Presentation ccsi 2012 yaseen taha mustafa

Background Method Application Results Conclusions Outlook References ?

SPATIO-TEMPORAL ESTIMATION OF LAI INHETEROGENEOUS FORESTS USING

SATELLITE REMOTE SENSING

Yaseen T. [email protected]

Faculty of ScienceUniversity of Zakho (UOZ)

Zakho-Duhok, Kurdistan region-Iraq

September 5, 2012

2nd International Conference on Climate Change & Social Issues

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Forest and its growth

What does it matter?

Why does it matter?

Who are interested?

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What does it matter?

Why does it matter?

Who are interested?

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LAI = total one-sided area of leaf tissue per unit groundsurface area (m2.m−2).

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Determination of LAI

Field measurements

Direct methods

Indirect methods

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Field measurements

Direct methods

Indirect methods

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Field measurements

Direct methods

Indirect methods

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Field measurements

Direct methods

Indirect methods

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Remote sensing (RS):

Moderate Resolution Imaging SpectroRadiometer (MODIS).

1 km resolutionEvery eight-day

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... Continue

Mustafa et al. (2011) have estimated MODIS LAI at 250 mspatial support using MODIS normalized difference vegetationindex (NDVI) product.

By establishing the relation between the NDVI MODIS andthe FPAR MODIS and LAI calculated from the predictedFPAR at 250 m.

It is still a course resolution and the high landscapeheterogeneity can be completely dissolved in 250 m pixels.

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Determination of subpixel area proportion

MODIS NDVI product

identify DOY of the

used pixel in the

composite NDVI image

Use MOD03

image of the

identified DOY pixel

MOD03 footprint

grided into 16 grid cells

MODIS LAI footprint

(reference footprint)

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Determine the proper MOD03

grid cell by finding the

shortest distance between

centers of MOD03 grid

cell and reference footprint

grid cell for each grid cell

calculate the intesected area

between MOD03 grid cell

and the reference polygons

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1

Example 2

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MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2 7 / 18

MODIS NDVI product

identify DOY of the

used pixel in the

Use MOD03

image of the

MOD03 footprint

MODIS LAI footprint

Reference footprint

Intersect gridded

reference foot-

print and gridded

MOD03 footprint

Example 1 Example 2 7 / 18

Decomposition

Linear Mixture Model (LMM) is used to decompose LAIMODIS into five values, one for each species.

x ik = ΣjAik,jy

ij + εik (1)

i (1, ..., 5) ⇒ image number;

x ik ⇒ LAIM for pixel k at image i ;

Ai ⇒ matrix of the area proportion values of species j(j = 1, ..., 5) in pixel k at image i ;

y ij ⇒ the LAI of tree species j at image i ; and

εik ⇒ the residual for pixel k at image i .

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Decomposition

x ik = ΣjAik,jy

ij + εik (1)

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Decomposition

x ik = ΣjAik,jy

ij + εik (1)

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Decomposition

x ik = ΣjAik,jy

ij + εik (1)

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Decomposition

x ik = ΣjAik,jy

ij + εik (1)

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Study area

Field data

MODIS data

MOD03MOD13QMOD15A

Location of the study area in the Speulderbos forest

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Decomposition

Decomposed MODIS LAI into five LAI values of five species, Beech, Douglas fir,Hemlock, Japanese larch, and Scotch pine.

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Decomposition

Decomposed MODIS LAI into five LAI values of five species after replacing the unexpected LAI values with theprecedent LAI values. (a) Beech, (b) Douglas fir, (c) Hemlock, (d) Japanese larch, and (e) Scotch pine.

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DecompositionThe mean values (µ), standard division (σ), RMSEs and REs forthe LAISAT, and LAIFD of five tree species during study time.

Source µ σ RMSESAT RESAT

Beech LAISAT 4.24 0.22 0.31 7.31%LAIFD 4.09 0.43

Douglas fir LAISAT 4.24 0.20 0.26 6.43%LAIFD 4.00 0.20

Hemlock LAISAT 4.27 0.36 0.37 8.68%LAIFD 4.04 0.54

Japanese larch LAISAT 4.13 0.11 0.17 3.47%LAIFD 4.05 0.26

Scotch pine LAISAT 4.31 0.18 0.23 4.47%LAIFD 4.16 0.35

Composed LAISAT 4.17 0.20 1.45 25.25%LAIFD 5.60 0.43

The accuracy is assessed using:

Root mean square error(RMSE)Relative error (RE)

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Conclusions

The decomposed MODIS LAI of individual species isestimated successfully;

This approach can be applied with any heterogeneousforest to infer spatio-temporal estimation of LAI.

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Outlook

Implement the approach in a larger area, and differentarea with different species.

Formulate and integrate statistical approaches (i.e.,EM-algorithm) to estimate missing satellite data.

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References

Kalacska, M., Sanchez-Azofeifa, A., Caelli, T., Rivard, B., & Boerlage, B.(2005). Estimating leaf area index from satellite imagery using Bayesiannetworks. IEEE Transactions on Geoscience and Remote Sensing 43 (8):1866-1873.

McCoy, R. M. (2005). Field methods in remote sensing. New York: GuilfordPress.

Mustafa, Y. T., Van Laake, P. E., & Stein, A. (2011). Bayesian NetworkModeling for Improving Forest Growth Estimates. IEEE Transactions onGeoscience and Remote Sensing 49 (2): 639-649.

Mustafa, Y. T., Stein, A., Tolpekin, V., & Van Laake, P. E. (2012) “Improvingforest growth estimates using a Bayesian network approach” PhotogrammetricEngineering & Remote Sensing 78 (1):45-51.

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An example of determining a proper grid cell of MOD03 by selecting a shortest distance between center of a gridcell (×) of the MOD03 footprint and center of a grid cell (∗) of the reference footprint (dashed line)

Flowchart .17 / 18

Selected MOD03 grid cell intersected with reference polygonsa b

0 0.50.25 km Tree speciesBeechDouglas Fir

HemlockJapanese Larch

Scotch PineNo Trees

( ) ( )

Flowchart .18 / 18

Presentation ccsi 2012 yaseen taha mustafa

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