Modeling and sensing
growth rates of tropical rainforests
Methodologies and needs
Marcos Heil Costa
Universidade Federal de Viçosa
Workshop on Modeling and Sensing Environmental Systems
LNCC – Petrópolis – August 2008
Outline
1. Introduction
2. Growth rates of tropical rainforests
a. Field measurements
b. Regional monitoring (diagnostic)
• Models
• Merging models and remote sensing
c. Prognostic
d. Intercomparison of methodologies
3. Discussion, conclusion and future needs
Introduction
 The most likely scenario for the next decades presents strong
modifications in the global environment, including increase of
the atmospheric concentration of CO2 and other trace gases,
climate change and intensification of the impacts caused by
the man's action.
 These changes may cause important modifications in the
composition, structure and distribution of the ecosystems on
the planet.
 A precise monitoring of the changes in the terrestrial
biosphere is extremely important.
Introduction
 The detection of the interannual variability and long-term
trends in the ecosystems structure and dynamics will provide
indications of change that would otherwise be unnoticed until
the beginning of the transformation of the biome.
 The structure and composition of an ecosystem depend on its
growth rate, or Net Primary Production (NPP) and mortality
rate.
 Monitoring strategies of NPP:
 Field measurements
 Modeling
 Remote sensing + modeling
Field measurements
Atlantic Rainforest Conservation Reserve
Aracruz Celulose SA – Teixeira de Freitas – BA
Field measurements
 Measurements of yearly biomass increments:
• Thousands of trees are
measured per site
• One measurement costs
about US$ 50k
Field measurements
Diagnostic Models
• 0-D models
– Forced by local meteorological data and
vegetation structure parameters
– Expensive, given the need for local
measurements
– Results validated against local NPP field
measurements
– Monitoring ability limited by availability of local
data
Diagnostic Models
• 2-D models
– Forced by climate data and regional
vegetation structure measurements taken
from remote sensing
– Monitoring ability limited by availability of:
• climate data: near real time
• Satellite data: continuity of sensors
– New remote sensing data may demand
algorithm upgrades
Framework of the RATE algorithm
FAPAR
MODIS
assimilated
Land cover
MODIS
LAI
MODIS
assimilated
Climate
variables
NCEP
RATE
algorithm
0,4º resolution
NPP
Algorithm
Validation
NPP field data
Prognostic Models
• Must predict their own future climate and
also NPP and vegetation structure
• This means a coupled climate-dynamic
vegetation model
• Careful validation against present time
field measurements essential
• Reconstruction of known past conditions
and trends desirable
Some data and results
Assimilation of LAI and FAPAR
LAI - KM67 - 2004
LAI - KM67 - 2004
2
2
7,00
7,00
6,00
6,00
5,00
4,00
LAI
LAI
5,00
3,00
4,00
3,00
2,00
2,00
1,00
1,00
0,00
0,00
1
25
49
73
97
121
145
169
193
217
241
265
289
313
337
1
361
2
3
4
5
6
7
8
9
10
11
10
11
12
Mês do Ano
Dia do Ano
LAI MODIS
LAI observado
LAI assimilado
FAPAR - KM67 - 2002
LAI assimilado
FAPAR - KM67 - 2002
1,000
1,000
0,900
0,900
0,800
0,800
0,700
0,700
0,600
0,600
FAPAR
FAPAR
2
(m /m )
2
(m /m )
0,500
0,400
0,300
0,500
0,400
0,300
0,200
0,200
0,100
0,100
0,000
1
25
49
73
97
121
145
169
193
217
241
265
Dia do Ano
289
313
337
361
0,000
1
2
3
4
5
6
7
8
9
Mês do Ano
FAPAR MODIS
FAPAR assimilado
FAPAR observado
FAPAR assimilado
12
Simulated NPP
Simulated NPP
Simulated NPP
Intercomparison of methodologies
• Two 0-D models forced by local met station data
– IBIS
– SITE
• One 2-D model forced by climate and remote
sensing data
– RATE
• One coupled climate-vegetation model
– CCM3-IBIS
Data availability
Site
City
Coordinates
Period
Vegetation
Data
available
Source
Model evaluated
Flona Tapajós
K67
Belterra – PA
55.04º W; 2.86º
2004
Floresta
tropical
amazônica
EM; NPP
Turner et
al. (2006)
SITE-EM, SITE-RA
RATE, IBIS-EM,
IBIS-RA
BA712
Teixeira de
Freitas – BA
39.67º W; 17.29º S
2006
Mata
atlântica
EM; NPP
Nunes
(2008)
SITE-EM, SITE-RA,
RATE, IBIS-EM,
IBIS-RA
Flona Tapajós
K67
Belterra – PA
55.02º W; 2.86º S
2001
Floresta
tropical
amazônica
NPP
Vieira et
al. (2004)
RATE
UFAC
Rio Branco – AC
68.03º W; 10.12º S
2001,
2002
Floresta
tropical
amazônica
NPP
Vieira et
al. (2004)
RATE
ZF-2
Manaus – AM
60.18º W; 2.97º S
2001,
2002
Floresta
tropical
amazônica
NPP
Vieira et
al. (2004)
RATE
Caxiuanã
Melgaço – PA
Várias parcelas entre
1°41’S e 1°46’S e
entre 51°30’W e
51°23’W
Longterm
Floresta
tropical
amazônica
NPP
Malhi et al.
(in press)
CCM3-IBIS
Flona Tapajós
K67
Belterra – PA
Várias parcelas entre
2°50’S e 3°18’S; entre
55°05’W e 54°55’W
Longterm
Floresta
tropical
amazônica
NPP
Malhi et al.
(in press)
CCM3-IBIS
Manaus
Manaus – AM
Várias parcelas entre
2°20’S e 2°38’S; entre
60°15’W e 59°58’W
Longterm
Floresta
tropical
amazônica
NPP
Malhi et al.
(in press)
CCM3-IBIS
0-D models forced by met data
NPP (kg -C m-2 yr -1 )
Site
Year
Model
Observed
Simulated
Relative
value
value
error
Flona Tapajós K67
2004
SITE -EM
1.055
1.049
-0.6%
Flon a Tapajós K67
2004
IBIS -EM
1.055
0.971
-8.0%
BA712
2006
SITE -EM
1.366
1.214
-11.1%
BA712
2006
IBIS -EM
1.366
1.458
6.7%
Average relative error
–
1.211
1.198
-3.3%
Average | relative error |
–
–
–
6.6%
2-D model forced by climate and
remote sensing data
NPP (kg-C m-2 year -1 )
Site
Year
Model
Observed
Simulated
value
value
Error
Flona Tapajós K 67
2001
RATE
1.230
1.338
8.8%
Flona Tapajós K 67
2004
RATE
1.055
1.255
19.0%
ZF-2
2001
RATE
1.063
1.226
15.3%
ZF-2
2002
RATE
1.356
1.237
-8.8%
UFAC
2001
RATE
1.343
1.307
-2.7%
UFAC
2002
RATE
1.299
1.255
-3.4%
BA712
2006
RATE
1.366
1.305
4.5%
Average of the relative error
–
–
1.245
1.275
4.7%
Average of | relative error |
–
–
–
–
8.9%
Coupled climate-vegetation model
NPP (kg -C m-2 year -1 )
Site
Model
Observed
Simulated
value
value
Erro r
Manaus (long -term)
CCM3 -IBIS
1.01
1.23
21.8%
Flona Tapajós ( long -term)
CCM3 -IBIS
1.44
1.16
-19.4%
Caxiuanã ( long -term)
CCM3 -IBIS
1.00
0.91
-9.0%
Average of relative errors
–
1.15
1.10
-4.3%
Average of | relative error |
–
–
–
16.7%
Synthesis
Group of models
0-D model s for ced by met station data
2-D m odels for ced by climate and
Average of
Maximum
| error |
| error |
(%)
(%)
(%)
3.3
6.6
11.1
4.7
8.9
19.0
4.3
16. 7
21.8
| error average |
remote sensing data
Coupled climate -biosphere model
Discussion
• Results indicate that current NPP simulation
models are capable of unbiased estimates of
average regional values of NPP, both in
diagnostic and in prognostic mode, with errors
smaller than 5%.
• The expected value of the error in each site is
smaller than 10% in the diagnostic mode and
smaller than 20% in the prognostic mode.
Main Conclusion:
State-of-the-art models are capable of
routinely monitoring the net primary
production of tropical ecosystems, with low
errors expected for these estimates.
Future needs/directions:
• More validation points, particularly in other
ecosystems
• Test of the LAI and FAPAR assimilation
algorithm for other ecosystems  field data
• Assimilation of other types of data
– CERES (flew 2002): skin temperature
– OCO/GOSAT (to fly 2008/2009): surface carbon
balance
– DESDynI (to fly 2010-2013): vegetation structure and
biomass
• Multi-model ensemble
Thank you!