Presentation is loading. Please wait.

Presentation is loading. Please wait.

GEOGG142 GMES Global vegetation parameters from EO Dr. Mat Disney Pearson Building room 113 020 7679 0592

Similar presentations


Presentation on theme: "GEOGG142 GMES Global vegetation parameters from EO Dr. Mat Disney Pearson Building room 113 020 7679 0592"— Presentation transcript:

1 GEOGG142 GMES Global vegetation parameters from EO Dr. Mat Disney Pearson Building room

2 2 More specific parameters of interest –vegetation type (classification) (various) –vegetation amount (various) –primary production (C-fixation, food) –SW absorption (various) –temperature (growth limitation, water) –structure/height (radiation interception, roughness - momentum transfer)

3 3 Vegetation properties of interest in global monitoring/modelling components of greenhouse gases –CO 2 - carbon cycling photosynthesis, biomass burning –CH 4 lower conc. but more effective - cows and termites! –H evapo-transpiration (erosion of soil resources, wind/water)

4 4 Vegetation properties of interest in global change monitoring/modelling also, influences on mankind –crops, fuel –ecosystems (biodiversity, natural habitats) soil erosion and hydrology, micro and meso-scale climate

5 5 Explicitly deal here with LAI/fAPAR –Leaf Area Index/fraction Absorbed Photsynthetically active radiation (vis.) Productivity (& biomass) –PSN - daily net photosynthesis –NPP - Net primary productivity - ratio of carbon uptake to that produced via transpiration. NPP = annual sum of daily PSN. BUT, other important/related parameters –BRDF (bidirectional reflectance distribution function) –albedo i.e. ratio of outgoing/incoming solar flux –Disturbance (fires, logging, disease etc.) –Phenology (timing)

6 6 definitions: LAI - one-sided leaf area per unit area of ground - dimensionless fAPAR - fraction of PAR (SW radiation waveband used by vegetation) absorbed - proportion

7 7 Appropriate scales for monitoring spatial: –global land surface: ~143 x 10 6 km –1km data sets = ~143 x 10 6 pixels –GCM can currently deal with 0.25 o o grids (25-30km - 10km grid) temporal: –depends on dynamics 1 month sampling required e.g. for crops Maybe less frequent for seasonal variations? Instruments??

8 8 optical 1 km –EOS MODIS (Terra/Aqua) 250m-1km fuller coverage of spectrum repeat multi-angular

9 9 optical 1 km –EOS MISR, on board Terra platform multi-view angle (9) 275m-1 km VIS/NIR only

10 10 optical 1 km –ENVISAT MERIS 1 km good spectral sampling VIS/NIR - 15 programmable bands between 390nm an 1040nm. little multi-angular –AVHRR > 1 km Only 2 broad channels in vis/NIR & little multi- angular BUT heritage of data since 1981

11 11 Future? –production of datasets (e.g. EOSDIS) e.g. MODIS products NPOESS follow on missions P-band RADAR?? –cost of large projects (`big science') high B$7 EOS little direct `commercial' value at moderate resolution data aimed at scientists, policy....

12 12 LAI/fAPAR  direct quantification of amount of (green) vegetation  structural quantity  uses:  radiation interception (fAPAR)  evapotranspiration (H 2 0)  photosynthesis (CO 2 ) i.e. carbon  respiration (CO 2 hence carbon)  leaf litter-fall (carbon again)  Look at MODIS algorithm  Good example of algorithm development  ATBD:

13 13 LAI  1-sided leaf area (m 2 ) per m 2 ground area  full canopy structural definition (e.g. for RS) requires  leaf angle distribution (LAD)  clumping  canopy height  macrostructure shape

14 14 LAI  preferable to fAPAR/NPP (fixed CO 2 ) as LAI relates to standing biomass  includes standing biomass (e.g. evergreen forest)  can relate to NPP  can relate to site H 2 0 availability

15 15

16 16 fAPAR  Fraction of absorbed photosynthetically active radiation (PAR: nm).  radiometric quantity  more directly related to remote sensing  e.g. relationship to RVI, NDVI  uses:  estimation of primary production / photosynthetic activity  e.g. radiation interception in crop models  monitoring, yield  e.g. carbon studies  close relationship with LAI  LAI more physically-meaningful measure

17 17 Issues  empirical relationship to VIs can be formed  but depends on LAD, leaf properties (chlorophyll concentration, structure)  need to make relationship depend on land cover  relationship with VIs can vary with external factors, tho’ effects of many can be minimised  NDVI  1 – e -kLAI

18 18

19 19 Estimation of LAI/fAPAR  initial field experiments on crops/grass  correlation of VIs - LAI  developed to airborne and satellite  global scale - complexity of natural structures

20 20 Estimation of LAI/fAPAR  canopies with different LAI can have same VI  effects of clumping/structure  can attempt different relationships dept. on cover class  can use fuller range of spectral/directional information in BRDF model  fAPAR related to LAI  varies with structure  can define through  clumped leaf area  ground cover

21 21 Estimation of LAI/fAPAR  fAPAR relationship to VIs typically simpler  linear with asymptote at LAI ~4-6  BIG issue of saturation of VI signal at high LAI (>5 say) need to define different relationships for different cover types

22 22 MODIS LAI/fAPAR algorithm  See ATBD: AND modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf -  RT (radiative transfer) model-based  define 6 cover types (biomes) based on RT (structure) considerations  grasses & cereals  shrubs  broadleaf crops  savanna  broadleaf forest  needle forest

23 23 MODIS LAI/fAPAR algorithm  have different VI-parameter relationships  can make assumptions within cover types  e.g., erectophile LAD for grasses/cereals  e.g., layered canopy for savanna  use 1-D and 3D numerical RT (radiative transfer) models (Myneni) to forward-model for range of LAI  result in look-up-table (LUT) of reflectance as fn. of view/illumination angles and wavelength  LUT ~ 64MB for 6 biomes

24 24 Method  preselect cover types (algorithm)  minimise RMSE as fn. of LAI between observations and appropriate models (stored in look-up-table – LUT)  if RMSE small enough, fAPAR / LAI output  backup algorithm if RMSE high - VI-based

25 25

26 26

27 27

28 28

29 29 Productivity: PSN and NPP  (daily) net photosynthesis (PSN)  (annual) net primary production (NPP)  relate to net carbon uptake  important for understanding global carbon budget -  how much is there, where is it and how is it changing  Hence climate change, policy etc. etc.

30 30 PSN and NPP  C0 2 removed from atmosphere –photosynthesis  C0 2 released by plant (and animal) –respiration (auto- and heterotrophic) –major part is microbes in soil....  Net Photosynthesis (PSN)  net carbon exchange over 1 day: (photosynthesis - respiration)

31 31 PSN and NPP  Net Primary Productivity (NPP)  annual net carbon exchange  quantifies actual plant growth  Conversion to biomass (woody, foliar, root) –(not just C0 2 fixation)

32 32 Algorithms - require to be model-based  simple production efficiency model (PEM) –(Monteith, 1972; 1977)  relate PSN, NPP to APAR  APAR from PAR and fAPAR

33 33  PSN = daily total photosynthesis  NPP, PSN typically accum. of dry matter (convert to C by assuming dry matter (DM) ~ 48% C)   = efficiency of conversion of PAR to DM (g/MJ)  equations hold for non-stressed conditions

34 34 to characterise vegetation need to know efficiency  and fAPAR: Efficiency fAPAR so for fixed 

35 35 Determining   herbaceous vegetation (grasses):  av gC/MJ for C 3 plants  higher for C 4  woody vegetation:  gC/MJ simple model for  :

36 36  gross - conversion efficiency of gross photosyn. (= 2.7 gC/MJ)  f - fraction of daytime when photosyn. not limited (base tempt. etc)  Y g - fraction of photosyn. NOT used by growth respiration (65-75%)  Y m - fraction of photosyn. NOT used by maintainance respiration (60-75%)

37 37 Biome-BGC model

38 38 From Running et al. (2004) MOD17 ATBD Biome-BGC model predicts the states and fluxes of water, carbon, and nitrogen in the system including vegetation, litter, soil, and the near- surface atmosphere i.e. daily PSN

39 39 From Running et al. (2004) MOD17 ATBD Biome-BGC model predicts the states and fluxes of water, carbon, and nitrogen in the system including vegetation, litter, soil, and the near- surface atmosphere i.e. daily PSN

40 40 From Running et al. (2004) MOD17 ATBD

41 41

42 42 NPP 1km over W. Europe, 2001.

43 43 Issues?  Need to know land cover  Ideally, plant functional type (PFT)  Get this wrong, get LAI, fAPAR and NPP/GPP wrong  ALSO  Need to make assumptions about carbon lost via respiration to go from GPP to NPP  So how good is BiomeBGC model?

44 44 0 = water; 1 = grasses/cereal crops; 2 = shrubs; 3 = broadleaf crops; 4 = savannah; 5= broadleaf forest; 6 = needleleaf forest; 7 = unvegetated; 8 = urban; 9 = unclassified MODIS LAI/fAPAR land cover classification UK is mostly 1, some 2 and 4 (savannah???) and 8. Ireland mostly broadleaf forest? How accurate at UK scale? At global scale?

45 45 Compare with/assimilate into models  Dynamic Global Vegetation Models  e.g. LPJ, SDGVM, BiomeBGC... Driven by climate (& veg. Parameters)  Model vegetation productivity –hey-presto - global terrestrial carbon, Nitrogen, water budgets.....  BUT - how good are they?  Key is to quantify UNCERTAINTY

46 46 MODIS Phenology 2001 (Zhang et al., RSE) Dynam. global veg. models driven by phenology This phenol. Based on NDVI trajectory.... greenup maturity senescencedormancy DOY 0 DOY 365

47 47 How might we validate MODIS NPP?  Measure NPP on the ground??  Scale? Methods?  Intercompare with Dynamic Global Vegetation Models??  e.g. LPJ, SDGVM, BiomeBGC... Driven by climate (& veg. Parameters) –how good are they? Can we quantify UNCERTAINTY? In both observations AND models Model-data fusion approaches

48 48 Summary: EO data: current  Global capability of MODIS, MISR, AVHRR...etc.  Estimate vegetation cover (LAI)  Dynamics (phenology, land use change etc.)  Productivity (NPP)  Disturbance (fire, deforestation etc.)  Compare with models  AND/OR use to constrain/drive models (assimilation)

49 49 Summary EO data: future?  BIG limitation of saturation of reflectance signal at LAI > 5  Spaceborne LIDAR, P-band RADAR to overcome this?  Use structural information, multi-angle etc.?  What does LAI at 1km (and lower) mean?  Heterogeneity/mixed pixels  Large boreal forests? Tropical rainforests?  Combine multi-scale measurements – fine scale in some places, scale up across wider areas….  EOS era (MODIS etc.) coming to an end?  NPOESS?  DESDyni?  ESA Explorer & Sentinel missions (BIOMASS etc.)

50 50 References Myneni et al. (2007) Large seasonal changes in leaf area of Amazon rainforests. Proc. Natl. Acad. Sci., 104: , doi: /pnas Cox et al. (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, Dubayah, R. (1992) Estimating net solar radiation using Landsat Thematic Mapper and Digital Elevation data. Water resources Res., 28: Monteith, J.L., (1972) Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol, 9: Monteith, J.L., (1977). Climate and efficiency of crop production in Britain. Phil. Trans. Royal Soc. London, B 281: Myneni et al. (2001) A large carbon sink in the woody biomass of Northern forests, PNAS, Vol. 98(26), pp Myneni et al. (1998) MOD15 LAI/fAPAR Algorithm Theoretical Basis Document, NASA. & modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdfhttp://cliveg.bu.edu/index.html Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm Theoretical Basis Document, NASA.


Download ppt "GEOGG142 GMES Global vegetation parameters from EO Dr. Mat Disney Pearson Building room 113 020 7679 0592"

Similar presentations


Ads by Google