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Comparison of L and P band radar time series for the monitoring of Sahelian area

Comparison of L and P band radar time series for the monitoring of Sahelian area. P.-L. Frison, G. Mercier, E. Mougin, P. Hiernaux. Context : Better understanding Sahelian surface processes and their interaction with monsoon variability

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Comparison of L and P band radar time series for the monitoring of Sahelian area

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  1. Comparison of L and P band radar time series for the monitoring of Sahelian area P.-L. Frison, G. Mercier, E. Mougin, P. Hiernaux

  2. Context: BetterunderstandingSahelian surface processes and their interaction withmonsoonvariability Improveourunderstanding and documentation of long term trend in vegetation in response to climate change radar data: 2 keyparameters: soilmoisture and vegetation Goal: Comparison of L band PALSAR and C band ASAR data for Sahelian surface monitoring. Relation between radar vs surface parameter temporal evolution

  3. Outline: • Study site • PALSAR and ASAR data • change detectionmethod • Results and discussion

  4. The Sahel Dry season (Nov. – Apr.) bare soil Semi-arid area shrub (0-20 %) trees (1-5 %) + Rainy season (May – Oct.) herbaceous layer (0-50 %) (annual grasses)

  5. Region of study: the Gourma - Mali Seno

  6. DATASET PALSAR acquisitions: L band (Jan. 2007 - Apr. 2009) ASAR acquisitions: C band (Jul. - Dec. 2005)

  7. Gourma Region (MALI) ASAR –Wide Swath - HH 20th Dec 2005

  8. GOURMA Region (MALI) PALSAR–WIDE BEAM- HH 1st Jan 2008

  9. ASAR C-band Gourma Region (MALI) PALSAR L-band C-band (ASAR): Shallow sand and silt soils L -band (PALSAR): Better discrimination of geological features Remnant of alluvial systems and lacustrine depressions

  10. ASAR C-band Gourma Region (MALI) PALSAR L-band C-band (ASAR): Shallow sand and silt soils L -band (PALSAR): Better discrimination of geological features Remnant of alluvial systems and lacustrine depressions

  11. PALSAR Fine Beam – HH polarization Temporal color composite image Water ponds Hombori mounts Low-land (accacia forest) 17 Jan. 2007 20 Oct. 2007 22 Jan.2009 GOURMA - MALI

  12. Change detection method Constraints: Large dynamic range (high differences over bright patterns) Even after multi-looking, presence of noise (speckle)  absolute or relative differences, ratios, rms,….. not significant Time seriescolor composite image Relative differences

  13. Change detection method Constraints: Large dynamic range (high differences over bright patterns) Even after multi-looking, presence of noise (speckle)  absolute or relative differences, ratio, rms,….. not significant Case of 3 channels Temporally stable regions  gray Change detection  colored areas

  14. Change detection method Constraints: Large dynamic range (high differences over bright patterns) Even after multi-looking, presence of noise (speckle)  absolute or relative differences, ratio, rms,….. not significant Case of 3 channels Temporally stable areas  gray areas (no saturation) Change detection  colored areas (saturation) Value B Hue G 0 Saturation R RGB space HSV space

  15. RGB Space 17 Jan. 2007 20 Oct. 2007 22 Jan.2009

  16. Value HSV Space Saturation Hue Areas that have changed

  17. Change detection for a 3-date color composite image Saturation image Color composite image 17 Jan. 2007 20 Oct. 2007 22 Jan.2009 PALSAR Fine Beam HH polarization

  18. Change detection method Case of N channels (N>3): P iterations: 1) random draw of 3 among the N available channels 2) Compute the saturation channel from HSV space Average of the P saturation channels Example: 12 Finebeam acquisitions at HH pol. N=12  12! / (9! * 3!) = 220 possible random draws P =50 (arbitrary)

  19. Change detection for a 3-date color composite image Saturation image Color composite image 17 Jan. 2007 20 Oct. 2007 22 Jan.2009 PALSAR Fine Beam HH polarization

  20. Change detection for 12 FineBeam acquisitions (HH polarization) Saturation image Color composite image 17 Jan. 2007 20 Oct. 2007 22 Jan.2009 Jan. 2007 – Apr. 2009 PALSAR Fine Beam HH polarization

  21. Temporal changes detected over 12 Fine Beam acquisitions Water ponds Fields (millet) dep. Orientation! PALSAR data HH polarisation Jan. 2007 – Mar. 2009 Significant penetration depth over sandy soils

  22. Temporal changes detected over 12 Fine Beam acquisitions Water ponds permanent Fields (millet) dep. Orientation! PALSAR data HH polarisation Jan. 2007 – Mar. 2009 Changes in dry season Changes in rainy season

  23. INFLUENCE OF POLARISATION Sandy soils Shallow soils HH HV ALOS/PALSAR – FBD 6th June 2008

  24. Change detection between HH and HV Shallow soils + Water ponds PALSAR DUAL POLARIZATION

  25. ALOS/PALSAR – WB1: HH Polarization 3-date color composite image Water ponds discrimination 2008 dry season: 1 Jan 16 Feb 2 Apr

  26. ALOS/PALSAR – WB1: HH Polarization Change detection (dry season) • Water pondsdiscrimination • Main water resource • Hydrological indicator • surface runoff • areas increase since begining of drought period (50’s)

  27. ASAR –Wide Swath – HH polarisation • 41 acquisitions • 22 acquisitions in ascending pass • 5 acquisitions same incidence angle (35°) – Jul. –Dec. 2005

  28. ASAR –Wide Swath - HH polarisation change detection (5 dates) 2nd Sept. 2005 low penetration over sandy soil upper surface changes sandy soils

  29. ASAR –Wide Swath - HH polarisation change detection (5 dates) 2nd Sept. 2005 low penetration over sandy soil upper surface changes sandy soils, water ponds

  30. Comparison P band / L band temporal change detection PALSAR (L-band) change detection (5 dates) High penetration dpeth over sandy soils water ponds millet fields low penetration over sandy soil upper surface changes sandy soils, water ponds

  31. Conclusion • RGB  HSV simple but performant for change detection ! histogram must be the same across the hole image! • Temporal change detection: penetration depth illustration L band (high penetration over sandy soils) Water ponds Millet fields • C band (low penetration over sandy soils) • mix of upper surface changes • sandy soils (soil moisture + vegetation) • water ponds • more difficult te discriminate special features • L band: some variation over sandy soils (soil moisture?) • Cross over with SMOS mission

  32. Thank you for your attention!

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