Introduction to LiDAR Technology and Applications in Forest Management Presented by Rory Tooke, Douglas Bolton and Nicholas Coops Integrated Remote Sensing Studio Faculty of Forestry University of British Columbia. Canada I + R + S + S University of British Columbia
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Introduction to LiDAR Technology and Applications … to LiDAR Technology and Applications in Forest Management Presented by Rory Tooke, Douglas Bolton and Nicholas Coops Integrated
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Introduction to LiDAR
Technology and Applications in
Forest Management
Presented by Rory Tooke, Douglas Bolton and Nicholas Coops
Integrated Remote Sensing Studio
Faculty of Forestry
University of British Columbia. Canada
I+R + S+S University of British Columbia
I+R + S+S What is LiDAR?
• LiDAR = Light Detection And Ranging
• Active form of remote sensing
• Measures the distance to target surfaces using narrow beams of near-infrared light (e.g.1064 nm).
• Primarily operated on airborne platforms for forestry applications – However spaceborne (GLAS) and field based LiDAR instruments
have also been developed.
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I+R + S+S LiDAR is Distance Measurement
R = Range or distance
c = Speed of light (299 792 km / sec)
tp = Time the pulse is emitted from the sensor
t = Time the pulse arrives back at the sensor
Divided by 2 to compensate for the round-trip distance
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LIDAR systems incorporate three technologies:
(i) laser ranging for accurate distance measurement,
(ii) satellite positioning using the Global Positioning System (GPS) to determine the geographic position and the height of the sensor, and
(iii) aircraft attitude measurement using an inertial measurement unit (IMU) to record the precise orientation of the sensor.
LIDAR Requires Three Enabling
Technologies
I+R + S+S LIDAR from Space (Theoretical)
Images and movies from NASA and used with permission
• The returned pulse is classified into one or more discrete returns
– Returns are recorded when the return energy exceeds the systems predefined threshold
– Early LiDAR systems were designed to record only the distance to the first target
– Later systems recorded multiple returns
– Last returns are particularly important for detecting the ground surface
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Discrete Return Data
• Cross-section of discrete return data
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Waveform Data
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• Waveform data is less common than discrete return data
– As technology advances, it is becoming easier to record the full waveform
• Much larger volume of data
• Methods of processing waveform data are not as advanced
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• Advantages of LiDAR technology:
– Assessment of vertical structure of forests at high spatial resolutions
– Accurate estimates of surface height
– Can operate independently of sunlight
• Growing interest in LiDAR in past two decades:
– In the beginning, primary interest was the development of digital elevation
models (DEM) • Looking past the vegetation
– In the past decade, the potential for LiDAR in forestry applications has been realized • Measure tree heights to sub-metre levels of accuracy • Estimate forest attributes such as stem volume and basal area
LiDAR Technology
12
I+R + S+S Different Types of LiDAR instruments
• Profiling LiDAR
• Small-footprint LiDAR
• Large-footprint LiDAR
• Ground based LiDAR
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I+R + S+S Profiling LiDAR
• Early airborne LiDAR instruments
• Measure height information along single transects with a fixed nadir view angle
• Advantages: – Relatively inexpensive
technology – Great sampling tool
• Limitation:
– Lack of spatial detail
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I+R + S+S LiDAR Scanning Pattern
• More advanced scanning
systems were later
developed (~1990s
onwards).
• Rotating mirror used to
direct pulses perpendicular
to flight direction
• Both small- and large-
footprint LiDAR use this
approach
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I+R + S+S Small-footprint LiDAR
• Beam diameters at intercepting surface < 1 m
• Typically record high sampling densities (>1 / m2)
• Accuracy ~15 cm vertically and 40 cm horizontally
• Operated on fixed wing or helicopter platforms
• Commercially available • Sensors now emit up to 260 000 pulses / sec – 3 years ago this was closer to 25 000 pulses / sec
• Increase from first / last return combinations to 5 returns per pulse
– Ability to separate returns by smaller distances (e.g. 2 m intervals) – Option to record full waveform is becoming more common
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I+R + S+S Large-footprint LiDAR
• These instruments use larger beam diameters at intercepting
surface (generally 5 to 25 m)
• Signal is averaged across the footprint
• Record the entire returned signal as a function of time
(waveform)
• Currently only experimental (e.g. SLICER and LVIS)
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I+R + S+S Ground based LiDAR
18 Figures provided by Martin van Leeuwen of the University of British Columbia
• Scanner is placed below the forest canopy
• Algorithms are deployed to detect individual tree stems
• Stems can be occluded by other stems
• Current research aims to make ground based LiDAR an operational
inventory tool
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• How do we derive meaningful measurements from a
LiDAR point cloud?
Working with Discrete Return LiDAR data
33 m 36 m
34 m 36 m
41 m 35 m
0
20
40
Height (m) Stem Volume (m3)
0
2.5
5
19 LiDAR visualizations produced with FUSION/LDA software – USDA Forest Service
I+R + S+S TERRAIN GENERATION
• LIDAR usually has high spatial sampling (0.1 – 4 m).
• Accuracy of 3-D location very good (<20 cm).
• Post-processing is done to ensure
– Recommend 2 GPS ground receivers with known positions making
absolute georeferencing possible
– Filtering of data to ascertain ground versus non-ground hits.
• Typical Accuracies: 15 cm in elevation and horizontal position
• Spot spacing much denser for slower aircraft
• More reliable/accurate DTM through denser spot spacing – more data collected
• Highest accuracy heights at nadir and decrease as swath angle increases
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Step 1: Extract probable
ground returns
- Ground points are often
classified by LiDAR vendor
Step 2: Create surface
from ground returns
Creating A Digital Elevation Model (DEM)
21 LiDAR visualizations produced with FUSION/LDA software – USDA Forest Service
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Creating A Digital Elevation Model (DEM)
• The density of
ground points
depends on the
vegetation
structural class
• Fewer pulses will
reach the surface
under dense
canopies
• Methods of
interpolation are
needed where
ground return
densities are low Analysis performed at Pacific Rim National Park,
Vancouver Island
I+R + S+S Interpolation Methods
• Interpolation is the estimation of values at unsampled locations.
• Algorithms fit a continuous surface through a set of measured points (e.g. LiDAR ground returns)
• Algorithms differ in their ease of use, mathematical complexity, and computational expense.
Inverse Distance Weighted (IDW)
Spline
Natural Neighbor
Measured Point
Fitted Surface
Sources: Johnston et al. 2001;
Maune et al. 2001
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I+R + S+S Creating A Digital Elevation Model (DEM)
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• Validating DEM
– Difficult task due to high level of accuracy
– Differential GPS is affected by vegetation cover (Naesset and Jonmeister, 2002).
– DEM accuracy may vary spatially across the landscape due to vegetation cover and ground slope
– Accuracy is generally within 1 m
Creating A Digital Elevation Model (DEM)
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I+R + S+S Digital Elevation Model (DEM) DEM of Alex Fraser Research Forest)
I+R + S+S Digital Elevation Model (DEM)
Current uses in operational
planning:
• Contour lines
– Road planning
– Block boundaries
– Stream modeling
• Operational slope classes
– < 35% slope: Conventional
ground skidding
– 35-50% slope: Requires
specialized equipment
– > 50% slope: Consider cable
yarding
Uses provided by:
Don Skea, AFRF
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Visualization for
Malcolm Knapp Research
Forest
Digital Surface Model (DSM)
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Lidar visualizations produced with FUSION/LDA software – USDA Forest Service
Visualization for
Malcolm Knapp Research
Forest
Digital Surface Model (DSM)
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Point
elevation
Derive Heights in Relation to the Surface
30 LiDAR visualizations produced with FUSION/LDA software – USDA Forest Service
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Point
elevation
Surface
elevation
Derive Heights in Relation to the Surface
31 LiDAR visualizations produced with FUSION/LDA software – USDA Forest Service
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0
20
40
Height (m)
Point
elevation
Surface
elevation
Point
height above
surface
Derive Heights in Relation to the Surface
32 LiDAR visualizations produced with FUSION/LDA software – USDA Forest Service
I+R + S+S Example of Multiple Regression Equations at the Plot Scale
Source: Næsset 2002
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Alberta SRD – 16 Dec 2011
A Canadian Foothills Project
Study Area
– Hinton FMA ~990,000 ha; 385,000 AVI polygons
• Clients, Partners & Collaborators
– West Fraser Mills, Hinton Wood Prods.
– AB Sustainable Resource Development
– CFS, Pacific Forestry Centre
• Dr. Mike Wulder, Dr. Gordon Frazer,
Joanne White, Geordie Hobart
– University of BC
• Dr. Nicholas Coops, Dr. Thomas Hilker, Danny Grills, Martin van Leeuwen, Chris Bater
Partners: WFM - Hinton Wood Prod.; Alberta SRD; CFS–PFC; UBC
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Alberta SRD – 16 Dec 2011
Existing Data Sources & Evolving Objectives
LiDAR & Ground Data
– AB SRD: Full LiDAR coverage (2004-2007) @ 0.75 to 1.1 hits/m2 & AVI data:
– HWP: Permanent Growth Sample Plot Data
Evolving Objectives
– Proof of Concept for mapping plot attributes from LiDAR-based predictions
I+R + S+S Processing Point Cloud “Canopy Metrics”
• Used available USDA FS Freeware package, “FUSION/LDV“, to tile, grid & calculate 61 Canopy Metrics @ 25m X 25m resolution (i.e. for each of nearly 14 million grid cells)
I+R + S+S Ground Calibration Prediction Model Development
• HWP maintains a network of systematically distributed Permanent Growth Sample Plots
– team used 788 of >3200 available plots to develop Prediction Models
– Ordinary Least Squares multiple regression and a non-parametric tool “Random Forests” (resident in “R” statistical software) used to create prediction models
– separate models developed for each of 3 “forest types”, based on AVI spp. composition
• Andersen, H.E., McGaughey, R.J. and Reutebuch, S.E. (2005). Estimating forest canopy fuel parameters using LiDAR data. Remote Sensing of Environment, 94, 441-449.
• Bater, C.W., Wulder, M.A., Coops, N.C., Nelson, R.F., Hilker, T., Næsset, E., In press. Stability of sample-based scanning LiDAR-derived vegetation metrics for forest monitoring. IEEE Transactions on Geoscience and Remote Sensing.
• Goodwin, N.R., Coops, N.C. and Culvenor, D.S. (2006). Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sensing of Environment, 103, 140-152.
• Holmgren, J. and Persson, R. (2004). Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90, 415-423.
• Hyyppä, J., Kelle, O., Lehikoinen, M. and Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 35, 969-975.
• Lefsky, M., Cohen, W., Parker, G. and Harding, D. (2002). LiDAR remote sensing for ecosystem studies. BioScience, 52, 19-30.
• Lovell, J.L., Jupp, D.L.B., Culvenor, D.S. and Coops, N.C. (2003). Using airborne and ground-based ranging LiDAR to measure forest canopy structure in Australian forests. Canadian Journal of Remote Sensing, 29, 607-622.
• Magnussen, S., Boudewyn, P., 1998. Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Can. J. For. Res. 28, 1016–1031.
• Maltamo, M., Packalén, P., Yu, X., Eerikäinen, K., Hyyppä, J. and Pitkänen, J. (2005). Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. Forest Ecology and Management, 216, 41-50.
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I+R + S+S References (cont.)
• Moffiet, T., Mengersen, K., Witte, C., King, R. and Denham, R. (2005). Airborne laser scanning: exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species. ISPRS Journal of Photogrammetry and Remote Sensing, 59, 289-309.
• Næsset, E. and Jonmeister, T. (2002). Assessing point accuracy of dGPS under forest canopy before data acquisition, in the field and after postprocessing. Scandinavian Journal of Forest Research, 17, 351-358.
• Næsset, E. (2002). Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80, 88-99.
• Nelson, R., Short, A. and Valenti, M. (2004). Measuring biomass and carbon in Delaware using an airborne profiling LIDAR. Scandinavian Journal of Forest Research, 19, 500-511.
• Roberts, S.D., Dean, T.J., Evans, D.L., McCombs, J.W., Harrington, R.L. and Glass, P.A. (2005). Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions. Forest Ecology and Management, 213, 54-70.
• Sambridge, M., Braun, J. and McQueen, H. (1995). Geophysical parameterisation and interpolation of irregular data using natural neighbours. Geophysical Journal International, 122, 837-857.
• Solberg, S., Næsset, E., Hanssen, K.H. and Christiansen, E. (2006). Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sensing of Environment, 102, 364-376.
• Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C. and Gerard, P.D. (2003). Characterising vertical forest structure using small-footprint airborne LiDAR Remote Sensing of Environment, 87, 171-182.