Transcript Slide 1
GCM 12/12/06: Retrieval of biophysical (vegetation) parameters from EO sensors Dr. Mat Disney [email protected] Pearson Building room 216 020 7679 0592 www.geog.ucl.ac.uk/~mdisney 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) 2 Vegetation properties of interest in global change 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) 3 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 4 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) 5 definitions: • LAI - one-sided leaf area per unit area of ground dimensionless • fAPAR - fraction of PAR (SW radiation waveband used by vegetation) absorbed - proportion 6 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?? 7 • optical data @ 1 km – EOS MODIS (Terra/Aqua) • 250m-1km • fuller coverage of spectrum • repeat multi-angular 8 • optical data @ 1 km – EOS MISR, on board Terra platform • multi-view angle (9) • 275m-1 km • VIS/NIR only 9 • 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 multiangular • BUT heritage of data since 1981 10 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 .... 11 LAI/fAPAR direct quantification of amount of (green) vegetation structural quantity uses: radiation interception (fAPAR) evapo-transpiration (H20) photosynthesis (CO2) i.e. carbon respiration (CO2 hence carbon) leaf litter-fall (carbon again!) Look at MODIS algorithm Good example of algorithm development see ATBD: http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html 12 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 13 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 (link to ET) 14 15 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 16 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 17 18 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 19 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 20 Estimation of LAI/fAPAR fAPAR relationship to VIs typically simpler linear with asymptote at LAI ~6 BIG issue of saturation of VI signal at high LAI (>5 say) • need to define different relationships for different cover types 21 MODIS LAI/fAPAR algorithm 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 22 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 23 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 24 25 26 27 28 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. 29 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) 30 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) 31 Algorithms - require to be model-based simple production efficiency model (PEM) – (Monteith, 1972; 1977) relate PSN, NPP to APAR APAR from PAR and fAPAR APAR IPAR fAPAR day 32 PSN APAR NPP APAR PSN = daily total photosynthesis NPP, PSN typically accum. of dry matter (convert to C by assuming DM 48% C) = efficiency of conversion of PAR to DM (g/MJ) equations hold for non-stressed conditions 33 to characterise vegetation need to know efficiency and fAPAR: • Efficiency • fAPAR so for fixed fAPAR NDVI PSN IPAR day 34 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 : gross f Yg Ym 35 gross f Yg Ym 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%) 36 37 38 NPP 1km over W. Europe, 2001. 39 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 40 •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 41 Compare/assimilate with 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 42 Why carbon? CO2, CH4 etc. greenhouse gases Importance for understanding (and Kyoto etc...) Lots in oceans of course, but less dynamic AND less prone to anthropgenic distrubance de/afforestation land use change (HUGE impact on dynamics) Impact on us more direct Data-Model Fusion [Using multiple streams of datasets with parameter optimization] C stock and flux measurements Inventory analyses Process-based information Climate data Remote sensing information CO2 column from space Inverse modeling Process-based modeling Retrospective and forward analyses Canadell et al. 44 2000 Carbon: how?? • Measure fluxes using eddy-covariance towers 45 •MODIS Phenology 2001 (Zhang et al., RSE) greenup •Dynam. global veg. models driven by phenology maturity •This phenol. Based on NDVI trajectory.... DOY 0 senescence dormancy DOY 365 46 -Carbon sinks/sources using AVHRR data to derive NPP -Carbon pool in woody biomass of NH forests (1.5 billion ha) estimated to be 61 20 Gt C during the late 1990s. - Sink estimate for the woody biomass during the 1980s and 1990s is 0.680.34 Gt C/yr. -From Myneni et al. PNAS, 98(26),1478414789 http://cybele.bu.edu/biomass/biomass.html 47 Limiting factors Dominant Controls water availability 40% temperature 33% solar radiation 27% Total vegetated area: 117 M km2 48 Since the early 1980s about, - half the vegetated lands greened by about 11% - 15% of the vegetated lands browned by about 3% - 1/3rd of the vegetated lands showed no changes. These changes are due to easing of climatic constraints to plant growth. Bottom line 49 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) 50 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 ???? 51 References • • • • • • 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 Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm Theoretical Basis Document, NASA. • http://cybele.bu.edu • http://www.globalcarbonproject.org 52