This article was downloaded by: [134.117.120.160]On: 15 January 2015, At: 07:33Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Click for updates

Canadian Journal of Remote Sensing: Journal canadiende télédétectionPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/ujrs20

Characterizing Scattering Behaviour and AssessingPotential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-Pol RADARSAT-2DataSarah N. Banksa, Douglas J. Kingb, Amine Merzoukic & Jason Duffea

a National Wildlife Research Centre, Environment Canada, 1125 Colonel By Drive, Ottawa,Ontario, K1S 5B6, Canadab Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa,Ontario, K1S 5B6, Canadac Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 CarlingAvenue, Ottawa, Ontario, K1A 0C6, CanadaPublished online: 25 Nov 2014.

To cite this article: Sarah N. Banks, Douglas J. King, Amine Merzouki & Jason Duffe (2014) Characterizing ScatteringBehaviour and Assessing Potential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-PolRADARSAT-2 Data, Canadian Journal of Remote Sensing: Journal canadien de télédétection, 40:4, 291-314,DOI: 10.1080/07038992.2014.979487

To link to this article: http://dx.doi.org/10.1080/07038992.2014.979487

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Canadian Journal of Remote Sensing, 40:291–314, 2014Copyright c© CASIISSN: 0703-8992 print / 1712-7971 onlineDOI: 10.1080/07038992.2014.979487

Characterizing Scattering Behaviour and AssessingPotential for Classification of Arctic Shore andNear-Shore Land Covers with Fine Quad-PolRADARSAT-2 Data

Sarah N. Banks1,*, Douglas J. King2, Amine Merzouki3, and Jason Duffe1

1National Wildlife Research Centre, Environment Canada, 1125 Colonel By Drive, Ottawa, Ontario, K1S5B6, Canada2Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario,K1S 5B6, Canada3Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue,Ottawa, Ontario, K1A 0C6, Canada

Abstract. Freeman–Durden and Cloude–Pottier decompositions were applied to polarimetric RADARSAT-2 data to characterizethe scattering behaviour of shore and near-shore land cover types for two study areas in the Beaufort Sea, Canada: RichardsIsland and Tuktoyaktuk Harbour. The impact of incidence angle was evaluated through comparison of Single Look ComplexFine Quad-Pol images acquired at three angles: shallow (45.3◦–49.5◦), medium (39.3◦–41.6◦), and steep (20.9◦–24.2◦), and thepotential for accurate classification was assessed using three unsupervised polarimetric SAR classifiers: Wishart-entropy/alpha,Wishart-entropy/anisotropy/alpha, and Freeman-Wishart. Results indicate that overall, medium, and shallow incidence angleimages provide more useful information than steep angle images and that these classifiers are capable of distinguishing somegeneral land cover types, including substrates, wetlands, vegetated tundra, and water. Results from this analysis will inform futureresearch related to classification and interpretation of RADARSAT-2 data for shoreline mapping.

Resume. Des decompositions Freeman–Durden et Cloude–Pottier ont ete appliquees a des donnees polarimetriques RADARSAT-2 pour caracteriser le comportement de diffusion des differents types de couverture terrestre des milieux littoraux et cotierspour deux zones d’etude dans la mer de Beaufort, au Canada: l’ıle Richards et le port de Tuktoyaktuk. L’impact de l’angled’incidence a ete evalue par comparaison des images singulieres complexes <<Single Look Complex (SLC)>> quad-pol finesacquises a trois angles; plus rasants (45.3◦–49.5◦), moyens (39.3◦–41.6◦) et abrupts (20.9◦–24.2◦), tandis que le potentiel pourune classification precise a ete evalue a l’aide de trois classificateurs RSO polarimetriques non diriges: Wishart-entropie/alpha,Wishart-entropie/anisotropie/alpha et Freeman–Wishart. Les resultats indiquent que, dans l’ensemble, les images avec des anglesd’incidence moyens a rasants fournissent des informations plus utiles que les images aux angles abrupts, et que ces classificateurssont capables de distinguer certains types generaux de couverture terrestre, y compris: les substrats, les zones humides, la toundravegetalisee et l’eau. Les resultats de cette analyse seront utiles dans les recherches futures liees a la classification et a l’interpretationdes donnees RADARSAT-2 pour la cartographie des rivages.

INTRODUCTIONIn the wake of the Exxon Valdez oil spill off the coast of

Alaska and the Nestucca spill off the coast of Washington statein 1989, specific protocols were developed to document anddescribe oiled shorelines. With contributions from EnvironmentCanada, these would later develop into the Shoreline Cleanupand Assessment Technique (SCAT). By 1994, a standardizedfield guide was developed so that methods could be applied

Recevied 25 April 2014; Accepted 9 October 2014.∗Corresponding author e-mail: sarah.banks@ec.gc.ca

consistently, regardless of location and response team (Owensand Sergy 1994). Since then, a number of agencies throughoutNorth America have adopted the SCAT approach and haveworked toward continued method standardization (Owens andSergy 2000). This is motivated by 4 facts: (i) oil deposits are typ-ically nonuniform in the affected area, (ii) the affected area canbe extensive, making logistical requirements difficult to assess,(iii) if identified, the most sensitive shorelines in terms of bio-logical and or human resources can be prioritized for protectionand cleanup, and (iv) responders require specific informationfor operational-level decisions (Lamarche et al. 2007).

291

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

292 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

Many organizations, including Environment Canada, havemoved toward maintaining and establishing pre-spill databasesin order to make response and cleanup efforts more efficient.This is possible because these databases enable quick accessand ready dissemination of information to responsible parties(Lamarche et al. 2003). For almost 30 years, audio commentariesand helicopter videography acquired at 150 km–185 km perhour and 100 m altitude have been used for this purpose (Owens1983). Initially, these databases were stored as paper atlases, buttoday most agencies use geographic information systems (Percyet al. 1997; Laflamme and Percy 2003; Lamarche et al. 2007).There are, however, a number of limitations associated with thecurrent mapping method: (i) it is expensive and time consuming,(ii) the experience of the videographer largely determines videoand map quality, and (iii) acquiring videography is difficult toplan logistically, because it requires the timing of acquisitionswith the lowest tides (Lamarche et al. 2007).

Synthetic Aperture Radar (SAR) may be well suited to de-tecting and differentiating a number of shore and near-shoreland cover classes, as well as contributing to the developmentof a semiautomated process that could be used for broad scalemapping. SAR sensors are capable of acquiring imagery underall weather and lighting conditions, and RADARSAT-2 data,specifically, are readily available to the Canadian federal gov-ernment. For Arctic shore and near-shore mapping applications,there is a need to better understand the information that canbe derived from such data, including the scattering behaviourof the various land cover types present and how this changeswhen images are acquired at different incidence angles. Becausethere is little information available in the current literature, thisstudy represents a continuation of Banks et al. (2014), whichfocused on the nonpolarimetric information contained in multi-angle fine Quad-Pol RADARSAT-2 data. The focus of this studyis to expand on that previous work, with the following specificobjectives:

(i) Characterize the scattering behaviour observed for variousshore and near-shore Arctic land cover types at multipleincidence angles, through analyses of two polarimetric de-compositions.

(ii) Assess the potential for unsupervised classification usingthree polarimetric SAR classifiers.

These analyses were conducted on the same six images usedby Banks et al. (2014), including three Single Look Complex(SLC) fine Quad-Pol RADARSAT-2 scenes acquired at differentincidence angles for two study areas.

BACKGROUNDThe following provides a brief description of the

Freeman–Durden and Cloude–Pottier decompositions, aswell as the Wishart-entropy/alpha (WH,α), Wishart-entropy/anisotropy/alpha (WH,α,A), and Freeman–Wishart (F-W) unsu-pervised polarimetric classifiers, which were used in this study.

Polarimetric DecompositionsPolarimetric decompositions are commonly applied to SAR

imagery to relate observed scattering behaviour to idealized re-sponses (scattering mechanisms) or for generating other usefulparameters that can also characterize scattering behaviour. In-coherent methods are often applied to natural surfaces (as inthis analysis), where local statistics are used to generate so-called decomposition parameters. Both Freeman–Durden andCloude–Pottier decomposition methods have been widely ap-plied to polarimetric SAR data (McNairn et al. 2009; Li et al.2012; Brisco et al. 2013).

The Freeman–Durden decomposition, based on the sym-metrized covariance (C3) matrix, partitions the backscatter ob-served for natural surfaces into respective contributions from 3simple scattering mechanisms represented by physical models.Surface scattering is modeled by a moderately rough surface,producing Bragg scattering. Even, or double-bounce, scatter-ing is modeled by 2 surfaces with different dielectric constantsthat lie orthogonally to one another. Diffuse or volume scat-tering is modeled by thin, cylindrically shaped, randomly ori-ented scatterers (Freeman and Durden 1998; Lee and Pottier2009).

The Cloude–Pottier decomposition, based on the coherencymatrix (T3), provides 3 useful parameters: entropy (H),anisotropy (A), and alpha angle (α). Entropy characterizes scat-tering randomness, where H = 0 is indicative of a single scat-tering mechanism or low depolarization, and H = 1 representsrandom scattering or high-signal depolarization. Anisotropy isthe normalized difference between the second and third eigen-values of the T3 matrix, wherein low values indicate that athird scattering mechanism is contributing significantly to totalpower, and high values indicate that only one secondary mech-anism is contributing significantly to total power (Cloude andPottier 1997). Alpha angle values range from 0◦ to 90◦, withlow (∼0 – 40.0◦), intermediate (∼40.0◦ to 52.5◦), and high (>52.5◦) values being indicative of surface, double-bounce, andvolume scattering, respectively (Cloude and Pottier 1996, 1997;Lee and Pottier 2009).

Cloude and Pottier (1997) also introduced a classificationscheme based on entropy and alpha values, which groups pixelsby scattering behaviour into 9 clusters, one of which represents a“nonfeasible” region resulting from high entropy values (> 0.9)preventing the identification of surface scattering when com-bined with low (0◦–40◦) alpha values. This classification alsorepresents the first step of the WH,α classifier, which uses theclusters that are produced to train a subsequent Wishart clas-sifier (Wishart distance-based maximum likelihood), applied inmultiple iterations. This classifier may assign pixels to new clus-ters, making it difficult to use results for interpreting scatteringbehaviour (Lee et al. 1999; Lee and Pottier 2009). The WH,A,α

classifier works similarly, but anisotropy values are used to gen-erate an additional 8 clusters during the initial classification. Athreshold value of 0.5 is applied where the first 8 clusters of theprevious segmentation have low anisotropy values (< 0.5), and

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 293

the next 8 have high anisotropy values (> 0.5; Lee and Pottier2009).

Lee et al. (2004) proposed a similar classification schemebased on the three parameters from the Freeman–Durdendecomposition. The advantage of this classifier is thatdominant scattering mechanisms observed initially by theFreeman–Durden decomposition are preserved. In the SARPolarimetry Workstation available in PCI Geomatica software(used in this analysis), various threshold values applied to powervalues are used to generate 9 clusters, in which clusters 1, 2, and3 represent low, medium, and high surface scattering, clusters 4,5, and 6 represent low, medium, and high double-bounce scat-tering, and clusters 7, 8, and 9 represent low, medium, and highvolume scattering.

Previous Applications Relevant to this ResearchThe subsequent review of current literature focuses on wet-

lands and substrate mapping applications, because many of theshore and near-shore land cover types present throughout thestudy areas can be categorized as such.

The usefulness of the Freeman–Durden and Cloude—Pottierdecompositions for wetlands mapping applications, includingfor interpreting scattering behaviour, has been well demon-strated. Brisco et al. (2013) achieved high overall classifica-tion accuracies for multiple wetland types in two study areasusing inputs from either decomposition; however, the authorsobserved the highest accuracies with the Freeman–Durden de-composition. Millard and Richardson (2013) found that the vol-ume component of the Freeman–Durden decomposition wasimportant in classifying multiple wetland types. Cable et al.(2014) characterized scattering behaviour of wetlands over timeand at multiple angles using a combination of Freeman–Durdenand Cloude–Pottier decomposition parameters and polarimet-ric response plots. Corcoran et al. (2011) used Cloude–Pottierdecomposition parameters to understand changes in scatteringbehaviour as a result of changes in vegetation density and hy-drologic dynamics of wetlands in northern Minnesota.

Similarly, both decompositions have been used in substratemapping applications. Choe et al. (2011) observed dominantsurface scattering over mudflats, and volume scattering overoyster reefs; da Silva et al. (2013) detected strong surface scat-tering from rocky outcrops, which represented relatively smoothsurfaces, double bounce from laterite blocks, and volume scat-tering in regions characterized by rougher surfaces. The authorsalso interpreted low entropy values as the result of interactionswith smoother rock surfaces, and found both entropy and alphaangle values were useful for identifying features in the region.Collingwood et al. (2014) found a direct relationship betweensurface roughness and anisotropy for two study areas in theHigh Arctic containing various substrates, including exposedbedrock, fine-grained moraine sediments, and some sparselyvegetated areas.

The WH,α and WH,A,α, and F-W classifiers have been usedpreviously by Shelat et al. (2012) to map surficial materials inUmiujalik Lake area, Nunavut. The authors found that incidenceangle had an effect on classification accuracy and that resultswere best at a medium angle (∼32◦). Overall, the authors foundthat the F-W classifier was better for discriminating gravel, sanddeposits, and bedrock than the WH,α and WH,A,α classifiers. Kochet al. (2012) could also easily detect wetlands with the WH,A,α

classifier applied to C-band RADARSAT-2 data.

MATERIALS AND METHODSAs described subsequently, the same reference data, satellite

images, and overall processing chain as described in Banks et al.(2014) were used in this analysis.

Study AreasThe first study area (∼25 km2) is centered on the West Point

of Richards Island, hereafter called WP (Figure 1). Shorelineshere are composed mostly of cliffs fronted by narrow sandbeaches (Rampton 1987a; 1988). In the southwestern portionof the area, islands approximately 1 m–2 m above sea level arecommon. These are composed mostly of peat and mud and arefronted by narrow peat beaches with high- and low-center poly-gons in the backshore. Extensive mudflats composed mostlyof fine silt, sand, and organic matter have developed, with thelargest approximately 12 km long and 3 km wide (Hequette andBarnes 1990). The second study area (∼25 km2) is centered onTuktoyaktuk Harbour, hereafter called TH (Figure 1). This sitemay be differentiated from the first by a lack of extensive mud-flats and low tundra islands, as well as the presence of a numberof anthropogenic features, including houses and roads. Spitsand barrier islands are more common here than in the WP studyarea, though the majority of shorelines are also narrow beachesat the base of tundra cliffs. Substrates north of the harbor arecomposed mostly of sand, whereas those to the south typicallycontain some pebble (Hequette and Barnes 1990). In this studyarea, inundated tundra can be found in a number of shelteredareas, and high- and low-center polygons are also present in thebackshore (Harper 1985; Rampton 1987b).

Reference DataHelicopter videography surveys, recorded simultaneously

with audio interpretations of the shore and near-shore land covertypes, were used to collect general information along the coastalzone of each study area on July 20 and 21, 2010; refer to Bankset al. (2014) for details. Additional field reference data were col-lected at 26 different landing sites between July 22 and July 26,2010. This information, in combination with 1.5 m orthophotosacquired in 2004 (provided by the Northwest Territories Centrefor Geomatics), was used to collect sample sites for each landcover type described in Table 1, as well as for assessing results of

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

294 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 1. 2004 orthophotos showing coverage of (a) the WP site and (b) the TH site (NWT Centre for Geomatics). Numbers areused to indicate shore and near-shore land cover types, including (1) narrow sediment beaches at the base of tundra cliffs, (2)low tundra islands fronted by narrow peat beaches, (3) extensive mud tidal flats, (4) anthropogenic features, (5) spits and barrierislands, and (6) inundated tundra.

the polarimetric classifiers. Comparisons among the orthopho-tos, helicopter videography, and available RADARSAT-2 im-agery indicated that the shore and near-shore land cover types,as well as the physical location of the land-water interface, didnot change significantly in either study area, which justifies theiruse for this purpose. Hereafter, land cover class names are pre-sented with the first letter of each word in the name capitalized(e.g., Anthropogenic). Note that in the Banks et al. (2014) paperthe Low Tundra and Tall Shrubs classes were referred to as HerbDominant Tundra and Shrub Dominant Tundra, respectively.

RADARSAT-2 Image AcquisitionsShallow, medium, and steep incidence angle SLC fine Quad-

Pol descending mode images were acquired over each site inthe summer of 2010 (Table 2). Because of the high demand forRADARSAT-2 imagery within the vicinity of the study areasand the requirement to submit acquisition requests months inadvance, it was not possible to plan around tides or weatherconditions. Despite this, tidal heights were relatively consistentbetween acquisitions (FOC 2010), and although prior to someacquisitions “drizzle” was reported at the closest weather stationnear Tuktoyaktuk, the impact this had on scattering behaviourcould not be assessed because the amount of precipitation was

not recorded, and it is not known whether this was simply a lo-calized event. The weather station at Tuktoyaktuk also recordedwind speed information on the dates of the acquisitions, though,as previously stated, it can be used reliably for interpretingresponses only for the images acquired over the TH site. Thisinformation is provided in Table 2 (EC 2010; Banks et al. 2014).

RADARSAT-2 Image Processing and Sample Site SelectionThe sigma nought (σ ◦) lookup table was applied to each

raw image in the form of the complex 2 × 2 scattering matrix.All scattering matrices were then converted to the symmetrizedC3 and the T3 matrices, which were subsequently filtered withthe enhanced Lee speckle filter with a 7 × 7 window. The C3matrices were then used to generate Freeman–Durden decompo-sition parameters and to apply the F-W classifier (Freeman andDurden 1998; Lee et al. 2004), while the T3 matrices were usedto generate the Cloude–Pottier decomposition parameters andto apply the WH,α and WH,A,α classifiers (Cloude and Pottier1997; Ferro-Famil et al. 2002, 2003). Each scene was then geo-referenced using a minimum of 20 ground control points (Jiaoet al. 2011; Banks et al. 2014).

Following image processing, all reference data were con-sidered in the selection of samples sites for generating global

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 295

TABLE 1Land cover classes considered in this analysis

Class Description

Anthropogenic All structures composed of human made materials impermeable to oil.Smooth/Unvegetated Mudflat Dominantly silt and clay sediments < 0.0625 mm in diameter.Rough/Vegetated Mudflat Mudflat that is roughened by caribou tracks and/or a sparse vegetation

cover.Peat Shoreline Dominant substrate type is peat.Sand Beaches/Flats Dominant grain size: 0.0625 mm to 2.0 mm, and up to 10 % other sediment.Mixed Sediment Beaches/Flats Sand mixed with any combination of granule (2.0 to 4.0 mm), pebble (4.0

to 64.0 mm), cobble (64.0 to 256.0 mm).Riprap Feature placed along shorelines to prevent erosion. Typically composed of

boulders.∗Wood/Substrate Mix Sand or mixed sediment beach/flat littered with some woody debris (up to

50 % coverage).∗Woody Debris Dominant substrate type is woody debris (> 50 % coverage). Typically

found in bays or along beaches.Marsh Dominant vegetation cover is glasswort or sedge, and coverage is sparse

(> 15 % of land). Salt or brackish water covers these areas at least once amonth during high tide.

Wetland Does not contain significant amount of sedges or rushes and is dominatedby grasses.

Inundated Low-Lying Tundra (ILLT) Mostly vegetated tundra that is at or near the water surface. Flooded duringstorm surges/spring high tide and can be permanently submerged insome areas.

∗Low-Center Tundra Polygons An area with actively growing ice wedges forming polygonal features inthe landscape. Polygon centers are below margins and in the summerinterstitial water is present above ice wedges.

∗High-Center Tundra Polygons An area with inactive ice wedges that once formed polygonal features inthe landscape. Polygon centers are above margins and in the summerthere is little to no interstitial water.

Eroding Tundra Composed mostly of retrogressive thaw slumps, though areas often containmudflows and surface wash.

∗Low Tundra Upland tundra composed mostly of low-lying herbaceous vegetation andshrubs.

∗Tall Shrubs Upland tundra composed mostly of tall shrubs (> ∼0.25 m ).Water Any water surface such as the ocean, lakes, or ponds.

∗Not considered previously for sensitivity mapping in Canada (Banks et al. 2014).

statistics (mean and standard deviation values), as well as forassessing the three unsupervised polarimetric classifiers. Sam-ple sizes ranged from 127 pixels (Peat in the TH site) to 1960pixels (Water in the TH site). In some cases, sample size waslimited by the width and extent of a given class, and whereappropriate and feasible because of availability of samples, anattempt was made to match sample size with perceived varianceof a given land cover class. To assess classifier results, accuracywas not assessed in a conventional way; instead, an attempt wasmade to determine whether clusters represented unique shoreor near-shore features, or combinations of shore and near-shorefeatures.

RESULTS AND DISCUSSION

Freeman–Durden DecompositionThe following section provides a discussion of the proportion

of total power (Table 3) attributable to each of the 3 scatteringmechanisms defined by the Freeman–Durden decomposition(Figure 2 and Figure 3). In most cases, the scattering mecha-nisms identified by the Freeman–Durden decomposition werealso consistent with those identified using polarimetric scatter-ing plots, generated with the Polarimetric Work Station (CCRS2011). As such, for brevity, these are not presented here (fordetails, refer to Banks 2012).

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

296 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 2. Percent (%) contribution to total power for all land covers present in the TH site. Surface, double-bounce and volumescattering are represented by blue, red, and green colors, respectively.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 297

FIG. 3. Percent (%) contribution to total power for all land covers present in the WP site. Surface, double-bounce, and volumescattering are represented by blue, red, and green colors, respectively.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

298 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

TABLE 2RADARSAT-2 image acquisitions over the TH and WP study areas, each covering approximately 25 km2

Study Area Beam ModeIncidenceAngle (◦) Orbit

AcquisitionDate (UTC) Time (UTC)

Wind Speed(Km/h)

WindDirection (◦)

TH FQ 31 48.4–49.5(shallow)

Descending 8/19/2010 15:06 20 100

FQ 21 40.2–41.6(medium)

Descending 8/15/2010 15:23 17 110

FQ 3 20.9–22.9(steep)

Descending 8/17/2010 15:56 32 140

WP FQ 27 45.3–46.6(shallow)

Descending 8/22/2010 15:19 20 30

FQ 20 39.3–40.8(medium)

Descending 8/25/2010 15:31 15 360

FQ 4 22.3–24.2(steep)

Descending 7/31/2010 16:00 2 70

Wind direction is provided in geographic or true direction from which the wind blows (i.e., not magnetic; EC, 2010; Banks et al. 2014).

On average, volume scattering was the dominant contributorto total power for the Anthropogenic class, and this was ob-served at all angles (Figure 2). This resulting confusion withvegetation is a known limitation that has been previously ob-served with the Freeman–Durden decomposition (Yamaguchiet al. 2005). In the Arctic, with sparse population and communi-ties, however, this confusion should not be a significant concern.Total power showed relatively low sensitivity to incidence an-gle because values ranged from only −2.57 dB to 0.06 dB atshallow and steep angles, respectively. High total power valuesmay be attributable to metallic objects, and or multiple scatter-ing sources, including buildings and roofs (Dong et al. 1997;Cloude and Pottier 1997).

For Water, both surface and volume scattering contributionswere high at shallow angles, making up 40 % to 50 % of thetotal observed power (Figure 2 and Figure 3). As was similarlyobserved by Cable et al. (2014), average horizontal transmit andvertical receive (HV) contributions for this class were close tothe noise floor (−33.30 dB for the TH site, and −34.35 dB forthe WP site). Freeman and Durden (1998) note that these lowreturns can result in confusion and misrepresentation of openwater. As incidence angles steepened, contributions from sur-face scattering increasingly dominated, at 61 %–70 % of totalpower for medium and 94 %–95 % of total power for steepangles. Total power also increased at steep angles, which couldbe from greater direct returns overall and/or from increasedsensitivity to Bragg scattering (Ulaby et al. 1986; Pietroniroet al. 2005). As a result, this made it easier to detect thesmooth and most saturated mudflats at these angles, becausereturns remained low for this class (Figure 4). This is consistentwith Baghdadi et al. (2004), who also found that mudflats inFrench Guiana were better distinguished from water at steepangles.

Substrate classes with relatively smooth surfaces (i.e.,Smooth/Unvegetated Mudflat, Rough/Vegetated Mudflat, Peat,Sand, and Wood/Substrate Mix) were dominated by surfacescattering at all angles (Figure 2 and Figure 3). Choe et al.(2011) found similar results for mud and sand flats along theKorean peninsula with RADARSAT-2 and Advanced Land Ob-serving Satellite (ALOS) data. The total power observed forthese classes was lower than for classes with rougher surfaces,including Mixed Sediment. This difference was also more ap-parent at shallow angles, with average surface scattering con-tributions for Smooth/Unvegetated Mudflat, Sand, and MixedSediment of −19.17 dB, −20.06 dB, and −13.77 dB, respec-tively. This indicates potential for general discrimination be-tween rough and smooth surfaces.

Compared with most other substrates, the volume scatter-ing contribution for Mixed Sediment and Riprap were relativelyhigh, even though surface scattering was dominant in most cases,especially at steep angles. These classes also had higher HV re-turns (Banks et al. 2014), as well as high mean and standarddeviation values for total power, surface, double-bounce andvolume scattering power. As a result, they generally appearedbrighter and more textured, making it possible to visually dis-tinguish these classes from Water and substrates with smoothersurfaces. Dominant surface scattering was also observed overthe Woody Debris class at all angles, which could be becauselogs are much larger than the wavelength of incident C-bandmicrowaves, resulting in mostly direct returns from the surface,opposed to multiple scattering interactions.

Most vegetated classes showed dominant volume scatteringat all incidence angles (Figure 2 and Figure 3). Due to vary-ing proportions of surface versus volume scattering, however,some classes were well distinguished, including Low Tundraand Tall Shrubs (Figure 5), because surface scattering made

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 299

TABLE 3Mean (x) and standard deviation (s), of the total power (SPAN) for each land cover type in the TH and WP sites

Shallow Medium SteepStudy Sample

Class Area Size x s x s x s

Anthropogenic TH 662 −2.58 3.08 −1.65 3.63 −0.09 3.61WP N/A

Water TH 1960 −20.48 1.09 −17.98 1.09 −4.37 1.37WP 1056 −21.97 1.04 −20.12 1.00 −7.54 1.23

Smooth/UnvegetatedMudflat

TH 268 −17.67 1.66 −15.80 1.50 −9.46 1.51

WP 1050 −16.08 1.62 −14.37 1.65 −8.08 1.91Rough/VegetatedMudflat

TH N/A

WP 937 −8.74 1.35 −6.45 1.55 −2.46 1.56Peat TH 127 −15.18 2.45 −11.84 2.45 −5.67 1.16

WP 171 −10.66 1.92 −9.13 1.51 −4.26 1.53Sand TH 834 −17.46 1.65 −14.59 2.00 −8.80 1.87

WP 1233 −16.03 3.01 −14.87 3.39 −6.12 2.75Mixed Sediment TH 760 −9.61 1.72 −8.14 1.50 −5.55 1.62

WP 688 −9.90 1.30 −8.62 1.39 −6.01 1.44Riprap TH 140 −9.13 1.34 −7.93 1.90 −4.89 1.22

WP N/AWood andSubstrate Mix

TH 662 −13.93 1.79 −11.60 1.69 −7.14 1.65

WP 428 −8.99 2.36 −8.17 2.62 −6.36 2.26Woody Debris TH 799 −9.92 1.31 −6.73 1.57 −5.94 1.09

WP 724 −4.56 1.34 −2.88 1.35 −3.64 1.16Marsh TH 249 −12.50 1.28 −9.81 1.13 −6.02 1.80

WP 923 −10.73 1.61 −9.46 1.80 −5.66 1.34Wetland TH 474 −7.22 1.05 −4.88 1.12 −2.85 1.25

WP 201 −8.51 1.54 −7.93 1.59 −5.96 1.60ILLT TH 705 −10.68 1.44 −8.59 1.43 −6.04 1.25

WP 725 −9.61 0.84 −8.60 0.83 −6.15 1.00High-CenterPolygons

TH N/A

WP 779 −8.78 0.76 −8.08 0.80 −5.88 0.83Low-CenterPolygons

TH 813 −10.70 1.29 −9.27 1.26 −5.40 1.31

WP 681 −10.42 1.35 −9.84 1.10 −6.64 1.19Eroding Tundra TH 489 −7.86 1.37 −6.06 1.41 −4.84 1.98

WP 477 −7.68 1.80 −6.41 2.05 −5.17 2.26Low Tundra TH 806 −11.50 1.19 −9.97 0.86 −7.79 1.26

WP 989 −10.50 1.25 −9.17 1.41 −7.99 1.62Tall Shrubs TH 942 −7.73 0.79 −6.85 0.80 −6.56 1.08

WP 818 −6.10 0.93 −5.63 1.02 −5.59 0.98

up 19 %–42 % of total power for the former compared to7 % to 23 % for the latter. At steep angles, some vegetatedclasses, including Marsh, showed dominant surface scatter-ing. Based on this analysis, incidence angle effects should

be considered when acquiring imagery in similar Arctic en-vironments. Total power values were also generally higherfor the most densely vegetated classes, including Tall Shrubs(Table 3).

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

300 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 4. (a) 2004 orthophotos over the WP study area, with FD decomposition results for (b) shallow, (c) medium, and (d) steepangles. Numbers indicate (1) Water, (2) Smooth/Unvegetated Mudflat, (3) Rough/Vegetated Mudflat, (4) ILLT, and (5) Wetland.

Some regions within certain vegetated classes showed highdouble-bounce scattering, and this was more pronounced at shal-low angles (Figure 5). Over wetlands, this response is commonlyattributed to waves striking the water surface and then verticallystanding vegetation (Brisco et al. 2011), however, in the caseof inundated low-lying tundra (ILLT) and Low-Center Poly-gons, this could also be attributable to microcliffs, which in thecase of the latter, develop as a result of ice wedge developmentand often contain standing water. Wetland in the TH site wasthe only class that showed dominant double-bounce scattering,though this was true only for shallow and medium angles; vol-ume scattering was dominant at steep angles. Although some

wetlands mapping (Brown et al. 1996) and soil moisture model-ing studies (Ulaby and Batlivala 1976; Daughtry et al. 1991) innon-Arctic environments have shown greater sensitivity to mois-ture at steep angles, these results show that acquisition geometryalso plays a role in inducing double-bounce returns. As shownin Figure 5, the reduced contribution from this parameter madea number of features more difficult to visually detect at steepangles.

Of all 3 scattering mechanisms, double-bounce contributionswere the least consistent between study areas, with the excep-tion of Wetland at shallow angles (Table 4). This may be ex-pected because it was one of the few classes to be dominated

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 301

FIG. 5. (a) 2004 orthophotos over the TH study area, with FD decomposition results for (b) shallow, (c) medium, and (d) steepangles. Numbers indicate (1)Water, (2) Tall Shrubs, (3) ILLT, (4) Low Tundra, and (5) Wetland.

by double-bounce scattering, whereas values for other classeswere typically low and distributions were highly skewed. Sur-face and volume-scattering contributions for classes such asSmooth/Unvegetated Mudflat, Peat, Wetland, and ILLT weretypically inconsistent between study areas. These are some ofthe same classes that showed similar results for backscatter-ing coefficients (Banks et al. 2014). Overall, volume-scatteringcontributions were the most consistent between study areas. Forindividual classes, Woody Debris showed the greatest inconsis-tency in values. Total power at shallow angles, for example, was−9.92 dB in the TH site, and −4.56 dB in the WP site. This wassimilarly observed for backscattering coefficients (Banks et al.2014).

Cloude–Pottier DecompositionResults from the entropy-alpha feature space segmentation

are provided in Figure 6 and Figure 7. Mean and standard devi-ation values for anisotropy are also provided in Table 5.

At shallow and medium angles, most sample data for theAnthropogenic class fell into Zone 4, features with mediumentropy, representing multiple scattering; and Zone 5, featureswith medium entropy, representing scattering from vegetatedsurfaces (Cloude and Pottier 1997). Although Cloude and Pot-tier (1997) suggest that Zone 4 may include urban areas, it isclear that the definition of Zone 5 (i.e., vegetated surfaces) doesnot correspond well to what was observed at C-band at theseincidence angles. At steep angles, most points fell within Zones

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

302 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

TABLE 4Percent of Freeman–Durden parameter values within the overlap region produced by the mean ± 2 standard deviations (x ± 2s)

for sample distributions to show the consistency between study areas for the same incidence angles

Odd Bounce Double Bounce Volume ScatteringSample

Class Size Shallow Medium Steep Shallow Medium Steep Shallow Medium Steep

Water 3016 81.37 99.20 87.14 14.36 13.30 0.00 98.64 96.02 0.00Smooth/UnvegetatedMudflat

1336 5.77 0.91 0.00 11.99 9.18 0.00 96.28 88.92 32.17

Peat 298 76.51 28.86 0.00 34.56 29.19 0.00 79.87 79.19 55.70Sand 2067 96.08 97.68 97.10 61.59 63.09 9.72 98.36 98.02 73.29Mixed Sediment 1448 92.13 96.41 96.48 63.74 62.15 24.52 96.62 99.45 96.62Wood/Substrate Mix 1090 92.73 97.61 96.41 70.93 70.10 18.22 89.51 92.46 93.65Woody Debris 1523 91.14 95.80 98.36 65.99 62.31 45.57 55.55 66.58 97.64Marsh 1172 74.32 81.74 98.72 68.00 67.66 32.85 84.81 96.42 97.53Wetland 675 91.82 87.20 52.23 95.09 77.83 18.60 98.07 96.58 34.23ILLT 1430 69.16 68.18 94.13 57.27 54.34 36.92 88.46 96.36 97.48Low-CenterPolygons

1494 81.39 78.18 97.66 73.43 69.88 33.60 98.13 99.26 97.66

Eroding Tundra 966 66.32 70.64 88.43 49.05 55.01 32.38 96.63 97.06 99.31Low Tundra 1795 71.87 68.36 92.76 36.77 30.92 18.16 97.77 97.88 99.16Tall Shrubs 1760 38.30 45.63 69.20 18.98 26.36 22.73 82.95 97.78 98.41

All values less than 80 % are bolded and italicized.

5 and Zone 6, the latter corresponding to surface scattering withmedium entropy. Cloude and Pottier propose that Zone 6 couldrepresent the effect of propagation through a canopy, but again,this does not correspond to the Anthropogenic features observedin this analysis.

At shallow and medium angles Water fell mostly withinZones 6 and Zone 9, with the latter representing surface scat-tering and low entropy. Cloude and Pottier (1997) proposedthat this can be indicative of scattering from water at P- andL-band, which also corresponds to what was observed in thisanalysis at C-band. At steep angles, the sample distribution fellonly in Zone 9, because values for both entropy and alpha werelow. All substrate classes showed distributions similar to Water,with most points falling within Zones 6 and 9 at shallow andmedium angles, and mostly Zone 9 at steep angles. This createdsubstantial overlap between distributions, making it difficult todifferentiate between Water and substrates, as well as amongsubstrate types. Some classes, particularly Woody Debris, hadsimilar distributions at all angles, indicative of less sensitivityto incidence angle effects.

At shallow and medium angles, some vegetated classesshowed potential for discrimination from substrates, becausedistributions were more centered on Zone 5 (e.g., ILLT), or Zone4 (e.g., Wetland; Figure 6 and Figure 7). Despite this, manypoints still fell into other zones, especially Zone 6 (with themost substrates). At steep angles, samples were mostly betweenZone 5, 6, and 9. High-Center Polygons and Wetland were theonly classes to show relatively unique distributions compared to

other vegetated classes, because at shallow and medium angles,distributions were more centered on Zones 4 and 5, whereas atsteep angles, the distribution for High-Center Polygons movedto Zones 5 and 6, and the distribution for Wetland centered onZones 6 and 9 in the WP site, and mostly on Zone 5 in the THsite.

A well-known limitation of the entropy-alpha segmentationis that the physical boundaries proposed by Cloude and Pottier(1997) do not always coincide with natural breaks in the data(Lee et al. 1999). As described by Cloude and Pottier (1997),selection of these boundaries was somewhat arbitrary, and atthe time they were proposed it was also anticipated that a num-ber of data and sensor parameters, such as measurement noisefloor, would have an impact on their definition. Despite this,the degree of overlap observed in this analysis was so severethat little improvement is anticipated even if new boundaries aredefined.

Results observed for anisotropy were less conclusive becausevalues were more alike among the land covers of interest. Thisis consistent with Corcoran et al. (2011), who observed a lowrange in values with steep angle (28◦) RADARSAT-2 imagery,with Water being the one exception, having high values (alsoobserved in this analysis). Despite this, some trends were iden-tified, including that at shallow and medium angles anisotropyvalues were relatively low for most land covers (< ∼0.50).Exceptions to this include Anthropogenic and Wetland (THstudy area only), indicating that power contributions were moreequally distributed among 2nd and 3rd scattering mechanisms.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 303

FIG. 6. Entropy-alpha feature space segmentation results for all land cover types present in the TH site. Shallow, medium, andsteep angle samples are represented in blue, red, and green, respectively.

At steep angles, anisotropy values were relatively high for An-thropogenic, Water and Smooth/Unvegetated Mudflat, indicat-ing that the second scattering mechanism is more dominant thanthe third. Values tended to increase as incidence angles steep-ened for Water and Smooth/Unvegetated Mudflat (e.g., 0.31◦ to0.66◦ from shallow to steep angles for Water in the TH studyarea). In contrast, Wetland values decreased as incidence anglessteepened, particularly in the TH study area (i.e., 0.64◦ to 0.32◦

from shallow to steep angles). Most other classes showed dif-ferences of less than 0.05 in mean values, across all incidenceangles.

Compared to backscattering coefficients (Banks et al. 2014)and Freeman–Durden decomposition parameters presented ear-lier, entropy, anisotropy, and alpha values were substantiallymore consistent between the TH and WP sites (Table 6). Over80 % of all samples from both sites fell within the commonrange produced by the mean ± 2 standard deviations (x ±

2s) for each sample distribution. The only exceptions includedSmooth/Unvegetated Mudflat, Peat, and Wetland. This indicatesthat the Cloude–Pottier parameters might be more appropriateas inputs to broad scale classifiers, though distributions alsoshowed more overlap between classes, indicating less potentialfor discrimination.

Wishart-Entropy/Alpha,Wishart-Entropy/Anisotropy/Alpha Classifiers

Both the WH,α and WH,A,α classifiers were applied to avail-able RADARSAT-2 data. Because the results of the latter seemedto better distinguish land cover units that corresponded to thoseof interest, these are primarily discussed here. Results are dis-played in Figure 8 for the TH site and Figure 9 for the WPsite, and the number of pixels that fell within each cluster forthe shallow angle images is provided for each site in Table 7

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

304 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 7. Entropy-alpha feature space segmentation results for all land cover types present in the WP site. Shallow, medium, andsteep angle samples are represented in blue, red, and green, respectively.

and Table 8. Values for other images are not provided, becausethe results for shallow and medium angles were similar anddisplayed greater potential for accurate classification than thesteep angle imagery.

Substantial confusion between Anthropogenic and Wetlandwas observed because both were predominately grouped intocluster 10 (Table 7). There was also confusion with other vege-tated classes, especially Eroding Tundra and Tall Shrubs. Withthis classifier, as incidence angles steepened, confusion be-tween land covers with similar surface roughnesses, includ-ing Water and various substrates, tended to increase. As canbe observed in Figure 8, the extensive sand spit was not dis-criminated from adjacent water at steep angles, but can bediscerned at shallow and medium angles. This is consistentwith observations made by Lee et al. (1999) using L-bandAIRSAR data acquired over San Francisco, California. She-lat et al. (2012) also observed similar results using an FQ1RADARSAT-2 scene to classify Arctic surficial materials. It is

possible that this confusion is as a result of greater wind speedsduring the acquisition of the steep angle image. This requiresfurther investigation, because wind conditions at the WP sitewere not known and the effects of moisture must be consideredas well.

Substrates also showed confusion with vegetated classes,though this tended to be worse for substrates with rougher sur-faces, including Mixed Sediment. Some areas composed of TallShrubs could be differentiated from adjacent Low Tundra (Fig-ure 8 and Figure 9), though, as shown in Table 7 and 8, thiswas not the case for all sample sites. It is also of interest to notethat the use of the anisotropy parameter improved the ability todistinguish between the extensive Wetland in the TH site (Fig-ure 8) and adjacent Tall Shrubs at shallow angles; there wasgreater confusion with the WH,α classifier. Although classifiedas multiple different clusters, some potential was observed tovisually distinguish Marsh, ILLT, High-Center Polygons, andLow-Center Polygons from adjacent tundra (i.e., Low Tundra

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 305

FIG. 8. (a) Orthophoto of TH study area, where numbers indicate (1) Water, (2) Tall Shrubs, (3) Sand, (4) ILLT, (5) Low Tundra,(6) Wetland, and (7) Woody Debris. Results from the Wishart-entropy/anisotropy/alpha (WH,A,α) classifier are displayed for thesame area at (b) shallow, (c) medium, and (d) steep angles.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

306 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 9. (a) Orthophoto of WP study area, where numbers indicate (1) Water, (2) Smooth/Unvegetated Mudflat, (3) Tall Shrubs,(4) ILLT, and (5) Rough/Vegetated Mudflat. Results from the Wishart-entropy/anisotropy/alpha (WH,A,α) classifier are displayedfor the same area at (b) shallow, (c) medium, and (d) steep angles.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 307

TABLE 5Mean (x) and standard deviation (s) of the anisotropy (A) parameter from the Cloude–Pottier decomposition at all angles and

both study areas

Shallow Medium Steep

Class Study Area Sample Size x s x s x s

Anthropogenic TH 662 0.54 0.20 0.53 0.20 0.55 0.20WP N/A

Water TH 1960 0.31 0.12 0.37 0.14 0.67 0.10WP 1056 0.36 0.13 0.35 0.13 0.68 0.10

Smooth/Unvegetated Mudflat TH 268 0.35 0.13 0.37 0.14 0.68 0.11WP 1050 0.38 0.14 0.36 0.14 0.54 0.14

Rough/Vegetated Mudflat TH N/AWP 937 0.34 0.12 0.35 0.13 0.34 0.13

Peat TH 127 0.45 0.13 0.43 0.14 0.44 0.16WP 171 0.41 0.15 0.38 0.14 0.37 0.14

Sand TH 834 0.39 0.14 0.43 0.15 0.50 0.17WP 1233 0.44 0.14 0.44 0.14 0.47 0.15

Mixed Sediment TH 760 0.35 0.14 0.35 0.14 0.38 0.15WP 688 0.38 0.14 0.36 0.14 0.34 0.14

Riprap TH 140 0.44 0.18 0.47 0.18 0.38 0.16WP N/A

Wood/Substrate Mix TH 662 0.39 0.14 0.39 0.14 0.43 0.15WP 428 0.45 0.17 0.41 0.15 0.39 0.16

Woody Debris TH 799 0.42 0.15 0.40 0.16 0.39 0.16WP 724 0.34 0.14 0.32 0.12 0.34 0.13

Marsh TH 249 0.40 0.13 0.40 0.14 0.37 0.14WP 923 0.38 0.14 0.38 0.14 0.39 0.16

Wetland TH 474 0.63 0.13 0.62 0.13 0.33 0.13WP 201 0.49 0.14 0.44 0.15 0.40 0.16

ILLT TH 705 0.36 0.14 0.37 0.15 0.34 0.13WP 725 0.31 0.13 0.32 0.12 0.32 0.13

High-Center Polygons TH N/AWP 779 0.41 0.13 0.41 0.13 0.33 0.12

Low-Center Polygons TH 813 0.39 0.14 0.41 0.14 0.33 0.12WP 681 0.37 0.13 0.36 0.14 0.33 0.13

Eroding Tundra TH 489 0.33 0.12 0.33 0.13 0.30 0.13WP 477 0.31 0.12 0.31 0.12 0.34 0.13

Low Tundra TH 806 0.29 0.11 0.28 0.11 0.29 0.11WP 989 0.30 0.12 0.32 0.11 0.31 0.12

Tall Shrubs TH 942 0.29 0.12 0.30 0.12 0.31 0.12WP 818 0.29 0.13 0.30 0.12 0.30 0.12

and Tall Shrubs), though these classes could not be differentiatedfrom one another (Figure 8 and Figure 9).

Freeman–Wishart ClassifierResults from the F-W classifier are displayed for the shallow

angle images in Table 9 for the TH site, and Table 10 for the WPsite, as well as visually in Figure 10 for TH site and Figure 11 forthe WP site. For brevity, values for other images are not providedbecause the results for shallow and medium angle images were

similar, and the potential for accurate classification with thesteep images was also lower in this case.

For the Anthropogenic class, most pixels were grouped intoclusters 7, 8, and 9, whereas a number were also identifiedas clusters 1 through 6, causing confusion with other classes.In both sites, Water pixels were grouped mostly into cluster 1(low surface scattering), with some also falling into cluster 7.This resulted in confusion with a number of substrates, par-ticularly those with relatively smooth surfaces, including Sand

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

308 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 10. (a) Orthophoto of TH study area, where numbers indicate (1) Water, (2) Tall Shrubs, (3) Sand, (4) ILLT, (5) Low Tundra,(6) Wetland, and (7) Woody Debris. Results from the Freeman-Wishart (F-W) classifier are displayed for the same area at (b)shallow, (c) medium, and (d) steep angles.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 309

FIG. 11. (a) Orthophoto of WP study area, where numbers indicate (1) Water, (2) Smooth/Unvegetated Mudflat, (3) Tall Shrubs,(4) ILLT, and (5) Rough/Vegetated Mudflat. Results from the Freeman-Wishart (F-W) classifier are displayed for the same area at(b) shallow, (c) medium, and (d) steep angles.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

310 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

TABLE 6Percent of Cloude–Pottier parameter values within the overlap region produced by the mean ± 2 standard deviations (x± 2s) for

sample distributions to show the consistency between study areas for the same incidence anlges

Entropy (H) Anisotropy (A) Alpha (α)

Class Sample Size Shallow Medium Steep Shallow Medium Steep Shallow Medium Steep

Water 3016 85.97 86.31 94.26 94.03 93.67 94.56 93.24 93.97 94.89Smooth/Unvegetated Mudflat 1336 90.74 77.85 93.78 94.69 95.98 76.78 84.45 78.91 94.84Peat 298 93.29 90.27 60.40 92.28 92.62 90.27 94.63 87.25 59.73Sand 2067 96.23 91.92 86.89 91.97 95.07 95.26 92.79 88.00 89.36Mixed Sediment 1448 93.92 94.48 85.50 94.68 96.13 93.23 91.92 93.23 84.81Wood/Substrate Mix 1090 89.97 90.80 85.92 93.19 94.02 94.20 91.26 86.84 87.49Woody Debris 1523 92.78 93.83 89.76 91.40 87.52 90.09 94.68 94.16 90.22Marsh 1172 94.88 93.94 89.16 94.03 94.62 93.43 91.38 93.86 94.45Wetland 675 94.49 91.96 93.60 80.95 73.51 92.56 88.10 77.08 18.01ILLT 1430 94.20 91.19 85.31 93.22 92.24 95.59 94.06 92.38 87.83Low-Center Polygons 1494 92.10 92.77 94.44 94.71 92.44 93.98 92.84 93.44 94.31Eroding Tundra 966 95.24 95.03 90.27 95.96 94.93 94.72 94.82 95.24 85.40Low Tundra 1795 93.20 91.75 94.71 96.49 93.31 94.82 90.70 86.91 93.48Tall Shrubs 1760 92.22 93.41 95.51 95.74 96.25 96.08 91.02 91.25 94.38

All values less than 80 % are bolded and italicized.

and Smooth/Unvegetated Mudflat. These substrates also tendedto have more pixels identified in cluster 1, whereas those withrougher surfaces (e.g., Rough/Vegetated Mudflat) tended to havemore pixels grouped into clusters 2 and 3, indicating mediumand high surface scattering, as well as clusters 7 to 9, indicat-ing some volume scattering. This indicates some potential for

discriminating generally between rough and smooth substrates,though not between substrates with similar roughnesses. Ad-ditionally, there was not much potential observed for discrim-inating vegetated classes and substrates with rougher surfacesbecause the former were also mostly grouped into clusters 8 and9 (medium and high volume scattering).

TABLE 7Results of the Wishart-entropy/anisotropy/alpha (WH,A,α) classifier applied to shallow angle image for the TH site

Cluster

Class Sample Size 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16

Anthropogenic 662 92 1 0 1 0 0 0 2 531 21 0 11 1 1 1Water 1960 0 0 1148 0 0 0 0 0 0 0 812 0 0 0 0Smooth/Unvegetated Mudflat 268 0 0 22 0 0 77 0 0 0 0 161 2 1 5 0Peat 127 0 0 2 1 0 39 0 0 0 0 19 0 20 46 0Sand 834 0 0 7 1 0 456 0 0 0 0 213 0 9 147 1Mixed Sediment 760 56 172 0 40 104 0 26 89 26 76 0 6 141 12 12Riprap 140 3 48 0 10 20 0 0 9 7 28 0 6 9 0 0Wood/Substrate Mix 662 0 1 0 1 12 78 0 0 0 0 1 1 245 311 12Woody Debris 799 0 205 0 101 185 0 6 8 4 87 0 15 181 0 7Marsh 249 0 4 0 29 10 1 1 0 0 0 0 8 143 15 0Wetland 474 0 0 0 0 0 0 0 0 416 31 0 27 0 0 0ILLT 705 7 24 0 243 86 0 42 10 32 95 0 37 77 6 46Low-Center Polygons 813 0 68 0 240 69 0 33 31 10 126 0 81 89 0 66Eroding Tundra 489 216 21 0 13 7 0 22 72 64 72 0 0 2 0 0Low Tundra 806 0 87 0 74 250 0 55 7 0 6 0 4 150 0 173Tall Shrubs 942 648 14 0 0 0 0 4 207 54 15 0 0 0 0 0

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 311

TABLE 8Results of the Wishart-entropy/anisotropy/alpha (WH,A,α) classifier applied to shallow angle image for the WP site

Cluster

Class Sample Size 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16

Water 1056 0 0 448 0 0 0 0 0 0 0 608 0 0 0 0Smooth/Unvegetated Mudflat 1050 0 0 0 0 1 804 0 0 0 0 100 0 35 110 0Rough/Vegetated Mudflat 937 0 9 0 1 403 0 0 63 1 419 0 0 7 0 34Peat 171 2 10 0 0 55 11 0 5 2 38 0 2 43 3 0Sand 1233 0 1 41 0 31 211 0 0 0 4 364 1 249 331 0Mixed Sediment 688 1 84 0 30 235 0 0 19 0 273 0 28 14 0 4Wood/Substrate Mix 425 31 26 0 9 48 0 4 23 14 139 0 49 48 2 32Woody Debris 724 0 0 0 0 0 0 14 3 0 2 0 0 0 0 705Marsh 923 138 34 0 156 84 0 3 15 27 97 0 225 137 6 1Wetland 198 61 1 0 6 0 0 0 0 98 11 0 19 1 1 0ILLT 725 246 101 0 204 1 0 1 32 41 88 0 8 0 0 3Low-Center Polygons 681 59 95 0 115 30 0 3 20 39 112 0 175 27 0 6High-Center Polygons 779 300 41 0 24 0 0 4 22 340 26 0 17 0 0 5Eroding Tundra 477 90 50 0 39 2 0 44 75 8 20 0 0 0 0 149Low Tundra 989 55 210 0 264 156 0 3 109 11 72 0 94 15 0 0Tall Shrubs 818 1 0 0 0 0 0 268 14 0 0 0 0 0 0 535

Visually, the classifier worked well for identifying Wetland,ILLT, and Low-Center Polygons because double bounce wasobserved in only parts of these land covers (Figure 10 andFigure 11). This was observed at only shallow and mediumangles though because, in general, there was a trend towarddecreased double bounce and volume scattering and an in-

crease toward greater surface scattering from shallow to steepincidence angles. This was also observed by Shelat et al.(2012), because most of the land covers in their study area,including water, till, vegetated till, sand and gravel, organicdeposits, boulder and bedrock, were grouped into clusters1 and 2.

TABLE 9Results of the Freeman–Wishart (F-W) classifier applied to the shallow angle image for the TH site

Cluster

Class Sample Size 1 2 3 4 5 6 7 8 9

Anthropogenic 662 19 55 11 87 141 9 44 285 19Water 1960 1616 1 0 4 0 0 339 0 0Smooth/Unvegetated Mudflat 268 261 7 0 0 0 0 0 0 0Peat 127 91 23 1 0 0 0 11 1 0Sand 834 506 94 0 0 0 0 183 49 2Mixed Sediment 760 68 176 67 0 0 0 10 329 110Riprap 140 24 49 21 0 2 0 2 36 6Wood/Substrate Mix 662 321 206 2 0 0 0 68 65 0Woody Debris 799 420 174 50 0 0 0 25 106 24Marsh 249 26 82 5 2 0 0 17 112 5Wetland 474 1 29 13 30 241 97 0 50 13ILLT 705 0 47 64 26 3 9 3 48 443Low Centre Polygons 813 55 113 31 11 14 5 26 477 81Eroding Tundra 489 6 14 37 0 0 1 2 210 219Low Tundra 806 24 67 15 0 0 0 60 618 22Tall Shrubs 942 3 6 13 0 0 0 0 404 516

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

312 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

TABLE 10Results of the Freeman–Wishart (F-W) classifier applied to the shallow angle image for the WP site

Cluster

Class Sample Size 1 2 3 4 5 6 7 8 9

Water 1056 667 0 0 4 0 0 385 0 0Smooth/Unvegetated Mudflat 1050 970 65 3 0 0 0 12 0 0Rough/Vegetated Mudflat 937 65 653 156 0 0 0 0 56 7Peat 171 13 63 15 0 0 0 5 68 7Sand 1233 731 257 11 7 0 0 138 87 2Mixed Sediment 688 56 345 82 0 0 0 6 182 17Wood/Substrate Mix 425 105 116 64 0 1 0 8 99 32Woody Debris 724 235 112 211 0 0 0 10 81 75Marsh 923 53 143 50 5 8 2 25 565 72Wetland 198 6 14 4 15 59 35 1 45 19ILLT 725 17 38 33 1 4 3 7 493 129Low-Center Polygons 681 13 49 38 2 10 0 5 514 50High-Center Polygons 779 9 18 8 9 67 24 5 482 157Eroding Tundra 477 7 7 29 0 0 0 4 254 176Low Tundra 989 14 42 42 0 0 0 7 792 92Tall Shrubs 818 1 3 2 0 0 0 0 117 695

CONCLUSIONThis analysis has contributed to improving our understand-

ing of the scattering behaviour of a number of shore and near-shore Arctic land covers at various incidence angles using SingleSLC Fine Quad-Pol RADARSAT-2data. This will improve ourability to interpret and extract useful information as part of fu-ture analyses under Environment Canada’s Emergency SpatialPre-SCAT for Arctic Coastal Ecosystems project. Potential forgeneral land cover classification of such shore and near-shoreArctic environments has also been demonstrated and will pro-vide a basis for testing other classifiers. Specifically, future workin classification will focus on the combined use of backscatter-ing coefficients, polarimetric data, digital elevation models, andoptical imagery. The unsupervised classifiers might similarlyprovide at least some indication of the shoreline types presentand could be used as part of preliminarily analyses or wherethere is little ground truth data available. The specific conclu-sions are as follows:

(i) Depending on the moisture conditions and land cover type,scattering behaviour can show sensitivity to changes inincidence angle, though in this study, large differences werenot observed when the difference in incidence angle wasbetween 6◦ and 8◦.

(ii) Although differences in total power were observed, the typeof dominant scattering mechanism for like incidence angleswas relatively consistent between the two study sites.

(iii) Greater potential for class discrimination was observed atshallow and medium angles compared to steep.

(iv) Overall potential has been observed to discriminate a num-ber for shore and near-shore land cover types, includingsubstrates, wetlands, vegetated tundra, and water, throughunsupervised classification.

Shoreline width, relative to image resolution proved to be amajor limitation of this study. As a result, sample sizes for someshoreline types were smaller than desired for representation ofthe spatial variability of a given class. This was especially thecase for classes such as Peat shorelines. The inconsistenciesobserved between study areas for the same land cover type,as well as the impact of moisture and waves on water, requirefurther consideration because it is anticipated that these issueswill complicate broad-scale mapping of certain classes.

ACKNOWLEDGMENTSThe authors would like to thank Steve Solomon for providing

field expertise, for initially advising the project, and for beingan exceptional colleague.

FUNDINGThe authors would like to thank Jason Duffe and Doug King

for providing funding for this research through the CanadianSpace Agency, a NSERC Discovery Grant, and through an En-vironment Canada–Carleton University collaborative researchagreement.

REFERENCESBanks, S. N. 2012. “Assessing RADARSAT-2 Polarimetric SAR for

mapping shoreline cleanup and assessment technique (SCAT) classes

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

VOL. 40, NO. 4, AUGUST/AOUT 2014 313

in the Canadian Arctic (MSc. Thesis, Department of Geography andEnvironment Studies, Carleton University, Ottawa).

Banks, S. N., King, D. J., Merzouki, A., and Duffe, J. 2014. “AssessingRADARSAT-2 for mapping shoreline cleanup and assessment tech-nique (SCAT) classes in the Canadian Arctic.” Canadian Journal ofRemote Sensing, (in press).

Baghdadi, N., Gratiot, N., Lefebvre, J.P., Oliveros, C., and Bour-guignon, A. 2004. “Coastline and mudbank monitoring in FrenchGuiana: Contributions of radar and optical satellite imagery.” Cana-dian Journal of Remote Sensing, Vol. 30(No. 2): pp. 109–122. doi:10.5589/m03-059.

Brisco, B., Kapfer, M., Hirose, T., Tedford, B., and Liu, J. 2011. “Eval-uation of C-band polarization diversity and polarimetry for wetlandmapping.” Canadian Journal of Remote Sensing, Vol. 37(No. 1): pp.82–92. doi: 10.5589/m11-017.

Brisco, B., Li, K., Tedford, B., Charbonneau, F., Yun, S., andMurnaghan, K. 2013. “Compact polarimetry assessment for riceand wetland mapping.” International Journal of Remote Sensing,Vol. 34(No. 6): pp. 1949–1964. doi: 10.1080/01431161.2012.730-156.

Brown, R. J., Brisco, B., D’Iorio, M. A., Prevost, C., Ryer-son, R. A., and Singhroy, V. 1996. “RADARSAT applications:Review of GlobeSAR program.” Canadian Journal of RemoteSensing, Vol. 22(No. 40): 404–419.

Canadian Centre for Remote Sensing (CCRS). (2011). Po-larimetric Work Station. The Canadian Centre for RemoteSensing.

Cable J. W., Kovacs, J. M., Shang, J., and Jiao, X. 2014. “Multi-temporal polarimetric RADARSAT-2 for land cover monitoring innortheastern Ontario, Canada.” Remote Sensing, Vol. 6(No. 3): pp.2372–2392. doi: 10.3390/rs6032372.

Choe, B. H., Kim, D. J., Hwang, J. H., Oh, Y., and Moon, W. M. 2011.“Oyster reef signature in tidal flats detected by multi-frequency po-larimetric SAR data.” In Proceedings of the International Geoscienceand Remote Sensing Symposium IGARSS, pp. 3775–3778. IEEE Ex-plore. doi: 10.1109/IGARSS.2011.6050052.

Cloude, S. R., and Pottier, E. 1996. “A review of target decompositiontheorems in radar polarimetry.” IEEE Transactions on Geoscience &Remote Sensing, Vol. 34(No. 2): 498–518. doi: 10.1109/36.485127.

Cloude, S. R., and Pottier, E. 1997. “An entropy based classificationscheme for land applications of polarimetric SAR.” IEEE Transac-tions on Geoscience and Remote Sensing, Vol. 35(No. 1): pp. 68–78.doi: 10.1109/36.551935.

Collingwood, A., Treitz, P., and Charbonneau, F. 2014. “Surface rough-ness estimation from RADARSAT-2 data in a High Arctic envi-ronment.” International Journal of Applied Earth Observation andGeoinformation, Vol. 27: pp. 70–80. doi: 10.1016/j.jag.2013.08.010.

Corcoran, J., Knight, K., Brisco, B., Kaya, S., Cull, A., and Murnaghan,K. 2011. “The integration of optical, topographic, and radar datafor wetland mapping in northern Minnesota.” Canadian Journal ofRemote Sensing, Vol. 37(No. 5): pp. 564–582. doi: 10.5589/m11-067.

da Silva, A., Paradella, W. R., Freitas, C. C., and Oliveira, C. G. 2013.“Evaluation of digital classification of polarimetric SAR data foriron-mineralized laterites mapping in the Amazon region.” RemoteSensing, Vol. 5(No. 6): pp. 3101–3122. doi: 10.3390/rs5063101.

Daughtry, C. S., Ranson, K. J., and Biehl, L. L. 1991. “C-Band backscattering from corn canopies.” International Jour-

nal of Remote Sensing, Vol. 12(No. 5): pp. 1097–1109. doi:10.1080/01431169108929713.

Dong, Y., Forster, B., and Ticehurst, C. 1997. “Radar backscatter anal-ysis for urban environments.” International Journal of Remote Sens-ing, Vol. 18(No. 6): pp. 1351–1364. doi: 10.1080/014311697218467.

Environment Canada (EC). 2010. “National climate data and informa-tion archive.” In Historical Climate Data [online]. August, 2012.http://climate.weather.gc.ca

Ferro-Famil, L., Pottier, E., and Lee, J. S. 2002. “Classification andinterpretation of polarimetric SAR data.” In Proceedings of the In-ternational Geoscience and Remote Sensing Symposium IGARSS,pp. 635–637. IEEE Explore. doi: 10.1109/IGARSS.2002.1025129.

Ferro-Famil, L., Pottier, E., and Lee, J. S. 2003. “Unsupervised clas-sification of natural scenes from polarimetric interferometric SARdata.” In Frontiers of Remote Sensing Information Processing, editedby C. H. Chen. Toh Tuck, Singapore: World Scientific Publishing.

Fisheries and Oceans Canada (FOC). 2010. “Tides currents and wa-ter levels.” In Water Level Observations [online]. August, 2012.http://www.tides.gc.ca/eng.

Freeman, A., and Durden, S. L. 1998. “A three component scatter-ing model for polarimetric SAR data.” IEEE Transactions on Geo-science and Remote Sensing, Vol. 36(No. 3): pp. 963–973. doi:10.1109/36.673687.

Harper, J. R. 1985. Beaufort Sea Physical Shore-Zone Analysis, ReportNo. 6691. Ottawa, Canada: Geological Survey of Canada.

Hequette, A., and Barnes, P. W. 1990. “Coastal retreat and shorefaceprofile variations.” Marine Geology, Vol. 91(No. 1–2): pp. 113–132.doi: 10.1016/0025-32279090136-8.

Jiao, X., McNairn, H., Shang, J., Pattey, E., Liu, J., and Cham-pagne, C. 2011. “The sensitivity of RADARSAT-2 polarimetricSAR data to corn and soybean leaf area index.” Canadian Jour-nal of Remote Sensing, Vol. 37(No. 1): pp. 69–81. doi: 10.5589/m11-023.

Koch, M., Schmid, T., Reyes, M., and Gumuzzio, J. 2012. “Evaluatingfull polarimetric C- and L-band data for mapping wetland condi-tions in a semi-arid environment in central Spain.” IEEE Journalof Selected Topics in Applied Earth Observation and Remote Sens-ing, Vol. 5(No. 3): pp. 1033–1044. doi: 10.1109/JSTARS.2012.220-2091.

Laflamme, A., and Percy, R. J. 2003. “Sensitivity mapping - with flare!An internet approach to environmental mapping.” Paper presentedat the International Oil Spill Conference Proceedings, pp. 95–102.Dartmouth, Canada, April, 2003.

Lamarche, A., Owens, E. H., Martin, V., and Laforest, S. 2003. “Com-bining pre-spill shoreline segmentation data and shoreline assess-ment tools to support early response management and planning.”Paper presented at the Proceedings of the Twenty-Sixth Arcticand Marine Oilspill Programme AMOP, pp. 219–231. Vancouver,Canada, June 2003.

Lamarche, A., Sergy, G. A., and Owens, E. H. 2007. Shoreline Cleanupand Assessment Technique SCAT Data Management Manual. Ot-tawa, Canada: Emergencies Science and Technology Division, Sci-ence and Technology Branch, Environment Canada.

Lee, J. S., Grunes, M. R., Ainsworth, T. L., Du, L. J., Schuler, D.L., and Cloude, S. R. 1999. “Unsupervised classification using po-larimetric decomposition and the complex Wishart classifier” IEEETransactions on Geoscience and Remote Sensing, Vol. 37(No. 5):pp. 2249–2258. doi: 10.1109/36.789621.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015

314 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

Lee, J. S., Grunes, M. E., Pottier, E., and Ferro-Famil, L. 2004. “Un-supervised terrain classification preserving polarimetric scatteringcharacteristics.” IEEE Transactions on Geoscience and Remote Sens-ing, Vol. 42(No. 4): pp. 722–731. doi: 10.1109/TGRS.2003.819883.

Lee, J. S., and Pottier, E. 2009. Polarimetric Radar Imaging: FromBasics to Applications. Boca Raton, FL: Taylor and Francis Group.

Li, K., Brisco, B., Yun, S., and Touzi, R. 2012. “Polarimetric de-composition with RADARSAT-2 for rice mapping and monitoring.”Canadian Journal of Remote Sensing, Vol. 38(No. 2): pp. 169–179.

McNairn, H., Chang, J. L., Jiao, X. F., and Champagne, C. 2009.“The contribution of ALOS PALSAR multipolarization and po-larimetric data to crop classification.” IEEE Transactions on Geo-science and Remote Sensing, Vol. 47(No. 12): pp. 3981–3992. doi:10.1109/TGRS.2009.2026052.

Millard, K., and Richardson, M. 2013. “Wetland mapping with LI-DAR derivatives, SAR polarimetric decompositions, and LIDARSAR fusion using a random forest classifier.” Canadian Journalof Remote Sensing, Vol. 39(No. 4): pp. 1–18. doi: 10.5589/m13-038.

Owens, E. H. 1983. “The application of videotape recording (VTR)techniques for coastal studies.” Shore and Beach, Vol. 51(No. 1): pp.29–33.

Owens, E. H., and Sergy, G. A. 1994. Field Guide to the Documentationand Description of Oiled Shorelines. Edmonton, Canada: Environ-ment Canada.

Owens, E. H., and Sergy, G. A. 2000. The SCAT Manual - Field Guideto the Documentation and Description of Oiled Shorelines 2nd ed.).Edmonton, Canada: Environment Canada.

Percy, R. J., LeBlanc, S. R., and Owens, E. H. 1997. “An integrated ap-proach to shoreline mapping for spill response planning in Canada.”Paper presented at the Proceedings of the International Oil SpillConference, pp. 277–288. Washington, United States, April, 1997.doi: 10.7901/2169-3358-1997-1-277.

Pietroniro, A., Toyro, J., Leconte, R., and Kite, G. 2005. “Remotesensing of surface water and soil moisture.” In Remote Sensing inNorthern Hydrology: Measuring Environmental Change, edited byC. R. Duguay and A. Pietroniro, pp. 119–142. Washington, DC,USA: American Geophysical Union.

Rampton, V. N. 1987a. “Surficial geology of the Mackenzie Delta.” InMarine Science Atlas of the Beaufort Sea: Geology and Geophysics,Vol. 40 of Miscellaneous Report. Ottawa, Canada: Geological Surveyof Canada.

Rampton, V. N. 1987b. “Surficial geology of the Tuktoyaktuk Penin-sula.” In Marine Science Atlas of the Beaufort Sea: Geology andGeophysics, Vol. 40 of Miscellaneous Report. Ottawa, Canada: Ge-ological Survey of Canada.

Rampton, V. N. 1988. Quaternary Geology of the Tuktoyaktuk Coast-lands, Northwest Territories, Memoir 423. Ottawa, Canada: Geolog-ical Survey of Canada.

Shelat, Y., Leblon, B., Larocque, A., Harris, J., Jefferson, C., Lentz, D.,and Tschirhart, V. 2012. “Effects of incidence angles on mappingaccuracy of surficial materials in the Umiujalik Lake area, Nunavut,using RADARSAT-2 polarimetric SAR images. Part 2. Polarimetricanalysis.” Canadian Journal of Remote Sensing, Vol. 38(No. 3): pp.404–423. doi: 10.5589/m12-031.

Ulaby, F. T., and Batlivala, P. P. 1976. “Optimum radar param-eters for mapping soil moisture.” IEEE Transactions on Geo-science and Remote Sensing, Vol. 14(No. 2): pp. 81–93. doi:10.1109/TGE.1976.294414.

Ulaby, F. T., Moore, R. K., and Fung, A. K. 1986. Microwave remotesensing: Active and passive, Vol. III - Volume scattering and emissiontheory. In Advanced Systems and Applications. Dedham, MA, USA:Artech House.

Yamaguchi, Y., Moriyama, T., Ishido, M., and Yamada, H. 2005. “Four-component scattering model for polarimetric SAR image decompo-sition.” IEEE Transactions on Geoscience and Remote Sensing, Vol.43(No. 8): pp. 1699–1706. doi: 10.1109/TGRS.2005.852084.

Dow

nloa

ded

by [

134.

117.

120.

160]

at 0

7:33

15

Janu

ary

2015