SlideShare a Scribd company logo
phenology,
forest structure,
biogeochemistry,
plant functioning
and
the carbon and water cycle
Eva van Gorsel
CMAR
March 11, 2014
OzFlux: a continental network of flux
stations to measure ecosystem
fluxes using nationally-consistent
approaches
Today, a new scientific revolution is emerging [...] where groups of
scientists are producing global scale information on carbon and
water fluxes. They are doing so by merging of information from
networks of flux towers, biophysical models, ecological databases
and satellite-based remote sensing to produce a new generation
of flux maps.
Dennis Baldocchi, UC Berkeley
Pheno Tumbarumba| Eva van Gorsel | Page 2
Pheno Tumbarumba| Eva van Gorsel | Page 3
after M.Williams et al., www biogesciences.net/t/1341/2009/
time scales involved in the exchanges of carbon and
water between plants and atmosphere
Pheno Tumbarumba| Eva van Gorsel | Page 4
time and length scales covered
Courtesy Peter Isaac
Pheno Tumbarumba| Eva van Gorsel | Page 4
time and length scales covered
Tower observations provide
information on ecosystem
processes for the exchanges of
energy, water and carbon on all
relevant time scales.
Courtesy Peter Isaac
Pheno Tumbarumba| Eva van Gorsel | Page 4
time and length scales covered
Tower observations provide
information on ecosystem
processes for the exchanges of
energy, water and carbon on all
relevant time scales.
Remote sensing observations are
rich in spatial (and temporal)
information content and can be
used to ‘scale up’ from local to
larger scales.
Courtesy Peter Isaac
Pheno Tumbarumba| Eva van Gorsel | Page 4
time and length scales covered
Tower observations provide
information on ecosystem
processes for the exchanges of
energy, water and carbon on all
relevant time scales.
Remote sensing observations are
rich in spatial (and temporal)
information content and can be
used to ‘scale up’ from local to
larger scales.
Scaling up through modelling
allows quantification through
space and time and physical
understanding.
Courtesy Peter Isaac
schematic data-model integration
Pheno Tumbarumba| Eva van Gorsel | Page 5
schematic data-model integration
Pheno Tumbarumba| Eva van Gorsel | Page 5
Pheno Tumbarumba| Eva van Gorsel | Page 7
Tumbarumba, Bago State Forest
http://www.ozflux.org.au/
Interannual variability of NEE
-616 -331 230 -445 -534 -731 -546 -894 -913 -814 -793-580 gCm-2
dry
wet
Pheno Tumbarumba | Eva van Gorsel | Page 9
Pheno Tumbarumba| Eva van Gorsel | Page 10
Tumbarumba, Bago State Forest
Bago State Forest (hardwood, 305 km2 )
1 km radius around the tower
Pheno Tumbarumba| Eva van Gorsel | Page 11
Tumbarumba, Bago State Forest
Bago State Forest (hardwood, 305 km2 )
1 km radius around the tower
On an annual time scale
•NEE and NDVI are highly correlated.
NDVI together with Sd and Tair
explain 87% of the variance in NEE.
Further modifiers of NEE are
•the usual (prec, swc,...)
•minimum air temperature
•hours per year with Tair <10C
•spring (minimum) temperatures
Continuous measuring program:
structure, vegetation composition
continuous measurements of Logitech C920 webcam
➢ Logitech C920 webcam
➢ can do 15MP, does 2MP using Rasperry Pi native
drivers
Through collaborations with TERN AusCover (Alex
Held, Darius Culvenor)
Pheno Tumbarumba | Eva van Gorsel | Page
Tumbarumba, Bago State Forest
Sensor Network
continuous, real-time measurements (13 nodes) of
➢ PAR (fapar, fipar) | tower: PAR in,ref
➢ temperature
➢ VPD
Multispectral sensor network (10 nodes)
➢ Incoming and reflected VIS, Red, Green, Blue, NIR
➢ Normalized NDVI ((NNIR-NRed)/(NNIR+NRed))
Tumbarumba updates | Eva van Gorsel | Page
Tumbarumba, sensor network
http://www.ozflux.org.au/
Tumbarumba updates | Eva van Gorsel | Page 20
Courtesy Christopher Picket-Heaps
Variability of drivers of carbon and water fluxes
Tumbarumba updates | Eva van Gorsel | Page 21
Courtesy Christopher Picket-Heaps
Variability of drivers of carbon and water fluxes
Tumbarumba updates | Eva van Gorsel | Page 22
variability of fapar and fipar
In-canopy measurements PAR 11/2010-07/2011
Tumbarumba updates | Eva van Gorsel | Page 23
seasonality of fapar
In-canopy measurements PAR 11/2010-07/2011
Tumbarumba updates | Eva van Gorsel | Page 24
seasonality of fapar
In-canopy measurements PAR 11/2010-07/2011
Sensor Network
in progress: hyperspectral sensors
➢ STS-VIS microspectrometer, 350 – 800 nm
➢ Approximate Optical resolution (FWHM):
1.0nm (10μm slit), 1.5nm (25μm slit), 6nm
(100μm slit), 12nm (200μm slit )
➢ Raspberry Pi driven
R2D2
in progress:
➢ Hyperspectral line scanner (400-1100 nm)
➢ FLIR
➢ Ocean Optics USB2000 (200-1100 nm)
➢ Pan Tilt device
R2D2
in progress:
➢ Hyperspectral line scanner (400-1100 nm)
➢ FLIR
➢ Ocean Optics USB2000 (200-1100 nm)
➢ Pan Tilt device
Challenges:
Integration of the system  (thanks to Dale Hughes!)
R2D2
in progress:
➢ Hyperspectral line scanner (400-1100 nm)
➢ FLIR
➢ Ocean Optics USB2000 (200-1100 nm)
➢ Pan Tilt device
Challenges:
Integration of the system 
Calibration of the system
R2D2
in progress:
➢ Hyperspectral line scanner (400-1100 nm)
➢ FLIR
➢ Ocean Optics USB2000 (200-1100 nm)
➢ Pan Tilt device
Challenges:
Integration of the system 
Calibration of the system
Deployment of the system
Data movement and storage
Tumbarumba updates | Eva van Gorsel | Page
Sensor Network
http://www.ozflux.org.au/
➢ multi/hyperspectral: changes in greenness, grass curing,…
➢ PAR measurements on tower and in canopy can be used as to
validate remote sensing fapar/fipar.
➢useful for validation of absolute values and seasonality
➢useful as input in land surface models
Near field remote sensing
➢unprecedented measurement system (?)
➢great opportunity to monitor structure, biochemistry, plant
functioning and stresses.
Thank youThis work is supported in part by the Terrestrial Ecosystem Research Network TERN and grants from
the Australian Climate Change Science Program and its predecessors through the DCCEE.
CSIRO/CMAR
Eva van Gorsel
t +61 2 6246 5611
e eva.vangorsel@csiro.au
w www.cmar.csiro.au
w www.ozflux.org.au
ENVIRONMENT/MARINE AND ATMOSPHERE
Pheno Tumbarumba| Eva van Gorsel | Page 6
the Australian focus
•Biochemistry
•Function
•Structure
Presentation title | Presenter name | Page

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ACEAS_pheno_tumba

  • 1. phenology, forest structure, biogeochemistry, plant functioning and the carbon and water cycle Eva van Gorsel CMAR March 11, 2014
  • 2. OzFlux: a continental network of flux stations to measure ecosystem fluxes using nationally-consistent approaches
  • 3.
  • 4. Today, a new scientific revolution is emerging [...] where groups of scientists are producing global scale information on carbon and water fluxes. They are doing so by merging of information from networks of flux towers, biophysical models, ecological databases and satellite-based remote sensing to produce a new generation of flux maps. Dennis Baldocchi, UC Berkeley Pheno Tumbarumba| Eva van Gorsel | Page 2
  • 5. Pheno Tumbarumba| Eva van Gorsel | Page 3 after M.Williams et al., www biogesciences.net/t/1341/2009/ time scales involved in the exchanges of carbon and water between plants and atmosphere
  • 6. Pheno Tumbarumba| Eva van Gorsel | Page 4 time and length scales covered Courtesy Peter Isaac
  • 7. Pheno Tumbarumba| Eva van Gorsel | Page 4 time and length scales covered Tower observations provide information on ecosystem processes for the exchanges of energy, water and carbon on all relevant time scales. Courtesy Peter Isaac
  • 8. Pheno Tumbarumba| Eva van Gorsel | Page 4 time and length scales covered Tower observations provide information on ecosystem processes for the exchanges of energy, water and carbon on all relevant time scales. Remote sensing observations are rich in spatial (and temporal) information content and can be used to ‘scale up’ from local to larger scales. Courtesy Peter Isaac
  • 9. Pheno Tumbarumba| Eva van Gorsel | Page 4 time and length scales covered Tower observations provide information on ecosystem processes for the exchanges of energy, water and carbon on all relevant time scales. Remote sensing observations are rich in spatial (and temporal) information content and can be used to ‘scale up’ from local to larger scales. Scaling up through modelling allows quantification through space and time and physical understanding. Courtesy Peter Isaac
  • 10. schematic data-model integration Pheno Tumbarumba| Eva van Gorsel | Page 5
  • 11. schematic data-model integration Pheno Tumbarumba| Eva van Gorsel | Page 5
  • 12. Pheno Tumbarumba| Eva van Gorsel | Page 7 Tumbarumba, Bago State Forest http://www.ozflux.org.au/
  • 13. Interannual variability of NEE -616 -331 230 -445 -534 -731 -546 -894 -913 -814 -793-580 gCm-2 dry wet Pheno Tumbarumba | Eva van Gorsel | Page 9
  • 14. Pheno Tumbarumba| Eva van Gorsel | Page 10 Tumbarumba, Bago State Forest Bago State Forest (hardwood, 305 km2 ) 1 km radius around the tower
  • 15. Pheno Tumbarumba| Eva van Gorsel | Page 11 Tumbarumba, Bago State Forest Bago State Forest (hardwood, 305 km2 ) 1 km radius around the tower On an annual time scale •NEE and NDVI are highly correlated. NDVI together with Sd and Tair explain 87% of the variance in NEE. Further modifiers of NEE are •the usual (prec, swc,...) •minimum air temperature •hours per year with Tair <10C •spring (minimum) temperatures
  • 16. Continuous measuring program: structure, vegetation composition continuous measurements of Logitech C920 webcam ➢ Logitech C920 webcam ➢ can do 15MP, does 2MP using Rasperry Pi native drivers Through collaborations with TERN AusCover (Alex Held, Darius Culvenor)
  • 17. Pheno Tumbarumba | Eva van Gorsel | Page Tumbarumba, Bago State Forest
  • 18. Sensor Network continuous, real-time measurements (13 nodes) of ➢ PAR (fapar, fipar) | tower: PAR in,ref ➢ temperature ➢ VPD Multispectral sensor network (10 nodes) ➢ Incoming and reflected VIS, Red, Green, Blue, NIR ➢ Normalized NDVI ((NNIR-NRed)/(NNIR+NRed))
  • 19. Tumbarumba updates | Eva van Gorsel | Page Tumbarumba, sensor network http://www.ozflux.org.au/
  • 20. Tumbarumba updates | Eva van Gorsel | Page 20 Courtesy Christopher Picket-Heaps Variability of drivers of carbon and water fluxes
  • 21. Tumbarumba updates | Eva van Gorsel | Page 21 Courtesy Christopher Picket-Heaps Variability of drivers of carbon and water fluxes
  • 22. Tumbarumba updates | Eva van Gorsel | Page 22 variability of fapar and fipar In-canopy measurements PAR 11/2010-07/2011
  • 23. Tumbarumba updates | Eva van Gorsel | Page 23 seasonality of fapar In-canopy measurements PAR 11/2010-07/2011
  • 24. Tumbarumba updates | Eva van Gorsel | Page 24 seasonality of fapar In-canopy measurements PAR 11/2010-07/2011
  • 25. Sensor Network in progress: hyperspectral sensors ➢ STS-VIS microspectrometer, 350 – 800 nm ➢ Approximate Optical resolution (FWHM): 1.0nm (10μm slit), 1.5nm (25μm slit), 6nm (100μm slit), 12nm (200μm slit ) ➢ Raspberry Pi driven
  • 26. R2D2 in progress: ➢ Hyperspectral line scanner (400-1100 nm) ➢ FLIR ➢ Ocean Optics USB2000 (200-1100 nm) ➢ Pan Tilt device
  • 27. R2D2 in progress: ➢ Hyperspectral line scanner (400-1100 nm) ➢ FLIR ➢ Ocean Optics USB2000 (200-1100 nm) ➢ Pan Tilt device Challenges: Integration of the system  (thanks to Dale Hughes!)
  • 28. R2D2 in progress: ➢ Hyperspectral line scanner (400-1100 nm) ➢ FLIR ➢ Ocean Optics USB2000 (200-1100 nm) ➢ Pan Tilt device Challenges: Integration of the system  Calibration of the system
  • 29. R2D2 in progress: ➢ Hyperspectral line scanner (400-1100 nm) ➢ FLIR ➢ Ocean Optics USB2000 (200-1100 nm) ➢ Pan Tilt device Challenges: Integration of the system  Calibration of the system Deployment of the system Data movement and storage
  • 30. Tumbarumba updates | Eva van Gorsel | Page Sensor Network http://www.ozflux.org.au/ ➢ multi/hyperspectral: changes in greenness, grass curing,… ➢ PAR measurements on tower and in canopy can be used as to validate remote sensing fapar/fipar. ➢useful for validation of absolute values and seasonality ➢useful as input in land surface models Near field remote sensing ➢unprecedented measurement system (?) ➢great opportunity to monitor structure, biochemistry, plant functioning and stresses.
  • 31. Thank youThis work is supported in part by the Terrestrial Ecosystem Research Network TERN and grants from the Australian Climate Change Science Program and its predecessors through the DCCEE. CSIRO/CMAR Eva van Gorsel t +61 2 6246 5611 e eva.vangorsel@csiro.au w www.cmar.csiro.au w www.ozflux.org.au ENVIRONMENT/MARINE AND ATMOSPHERE
  • 32. Pheno Tumbarumba| Eva van Gorsel | Page 6 the Australian focus •Biochemistry •Function •Structure
  • 33. Presentation title | Presenter name | Page

Editor's Notes

  1. IPAR and fAPAR are defined as follows: fAPAR = (PARo - PARt - PARr + PARs) / PARofIPAR = (PARo – PARt+ PARs)/PARoPARo: incident solar PAR PARr: canopyreflectance PAR PARt: PAR transmitted through the canopy to the soilPARs: soil reflectance PAREiji KODANI (2004). Estimation of fIPAR in deciduous forest standsin summer and winter using airborne MSS. IEEE
  2. IPAR and fAPAR are defined as follows: fAPAR = (PARo - PARt - PARr + PARs) / PARofIPAR = (PARo – PARt+ PARs)/PARoPARo: incident solar PAR PARr: canopyreflectance PAR PARt: PAR transmitted through the canopy to the soilPARs: soil reflectance PAREiji KODANI (2004). Estimation of fIPAR in deciduous forest standsin summer and winter using airborne MSS. IEEE
  3. IPAR and fAPAR are defined as follows: fAPAR = (PARo - PARt - PARr + PARs) / PARofIPAR = (PARo – PARt+ PARs)/PARoPARo: incident solar PAR PARr: canopyreflectance PAR PARt: PAR transmitted through the canopy to the soilPARs: soil reflectance PAREiji KODANI (2004). Estimation of fIPAR in deciduous forest standsin summer and winter using airborne MSS. IEEE