Lidar Image Processing Import, Interpolation, Application

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

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)

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

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

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.)

Typical text information associated with Lidar acquisition

Lidar point pattern overlaid on shaded DEM

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.

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

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

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

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.

IDW: From ArcGIS Manual

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

From Wikipedia

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

Raster elevation with lidar anomalies

Raster elevation after filtering

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.

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).

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

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”

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

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)