Assessing carbon stocks & flux:
opportunities and challenges
Jeremy Fried, Xiaoping Zhou & Susanna Melson
PNW FIA Portland
PNW FIA 2008 Client Meeting
Sacramento, CA
13 May 2008
Agenda
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I. Rapid estimation of a 1990 baseline
II. Model selection error
III. Where to from here?
Part I
Rapid estimation of a 1990 forest
carbon baseline for California
The genesis
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PSW/ARB/CDF sought FIA’s help to estimate statewide
1990 baseline forest carbon stock and flux by carbon pool
Component
Percent of forest C*
Live, above-ground (tree, shrub, forb)
40
Soil organic carbon
38
Below-ground live (roots)
10
Litter
8
Dead wood
4
Total
100
* Source of approximate average proportions: B. Krumland
The driver
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California AB32 mandates rollback to 1990
emissions by 2020; initiated GHG inventory
Targets for carbon emissions for every sector of the
economy had to be set by January 2008
It was increasingly clear that the numbers and
model basis on which ARB was relying for forest
carbon were not making sense
Result was an 11th hour effort to estimate via forest
inventory plot data
The attribute of interest
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ARB sought information on both carbon stocks, by
forest component and annual carbon flux,
approximated as stock change
We use flux and stock change synonymously, so
positive values = sequestration
Carbon stocks derived from inventory-calculated
volume and biomass and carbon conversion factors
Stock change calculated by differencing inventories
How were forest pools assessed?
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Above ground live tree (AGLT) biomass was
calculated for each tree as the product of volume
and specific gravity
Jenkins equations, developed by very broad type
groups, used to estimate other pools
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Understory and down wood carbon estimated as
proportion of live tree carbon
Standing dead = f(growing stock volume)
Litter = f(stand age)
1990 baseline a tough target
NFS
Reserved
ONF
Timberland
ONF
Other
Forest
Collection
dates
Remeas.
NIMS
2001-06
No
Yes
Yes
Yes
Yes
Yes
IDB
1991-94
[ONF]
1993-2000
[NFS]
No
Yes
Yes
Yes
No
Partial
94Change
1991-94
1981-83
Yes
No
No
Yes
No
Partial
ONF=outside of national forest
NFS
ONF
Reserved
DataBase
Limitations of available data
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Changes between 1990s (periodic, IDB) and 2000s
(annual, NIMS) definitions of:
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what
what
what
what
what
is
is
is
is
is
a tree
a forest
reserved
timberland
a plot (plot design)
Little definition change from 80s to 90s, but data
covers only ONF timberland, which comprises
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24 percent of California’s forest area
28 percent of California’s above-ground, live tree biomass
Line of attack
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Compare IDB to NIMS, assume 1990 flux same
Considered 8 forest strata:
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NFS Timberland
NFS Other Forest
NFS Reserved
Other Public Timberland
Other Public, Other Forest
Other Public Reserved
Private Timberland
Private Other Forest
And various aggregations of these strata
Old Growth
Regeneration
8 m radius
The covariance bugaboo
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Sampling error (SE) of the difference between two inventories:
S .E . = σ + σ − 2CoVar1, 2
2
1
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Where these terms are, in order:
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2
2
variance of the total (carbon, biomass or any other inventory attribute)
from inventory #1
variance of the total from inventory #2
covariance between the two inventories.
Except for ONF timberland, we measured mostly different
places on the ground, and different trees
Estimating covariance is devilishly difficult, if not impossible
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Only alternative is to assume zero covariance, so SE is large
Problems comparing IDB to NIMS
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Millions of acres with no sample information
in IDB (e.g., Other Public Reserved, ONF
other forest)
Timberland protocol change produced
artifact of ~20% increase in estimate of
timberland area
Reserved definitions changed
Calculated sampling errors were huge
Stock change never significantly different
from zero at α=0.05
NIMS subsets
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Differenced 2001-2003 from 2004-2006 and divided
by 3 to get annual density flux
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Covered all lands, definitions consistent, but
Sample size small, sampling errors huge!
Covariance definitely zero (no plot overlap)
Estimating 1990 stock as
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2004 stock – (14 * annual flux)
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resulted in negative carbon in one stratum!
Still no differences significant at α=0.05
ONF Timberland Results (from 94 change table)
Tg of carbon
Aboveground
live tree
Year
Survey 1984
1990 Estimates
Survey 1994
Annual Flux
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274
296
304
2.9
BelowUnderground
story
biomass vegetation
59.5
10.1
63.2
10.8
64.4
11.1
0.5
0.09
Dead
wood
Soil
organic
Litter
Total
59.7
61.9
62.8
0.29
134.4
133.7
134.0
-0.09
94.1
93.0
93.2
-0.14
632
658
670
3.5
Aboveground live tree is largest pool and accounts
for greatest stock change
Jenkins equations for soil organic and litter carbon
relatively insensitive to attributes assessed by FIA
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e.g., stand age and forest type
2001-2003 vs. 2004-2006
Carbon Flux (Mg/ac/yr)
Stratum
All forest land
Mean
National Forest
Total
0.309
Other public and private
Other public
1.488
Private
0.065
Total
0.491
All forestland
0.369
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Forest land
Forestland Groups
Timberland
Nonreserved,
excl. timberland
Reserved
SE
Mean
SE
Mean
SE
Mean
SE
0.445
0.059
0.579
0.420
0.536
0.655
1.120
1.751
0.502
0.596
0.384
-1.443
-0.656
-0.719
-0.391
2.684
0.658
0.644
0.433
-0.305
0.203
-0.018
0.104
0.459
0.387
0.316
0.273
3.274
3.088
3.274
1.966
3.088
1.500
Sampling error implies that the best estimate of
flux is zero; no result is significant at α=0.05
Reserved lands appear likely to have high
sequestration (at α=0.33)
Carbon stocks findings
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23% of California’s live tree carbon is on
reserved lands (18% of CA forest area)
50% of reserved carbon is in NFS; the rest is
in state and national parks
Carbon stocks on all NFS strata combined
represent > 50% of CA forest carbon
Pool fractions different than average
Component
%
National
Avg.
% CA
Timberland
Live, above-ground (tree, shrub, forb)
40
45
Soil organic carbon
38
20
Below-ground live (roots)
10
10
Litter
8
14
Dead wood
4
9
100
100
Total
Lessons learned
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Lesson #1: While flux may be derived from stock
change, it cannot be reliably derived from change
in independently estimated stocks (i.e., periodic to
annual)
Lesson #2: Contemporary, statistically significant
estimates of flux can’t be expected until
remeasurement data is in hand
Lesson #3: Freezing protocols is essential for
future ability to assess change
Part II
Model Selection Error
How good are FIA estimates?
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Estimates derived from a statistical sample
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Precision and accuracy affected by
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Not a census, not “truth”
Measurement error (ME)
Sampling error (SE)
Model selection error (MSE)
Magnitude: MSE>SE>ME
Accuracy (bias) impact: MSE>SE>ME
In sphere of FIA control: ME>SE>MSE
Focus of attention: ME>SE>MSE
MSE impact on carbon HUGE
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We don’t measure carbon
We don’t measure biomass
We don’t measure volume
We don’t measure diameter or height
We assess height and measure circumference
All the rest is smoke and mirrors, er, make
that models!
So how many models are there?
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On average, for a given species and location,
~ 2 dozen published models plausibly apply
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Covering bole, branch, bark, foliage, whole tree,
bole and bark, bole and branches, etc.
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as volume or biomass
for various ranges of dbh
With/without add’l explanatory variables such as height, LCR
These can be combined in myriad ways to
estimate above-ground live tree (AGLT) carbon
For Douglas-fir, these combinations result in 10
million possible calculation pathways
Jenkins meta-analysis approach
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Compiled all available
DBH-based equations
for AGLT biomass for
U.S. species
Used regression to
develop a set of
national-scale, AGLT
biomass equations of
the form:
Exp(β0 + β1 ln (dbh))
Example: 6 Douglas-fir equations
Jenkins AGLT biomass parameters
Jenkins equations: concerns/cautions
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Species groups are very broadly defined
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e.g. ponderosa pine is in the same group as
loblolly pine
AGLT biomass equations rely on diameter
as the sole explanatory variable
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This ignores the effect of tree taper
Loblloy Pine vs Ponderosa Pine
(Height vs DBH)
160
Height (Feet)
140
120
100
80
60
40
20
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30
DBH (inch)
Loblolly Pine
Pondrosa Pine
West Oregon vs. east Oregon
(Ponderosa Pine --- Height vs DBH)
140
Height (feet)
120
100
80
60
40
20
0
2
4
6
8
10
12
14
16
18
20
22
24
DBH (inch)
West Oregon
East Oregon
26
28
30
B iom ass (K g)
AGLT biomass of regional Loblolloy &
Ponderosa vs. Jenkins Pine
4500
4000
3500
At dbh=20”, Jenkins:
Underestimates Loblolly pine by 25%
Overestimates Pondrosa pine by 36%
3000
2500
2000
1500
1000
500
0
4
5
6
8
10 12 14 16 18 20 22 24 26 28 30
DBH (inch)
Loblolly_BioT_FIA)
Pondrosa_BioT_FIA)
Jenkins_Pine_BioT
Use of group-averaged wood density
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Specific gravity values from literature for
all species that make up a group are
averaged and applied to volume before
data are developed for distribution fitting
(Jenkins, 2004)
In the pine group, wood specific gravity
ranges from 0.34 (sugar pine) to 0.54
(longleaf pine in the south)
Local volume equation approach
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Tree specific: regionally applicable, species-specific
equations are used for each major tree species
Branch, bark and bole volume equations depend on
dbh and height
Bole biomass is calculated from the cubic volume
total stem estimate and species-specific wood
density
Bole, branch and bark biomass are summed to
derive total woody biomass
AGLT biomass in California for selected species
600
28%
Million metric to
500
400
300
200
-40%
11%
100
0
Ponderosa pine
Douglas fir
PNW_Eqn
Jenkins_Eqn
Redwood
AGLT biomass in California (from NIMS)
Ownership
Forest Land
Group
Area
PNW
Jenkins
Diff.
%
NFS
Timberland
9.3
709
850
20
Other Non_reserved
2.3
41
54
32
Other Reserved
3.4
231
285
23
Timberland
0.9
63
68
8
Other Non_reserved
1.3
18
20
22
Other Reserved
2.5
226
235
4
Timberland
8.9
610
659
8
Other Non_reserved
4.3
103
134
30
0
0
0
Other Pub
Private
Other Reserved
Area in million acres and Biomass in
Million metric tons
Above-ground, live tree
carbon, 2005
Mg carbon/ac
Land Stratum
PNW vol. eqns.
Jenkins eqns.
Timberland
35.1
40.2
Other unreserved
10.5
13.2
Other reserved
41.8
46.4
Area-wtd. Total
30.8
35.3
Jenkins is 11-25% high!
Above-ground, live tree,
carbon flux, 2002-2005
Kg carbon/ac/year
Land Stratum
PNW vol. eqns.
Jenkins eqns.
Timberland
-406
-454
Other unreserved
103
-64
Other reserved
1938
1333
Area-wtd. Total
352
200
Jenkins flux is always lower!
But none of these differences are significantly different from 0
at α=0.05, due to the large sampling error.
Some relationships don’t make sense
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Jenkins dead wood=proportion of live above
ground
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But following disturbance (harvest, fire, blow-down or
bug-kill), dead wood can be many times the live tree
carbon, NOT a fraction of it (multiplier can be >1)
While some kinds of harvest may leave little if any
dead wood, possibly more acres today are disturbed
by other forces
Where to?
CCAR/CCX data needs
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Carbon stock/acre by ecoregion, site quality,
owner group, forest type
Precision standards TBD but likely >> current
Regular updates to assess stock change
Spatially resolved at ownership/parcel scale
Nationally and temporally consistent, unbiased
and distortion-free accounting process
What could get us there?
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Stop changing the forest inventory design/definitions
Invest $$$ in volume & biomass equations
Substantially increase plot density (3-4X)
Focused, well-supported techniques research:
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integrate plot, LiDAR and spectrally sensed information to
enable spatially comprehensive, sufficiently precise models
Except #1, these are unfunded
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Perhaps 70+ million dollars for CA to do all these things
Our short-term reality
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Assess down wood/snag carbon via FIA plots
and compare with Jenkins
Assess live tree carbon stock change (and
precision) for remeasured R5 plots
Complete work with Susanna Melson and
Mark Harmon on equation selection sensitivity
analysis
Try to build support for estimating better
volume and biomass equations
Questions welcome