Spatio-temporal analyses of primary production

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

1. Identification of available global remote sensing data sets

2. Development of tools and data processing3. Results4. Further work

Remote sensing of vegetation

Remote sensing of vegetation

where:

NDVI : Normalized difference vegetation indexNIR : Reflectance in the near infraredRED: Reflectance in the red spectrum

NDVI time series

Phenological parameters derived from time series

Source: Bachoo et al., 2007

• 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

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

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/

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

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

Analysis of NDVI time series

Clip NDVI files to the region of interest (Script 1)

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)

Computation of Vegetation anomalies

1) Compute local (per pixel) NDVI means

2) Compute deviation from mean for each period of each year

NDVI time series

Spatial analysis of anomalies

Vegetation anomalies from GIMMS data (deviation from average yearly max)

1984 1999

Vegetation anomalies from GIMMS data (deviation from average yearly max)

2000

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

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

GIMMS anomalies

Spot vegetation anomalies

Fakara site, GIMMS data

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Gabi site, GIMMS data

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Spatial dependence of anomalies, Niger(from Spot vegetation)

January 2000 January 2002 January 2007

Spot vegetation anomalies for sites in Kenya

Samburu

Kadjiado

Samburu

Kadjiado

Samburu

2000 2001 2002 2003 2004 2005 2006 2007 20080.1

0.15

0.2

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0.35

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Rainfed herbaceous crop

Scattered herbaceous crop (field density 20-40%)

Isolated herbaceous crop (field density 10-20%)

Year

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VI

2000 2001 2002 2003 2004 2005 2006 2007 20080.1

0.2

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Samburu NDVI Time series per Land Cover Type

Closed trees

Closed shrubs

Shrub savannah

Closed herbaceous vegetation on permanently flooded land

Year

ND

VI

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

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

2004 2004.5 2005 2005.5 2006 2006.5 2007 2007.5 20080

0.1

0.2

0.3

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Kadjiado NDVI time series per land cover type

Closed trees

Forest plantation - undifferen-tiated

Open to closed herbaceous vegetation

Bare areas

Year

ND

VI

2000 2001 2002 2003 2004 2005 2006 2007 20080

0.1

0.2

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Rainfed herbaceous crop (Samburu)

Rainfed herbaceous crop (Kadjiado)

Year

ND

VI

Fakara Veg anomalies 2002

Gabi, Veg anomalies 2004

Zermou, Veg anomalies 2004

IRD soil map boundaries andVeg anomalies 2004

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

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