Quantifying CO 2 fluxes of boreal forests in Northern Eurasia
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1 Quantifying CO 2 fluxes of boreal forests in Northern Eurasia Integrated analyses of in-situ eddy flux tower, remote sensing and biogeochemical model Xiangming Xiao Institute for the Study of Earth, Oceans and Space University of New Hampshire, Durham, NH April 12, 2006 NASA LCLUC Science Team Meeting
2 International collaborative project University of New Hampshire Changsheng Li (PI), Xiangming Xiao Space Research Institute (IKI), Russian Academy of Science Sergey Bartalev Institute of Problems in Ecology and Evolution (IPEE), Russian Academy of Sciences Juliya Kurbatova Max Planck Institute for Biogeochemistry, Jena, Germany E.D. Schulze
3 Boreal forest and global carbon cycle Boreal forest distribution Siberia of Russia: Europe (including European Russia): North America: 600 million ha 300 million ha 500 million ha Carbon in boreal forest: ~50% of global carbon in vegetation and soils There are large uncertainty in CO 2 fluxes of boreal forests NEE = GPP R e
4 Eddy flux tower for CO 2, water and energy - Fluxnet Tver flux sites Krasnoyarsk flux sites Yakutia flux sites
5 Scientific questions to be addressed: (1) What factors control the seasonal and interannual variations of NEE, GPP and R e at individual flux tower sites in northern Eurasia? (2) What factors control the spatial variations of NEE, GPP and R e across the flux tower sites in northern Eurasia? (3) What are the impacts of disturbance and land use change on carbon fluxes in Siberia since 1998?
6 Project Objectives (1) Developing a prototype tool by integrating CO 2 flux tower measurements, remote sensing analysis and biogeochemical models to interpret the observed CO 2 flux data across the tower sites in northern Eurasia; (2) Quantifying temporal dynamics and spatial patterns of CO 2 fluxes from boreal forests in Russia over the period of
7 Research approach and methods leaf canopy ecosystem landscape region field/lab work chlorophyll nitrogen LAI species, soils land cover eddy flux tower NEE, GPP, R e radiative transfer model chlorophyll FPAR chl LAI Landsat land cover MODIS FPAR chl FPAR chl VPM GPP GPP GPP DNDC NEE, R e NEE, R e NEE,R e
8 Expected results 1. Validation of satellite-based Vegetation Photosynthesis Model (VPM) 2. Validation of process-based DNDC model 3. Evaluating radiative transfer model (PROSAIL2) 4. A multi-scale satellite image database for the flux tower sites in northern Eurasia, and its analysis 5. Regional datasets of GPP of boreal forests in Siberia for from the VPM model 6. Regional datasets of NEE of boreal forests for from coupled VPM-DNDC models
9 Activities and results in Year 1 1. Project meeting in Moscow, June Continuous operation of eddy flux towers in CFBR sites 3. Field sampling and survey in CFBR sites 4. Evaluating improved radiative transfer model 5. Evaluating VPM model 6. Evaluating DNDC model 7. Analysis of Landsat images 8. Analysis of MODIS and VEGETATION images
10 Eddy flux tower at CFBR NEE of CO 2 Water flux Energy flux
11 Leaf sampling at CFBR sites chlorophyll nitrogen leaf water content leaf specific weight
12 Plant Photosynthesis or GPP Leaf chlorophyll content and light absorption by chlorophyll are the fundamental controlling factors of GPP
13 Scaling-up of light absorption from chlorophyll to leaf and canopy non-photosynthetic vegetation (NPV)
14 Partition light absorption at chlorophyll, leaf and canopy levels Leaf level canopy = green leaf + NPV (stem, branch) FPAR canopy = FPAR leaf + FPAR NPV Chlorophyll level Leaf/canopy = Chlorophyll + NPV (veins, cell wall) FPAR canopy = FPAR chl + FPAR NPV NPV = non-photosynthetic vegetation
15 Radiative transfer model (PROSAIL2) Coupled leaf- (PROSPECT) and canopy-level (SAIL2) model Retrieve both leaf- and canopy-level variables, and Calculate FPAR canopy, FPAR leaf and FPAR chl Zhang et al., 2005, RSE; Zhang et al., 2006a,b
16 A comparison between the PROSAIL2 model [Zhang, et al., 2005] and the radiative transfer model used for MODIS standard LAI/FPAR products (MOD15) [Myneni, et al., 2002] MODIS bands Name Description Unit PROSAIL2 MOD15 Green, Red, NIR 1, NIR 2, SWIR 1 Red, NIR 1 MODIS Spatial resolution 500-m 1-km Inversion Inversion algorithm MCMC Look-up table Structure 1-D 3-D Leaf-level C ab Leaf chlorophyll (a+b) µg/cm 2 Retrieved, Fixed C brown Other pigments in leaf Retrieved, Not included C m Leaf dry matter g/cm 2 Retrieved, Fixed C w Leaf equivalent water thickness cm/cm 2 Retrieved, Fixed N Leaf internal structure Retrieved, Fixed Canopylevel FPAR LAI Leaf area index m 2 /m 2 Retrieved, Retrieved SAI Stem area index % Retrieved, Not included CF Cover fraction % Retrieved, Retrieved Fraction of PAR absorption FPAR canopy, FPAR leaf, FPAR chlorophyll FPAR canopy
17 Radiative transfer model coupled leaf- (PROSPECT) and canopy (SAIL2) model daily MODIS data (500-m spatial resolution) Retrieve leaf- and canopy variables at CFBR sites Number of iterations (x10 6 ) Leaf chlorophyll content (µg/cm 2 ) Leaf area index (m 2 /m 2 ) Daily MODIS data from June 1-16, 2004 were used for inversion of PROSAIL2
18 0.8 FPAR canopy 0.9 FPAR leaf Number of model iterations (x10 6 ) FPAR chl 0.65 Daily MODIS data from June 1-16, 2004 Radiative transfer model coupled leaf- (PROSPECT) and canopy (SAIL2) model daily MODIS data (500-m spatial resolution) Retrieve leaf- and canopy variables at CFBR sites
19 Uncertainty in estimating GPP model variable/process and parameters absorption of PAR Structural perspective PAR absorbed by canopy (PSN, GLO-PEM) GPP = ε g FPAR canopy PAR Biochemical perspective PAR absorbed by chlorophyll (VPM) GPP = ε g FPAR chl PAR
20 Satellite-based Vegetation Photosynthesis Model (VPM) Climate data CO 2 eddy flux tower Surface reflectance (VGT, MODIS) Validation Literature LSWI EVI GPP = (ε 0 T scalar W scalar P scalar ) FPAR chl R scalar PAR T scalar, W scalar, P scalar, R scalar downward regulation scalars for air temperature, leaf water content, leaf phenology and non-linear light response VPM is evaluated at ~40 eddy flux tower sites of AmeriFlux, EuroFlux, and ChinaFlux
21 Gross primary production predicted by VPM model wet spruce forest and mixed forest sites Gross primary production (g C/m 2 /day) by VPM CFR1 site spruce forest (one VGT pixel) Gross primary production (g C/m 2 /day) by VPM CFR2 site mixed forest (one VGT pixel) Jan Mar May Jul Sep Nov Jan Jan Mar May Jul Sep Nov Jan Date Date
22 Structure of Forest-DNDC
23 DNDC-predicted photosynthesis, tree respiration and soil respiration for a wetland spruce forest at CFBR in 2004
24 Landscape-scale study field surveys in CFBR land cover types tree species soils
25 Landscape-scale analysis Collection of ground truth data for the test sites GIS database for Central Forest Natural Biosphere reserve, test site in Tver region SPRUCE BIRCH-SPRUCE SPRUSE-BIRCH BIRCH NEMORAL NEMORAL-SPRUSE MIXED PINE BOG No Forest
26 Land cover map of CFBR site (right) derived from Landsat-ETM+ image (left) Flux tower #1b Flux tower #1a TM4-TM5-TM3, Pine Two-layer mixed forest Bogs Water Spruce Coniferous dominant mixed forest Grassland / Cropland Unvegetated surface Deciduous Deciduous dominant mixed forest Forest regrowh and shrubs Burnt area
27 Regional-scale study Develop website Image data MODIS data from 2000 present VGT data from 1998 present Vegetation indices Phenology LSWI, EVI, NDVI,
28 Regional-scale study Geospatial databases for VPM-DNDC simulations Climate NCEP/NCAR global climate reanalysis Soil Land Resources of Russia (IIASA) Vegetation Land Resources of Russia (IIASA)
29 Plan for Year 2 1. Continue the field work in CFBR 2. Evaluate VPM/DNDC/PROSAIL2 models for flux tower sites in central Siberia 3. Couple VPM/DNDC models 4. Conduct landscape-scale analysis 5. Initiate regional analysis
30 Thank you
31 A data-model assimilation system coupled VPM DNDC models Daily and 8-day MODIS ( ) and 10-day VGT (4/ ) Forest, soils, fire, NDVI EVI LSWI Leaf phenology VPM Climate DNDC Daily GPP CO 2 flux tower NPP, R e, NEE, N 2 O, CH 4 Annual GPP NPP Annual NPP NEE Flowchart of a data-model assimilation system for quantifying carbon fluxes and trace gases emissions.
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