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Murray Woods
Southern Science & Information
OMNR – North Bay
Operational
Implementation
of LiDAR
Inventories in
Boreal Ontario
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Advanced
Forest
Resource
Inventory
Technologies
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Kevin Lim Lim Geomatics
Image source: http://www.icejerseys.com/images/rbk_edge_replica_jerseys/TorontoMapleLeafsReebokPremierReplicaAltJersey_big.jpg
Doug Pitt
Paul Treitz
Queen’s U
Margaret Penner Forest Analysis Ltd.
Dave Nesbitt
Jeff Dech
Nipissing U
Don Leckie François
Gougeon
ITC Line
Dave Etheridge
TEAM AFRIT
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Ontario’s Enhanced Inventory Program
LiDAR Derived Inventory Primer
Current LiDAR Inventory Success Stories
Operational Economics
Ongoing Enhancements
Emerging Technologies
Outline
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Ontario’s Enhanced Inventory Program
Move to a 10-year cycle
Evolve from a periodic inventory to a continuous inventory update
Inventory will be ecologically based
Move to a higher quality information through the implementation of new technologies
Enhancement of the “field” component
Credit: Ontario’s eFRI Program
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Ontario’s Enhanced Inventory Program – Digital Imagery
• FRI specifications of:
• 35 cm resolution for RGB and colour
infrared
• 20 cm resolution panchromatic
• Level 1, stereo imagery
• Continuous strip of imagery viewed in
a softcopy environment for
interpretation in FRI
• Level 2, orthophoto
• 5x5km tiles photos with uniform scale
• Digital Surface Model (DSM), an
elevational map of the tops of vegetation
on the landbase
• Leica ADS40/80 Push-Broom Sensor
Credit: Ontario’s eFRI Program
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• Source of information
• Management consideration
• Horizontal structure
• Incidental species
• Vertical structure
• Stage of development
• Depletion type
• Depletion year
• Access
• Crown Closure and Stocking
• Opportunity for SFL holders to acquire additional Remote
Sensing information
Ontario’s Enhanced Inventory Program – New Attributes
Credit: Ontario’s eFRI Program
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“Active” remote sensing
technology; transmit & receive
~35,000- 500,000 pulses of NIR
laser light per second
Discrete System - Each pulse
can produce multiple returns
(up to 4)
GPS provides the exact X-Y-Z
position of each return
LiDAR Light Detection And Ranging
Used with permission of Doug Pitt
http://ngom.usgs.gov/task4_2/images/eaarl2a.jpg
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Study Sites – Northeastern Ontario ~ 2M ha
~1.0 /m2 ~0.5 /m2 Pulse density
< 30 cm < 50 cm Vert. Accuracy
1,000 m 900 m Line spacing
2,400 m 2,740 H
30 deg. 20 deg. FOV
32 Hz 30 Hz Scan Rate
119,000 Hz 32,300 Hz Pulse Rate
Cessna 310 King Air 90 Platform
Leica ALS50 Leica ALS40 Sensor
Hearst Romeo Malette Parameter
LiDAR Specifications
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Field Sampling vs. 100% Enumeration
• 100% enumeration of the
landbase with LiDAR
measurements of vertical
structure
• permits the use of
regression estimators to
scale predicts from
Prediction Units to:
groups, stands, forest
LiDAR Derived Forest Inventory
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• LiDAR offers more than a DTM
• Additional forest inventory information contained in point cloud data
• Focus of AFRIT is “Area” based modeling NOT “Individual Tree”
• Prediction Unit = 20m X 20m (400m2)
LiDAR Derived Forest Inventory
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LiDAR Derived Inventory – Pairing with Ground Data
Field Plot Measurement
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• Height (AVG, Top) • QMDBH • Volume (GTV, GMV) • Basal area • Biomass • Density*
* Derived from DBHq & BA
LiDAR predictive Models for:
• Sawlog Volume • Close Utilization
Volume • Dom/Codom Ht • Mean Tree GMV • Size Class Distributions
Pre
dic
ted
Observed
LiDAR Derived Inventory
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LiDAR Derived Inventory – 400m2 Surfaces
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LiDAR
Information can
add more value
to our rich image
and inventory
interpretation
LiDAR Derived Inventory – 400m2 Surfaces
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Enhanced Modeling Approaches
Regression
y = bo+ b1 x1+ b2 x2
y1 = bo+ b1 x1+ b2 x2
y2 = bo+ b1 x1+ b2 x3
yp = bo+ b1 x2+ b2 x3
y = eb0 · x1b1 · x2
b2 · … · x5b5
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Enhanced Modeling Approaches
Regression RandomForest
y = bo+ b1 x1+ b2 x2
y1 = bo+ b1 x1+ b2 x2
y2 = bo+ b1 x1+ b2 x3
yp = bo+ b1 x2+ b2 x3
y = eb0 · x1b1 · x2
b2 · … · x5b5
Option for hundreds to thousands of trees
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SUR vs. RandomForest - Predicted vs. Observed
SUR Regression Model RandomForest
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0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve
fre
qu
en
cy
0
pred3
+
0
0.05
0.1
0.15
0.2
0.25
0.3
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve
fre
qu
en
cy
0.2
pred3
+
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve
fre
qu
en
cy
0.4
pred3
+
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve
fre
qu
en
cy
0.3
pred3
+
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve
fre
qu
en
cy
0.5
pred3
+
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve
fre
qu
en
cy
0.6
pred3
+
Black Spruce Validation Data
– Aggregated by VCI class
LiDAR Derived Inventory – Size-Class Distributions
Density and Volume by 2cm Diameter Classes
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Mixedwood Validation Data
– Aggregated by VCI class
LiDAR Derived Inventory – Size-Class Distributions
Density and Volume by 2cm Diameter Classes
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve f
req
ue
ncy
0.6
pred3
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve f
req
ue
ncy
0.3
pred3
0
0.05
0.1
0.15
0.2
0.25
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Diameter class (cm)
Re
lati
ve f
req
ue
ncy
0.5
pred3
+
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• Are LiDAR-enhanced
inventories leading to
better forest management
decisions?
• What are the cost savings
associated with enhanced
decision making?
Objectives
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LiDAR provides better volume prediction (statistically tested)
– Yield Curve: 14.5%
– LIDAR: 5.4 %
Yield Curve BW SP PJ PO
208 12% 10% 0% -22%
214
216 -2% 33% 3% -34%
220 14% -15% 0% 1%
244 -9% 34% -12% -14%
245 -2% 9% -1% -6%
246 -2% 20% -1% -17%
251 6% 33% 6% -45%
252 9% 55% 1% -66%
254 -1% 4% 10% -12%
256 1% -6% 5%
257 7% -17% 9%
259 1% 0% -1%
275
LIDAR BW SP PJ PO
208 2% -6% 0% 3%
214
216 -16% 6% 1% 8%
220 0% -11% 4% 7%
244 -10% 2% 2% 5%
245 0% 5% 1% -6%
246 0% 5% 1% -6%
251 1% 11% 5% -17%
252 4% 23% 2% -29%
254 1% -4% 0% 4%
256 2% -1% -1%
257 2% -5% 3%
259 -3% -2% 5%
275
10 % & under greater than 10 %
Difference from harvested volume (scaled)
Approach – Volume Comparison
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Comparison of 2 scenarios
1. Actual cut over – ACTUAL • Area harvested within approved plan
2. Redesign blocks within proposed plan – LIDAR • New harvest planning using LiDAR for area similar to initial plan
• Validated by Tembec FRM
• Stay within approved 5 year harvest blocks
Approach – Cost Analysis
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Actual harvested area, obtained using digital imagery - ACTUAL
Approach – Cost Analysis: Scenarios
DBHq Actual Harvest
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Redesign plan, based on full suite of LiDAR data products – LIDAR
DBHq (cm) GMV (m3 ha)
Approach – Cost Analysis: Scenarios
ACTUAL LIDAR
Removed area of less
GMV from Scenario
* As a result – less roads need to be constructed and maintained $$$
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Three groups of cost items :
A. Inventory acquisition and processing - 6 items
B. Forest operations – 20 items
C. Mill - 4 items
$ - 0.10 m3
$ 1.40 m3
$ 0.30 m3
Savings $ 1.60 m3
X 500,000 m3 / year = $ 800,000 / year
Payback = 1.3 years
Results: Actual vs. LiDAR Scenario
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Ongoing Enhancements
• ITC – Individual Tree Classification
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Ongoing Enhancements
• Site Productivity - Predicted Texture
Clement Akumu, John Johnson, Peter Uhlig, Sean McMurray, Dave Etheridge
Environmental input variables:
• Elevation (10m)
• Slope (%)
• Surface shape (Curvature)
• Mode of deposition (NOEGTS)
• Landcover
• Slope Position from TPI
• (macro window = 1km)
• (medium window = 500m)
• (micro window = 20m)
• Wetness Index
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Ongoing Enhancements
• Site Productivity - Wet Areas Mapping
Paul Arp, Jae Ogilvie - UNB
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Ongoing Enhancements
• Fibre Analysis – Jeff Dech, Bharat Pokharel, Megan Smith, Art Groot
Comparative analysis of wood fibre attributes and transition years among four contrasting ecosite groups
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Ongoing Enhancements
• LiDAR Species Classification…from Low Density LiDAR
n = 346 plots; 86 used for validation:
HWD MWH MWC CON
HWD 91 4 4
MWH 100
MWC 9 9 82
CON 4 96 A
ctu
al
%
Predicted %
Prediction for each 400m2
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• Rapid Technology Evolution
– Discrete LiDAR Full Waveform Image Pixel Correlation
SGM 2009 LiDAR 2012 LiDAR 2005
Petawawa Research Forest Plot nPW12
Emerging Technologies
Area-Based Individual Tree (crown attributes – tree quality – fibre properties)
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• LiDAR Species Classification…from High Density LiDAR
Error matrix of classification
Baoxin Hu Dept. of Earth and Space Science and Engineering York University, Toronto, Canada
Future Opportunities
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• LiDAR has the ability to complement eFRI information
• add spatial resolution of metrics - additional values
• Opportunity to develop scalable inventory information to ensure:
• forest sustainability
• spatial habitat modeling, product quality, etc.
• linkage between strategic & operational planning
• maximizing value – “right wood to the right mill at the cheapest cost”
• Software tools are available and expanding
• Economics of acquiring LiDAR datasets are being realized by industry and Gov’t
• Existing and Emerging technologies offer exciting future opportunities
An Exciting Future ahead
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Thank you - Questions?
705 475-5561
705 541-5610
Woods, M. D. Pitt, M. Penner, K. Lim, D. Nesbitt, D. Etheridge and P. Treitz. 2011. Operational
implementation of a LiDAR inventory in Boreal Ontario. For. Chron. 87(4). 512-528
Treitz. P., K. Lim, M. Woods, D. Pitt, D. Nesbitt. and D. Etheridge. 2012. LiDAR sampling
density for forest resource inventories in Ontario. Canada Remote Sens. 2012, 4, 830-848
Thomas V., K. Lim, M. Woods. 2008. LiDAR and Weibull modeling of diameters and basal
area distributions. For. Chron. Vol. 84(6) 866-875
Woods, M., K. Lim and P. Treitz. 2008. Predicting forest stand variables from LiDAR data
in the Great Lakes – St Lawrence forest of Ontario. For. Chron. Vol. 84(6). 827-839.