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    Notarnicola_TH2_TO4.2.ppt Notarnicola_TH2_TO4.2.ppt Presentation Transcript

    • EXPLOITATION OF COSMO-SKYMED IMAGE TIME SERIES FOR SNOW MONITORING IN ALPINE REGIONS
      • T. Schellenberger 1 , B. Ventura 1 , C. Notarnicola 1 ,
      • M. Zebisch 1 , T. Nagler 2 , H. Rott 2 ,
      1 EURAC-Institute for Applied Remote Sensing, Viale Druso 1, Bolzano, Italy 2 ENVEO IT GmbH, Innsbruck, Austria
    • OUTLINE
      • Introduction
      • Description of the acquired COSMO-SkyMed (CSK) images and of the test areas
      • Description of the multi-temporal approach
      • Experimental results:
        • Time series of the snow maps from CSK images acquired in winter 2010-2011 and comparison with optical images
        • Probability error maps for snow cover
        • Comparison of the CSK backscattering coefficients with the simulations derived from an electromagnetic model
      • Conclusions and future steps
    • Introduction and motivation
      • Cosmo-SkyMed (CSK © ) constellation represents a great challenge to extent this previous knowledge to the X-band data for snow detection.
      • A high resolution (up to 1 m) and an higher repetition time (8 days in standard mode with the full constellation) gives the chance to analyze the problem of detecting wet snow and derive Snow Cover Area (SCA) with a greater spatial and temporal resolution.
      • The main objectives of the work are:
        • To adapt the already developed multi-temporal techniques to X-band CSK images;
        • To analyze the temporal variability of the key parameters used for the distinction of the snow from the no-snow areas.
      • The activities are carried out in the framework of the project SNOX- snow cover and glacier monitoring in alpine areas with COSMO-SkyMed X-band data” ID 2152 funded by the Italian Space Agency .
    • Test sites - Description Bolzano The test areas in South Tyrol. The pink markers indicate the placement of the manual ground measurement stations , while the green ones of the automatic ground measurement stations
    • COSMO-SkyMed characteristics and Test sites images COSMO-SkyMed Main characteristics Band X – 9.60 GHz Wavelength 3.12 cm Polarization HH, VV, HH-HV, VV-VH Swath 100 km Revisit Time 8 days Radiometric accuracy < 1 dB Acquisition mode Spotlight (Enhanced) Stripmap (Himage, PingPong) ScanSAR (WideRegion, HugeRegion) Geometrical Resolution (ground range, m - azimuth, m) ≤ 1.0 - ≤1.0 Spotlight (Enhanced) ≤ 5.0 - ≤5.0 Stripmap (Himage) ≤ 20.0 - ≤20.0 Stripmap (PingPong) ≤ 30.0 - ≤30.0 ScanSAR (WideRegion) ≤ 100.0 - ≤100.0 ScanSAR (HugeRegion) Test sites Acquisition Technical characteristics test site South Tyrol 1 (Ulten valley) South Tyrol 2 (Brennero) mode Stripmap PingPong Stripmap PingPong Level 1A-SCSB 1A-SCSB swath 30km x 30km 30km x 30km Preferred incidence angle >25° e <35° >25° e <35° image area 30 km x 30 km 30 km x 30 km Number of scene 22 22 look right right orbit asc or desc asc or desc polarization VV, VH VV, VH
    • COSMO-SkyMed data sets List of the acquired COSMO-SkyMed images: in green are indicated the “melting season” data; in black the “winter season” data; in blue the “summer season” (snow free) images. Area Date Mode Polarization Look Side Pass Beam Proc. Level Ulten 20100426 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20100427 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20100901 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20100902 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20100917 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20101128 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20101223 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20110123 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20110312 Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten 20110405 Ping Pong VV/VH Right Ascending 10 1A-SCSB Brenner 20110404 Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner 20110421 Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner 20110424 Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner 20110506 Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner 20110507 Ping Pong VV/VH Right Descending 11 1A-SCSB
    • Overview of methods for snow cover area detection with SAR images AUTHOR Method + / - Koskinen et al. (1997) /Luojus et al., (2009)
      • wet snow:
      • difference technique (using 3 images)
      • map of snow cover fraction
      • SCA in forest zone
      • No dry snow detection
      • difference technique not useful for waterbodies
      Nagler & Rott (2005)
      • wet snow:
      • difference technique (using 2 images)
      • dry snow:
      • upper boundary of wet snow cover as the lower boundary of dry snow
      • No SCA in forestzone
      • difference technique not useful for waterbodies
      Storvold et al. (2005)
      • wet snow
      • difference technique (using 2 images)
      • dry snow
      • mean altitude of wet snow pixel
      • negative air temperature
      • wet snow pixel in the surrounding of potential dry snow
      • misclassification occurs under cold conditions  no wet snow exists  pixel are classified as bare ground
      • difference technique not useful for waterbodies
      Venkataraman et al. (2009)
      • Application to TERRASARX images (-3 dB threshold)
      • Comparison with ASAR and ALOS-PALSAR
      • Similar threshold for C and X band images
      • No dry snow
    • Detecting snow cover area with SAR images Distribution for “snow” and “ no snow” areas The method derived from Nagler (1996) is based on the difference in backscattering behavior between snow covered and snow free images.
    • Filter effects analysis effective number of looks (enl) The enl were calculated for a small homogeneous area in unfiltered and filtered multilooked intensity images. To account for temporal changes six SAR images of April, September and November 2010 were chosen, and the mean enl over these images was derived for each filter type.
      • Generally the enl in the VH image is lower than in the VV channel, when comparing the images of one date.
      • The unfiltered intensity image shows mean enl values of 0.76 (VH) and 1.03 (VV).
      • The Frost filter increases the enl between 3.4 times and 4.8 times depending on the filter size and polarization channel.
      • Gamma Filter increases the enl ~3.2 times in the VH image and ~4.2 in the VV image (almost independent of the window size).
    • Effect of filtering on the distribution of the ratio values comparing different filter sizes
      • - Frost–Filter : the effect of the filtersize is low
      • - Gamma DEMAP–Filter : the effect of the filtersize is low
    • Time Series of Ratio Values
      • Distribution of ratio values (dB) under three different conditions in areas without vegetation:
      • - 26.04.2010: wet snow
      • - 01.09.2010: no snow
      • - 28.11.2010: dry snow – wet snow
    • Threshold for wet snow classification Distribution of ratio R for different land cover classes Grassland Rocks Forest Threshold for mapping wet snow with CSK Frost (7 × 7) ratio-images in dependence of polarization and land cover Rock (dB) Grassland (dB) VV -2.3 -2.2 VH -1.3 -2.0
    • Dependence of SCA on the threshold
      • As the snow and no-snow ratio distributions partially overlap, the choice of the thresholds is a key aspect of the analysis. To study the dependence of SCA on the threshold, SCA derived with a threshold of -2.3 dB based on statistical analysis was compared to SCA based on a -3.0 dB threshold, commonly used for detecting snow with C-band data and TerraSAR-X data. Using a lower threshold of -3.0 dB leads to a smaller SCA and snow-covered pixels, which show a higher ratio, are wrongly classified as snow-free. In contrast, when using a higher threshold, snow-free pixel which have lower ratio values than -2.3 dB are no longer classified as snow.
      -2.3 dB – Normal -3.0 dB - Italics 26April 2010 - % SNOW - LANDSAT NO SNOW - LANDSAT SNOW - CSK 57.7 63.7 2.3 3.0 NO SNOW - CSK 42.3 36.3 93.6 97.1 12March 2011 - % SNOW-LANDSAT NO SNOW-LANDSAT SNOW - CSK 51.4 53.0 2.5 2.4 NO SNOW - CSK 48.6 46.9 97.5 97.6 5April 2011 - % SNOW - LANDSAT NO SNOW - LANDSAT SNOW - CSK 72.1 70.4 6.4 6.3 NO SNOW - CSK 27.9 29.6 93.6 93.7
    • Dependence of the threshold r 0 on the reference images
      • The choice of the reference image has a considerable impact on the threshold and hence on the classification result.
      • The strong influence of the reference image on the threshold is also reported by Luojus et al., 2009 for C-band data.
      • When for each reference image, these different thresholds are applied the resulting SCA area varies up to 6%. When using a fixed threshold of -3 dB (applied to grassland and rocks land-use classes), the resulting SCA area varies up to 12%.
      SCA (%) r 0 (dB) Ref. image 60.0 -3.0 01-02-17 Sept 2010 (mean) 51.5 -3.0 01 Sept 2010 55.5 -3.0 02 Sept2010 63.0 -3.0 17 Sept 2010 SCA (%) r 0 (dB) Ref. image 65.9 -2.3 01-02-17 Sept 2010 (mean) 61.8 -1.7 01 Sept 2010 66.1 -1.6 02 Sept2010 68.2 -2.3 17 Sept 2010
    • Preprocessing outputs COSMO-SkyMed geocoded image, March 12 th 2011, VV COSMO-SkyMed geocoded image, March 12 th 2011, VH COSMO-SkyMed -ASI ©– All rights reserved
    • Time series of CSK snow maps: November COSMO-SkyMed November 28 th 2011 MODIS snow line November 26 th 2011 Snow No Snow
    • Time series of CSK snow maps: January COSMO-SkyMed January 23 rd 2011 MODIS snow line January 21 st 2011 Snow No Snow
    • Time series of CSK snow maps: March COSMO-SkyMed March 12 th 2011 LANDSAT March 6 th 20011 Snow No Snow
    • Time series of CSK snow maps: April COSMO-SkyMed April 5 th 2011 LANDSAT April 7 th 20011 Snow No Snow
    • Comparison CSK and Landsat snow cover maps -3.0 dB March 12 th 2011 April 5 th 2011 % NO SNOW - LANDSAT SNOW - LANDSAT NO SNOW - CSK 53.0 2.4 SNOW - CSK 46.9 97.6 % NO SNOW - LANDSAT SNOW - LANDSAT NO SNOW - CSK 70.4 6.3 SNOW - CSK 29.6 93.7 Snow for LANDSAT and CSK No Snow for LANDSAT and CSK Snow only for CSK Snow only for LANDSAT
    • Comparison CSK and Landsat snow cover maps -3.0 dB March 12 th 2011 April 5 th 2011 % NO SNOW - LANDSAT SNOW - LANDSAT NO SNOW - CSK 53.0 2.4 SNOW - CSK 46.9 97.6 % NO SNOW - LANDSAT SNOW - LANDSAT NO SNOW - CSK 70.4 6.3 SNOW - CSK 29.6 93.7 Snow for LANDSAT and CSK No Snow for LANDSAT and CSK Snow only for CSK Snow only for LANDSAT
    • Probability of error in change detection technique Probability of error (in %) of the ratio method versus the change in radar backscatter (in dB) between two dates, for a number of looks N varying between 1 and 256 (from Rignot & van Zyl, 1993). Commission error Omission error
    • Probability of error map No snow Snow < 5% 5% - 10% 10% - 25% 25% - 50% > 50%
    • Comparison between e.m. model simulations and CSK backscattering coefficients By using the IEM model, the main hypothesis is that we are dealing with surface scattering. This hypothesis is verified only the case of wet snow. λ (cm) = 3.1 l (cm) = 5.0 -10.0 ε snow = [1.5-2.1] s (cm) = 0.5 – 1.0
    • Conclusions and future steps The possibility to discriminate wet snow from snow-free areas in COSMO-SkyMed X-band images using a multi-temporal approach was studied in dependence of different key parameters. SCA increases up to 8% when a threshold of -2.3 dB is applied instead of a threshold of -3.0 dB. Analyzing the dependence of the threshold on the reference image showed that the threshold, and hence the classification result, strongly depends on the reference image. An average of suitable reference images is advisable in order to reduce the impact of conditions deriving from a single image. The snow cover maps can be associated to a probability of error map which indicates the level of error in the different areas. Future steps will include: The analysis will be extended to another test area where the CSK acquisitions were in the afternoon. The multi-temporal approach will be extended to VH polarization. A comparison with TERRASARX images is foreseen The problem of the snow cover extension beyond wet snow will be faced.
    • Thank you for the attention! Comments/questions?