Modeling Lateral Line-of-Sight with LiDAR Jayson Murgoitio
Idaho State University Boise Center Aerospace Lab
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Overview Introduction - relevance LOS calculation problems
Research and methodology The way ahead and implications
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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
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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
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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
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Data Example of data problems for LOS calculations:
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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
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LOS calculations LiDAR Data functions in 2 ways: Bare-earth
terrain Vegetation presence, stand density, and height
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LOS Calculations No LOS attenuation by trees! Too much
attenuation by trees!
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Accuracy challenges LOS calculations are resource intensive,
time intensive Model of terrain and vegetation = not reality How
the computer interprets trees:
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Somewhere between Live Free or Die and Famous Potatoes the
truth lies. - George Carlin on license plates
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Somewhere between the bare earth and vegetation rasters, the
truth lies. - Me on LOS calculations
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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
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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
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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
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Study Area Elk Meadows, West of Stanley, Idaho Lodgepole Pine
dominant forest with minimal underbrush Relatively flat terrain
(slope < 2 degrees) 15 plot locations
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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
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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
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Base Image @ 10m All obstructions = 52% visible Trunk
obstruction = 72% visibleRecreated Target = 100% visible
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Translation to LOS model
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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
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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
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Create a 3D model of tree trunks for LOS calculation
Incorporating Vegetation in 3D LOS Analysis
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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
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Cyclone Processing Workflow Input simulated 1m 2 target Fence
point cloud for target area Calculate percentage obstruction from
point cloud
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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
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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
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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.
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Aerial LiDAR Summary Line-of-sight applicability Problems with
current calculation Integration of aerial and terrestrial LiDAR
Terrestrial LiDAR Combined