gross primary production algorithm development and validation
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Gross primary production algorithm development
and validation
K.Muramatsu(Nara Women’s Univ.)S. Furumi (Nara Saho College )
M. Daigo (Doshisha Univ.)Yukiko Mineshita ( NWU M2 student)
Yuri Hattori (NWU B4 student)14年1月14日火曜日
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Framework of GPP estimation
Photosynthesis velocity = Capacity x depression
Use light response curves Estimate directly GPP, not use LAI
Correspond to photosynthesis process Characteristics of the algorithm
●●● stomatal
opening and closing
Photosynthesis process: only the light exposure area
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Light reaction Carbon fixation
chlorophyll
light
Chemical energy
CO2
Carbon fixation
Calvin cycle
~ amount of absorbed light ~ amount of absorbed CO2
chlorophyll contents, light intensity
stomatal opening and closing, Weather, soil moisture
stomata
photosynthesis process
related to colorplant physiological parameter plant physiological actions
gas exchange is controlled fcapacity (PAR, chlorophyll) fstomata (xxx, xxx, •••)
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GPP capacity estimation framework
initial slope
PAR
photosynthesis velocity with low stress Pmax_capacity
From the plant physiological studyFor a leafInitial slope: efficiency of light conversion to carbon related to chlorophyll contentsPmax_capacity: volume of chloroplasts [Ono et. al, 1995, Oguchi 2003]
Pmax_capacity related to chlorophyll contents
slope x Pmax_capacity x PAR1+slope xPAR
CIgreen=G/NIR -1. [Gitellson et. al, 2006]
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Characteristics of CIgreen(=NIR/G-1 )
i) High sensitivity to Chlorophyll contents:Green>Red
ii) Linear relationship with Chlorophyll contents of a leafiii) Linear relationship with Pmax_capacity_2000
initial slope
PAR
Photosynthesis velocity with low stress Pmax_capacity
slope x Pmax_capacity x PAR1+slope xPAR
2000
Pmax_capacity_2000
Results from Previous study
(μmol)
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Previous studyPmax_capacity2000 vs. CIgreen
for each vegetation functional types
J.Thanyapraneedkul. et.al, 2012, Remote Sensing, 4, 3689-3720
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DNFEF
Grass
PF
DBFDBF
???
Use Grass parametersOpen Shrub ?Closed Shrub ?
GPP capacity estimation :
Evergreen Broad Leaf forests ?
Amazon
Parameters determined mainly using Japan FLUX
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Km67: natural Forest IGBP: EBF
US-Los: ハンノキ,湿地IGBP: Closed shrub bland
US-Ses: Desert shrub landIGBP: Open shrub land
US-Wjs: Savanna(EGF)IGBP: Open shrub land
US-Whs: Grazing areaIGBP: Open shrub land
CA-LetIGBP: Grass
Km83: Logged Forest IGBP: EBF
Study site , this year
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Open shrub and GRASS
Grass (CA-Let) and Open shrubs (US-Wjs, US-Ses, US-Whs)On the same line
added data (Open Shrub)
Previous study (Grass)
0
0.5
1
1.5
2
0 2 4 6 8 10 12
Pmax
capa
city
2000
[mgC
O2/
m2/
s]
CI=NIR/Green-1
CI-2000
CA-LetUS-WjsUS-SesUS-Whs
0.42*x-0.33
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Amazonian Forest: Pmax_capacity_2000 has almost constant valueExcept for Amazonian Forest On the same line US-Los(Closed shrub), JP-FJY(ENF), JP-TMK (DNF), TH-SKR(EBF) Another line JP-TKY Slopes of them are the almost same value
Closed shrub and Amazonia Forest (EBF)
BEF( Amazonia F.)
closed shrub
Previous study NDFNEFBEF
BDF (Previous study)
Added data
0
0.5
1
1.5
2
0 2 4 6 8 10 12
Pmax
capa
city
2000
[mgC
O2/
m2/
s]
CI=NIR/Green-1
CI-2000
JP-TMKJP-FJY
TH-SKRUS-Los
km67km83
JP-TKY0.14*x+0.210.17*x-0.35
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The relationship between CI vs. Pmax_capacity_2000 Three vegetation groups
1) Grass and Shrubs 2) Woody plant except for Amazonian Forest 3) Amazonian Forest
In the same group: The slope are almost same values
If there is the determination rule of the intercept, determination of parameters can be generalized.
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0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12
CI
MONTH
CI
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12
CI
MONTH
CI
CI seasonal changes of Shrubs
Not growing season’s CI value is this start point
US-Wjs US-Whs
0
0.5
1
1.5
2
2.5
0 2 4 6 8 10
Pmax
capa
city
2000
[mgC
O2/
m2/
s]
CI=NIR/Green-1
CI-2000
CA-LetUS-WjsUS-SesUS-Whs
0.42*x-0.33
For Woody plant group: considering
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Stomata opening closing
Daily change of stomatal conductance
Gas exchange is controlled by stomata opening and closing. When absorbing CO2, H2O is evapolated → Leaf temperature rising is suppressed
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
08: 09: 10: 11: 12: 13: 14: 15: 16: 17: 18:
Stom
atal
Con
duct
ance
( m
ol H
20 m
-2 s
-1 )
Time of Day
Observation with LI-6400
http://www.jspp.org/17hiroba/kaisetsu/kinoshita.html
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At Takayama (Gifu, Pref.) and Ymashiro (Kyoto Pref.) site
Canopy: MeasurementsPhotosynthesisLeaf Temperature : Thermal imager Brightness thermometer
Leaf : Measurements
Canopy temperature: Thermal imager
For three vegetation species: ミズナラ•ダケカンバ•コナラ Quercus crispula, Betula ermanii, Quercus serrata
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Daily change of leaf temperature
Quercus crispula Betula ermanii
The relationship among conductance, leaf temperature, weather conditions (air temperature,humidity, solar radiation) Calculate LUT using the moel[Baldocchi,1994]
ミズナラ ダケカンバ
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��� ������������
VPD(0~4)�
VPD(5~9)�
VPD(10~14)�
VPD(20~24)�
VPD(25~29)�
VPD(30~34)�
VPD(35~39)�
VPD(40~44)�
VPD(45~)�
VPD(15~19)�
LUT
Estimation results of stomatal conductance
stomata conductance(mol H20m-2s-1)
VPD: Vapor pressure deficit(kPa)
quercus serrata コナラLeaf T
PARPAR
Leaf temperature
Pattern : O.K.Absolute values:Calibration
Thermal imager
Measurements(LI6400)
Thermal imager
Thermocouple (LI6400)
Brightness thermometer
Brightness thermometer
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This year:
Continue the field measurements for scaling up from leaf to canopy at Takayama and Yamashiro site
MODIS data analysis for scaling up from canopy to satellite Using MODIS11C3 products (monthly averaged daily LST data )
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36.
44.
Example of field measurements at Yamashiro site
(℃)
(℃)
5 branches: daily change of photosynthesis measurement
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0 0.05
0.1 0.15
0.2 0.25
0.3 0.35
0.4
6 8 10 12 14 16 18 20
Con
duct
ance
Time
b1b2b3b4b5
0
5
10
15
20
6 8 10 12 14 16 18 20
Phot
osyn
thes
is
Time
b1b2b3b4b5
25
30
35
40
45
6 8 10 12 14 16 18 20
Brig
htne
ss T
empe
ratu
re
Time
b1
25
30
35
40
45
6 8 10 12 14 16 18 20
Brig
htne
ss T
empe
ratu
re
Time
b2
25
30
35
40
45
6 8 10 12 14 16 18 20
Brig
htne
ss T
empe
ratu
re
Time
b3
25
30
35
40
45
6 8 10 12 14 16 18 20
Brig
htne
ss T
empe
ratu
re
Time
b4
25
30
35
40
45
6 8 10 12 14 16 18 20
Brig
htne
ss T
empe
ratu
re
Time
b5
Stomatal conductance
(mol/m2/s)
(℃) (℃)
(℃)(℃) (℃)
photosynthesis
(μmol/m2/s)
Brightness Temperature (℃)b1 b2
b3 b4 b5
LUT is checked using these data.
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MODIS data analysis study the daily variation range of surface
temperature for evergreen and deciduous types
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• ¥
Jan. Jan.
DeciduousEvergreendesert
MODIS daily variation of LST(max.-min. ) vs. CI
0.1CI 0.1CI
0.1CI0.1CI
DeciduousEvergreendesert
0.1CI 0.1CI
Jul. Jul.
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Study Plan
• GPP capacity estimation
• Another vegetation types : FLUX site
• consider the generalized rule for the estimation formula
• Stomatal opening and closing
• Consider scaling up method ( LUT for global study )
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Preparation for Validation
• Collect the validation data
• FLUX data sets
• find the available ( we can use ) data
• Calculate typical value of the site
• Ground observation data sets
• Nara prefecture forest, Yatsugatake site,,,
• Ecological study sites : Nasahara-san G
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Thank you!
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