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Scott SaleskaPaul Moorcroft
David Fitzjarrald (SUNY-Albany)
Geoff Parker (SERC)
Plinio Camargo (CENA-USP)
Steven Wofsy
Natural disturbance regimes and tropical forest carbon balance:
integrating canopy structure, flux measurements, and modeling across the landscape
(Harvard)
Prior results I: eddy flux measurement show net loss of C in Tapajos National forest of
Amazônia, attributable to recent disturbance event(s)
6
5
4
3
2
1
0
-1
-2
Jul Jan Jul Jan Jul Jan Jul
0
100
200
prec
ip
(mm
/wee
k)
0
100
200
prec
ip
(mm
/wee
k)
km83 sitekm67 sitekm83 sitekm67 site
eddyflux
bio-metry
upta
kelo
ss t
o
atm
osp
here
Acc
um
ula
ted
Mg(
C)
ha
-1
Source: Saleska et al. (2003) Science2001 2002 2003
Source: Moorcroft, Hurtt & Pacala (2001)
Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest
Gap Age (time since disturbance in
years)
2520
15
10
5
-5
0
-10
-15
-20
-25
Note
eco
logic
al si
gn c
onventi
on!
npp = net primary productivity (uptake)rh = heterotrophic respiration (loss) nep = net ecosystem productivity (change in carbon balance)
Positive productivity (=uptake from atm)
Negative productivity (=loss
to atm)
Mg (
C)
ha
-2 y
r-1
Source: Moorcroft, Hurtt & Pacala (2001)
Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest
Gap Age (time since disturbance in
years)
2520
15
10
5
-5
0
-10
-15
-20
-25
Note
eco
logic
al si
gn c
onventi
on!
npp = net primary productivity (uptake)rh = heterotrophic respiration (loss) nep = net ecosystem productivity (change in carbon balance)
Positive productivity (=uptake from atm)
Negative productivity (=loss
to atm)
Mg (
C)
ha
-2 y
r-1
Tapajos Km 67 site?(loss)
Source: Moorcroft, Hurtt & Pacala (2001)
Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest
Gap Age (time since disturbance in
years)
2520
15
10
5
-5
0
-10
-15
-20
-25
Tapajos Km 67 site?(loss)
(1)where measurement sites fall on this graph
(2)frequency distribution of gap ages across the landscape
Extrapolating measurements to landscape or region requires knowing:
Mg (
C)
ha
-2 y
r-1
Note
eco
logic
al si
gn c
onventi
on!
Source: Moorcroft, Hurtt & Pacala (2001)
Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest
Gap Age (time since disturbance in
years)
2520
15
10
5
-5
0
-10
-15
-20
-25
Tapajos Km 67 site?(loss)
(1)where measurement sites fall on this graph
(2)frequency distribution of gap ages across the landscape
Extrapolating measurements to landscape or region requires knowing:
Mg (
C)
ha
-2 y
r-1
selection bias towards pristine-
looking sites?(uptake)
Note
eco
logic
al si
gn c
onventi
on!
Poses hard question: how to reliably estimate C-balance at large spatial
scales
Requirements:
A. Model that links disturbance state and forest carbon balance
B. Measurements of forest disturbance state to constrain model at large scales
C. Ability to test forest structure-constrained model predictions at points distributed across landscapes/regions
A. Modeling: the Ecosystem Demography (ED)
Model • Plant community dynamics
• carbon and nitrogen biogeochemistry
• Explicit representation of size- and age-structure of ecosystem heterogeneity
(Moorcroft, Hurtt and Pacala, 2001; Medvigy et al., 2004)
Patch age (yr)
Max
Can
opy
Ht (
m)
0 20 40 60 80 100
0
10
20
30
ED simulations relating forest size and age structure to carbon balance: Max canopy height and …
Tapajos National Forest
Patch age (yr)
Max
Can
opy
Ht (
m)
0 20 40 60 80 100
0
10
20
30
-30
-20
-10
0
10
NE
P (
MgC
/ha/
yr)
Tapajos National Forest
ED simulations relating forest size and age structure to carbon balance: Max canopy height and … Net Ecosystem Production (NEP)
Aircraft-based Lidar gives canopy structure
(at landscape scale)
Source: Hurtt et al. (2004)
B. Measurements of forest disturbance state (as embodied in canopy structure)
0 1 2 3 4 5 6 7km 67 H arvard transects July 2003
estim ated surface area density
500 550 600 650 700 750 800 850 900 950 1000
20
40
0 50 100 150 200 250 300 350 400 450 500
10
20
30
40
50
500 550 600 650 700 750 800 850 900 950 1000
10
20
30
40
50
0 50 100 150 200 250 300 350 400 450 500
20
40
he
igh
t, m
0 50 100 150 200 250 300 350 400 450 500
20
40
500 550 600 650 700 750 800 850 900 950 1000
horizonta l d istance, m
20
40
transect 1 first ha lf
transect 1 second half
transect 3 second half
transect 3 first half
transect 2 first ha lf
transect 2 second half
distance along transect (m)
Source:Fitzjarrald &
Parker, personal
communication
heig
ht
(m) km 67 tower site,
July 2003
B. Measurements of forest disturbance state (as embodied in canopy structure)
Ground-based Lidar gives canopy structure (local scale)
0 1 2 3 4 5 6 7km 67 H arvard transects July 2003
estim ated surface area density
500 550 600 650 700 750 800 850 900 950 1000
20
40
0 50 100 150 200 250 300 350 400 450 500
10
20
30
40
50
500 550 600 650 700 750 800 850 900 950 1000
10
20
30
40
50
0 50 100 150 200 250 300 350 400 450 500
20
40
he
igh
t, m
0 50 100 150 200 250 300 350 400 450 500
20
40
500 550 600 650 700 750 800 850 900 950 1000
horizonta l d istance, m
20
40
transect 1 first ha lf
transect 1 second half
transect 3 second half
transect 3 first half
transect 2 first ha lf
transect 2 second half
distance along transect (m)
heig
ht
(m)
Ground-based Lidar
km 67 tower site, July 2003
Source:Fitzjarrald &
Parker, personal
communication
0 1 2 3 4 5 6 7km 67 H arvard transects July 2003
estim ated surface area density
500 550 600 650 700 750 800 850 900 950 1000
20
40
0 50 100 150 200 250 300 350 400 450 500
10
20
30
40
50
500 550 600 650 700 750 800 850 900 950 1000
10
20
30
40
50
0 50 100 150 200 250 300 350 400 450 500
20
40
he
igh
t, m
0 50 100 150 200 250 300 350 400 450 500
20
40
500 550 600 650 700 750 800 850 900 950 1000
horizonta l d istance, m
20
40
transect 1 first ha lf
transect 1 second half
transect 3 second half
transect 3 first half
transect 2 first ha lf
transect 2 second half
distance along transect (m)
Source:Fitzjarrald &
Parker, personal
communication
heig
ht
(m)
fraction
heig
ht
(m)
Future Work (2). Observations over larger spatial scales
Ground-based Lidar
km 67 tower site, July 2003
Gap fraction (canopy < 10
m) 25%
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
10 km
C. Testing model predictions across landscape
Question:
What is the range of disturbance
across the landscape scale in
the Tapajos National Forest?
Km 67
10 km
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
Question:
What is the range of disturbance
across the landscape scale in
the Tapajos National Forest?
C. Testing model predictions across landscape
20 ha of biometry transect
s in tower
footprint
estab- lished 1999
Tapajos National Forest region, central eastern Amazônia
Km 117
Km 72
Km 67
T3
T2
T1
T4
10 km
25m
15m500m 1000m 2000m 2500m0m 1500m
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
Question:
What is the range of disturbance
across the landscape scale in
the Tapajos National Forest?
C. Testing model predictions across landscape
40 ha of new
Transects
at Km72 (T1 &
T2) and Km117 (T3 & T4)
estab-lished
summer 2003
20 ha of biometry transect
s in tower
footprint
estab- lished 1999
Tapajos National Forest region, central eastern Amazônia
Km 117
Km 72
Km 67
T3
T2
T1
T4
10 km
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Question:
What is the range of disturbance
across the landscape scale in
the Tapajos National Forest?
C. Testing model predictions across landscape
40 ha of new
Transects
at Km72 (T1 &
T2) and Km117 (T3 & T4)
estab-lished
summer 2003
20 ha of biometry transect
s in tower
footprint
estab- lished 1999
Tapajos National Forest region, central eastern Amazônia
Large-scale Observations:
1. Live aboveground biomass
2. Vines
3. Coarse Woody Debris
4. Soil characteristics
5. Canopy structure (via ground-based Lidar)
6. Remote-sensing Lidar campaign (airborne LVIS, airborne Lidar or IceSat data)
At spatially-distrib-
uted transect
s
Spatially continuous in 20 x 60 km box
Canopy Height
Gap A
ge
Age-Height Relation
Gap Age (time since disturbance in
years)
Mg (
C)
ha
-2 y
r-1
Flux-Age relation
(Moorcroft, et al., 2001)
(A) ED model(includes canopy structure)
Canopy Height
Gap A
ge
Age-Height Relation
Gap Age (time since disturbance in
years)
Mg (
C)
ha
-2 y
r-1
Flux-Age relation
(Moorcroft, et al., 2001)
(A) ED model(includes canopy structure)(B) Observation:
canopy height distribution
Heig
ht
Tree Density
Model prediction:carbon balance
across landscape
Canopy Height
Gap A
ge
Age-Height Relation
Gap Age (time since disturbance in
years)
Mg (
C)
ha
-2 y
r-1
Flux-Age relation
(Moorcroft, et al., 2001)
(A) ED model(includes canopy structure)(B) Observation:
canopy height distribution
Heig
ht
Tree Density
Model prediction:carbon balance
across landscape
• Eddy fluxes (over time)
• Landscape-scale plots
(over space)
(C) Carbon Flux Observations
Test
Canopy Height
Gap A
ge
Age-Height Relation
Gap Age (time since disturbance in
years)
Mg (
C)
ha
-2 y
r-1
Flux-Age relation
(Moorcroft, et al., 2001)
(A) ED model(includes canopy structure)(B) Observation:
canopy height distribution
Heig
ht
Tree Density
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
Initial Results(and one big caveat)
Canopy Height Distribution
ED
Km 67
fraction
Hei
gh
t (m
)
Canopy Height Distribution
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
Large-scale sites (km 72 & km 117)
ED
Km 67
fraction
Hei
gh
t (m
)Initial Results
(and one big caveat)
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
-6-4
-20
Canopy Height Distribution
Large-scale sites (km 72 & km 117)
Km67 Km72 km117site
ED
Km 67
Corresponding ED-predicted Fluxes(95% confidence intervals from bootstrapping height data)
fraction
Hei
gh
t (m
)
Car
bo
n U
pta
ke (
Mg
C/h
a/yr
Loss
| ga
in
Initial Results(and one big caveat)
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
-6-4
-20
Canopy Height Distribution
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
Large-scale sites (km 72 & km 117)
ED
Km 67
Corresponding ED-predicted Fluxes(95% confidence intervals from bootstrapping height data)
eddy flux C-balance range
biometry C-balance range
fraction
Hei
gh
t (m
)
Car
bo
n U
pta
ke (
Mg
C/h
a/yr
Loss
| ga
in
Initial Results(and one big caveat)
Km67 Km72 km117site
5 10 155 10 15 20 25 30 355 10 15 20 25 30 35 40 45 505 10 15 20
5
10
15
20
25
30
35
40
45
5 10 15 20 25 30 35 40 455 10 15 20 25
5
10
15
20
25
30
35
40
45
5 10 15 20 25 30 35 40 45 50
10 20 30 40 50 60 70 80
vertica l s lice of canopy surface areaat D rano transects km 67 Tapajos
10 20 30 40 50 60
10
20
30
40"A" "B+C"
"D " "E" "F" "G "
"H " "I" "J"
The caveat: scale used for LIDAR data aggregation
Meters of transect
Hei
ght
(m)
Bin-size, 10 m
Gap width = 40m
5 10 155 10 15 20 25 30 355 10 15 20 25 30 35 40 45 505 10 15 20
5
10
15
20
25
30
35
40
45
5 10 15 20 25 30 35 40 455 10 15 20 25
5
10
15
20
25
30
35
40
45
5 10 15 20 25 30 35 40 45 50
10 20 30 40 50 60 70 80
vertica l s lice of canopy surface areaat D rano transects km 67 Tapajos
10 20 30 40 50 60
10
20
30
40"A" "B+C"
"D " "E" "F" "G "
"H " "I" "J"
The caveat: scale used for LIDAR data aggregation
Meters of transect
Hei
ght
(m)
Bin-size, 20 m
Gap width = 20m
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
The caveat: scale used for LIDAR data aggregation
fraction
Hei
gh
t (m
)
Baseline km67 data (horiz bin=2m)
Km 67 LIDAR data only
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
fraction
Hei
gh
t (m
)
Baseline km67 data (horiz bin=2m)
Km 67 LIDAR data only
4m bin
The caveat: scale used for LIDAR data aggregation
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
fraction
Hei
gh
t (m
)
Baseline km67 data (horiz bin=2m)
Km 67 LIDAR data only
10m bin
4m bin
The caveat: scale used for LIDAR data aggregation
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
fraction
Hei
gh
t (m
)
Baseline km67 data (horiz bin=2m)
Km 67 LIDAR data only
20m bin
10m bin
4m bin
The caveat: scale used for LIDAR data aggregation
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
fraction
Hei
gh
t (m
)
Baseline km67 data (horiz bin=2m)
Km 67 LIDAR data only
20m bin
10m bin
4m bin
-6-4
-20
24
0 10 20 30 40
LIDAR horiz bin width
LIDAR-constrained ED prediction
Car
bo
n U
pta
ke (
Mg
C/h
a/yr
Loss
| ga
in
4m bin
10m bin
20m bin
The caveat: scale used for LIDAR data aggregation
0.0 0.04 0.08 0.12
2
6
10
14
18
22
26
30
34
38
42
46
50
fraction
Hei
gh
t (m
)
Baseline km67 data (horiz bin=2m)
Km 67 LIDAR data only
20m bin
10m bin
4m bin
-6-4
-20
24
0 10 20 30 40
LIDAR horiz bin width
LIDAR-constrained ED prediction
Car
bo
n U
pta
ke (
Mg
C/h
a/yr
Loss
| ga
in
Scale of ED model
4m bin
10m bin
20m bin
The caveat: scale used for LIDAR data aggregation
0 20 40 60 80 100
-30
-20
-10
0
10
Future work• Incorporate CWD explicitly into ED model:
Loss
| ga
in
Patch Age (yrs)
Car
bo
n U
pta
ke (
Mg
C/h
a/yr Current ED
0 20 40 60 80 100
-30
-20
-10
0
10
Loss
| ga
in
Patch Age (yrs)
Car
bo
n U
pta
ke (
Mg
C/h
a/yr Current ED
Expected effect of CWD module: smooth out decomp losses
Future work• Incorporate CWD explicitly into ED model:
0 20 40 60 80 100
-30
-20
-10
0
10
Loss
| ga
in
Patch Age (yrs)
Car
bo
n U
pta
ke (
Mg
C/h
a/yr Current ED
Expected effect of CWD module: smooth out decomp losses
Less loss early
More loss late
Future work• Incorporate CWD explicitly into ED model:
Conclusions1. LIDAR detects variation in canopy structure across the
landscape (km 67 different from km’s 72 and 117).
2. ED model can map LIDAR-detected canopy structure to distribution of patch age, and thence to carbon balance; and it predicts significantly different balances across the landscape
3. ED model-predicted fluxes are highly sensitive to spatial scale of LIDAR-data aggregation: key to match spatial scale of LIDAR data to scale of model
4. When the scales of observation and model are matched, the modeled carbon balance does not agree with observed balance at km67
5. incorporation of CWD in ED model will likely improve the ability of ED to predict observed carbon balance.
LAI (cm2/m2 in each 2m ht bins)
Heig
ht
(m)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 1 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 2 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 3 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 4 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 5 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 6 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 8 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 9 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 10 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 11 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 12 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 13 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 14 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 15 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 16 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 17 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 18 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 19 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 20 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 21 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 22 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 23 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 24 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 25 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 26 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 28 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 29 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 30 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 31 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 32 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 33 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 34 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 35 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 36 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 37 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 38 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 39 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 40 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 41 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 42 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 43 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 44 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 45 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 46 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 47 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 48 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 49 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
0
10
20
30
40
0 1 2 3 4 5
Gap age: 50 yrs
-30
-20
-10
0
10
20Uptake
(TC/ha/yr)
01
02
03
04
05
0
0.0 0.02 0.04 0.06 0.08 0.10 0.12
LAI distributions (bars=ED, lines=Km67 Lidar data)
prob. density
he
igh
t (m
)
cum. probability
0.0 0.2 0.4 0.6 0.8 1.0
01
02
03
04
05
0
Km67 SAI(=Surface Area Index) is lower than LAI in ED
ED mean LAI height
Km67 mean SAI height
01
02
03
04
05
0
0.0 0.02 0.04 0.06 0.08 0.10 0.12
SAI patchwise mean-hts (bars=2-m segs, lines=8m, 20m segs)
prob. density
he
igh
t (m
)
cum. probability
0.0 0.2 0.4 0.6 0.8 1.0
01
02
03
04
05
0
Effect of Aggregation on patchwise mean LAI height
2m bins
20 m bins
Aggregating SAI bins has no effect on mean, but narrows the distribution
As distrib. narrows, lose lowest heights associated with big negative (loss) fluxes
As distrib. narrows, lose high heights but these have similar positive fluxes to those just below
Mean height
km67 fluxes from Lidar-constrained ED vs. aggregation length
aggreg length (m)
Flu
x (t
C/h
a/yr
)-4
-20
24
0 10 20 30 40
o Max ht approach
Mean SAI-ht approach
LAI approach is less sensitive than max-ht. approach, but it is still scale-dependent.
Km 67
10 km
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
(B)
Km 67
10 km
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
(B)
Km 67
Km 83
10 km
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
(B)
Km 117
Km 72
Km 67
Km 83
T3
T2
T1
T4
10 km
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
(A) (B)
Km 117
Km 72
Km 67
Km 83
T3
T2
T1
T4
10 km
Center Path25m
15m CWD Line
DBH> 30 cm , dead or alive
DBH> 10 cm, dead or alive, including lianas
500m 1000m 2000m 2500m0m
0m 10mLine Intercept CWD
measure CWD > 7.5 cm DBH For CWD >30cm DBH, measure orientation
1500m
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
Key for CWD PlotsPlottype
Woodsize (cm)
No.plots
Area(m2)
> 30
10 - 30
2 - 10
32
64
64
1200
25
1
tower
(A) (B)
(C)
Recommended