Modeling Lateral Line-of-Sight with LiDAR

1. Modeling Lateral Line-of-Sightwith LiDAR Jayson Murgoitio Idaho State University Boise Center Aerospace Lab

2. Overview • Introduction - relevance • LOS calculation problems • Research and methodology • The way ahead and implications

3. Introduction • Line-of-sight (LOS) • Ability to see point A from point B • Viewshed – viewable area from a point • Intrinsic GIS problem • Applications - common • Communications • Transportation • Defense and security • Surveillance

4. Introduction - relevance • LOS Applications – specialized • Geomorphology: • Delineation of landscape morphometric classes • Planning: • Landscape architecture, utility screening, green space • Geo-Archaeology: • Intervisibility patterns in landscape modeling and reconstruction • Remote sensing: • Sensor placement, image interleave feasibility, measurement accuracy

5. Problems with LOS calculations • Input data can cause error: • Bare earth elevation = no vegetation blockage • Vegetation model is too restrictive for micro- scale calculations • Too much visibility attenuated because of vegetation • Garbage In, Garbage Out • Good data produces good results • Especially true for small spatial subsets

6. Data Example of data problems for LOS calculations:

7. LOS calculations • LiDAR Data functions in 2 ways: • Bare-earth terrain • Vegetation presence, stand density, and height Trees

8. LOS Calculations No LOS attenuation by trees! Too much attenuation by trees!

9. Accuracy challenges • LOS calculations are resource intensive, time intensive • Model of terrain and vegetation = not reality How the computer interprets trees:

10. “Somewhere between ‘Live Free or Die’ and ‘Famous Potatoes’ the truth lies.” -George Carlin on license plates

11. “Somewhere between the bare earth and vegetation rasters, the truth lies.” - Me on LOS calculations

12. Research • Problem: How to more accurately utilize LiDAR data to predict LOS through vegetation? • Resources: • LiDAR use in forestry: Tree location, size, characteristics • Algorithms for light attenuation through a medium • Advanced 3D modeling technology • Advanced storage and processing capabilities

13. Methodology • Complimentary point cloud for model of tree characteristics • Height and area = Aerial LiDAR • Branch and stem density = Terrestrial LiDAR • Use Beer-Lambert Law of Attenuation as framework • Digital photography to measure actual sightlines • Compare calculated versus actual

14. Beer Lambert law of attenuation Absorption = A­𝜆 x L x D • Provides a framework for LOS calculations • Spatial data input as variables • A­𝜆 (Absortivity coefficient) ~ how much light absorbed per obstruction • L(Path length) ~ distance light travels through obstructions • D (Density) ~ how many obstruction instances per unit of measure

15. Study Area • Elk Meadows, West of Stanley, Idaho • Lodgepole Pine dominant forest with minimal underbrush • Relatively flat terrain (slope < 2 degrees) • 15 plot locations

16. Digital Photography • 1 observation point and 3 sightlines per plot • 1m x 1m target – take digital photo from 5 to 50 m at 5 meter intervals for each sightline • Process each photo to determine percentage of target visible • Pixel count

17. All obstructions = 52% visible Base Image @ 10m Trunk obstruction = 72% visible Recreated Target = 100% visible

18. Translation to LOS model

19. Aerial LiDAR Dataset • Collected Aug 4-5th 2010 • Leica ALS50 Phase II • Flown at 900m AGL • 8.68 points/m2 • Vert. Accuracy 3.28cm • Subset for slope and vegetation cover using 2009 NAIP imagery

20. Aerial LiDAR Processing • Delineate individual trees • Translate tree height to diameter breast height (trunk at 1.37m AGL) • Formula from Central Idaho Variant of Forest Vegetation Simulator • Specific variables for Lodgepole Pine ESRI ArcScene Trimble eCognition BCAL LiDAR Tools for ENVI

21. Incorporating Vegetation in 3D LOS Analysis • Create a 3D model of tree trunks for LOS calculation

22. Terrestrial LiDAR Dataset • Collected June 30th 2011 • LeicaScanStation C10 • TOPCON GR3 static control • 360 degree scan collected for 5 survey points • Leica Cyclone for processing

23. Cyclone Processing Workflow Calculate percentage obstruction from point cloud Fence point cloud for target area Input simulated 1m2 target

24. Utilizing the data • Vegetation model gives us: • Length of LOS beam (relative to slope, terrain) • Delineated and geolocated trees for obstruction density, and trunk structure (Aerial LiDAR) • Absorptivity – branch and stem obstruction through TLS point cloud analysis • Utilize vegetation model for enhanced LOS calculations

25. The way ahead… • Aerial LiDAR • Tree delineation, trunk size determination • Terrestrial LiDAR • Validate aerial processing • add branch and stem density • Compare LiDAR derived vegetation model to digital imagery

26. Implications • LOS metric can be leveraged for further use • Visual analysis/measurement • Provides a quantifiable method for evaluation: “That looks like a dense stand of trees.” -- versus – “That stand of trees has a modeled average visual density of 340 obstruction points per cubed meter.”

27. Terrestrial LiDAR Summary Aerial LiDAR Combined • Line-of-sight applicability • Problems with current calculation • Integration of aerial and terrestrial LiDAR

28. Questions?