEWTerrain.7. Follow the Terrain Wizard. We let GIS Calculate the Pyramids.8. In ArcCatalog Use 3D-Analyst ToolsConversionFrom Terrain”Terrain to Raster” tool to convert Terrain to a DEM. (3 min./tile)9. For hydrology, “CELLSIZE 15” seems to be good compromise.10. For Cross-Section work, may want to use 3D-Analyst ToolsConversionFrom Terrain”Terrain to TIn” tool to convert Terrain to a TIN.11. A county-wide terrain and DEM may need to run over the weekend.
  26. 26.  Per ESRI, a terrain dataset is a multiresolution, TIN- based surface built from measurements stored as features in a geodatabase. Terrains reside in the geodatabase, inside feature datasets with the features used to construct them. Terrains have participating feature classes and rules, similar to topologies. Common feature classes that act as data sources for terrains include the following: ◦ Multipoint feature classes of 3D mass points such as lidar ◦ 3D point and line feature classes, i.e. breaklines ◦ Study area boundaries that define the bounds of the terrain dataset
  27. 27. Courtesy of MN GIS/LIS Fall Workshops 11/01/2012 Slide courtesy of USGS
  28. 28.  From the 3D Analyst Tools, double-click the Terrain To Raster geoprocessing tool to open it. Input Terrain ◦ add the terrain dataset Output Raster ◦ specify the location where the raster dataset is to be created. ◦ Recommend including grid size in name Output data type ◦ Either 32-bit floating point or 32-bit integer. ◦ Floating point is the default value.
  29. 29.  Interpolation method ◦ Either Linear or Natural Neighbors. ◦ Both are TIN-based interpolation methods applied through the triangulated terrain surface. ◦ The Linear option finds the triangle encompassing each cell center and applies a weighted average of the triangles nodes to interpolate a value. ◦ The Natural Neighbors option uses the Voronoi neighbors of cell centers. ◦ Consider the natural neighbors method for interpolating a terrain surface. ◦ Natural neighbor interpolation takes longer processing time; however, the generated surface is much smoother than that produced with a linear interpolation. It is also less susceptible to small changes in the triangulation.
  30. 30.  Sampling Distance ◦ Either Observations or Cellsize, which controls the horizontal resolution of the raster. ◦ Observations method  calculates the cell size based on the set value this number represents and the number of cells you want on the longest edge of the raster surface. ◦ Cellsize method  You set the cell size explicitly  i.e. “CELLSIZE 15” outputs a raster with 15’ square representing the surface.
  31. 31.  Resolution ◦ The resolution parameter indicates which pyramid level of the terrain dataset to use for conversion. ◦ To output a raster dataset at full resolution, set this parameter to 0. To extract a subset of the terrain, click the Environments button on the bottom of the geoprocessing tool. Click the General Settings tab and define the extent of the output DEM.
  32. 32.  DEM Cellsize Matters TIN vs. DEM Terrain (represents, no labels, resolution) Shapefile Land Use Matters 2’ as base.
  33. 33.  A data infrastructure for storing and integrating hydro data within ArcGIS ◦ A set of hydro objects ◦ A set of standardized attributes ◦ A vocabulary for describing data ◦ A toolset for data model applications
  34. 34. Arc Hydro ToolsRegression Tools
  35. 35. 1. Terrain Preprocessing – topographic and hydrographic layers2. Location specific layers – generated by the user3. Statewide parameter layers – used for flood flow prediction
  36. 36.  Multiple ways of representing elevation ◦ Contours and points (Vector) ◦ Triangulated irregular network (TIN) ◦ Digital elevation model (Raster) Each has advantages and disadvantages DEM is used for Arc Hydro terrain analysis and watershed delineation
  37. 37. DEMs have become a common way of representing elevation where every grid cell is givenan elevation value. This is allows for very rapid processing and supports a wide-array ofcritical analyses.
  38. 38. Cell sizeNumber of rows NODATA cell (X,Y) Number of Columns
  39. 39.  Each cell usuallyGraphical stores the average elevation of grid cell  Alternatively, it may store the value at the center of the grid cell 67 56 49 Elevations areDigital  53 44 37 presented graphically in shades or colors 58 55 22
  40. 40.  Spurious sinks ◦ Spurious sinks are a byproduct of the DEM creation / interpolation process ◦ Spurious sinks ought to be removed True sinks ◦ Some landscapes have natural depressions ◦ e.g. pothole lakes ◦ True sinks may be retained or removed Sinks are removed by raising the elevation of the sink to the elevation of the outlet
  41. 41. DEM with unfilled sinks DEM with filled sinks Depth of sink Images from ESRI Map Book Gallery http://www.esri.com/mapmuseum/mapbook_gallery/volume19/conservation5.htmlSinks that are removed (filled) will contribute to downstream flow
  42. 42.  New Arc Hydro tools available to screen, evaluate, and leave / remove true sinks (in the exercise, all sinks will be filled)
  43. 43. Graphical 67 56 49 2 2 4Digital 53 44 37 1 2 4 58 55 22 128 1 2 Elevation Flow Direction
  44. 44. 32 64 12816 18 4 2
  45. 45.  Flow accumulation is the number of upstream grid cells that contribute flow to 2 2 4 0 0 0 a given grid cell  Calculated from 1 2 4 0 3 2 flow direction128 1 2 0 0 8Flow Direction Flow Accumulation
  46. 46.  Streams are defined from the flow accumulation grid based on a threshold Reclassify grid ◦ If [Cell] > Threshold Then [Cell]=Stream ◦ If [Cell] < Threshold Then [Cell]=Not Stream
  47. 47. All the cells in a particular segment have the same grid codethat is specific to that segment
  48. 48. Edge Junction
  49. 49.  Arc Hydro uses AGREE 100 method to “burn-in” streams Original Surface  Adjusts elevation of DEM 90 Modified Surface based on input vector line features 80  Drop/raise elevation of cells corresponding to lines byElevation (m) 70 smoothdrop  Buffer lines by 60 smoothdistance  Elevation of cells inside 50 buffer are adjusted to a straight line from edge of 40 buffer to line.  Drop/raise the elevation of 30 the cells corresponding to the lines by sharpdrop 20 40 60 80 0 100 120 140 160 180 200 220 240 260 Lateral Distance (m)
  50. 50. Correctdrainage pathDeriveddrainage path
  51. 51. Correct(burned)drainage pathDeriveddrainage path
  52. 52.  Difference between LiDAR data and 30-meter DEM ◦ LiDAR is detailed enough to show road grades / ditches ◦ More effort required to burn proper drainage paths Burn short segments at culvert crossings
  53. 53.  Catchment – the area draining to a single segment of stream between two junctions Subwatershed – the drainage area between two user defined drainage points Watershed – the entire drainage area upstream of a user defined drainage point
  54. 54.  Catchment Grid Delineation – creates a grid of catchment areas draining into each stream segment Catchment Polygon Processing – coverts catchments into a polygon feature class Adjoint Catchment Processing – For each catchment that is not a head catchment, a polygon representing the whole upstream area draining to its inlet point is constructed
  55. 55. 1. Terrain Preprocessing – topographic and hydrographic layers2. Location specific layers – generated by the user3. Statewide parameter layers – used for flood flow prediction
  56. 56.  WatershedPoint – user defined outlet point Watershed – resulting watershed polygon LongestFlowPath3D – longest flowpath line Slp1085Point – points at 10 and 85 percent along the longest flowpath (used for slope calculation)
  57. 57.  Rainfall-runoff model: HEC-HMS ◦ Run a synthetic/observed storm over a subdivided watershed model ◦ HEC-GeoHMS extension can be used to set up model geometry Flood-Frequency Characteristics – based on the USGS Water-Resources Investigations Report 03- 4250, “Flood-Frequency Characteristics of Wisconsin Streams” by J.F. Walker and W.R. Krug (2003). http://pubs.usgs.gov/wri/wri034250/ ◦ Step 1: Extract watershed parameters ◦ Step 2: Plug parameter values into Regional Regression Equations
  58. 58.  Layer was obtained from USGS Each region has a different set of regression equations
  59. 59.  Layer was obtained from USGS Weighted average of the watershed Original source: 1:250,000 scale soil maps of Wisconsin (Hole et. al. 1968)
  60. 60.  Rainfall value determined at the watershed outlet Rainfall data from Huff and Angel, 1992
  61. 61.  Snowfall was clipped from a nationwide grid from the Climate Source The same source data was used to create the snowfall contours in USGS Figure 2 Snowfall value determined at the watershed outlet http://www.climatesource.com/us/fact_sheets/fact_snowfall_us.html
  62. 62.  USGS method is to determine forest and storage from the symbols shown on the USGS 24K quad maps (DRGs) Forest and Storage grids were developed from 1992 WISCLAND land cover classification for use in Arc Hydro Weighted average of the watershed
  63. 63.  After extracting parameters, run Regression Calculator from Arc Hydro Before applying to real-world situations, users should… ◦ Understand equation limitations ◦ Know the stand errors of estimate ◦ Be familiar with other calculation techniques listed in the USGS report
  64. 64.  Arc hydro link ◦ http://resources.arcgis.com/content/hydro-data- model Water resources ◦ http://www.esri.com/industries/water_resources/re sources/data_model.html
  65. 65. Stream Power Index
  66. 66.  Online Tutorialhttp://wrc.umn.edu/randpe/agandwq/tsp/lida r/trainingvideos/index.htm
  67. 67.  Slope Raster (%) X Flow Accumulation LN (above) Symbology (is a guess yet…)
  68. 68.  Field proofing Tweak symbology Targeted Conservation
  69. 69. Lidar Point Cloud of Structures Courtesy of MN GIS/LIS Fall Workshops 11/01/2012 Slide courtesy of USGS
  70. 70.  Emergency Management Plat Book Zoning Stormwater Other
  71. 71.  ENVI EX 4.8 (www.excelisvis.com) ENVI, EX + IDL ArcGIS + 3D Analyst
  72. 72.  2010 6” Color Aerial 2010 6” CIR Aerial 2005 6” Black & White Aerial 2005 LiDAR Data (stood alone)
  73. 73.  Acquisition Building Lean Intermediate Shapefile Angled Buildings Grid
  74. 74. Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  75. 75. Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  76. 76. Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  77. 77. Courtesy of MN GIS/LIS Fall Workshops 11/01/2012
  78. 78. Courtesy of MN GIS/LIS Fall Workshops 11/01/2012 Slide courtesy of USGS
  79. 79.  Planimetrics Change Detection Land Use Wetland ID
  80. 80. Slides courtesy of:Sauk County LCDMN-DNRNRCSUSGSPRESENTER: JEREMY FREUND, P.E. OUTAGAMIE COUNTY LCD JEREMY.FREUND@OUTAGAMIE.ORG

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