Comparison of L and P band radar time series for the monitoring of Sahelian area
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Transcript of Comparison of L and P band radar time series for the monitoring of Sahelian area
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Comparison of L and P band radar time series for the monitoring of Sahelian area
P.-L. Frison, G. Mercier, E. Mougin, P. Hiernaux
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Context:• Better understanding Sahelian surface processes and their interaction with monsoon variability
• Improve our understanding and documentation of long term trend in vegetation in response to climate change
• radar data: 2 key parameters: soil moisture 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
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Outline:
• Study site
• PALSAR and ASAR data
• change detection method
• Results and discussion
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The Sahel
Semi-arid area
herbaceous layer (0-50 %) (annual grasses)
Dry season (Nov. – Apr.)
bare soil
Rainy season (May – Oct.)
shrub (0-20 %)
trees (1-5 %)+
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Region of study: the Gourma - Mali
Seno
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PALSAR acquisitions: L band (Jan. 2007 - Apr. 2009)
DATASET
Mode # Polarization Resolution Incidence angle
Pass
Fine Beam 7 HH 15 m 40° Ascending
Fine Beam 5 HH / HV 15 m 40° Ascending
Wide Swath 6 HH 100 m 30° Descending
ASAR acquisitions: C band (Jul. - Dec. 2005)
Mode # Polarization Resolution Incidence angle
Pass
Wide Swath 41 HH 150 m 18-35° Ascending+
Descending
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Gourma Region (MALI)ASAR –Wide Swath - HH
20th Dec 2005
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GOURMA Region (MALI)PALSAR–WIDE BEAM- HH
1st Jan 2008
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Gourma Region (MALI)ASARC-band
PALSARL-band
C-band (ASAR):Shallow sand and silt soils
L -band (PALSAR):Better discrimination of geological features
Remnant of alluvial systems and lacustrine depressions
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Gourma Region (MALI)ASARC-band
PALSARL-band
C-band (ASAR):Shallow sand and silt soils
L -band (PALSAR):Better discrimination of geological features
Remnant of alluvial systems and lacustrine depressions
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PALSAR Fine Beam – HH polarizationTemporal color composite image
GOURMA - MALI17 Jan. 200720 Oct. 200722 Jan.2009
Water ponds
Hombori mounts
Low-land (accacia forest)
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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 series color composite image Relative differences
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Temporally stable regions gray
Change detection colored areas
Case of 3 channels
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
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Temporally stable areas gray areas (no saturation)
Change detection colored areas (saturation)
RGB space R
B
G0
HSV space
Case of 3 channels
Value
Saturation
Hue
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
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17 Jan. 200720 Oct. 200722 Jan.2009
RGB Space
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Value
Hue
Saturation
HSV Space
Areas that have changed
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Color composite image Saturation image
17 Jan. 200720 Oct. 200722 Jan.2009
Change detection for a 3-date color composite image
PALSAR Fine BeamHH polarization
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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)
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Color composite image Saturation image
17 Jan. 200720 Oct. 200722 Jan.2009
Change detection for a 3-date color composite image
PALSAR Fine BeamHH polarization
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Color composite image Saturation image
Change detection for 12 FineBeam acquisitions (HH polarization)
17 Jan. 200720 Oct. 200722 Jan.2009
PALSAR Fine BeamHH polarization
Jan. 2007 – Apr. 2009
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Temporal changes detected over 12 Fine Beam acquisitions
PALSAR dataHH polarisationJan. 2007 – Mar. 2009
Water ponds
Fields (millet) dep. Orientation!
Significant penetration depth over sandy soils
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Changes in dry seasonChanges in rainy season
Temporal changes detected over 12 Fine Beam acquisitions
Water pondspermanent
PALSAR dataHH polarisationJan. 2007 – Mar. 2009
Fields (millet) dep. Orientation!
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ALOS/PALSAR – FBD6th June 2008
HH HV
Sandy soils
Shallow soils
INFLUENCE OF POLARISATION
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Change detection between HH and HV
PALSAR DUAL POLARIZATION
Shallow soils+
Water ponds
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ALOS/PALSAR – WB1: HH Polarization
Water ponds discrimination
2008 dry season:1 Jan16 Feb2 Apr
3-date color composite image
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Change detection (dry season)
ALOS/PALSAR – WB1: HH Polarization
Water ponds discrimination
• Main water resource • Hydrological indicator
surface runoffareas increase since begining of drought
period (50’s)
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ASAR –Wide Swath – HH polarisation
41 acquisitions22 acquisitions in ascending pass
5 acquisitions same incidence angle (35°) – Jul. –Dec. 2005
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ASAR –Wide Swath - HH polarisation
2nd Sept. 2005 change detection (5 dates)
low penetration over sandy soil upper surface changes
sandy soils
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ASAR –Wide Swath - HH polarisation
2nd Sept. 2005 change detection (5 dates)
low penetration over sandy soil upper surface changes
sandy soils, water ponds
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change detection (5 dates)PALSAR (L-band)
low penetration over sandy soil upper surface changes
sandy soils, water ponds
High penetration dpeth over sandy soilswater ponds millet fields
Comparison P band / L band temporal change detection
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Conclusion
• RGB HSV simple but performant for change detection!
• Temporal change detection: penetration depth illustration
histogram must be the same across the hole image!
C band (low penetration over sandy soils)
mix of upper surface changessandy 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
L band (high penetration over sandy soils)Water pondsMillet fields
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Thank you for your attention!