monitoring vegetation dynamics using meris fused...
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Monitoring vegetation dynamics using MERIS fused images
Raul Zurita-Milla(1), G. Kaiser(2), J. Clevers(1), W. Schneider(2) and M.E. Schaepman(1)
(1) Wageningen University, The Netherlands(2) University of Natural Resources and Applied Life Sciences, Austria
Mapping and monitoring heterogeneous landscapes:spatial, spectral and temporal unmixing of MERIS data
Raúl Zurita-Milla17th September 2008
Overview
Monitoring vegetation dynamics Data fusion
Complex landscapesStudy area and datasetsMERIS fused imagesResults
Image qualityMERIS vegetation indicesValidation
Conclusions
Monitoring vegetation dynamics (I)
Earth systemCarbon cycleBiosphere Climate
Time series of VIsAVHRRSPOT-VEGETATION
Other applicationsForecasting crop yieldMonitoring habitatsEpidemiology
New sensors, new products
MODISEVI (MOD13) minimizes canopy background and atmospheric effects
MERISMTCI canopy chlorophyll contentMGVI FAPAR
R8-R9R9-R10MTCI =
MGVI = f (R8*,R13*)
Complex landscapes (I)
?
Coarse and medium spatial resolution sensors cannot capture all the details/dynamics of complex landscapes
Landsat-like sensors have a past track record in monitoring vegetation dynamics at sufficient spatial but, in general, not temporal resolution
Complex landscapes (II)
Objective:Evaluate the synergetic use of MERIS FR images and (existing) high spatial resolution datasets for monitoring heterogeneous (and frequently cloudy) landscapes.
10 July 2003Landsat TM
Materials: high spatial resolution
LGN510 July 2003
Landsat TM
Materials: MERIS FR data
18/02 16/04 31/05 14/07
06/08 15/10 08/12
Methodology (I)
18/02 16/04 31/05 14/07 06/08 15/10 08/12
18/02 16/04 31/05 14/07 06/08 15/10 08/12
fusion
Methodology (II): study area
Methodology (I)
18/02 16/04 31/05 14/07 06/08 15/10 08/12
18/02 16/04 31/05 14/07 06/08 15/10 08/12
fusion
Methodology (III): the LMM
pv = m1·f1 + m2·f2+ m3· f3+ m4·f4 + e
( ) nbiefpvnc
ciccii ,...,2,1
1
=+⋅= ∑=
μ
∑=
=nc
ccf
1
0.110 ≤≤ cf
PV(nbx1) = M(nbxnc) · F(ncx1) + E(nbx1)
nc: number of classes (endmembers)
nb
nb: number of bands
pv = m1·f1 + m2·f2+ m3· f3+ m4·f4 + e
… …
1
2 3 4
Methodology (IV): unmixing-based data fusion
Data fusion:
“EnergyUnconstrained”
iSatci L≤< μ0
Zurita-Milla et al. (2008). IEEE GRSL, 5, 453-457
High Resolution (LGN)
Low Resolution(MERIS FR)
PV(k2 x 1) = F(k2x nc) · M(nc x 1) + E(k2 x 1)
Low Resolution downscaled
k: neighborhood sizenc: number of classes
Methodology (V): points of attention
Image co-registrationManual image-to-image not operationalRe-projection issuesAMORGOS
Duplicates removalPixel size = f (swath)
Source: http://www.noc.soton.ac.uk/bilko/envisat/html/pop/mer_prodgrid.html
Fractional cover estimation‘
‘
Fraction aggregation threshold (5%)PSF effects
PV(k2 x 1) = F(k2x nc) · M(nc x 1) + E(k2 x 1)
Methodology (V): points of attention
Image co-registrationManual image-to-image not operational/ errorsRe-projection issuesAMORGOS (3.0)
Duplicates removalPixel size = f (swath)
Methodology (VI): image quality
At 25m (only for July!)
( )∑=
=N
12/
21100
i iMiRMSENl
hERGAS
At 300m
Where: h and l are the resolutions of the high and the low spatial resolution images.
N is the number of spectral bands involved in the fusion.
M is the mean value of the MERIS band-i.
RMSEi is the root mean square error computed between the band-i of the MERIS image and its corresponding band of the degraded fusedimage.
Where: M is the mean value of the TM band-i. RMSEi is computed between the band-i of the
TM image and its (spectrally) corresponding band of the fused image.
Results (I): a quick look
RGB color composite of a subset of the MERIS FR image (a), fused image (b), and the TM image (c)
(a) (b) (c)
Results (II): image co-registration
Results (III): Quality assessment (25 m)
5 7 9 111.95
2
2.05
2.1
2.15
2.2
2.25
2.3
2.35
ER
GA
S 2
5 m
Window size5 7 9 11
0.44
0.46
0.48
0.5
0.52
0.54
0.56
0.58
0.6
0.62
0.64
ER
GA
S 3
00 m
Window size
original fractionsaggregated fractions [5%]
5% aggregation changed < 0.6% of the LGN5 pixels
Results (IV): Quality assessment (300 m)
Monitoring vegetation dynamics (I)MTCI
Fused images allow monitoring of individual fields and small patches of vegetation
MTCI
Monitoring vegetation dynamics (I)MTCI
FU
SE
DM
ER
IS
18−02 16−04 31−05 14−07 06−08 15−10 08−12
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Fused images allow monitoring of individual fields and small patches of vegetation
Monitoring vegetation dynamics (I)MGVI
Fused images allow monitoring of individual fields and small patches of vegetation
Monitoring vegetation dynamics (II)
MGVI
Monitoring vegetation dynamics (III)MTCI
“Quality check”“Validation”
Monitoring vegetation dynamics (IV)MGVI
ConclusionsThe unmixing-based data fusion approach succeed in synthesizing MERIS fused images with a very good spectral quality
The NDVI, MTCI and MGVI profiles extracted from the temporal series of fused images show consistent patterns for each of the land cover types under study.
Monitoring vegetation dynamics (phenology) at high spatial and temporal resolution is possible by combining time series of MERIS FR data with high spatial resolution images.
Thank you for your attention!