1 Remote Sensing and Image Processing: 9 Dr. Hassan J. Eghbali

2 Application –Remote sensing of terrestrial vegetation and the global carbon cycle Today….. Dr. Hassan J. Eghbali

3 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 anthropogenic disturbance  de/afforestation  land use change (HUGE impact on dynamics)  Impact on us more direct Dr. Hassan J. Eghbali

4 The Global Carbon Cycle (Pg C and Pg C/yr) Atmosphere 730 Accumulation Fossil fuels & cement production 6.3 Net terrestrial uptake 1.4 Net ocean uptake 1.7 Fossil organic carbon and minerals Ocean store 38,000 Vegetation 500 Soils & detritus 1,500 Runoff 0.8 Atmosphere ocean exchange 90 Atmosphere land exchange 120 Burial 0.2 (1 Pg = g) Dr. Hassan J. Eghbali

5 CO 2 – The missing sink Dr. Hassan J. Eghbali

6 CO 2 – The Mauna Loa record Dr. Hassan J. Eghbali

7 Why carbon?? Thousands of Years (x1000) 180 ppm 280 ppm Dr. Hassan J. Eghbali

8 Why carbon? Cox et al., 2000 – suggests land could become huge source of carbon to atmosphere see Dr. Hassan J. Eghbali

9 Why vegetation? Important part of terrestrial carbon cycle Small amount BUT dynamic and of major importance for humans –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) Dr. Hassan J. Eghbali

10 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 Dr. Hassan J. Eghbali

11 So…… Terrestrial carbon cycle is global Temporal dynamics from seconds to millenia Primary impact on surface is vegetation / soil system So need monitoring at large scales, regularly, and some way of monitoring vegetation…… Hence remote sensing…. –in conjunction with in situ measurement and modelling Dr. Hassan J. Eghbali

12 Back to carbon cycle  Seen importance of vegetation  Can monitor from remote sensing using VIs (vegetation indices) for example  Relate to LAI (amount) and dynamics  BUT not directly measuring carbon at all….  So how do we combine with other measures Dr. Hassan J. Eghbali

13 Vegetation and carbon  We can use complex models of carbon cycle  Driven by climate, land use, vegetation type and dynamics, soil etc.  Dynamic Global Vegetation Models (DGVMS)  Use EO data to provide….  Land cover  Estimates of “phenology” veg. dynamics (e.g. LAI)  Gross and net primary productivity (GPP/NPP) Dr. Hassan J. Eghbali

14 Basic carbon flux equations GPP = Gross Primary Production –Carbon acquired from photosynthesis NPP = Net Primary Production –NPP = GPP – plant respiration NEP = Net Ecosystem Production –NEP = NPP – soil respiration Dr. Hassan J. Eghbali

15 Basic carbon flux equations Units: mass/area/time –e.g. g/m 2 /day or mol/m 2 /s Sign: +ve = uptake –but not always! –GPP can only have one sign Dr. Hassan J. Eghbali

16 Dynamic Vegetation Models (DVMs) Assess impact of changing climate and land use scenarios on surface vegetation at global scale Couple with GCMs to provide predictive tool Very broad assumptions about vegetation behaviour (type, dynamics) Dr. Hassan J. Eghbali

17 Max Evaporation Soil Moisture Litter Transpiration Soil Moisture LAI Soil C & N NPP Soil Moisture H 2 O 30 Phenology HydrologyNPP CenturyGrowth e.g. SDGVM (Sheffield Dynamic Global Veg. Model – Woodward et al.) Dr. Hassan J. Eghbali

18 Potentials for integrating EO data Driving model –Vegetation dynamics i.e. phenology Parameter/state initialisation –E.g. land cover and vegetation type Comparison with model outputs –Compare NPP, GPP Data assimilation –Update model estimates and recalculate Dr. Hassan J. Eghbali

19 Parameter initialisation: land cover EO derived land cover products are used to constrain the relative proportions of plant functional types that the model predicts evergreen forest deciduous forest shrubsgrassescrops Land cover PFTs Dr. Hassan J. Eghbali

20 Parameter initialisation: phenology Day of year of green-up Spring crops Green up Senescence green-up occurs when the sum of growing degree days above some threshold temperature t is equal to n Dr. Hassan J. Eghbali

21 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 Dr. Hassan J. Eghbali

22 Model/EO comparisons: GPP Simple models of carbon fluxes from EO data exist and thus provide a point of comparison between more complex models (e.g. SDGVM) and EO data e.g. for GPP = e.fAPAR.PAR e = photosynthetic efficiency of the canopy PAR = photosynthetically active radiation fAPAR = the fraction of PAR absorbed by the canopy (PAR.fAPAR=APAR) Dr. Hassan J. Eghbali

23 Model/EO comparisons: GPP Dr. Hassan J. Eghbali

24 Model/EO comparisons: NPP Dr. Hassan J. Eghbali

25 Summary: Current EO data  Use global capability of MODIS, MISR, AVHRR, SPOT-VGT...etc.  Estimate vegetation cover (LAI)  Dynamics (phenology, land use change etc.)  Productivity (NPP)  Disturbance (fire, deforestation etc.)  Compare with models and measurements  AND/OR use to constrain/drive models Dr. Hassan J. Eghbali

26 Dr. Hassan J. Eghbali

27 Future? OCO, NASA 2007 Orbiting Carbon Observatory – measure global atmospheric columnar CO2 to 1ppm at 1  x1  every days Dr. Hassan J. Eghbali

28 Future? Carbon3D 2009? Dr. Hassan J. Eghbali

29 Future? Carbon3D? 2009? Dr. Hassan J. Eghbali