10/03/2005NOV-3300-SL M. Weiss, F. Baret D. Allard, S. Garrigues From local measurements to high spatial resolution VALERI maps

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL OVERVIEW OF THE VALERI METHODOLOGY HP LAI2000 GPS SPOT Image Level 2 Map LAI, fCover, fAPAR + Flag (High Resolution) Co-Kriging Map LAI, fCover, fAPAR (Medium Resolution) Block Kriging Level 1 Map LAI, fCover, fAPAR (High Resolution) Transfer Function (TF)

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Spatial sampling of the Measurements  Objectives =  set the minimum number of ESUs at the optimal location to provide robust relationships between LAI and high resolution spatial images  Get a good description of the geostatistics over the site  In practice =  Sample in proportion all cover types & variability inside  Spread spatially equal within 1km² for variogram computation  Not too close to a landscape boundary  Sometimes difficulty to access the fields  Manpower must be reasonable =3 to 5 ESU per 1km²(  0.18% of the site) => Need to evaluate the sampling afterwards

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Evaluation of the spatial sampling (1)  30 to 50 ESUs to compare with SPOT pixels Comparing directly the two NDVI histograms is not statistically consistent Monte-Carlo procedure to compare the actual cumulative ESU NDVI frequency with randomly shifted sampling pattern 1 – Computing the NDVI cumulative frequency of the 50 exact ESU location 2 – Applying a unique random translation to the sampling pattern 3 – Computing the NDVI cumulative frequency of the shifted pattern 4 – Repeating steps 2 and 3, 199 times with 199 random translation vectors

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Evaluation of the spatial sampling (2) Statistical test on the population of cumulative frequencies For a given NDVI level, if the actual ESU density function is between the 5 highest and 5 lowest frequency value, the hypothesis that ESUs and whole site NDVI distributions are equivalent.

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Evaluation of the spatial sampling (3)  SPOT image classification & comparison of SPOT/ESU distributions

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Evaluation of the spatial sampling (4)  The convex-hull criterium  Strict convex-hull summits = ESU reflectance values in each band  Large convex-hull summits = ESU reflectance values in each band ± 5%relative  Pixels inside the convex-hull:  transfer function used as an interpolator  Pixels outside the convex-hull  Transfer function used as an extrapolator

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Evaluation of the spatial sampling (5) TURCO 2003 Red = interpolation Dark & light blue = strict & large convex-hull 2 bands3 bands4 bands

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Determination of the transfer function (1)  Preliminary analysis of the data Haouz, 2003 Larose, 2003 Averaging Robust Regression /LUT Robust regression /LUT

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Determination of the transfer function  Test of 2 methods  Use of robust regression  iteratively re-weighted least squares algorithm (weights computed at each iteration by applying bisquare function to the residuals).  Results less sensitive to outliers than ordinary least squares regression.  Use of LUT composed of the ESU values  LUT with nbESU elements (3,4 reflectances + measured LAI)  Cost Function:  Estimated LAI = Average value over x data minimizing the cost function  Choice of the best band combination by taking into account 3 errors:  Weighted RMSE  RMSE  Cross-validation RMSE

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Determination of the transfer function

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Collocated kriging (1) Minimisation of the estimation variance:    f(  LAI, LAI,  LAI, LAIreg,  LAIreg, LAIreg ) )    = 1 LAIreg = LAI issued from transfer function LAI(x  ) = LAI measured at ESU 

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Collocated kriging (2) Romilly 2000  Ordinary Kriging  Few measurements  No actual spatialisation  Collocated Kriging  High influence of HR image  Require linear LAI-   Highly decreases the estimation variance

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Conclusions: data base status  The spatial sampling & associated methodology are quite well established  Level 0 : averaging the ESU values  Level 1 : provide HR LAI maps from transfer function  Level 2 : provide HR LAI maps from collocated kriging  Level 0.5: LAI maps derived from SPOT image classification  For some very homogeneous sites, only level 0.5 Aek Loba 2001 Counami 2001,2002 Year 2000 & 2003 completed Years 2001 & 2002 partially completed Year 2004 not investigated

From local measurements to high spatial VALERI maps 10/03/2005NOV-3300-SL Many thanks for all your contributions & May the force be with you