What we`re working on now… - Canadian Institute of Forestry

SISCO
Southern Interior Silviculture Committee
Enhanced Forest Inventory
A case study in the Alberta foothills
Roger Whitehead & Jim Stewart
CFS, Canadian Wood Fibre Centre
Glenn Buckmaster
West Fraser Mills, Hinton Wood Products
Mike Wulder, Joanne White & Gordon Fraser1
CFS, Pacific Forestry Centre
1Current affiliation – University of Victoria
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Outline

Site & Data Sources

What we did

Model predictions

Validation & discussion

What we’re working on now
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Study Area & Data Sources

Hinton FMA
– West Fraser Mills, Hinton WP
– 988,870 ha;
– 185,000 AVI polygons

LiDAR & AVI data
– Alberta ESRD /WF- HWP

Ground Calibration data
– HWP’s established network of
Permanent Growth Sample Plots
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LiDAR data

Alberta ESRD
provided HWP with
full FMA coverage
– multiple discrete return
(max 4 returns)
– small footprint (30 cm)
– 0.75 points/m2
– collected 2004–2007
– pt cloud, CHM, DEM
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Data Cloud  Canopy Metrics

Used USDA FS freeware
package FUSION/LDV to
– tile, grid & calculate >50
canopy metrics
–
– 25m X 25m grid
13,665,234 grid-cells
– forest type assigned from
AVI stand-level inventory
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Ground calibration…

WF-HWP maintains >3200
PSP  empirical yield curves

we used 735 of those plots to
train prediction models
– timing of last msmt & quality of GPS
– used HWP mensuration data & calcns
of top ht, volumes, BA & trees/m3
– biomass from regr. equations of
Lambert (2005) or Ung (2008)

separate models for each of 3
forest types
– conifer, deciduous, & mixedwood
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LiDAR–based Prediction of Attributes

Used Random Forests (“R”)
to create prediction models
– Top height, Co-dominant
& Mean height
– DBHq & BA
– Total Volume &
Merchantable Volume
Total Above Ground
Biomass (tonnes/ha)
– Above Ground Biomass
– Mean piece-size (trees/m3)
Partners: WFM - Hinton Wood Prod.; Alberta SRD; CFS–PFC; UBC
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Mapped as GIS raster layers


25m cell level
AVI Polygon level
33 m3/ha
384 m3/ha
14 m3/ha
247 m3/ha
331
m3/ha
Merch. Volume (m3/ha)
For ~1 million ha
Hinton FMA
525 m3/ha
276 m3/ha
164 m3/ha
0 m3/ha
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Yeah, but… are any predictions correct?
Weight-scaled volume from 272 cutblocks harvested since LiDAR acquisition
compared to predictions from LiDAR vs. Cover Type Adjusted Volume Tables
Block Size
(m3 X1000)
Source of
Prediction
Predicted Volume
– Scaled Volume
Statistically
significant?
<5
n = 138
LiDAR
CT Vol. Table
-6.7%
-23.7%
No
Yes
5 – 10
n = 76
LiDAR
CT Vol. Table
+1.8%
-17.4%
No
Yes
10 – 15
n = 25
LiDAR
CT Vol. Table
-1.2%
-22.3%
No
Yes
15 – 20
n = 15
LiDAR
CT Vol. Table
-4.4%
-23.5%
No
Yes
>20
n = 18
LiDAR
CT Vol. Table
+6.6%
-17.4%
No
No
Vol.T. underestimated scaled volume by 19.8%
LiDAR overestimated scaled volume by 0.6%
Information courtesy
Hinton Wood Products
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Why are the Volume Tables so far off ?

Volume Table predictions
– rely on AVI polygon height
– don’t handle within-polygon
variability well

Polygon-level LiDAR
predictions
– don’t rely on age or SI50
– aggregate all cell-level
predictions

What about the bias?
– It’s the oper planner’s fault!
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Existing PSPs  calibrate LiDAR Model?

PGS plots used were not welldistributed across variation in
LIdar metrics
– For many situations on FMA,
models are in extrapoln mode
– Validation needed for area
outside PSP space

customized sample design
should  still better models
Frazer et. al, 2011
Partners: WFM - Hinton Wood Prod.; Alberta SRD; CFS–PFC; UBC
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Structurally-guided sample design
Existing PGS plots
Structurally guided sample
White et al, 2013
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Required sample size will depend on…



acceptable error
confidence level required
# of “forest types” modeled
ACCEPTABLE ERROR
CONFIDENCE LEVEL
REQUIRED SAMPLE SIZE
(per “forest type”)
5%
95%
386
10%
95%
96
10%
90%
68
White et al, 2013
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Sampling Intensity, results & cost…

Hinton PSPs maintained since 1950s
– 3,202 plots est. systematically across FMA
– designed  empirical yield curves

“Better results” from LiDAR models
– but only 735 plots were used

A LiDAR-specific sample design…
– still better results, with even fewer plots
– allow prediction of other attributes
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What we’re working on now…

“Best Practices” Guideline
– support dev. of “standards” for LiDAR use

Strategic, tactical and operational planning
– support acceptance of LiDAR in FMPs, etc,
– Linkage to FPInnovations’ Value Maximization
& Decision Support Program
• FPInterface  net value predictions
• Document cost-benefit across value chain
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What we’re working on now…

Evaluating model improvement (?) with
structurally-balanced sample design

High Resolution LiDAR & Digital Imagery
with Semi Global Matching  CHM
– proposal to re-fly the Hinton FMA
– explore potential to “grow” the inventory

Going Coastal ???
– explore object-based predictions with high-res?
– predicting species & “product profiles” ?
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