Eva van Gorsel_ From plant canopy to ecosystem to the globe: upscaling OzFlux data using AusCover remote sensing data, eMAST modeling and integration
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![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](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Ähnlich wie Eva van Gorsel_ From plant canopy to ecosystem to the globe: upscaling OzFlux data using AusCover remote sensing data, eMAST modeling and integration
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Eva van Gorsel_ From plant canopy to ecosystem to the globe: upscaling OzFlux data using AusCover remote sensing data, eMAST modeling and integration
- 1. From plant canopy to ecosystem to the globe: Upscaling OzFlux data using AusCover remote sensing data, eMAST modeling and integration E. van Gorsel, J. Beringer, J.A.J. Berni, A. Cabello, H. Cleugh, V. Haverd, A. Held, A. Huete, L. Hutley, P. Isaac, N. Kljun and C. Prentice
- 2. 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
- 3. do we need/want an Australian focus? leaf chemistry leaf angle distribution plant structure stand density ...
- 4. time scales involved in the exchanges of carbon and water between plants and atmosphere after M.Williams et al., www biogesciences.net/t/1341/2009/
- 5. Globe: 10'000 km spatial scales involved... Continent: 1000 km Landscape: 1-100 km Canopy: 100-1000 m Plant: 1-100 m ... span about 14 orders Leaf: 0.01-0.1 m of magnitude after D. Baldocchi, 5th annual flux course, 'Biosphere Stomata: 10-5 m Breathing' Chloroplast: 10-6 m
- 6. 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 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 P. Isaac
- 8. data-model integration 2 examples Tumbarumba Bago State Forest
- 9. data-model integration at Supersite Tumbarumba Jimenez-Berni et al. (2011)
- 10. Data collection hemispheric photography terrestrial LiDAR airborne LiDAR
- 11. LAI 5km Calibrate airborne LAIe by histogram matching with EVI foliage Derive optimised extinction profile coefficent. Scale up using Beer’s Law assumption and optimised extinction coefficient. Hopkinson et al. , submitted to RSE
- 12. data-model integration at Supersite Tumbarumba Jimenez-Berni et al. (2011)
- 13. Data collection leaf collection leaf collection leaf level flux measurements spectral analysis leafs hyperspectral imagery
- 14. derivation of key model parameters At leaf level the ratio of band 750/710 is well correlated with Chlorophylla+b concentration (Zarco-Tejada et al, 2001). Use of radiative transfer model The maximum carboxilation velocity, Vc,max, do scale up to ecosystem level. is to a first approximation taken as linearly Chla+b = f(LAI) related to Chla+b. Linear relationship is derived from leaf Jimenez-Berni et al. (2011) level gas exchange measurements.
- 15. data-model integration at Supersite Tumbarumba Jimenez-Berni et al. (2011)
- 16. Cable runs for Tumbarumba site Control run: NEE (µmolm-2s-1) area averaged input value of LAI and Vc,max. CABLE Simulation for 14:00 30/11/2009 Case study: input of spatially resolved LAI and Vc,max 4 maps with subsequent footprint 0 weighting max difference ctrl vs footprint weighted: LAI (16%), lE (7%), GPP (9%) -> improved agreement when we take -12 complex surface characteristics into account . Courtesy Kljun
- 17. data-model integration 2 examples NATT – the North Australian Tropical Transect Special Issue Agricultural and Forest Meteorology: Savanna Patterns of Energy and Carbon Integrated Across the Landscape (SPECIAL). Volume 151, Issue 11 (2011)
- 18. Adelaide River Howard Springs Fogg Dam Daly River Rainfall gradient Rainfall gradient Dry Creek Sturt Plains Beringer et al. (2010)
- 20. Above –ground biomass, stem density, LAI and canopy height declined with rainfall Biomass ranged from 35 to 5 t C ha-1 along the 1714 to 400 mm rainfall range with LAI ranging from 1.5 to ~0 1.2 12 Sand d) Sand a) Loam Loam SPECIAL SPECIAL 0.9 9 R² = 0.76 2 h -1) a R² = 0.65 6 0.6 O A L o e y s v r I t m B e a s r ( l 3 0.3 0 0.0 0 500 1000 1500 2000 0 500 1000 1500 2000 Rainfall (mm) Rainfall (mm) Hutley et al. (2010)
- 21. Satellite remote sensing (MODIS) of Leaf Area Index (LAI) agreed very well with ground based hemispherical photos and LAI2000. 3.0 Day 89 MODIS Collection 5 LAI (m /m2) and MAP (m) Day 257 MAP (m) 2.5 2.0 2 1.5 1.0 0.5 0.0 -12 -14 -16 -18 -20 Latitude Sea et al. (2010)
- 22. Leaf Level Physiology A/Ci curves light use curves leaf mass Cernusak et al. (2010)
- 23. • Maximum Rubisco carboxylation velocity (Vcmax), Gs and Ci/Ca nearly constant • Leaf mass per area increased strongly along the rainfall gradient • Variation in ecosystem-level gas Eucalyptus miniata exchange not dominated by Eucalyptus tetrodonta Eucalyptus tectifica photosynthetic performance rather Corymbia latifolia Corymbia terminalis changes in LAI along transect. Eucalyptus pruinosa Eucalyptus coolabah Corymbia aparrerinja Eucalyptus miniata Eucalyptus tetrodonta Eucalyptus tectifica 300 A Leaf mass per area (g m-2) Corymbia latifolia Corymbia terminalis Eucalyptus pruinosa Eucalyptus coolabah 250 Corymbia aparrerinja 300 A 200 f mass per area (g m ) -2 250 150 200 1.2 B )
- 24. canopy-scale properties along the transect • Of the meteorological drivers only D, the vapour pressure deficit, decreases significantly along gradient. • The canopy response to D is similar along gradient. • Primary driver of flux variability in evaporative fraction and water use efficiency is land use. • Canopy scale maximum conductance, quantum efficiency and maximum assimilation don’t haveve 6 Howard Springs significant dependence on precipitation gradient Adelaide River 4 Daly River WUE Dry River Observed spatial variability in fluxes is mainly driven by 2 LAI, not by vegetation photosynthetic capacity. 0 0 10 20 30 40 0 D (g kg-1) Courtesy P. Isaac
- 25. Scaling of productivity Kanniah et al. (2010)
- 26. Scaling up flux information over all temporal and spatial scales involved? •it can be done •increasingly well •through TERN we have unprecedented data sets (consistent within and co- located facilities) that allow integrated information. Courtesy P. Isaac
- 27. Thank you and thank you to all technical staff who keep our measurements going as well as to the cohorts who collect data in the field contact: Eva van Gorsel t +61 2 6246 5611 e eva.vangorsel@csiro.au w www.ozflux.org.au
Hinweis der Redaktion
- Quantification of carbon, water+energy fluxes is critical information needed for a sound management of Australian landscapes and to maintain key ecosystem services We want to quantify these fluxes everywhere and all the time Dennis Baldocchi calls this era an era of scientific revolution because it is only now that we start to see a critical mass in infrastructure + resulting data needed to do the science
- Despite its great importance to understand and manage the impact of land use on carbon sequestration and water availability, such knowledge has not been readily available for many of Australia’s unique ecosystems.
- Vegetation is sufficiently different And in many aspects probably a worst case scenario for remote sensing applications. What works in other parts of the world need not work here.
- Schematic of a ecosystem processes at hourly, daily and annual-decadal time scales. Measurements at flux stations are used to improve process understanding, evaluate model parameters and model performance at scales of hours to decades.
- This task requires understanding and quantifying a set of coupled and highly nonlinear biophysical processes that span 14 orders of magnitude in time and space [ Jarvis,1995; Osmond et al., 1980 ].
- Plot-based terrestrial lidar foliage profiles are used as training datasets for the derivation of a scaling function applied to calibrate effective leaf area index (LAIe) from a coincident ALS point cloud.
- Regional map of the field area showing the six measurement sites down the North Australian Tropical Transect (NATT), where rainfall strongly declines from the coast (1700 mm) inland to Sturt Plains (700 mm). Leaf area and basal area decline from Howard Springs to Sturt Plains. Fogg Dam is a seasonally flooded wetland with sedge grasses that were still partially green at the time of the intensive field campaign and where soil water contents were high. Photos are shown to illustrate the differences in structure of savanna vegetation. Aircraft grid patterns over selected sites (red circles) are shown and these are used for characterization and validation. Linking aircraft flux transects were broken into northern, middle and southern transects (green ellipses). Location of the budget flights at Daly River are shown (blue circle).
- Leaf-level photosynthetic parameters of species in the closely related genera Eucalyptus and Corymbia were assessed along a strong rainfall gradient in northern Australia. Both instantaneous gas exchange measurements and leaf carbon isotope discrimination indicated little variation in intercellular CO 2 concentrations during photosynthesis ( c i ) in response to a decrease in mean annual precipitation from 1700 mm to 300 mm. Correlation between stomatal conductance and photosynthetic capacity contributed toward the maintenance of relatively constant c i among the sampled leaves, when assessed at ambient CO 2 concentration and photon irradiance similar to full sunlight. Leaf mass per area was the most plastic leaf trait along the rainfall gradient, showing a linear increase in response to decreasing mean annual precipitation. The maximum Rubisco carboxylation velocity, V cmax , expressed on a leaf-area basis, showed a modest increase in response to decreasing rainfall. This modest increase in V cmax was associated with the strongly expressed increase in leaf mass per area. These results suggest that variation in ecosystem-level gas exchange for the over-story eucalypts in north-Australian savannas will likely be dominated by changes in leaf area index in response to increasing aridity, rather than by changes in photosynthetic performance per unit leaf area.
- Before going calculating canopy scale properties plant response was taken into account! Canopy scale maximum conductance (inverted penman monteith) quantum efficiency (analoguous to A/ci curve) and maximum assimilation (by fitting LUE curve where A is down-regulated by D according to the modified Leuning form of D response) don’t have significant dependence on precipitation gradient WUE = GPP/ET (leaf level to canopy level is normally confounded by soil resp (which is constant here) and soil evap (small in dry season) )
- To determine GPP for the savannas of the NT region, a simple light use efficiency (LUE) model was used along with gridded satellite remote sensing (MODIS) fPAR (fraction of absorbed photosynthetically active radiation) and gridded meteorological data. GPP=APAR×LUE×TMIN scalar×VPD scalar Changes in GPP along the NATT (Fig. 4, Table 1) are influenced by the interaction among four major environmental variables: fPAR (R2 = 0.85), VPD (R2 = 0.85), rainfall (R2 = 0.96) and LAI (R2 = 0.96). It was found that daily average temperature was only moderately correlated to GPP (R2 = 0.51). Figure 11 – GPP for the entire savanna region within the Northern Territory for the campaign period (September 2008). GPP derived from MODIS GPP algorithm (Myneni et al. 2002) but used a savanna light use efficiency based on our six sites down the NATT (LUE defined as carbon uptake per unit of radiation absorbed), regional specific meteorology (Jeffrey et al. 2001) and the fraction of absorbed Photosynthetically Active Radiation (fPAR) (MOD15A2 collection5) (Kanniah et al. 2009). Changes in rainfall along the gradient are associated with a strong gradient in GPP due to changes in the savanna structure and composition. Figure 4: Annual GPP along a major rainfall gradient in the Northern Australian Tropical Transect (NATT). The mid -point in each of the boxes is the mean, the boxes are standard error and the whiskers are the minimum and maximum values. Zones A, B and C represent the wet, middle and dry end of the NATT. Data represent GPP from 2000 to 2007. Locations marked with asterisk are the six sites investigated during SPECIAL Campaign).