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Light Detection And Ranging (LiDAR) introduction Some applications of Aerial LiDAR imagery
Dr. Venkat Devarajan Professor, Electrical Engineering, UTA
Director, Virtual Environment Lab (VEL) Email: [email protected]
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Airborne LiDAR
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Target is illuminated by laser and distance is measured by analyzing reflected beam
Courtesy:USGS
Range R = v.t/2
LiDAR data acquisition
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1) Aircraft 2) Scanning Laser Emitter -Receiver Unit 3) Differential GPS 4) IMU 5) Computer
Available information • X,Y, Z of the reflecting points • Reflected beam intensity • Return count from a point • Time stamp of each pulse
Image source: Imaging Notes Magazine, Volume 26 Number 2
Aerial LiDAR Systems and Scanning Mechanism
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Courtesy: Claus Brenner, Institute of Cartography and Geoinformatics University of Hannover, Germany
Range of commercial Lasers
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Courtesy: J. Stoker, USGS
LiDAR Footprint
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Accuracy and Resolution in laser Ranging
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Courtesy: Amar Nayegandhi
Terrestrial LiDAR
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Image Courtesy: Sanborn and Fargo
Terrestrial LiDAR collected from a vehicle (left) and a boat (right)
LiDAR Bathymetry
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Sonar and LiDAR complement each other when making nautical charts.
Courtesy: Optech
LiDAR Data Point Cloud
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Courtesy: USGS
NPD: Nominal Point Density is the number of returns per square meter
LiDAR intensity image
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Baltimore Harbor 1st return LiDAR and corresponding intensity image
Courtesy: NRCS DEM whitepaper
Reflectivity of various surface/materials @ 0.9μm
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Highly reflective objects sometimes saturates some laser detector and return signal from low-reflective object might be too weak to register as valid.
Spectral Reflectance of vegetation, water and soil
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Multiple LiDAR Return
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Courtesy: J. Stoker USGS
Contemporary LiDAR systems are capable of giving at least three returns per pulse
Multiple LiDAR Return
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Courtesy: Hans-Eric Anderson
LiDAR Deliverables: Digital Elevation Model (DEM)
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Source: USGS NED overview
Higher resolution source migration
LiDAR Deliverables: Digital Surface model (DSM) and Digital Terrain Model (DTM)
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Source: NRCS DEM Whitepaper
LiDAR Deliverables: Triangulated Irregular Network (TIN)
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Source: Valerie Garcia, NCSU
LiDAR Deliverables: Hillshaded, Color-Ramped DEM
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Courtesy: LiDAR 101, NOAA
LiDAR Applications: Feature Extraction using LiDAR
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Courtesy: LiDAR 101, NOAA
LiDAR Applications: LiDAR data used to asses damage caused by fire
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Courtesy: J. Stoker
LiDAR Applications: LiDAR data used to asses damage cased by
Hurricane Isabel
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Courtesy: J. Stoker
LiDAR Applications: LiDAR for Urban Modeling
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Courtesy: J. Stoker, USGS
LiDAR Applications: Power Line Mapping with LiDAR
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Courtesy: Reigl USA
LiDAR rapidly provides most comprehensive and accurate assessment of power lines and their surroundings
TIN along the side of a ridge with/without break line
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The dam is successfully modeled with break line
TIN without/with Hydro Breaklines
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Source: Furgo earth data
Water body with unenforced boundary and after breakline enforcement
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Source: PE&RS mar 2012
Contours entering the water body is not desirable. So breakline enforcement is necessary
Importance of Intensity Image for water body detection
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Source: Dewberry LiDAR QA report, Sabine/Shelly Counties, Tx
Intensity image confirms a large riverbank area is ground not water
Jagged Shoreline from Manual Delineation
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Source: Dewberry LiDAR QA report, Sabine/Shelly Counties, Tx
Insufficient number of vertices makes islands and shorelines appear jagged
Flow Chart of VEL/UTA Auto Hydro Breakline Generation
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2m DEM and 2m pixel intensity image of a test area in L’Anguille river basin
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Water surface is relatively smooth
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Three 40000 m2 area were chosen inside three water bodies and the water surface elevation variation was found to be 1.9233 in2, 1.6721 in2 and 1.3482 in2
Flow Chart for water body detection in Method 1
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Histogram Generation and Peak Detection
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0 200 400 600 800 1000 1200-0.5
0
0.5
1
1.5
2
2.5x 10
4
138 140 142 144 146 148 150 152 154
4200
4250
4300
4350
4400
4450
4500
4550
4600
X: 146.1Y: 4590
X: 142.3Y: 4239
Detecting areas associated with one particular peak
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Detected water bodies after removing all the false detection using method 1
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Steps in detecting water bodies using method 2
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2m pixel Intensity Image
Detect all the pixels which Falls below 20 percentile
Keep areas greater than ½ acre in size
Compare elevation with surrounding area
Reject some areas as false detection Continuity test
Compare intensity with surrounding area
Final detection in method 2
Overall Philosophy: Small water bodies might remain undetected by method 1 as those might not appear as a sharp peak in the elevation histogram and hence method 2 is used
Water bodies detected using method 2
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Water body detected after merging detection from elevation and intensity data
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LIDAR Strip Adjustment and Mosaicking
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Biased Strip
Reconstructed Strip
Ground Detection
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Original Image Elevation map in 9m by 9m resolution Ground detection is necessary to create bare earth model
Ground Detection
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Ground mask detection using ground filtering Generated DEM
Ground Detection
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Ground mask with more precise resolution Original image
Comparison with other technologies
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Photogrammetric technology and Manual survey are other available technologies Pros
• LiDAR provides higher accuracy and faster data collection • Data acquisition is possible both day and night. • Cloud shadow, mountain and building shadow is not a problem • Bare earth modeling is also possible in dense forest region • Lower cost. Significantly low for large project • Can be integrated with other technology
Cons • New technology. Algorithms and procedures are under development • High spatial resolution and very large dataset. So high computation time