1 / 21

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

CREATION OF LARGE AREA FOREST BIOMASS MAPS FOR NE CHINA USING ERS-1/2 TANDEM COHERENCE. Oliver Cartus (1) , Christiane Sch mullius (1) Maurizio Santoro (2) , Pang Yong (3) , Li Zengyuan (3). (1) Department of Earth Observation, Friedrich-Schiller University Jena , Germany.

triage
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 (3) Chinese Academy of Forestry, Institute of Forest Resource Information Technique Beijing, China (2) Gamma Remote Sensing Gümligen, Switzerland

  2. 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 … for small managed test sites Kättböle, Sweden, RMSE = 21 m3/ha Conclusion: multi-temporal winter coherence data is most suitable (Santoro et al, 2002) 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. Background Coherence - stem volume relationship strongly varies with meteorological and environmental conditions

  4. Background It could be shown that ERS-1/2 „tandem“ coherence can be used for biomass estimation in boreal forest at large scale … 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 SIBERIA Project – Central Siberia (Wagner et al., 2003) Area covered: 1.000.000 km2 ; Accuracy > 90% Method cannot be used for multi-seasonal & multi-baseline data

  5. Overview • 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

  6. Forest inventory data Red = RS >80 % Blue = RS<30 % For each stand measurements of: Stem volume [m^3/ha] Height, DBH, dominant Species, Relative Stocking RS [%] are available.

  7. ERS-1/2 tandem data 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 ERS-1/2 Mosaic R: Coherence G: Sigma nought (ERS-1) B: Sigma nought ratio 223 coherence scenes Baselines: 0 - 400 m

  8. Coherence measurements at the test sites r = -0.746 r = -0.895 RS > 50 % Area > 3 ha r = -0.678 r = -0.746 RS > 30 % Area > 3 ha (Santoro et al. 2007)

  9. Interferometric Water Cloud Model ground coherence  temporal decorrelation canopy coherence  temporal and volume decorrelation Forest coherence is the sum of 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)

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

  11. Model training based on VCF 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

  12. Temporal decorrelation Compensation for residual ground coherence

  13. Forest transmissivity β Regression-based estimation of all 5 unknowns

  14. Regression- vs. VCF-based model training Dashed line- regression Solid line - VCF

  15. Variability of coherence within frames Variability of ground coherence Variability of coherence of dense canopies Sandy soils, Peat soils

  16. Variability of coherence within frames Training for the whole frame Restricted

  17. Stem volume retrieval >3ha > 6ha

  18. 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. Forest Map of Northeast China

  20. 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

  21. Topography Increasing influence of spatial decorrelation for longer baselines Topographic modification of temporal decorrelation (wind field?) of dense forests

More Related