scott saleska paul moorcroft david fitzjarrald (suny-albany) geoff parker (serc) plinio camargo...

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




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)




years)

2520

15

10

5

-5

0

-10

-15

-20

-25


(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!




years)

2520

15

10

5

-5

0

-10

-15

-20

-25


(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 first 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)



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


20

40








heig

ht

(m)

Ground-based Lidar

km 67 tower site, July 2003


Parker, personal

communication



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


20

40









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


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

2 - 10

32

64

64

1200

25

1

tower

Question:





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


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

2 - 10

32

64

64

1200

25

1


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

2 - 10

32

64

64

1200

25

1

tower

Question:





40 ha of new

Transects

at Km72 (T1 &

T2) and Km117 (T3 & T4)

estab-lished

summer 2003


s in tower

footprint

estab- lished 1999


Km 117

Km 72

Km 67

T3

T2

T1

T4

10 km


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

2 - 10

32

64

64

1200

25

1


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

2 - 10

32

64

64

1200

25

1

Question:





40 ha of new

Transects

at Km72 (T1 &

T2) and Km117 (T3 & T4)

estab-lished

summer 2003


s in tower

footprint

estab- lished 1999


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


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


years)

Mg (

C)

ha

-2 y

r-1

Flux-Age relation


(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


years)

Mg (

C)

ha

-2 y

r-1

Flux-Age relation




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


years)

Mg (

C)

ha

-2 y

r-1

Flux-Age relation




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

)


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



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


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


0.0 0.04 0.08 0.12

2

6

10

14

18

22

26

30

34

38

42

46

50


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


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"


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


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

)



4m bin


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

)



10m bin

4m bin


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

)



20m bin

10m bin

4m bin


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

)



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


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

)



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


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


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


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


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

2 - 10

32

64

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1200

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Woodsize (cm)

No.plots

Area(m2)

> 30

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64

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tower

(B)

Km 67

Km 83

10 km


Woodsize (cm)

No.plots

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> 30

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tower

(B)

Km 117

Km 72

Km 67

Km 83

T3

T2

T1

T4

10 km


Woodsize (cm)

No.plots

Area(m2)

> 30

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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


Woodsize (cm)

No.plots

Area(m2)

> 30

10 - 30

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32

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> 30

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tower

(A) (B)

(C)

scott saleska paul moorcroft david fitzjarrald (suny-albany) geoff parker (serc) plinio camargo...

Documents

loss slide

uptake loss

net loss of c

tapajos national forest

landscapesregions slide

landscape harvard slide

tropical forest carbon

forest carbon balance