monitoring vegetation dynamics using meris fused...

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!