Airborne LIDAR The Technology Slides adapted from a talk given by Mike Renslow - Spencer B. Gross, Inc. Frank L.Scarpace Professor Environmental Remote Sensing Center Civil and Environmental Engineering University of Wisconsin-Madison

PRESENTATION OUTLINE Background of LIDAR Brief Technical Description Quality Control/Assurance Procedures LIDAR Data Characteristics Processing LIDAR Data (2 Steps) Data Set Characteristics

BACKGROUND LIDAR (Light Detection And Ranging) –30 Year Old Technology –Became Cost Effective Very Recently System Components –Laser Scanner, ABGPS, IMU, Precise Clock (Multiple Planes of Reference) –Robust Computer Support –Requires Calibration (Bore Sighting)

BACKGROUND Capacity to Capture Multiple Return Values / Pulse –Derive many, many X, Y, Z Values –Positional Data and Intensity Data Multiple Configurations Possible –Remarkably Large Data Files Accuracy –Standard Deviation cm –Vertical RMSE at 20 cm on Discrete ‘Hard Hit’ Points –Horizontal Accuracy at 2X the ‘Footprint” Size

Oscillating Mirror Scan Pattern Rotating Mirror Scan Pattern

LIDAR has Multiple Return

POINT CLOUD OF ALL LIDAR POINTS IN DOUGLAS FIR FOREST

LIDAR &Terrain Interaction For example; a calm still lake, will only reflect energy back within a few degrees of the nadir beam of the laser. A “wavy” lake on the other hand, will reflect energy back from wider incident angles. Diffuse surfaces (ground or tree) reflect energy back omnidirectionaly.

LIDAR Intensity Collection

Laser Intensity Raster - Detail

TIN surface of Raw LIDAR Data

‘Raw’ FIRST Return LIDAR Data

Raw LAST Return LIDAR Data

Automatic programs begin the noise and vegetation/surface feature removal process These remove approximately 80% of vegetation (depending on the land cover and terrain characteristics) This part typically uses about 20% of the vegetation removal time budget Automatic Vegetation Removal

Trend Surface Analysis Green Points = Vegetation Brown Points = Trend Surface

Before

...after

Final vegetation and feature removal requires manual intervention. Custom selection routines are used in 3D and GIS Software to analyze the data and identify target points. Accurate interpretation of the LIDAR data requires supporting imagery. Removal of the remaining 20% of the vegetation and features will account for about 80% of the time budget Manual Editing

...after

…final

LIDAR vs. Traditional Mapping 1”=100’ Scale Terrain Mapping Example  Compiled Mass Points are more widely spaced: 60 feet vs. 12 feet.  Compiled DTMs use breaklines; LIDAR usually does not (breaklines can be added from photogrammetric techniques).  Compiler can place points; LIDAR is indiscriminate.  Compiler must be able to SEE THE GROUND, LIDAR is self-illuminating & ‘looks’ down into the vegetation.

Typical Wooded Area Example

Detail with LIDAR Ground Points

Processed TIN Surface

Contours are a cartographic construct used to visualize topography. Contours produced directly from the LIDAR TIN are usually not aesthetically pleasing. LIDAR data can be converted into a DEM Grid at the nominal post spacing which retains fidelity to the original data and which appropriately smoothes the contours. DEM and Contour Generation

Contours Generated from the DSM

Contours Generated from the DEM

Conclusions LIDAR is a powerful new technology for determining terrain elevations. There are still questions as to the horizontal accuracy. Appears to be a good companion technology to the existing photogrammetric methods of measuring terrain.