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Transcript of 1/13/2003IRI Presentation E D C F E W S An empirical study of the links between NDVI and atmospheric...
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1/13/2003 IRI Presentation
E D C
F E W S
An empirical study of the
links between NDVI and
atmospheric variables in Africa with
applications to forecasting vegetation change and
precipitation
Chris FunkUCSB, Climate Hazard Group
Molly Brown,NASA Global
Inventory Modeling and
Mapping
Systems
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Objective: Improved FEWS NET Executive Summaries
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Background – Useful NDVI projections
• NDVI is a satellite measurement of vegetation used to monitor drought
• NDVI linked to locusts (Tucker, 1985; Hielkema et al., 1986), Malaria (Hay et al., 1998), and Rift Valley Fever (Linthicum et al., 1999)
– RVF in 1998/1999 cost the Greater Horn ~$100 million
• NDVI is linked lagged precipitation– (e.g. Nicholson, 1990; Potter and Brooks, 1998; Richard and
Poccard, 1998, and others)
• It seems logical to try to use lagged rainfall to project future NDVI
– These projections are distinct from and compatible with NDVI forecasts based on downscaled climate information
– e.g. the work of Matayo Indeje at the IRI
• Future work should look at combining approaches• Best Skill = Persistence + lagged Rain + Climate Forecast
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Monthly Data
• NDVIe data from NASA GIMMS (1 global and 0.1 degree African)
• GPCP rainfall rescaled to 1 degree• Tim Love’s CPC FEWS NET African Rainfall
Climatology data (0.1 degree, Africa)– Cold cloud duration precipitation estimates blended
with automatic gauge data (Love et al., 2004)
• NCAR Reanalysis relative humidity fields– Class ‘C’ variable (Kalnay et al., 1996)
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Talk Overview• Empirical Models of NDVI Change
– Describe model– Test model
• Works in most semi-arid regions• But, only some regions have decent cross-validated skill when the seasonal
cycle is removed• However, most of Africa is explained well by either the seasonal cycle or
the projection model
– From a decision support perspective we can tell people what the NDVI conditions will be a few months in advance
• Empirical analysis of lagged NDVI/precipitation relationships– NDVI can maybe help predict precipitation in a few regions
• Brazil and Eastern Australia (perhaps).
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Month ahead Max-to-Min NDVI Change Formulation
1 min 1(100 )t tLoss N N RH
max 1 1ln(1 )t tGrowth N N P
We assume geographicallyvarying Nmin and Nmax are fixed.
These have been shown to be strongly related to average precipitation, temperature and latitude (Potter and Brooks, 1998)
More veg higherevapotranspiration
Higher RH lessevapotranspiration
Less veg higherrainfall efficiency
More rain moreveg
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Month ahead Max-to-Min NDVI Change Formulation
1 max 1 1 2 1 min 1ˆ ln(1 ) (100 )t t t t tN b N N P b N N RH
We modelNDVI change
‘Growth’ term ‘Loss’ term
Growth stops whenwe reach historic maxNDVI Growth assumed to
log-linear with precipitation
Loss stops whenwe reach historic minNDVI
Loss assumed linearlyrelated to 100-RH
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Zimbabwe test site revisited
GPCP-based NDVI Growth estimate and NDVI change
R2 = 0.6
-0.2
0
0.2
0 0.1 0.2 0.3 0.4 0.5
NDVI Growth Estimate
ND
VI
Ch
ang
e
NDVI Loss estimate and NDVI Change
R2 = 0.5-0.2
-0.1
0
0.1
0.2
0 0.05 0.1 0.15
NDVI Loss EstimateN
DV
I Cha
nge
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Observed and Estimated NDVI Change for Zimbabwe Test Site
-0.1
-0.05
0
0.05
0.1
0.15
Observed NDVI Change Estimated NDVI Change
Extremes under-estimated – could consider extending max/minbeyond historic values
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Observed and Estimated NDVI for Zimbabwe Test Site
¤ Extremes under-estimated – could consider extending max/min beyond historic values¤ Some inter-annual variability is captured
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Observed NDVI Change Estimated NDVI Change
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1-month max-to-min NDVI Change models
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Future
Climatological Averages
Past
Observed Data
5-month max-to-min NDVI change models
G1 G2 G3 G4 G5L1 L2 L3 L4 L5
FoL1
G1 G2 G3 G4 G5L1 L2 L3 L4 L5
FoL2
G1 G2 G3 G4 G5L1 L2 L3 L4 L5
FoL3
G1 G2 G3 G4 G5L1 L2 L3 L4 L5
FoL4
¤ Climatological averages could be replaced with forecast precipitation and relative humidity¤ This effort meant to complement forecasting efforts by the IRI, CPC and others
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Cross-validation results for Africa
¤ R2 images contain the seasonal cycle¤ Skill = 1.0 – Var(Nobs- Nest)/Var(Nobs), Michaelsen, 1987¤ Some drought-prone semi-arid locations show good skill
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Focus on 4 month forecast
¤ In southern Africa low rainfall regions predicted okay, but seasonal cycle appears dominant¤ In eastern Greater Horn region, good skills found – applications to pasture, malaria and RVF feasible
Livestock dependentRVF-prone
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Sample Application – NE Kenya NDVI projections
• In 1997/98 an extensive outbreak of Rift Valley Fever occurred in northeastern Kenya
• Apx. 27,500 cases occurred in Garissa district, making this the largest recorded outbreak in East Africa, (Woods, Karpati and others, 2002)
• Early warning can allow prevention, monitoring and mitigation activities
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Sample Application – NE Kenya NDVI projections
NE Kenya Test Site - Observed and forecast monthly NDVI anomalies
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
1995 1996 1997 1998 1999 2000 2001 2002
Year-month
ND
VI
ano
mal
y
Obs Lag1 Lag2 Lag3 Lag4
TestSite
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Sample Application – NE Kenya NDVI projections
TestSite
O - Lag 1- R2 = 0.86
X - Lag 4 - R2 = 0.83
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
Caveats:Large area increases accuracy of estimatesARC rainfall known to be accurate in KenyaStill … this analysis bodes well for RVF detection
-0.07
-0.02
0.03
0.08
0.13
0.18
1995 1996 1997 1998 1999 2000 2001 2002Year
Dec
emb
er N
DV
I A
no
mal
ies
Obs Lag4
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Conlcusions
• We can model NDVI change in semi-arid regions with a simple max-to-min growth/loss formulation
• Skill levels are high in semi-arid, low in tropical forests and places with a strong seasonal cycle
• Future work will look at incorporating forecast information
• We hope to create integrated monitoring/projection information products