10/03/2005NOV-3300-SL-2857 1 M. Weiss, F. Baret D. Allard, S. Garrigues From local measurements to...
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Transcript of 10/03/2005NOV-3300-SL-2857 1 M. Weiss, F. Baret D. Allard, S. Garrigues From local measurements to...
10/03/2005 NOV-3300-SL-2857 1
M. Weiss, F. BaretD. Allard, S. Garrigues
From local measurements to high spatial resolution VALERI maps
10/03/2005 NOV-3300-SL-2858 2
From local measurements to high spatial VALERI maps
OVERVIEW OF THE VALERI METHODOLOGY
HPLAI2000GPS
SPOT Image
Level 2 Map LAI, fCover, fAPAR
+ Flag(High Resolution)
Co-Kriging
Map LAI, fCover, fAPAR
(Medium Resolution)
BlockKriging
Level 1 Map LAI, fCover, fAPAR(High Resolution)
Transfer Function
(TF)
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From local measurements to high spatial VALERI maps
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
10/03/2005 NOV-3300-SL-2858 4
From local measurements to high spatial VALERI maps
Evaluation of the spatial sampling (1)
30 to 50 ESUs to compare with 22500 SPOT pixelsComparing 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 pattern4 – Repeating steps 2 and 3, 199 times with 199 random translation
vectors
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From local measurements to high spatial VALERI maps
Evaluation of the spatial sampling (2)
Statistical test on the population of 199+1 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.
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From local measurements to high spatial VALERI maps
Evaluation of the spatial sampling (3)
SPOT image classification & comparison of SPOT/ESU distributions
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From local measurements to high spatial VALERI maps
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-hullTransfer function used as an extrapolator
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From local measurements to high spatial VALERI maps
Evaluation of the spatial sampling (5)
TURCO 2003
Red = interpolationDark & light blue = strict & large convex-hull
2 bands 3 bands 4 bands
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From local measurements to high spatial VALERI maps
Determination of the transfer function (1)
Preliminary analysis of the data
Haouz, 2003 Larose, 2003
Averaging Robust Regression
/LUT
Robust regression
/LUT
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From local measurements to high spatial VALERI maps
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
NbBands
kki
kj
kij
i NbBandsC
1
21
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From local measurements to high spatial VALERI maps
Determination of the transfer function
10/03/2005 NOV-3300-SL-2858 12
From local measurements to high spatial VALERI maps
Collocated kriging (1)
)( )( )(1
*oreg
n
o xLAIxLAIxLAI
Minimisation of the estimation variance: f(LAI, LAI , LAI, LAIreg , LAIreg,
LAIreg ) )= 1
LAIreg = LAI issued from transfer functionLAI(x) = LAI measured at ESU
)S - (1 .931 28.1.281 17.1 ) - (1 38.3 53.3
53.3 73.3),( ),(
),( ),( 21
SLAILAILAILAI
LAILAILAILAIregregregreg
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From local measurements to high spatial VALERI maps
Collocated kriging (2)
Rom
illy 2
00
0
Ordinary KrigingFew measurementsNo actual spatialisation
Collocated KrigingHigh influence of HR imageRequire linear LAI-Highly decreases the estimation variance
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From local measurements to high spatial VALERI maps
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 2001Counami 2001,2002
Year 2000 & 2003 completedYears 2001 & 2002 partially completedYear 2004 not investigated