FSU Jena – Department of Earth Observation CREATION OF LARGE AREA FOREST BIOMASS MAPS FOR NE CHINA USING ERS-1/2 TANDEM COHERENCE Oliver Cartus (1), Christiane

FSU Jena – Department of Earth Observation

CREATION OF LARGE AREA FOREST BIOMASS MAPS FOR NE CHINA USING ERS-1/2 TANDEM COHERENCE

Oliver Cartus (1), Christiane Schmullius (1)

Maurizio Santoro (2), Pang Yong (3), Li Zengyuan (3)

(1) Department of Earth Observation,

Friedrich-Schiller University

Jena, Germany

(2) Gamma Remote Sensing

Gümligen, Switzerland

(3) Chinese Academy of Forestry,

Institute of Forest Resource

Information Technique

Beijing, China


Background

The ERS-1/2 tandem mission has created a huge interferometric dataset (1995-2000)

It is known that ERS-1/2 „tandem“ coherence

can be used for biomass estimation in boreal forest with high accuracy

Kättböle, Sweden, RMSE = 21 m3/ha

Conclusion: multi-temporal winter coherence data is most suitable

(Santoro et al, 2002)

… for small managed test sites

Coherence depends on meteorological and environmental conditions

The behaviour of coherence found in a small test site cannot be

transferred to large areas automatically


Background

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Stem volume [m3/ha]

Coh

eren

ce

Bolshe NE - 01-02 Jan. 96

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Stem volume [m3/ha]

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Chunsky N - 29-30 Dec.95

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Stem volume [m3/ha]

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ce

Primorsky E - 09-10 Oct. 97

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Stem volume [m3/ha]

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eren

ce

Bolshe NE - 22-23 Sep. 97

Coherence - stem volume relationship strongly varies

with meteorological and environmental conditions


SIBERIA Project – Central Siberia (Wagner et al.,

2003)

Area covered: 1.000.000 km2 ; Accuracy > 90%

It could be shown that ERS-1/2 „tandem“ coherence

can be used for biomass estimation in boreal forest at large scale

Background

… with an ERS-1/2 tandem dataset acquired only in fall and with a narrow range of baselines

Histogram-based training of an empirical model, which relates coherence to stem

volume, could be done

Method cannot be used for multi-seasonal & multi-baseline data


Data: Overview of test sites and ERS-1/2 coherence imagery

Coherence measurements at the test sites

Coherence modelling

Model training: A new VCF-based model training procedure

Regression-based vs. VCF-based training procedure

Classification Accuracy

Application of the new approach for Northeast China

Overview


Forest inventory data

For each stand measurements of:

Stem volume [m^3/ha]

Height, DBH, dominant Species,

Relative Stocking RS [%]

are available.

Red = RS >80 %

Blue = RS<30 %


ERS-1/2 Mosaic

R: Coherence

G: Sigma nought (ERS-1)

B: Sigma nought ratio

223 coherence scenes

Baselines: 0 - 400 m

ERS-1/2 tandem data

Acq. date Area Bn Weather conditions

29.12.199530.12.1995

Chunsky N 171 m

T1≈-10° C,

T2≈-23° C,

WS1≈6 m/s,

WS2≈ 0 m/s,

SD: 18 cm

01.01.199602.01.1996

Bolshe NE 144 m

T≈-20 °C, WS1≈ 5-6 m/s,

WS2 < 3 m/s,

SD: 16 cm, Snowfall

14.01.199615.01.1996

Chunsky N & E 65 m

T1≈-18° C,

T2≈-23° C,

WS < 2 m/s,

SD: 27 cm

22.09.199723.09.1997

Bolshe NE 260 mT1≈16 °C, T2≈19°C,

WS< m/s, Rain on 21st

25.09.199726.09.1997

Bolshe NE & NW 233 mT1≈20 °C, T2≈13°C,

WS < 2 m/s

27.10.199728.10.1997

Bolshe NE 158 m T≈2 °C, WS < 1 m/s

28.05.199829.05.1998

Bolshe NE & NW 313 mT1≈26 °C, T2≈19°C,

WS < 3 m/s

Processing:

Co-registration, 2x10 multi-looking, common-band filtering, adaptive coherence estimation (3x3 to 9x9), Geo-coding using the SRTM-C DEM,

Pixel size = 50x50 m


Coherence measurements at the test sites

RS > 50 %

Area > 3 ha

RS > 30 %

Area > 3 ha

(Santoro et al. 2007)

r = -0.746 r = -0.895

r = -0.746r = -0.678


,,(*1)(0

0

0

0

nvolV

for

vegveg

V

for

grgrfor BheeV

Ground contribution Vegetation contribution

• gr and 0gr represent ground temporal coherence and backscatter

• veg and 0veg represent vegetation temporal coherence and backscatter

• is related to the forest transmissivity (~0.003 - 0.007 for ERS)

• Volume decorrelation related to

• h, Height allometric equation to express it as a function of stem volume

• Bn, perpendicular baseline

• α, two-way tree attenuation 1 – 2 dB/m depending on season (Askne et al. 1997)

Voveg

Vogr

ofor ee 1

ground coherence temporal decorrelation

canopy coherence temporal and volume decorrelationForest coherence is the sum of

Interferometric Water Cloud Model


Question:

How to calculate the unknowns of the model for each frame without ground-truth data?


What is VCF?

The Modis Vegetation Continuous Field product (VCF) provides global sub-pixel estimates of landscape components (tree cover, herbaceous cover and bare cover) at 500 m pixel size (Hanson et al. 2002). Why is VCF important in this context?

Because coherence and VCF contain similar information

Model training based on VCF


Temporal decorrelation

Compensation for residual ground coherence


Forest transmissivity β

Regression-based estimation of all 5 unknowns

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Stem volume [m3/ha]

Are

a F

ill F

acto

r

Valid range of area fill factor

alfa = 1 dB/m

alfa = 2 dB/m

= 0.007

= 0.003

29-30 Dec 14-15 Jan 14-15 Jan 01-02 Jan 09-10 Oct 22-23 Sep 25-26 Sep 27-28 Oct 28-29 May25-26 Sep 28-29 May0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

[h

a/m

3 ]

29-30 Dec 14-15 Jan 14-15 Jan 01-02 Jan 09-10 oct 22-23 Sep 25-26 Sep 27-28 Oct 28-29 May25-26 Sep 28-29 May0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

V


Regression- vs. VCF-based model training

Dashed line- regression

Solid line - VCF

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Stem volume [m3/ha]

Coh

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Bolshe NE 22-23 Sep. 97

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

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100

200

300

400

500

GT stem volume [m3/ha]Est

imat

ed s

tem

vol

ume

[m3/

ha]


RMSE [m3/ha]:97.5532

Rel. RMSE:0.55211

R2:0.55721

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce

Bolshe NE 27-28 Oct. 97

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]

Bolshe NE 27-28 Oct. 97

RMSE [m3/ha]:108.048

Rel. RMSE:0.61934

R2:0.57076

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce


0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]


RMSE [m3/ha]:86.9621

Rel. RMSE:0.70848

R2:0.7433

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce

Bolshe NW 25-26 Sep. 97

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]

Bolshe NW 25-26 Sep. 97

RMSE [m3/ha]:127.5837

Rel. RMSE:0.51953

R2:0.29388

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce

Bolshe NW 28-29 May 98

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

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100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]

Bolshe NW 28-29 May 98

RMSE [m3/ha]:146.2137

Rel. RMSE:0.59977

R2:0.17445

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Stem volume [m3/ha]

Coh

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Chunsky N 29-30 Dec. 95

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]


RMSE [m3/ha]:60.5459

Rel. RMSE:0.40057

R2:0.76815

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1

Stem volume [m3/ha]

Coh

eren

ce

Chunsky N 14-15 Jan. 96

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]


RMSE [m3/ha]:79.591

Rel. RMSE:0.52838

R2:0.84288

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce

Chunsky E 14-15 Jan. 96

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]


RMSE [m3/ha]:83.7119

Rel. RMSE:0.64967

R2:0.73466

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce

Bolshe NE 01-02 Jan. 96

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]


RMSE [m3/ha]:73.3475

Rel. RMSE:0.42926

R2:0.59476

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0.5

1

Stem volume [m3/ha]

Coh

eren

ce

Primorsky E 09-10 Oct. 97

0 100 200 300 400-15

-10

-5

0

Stem volume [m3/ha]

Inte

nsity

[dB

]

0 100 200 300 400 5000

100

200

300

400

500


imat

ed s

tem

vol

ume

[m3/

ha]

Primorsky E 09-10 Oct. 97

RMSE [m3/ha]:77.3648

Rel. RMSE:0.58907

R2:0.74532


Variability of coherence within frames

Sandy soils, Peat soils

Variability of ground coherence Variability of coherence of dense canopies


0 0.2 0.4 0.6 0.8 10

0.5

1

FID Training

VC

F T

rain

ing

gr

& veg

-12 -10 -8 -6-12

-10

-8

-6

FID Training

VC

F T

rain

ing

0gr

& 0veg

[dB]

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0.5

1

FID Training

VC

F T

rain

ing

gr

& veg

-12 -10 -8 -6-12

-10

-8

-6

FID Training

VC

F T

rain

ing

0gr

& 0veg

Variability of coherence within frames

Training for the whole frame

Restricted


Stem volume retrieval

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102030405060708090

100

RS [%]

Rel

. RM

SE

[%]


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102030405060708090

100

RS [%]

Rel

. RM

SE

[%]


VCFFID

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102030405060708090

100

RS [%]

Rel.

RMSE

[%]


0 20 40 60 800

102030405060708090

100

RS [%]

Rel.

RMSE

[%]


0 20 40 60 800

102030405060708090

100

RS [%]

Rel.

RMSE

[%]


0 20 40 600

50

100

150

Min. rel. stock [%]

RM

SE

[m

3 /ha]


0 20 40 600

102030405060708090

100

Min. rel. stock [%]

Rel

. R

MS

E [

%]

>3ha

> 6ha


Test site & image 0-20 20-50 50-80>80

[m3/ha]Overall

Acc. [%]kappa

Chunsky N29-30 Dec.95

78.680.4

38.826.4

12.48.0

93.997.2

81.182.1

0.690.68

Chunsky E14-15 Jan.96

65.673.3

39.826.9

29.231.4

87.484.9

70.572.5

0.540.54

Bolshe NE22-23 Sep.97

8.172.3

14.128.7

51.130.0

92.884.6

37.069.0

0.220.52

Bolshe NW25-26 Sep.97

81.082.0

67.867.8

34.130.9

78.176.8

75.675.0

0.620.62

Classification accuracy

Classes according to the SIBERIA map:

0-20,20-50,50-80,>80 m^3/ha

Green: VCF-based training

Red: Regression-based training

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SD

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40

60

80

100

SD

Acc

urac

y [%

]

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AllUnfrozenFrozen


Forest Map of Northeast China


The new VCF-based classification approach is a fast and easy to apply method to map forest stem volume

Weak points: 1) Low accuracy of intermediate classes (20-50,50-80 m3/ha)

multi-temporal combination of results obtained from winter coherence images – unfortunately not

possible with the ERS dataset available

2) Siberian boreal forest – Chinese cold-temperate forests: Are there differences in coherence?

Conclusions


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Aspect angle [°]

Coh

eren

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18-19 Jan. 96, Bn = 148 m

15°

10°

5°

0°

0 100 200 3000

0.5

1

Aspect angle [°]

Coh

eren

ce

28-29 Mar. 96, Bn = 101 m

0 100 200 3000

0.5

1

Aspect angle [°]

Coh

eren

ce

22-23 Feb. 96, Bn = 40 m

Topography

Increasing influence of spatial decorrelation for longer baselines

Topographic modification of temporal decorrelation (wind

field?) of dense forests

Documents

FSU Jena – Department of Earth Observation CREATION OF LARGE AREA FOREST BIOMASS MAPS FOR NE CHINA USING ERS-1/2 TANDEM COHERENCE Oliver Cartus (1), Christiane