Transcript Vegetation monitoring for climate studies

Remote Sensing and Image
Processing: 9
Dr. Mathias (Mat) Disney
UCL Geography
Office: 301, 3rd Floor, Chandler House
Tel: 7670 4290 (x24290)
Email: [email protected]
www.geog.ucl.ac.uk/~mdisney
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Today…..
• Application
– Remote sensing of terrestrial vegetation and the global
carbon cycle
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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
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The Global Carbon Cycle (Pg C and Pg C/yr)
Atmosphere 730
Accumulation + 3.2
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
Runoff
0.8
Ocean store
38,000
Fossil organic carbon
and minerals
(1 Pg = 1015 g)
Burial
0.2
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CO2 – The missing sink
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CO2 – The Mauna Loa record
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Why carbon??
280 ppm
180 ppm
Thousands of Years (x1000)
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Why carbon?
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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
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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……
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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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e.g. SDGVM (Sheffield Dynamic Global Veg.
Model – Woodward et al.)
Soil Moisture
Phenology
LAI
Soil Moisture
Hydrology
Soil
Moisture
H2O30
Century
Transpiration
Soil C & N
Litter
NPP
NPP
Max
Evaporation
Growth
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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
grasses
crops
shrubs
PFTs
Land cover
evergreen
forest
deciduous
forest
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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
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•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
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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)
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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.
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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 1x1 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?
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