Remote Sensing and Image Processing: 9

Transcription

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

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

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 3

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

5 CO 2 The missing sink 5

6 CO 2 The Mauna Loa record 6

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

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

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) 9

10 spatial: Appropriate scales for monitoring 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 10

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 11

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 12

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) 13

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 14

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 15

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) 16

17 e.g. SDGVM (Sheffield Dynamic Global Veg. Model Woodward et al.) Soil Moisture Phenology LAI Soil Moisture Hydrology Transpiration NPP Soil Moisture H 2 O 30 Soil C & N NPP Max Evaporation Century Litter Growth 17

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 18

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 grasses crops shrubs PFTs Land cover evergreen forest deciduous forest 19

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

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

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) 22

23 Model/EO comparisons: GPP 23

24 Model/EO comparisons: NPP 24

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 25

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27 Future? OCO, NASA 2007 Orbiting Carbon Observatory measure global atmospheric columnar CO2 to 1ppm at 1 x1 every days 27

28 Future? Carbon3D 2009? 28

29 Future? Carbon3D? 2009? 29