1. 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

2. 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 m 3 /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

3. FSU Jena – Department of Earth Observation Background Coherence - stem volume relationship strongly varies with meteorological and environmental conditions

4. FSU Jena – Department of Earth Observation SIBERIA Project – Central Siberia (Wagner et al., 2003) Area covered: 1.000.000 km 2 ; 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

5. 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

6. 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 %

7. 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. dateAreaBnBn Weather conditions 29.12.1995 30.12.1995 Chunsky N171 m T 1 ≈-10° C, T 2 ≈-23° C, WS 1 ≈6 m/s, WS 2 ≈ 0 m/s, SD: 18 cm 01.01.1996 02.01.1996 Bolshe NE144 m T≈-20 °C, WS 1 ≈ 5-6 m/s, WS 2 < 3 m/s, SD: 16 cm, Snowfall 14.01.1996 15.01.1996 Chunsky N & E65 m T 1 ≈-18° C, T 2 ≈-23° C, WS < 2 m/s, SD: 27 cm 22.09.1997 23.09.1997 Bolshe NE260 m T 1 ≈16 °C, T 2 ≈19°C, WS< m/s, Rain on 21 st 25.09.1997 26.09.1997 Bolshe NE & NW233 m T 1 ≈20 °C, T 2 ≈13°C, WS < 2 m/s 27.10.1997 28.10.1997 Bolshe NE158 mT≈2 °C, WS < 1 m/s 28.05.1998 29.05.1998 Bolshe NE & NW313 m T 1 ≈26 °C, T 2 ≈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

8. 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 = -0.746r = -0.895 r = -0.746 r = -0.678

9. FSU Jena – Department of Earth Observation Ground contributionVegetation contribution  gr and  0 gr represent ground temporal coherence and backscatter  veg and  0 veg 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 B n, perpendicular baseline α, two-way tree attenuation  1 – 2 dB/m depending on season (Askne et al. 1997) ground coherence  temporal decorrelation canopy coherence  temporal and volume decorrelation Forest coherence is the sum of Interferometric Water Cloud Model

10. FSU Jena – Department of Earth Observation Question: How to calculate the unknowns of the model for each frame without ground-truth data?

11. 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

12. FSU Jena – Department of Earth Observation Temporal decorrelation Compensation for residual ground coherence

13. FSU Jena – Department of Earth Observation Forest transmissivity β Regression-based estimation of all 5 unknowns

14. FSU Jena – Department of Earth Observation Regression- vs. VCF-based model training Dashed line- regression Solid line - VCF

15. 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

16. FSU Jena – Department of Earth Observation Variability of coherence within frames Training for the whole frame Restricted

17. FSU Jena – Department of Earth Observation Stem volume retrieval >3ha > 6ha

18. FSU Jena – Department of Earth Observation Test site & image0-2020-5050-80 >80 [m 3 /ha] Overall Acc. [%] kappa Chunsky N 29-30 Dec.95 78.6 80.4 38.8 26.4 12.4 8.0 93.9 97.2 81.1 82.1 0.69 0.68 Chunsky E 14-15 Jan.96 65.6 73.3 39.8 26.9 29.2 31.4 87.4 84.9 70.5 72.5 0.54 Bolshe NE 22-23 Sep.97 8.1 72.3 14.1 28.7 51.1 30.0 92.8 84.6 37.0 69.0 0.22 0.52 Bolshe NW 25-26 Sep.97 81.0 82.0 67.8 34.1 30.9 78.1 76.8 75.6 75.0 0.62 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

19. FSU Jena – Department of Earth Observation Forest Map of Northeast China

20. 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 m 3 /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

21. FSU Jena – Department of Earth Observation Topography Increasing influence of spatial decorrelation for longer baselines Topographic modification of temporal decorrelation (wind field?) of dense forests