1. Lidar Image Processing
Import, Interpolation, Application

2. Raw Lidar = x,y,z point data
Processed Lidar = 1 or more surfaces representing elevation of ground, top of canopy, etc.

3. Lidar Processing
Use of Lidar data requires processing to get from thousands to billions of x,y,z points to accurate, internally consistent elevation surfaces
Processing can be laborious due to number of points
Information on first, last, and intermediate returns supplied by contractor is used to separate Lidar surfaces
Conversion of x,y,z point (vector) data to raster digital elevation models (DEMs)

4. Lidar Processing (cont.)
Filtering of resulting DEM to remove anomalous elevations (sinks and peaks that are unlikely to be real) or returns that are not from the ground
Correction for poor interpolation through structures like buildings, etc. to get to bare earth elevation

5. How are Lidar data received?
Researchers receive Lidar data from contractors (or instruments) as files of geometrically rectified x,y,z point data (often text files)
Data typically include first (top of canopy?) and last (bare earth?) returns and sometimes intermediate returns
Tabular data with x,y,z coordinates can be imported into GIS or other software for processing

6. Conversion of Lidar text files to point data
Can import x,y,z data into ArcGIS (for example) from CSV files or spreadsheets and other ways.
Results in a dense network of point data with various attributes including elevation (z), intensity (sometimes), and information on return # (1st, 2nd, 3rd, etc.)

7. Typical text information associated with Lidar acquisition

8. Lidar point pattern overlaid on shaded DEM

9. Analysis of point cloud
First task is to separate anomalies in the point cloud from real surfaces
Some lidar returns are from non-relevant surfaces – tops of fire hydrants, birds, etc. These must be identified and removed
Typically accomplished with software that looks at unusual deviations (outliers) from surrounding points and some manual editing
Anomalies discarded to create bare earth elevation layer that can be compared to other return surfaces to get heights, etc.

11. Conversion of point data to DEM
Use geostatistical tools to convert (interpolate) points to an accurate representation of a surface in raster format
Splining
Inverse Weighted Distance (IDW)
Kriging
Others

12. Splining
Interpolates a surface from point data constrained by two criteria
The surface must pass through each data point
The surface must minimize curvature (fit all the points while minimizing 2nd derivative between points)
Splining is deterministic – directly based on measured values; always produces same result for same input data
Interpolation is piecewise – rather then trying to fit one global high order polynomial function to the point cloud, the method uses many local functions (can take different forms – linear, quadratic, etc.).
Can use alternative splining methods that either smooth the result or maintain a “coarser” surface
Good for gently varying surfaces like elevation

13. Linear Spline (Wikipedia)
Blue line is a linear spline. Red line is the function (elevation) it is approximating.

14. Inverse Distance Weighted (IDW)
Uses a linear weighting of surrounding points (in a neighborhood) to predict value at unmeasured locations
Weights are a function of distance from the unmeasured location – assumes points close together are more alike than points far apart
Like splining, this is a deterministic method
Unlike splining, does not have to pass through measured points.

15. IDW: From ArcGIS Manual

16. Kriging
Interpolates point values to create a surface by weighting the points (measured values) to predict unmeasured values between points
Non-deterministic – uses a statistical model (probability function) that considers autocorrelation and allows prediction of certainty of the interpolation
Good choice when data are spatially correlated or have directional bias
Does not have to pass through measured points

17. From Wikipedia

18. Removal of remaining anomalies
Raster data may still show anomalous lidar returns
Can filter them using moving window or other filters

19. Raster elevation with lidar anomalies

20. Raster elevation after filtering

21. Correction for erroneous interpolation
Some features can cause errors in derived “bare earth”
Buildings
Highway overpasses
Other human structures or objects
Etc.
These errors usually sorted out using other imagery (e.g., aerial photos) to identify building outlines, highways, etc.
Other tall moveable objects like trucks, trains, etc., can be more difficult to remove.

22. Air photo (left) shows building that was “removed” using a GIS buildings layer (Seattle) for calculation of bare ground DEM. But—lack of real ground data over building footprint requires interpolation across large distance potentially causing error (right).

23. Highway overpass confused with top of tree canopy because all returns higher than a threshold above bare earth were grouped as trees.

24. Creating hydrologic maps with Lidar –derived data
Hydrologic maps include flow routes, waterways and watersheds
Require very accurate DEMs to correctly route the flow of water
Some features in DEMs can have undue influence on water flow, e.g., depressions or “sinks”

25. Sinks
Sinks are depressions in DEMs caused by faulty data, rounding error, etc. They can result in discontinuous water flow

26. Summary
Lidar processing can be intense due to the large amount of data (billions of points)
Processing goal is to separate returns into discrete surfaces and then remove anomalies from each
Often requires a mixture of manual interpretation and automated processing
Result can be highly accurate DEMs and information on heights of other objects (like trees)