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 ...

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 Spatio-temporal analyses of primary production Presentation Transcript

  • 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
  • 1. Identification of available global remote sensing data sets 2. Development of tools and data processing 3. Results 4. Further work
  • Remote sensing of vegetation
  • Remote sensing of vegetation where: NDVI : Normalized difference vegetation index NIR : Reflectance in the near infrared RED: 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 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/
  • 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 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)
  • 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
  • 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
  • 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
  • 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
  • Spatial dependence of anomalies, Niger (from Spot vegetation) SeptB 2000 SeptB 2002
  • Spot vegetation anomalies for sites in Kenya January 2000 January 2002 January 2007 Samburu Kadjiado
  • Samburu
  • Kadjiado
  • Samburu
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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 (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
  • 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