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SAR polarimetric change detection forflooded vegetationB. Brisco a , A. Schmitt b , K. Murnaghan a , S. Kaya a & A. Roth ba Canada Centre for Remote Sensing, Earth Sciences Sector,Natural Resources Canada, Ottawa, ON, Canadab German Remote Sensing Data Center, German Aerospace Center,Oberpfaffenhofen, Wessling, Germany

Available online: 21 Sep 2011

To cite this article: B. Brisco, A. Schmitt, K. Murnaghan, S. Kaya & A. Roth (2011): SARpolarimetric change detection for flooded vegetation, International Journal of Digital Earth,DOI:10.1080/17538947.2011.608813

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SAR polarimetric change detection for flooded vegetation

B. Briscoa, A. Schmittb, K. Murnaghana*, S. Kayaa and A. Rothb

aCanada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada, Ottawa,ON, Canada; bGerman Remote Sensing Data Center, German Aerospace Center,

Oberpfaffenhofen, Wessling, Germany

(Received 21 February 2011; final version received 26 July 2011)

Due to spatial and temporal variability an effective monitoring system for waterresources must consider the use of remote sensing to provide information.Synthetic Aperture Radar (SAR) is useful due to timely data acquisition andsensitivity to surface water and flooded vegetation. The ability to map floodedvegetation is attributed to the double bounce scattering mechanism, oftendominant for this target. Dong Ting Lake in China is an ideal site for evaluatingSAR data for this application due to annual flooding caused by mountain snowmelt causing extensive changes in flooded vegetation. A curvelet-based approachfor change detection in SAR imagery works well as it highlights the change andsuppresses the speckle noise. This paper addresses the extension of this changedetection technique to polarimetric SAR data for monitoring surface water andflooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate thiscurvelet-based change detection technique applied to wetlands although it isapplicable to other land covers and for post disaster impact assessment. Thesetools are important to Digital Earth for map updating and revision.

Keywords: SAR polarimetry; change detection; wetlands; floods; earth observa-tion; land cover; microwave remote sensing

Introduction

Both water management and wetland conservation are increasingly important as

water becomes a more expensive commodity and the value of wetlands for providing

ecological goods and services is better understood. Due to high variability in both

space and time an effective monitoring system for wetlands and water resources must

consider the use of remote sensing to provide timely, cost-effective information

(Kasischke and Bourgeau-Chavez 1997, Pope et al. 1997).

Synthetic Aperture Radar (SAR) is an attractive sensor for monitoring wetlands

due to the timely data acquisition capabilities and the sensitivity to surface water and

flooded vegetation. The ability to map and monitor flooded vegetation with SAR

provides information often unavailable from optical sensors and of value for a wide

range of applications, including hydrology, ecology, meteorology, and flood mapping

(Brisco et al. 2008, 2009). The ability to map flooded vegetation is largely attributed

to the double bounce scattering mechanism which is often dominant for this type of

target and leads to enhanced backscatter and a 180-degree phase difference in the co-

polar channels. This allows for the use of polarimetric decomposition techniques to

*Corresponding author. Email: Kevin.Murnaghan@NRCan.gc.ca

International Journal of Digital Earth,

2011, 1�12, iFirst article

ISSN 1753-8947 print/ISSN 1753-8955 online

# Her Majesty the Queen in Right of Canada 2011

http://dx.doi.org/10.1080/17538947.2011.608813

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identify regions of flooded vegetation (Brisco et al. 2011). A few well-known

decomposition techniques, such as the Cloude�Pottier, the Freeman�Durden, the

Pauli, the Van Zyl, the Yamaguchi, and the Touzi decomposition methods (Van Zyl

1989, Cloude and Pottier 1997, Freeman and Durden 1998, Touzi et al. 2004,Yamaguchi et al. 2005), are now implemented in commercial software which are

readily available to the scientific community.

Dong Ting Lake in China is well known to have an annual severe flooding event

due to mountain snow melt which causes extensive changes in the location and

amount of flooded vegetation. This makes it an ideal site for evaluating the use of

SAR data for water resource applications. Change detection techniques have been

developed for remote sensing since the acquisition of multi-temporal images

facilitates this type of analyses for information extraction. Schmitt et al. (2010)developed a curvelet-based approach for detection changes in SAR imagery related

to disaster applications. This methodology works well by highlighting the change and

suppressing the speckle noise, which is inherent in SAR data. It was originally

developed only for magnitude SAR data; however, the authors have developed an

extension to make it applicable to decompositions of complex data from polarimetric

SAR systems. Change detection is an important tool for map updating as well as for

impact assessment after disasters such as flooding or earthquakes.

This paper addresses the extension of this change detection technique topolarimetric SAR data from RADARSAT-2 for the detection and monitoring of

flooded vegetation. RADARSAT-2 images of Dong Ting Lake in China are used to

demonstrate the use of curvelet-based change detection technique and to evaluate the

Freeman�Durden polarimetric decomposition technique for mapping these changes

in flooded vegetation.

Study site and data description

Dong Ting Lake, the second largest lake in China, is located in Hunan Province. It is

a flood-basin of the Yangtze River and thus varies seasonally in size (Figure 1). It can

expand to 2691 km2 during the annual floods and shrink to 709.9 km2 in the dry

season (Huang 1999). There are environmental issues associated with the lake and

thus remote sensing can play a vital role in monitoring the lake water levels and

providing information to hydrological engineers and environmentalists working in the

area (Smith 1997, Du et al. 2001, Peng et al. 2005,Mao et al. 2006, Zhang et al. 2006).

Two RADARSAT-2 polarimetric images from 2008 covering low water and highwater levels were acquired to provide the basis for the change detection (Table 1).

Polarization color composites of these images showing the dramatic changes in water

level are shown in Figure 2. This shows dramatic changes in the surface water

between the two RADARSAT images. One can also see the cultivated region, which

is predominately rice paddies, in the reddish hue with green borders, likely due to

high horizontal transmit/horizontal receive (HH) backscatter. This area is relatively

stable between acquisitions with respect to water levels because of the dikes and

canals. There is also an area of aquaculture with some of the ponds in the lower leftof the image. This area is dominated by open water characterized by specular

reflectance of the radar signature, as well as some areas with variable levels of plant

growth. During the high water period, the areas of natural vegetation along the river

are mostly whitish in color due to high backscatter at all polarizations. These areas

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are primarily green in color during the low water period due to high horizontal

transmit/vertical receive (HV) backscatter. It is this area in particular that forms the

basis for evaluating the curvelet-based change detection using polarimetric decom-

position.

Curvelet-based change detection

Change detection on SAR images is constrained by two inherent sensor character-

istics: the side-looking acquisition geometry and the radiometry. The difficulties with

the geometry can be resolved by using repeat pass acquisitions that share the same

incidence angle and hence, the same geometric distortions. Those images are

Figure 1. Optical satellite images of Dong Ting Lake showing the dramatic change in water

extent between the flood stage and the dry season.

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subsequently co-registered by shifting the second image in azimuth and range to fit

best the first one. The handling of the radiometric properties is more complicated. If

a simple pixel by pixel comparison is performed, a very high false target rate is

produced due to the SAR inherent speckle effect. Thus, the idea presented in this

paper compares structures apparent in the image instead of single pixel values.

In order to reconstruct the image, the curvelet transform (Candes and Donoho

1999) is utilized. Since this transform was originally designed for the compression of

optical images, it is able to approximate curved singularities with very few

coefficients in a nonadaptive manner. The basic elements � called Ridgelets because

of their ridge-like geometric form � are transformed to a wide range of scales,

orientations, and positions. The pixel values are then calculated as the sum of the

contribution of each element weighted by the corresponding curvelet coefficient.

Table 1. Dong Ting Lake RADARSAT-2 FQ16 data description for polarimetric data

acquired on 6 June and 17 August 2008.

Parameter Value

Azimuth Spacing 5.16 m

Range Spacing 4.73 m

Incidence Angle 36.8 degrees

Looks 1 X 1

Figure 2. RADARSAT-2 6 June 2008 (left) and 17 August 2008 (right) polarization color

composites (HH-red, HV-green, VV-blue) of Dong Ting Lake showing high and low water

conditions, respectively.

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This implies that for the multiplicative SAR images in the intensity domain a

logarithmic scaling has to be chosen: the higher the absolute value of a curvelet

coefficient, the higher its influence and the stronger the corresponding structure in

the original image.For image enhancement, it is convenient to have a measure for the intensity of

structures. Noisy structures associated with very low curvelet coefficients can be

removed by setting the corresponding coefficients to zero. Higher coefficients

referring to more intense structures are weighted by a special function to keep

strong structures unchanged and to lower minor structures slightly. Otherwise, if a

hard thresholding was done, overlays and many artifacts would disturb the resulting

image (Schmitt et al. 2009a). The same image enhancement procedure can be applied

to difference images. According to the formulas given in Schmitt et al. (2010), thecurvelet coefficient difference is equal to the difference between the logarithmic

amplitudes and hence, equal to the images’ quotient representing the relative change

of the SAR amplitudes. The suitability of the SAR change detection approach for

single polarized amplitude images has been proven using many sample applications

as reported by Schmitt et al. (2009b).

Polarimetric decomposition

Polarimetric SAR data provide four complex polarization channels. Although the

redundancy of the data can be reduced to three complex channels because the cross-

polarized channels are equal for the backscattering case � where the transmitting and

receiving antenna are in the same location � the remaining channels have to be

recombined mathematically to enable a physical interpretation of the underlying

scattering mechanism. The Freeman�Durden approach (Freeman and Durden 1998)

first correlates the three channels mutually to get the so-called covariance matrix

which is summed up over several pixels to capture depolarizing effects that cannot bedescribed by the original data format.

This matrix containing the channel intensities as well as correlations between the

channels is then decomposed into the contributions of three scattering mechanisms:

surface, diplane (or ‘double-bounce’), and volume. The surface scattering channel

represents the intensity backscattered by a rough surface (in relation to the sensor

wavelength). The diplane channel captures double reflections occurring where

perpendicular reflecting planes meet; for example tree trunks and the surrounding

flooded soil. The volume scattering channel shows high intensities where multiplescattering mechanisms act in one resolution cell; for example over a forest canopy. One

drawback of this decomposition is the initial assumption of target symmetry which is

not true for all targets. Over built-up areas, for example, a high contribution of helix �asymmetric � scattering is recognized by other decompositions. But, as the focus of

this study lies on natural landscapes this fact has been considered and found negligible.

The three decomposition channels providing backscattering intensities can easily

be introduced to the change detection algorithm presented in the previous section.

After the logarithmic scaling, the intensity data are equal to amplitude datamultiplied by a factor of 2 which implies the same radiometric characteristics as for

single polarized amplitude images. The change detection algorithm is applied to all

three channels separately so that an increase of the total intensity, for example,

shortly after a rain fall, is reflected by an equal increase in all three channels, whereas

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the change of a special scattering event only appears in the dedicated scattering

mechanism intensity. In this way, the development of scattering mechanisms

indicating temporal changes in the imaged natural landscape can be monitored.

Results and discussion

The results of the Freeman�Durden decomposition with red as double bounce, blue

as surface scattering, and green as volume scattering for the two images are shown in

Figure 3. The image on the left is for the high water date (June 6) while the low water

date (August 17) is shown on the right. Note that when the water is high there is

increased occurrence of double bounce scattering in the flooded vegetation along the

river and canals floodplains so these areas show up as a pinkish color. Meanwhile,

the cultivated areas are bluish green in color due to a mix of volume and surface

scattering. Open surface water and the area of aquaculture are blue in color due to

the dominance of surface scattering. These results are typical when using polari-

metric decomposition of RADARSAT-2 data for wetland areas (Touzi et al. 2007,

Brisco et al. 2011).However, for the date with low water there is little difference between the natural

vegetation areas along the river and the cultivated regions. Both areas are largely

green due to the dominance of volume scatter rather than double bounce or surface

scatter. Some areas of both the natural vegetation and cultivated region are whitish

Figure 3. Freeman�Durden decompositions for 6 June 2008 (left) and 17 August (right)

RADARSAT-2 polarimetric data. Red is double bounce, green is volume, and blue is surface

scattering.

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in color, indicating some double bounce scattering in these regions. Once again, the

open water is characterized by surface scattering resulting in a blue color.

Figure 4 shows the change in double bounce scattering between the two dates

with the magnitude of change ranging from �10 dB in red to �10 db in dark blue

tones. Figure 5 shows the change in volume scattering and Figure 6 shows the change

in surface scattering. The same color bar applies to all the change images. The light

blue, green, and yellow colors represent small changes on the order of 1�2 dB. The

change in double bounce is on the order of 10 or more dB for the natural vegetation

areas as these regions have much less double bounce scattering when the water level is

low (Figure 4). On the other hand, areas along the river that were flooded in the early

date and are not flooded in the later acquisition show 10 dB changes due primarily to

the change from surface scatter to double bounce and volume scattering.

The change in volume and surface scattering between the two SAR images are

shown in Figures 5 and 6, respectively. Most of the cultivated region and the natural

vegetation along the river exhibit little change in volume scattering. These areas of

significant vegetation have relatively high amounts of volume scattering on both

dates, as expected. Some vegetated areas along the waterways and in the lake show a

Figure 4. Change in double bounce scattering between 6 June and 17 August for Dong Ting

Lake and surrounding area.

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loss of 10 dB in volume scattering which is rather surprising as these areas were

mostly flooded vegetation in the first acquisition. There are also isolated patches of

vegetation along the waterways and in the cultivated region that show an increase of

10 dB in volume scattering. These are likely areas that, while flooded, had little

volume scattering but are now areas of lush vegetation with high volume scattering.

Areas that were flooded in the first date but not in the second date show a loss of

10 dB in surface scattering (Figure 6). The areas of natural vegetation along the

waterways and the cultivated region show little change in surface scattering.

Similarly, the aquaculture region exhibits little change. There are some areas along

the waterway that show a 10 dB increase in surface scattering. These are most likely

areas that were flooded on the second date and not on the first due to water control

activities such as local dikes and other human activities.

Concluding remarks

The curvelet-based approach to change detection has been successfully adapted to

polarimetric SAR data. The methodology appears very promising for information

Figure 5. Change in volume scattering between 6 June and 17 August for Dong Ting Lake and

surrounding area.

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extraction from SAR data using polarimetric decomposition techniques. In this

study, we used the Freeman�Durden decomposition to monitor changes in double

bounce, volume, and surface scattering which could be related to the water level

conditions in Dong Ting Lake, China. Additional work to validate this change

detection methodology is underway using a variety of test sites in North America. As

a member of the Terrestrial Wetland Global Change Research Network

(TWGCRN), the Canada Centre for Remote Sensing (CCRS) is investigating the

use of SAR polarimetry for monitoring seasonal changes in critical ecosystems and

relating these changes to habitat management for species at risk. The initial results

presented here will be further explored over a variety of TWGCRN test sites in North

America. End-user ground truth and product assessment support from the Canadian

Forestry Service (CFS) and United States Geological Survey (USGS) will allow more

detailed validation of the technique. We are also exploring the other polarimetric

decomposition techniques such as the Cloude�Pottier and Touzi algorithms to

evaluate how their output parameters can be applied to this methodology. We will

report on these results in a follow-up publication. Although applied to wetlands this

technique is applicable to other land covers such as urban, agriculture, or forestry

Figure 6. Change in surface scattering between 6 June and 17 August for Dong Ting Lake and

surrounding area.

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and can also be used for post disaster impact assessment. These tools are important

to Digital Earth as map updating and revision will be an important ongoing activity.

Acknowledgements

This collaboration between DLR and CCRS was partially funded by the Bayern-Pfalz-Foundation in Munich (Germany) and the German Academic Exchange Service (DAAD).The Remote Sensing Science (RSS) program at CCRS also supported this research project.The authors would like to thank both organizations for their financial and administrativesupport. The RADARSAT-2 data were acquired under the Capacity Building Centre forEarth Observation (CBCEO) agreement between the Chinese Academy of Sciences (CAS) andEarth Science Sector (ESS). ESS Contribution number/Numero de contribution du SST:20100490.

Notes on contributors

Brian Brisco received a PhD degree in remote sensing/physical geography from the Universityof Kansas in 1985 and an MSc degree in soil science and a BSc degree in ecology, both fromthe University of Guelph. Brian has been involved in remote sensing since 1975 participatingin the SURSAT project from 1977 to 1980 before working at the Remote Sensing Laboratoryat the University of Kansas. He worked for Intera from 1989 until 1997 as a research associateafter completion of an NSERC post-doctoral fellowship at the CCRS. From 1997 to 2006 heworked for Noetix Research Inc. as the Director of Research and Applications Development.Brian is currently a Senior Research Scientist at CCRS.

Andreas Schmitt received a diploma degree (Dipl.-Ing.) in Geodesy and Geoinformatics fromthe University of Karlsruhe (Germany) in 2008. Since then he is working as PhD student in theGerman Remote Sensing Data Center (DFD) within the German Aerospace Center (DLR) inOberpfaffenhofen. His research topic is the automatic change detection in multi-temporal andmulti-polarized SAR images. In summer 2010 he spent three months working with CCRS inOttawa on developing polarimetric SAR change detection techniques for wetland monitoring.

Kevin Murnaghan received his Bachelor of Science, honors applied physics degree from theUniversity of Waterloo in 1996. He started as a Research Associate at the CCRS in 1997 andworked on calibration of RADARSAT-1, and processing and calibration for the airborneSAR-580 polarimetric platform. Kevin joined CCRS in 2006 and is currently working on geo-hazard monitoring using InSAR, water resource mapping, and permafrost monitoring. Kevinis currently an Environmental Scientist at CCRS.

Shannon Kaya is an environmental scientist with a specialization in SAR and its application toa terrestrial water resources and environmental health. Shannon received a Master’s degreefrom the department of Geography and Earth Studies in 2001 and a Bachelor’s degree in 1996,both from Carleton University in Ottawa, Ontario. She is also an alumnus of the InternationalSpace University, Satellite Applications Department. Shannon has been involved in remotesensing since 1996 when she began her career at the CCRS. Her research has focused on theuse of SAR for environmental applications related to terrestrial water resources, including ricecrop monitoring, wetland mapping, and ecosystem approaches to human health issues.

Achim Roth received the degree as graduate engineer in geodesy from the University ofKarlsruhe, Germany, in 1987. He joined the German Aerospace Center in 1987 for thedevelopment and implementation of an operational SAR-geocoding system for the ERS- andX-SAR-missions. Since 1991 he is leading the team ‘SAR Topography’ at DLR’s GermanRemote Sensing Data Center DFD. From 2000 until 2004 he was the SRTM/X-SAR GroundSegment Manager. Since 2002 he is the TerraSAR-X Science Coordinator. The team’s researchfocuses on the development of geo-information products, the corresponding retrieval

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techniques, and their implementation as operational processors. It contributed to the ShuttleRadar Topography Mission (SRTM) and is currently involved in the TerraSAR-X andTanDEM-X missions.

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