spatio-temporal analyses of primary production
DESCRIPTION
Contribution to the SLP project: ’Identifying livestock-based risk management and coping options to reduce vulnerability to droughts in agro-pastoral and pastoral systems in East and West Africa’ Presentation by Bruno Gérard to the SLP Workshop in Niamey, March 2009.TRANSCRIPT
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Spatio-temporal analyses of primary production Contribution to the SLP project: ’Identifying livestock-based risk management and coping options to
reduce vulnerability to droughts in agro-pastoral and pastoral systems in East and West Africa’Bruno Gérard
SLP Workshop in Niamey, March 2009
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Year 2007, D172
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1. Identification of available global remote sensing data sets
2. Development of tools and data processing3. Results4. Further work
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Remote sensing of vegetation
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Remote sensing of vegetation
where:
NDVI : Normalized difference vegetation indexNIR : Reflectance in the near infraredRED: Reflectance in the red spectrum
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NDVI time series
Phenological parameters derived from time series
Source: Bachoo et al., 2007
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• So importance of spatial but especially temporal resolution for vegetation monitoring
• One information over the season is not good enough to capture vegetation dynamics
-> coarse resolution imagery of global coverage is prefered to fragemented high resolution information
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Identification of available global remote sensing data sets
1. The Global Inventory Modeling and Mapping Studies (GIMMS)
Used in many vegetation changes recent studies
2. Spot Vegetation data
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The Global Inventory Modeling and Mapping Studies (GIMMS)
Time series of normalized difference vegetation index (from NOAA AVHRR) over a 22 year periodPeriod: January 1983 to December 2003, max compositing every 15 daysSpatial Resolution of GIMMS end-product: 8 kmhttp://glcf.umiacs.umd.edu/data/gimms/
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Spot Vegetation data
• Earth observation sensor onboard of the Spot satellite with a daily coverage of the entire earth at a spatial resolution of 1 km • VEGETATION instrument (SPOT 4 satellite) and VEGETATION 2 (SPOT 5 satellite)• Period study: 2000-2007 10 days mean compositing
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Analysis of NDVI time series
Python Scripting: Why scripting this analysis?
• Large number of files to process(582 tif files, size > 100 GB)
• Risk of errors in case of manual processing
• Local NDVI statistics need to be recomputed when NDVI input files are updated (additional year)
• Similar processing with the two data sets
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Analysis of NDVI time series
Clip NDVI files to the region of interest (Script 1)
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Analysis of NDVI time series
Clip NDVI files to
the region of interest
(Script 1)
Compute the NDVI local
statistics for each
decade over the
studied
period(Script
2)
Calculate the NDVI
deviation from
the average
over the
studied period
for each
decade(Script
3)
Extract NDVI or anomalies time series
using a shape file for points
or areas
of interest
(Script
5)
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Computation of Vegetation anomalies
1) Compute local (per pixel) NDVI means
2) Compute deviation from mean for each period of each year
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NDVI time series
Spatial analysis of anomalies
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Vegetation anomalies from GIMMS data (deviation from average yearly max)
1984 1999
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Vegetation anomalies from GIMMS data (deviation from average yearly max)
2000
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NDVI time series
Filtering noisy NDVI series with Savistky-Golay filter
Smoothes and approximates data by replacing each data value xi (i = 1, . . . ,N) N is the number of data points) with the value of an approximated function at that point.
Function is a quadratic polynomial fitted to the set of points X in a moving window centered at xi. The width of the window controls the degree of smoothing.
Quadratic polynomial: f(t) = c1 + c2t + c3t2
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NDVI time series
Filtering noisy NDVI series with Savistky-Golay filter (cont.)
wi : weight at point i σ: standard deviation μ: mean
-> LSE algorithm is driven towards being asymmetrically biased so as to fit the upper envelope of NDVI values
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GIMMS anomalies
Spot vegetation anomalies
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Fakara site, GIMMS data
1000
3000
da
ta
-500
050
010
00
sea
son
al
1400
1800
2200
2600
tre
nd
-150
00
1000
1985 1990 1995 2000
rem
ain
de
r
time
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Gabi site, GIMMS data
010
0020
0030
00
da
ta
-600
-200
200
sea
son
al
1000
1400
tre
nd
-100
00
500
1985 1990 1995 2000
rem
ain
de
r
time
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Mande site, GIMMS data
2000
5000
8000
da
ta
-200
00
1000
sea
son
al
3600
4200
4800
tre
nd
-200
00
2000
1985 1990 1995 2000
rem
ain
de
r
time
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SeptB 2000 SeptB 2002
Spatial dependence of anomalies, Niger(from Spot vegetation)
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January 2000 January 2002 January 2007
Spot vegetation anomalies for sites in Kenya
Samburu
Kadjiado
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Samburu
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Kadjiado
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Samburu
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2000 2001 2002 2003 2004 2005 2006 2007 20080.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6Samburu NDVI Time series per Land Cover Type
Rainfed herbaceous crop
Scattered herbaceous crop (field density 20-40%)
Isolated herbaceous crop (field density 10-20%)
Year
ND
VI
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2000 2001 2002 2003 2004 2005 2006 2007 20080.1
0.2
0.3
0.4
0.5
0.6
0.7
Samburu NDVI Time series per Land Cover Type
Closed trees
Closed shrubs
Shrub savannah
Closed herbaceous vegetation on permanently flooded land
Year
ND
VI
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2000 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 20040.1
0.2
0.3
0.4
0.5
0.6
0.7Samburu NDVI Time series per Land Cover Type
Closed trees
Closed shrubs
Shrub savannah
Closed herbaceous vegetation on permanently flooded land
Year
ND
VI
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2004 2004.5 2005 2005.5 2006 2006.5 2007 2007.5 20080.1
0.2
0.3
0.4
0.5
0.6
0.7
Kadjiado NDVI time series per land cover type
Rainfed herbaceous crop
Irrigated herbaceous crop
Open to closed herbaceous vegetation
Year
ND
VI
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2004 2004.5 2005 2005.5 2006 2006.5 2007 2007.5 20080
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Kadjiado NDVI time series per land cover type
Closed trees
Forest plantation - undifferen-tiated
Open to closed herbaceous vegetation
Bare areas
Year
ND
VI
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2000 2001 2002 2003 2004 2005 2006 2007 20080
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Rainfed herbaceous crop (Samburu)
Rainfed herbaceous crop (Kadjiado)
Year
ND
VI
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Fakara Veg anomalies 2002
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Gabi, Veg anomalies 2004
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Zermou, Veg anomalies 2004
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IRD soil map boundaries andVeg anomalies 2004
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Merge information coming from two spatial prediction models (econometric and kriging) through the Bayesian data fusion (BDF)See example from Tracking Vulnerability paper by Marinho and Gérard (2008)
FEWS Food economy
zones
Household vulnerability survey data
(528 villages and 10,564 households
Vulnerability indicators at
arrondissement level
Vegetation anomalies at harvest time
as an agricultural season indicator
Small area estimation approach
Kriging to estimate vulnerability at non surveyed villages
Bayesian Data Fusion
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Merge information coming from two spatial prediction models (econometric and kriging) through the Bayesian data fusion (BDF)See example from Tracking Vulnerability paper by Marinho and Gérard (2008)
FEWS Food economy
zones
Household vulnerability survey data
(528 villages and 10,564 households
Vulnerability indicators at
arrondissement level
Vegetation anomalies at harvest time
as an agricultural season indicator
Small area estimation approach
Kriging to estimate vulnerability at non surveyed villages
Bayesian Data Fusion