GEOGG142 GMES Global vegetation parameters from EO

1. GEOGG142 GMESGlobal vegetation parameters from EO Dr. Mat Disney mdisney@geog.ucl.ac.uk Pearson Building room 113 020 7679 0592 www.geog.ucl.ac.uk/~mdisney

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. Vegetation properties of interest in global monitoring/modelling • components of greenhouse gases • CO2 - carbon cycling • photosynthesis, biomass burning • CH4 • lower conc. but more effective - cows and termites! • H20 - evapo-transpiration • (erosion of soil resources, wind/water)

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. 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. 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. Appropriate scales for monitoring • spatial: • global land surface: ~143 x 106 km • 1km data sets = ~143 x 106 pixels • GCM can currently deal with 0.25o - 0.1o 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. optical data @ 1 km • EOS MODIS(Terra/Aqua) • 250m-1km • fuller coverage of spectrum • repeat multi-angular

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

10. optical data @ 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. 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. LAI/fAPAR • direct quantification of amount of (green) vegetation • structural quantity • uses: • radiation interception (fAPAR) • evapotranspiration (H20) • photosynthesis (CO2) i.e. carbon • respiration (CO2 hence carbon) • leaf litter-fall (carbon again) • Look at MODIS algorithm • Good example of algorithm development • ATBD:http://cybele.bu.edu/modismisr/atbds/modisatbd.pdf

13. LAI • 1-sided leaf area (m2) per m2 ground area • full canopy structural definition (e.g. for RS) requires • leaf angle distribution (LAD) • clumping • canopy height • macrostructure shape

14. LAI • preferable to fAPAR/NPP (fixed CO2) as LAI relates to standing biomass • includes standing biomass (e.g. evergreen forest) • can relate to NPP • can relate to site H20 availability

16. fAPAR • Fraction of absorbed photosynthetically active radiation (PAR: 400-700nm). • 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. 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

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. 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. 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. MODIS LAI/fAPAR algorithm • See ATBD: http://cliveg.bu.edu/index.html 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. 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. 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

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. PSN and NPP • C02 removed from atmosphere • photosynthesis • C02 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. PSN and NPP • Net Primary Productivity (NPP) • annual net carbon exchange • quantifies actual plant growth • Conversion to biomass (woody, foliar, root) • (not just C02 fixation)

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

35. Determining  • herbaceous vegetation (grasses): • av. 1.0-1.8 gC/MJ for C3 plants • higher for C4 • woody vegetation: • 0.2 - 1.5 gC/MJ • simple model for :

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

37. Biome-BGC model

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

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

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. 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? 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

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

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. 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. 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? http://www.ipo.noaa.gov/ • DESDyni? http://desdyni.jpl.nasa.gov/ • ESA Explorer & Sentinel missions (BIOMASS etc.)

50. References • Myneni et al. (2007) Large seasonal changes in leaf area of Amazon rainforests. Proc. Natl. Acad. Sci., 104: 4820-4823, doi:10.1073/pnas.0611338104. • Cox et al. (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184-187. • Dubayah, R. (1992) Estimating net solar radiation using Landsat Thematic Mapper and Digital Elevation data. Water resources Res., 28: 2469-2484. • Monteith, J.L., (1972) Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol, 9:747-766. • Monteith, J.L., (1977). Climate and efficiency of crop production in Britain. Phil. Trans. Royal Soc. London, B 281:277-294. • Myneni et al. (2001) A large carbon sink in the woody biomass of Northern forests, PNAS, Vol. 98(26), pp. 14784-14789 • Myneni et al. (1998) MOD15 LAI/fAPAR Algorithm Theoretical Basis Document, NASA. http://cliveg.bu.edu/index.html & modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf • Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm Theoretical Basis Document, NASA. • http://www.globalcarbonproject.org