1. 1 Remote Sensing and Image Processing: 9 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: 7670 4290 (x24290) Email: mdisney@geog.ucl.ac.uk www.geog.ucl.ac.uk/~mdisney

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

3. 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

4. 4 The Global Carbon Cycle (Pg C and Pg C/yr) Atmosphere 730 Accumulation + 3.2 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 = 10 15 g)

5. 5 CO 2 – The missing sink

6. 6 CO 2 – The Mauna Loa record

7. 7 Why carbon?? Thousands of Years (x1000) 180 ppm 280 ppm

8. 8 Why carbon? Cox et al., 2000 – suggests land could become huge source of carbon to atmosphere see http://www.grida.no/climate/ipcc_tar/wg1/121.htm

9. 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)

10. 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 - 0.1 o grids (25-30km - 10km grid) temporal: –depends on dynamics –1 month sampling required e.g. for crops

11. 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

12. 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

13. 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)

14. 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

15. 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

16. 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)

17. 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.)

18. 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

19. 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

20. 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

21. 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

22. 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)

23. 23 Model/EO comparisons: GPP

24. 24 Model/EO comparisons: NPP

25. 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

26. 26

27. 27 Future? OCO, NASA 2007 Orbiting Carbon Observatory – measure global atmospheric columnar CO2 to 1ppm at 1  x1  every 16-30 days http://oco.jpl.nasa.gov/index.html

28. 28 Future? Carbon3D 2009? http://www.carbon3d.uni-jena.de/index.html

29. 29 Future? Carbon3D? 2009?