Spatio-temporal analyses of primary production
Contribution to the SLP project: ’Identifying livestock-based risk manageme...
1. Identification of available global remote
   sensing data sets
2. Development of tools and data processing
3. Results
4...
Remote sensing of vegetation
Remote sensing of vegetation




where:

NDVI : Normalized difference vegetation index
NIR : Reflectance in the near infra...
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 seas...
Identification of available global
        remote sensing data sets
1. The Global Inventory Modeling and Mapping
   Studie...
The Global Inventory Modeling and
       Mapping Studies (GIMMS)
Time series of normalized difference vegetation
index (fr...
Spot Vegetation data
• Earth observation sensor onboard of the Spot
satellite with a daily coverage of the entire earth
at...
Analysis of NDVI time series
Python Scripting: Why scripting this analysis?

• Large number of files to process
  (582 tif...
Analysis of NDVI time series
Clip NDVI files to the region of interest (Script 1)
Analysis of NDVI time series


                                       Calculate the    Extract NDVI or
                   ...
Computation of Vegetation anomalies
1) Compute local (per pixel) NDVI means




2) Compute deviation from mean for each
  ...
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 eac...
NDVI time series
Filtering noisy NDVI series with Savistky-Golay filter (cont.)




wi : weight at point i
σ: standard dev...
GIMMS anomalies




Spot vegetation anomalies
remainder           trend            seasonal                 data
                                 1400 1800 2200 2600   ...
remainder           trend           seasonal            data
                              1000     1400                  ...
remainder            trend             seasonal          data
                               3600   4200    4800          ...
Spatial dependence of anomalies, Niger
                 (from Spot vegetation)




SeptB 2000                   SeptB 2002
Spot vegetation anomalies for sites in Kenya

January 2000                   January 2002                      January 200...
Samburu
Kadjiado
Samburu
Samburu NDVI Time series per Land Cover Type
        0.6

       0.55
                                                    ...
Samburu NDVI Time series per Land Cover Type
       0.7


                                                                ...
Samburu NDVI Time series per Land Cover Type
       0.7


                                                                ...
Kadjiado NDVI time series per land cover type
       0.7


       0.6                                                     ...
Kadjiado NDVI time series per land cover type
       0.8

       0.7
                                                     ...
0.7



  0.6



  0.5



  0.4                                                                 Rainfed herbaceous
        ...
Fakara Veg anomalies 2002
Gabi, Veg anomalies 2004
Zermou, Veg anomalies 2004
IRD soil map boundaries and
Veg anomalies 2004
Merge information coming from two spatial prediction models
(econometric and kriging) through the Bayesian data fusion (BD...
Merge information coming from two spatial prediction models
(econometric and kriging) through the Bayesian data fusion (BD...
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
Spatio-temporal analyses of primary production
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Spatio-temporal analyses of primary production

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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.

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Spatio-temporal analyses of primary production

  1. 1. 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 1500000 Year 2007, D172 10 10 20 20 1490000 5 15 15 Y 1480000 10 10 20 15 5 10 15 1470000 20 455000 465000 475000 485000 X
  2. 2. 1. Identification of available global remote sensing data sets 2. Development of tools and data processing 3. Results 4. Further work
  3. 3. Remote sensing of vegetation
  4. 4. Remote sensing of vegetation where: NDVI : Normalized difference vegetation index NIR : Reflectance in the near infrared RED: Reflectance in the red spectrum
  5. 5. NDVI time series Phenological parameters derived from time series Source: Bachoo et al., 2007
  6. 6. • 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
  7. 7. 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
  8. 8. The Global Inventory Modeling and Mapping Studies (GIMMS) Time series of normalized difference vegetation index (from NOAA AVHRR) over a 22 year period Period: January 1983 to December 2003, max compositing every 15 days Spatial Resolution of GIMMS end-product: 8 km http://glcf.umiacs.umd.edu/data/gimms/
  9. 9. 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
  10. 10. 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
  11. 11. Analysis of NDVI time series Clip NDVI files to the region of interest (Script 1)
  12. 12. Analysis of NDVI time series Calculate the Extract NDVI or Compute the Clip NDVI files NDVI deviation anomalies NDVI local to the region from the time series statistics for of interest average over using a shape each decade the studied file for points over the period for each or areas of studied period (Script 1) decade interest (Script 2) (Script 3) (Script 5)
  13. 13. Computation of Vegetation anomalies 1) Compute local (per pixel) NDVI means 2) Compute deviation from mean for each period of each year
  14. 14. NDVI time series Spatial analysis of anomalies
  15. 15. Vegetation anomalies from GIMMS data (deviation from average yearly max) 1984 1999
  16. 16. Vegetation anomalies from GIMMS data (deviation from average yearly max) 2000
  17. 17. 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
  18. 18. 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
  19. 19. GIMMS anomalies Spot vegetation anomalies
  20. 20. remainder trend seasonal data 1400 1800 2200 2600 1000 3000 1985 1990 time 1995 Fakara site, GIMMS data 2000 -1500 0 1000 -500 0 500 1000
  21. 21. remainder trend seasonal data 1000 1400 0 1000 2000 3000 1985 1990 time 1995 Gabi site, GIMMS data 2000 -1000 0 500 -600 -200 200
  22. 22. remainder trend seasonal data 3600 4200 4800 2000 5000 8000 1985 1990 time 1995 Mande site, GIMMS data 2000 -2000 0 2000 -2000 0 1000
  23. 23. Spatial dependence of anomalies, Niger (from Spot vegetation) SeptB 2000 SeptB 2002
  24. 24. Spot vegetation anomalies for sites in Kenya January 2000 January 2002 January 2007 Samburu Kadjiado
  25. 25. Samburu
  26. 26. Kadjiado
  27. 27. Samburu
  28. 28. Samburu NDVI Time series per Land Cover Type 0.6 0.55 Rainfed herbaceous crop 0.5 0.45 0.4 Scattered herbaceous crop (field density 20-40%) NDVI 0.35 0.3 Isolated herbaceous crop 0.25 (field density 10-20%) 0.2 0.15 0.1 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year
  29. 29. Samburu NDVI Time series per Land Cover Type 0.7 Closed trees 0.6 0.5 Closed shrubs NDVI 0.4 Shrub savannah 0.3 Closed herbaceous vegetation 0.2 on permanently flooded land 0.1 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year
  30. 30. Samburu NDVI Time series per Land Cover Type 0.7 Closed trees 0.6 0.5 Closed shrubs NDVI 0.4 Shrub savannah 0.3 Closed herbaceous vegetation 0.2 on permanently flooded land 0.1 2000 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 2004 Year
  31. 31. Kadjiado NDVI time series per land cover type 0.7 0.6 Rainfed herbaceous crop 0.5 Irrigated herbaceous crop NDVI 0.4 Open to closed herbaceous vegetation 0.3 0.2 0.1 2004 2004.5 2005 2005.5 2006 2006.5 2007 2007.5 2008 Year
  32. 32. Kadjiado NDVI time series per land cover type 0.8 0.7 Closed trees 0.6 Forest plantation - 0.5 undifferentiated Open to closed herbaceous NDVI 0.4 vegetation 0.3 Bare areas 0.2 0.1 0 2004 2004.5 2005 2005.5 2006 2006.5 2007 2007.5 2008 Year
  33. 33. 0.7 0.6 0.5 0.4 Rainfed herbaceous crop (Samburu) NDVI 0.3 Rainfed herbaceous crop (Kadjiado) 0.2 0.1 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year
  34. 34. Fakara Veg anomalies 2002
  35. 35. Gabi, Veg anomalies 2004
  36. 36. Zermou, Veg anomalies 2004
  37. 37. IRD soil map boundaries and Veg anomalies 2004
  38. 38. 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) Vulnerability Vegetation FEWS Food Household vulnerability indicators at anomalies at economy survey data arrondissement harvest time (528 villages and 10,564 zones level as an agricultural households season indicator Small area Kriging to estimate estimation vulnerability at non approach surveyed villages Bayesian Data Fusion
  39. 39. 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) Vulnerability Vegetation FEWS Food Household vulnerability indicators at anomalies at economy survey data arrondissement harvest time (528 villages and 10,564 zones level as an agricultural households season indicator Small area Kriging to estimate estimation vulnerability at non approach surveyed villages Bayesian Data Fusion
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