Slide 1

Modeling Lateral Line-of-Sight with LiDAR Jayson Murgoitio Idaho State University Boise Center Aerospace Lab

Slide 2

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

Slide 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

Slide 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

Slide 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

Slide 6

Data Example of data problems for LOS calculations:

Slide 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

Slide 8

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

Slide 9

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

Slide 10

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

Slide 11

Somewhere between Live Free or Die and Famous Potatoes the truth lies. - George Carlin on license plates

Slide 12

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

Slide 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

Slide 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

Slide 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

Slide 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

Slide 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

Slide 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

Slide 19

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

Slide 20

Translation to LOS model

Slide 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

Slide 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

Slide 23

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

Slide 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

Slide 25

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

Slide 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

Slide 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

Slide 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.

Slide 29

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

Slide 30

Questions?