Format for storing LiDAR data (LAS) or LiDAR data in zipped form (LAZ).  LiDAR (Light Detection and Ranging) measures terrain elevations by shining a pulsed laser from an airborne or spaceborne platform onto a surface and measuring reflected pulses.  Manifold imports LiDAR data from LAS format (LAZ is identical except for being compressed) as points in a drawing, with coordinate system normally assigned.


ico_nb_arrow_blue.png This is an experimental dataport subject to change.




To import from LAS format:


  1. Choose File-Import from the main menu.

  2. In the Import dialog browse to the folder containing data of interest.

  3. Double-click the file ending in .las or .laz for the data of interest.

  4. A table and a drawing will be created.





The current LiDAR dataport does not automatically build a spatial index on points: if we want to display the drawing we must first add a spatial index.    We double-click the pentagon table to open it.




We launch Edit - Schema and then click the <new index> command.   We Add an rtree index called Geom_x.



Next, we click the <new field> command under the newly-created Geom_x index and we Add the Geom field as a key field.  We press OK.  We can now open the pentagon Drawing.


We can double-click on drawings to view them.   For a more interesting display, we first create a new data source using a Bing street maps image server as shown in the Example: An Imageserver Tutorial topic.   We then create a map and drag and drop the Bing layer into the map, and then we drag and drop the pentagon Drawing into the map.




This particular data set contains over eight million points.  LiDAR data sets can contain billions of points.  The drawing appears in default formatting as a mass of gray points.   It is located in the right place since LAS format provides projection information.   We can use the Style  panel to assign a palette to color the elevation values in the drawing.




We use the Style panel in the Contents pane to color the points using a palette, assigning the same palette to coloring both the stroke color and fill color for points.    We have used the CB Spectral palette in reversed order.




That provides a more sensible display.  In the illustration above, we have used square symbols for points.  The display shows LiDAR elevations for the Pentagon in the United States.





Zooming in, we can see better detail.   However, viewing LiDAR as colored points is neither efficient nor appealing.  We can do better by interpolating the vector point layer into a raster image using the Kriging transform template, as discussed in the Example: Vector to Raster using Kriging topic.


We drag and drop the resulting image into our map.  It appears all white, for lack of contrast, until we use the Style panel to apply interpolated grayscale color:




We use the settings above, two intervals with interpolation from black to white, to apply grayscale color based on the single channel, the height, in the image.




The result is a grayscale image where higher heights are brighter.


eg_import_las01_07c.png  eg_import_las01_07d.png


We can apply a color palette, the CB Spectral palette in reverse order, plus hill shading, as seen above.




The image appears very detailed because we have used Kriging to create a raster surface with a pixel at every meter.  A more detailed look at the same image is in the Gallery page of the Manifold website.


See Also











Edit - Schema


Contents - Transform






GRD, Surfer .GRD


Example: Spectacular Images and Data from Web Servers


Example: An Imageserver Tutorial


Example: Vector to Raster using Kriging