1 Remote Sensing and Image Processing: 9 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd...

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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

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• Application– Remote sensing of terrestrial vegetation and the global

carbon cycle

Today…..

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Why carbon?

CO2, CH4 etc.greenhouse gasesImportance for understanding (and Kyoto etc...)Lots in oceans of course, but less dynamic AND less prone to anthropogenic disturbance

de/afforestationland use change (HUGE impact on dynamics)Impact on us more direct

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The Global Carbon Cycle (Pg C and Pg C/yr)

Atmosphere 730Accumulation + 3.2

Fossil fuels &cement production 6.3

Net terrestrialuptake 1.4

Net oceanuptake 1.7

Fossil organic carbon and minerals

Ocean store 38,000

Vegetation 500Soils & detritus 1,500

Runoff 0.8

Atmosphere ocean exchange 90

Atmosphere land exchange 120

Burial 0.2

(1 Pg = 1015 g)

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CO2 – The missing sink

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CO2 – The Mauna Loa record

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Why carbon??

Thousands of Years (x1000)

180 ppm

280 ppm

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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

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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)

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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

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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

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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

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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)

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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

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Basic carbon flux equations

• Units: mass/area/time– e.g. g/m2/day or mol/m2/s

• Sign: +ve = uptake – but not always!– GPP can only have one sign

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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)

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Max Evaporation

Soil Moisture

Litter

Transpiration

Soil Moisture

LAI

Soil C & N NPPSoil

Moisture H2O30

Phenology

Hydrology NPP

Century Growth

e.g. SDGVM (Sheffield Dynamic Global Veg. Model – Woodward et al.)

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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

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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

shrubsgrasses crops

Land cover

PFTs

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Parameter initialisation: phenology

Day of year of

green-upSpring crops

Green up

Senescence

green-up occurs when the sum of growing degree days above some threshold temperature t is equal to n

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•MODIS Phenology 2001 (Zhang et al., RSE)

•Dynam. global veg. models driven by phenology

•This phenol. Based on NDVI trajectory....

greenup maturity

senescence dormancy

DOY 0

DOY 365

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Model/EO comparisons: GPPSimple 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)

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Model/EO comparisons: GPP

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Model/EO comparisons: NPP

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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

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Future? OCO, NASA 2007

•Orbiting Carbon Observatory – measure global atmospheric columnar CO2 to 1ppm at 1x1 every 16-30 days

•http://oco.jpl.nasa.gov/index.html

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Future? Carbon3D 2009?

http://www.carbon3d.uni-jena.de/index.html

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Future? Carbon3D? 2009?