fsu jena – department of earth observation creation of large area forest biomass maps for ne china...
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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
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
Background
0 50 100 150 200 250 300 3500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Stem volume [m3/ha]
Coh
eren
ce
Bolshe NE - 01-02 Jan. 96
0 50 100 150 200 250 300 350 4000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Stem volume [m3/ha]
Coh
eren
ce
Chunsky N - 29-30 Dec.95
0 50 100 150 200 250 300 350 400 450 5000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Stem volume [m3/ha]
Coh
eren
ce
Primorsky E - 09-10 Oct. 97
0 50 100 150 200 250 300 350 4000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Stem volume [m3/ha]
Coh
eren
ce
Bolshe NE - 22-23 Sep. 97
Coherence - stem volume relationship strongly varies
with meteorological and environmental conditions
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
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 %
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
,,(*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
FSU Jena – Department of Earth Observation
Question:
How to calculate the unknowns of the model for each frame without ground-truth data?
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
Temporal decorrelation
Compensation for residual ground coherence
FSU Jena – Department of Earth Observation
Forest transmissivity β
Regression-based estimation of all 5 unknowns
0 50 100 150 200 250 300 350 4000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
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
FSU Jena – Department of Earth Observation
Regression- vs. VCF-based model training
Dashed line- regression
Solid line - VCF
0 100 200 300 4000
0.5
1
Stem volume [m3/ha]
Coh
eren
ce
Bolshe NE 22-23 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
GT stem volume [m3/ha]Est
imat
ed s
tem
vol
ume
[m3/
ha]
Bolshe NE 22-23 Sep. 97
RMSE [m3/ha]:97.5532
Rel. RMSE:0.55211
R2:0.55721
0 100 200 300 4000
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
GT stem volume [m3/ha]Est
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
0 100 200 300 4000
0.5
1
Stem volume [m3/ha]
Coh
eren
ce
Bolshe NE 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
GT stem volume [m3/ha]Est
imat
ed s
tem
vol
ume
[m3/
ha]
Bolshe NE 25-26 Sep. 97
RMSE [m3/ha]:86.9621
Rel. RMSE:0.70848
R2:0.7433
0 100 200 300 4000
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
GT stem volume [m3/ha]Est
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
0 100 200 300 4000
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
]
0 100 200 300 400 5000
100
200
300
400
500
GT stem volume [m3/ha]Est
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
0 100 200 300 4000
0.5
1
Stem volume [m3/ha]
Coh
eren
ce
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
GT stem volume [m3/ha]Est
imat
ed s
tem
vol
ume
[m3/
ha]
Chunsky N 29-30 Dec. 95
RMSE [m3/ha]:60.5459
Rel. RMSE:0.40057
R2:0.76815
0 100 200 300 4000
0.5
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
GT stem volume [m3/ha]Est
imat
ed s
tem
vol
ume
[m3/
ha]
Chunsky N 14-15 Jan. 96
RMSE [m3/ha]:79.591
Rel. RMSE:0.52838
R2:0.84288
0 100 200 300 4000
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
GT stem volume [m3/ha]Est
imat
ed s
tem
vol
ume
[m3/
ha]
Chunsky E 14-15 Jan. 96
RMSE [m3/ha]:83.7119
Rel. RMSE:0.64967
R2:0.73466
0 100 200 300 4000
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
GT stem volume [m3/ha]Est
imat
ed s
tem
vol
ume
[m3/
ha]
Bolshe NE 01-02 Jan. 96
RMSE [m3/ha]:73.3475
Rel. RMSE:0.42926
R2:0.59476
0 100 200 300 4000
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
GT stem volume [m3/ha]Est
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
FSU Jena – Department of Earth Observation
Variability of coherence within frames
Sandy soils, Peat soils
Variability of ground coherence Variability of coherence of dense canopies
FSU Jena – Department of Earth Observation
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]
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
Variability of coherence within frames
Training for the whole frame
Restricted
FSU Jena – Department of Earth Observation
Stem volume retrieval
0 20 40 60 800
102030405060708090
100
RS [%]
Rel
. RM
SE
[%]
Bolshe NE 01-02 Jan. 96
0 20 40 60 800
102030405060708090
100
RS [%]
Rel
. RM
SE
[%]
Chunsky N 29-30 Dec. 95
VCFFID
0 20 40 60 800
102030405060708090
100
RS [%]
Rel.
RMSE
[%]
Chunsky N 14-15 Jan. 96
0 20 40 60 800
102030405060708090
100
RS [%]
Rel.
RMSE
[%]
Chunsky E 14-15 Jan. 96
0 20 40 60 800
102030405060708090
100
RS [%]
Rel.
RMSE
[%]
Chunsky E 14-15 Jan. 96
0 20 40 600
50
100
150
Min. rel. stock [%]
RM
SE
[m
3 /ha]
Bolshe NE 01-02 Jan. 96
0 20 40 600
102030405060708090
100
Min. rel. stock [%]
Rel
. R
MS
E [
%]
>3ha
> 6ha
FSU Jena – Department of Earth Observation
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
0 10 20 30 40 500
0.2
0.4
0.6
0.8
1
SD
0 10 20 30 40 500
20
40
60
80
100
SD
Acc
urac
y [%
]
0 0.5 10
0.5
1
AllUnfrozenFrozen
FSU Jena – Department of Earth Observation
Forest Map of Northeast China
FSU Jena – Department of Earth Observation
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
FSU Jena – Department of Earth Observation
0 100 200 3000
0.5
1
Aspect angle [°]
Coh
eren
ce
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
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