Nicolas Ackermann Supervisor : Prof. Christiane Schmullius

1. Biomass retrieval in temperate forested areas with a synergic approach using SAR and Optical satellite imagery Nicolas Ackermann Supervisor: Prof. Christiane Schmullius Co-supervisors: Dr. Christian Thiel, Dr. Maurice Borgeaud WSL Davos, 8th December 2011

2. Presentationoutline • Context • Objectives • Application: Biomassretrieval in the Thuringian Forest (Germany) • Test site and data • Pre-processing • Analysis of the data • Biomass retrieval • Fusion • Schedules

3. Context • Biomass – Carbon assessment: • 1/3 of land surface is covered by forests • Temperate forests : ~1/4 of world’s forests => Pool of Carbon • Kyoto Protocol: “quantify emission limitation and reduction commitments” • ENVILAND2: • Objective: • automated processing chain • land cover products • optical and SAR • synergistic approach • Status • ENVILAND1 : scale integration + spatial integration (2005-2008) • ENVILAND2: level 3 products(kick-off: November 2008) Temperate terrestial biome World forest distribution (National Science Foundation)

4. Objectives Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery ALOS-PALSAR Rapideye Forested areas in the Thuringian Forest • Priorities: • Algorithms simple and robust • Algorithms spatially and temporally transferable • Global / regional scale • Automatisation

5. Test site and data

6. Thuringia Forest (Germany) Surface: 110 km x 50 km Terrain variations Tree species composition Scots pines Norway Spruce European Beech Climate cool and rainy frequently clouded Peculiarities logging for forest exploitation Kyrill storm (February 2007) Test site

7. Test site Pine Spruce

8. Test site Beech

9. Test site Topography

10. Test site Forest understory

11. Test site

12. Available Data • SAR data • ALOS PALSAR (L-Band, 46 days) • TerraSAR-X (X-Band, 11 days) • Cosmo-SkyMed (X-Band, 1 day) • E-SAR (L-Band, aerial system) • Optical data • RapidEye • Kompsat-2 • HyMap • Ancillary data • DEM: SRTM 25[m], LaserDEM 5[m] • Laser points (2004), • Orthophotos (2008) • Forest inventory (1989-2010) • Photos with GPS coord. (2009) • Weather data • Field work

13. Available satellite data Total: 247 scenes Satellite data - Thuringian Forest test site

14. Forest inventory • Principle available parameters • Species • Age, Height, DBH • Basal area, Baumanteil, Relative Stocking, Bonity • Forest layers • Stem Volume • Acquisition date • Parameters for reliable stands selection • Storm damaged surfaces • Buffer (25m) • Tree coverage • Area (2ha) • Relative Stocking • Acquisition date • Orthophotos comparisons Data analysis & Modeling

15. Forest inventory • Principle available parameters • Species • Age, Height, DBH • Basal area, Baumanteil, Relative Stocking, Bonity • Forest layers • Stem Volume • Acquisition date • Parameters for reliable stands selection • Storm damaged surfaces • Buffer (25m) • Tree coverage • Area (2ha) • Relative Stocking • Acquisition date • Orthophotos comparisons Data analysis & Modeling

16. Forest inventory • Forest stands Low stem volume (0-100 [m3/ha]) High stem volume (500-700 [m3/ha])

17. Pre-processing

18. Topographic normalisation • Correction main components (Castel et al., 2001) Local incident angle Ground scattering area

19. Topographic normalisation • Optical crown depth (Castel et al., 2001) Volume scattering: a) Tilted surface facing the radar, b) flat surface, tilted surface opposite to the radar (Castel, 2001)

20. High intensity for steep slopes facing radar. Topographic normalisation Slopes oriented in Radar flight direaction Aspect [°] Aspect [°] 0° Gamma nought [dB] Sensor orientation : 350° Sensor azimuth angle : +90° PALSAR 34° HV Asc. 06may08 Non normalised PALSAR 34° HV Asc. 06may08 Normalised

21. Overcorrection? • Crown optical depth? • Other effects? Topographic normalisation Slopes away from the radar Slopes facing the radar Gamma nought [dB] Aspect [°] PALSAR 34° HV Asc. 06may08 Normalised

22. Topographic normalisation Gamma nought [dB] Gamma nought [dB] Aspect [°] Aspect [°] PALSAR 34° HV Asc. 06may08 Normalised PALSAR 34° HV Asc. 06may08 Normalised + Normalised with n coefficient

23. Analysis of the data

24. RapidEye spectral reflectance • Visual interpretations RE R, Red-edge, NIR, 5m, 13th June 2009 Tree species composition Red: European Beech Blue: Norway Spruce • Good separation between Beech and Spruce with a higher reflectance for Beech. Color composite: R (NIR), G (Red-edge), B (R)

25. X-band backscatter • Visual interpretations TSX HS, 37.3°, HH, A, 10m, 12aug09 Tree species composition Yellow: European Beech Red: Norway Spruce • Net contrast between Norway Spruce and European Beech

26. L-band backscatter • Visual interpretations PALSAR FBD, 38.7°, HV, A, 25m, 20jul08 Stem Volume Yellow: 0-100 [m3/ha] (young stands) Blue: 100-400 [m3/ha] (mature stands) Red: 400-750 [m3/ha] (old mature stands) • Similar grey levels for the different stem volume.

27. Little increase of backscatter intensity with Precipitations Weather – TSX intensity Precipitations Precipitation [mm] Temp [°] Wind [m/s] Gamma nought [dB] Acquisition date Series of TSX HS, 34.4°, HH, Asc.

28. Temperature approaching • 0 [°C] implies a decrease • of the backscatter intensity Weather - PALSAR intensity Precipitation [mm] Temp [°] Wind [m/s] Snow + high Water equivalent frozen frozen frozen Gamma nought [dB] Acquisition date Series of PALSAR FBD, FBS, 34.6°, HH, Asc.

29. A precipitation event highly affects the degree of coherence Weather - PALSAR coherence Precipitation map PALSAR Coherence HH 23jul09_X_ 07sept09

30. Weather - PALSAR coherence Urban Layover Precipitation [mm] Coherence Azimuth PALSAR FBD, 34.4°, HH, Asc.

31. Biomass retrieval

32. Biomass estimation • Two approaches • K-Nearest Neighbor (non-parametric) • Regressions (parametric) • Investigations • PALSAR Intensity • PALSAR Coherence • Rapideye spectral reflectance • Assumption: stands with similar forest properties have also similar spectral characteristics

33. Biomass estimation • Two approaches • K-Nearest Neighbor (non-parametric) • Regressions (parametric) • Investigations • PALSAR Intensity • PALSAR Coherence • Rapideye spectral reflectance Satellite [-DN-] y = aebx Stem Volume [m3/ha] (Ground data)

34. Biomass estimation • Two approaches • K-Nearest Neighbor (non-parametric) • Regressions (parametric) • Investigations • PALSAR Intensity • PALSAR Coherence • Rapideye spectral reflectance Satellite [-DN-] y = aebx Stem Volume [m3/ha] (Ground data)

35. Interferometriccoherence • Coherence • The degree of coherence can be related to several factors, each expressing a specific source of decorrelation. • The volume decorrelation is related to objects presenting a vertical extension. This factor is spatialbaseline dependent. The temporal decorrelation is related to thestability of the objects between the two acquisitions.

36. Coherence seems to be related to the structure of the trees. Highest coherence for Spruce. ALOS PALSAR coherence • Coherence versus Stem Volume PALSAR HH 07sep09-23oct09 Interferometriccohreence - 46 days repat pass - r2Spruce=0.41 r2Pine=0.21 r2Beech=0.18 Stem volume [m3/ha]

37. Precipitation on one of the interferomteric acquisition leads to deccorelation ALOS PALSAR coherence • Temporal decorrelation: precipitations Norway Spruce PALSAR HH 23jul09 - 07sept09 - 46 days repat pass - Precipitations [mm] Interferometriccohreence Acq. 1 Acq. 2 3.9 Daily 28.6 Hourly 5.1 0.0 Stem volume [m3/ha]

38. An Increase of normal baseline increases coherence and correlation. ALOS PALSAR coherence • Volume decorrelation: perpendicular Baseline (Bn [m]) r2S=0.09 r2S=0.21 r2S=0.40 Norway Spruce 46 days temporal baseline Interferometriccoherence r2S=0.41 r2S=0.02 r2S= 0.00 Stem volume [m3/ha]

39. ALOS PALSAR coherence • Training • Process stands mean values over coherence image and fit an empirical model • Remove outliers • Testing • Inverse model and derive Growing Stock Volume (GSV) map • Process stands mean values over GSV image • Calculate statistics (RMSE, accuracy matrix) and compute scatterplot • Productsgeneration • GSV continuous values • GSV discrete values – classes • GSV discrete values – forest stands • GSV difference map – forest stands

40. ALOS PALSAR coherence • Training • Model: • Outliers: it: r2 1. 0.51 9. 0.72 2. 0.60 10. 0.72 3. 0.65 11. 0.72 Norway Spruce Interferometric Coherence 4. 0.68 12. 0.72 5. 0.69 13. 0.72 6. 0.70 14. 0.72 7. 0.71 15. 0.72 8. 0.72 Stem volume [m3/ha]

41. ALOS PALSAR coherence • Testing • Inverse model and derive Growing Stock Volume (GSV) map • Process stands mean values over GSV image • Statistics (RMSE, accuracy matrix) Estimation: 0.2*Vref : 41% + : 32% 1:1 - : 26% -0.2*Vref GSV estimated [m3/ha] RMSE: 119 [m3/ha] GSV Forest inventory [m3/ha]

42. ALOS PALSAR coherence • Products [m3/ha] [m3/ha] • GSV discrete values – forest stands • GSV difference map – forest stands

43. Does the coherence or forest inventory induce spatial systematic errors in the estimates? ALOS PALSAR coherence • Products: GSV Difference map (Norway Spruce) Mostly underestimated [m3/ha] Mostly overestimated Estimation: : 41% + : 32% - : 26% Norway spruce

44. ALOS PALSAR coherence • Modeling results of Empirical regressions (Norway Spruce) Error ≈ ±150 [m3/ha] • Potential sources: • Site topography • Radar system • Estimation method • Forest inventory

45. Summary - available information Information combination Algorithms to retrieve biophysical parameters Pre-processing Forest/ non-Forest map SAR backscatter Forest/non Forest SAR backscatter Bands ratio, Thresholding SAR data - ALOS PALSAR, TSX - InSAR Coherence Tree species Regressions InSAR Coherence Biomass map Synergy / Fusion Optical data - RapidEye - Spectral Reflectance Spectral Reflectance Biomass map All species K-NN DEM Biomass map Beech Tree species map Weather data - Precipitation, T°, Wind - Biomass map Spruce

46. Summary - available information Information combination Algorithms to retrieve biophysical parameters Pre-processing Forest/ non-Forest map SAR backscatter Forest/non Forest SAR backscatter Bands ratio, Thresholding SAR data - ALOS PALSAR, TSX - InSAR Coherence Tree species Regressions InSAR Coherence Biomass map Synergy / Fusion Optical data - RapidEye - Spectral Reflectance Spectral Reflectance Biomass map All species K-NN DEM Biomass map Beech Tree species map Weather data - Precipitation, T°, Wind - Biomass map Spruce

47. Summary – available information • Howcanwecreate a biomassmap • using the entireavailable information?

48. Fusion / Algorithms

49. Fusion approach - motivations • Approach • The combination of different sensors can be achieved by several approaches. As part of this PhD and the project Enviland2, it was decided to focus the work on the fusion of different established products, such as a map of biomass or a mask forest/non forest. • The fusion and/or synergy would be performed where EO data are spatially overlapping. • The idea would be to obtain a final biomass map which is constituting the best potential result in regards to the initial data provided by the user. • Motivation • The chosen approach was motivated by its simplicity but also by its flexibility (different sources of biomass maps). • As the number of different sources of information/sensors have increased considerably these recent years, it is necessary to have an approach which allows the combination of the high amount of available data. • The combination of several datasets would allow to increasethe accuracy of the biomass map, its spatial extension and its updated frequency.

50. Fusion approach • Mapaccuracyimprovement • Biomass base map • Transferability concept • Spatial: clouds, mosaicing • Temporal: lack of data SAR t Optical t Temporal transferability SAR data • Quality Factor (max=1) • 0,8 • 0,5 • 0,7 • 0,7 • 0,9 • 0,8 Optical data 3. • Possiblecases • Optical image clear • Optical image cloudcovered • SAR image clear • SAR image cloudcovered • Superimposed Optical and SAR images clear • Superimposed Optical and SAR image cloudcovered 1. 6. 4. 2. 5. Spatial transferability