1. Modeling Lateral Line-of-Sight with 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. Light Detection And Ranging (LiDAR) Measures time between laser emission and reception to calculate distance Highly accurate, high resolution Collection platforms – Spaceborne – Aerial – Vehicle – Terrestrial Photo Sources: http://www.personal.psu.edu/sns162/index_p3_report.htm http://www-modis.bu.edu/lidar/introduce.html

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

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

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

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

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

13. 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

14. 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 Methodology

15. Beer Lambert law of attenuation Provides a framework for LOS calculations Spatial data input as variables – A­ (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 Absorption = A­ x L x D

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

17. 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

18. Processing – Digital Photos Determine pixel count of target: – Full (unobstructed) view – Branches, stems, trunk obstructions – Trunk obstructions only Performed in Paint.net (open source) – Color selection (RGB) with 15 – 25% matching threshold – Manipulate image for pixel counts

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

20. Translation to LOS model

21. Aerial LiDAR Dataset Collected Aug 4-5 th 2010 Leica ALS50 Phase II Flown at 900m AGL 8.68 points/m 2 Vert. Accuracy 3.28cm Subset for slope and vegetation cover using 2009 NAIP imagery

22. 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 BCAL LiDAR Tools for ENVI Trimble eCognition ESRI ArcScene

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

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

25. Cyclone Processing Workflow Input simulated 1m 2 target Fence point cloud for target area Calculate percentage obstruction from point cloud

26. 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

27. 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

28. 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.”

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

30. Questions?