Processing and Use of LiDAR data (encouraged to work in pairs)

  1. Explore the Easter Mexico fault rupture in photographs and maps
    1. Google Earth kml fle with Barrett Salisbury photos from May 2010, one month after the rupture (photos referred to in the kml file are in \\geodata\vol2\GEOL260\sharedwork\El_Mayor.
    2. write-up about the fault rupture (their photo 3-14 is Barrett's photo 5)
    3. US Geological Survey EQ page

  2. Copy the ArcGIS geodatabase called "lidar" from \\geodata\vol2\GEOL260\sharedwork\El_Major\
    -contains "study area" polygon, and google image that has been geolocated to the lidar data (which is shifted from where it is positioned in Google Earth.).

  3. Download the data
    1. Go to Open Topography portal to retrieve Google images for the "El Mayor-Cucapah Earthquake (4 April 2010) Rupture LiDAR Scan" LiDAR project area. (The images are actually just links to the data stored at the supercomputer center at UC San Diego)
    2. Export the "study area" to kmz and display it in Google Earth.
    3. Go to the "Find Data" tab at OpenTopography and use that location map to download the "point cloud" for approximately the area shown by the "study area" polygon using the following parameters
      1. zoom in to our lab study area in the Google Map within the OpenTopography window
      2. push the "select a region" button and outline the area
        - check below the image that the number of data points is not much greater than 500,000 points
      3. get the point cloud, using "ALL" data (not veg or ground separately)
      4. check the ASCII (text) format for delivery
      5. deselect the DEM data (you'll be generating your own).
      6. copy the coordinate system for ues in ArcGIS
      7. put in your email, it will notifiy you by email when its done
      8. UnZip it and unTAR it.
      9. Iimport the data into ArcGIS :)
  4. Explore the data
    1. Determine how many overlapping scans are represented in the data and map the distribution of each of these scans.
      1. image of the flightlines from the metadata here at
      2. here are the metadata for the .las format, which is similar to the .txt format. Look at the version 2.0 metadata link.
    2. You can set the "definition" to just one of the scans for further work (for simplicity and speed) except for the surface creation.
    3. examine the data using the intensity value
      - and determine some of the reasons for the intensity differences on surfaces or objects and show them on a map (use the photos and google earth).
    4. distinguish the data by scan direction.
    5. ..... and by return number
    6. examine the point cloud near one or more vegetation anomalies. What does the elevation distribution look like?
      from this location I get
    7. export at the most 15,000 data points from a subset of one scan (or more, but I don't recommend it) scans that has at least one significant vegetation example and a portion of the fault scarp. Save it as a new feature class and view the point cloud distribution in ArcScene. For example. . .
      1. What does the vegetation look like?
      2. What does the fault scarp look like
      3. Can you see the where the changes in intensity occur?
      4. Is the vegetation always the first of multiple returns?
      5. Can you determine the offset of the fault?
  5. Interpolating a DEM surface from the lidar point cloud
    1. How would you filter the data to get a "bare earth" or ground surface DEM? The pros use a "progressive curvature filter."
    2. What size grid cells would maximize the data detail, but allow sufficient averaging to remove the "corduroy" and filter vegetation?
    3. What tools could you use (point to raster? spline? idw? kriging?)
      (note that many tools obey the definition query you would use to subset the data by scan)
    4. make both first return and bare earth surfaces, and try another that shows the vegetation height..
  6. How much did the fault move?
    1. use a flat surface to determine the vertical offset on the fault by drawing profile lines.
    2. use a linear feature (swale, stream bank, channel, etc) to determine the horizontal offset.
  7. The deliverables on your web page.
    1. Exported maps explaining the point cloud that show the features of both the lidar scans and the ground features (15 pts)
    2. Exported 3D views showing the characteristics of the point cloud (10 pts)
    3. Models of the terrain surface with comments on the method of filtering and surface creation (15 pts)
    4. Determining the characteristics of the fault offset ( 10 pts)