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Nicolas Ackermann Supervisor : Prof. Christiane Schmullius

PhD Colloqium 2010. Biomass retrieval in temperate forested areas with a synergic approach using SAR and Optical satellite imagery: state November 2010. Nicolas Ackermann Supervisor : Prof. Christiane Schmullius Co- supervisors : Dr. Christian Thiel , Dr. Maurice Borgeaud

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Nicolas Ackermann Supervisor : Prof. Christiane Schmullius

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  1. PhD Colloqium 2010 Biomass retrieval in temperate forested areas with a synergic approach using SAR and Optical satellite imagery: state November 2010 Nicolas Ackermann Supervisor: Prof. Christiane Schmullius Co-supervisors: Dr. Christian Thiel, Dr. Maurice Borgeaud FSU Jena, the 30th November 2010

  2. Presentationoutline • Context • Objectives • Application: Biomass retrieval in the Thuringian Forest (Germany) • Test site and data • Pre-processing • Analysis of the data • Biomass retrieval • 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 SPOT-5 Forested areas in Thuringian Forest • Priorities: • Algorithms simple and robust • Algorithms spatially and temporally transferable • Global /regional scale • Automatisation

  5. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Processing phases Pre-processing of the data Test site selection Fusion processing Biomass retrieval Analysis of the data SAR data Test site Regions of interests Methodology Methodology Optical data Results Results Data availability SAR and Optical data analysis Ground data Ground dataanalysis Validation Validation Completed: 80% 40% 10% 95% 50%

  6. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Test site selection

  7. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Test site • Thuringia Forest (Germany) • Surface: 110 km x 50 km • Terrain variations • 90% of forest over hilly areas • range: 800m - 900m • Forest proprieties: • main species: Scots pines, Norway Spruce, European Beech • large biomass dispersion • Climate • cool and rainy • frequently clouded • Peculiarities • logging for forest exploitation • Kyrill storm (February 2007) 25 km

  8. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Extended test site • Initial test site limitations • Only contains Norway Spruce • Not well covered by RapidEye and PALSAR • Not sufficiently reliable forest stands • Mostly located topographic area Extended test site overcomes these limitations 25 km

  9. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Available Data (state November 2010) • SAR data • ALOS PALSAR (L-Band, 46 days) • TerraSAR-X (X-Band, 11 days) • Cosmo-SkyMed (X-Band, 1 day) • Optical data • RapidEye • Kompsat-2 • Ancillary data • DEM: SRTM 25[m], LaserDEM 5[m] • Laser points (2004), • Orthophotos (2008) • HyMap (2008,2009) • Forest inventory (1989-2009) • Photos with GPS coord. (2009) • Weather data • Field work Interferometric coherence Multispectral radiometric normalisation SAR topographic normalisation, SAR analysis SAR analysis, Forest inventory validation

  10. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Available Satellite Data Total: 225 scenes Satellite data - Thuringia Forest test site (state November 2010)

  11. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Available Data • Temporal overview satellite data Kompsat2 RapidEye CSK Himage TSX HS TSX SL TSX SM ALOS PALSAR PLR ALOS PALSAR FBD ALOS PALSAR FBS

  12. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Pre-processing of the data

  13. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval RapidEye • Calibrated 16 Bit product [W m-2 sr-1 µm-1] Multispeectral data - RapidEye L1B - Orthorectification (manual GCPs) • Orthorectified • Radiance • [W m-2 sr-1 µm-1] - • Orthoengine PCI Geomatica Atmosphere correction • ATCOR PCI Geomatica • Sun/Surface/Sensor normalisation • Atmospheric corrections • Relief radiometric normalisation • Scaling Atmosphere corrected - Reflectance [%] - RapidEye, R, G, B, 13th June 2009 (atmosphere corrected)

  14. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval RapidEye Atmosphere corrected - Reflectance [%] - Hyperspectral data - HyMap L2 - Reference refined - Reflectance[%] - • External reference data: HyMap 2008, 2009 (DLR) Training ROIs (automatic) Training ROIs Validation (JM distance) MAD normalisation Empirical line correction Validation (JM distance) 1. 4. 3. • MAD and Empirical line correction are based on linear regressions • Validate with Jeffries-Matusita distance (JM) => require a low spectral separability Radiometric refined - Reflectance [%] - Testing ROIs Testing ROIs 5. 2. Clouds/Clouds shadow masking 6. • Definiensecognition • Multisegmentation (scale 10) • Brightness ratio ((Bmax-B)/(Bmax-Bmin)) • NDVI Cloud/shadow masked - Reflectance [%] -

  15. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval RapidEye Non forest elements can be neglected RapidEye – Clouds mask R: NIR G: Red-edge B: R RapidEye (25th Mai 2009) • + Performs well the masking • - Manual approach for each scene RapidEye – Clouds shadow mask

  16. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval PALSAR / TSX • Crown Optical depth considerations • n coefficient takes different values according to the investigated surface • In forested areas, n can be related to the crown optical depth, which in turns depends of two physical parameters : Ke and Hc • Ke can be a function of the tree species composition and the weather conditions (frozen, humid, dry, etc.) • Hc can be a function of the local slope and the crown height • Analysis methodology • Define a reference and perform a sensibility analysis by comparing the coefficent of variation (CV) for different environnemental conditions • Estimate the optimal n coefficient by iteration using as a criteria the minimum of CV

  17. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval PALSAR / TSX • Example • Reference parameters • Number of stands may influence the statistics (min fixed: 25-30 stands) • Incident angle = 38.7[°] cvmin = 0.135138 cv = 0.136754 n optimal= 0.21 n = 1 = -7.7 [dB] Gamma nought [dB] - FBS Gamma nought [dB] - FBS = -7.6 [dB] Local incident angle [°] Local incident angle [°]

  18. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Weather data Station 2 Station 1 • Weather data • DWD: Deutsche Wetter Dienst • Acquisition period: 2006-2010 • Parameters: Precipitation, Snowdepth, Water-equivalent, Wind, Temperature, Sunshineduration, relative Humidity • Pre-processing • Collaboration with FSU geoinformaticinstitute • JAMS (Jena Adaptable Modelling System) Software • 2 temporal scales : daily / hourly Station 3 Station 5 Station 4 90m JAMS Software Daily input conversion Daily Selected [ .dat ] Daily Regionalisation Daily Regionalised [ .txt ] Daily output generation Daily [ raster ] Weather data [ .xml ] Hourly Input conversion Hourly Selected [ .dat ] Hourly Regionalisation Hourly Regionalised [ .txt ] Hourly output generation Hourly [ raster ]

  19. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Weather data Precipitations [mm] Temperature [°C] • Weatherparameters outputs • Raster data • 90m and 300m spatial resolution • Initial site / extended site • Excel table • Describemeanweather values of the overlappingselectedforest stands and satellite data High: 36 High: 10 Figure depicting the raster data (31mar08 - 03apr08) Table summarizing weather data (simplified version) 0 0

  20. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Analysis of the data

  21. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Processingchart-flow Algorithms to retrieve biophysical parameters Algorithms to retrieve Forest biomass Pre-processing SAR backscatter Forest/non Forest Tree species Crown cover SAR backscatter Bands ratio, Textur, Thresholding WCM SAR data - ALOS PALSAR, TSX - Biomass map InSAR Phase / Coherence Forest/non Forest Forest layers Tree Height InSAR Phase / Coherence InSAR Height, Bands ratio, Textur, Thresholding IWCM Spectral Reflectance Forest/non Forest Tree species Crown cover Optical data - RapidEye - Spectral Reflectance NDVI, Thresholding DEM Data analysis Biomass retrieval Weather data - Precipitation, T°, Wind -

  22. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval RapidEyepreliminary investigations • RapidEye (R,G,B, Red-edge, NIR) – Test site Schmiedefeld RE R, Red-edge, NIR, 5m, 13th June 2009 Tree species composition Red: European Beech Blue: Norway Spruce • The forest stands are well overlapping over the satellite image. • Good separation between Beech and Spruce with a higher reflectance for Beech. • Generally, very high reflectance in Red-edge and NIR for open area (grass). • Urban show more reflectance in R than the other surface, which depicts the bright blue color. • The wavelength are, as expected, absorbed by water in R, Red-edge and NIR channels (Dark blue). Color composite: R (NIR), G (Red-edge), B (R)

  23. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval RapidEyepreliminary investigations • RapidEye (R,G,B, Red-edge, NIR) – Test site Schmiedefeld RE B, G, R, Red-edge, NIR, 5m, 13th June 2009 Reflectance [%] • The reflectance can be better differenciate for the different classes in high wavelength spectrum (Red-edge, NIR channel). • Open area, Beech and Spruce have respectively ~60 [%],~40 [%] und ~20 [%] reflectance in NIR => potential of discrimination of these classes • Slight differences bewtween signatures from low stem volume in comparison to high stem volume => maybe lead to a small sensitivity to forest biomass Wavelength [nm]

  24. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval RapidEyepreliminary investigations • RapidEye (NIR) RE NIR, 5m, 25th September 2009 Reflectance Blue: Norway Spruce (S) Yellow: European Beech(B) Red: Scot Pine (P) R2S=0.10 R2B=0.25 R2P=0.04 Y(x) = A * x + B • Important physical considerations: • Tree structure : shadow and additive reflectance • Tree species composition: chlorophylle pigments • Abiotic factors : soil moisture, air relative humidity, atmosphere effects • Little negative linear correlation between RE NIR and stem volume for Beech (r2=0.25), Spruce (r2=0.1) and Pines (r2=0.04) • The dispersions of the points are relatively homogeneous. Islotated high and low reflectance values are still occuring => clouds and clouds shadow => affect the statistics • Good separation of Norway Spruce and Beech. Reflectance [%] – RE NIR Stem volume [m3/ha]

  25. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Frame location ALOS PALSAR 1 2 • ALOS PALSAR coherence Spatial baseline Y(x) = A * x + B PALSAR FBD, 38.7°, HH, A, 25m R2S=0.22 R2S=0.10 R2S=0.29 Interferometric coherence Blue: Norway Spruce (S) Yellow: European Beech(B) Red: Scot Pine(P) R2B=0.012 R2B=0.009 R2B=0.002 R2P=0.06 R2P=0.009 Frame 1 • Little negative correlation between ALOS PALSAR coherence and Stem Volume • Higher coherence and higher correlation for Spruce in comparison with Beech and Pines (branches structure differs but stem volume distribution also) • Weather effect (precipitations) and high perpendicular baseline can affect the correlation and the level of coherence Interferometric Coherence R2S=0.36 R2S=0.35 R2S=0.19 R2B=0.15 R2B=0.12 R2B=0.038 R2P=0.088 R2P=0.08 R2P=0.044 Frame 2 Stem Volume [m3/ha] Precipitations: 23jul09: 28.6mm 07sept09: 3.9mm

  26. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Biomass retrieval

  27. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval ALOS PALSAR • ALOS PALSAR coherence – multitemporal approach Linear Non-linear Coherence stack Forest inventory 1. 1., 2. Select and fit model 4. Compute RMSE and weights 25% stands - Training - 75% stands - Testing - 2. Scatterplot + model Weights 3. Inverse model and retrieve Stem volume Compute Growing Stock volume map 3. 5. 4. Estimated Growing stock Volume Growing stock Volume map 5. SANTORO et al., 2000; SANTORO, et al., 2002b

  28. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Frame location ALOS PALSAR 1 2 • ALOS PALSAR coherence – Growing stock Volume map PALSAR FBD, 38.7°, HH, A, 25m Multitemporal coherence biomass map • Similarly to spatial averaging, the multitemporal combination act as a filter and decreases the noise. • RMSEi>200 [m3/ha] is very high, in particular due to the high dispersion of the coherence. The methodology should be tested for each species separately and by inversing testing and training stands. • Water and urban can be recognized, with respectively low (dark green) and high (gray) coherence RMSE>200 [m3/ha]

  29. Test site selection Pre-processing of the data Analysis of the data Biomass retrieval Field campagn 2010 • Forest campagn • objective: collect information on the undergrowth in order to interpret some of the obtained results • Focus on low stem volume forest stands Undergrowth in a young regenerating forest stand

  30. Schedules – nextsteps • DECEMBER-JANUARY • Data analysis: • Process SAR intensityandcoherence 2010 • Validateobtainedresultsusingancillarydata (weather, forestcampagn) • Pre-processing: • Completepre-processingMultispectraldata • Pre-processingweatherparametersnowdepthandwaterequivalent • JANUARY-APRIL • Data analysis: • Completeanalysismultispectraldata • Completeanalysis SAR data • Modeling: • Developmodelingapproachfor SAR data • APRIL-AUGUST • Fusion: • Developfusionapproachfor SAR – Multispectraldata • Completemodelingwiththe SAR data • PhD Dissertation: • Final results • Papers

  31. Vielen Dank für Ihre Aufmerksamkeit! Thuringia Forest – July 2010

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