Notarnicola_TH2_TO4.2.ppt

IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011

Vancouver, Canada - July, 24-29, 2011

EXPLOITATION OF COSMO-SKYMED IMAGE

TIME SERIES FOR SNOW MONITORING

IN ALPINE REGIONS

T. Schellenberger1, B. Ventura1, C. Notarnicola1,

M. Zebisch1, T. Nagler2, H. Rott2,

1EURAC-Institute for Applied Remote Sensing, Viale Druso 1, Bolzano, Italy2 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

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

Bolzano



COSMO-SkyMed data sets

Area Date ModePolarizatio

nLook Side

PassBeam

Proc. Level

Ulten2010042

6Ping Pong VV/VH Right Ascending 10 1A-SCSB

Ulten2010042


Ulten2010090


Ulten2010090


Ulten2010091


Ulten2010112


Ulten2010122


Ulten2011012


Ulten2011031


Ulten2011040


Brenner

20110404

Ping Pong VV/VH RightDescendin

g11 1A-SCSB

Brenner

20110421


g11 1A-SCSB

Brenner

20110424


g11 1A-SCSB

Brenner

20110506


g11 1A-SCSB

Brenner

20110507


g11 1A-SCSB

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.



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.

ABrrr 0



Filter effects analysiseffective 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 classificationDistribution of ratio R for different land cover classes

Grassland Rocks

Forest

Rock (dB)

Grassland (dB)

VV -2.3 -2.2

VH -1.3 -2.0

Threshold for mapping wet snow with CSK Frost (7 × 7) ratio-images in dependence of polarization and land

cover



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.

26April 2010 - %

SNOW - LANDSAT

NO SNOW - LANDSAT

SNOW - CSK57.763.7

2.33.0

NO SNOW - CSK

42.336.3

93.697.1

12March 2011 - %

SNOW-LANDSAT

NO SNOW-LANDSAT

SNOW - CSK51.453.0

2.52.4

NO SNOW - CSK48.646.9

97.597.6

5April 2011 - %

SNOW - LANDSAT

NO SNOW - LANDSAT

SNOW - CSK72.170.4

6.46.3

NO SNOW - CSK

27.929.6

93.693.7

-2.3 dB – Normal-3.0 dB - Italics



Dependence of the threshold r0 on the referenceimages

•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 (%) r0 (dB) Ref. image

60.0 -3.001-02-17 Sept 2010

(mean)

51.5 -3.0 01 Sept 2010

55.5 -3.0 02 Sept2010

63.0 -3.0 17 Sept 2010

SCA (%) r0 (dB) Ref. image

65.9 -2.301-02-17 Sept 2010

(mean)

61.8 -1.7 01 Sept 2010

66.1 -1.6 02 Sept2010

68.2 -2.3 17 Sept 2010



Preprocessing outputsCOSMO-SkyMed geocoded image, March 12th 2011, VV COSMO-SkyMed geocoded image, March 12th 2011, VH

COSMO-SkyMed -ASI ©– All rights reserved

Time series of CSK snow maps: November

COSMO-SkyMedNovember 28th2011

MODIS snow lineNovember 26th 2011

Snow

No Snow



Time series of CSK snow maps: January

COSMO-SkyMedJanuary 23rd 2011

MODIS snow lineJanuary 21st 2011

Snow

No Snow



COSMO-SkyMedMarch 12th 2011

Time series of CSK snow maps: March

LANDSATMarch 6th 20011

Snow

No Snow



Time series of CSK snow maps: April

COSMO-SkyMedApril 5th 2011

LANDSATApril 7th 20011

Snow

No Snow



Comparison CSK and Landsat snow cover maps -3.0 dB

%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

March 12th 2011 April 5th 2011

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 snowSnow

< 5%5% - 10%

10% - 25%

25% - 50%

> 50%

Comparison between e.m. model simulations and CSK backscattering coefficients

λ (cm) = 3.1 l (cm) = 5.0 -10.0εsnow= [1.5-2.1] s (cm) = 0.5 – 1.0

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.



Conclusions and future stepsThe 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?

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