Prof. Dr. Christiana Schmullius Dr. Leif Eriksson, Dipl

SAR Day 2 Lecture 4 Introduction to Modelling C. Schmullius 1

Prof. Dr. Christiana SchmulliusDr. Leif Eriksson, Dipl.-Geogr. Tanja Riedel

Dr. Maurizio Santoro, Dr. Christian Thiel

Department for Geoinformatics and Remote SensingFriedrich-Schiller-University Jena, Germany


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ERS coherence image

JERS intensity image

Use model to calculate

class means

Maximum LikelihoodClassifier

Iterated ContextualProbability Classifier

(ICP)


�� • Histograms vary from scene to scene.

– How to capture variance? From the scenes themselves.

Simulated Histograms of Stem Volume Classes and Overall Class „Forest“.

Characteristic Values

Wagner et al., RSE, 2003


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• Ground truth data determine model for ERS coherence and JERS-1 intensity

Coherence Model:γ75 .. CharacteristicCoherence Percentile

( ) 1227575 58.033.0)(

v

ev−

⋅⋅++= γγγ

Test data to determine coherency model.



�� ( ) ( ) γγγγγ V

v

ev−

∞∞ ⋅−+= 0

750 γγ γγ ⋅+= ba

( ) γγγγ γγVv

ebav−

⋅−++= 7575 )1()(

( ) 1.1227575 581.0330.0)(

v

ev−

⋅⋅++= γγγ

75γγ ≈∞

v = growing stock volume

γ0 = coherence at v = 0 m3/ha (non-forest)

γ∞ = coherence for asymptotic values of v(corresponding to dense forest)

γ75 = value where the coherence distribution reach 75% of the maximum value (see fig.)

Vγ = characteristic v value where the exponential function has decreased by e-1



( ) σσσσσ Vv

ev−

∞∞ ⋅−+= 00 )(

34.10775

0 46.2)(v

ev−

⋅−= σσ


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SIBERIA classes Land cover type

Water River, lake, inland water

Smooth areas Agricultural fields, river sand bar

Open areas Bogs, meadows, hayfields, pasture, clear-cut, burnt forest, young regrowth

Forest 20-50 m3/ha

Forest 20-50 m3/ha

Forest 50-80 m3/ha

Forest 50-80 m3/ha

Forest>80 m3/ha

Forest >80 m3/ha


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Radar Image Mosaic

111 Radar Image Maps

Forest Cover Mosaic

96 Forest Cover Maps


Pang Yong, Annual Progress Report 2004, CAF

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The retrieval process can be divided in four parts: 1. Model selection2. Model training3. Retrieval4. Accuracy assessment

Growing stock volume = stem volume [m3/ha]

Leif Eriksson et al., ForestSAT 2005

Stem volume retrieval – Methodology 1


Regression model

�: coherence

V: growing stock volume

Regression parameters

A: dynamic range

B: slope

C: offset

( ) CeAV VB +∗= ∗γ


Stem volume retrieval – Methodology 2


Best results for JERS-1:RMSE = 60 m³/haRelative RMSE = 43 %R² = 0.75

Best results for ERS-1/2:RMSE = 57 m³/haRelative RMSE = 37 %R² = 0.73


Stem volume retrieval – Results


Regression model

JERS-1 coherence

JERS-1 backscatter

SIBERIA algorithm

JERS-1 coherence

JERS-1 backscatter

SIBERIA algorithm

ERS-1/2 coherence

JERS-1 backscatter

Forest inventory


Interferometric Water Cloud Model


( )Voveg

Vogr

ofor ee ββ σσσ −− −+= 1

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( ) ( )1

1−−−−− −+= αωββ

σσ

γσσ

γγ hjofor

oveg

vegVV

ofor

ogr

grfor eee

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Interferometric Water Cloud Model

Santoro et al., RSE, 2002


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Local survey



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��Model-based retrieval procedure is robust and consistent�Multi-temporal combination to be preferred�C-band „tandem“ coherence: ideal�L-band backscatter: reliable

�Accuracy comparable to ground-based surveys

��Importance of reference ground-truth and SAR data�C-band „tandem“ coherence: depends on weather conditions�L-band backscatter: few images at ideal conditions


Conclusions – Stem volume retrieval


Test Questions

1)

2)

3)

4)


Documents

Prof. Dr. Christiana Schmullius Dr. Leif Eriksson, Dipl