Multisensor Hydrologic Assessment of a Freshwater Wetland

www.elsevier.com/locate/rse

Multisensor Hydrologic Assessment of aFreshwater Wetland

Jessika Toyra,* Alain Pietroniro,† and Lawrence W. Martz*

This article evaluates the use of synthetic aperture radar when the SPOT scene was classified in combination withthe Radarsat S7 scene. Elsevier Science Inc., 2001. All(SAR) and visible/infrared (VIR) satellite imagery for map-Rights Reserved.ping the extent of standing water in the Peace-Athabasca

Delta during spring and summer of 1998. SAR images con-tain data about the geometric and electrical characteristics

INTRODUCTIONof the objects, while VIR images contain information aboutthe reflectivity of objects. Radar pulses can also penetrate Although wetlands only cover about 6% of the land surfacevegetation to some degree depending on the wavelength and of the world, they provide an important role in the global

ecosystem (Mitsch and Gosselink, 1986). Wetlands supportvegetation thickness. It is hypothesized that since Radarsata unique habitat for a great variety of hydrophytic plants,and SPOT images contain complementary information, floodfish, wildlife, and insects. From a hydrologic perspective,mapping will be more efficient when the two image typeswetlands improve water quality, recharge ground water,are used in combination. Radarsat SAR and SPOT multi-control floods, and protect shorelines. Since the availabilityspectral imagery from May 1998 and July 1998 were usedof water is the dominant mechanism driving the ecosystemfor the flood mapping. A Radarsat S2 image (27.58 incidenceresponse, accurate and frequently updated maps of flood-angle) was obtained for May 1998. To evaluate the inci-water extent are necessary for wetland management. Thisdence angle effect on flood mapping, a Radarsat S1 imageinformation is difficult to obtain using ground surveys due(27.58 incidence angle) and a Radarsat S7 image (47.08to logistical problems and high costs. Satellite remote sensingincidence angle) were obtained for July 1998. The Radarsathas become an important and cost-effective tool for provid-

scenes were calibrated and filtered, and all imagery were ing this information in a temporal context (Carter, 1982).orthorectified to minimize geometric distortion. A Mahala- Many studies have utilized the low reflectance of waternobis distance algorithm was used to classify the SPOT in the infrared bands of SPOT, Landsat TM, and Landsatscenes, the Radarsat scenes, and a combination of the two MSS satellites to map the extent of surface water (Mooreinto open water, flooded vegetation, and nonflooded land. and North, 1974; Rose and Rosendahl 1983; Ackleson et al.,The results indicate that flood mapping in both spring and 1985; Bennett 1987; Jaques 1989; Pietroniro and Prowsesummer conditions has significantly higher accuracy when 1996). In the case of forested wetlands, Carter (1982) hasRadarsat and SPOT imagery are used in combination, rather suggested that the extent of flooding will be most efficientlythan separately. However, it is important to use Radarsat detected on visible and infrared (VIR) imagery when vege-imagery acquired at low incidence angles. Classification tation is absent or during the dormant, leafless season. As

the deciduous vegetation becomes foliated, tree canopiesof the SPOT scene combined with the Radarsat S1 sceneobscure the floodwaters and most flood boundaries areachieved significantly better results than those obtaineddifficult to distinguish. On the other hand, the vegetationcover and composition that can be observed on visible andinfrared imagery may provide indirect information about

* Department of Geography, University of Saskatchewan, Saska-the floodwater boundaries.toon, Saskatchewan, Canada

† National Water Research Institute, Environment Canada, Saska- Synthetic Aperture Radar (SAR) is useful for floodtoon, Saskatchewan, Canada mapping because of its sensitivity to moisture differences

Address correspondence to J. Toyra, Dept. of Geography, Univ. of and its potential to penetrate clouds and vegetation. OpenSaskatchewan, 9 Campus Dr., Saskatoon, SK, Canada, S7N 5A5. E-mail:

water acts as a specular reflector in the microwave [email protected] 3 March 2000; revised 10 July 2000. trum and yields a very low radar backscatter signal, while

REMOTE SENS. ENVIRON. 75:162–173 (2001)Elsevier Science Inc., 2001. All Rights Reserved. 0034-4257/00/$–see front matter655 Avenue of the Americas, New York, NY 10010 PII S0034-4257(00)00164-4

Multisensor Hydrologic Assessment of a Freshwater Wetland 163

dry and flooded vegetation generally produce a mediumstrength and a strong return signal, respectively. Wanget al. (1995) suggested that the total backscatter for anonflooded forest stand is dominated by surface scatteringby the forest floor and volume scattering in the canopy.Wang et al. (1995) also pointed out that it is the cornerreflection between the tree trunks and the ground, ampli-fied by the high dielectric constant of the standing waterthat causes the high returns of flooded forest stands.

Since canopy penetration is reduced at shorter wave-lengths, it has commonly been concluded that forest floodmapping is best performed using L-band radar systems(Richards et al., 1987b; Hess et al., 1990; Hall, 1996).Leconte and Pultz (1991), Pultz et al. (1991), Ustin et al.(1991), Crevier and Pultz (1996), Saper et al. (1996), andAdam et al. (1998) presented evidence that HH polarizedC-band radar systems are able to detect forest flooding inthe spring when no leaves are present. Crevier and Pultz(1996), Adam et al. (1998), and Toyra et al. (in press) foundthat HH polarised C-band images, acquired at smallerincidence angles, have a greater enhancement in floodedforest stands (with no leaves present) than those acquired

Figure 1. The Peace-Athabasca Delta, Alberta, Canada (afterat larger angles. An increase in incidence angle lengthensKaiser, 1997).the path that the radar signal has to travel through the

canopy layer, which means that the canopy attenuation onthe radar signal also increases.

to derive radar backscatter values and filtered to reduceInformation contained in SAR and VIR imagery isspeckle noise. All scenes were orthorectified to minimizecomplementary and more information should be obtainedgeometric distortion. The Radarsat and the SPOT scenesfor wetland flood mapping if they are used in combination.were classified alone and in different combinations intoSkidmore et al. (1986) used a combination of Landsat MSSopen water, flooded vegetation, and nonflooded land usingand SIR-B data to classify different forest types with varyingthe Mahalanobis distance algorithm. The classified sceneswater levels in the Riverina Forest, Australia. The resultswere compared against reference data and the classificationindicated that the classification accuracy was higher whenaccuracy was evaluated using confusion matrices and thethe two image types were used in combination as opposedKappa coefficient of agreement.to individually. Li et al. (1995) showed that wetland identifi-

cation is possible using either Landsat TM or multi-tempo-Study Arearal ERS-1 data, but that the accuracy increases when theThe Peace-Athabasca Delta (588409N, 1118309W) is a 3900two image types are used in combination.km2 fresh water wetland complex located at the westernThis article evaluates the use of Radarsat SAR andend of Lake Athabasca in northeastern Alberta, Canadamultispectral SPOT imagery for mapping the extent of(see Fig. 1). The Peace-Athabasca Delta was recognizedstanding water in the Peace-Athabasca Delta wetland com-as a Wetland of International Significance by the Ramsarplex during the summer season of 1998. The RadarsatConvention in 1982. About 80% of the delta is also locatedSAR sensor transmits and records HH polarized C-bandwithin the Wood Buffalo National Park, which became amicrowave radiation (5.3 GHz frequency), while the SPOTUNESCO World Heritage Site in 1983.sensor records reflected radiation in the green, red, and

near-infrared spectrum. Satellite imagery acquired at both Grasses (Calamagrostis and Scolochloa), sedges (Carex),and willows (Salix) are the dominant vegetation types in thespring (May) and summer (July) conditions was used to

evaluate the potential of flood mapping during different active and semiactive portions of the delta, while coniferous(Picea and Pinus), deciduous (Alnus and Populus) or mixedstages of foliation. The spring flood mapping was published

previously by Toyra et al. (in press) and is included in this forests grow in the inactive portions, on bedrock outcropsand along the higher levees. Numerous smaller wetlandpaper for the sake of completeness. In July, two Radarsat

scenes acquired at different incidence angles were used basins are scattered around the delta. This type of environ-ment is essential for such wildlife as waterfowl and musk-1) to assess the effect of the incidence angle on flood

mapping in fully foliated conditions and 2) to evaluate if rats. Since a major portion of the wetland basins is isolatedfrom the channel network, they depend on large, periodicflood mapping improves when multiangle Radarsat data

are used in combination. The Radarsat data were calibrated overland floods to maintain their productivity. These flood

164 Toyra et al.

events most commonly result from break-up ice jams in wetland mapping during different stages of foliation, it wasdecided that imagery should be acquired in the spring andthe river channels (Prowse et al., 1996).

The Peace-Athabasca Delta experienced an absence in mid-summer. In contrast to SPOT imagery, Radarsatimage quality is not dependent upon the cloud conditionsof large floods between 1974 and 1996, resulting in consid-

erable changes in water boundaries and possible changes at the time of the satellite overpass. Therefore, fieldworkwas planned around selected Radarsat overpasses in thein vegetation patterns. The absence of large overland floods

has been related to reduced occurrences of break-up ice spring and summer of 1998 and cloud-free SPOT scenes,acquired as close as possible to the Radarsat overpasses,jams due to a combined effect of flow regulation along the

Peace River and a decline in spring snowpacks (Prowse et were selected post facto. A Radarsat overpass on 17 Mayand two subsequent Radarsat overpasses occurring on 21al., 1996). After 22 years of no major flood events, the

delta flooded again in the spring and summer of 1996 and and 22 July were chosen to represent the spring and sum-mer season, respectively. The spring overpass was selectedin the spring of 1997. No flood event occurred in 1998.

Although the floodwaters were draining, a large portion of to occur after the ice on the lakes and rivers had meltedthe delta remained inundated during the spring and sum- and prior to full foliation of the deciduous vegetation.mer of 1998. Representative flooded and non-flooded areas of each

It is necessary to monitor the extent and duration of land cover class were visited in the field at the time whenflooding within the delta to assess changes in vegetation the imagery was acquired. The main land cover types can beand wildlife as they relate to the hydrology of the region. divided into open water, sedge and grass meadow, shrubs,Given the large spatial extent, remoteness and dynamic deciduous forest, and coniferous forest. Field data collec-nature of the Peace-Athabasca Delta, hydrologic monitor- tion included sampling preestablished vegetation transectsing is only feasible using remote sensing. (Timoney 1996) and field reconnaissance using helicop-

ter surveys.The 17 May Radarsat image was acquired in standard

METHODOLOGY mode S2 with an incidence angle ranging between 248and 318. Smaller incidence angles should shorten the pathThe process of extracting meaningful information from

remotely sensed imagery can be divided into four main through the canopy and reduce the canopy attenuation onthe radar signal. The 21 July Radarsat image was acquiredsteps: defining the problem, data collection, data analysis,

and information presentation (Jensen, 1996). In our case, in standard mode S1 with an evaluation angle rangingbetween 208 and 278, while the 22 July Radarsat imagethe problem was to evaluate the use of Radarsat and SPOT

imagery for wetland flood mapping. Data collection in- was acquired in standard mode S7 with an evolution anglebetween 458 and 498. This allows a comparison of thevolves acquiring satellite imagery and collecting ground

reference data. In this study, the data analysis was divided incidence angle effects on different types of flooded vegeta-tion. Additional information may also be obtained if theinto two sections: image preprocessing and image pro-

cessing. In the image preprocessing step, the Radarsat data two Radarsat images are combined. Two nearly cloud-freeSPOT scenes were acquired on 16 May 1998, and 26 Julywere calibrated and filtered, and all image scenes were

corrected from geometric distortion. In the processing 1998. Since there was no sudden changes of water bound-aries in the spring and mid-summer of 1998 due to floodsstep, the imagery was classified using a statistical pattern

recognition algorithm. The accuracy of each classification or drastic floodwater drainage, it was assumed that thedifferences in water boundaries were insignificant duringwas calculated by comparing randomly sampled pixels

against reference data. The results are presented in form the one day period between 16 and 17 May, and during the5-day period between 21 and 26 July. Detailed informationof thematic maps and error reports (geometric and classifi-

cation accuracy). A detailed description of the methodology about the images is listed in Table 1.is provided in Toyra (1999) and Toyra et al. (in press).

Image PreprocessingReference Data Collection and Satellite Imagery The Radarsat scenes were first returned to the Canada

Centre for Remote Sensing (CCRS) for compensation ofReference data to verify satellite image interpretation mayinclude field measurements coupled with ancillary data the power loss caused by the automatic gain control (AGC)

in the Radarsat sensor (Vachon et al., 1997). Vachon et al.sources, such as aerial photographs or maps. When surfaceconditions are dynamic over the scale of weeks, such as (1997) demonstrated that an AGC power loss of about 7

dB can be corrected to a residual error of less than 1 dB.during flood draw-down in the Peace-Athabasca Delta, itis necessary to conduct the field measurements at or near After pre-processing at CCRS, the Radarsat scenes were

calibrated using PCI software (version 6.2) to derive radarthe time of the satellite overpass (Lee, 1975). In recognitionof this, field surveys were conducted over the week brack- backscatter (r8) values (Srivastava, 1998). The variations

in local incidence angle were ignored because of the ex-eting the time of the satellite overpasses.To evaluate the potential of the satellite images for tremely flat topography of the Peace-Athabasca Delta re-


Table 1. Information about the Radarsat and SPOT Scenes

Resolution (m)Angle Across3Along Pixel Size

Imagery Date Mode Processing Level (deg) Track (m)

Radarsat 17 May 1998 Standard Beam (S2) Path Image Plus (SGX) 27.5 20327 838Radarsat 21 July 1998 Standard Beam (S1) Path Image Plus (SGX) 23.5 24327 838Radarsat 22 July 1998 Standard Beam (S7) Path Image Plus (SGX) 47.0 19327 838SPOT 16 May 1998 Multispectral Path Image 29.7 20320 20320SPOT 26 July 1998 Multispectral Path Image 25.1 20320 20320

gion. According to Srivastava et al. (1998), the calibrated they are not misregistered (Pohl and Van Genderen 1998).To ensure a good fit between the Radarsat and the SPOTbackscatter values are accurate to within 1.6 dB. The Ra-

darsat noise speckle was reduced by a multilooking process, scenes, the Radarsat scenes were reregistered to their re-spective SPOT scene using eight tiepoints and a first-orderwhere four independent observations of each resolution

cell are averaged (Bruniquel and Lopes, 1997), and by polynomial equation with a nearest neighbor resamplingprocedure. Since the Radarsat and the SPOT scenes haveapplying a 7 by 7 pixel Kuan adaptive filter (Kuan et al.,

1987). Since the radar backscatter values are in decibels, different pixel sizes (see Table 1), the Radarsat scenes wereresampled to a pixel size of 20 m, to match the resolutionthey were converted to intensity values prior to Radarsat

image filtering and classification. The intensity values were, of the SPOT scenes.thereafter, converted back to radar backscatter values (dB).

The Radarsat and the SPOT scenes were orthorectified Image Processingusing a method described by Toutin (1995), which uses The image channels were classified into open water, floodedsatellite ephemeris data, ground control points (GCPs), vegetation, and non-flooded land using a Mahalanobis dis-and a digital elevation model (DEM) to correct the imagery tance supervised classification scheme (Schott, 1997). Sep-from geometric distortions. The geographic coordinates of arate sets of training areas were collected for the May 1998the GCPs were established using differential global posi- imagery and the July 1998 imagery. Because of the widetioning system (GPS) and 1:50,000 NTS map sheets that range of digital values belonging to each class, the threecover more stable areas and objects, such as cultural fea- general land cover classes were divided into about 15 sub-tures and small bedrock islands. The GPS sites have an classes to reduce the spectral variance of the class signature.accuracy of less than 1 m, while the accuracy of the map The subclasses were merged back into the three mainsheets varies between 30 m and 50 m depending on the classes after the classification was completed. Representa-map sheet. Table 2 describes the number of GCPs that tive areas of each subclass were identified on the imagerywere collected for the Radarsat and the SPOT scenes and based on reference data and used as training areas fortheir RMS residual error. Because of the low relief in the the classification algorithm. To improve the validity of thedelta, an average elevation of 214 m was used for the statistical estimates, Swain and Davis (1978) recommendedorthorectification instead of a DEM. All of the images were that the training areas should include a minimum of 10Nre-sampled to a UTM (NAD27) coordinate system using pixels, and if possible 100N pixels, N being the number ofa nearest neighbor algorithm (Schott, 1997). The total RMS image channels used in the classification. In this case,error of each scene was less than 10 m, giving the orthorec- between 20N and 100N pixels were collected for eachtified scenes a planimetric error of a pixel or less. subclass. All training areas were collected with the seed

Only cloud-free areas that were covered by both the pixel method (Lillesand and Kiefer, 1994).Radarsat and the SPOT scenes were used in the image In total, 10 different combinations of image channelsanalysis. When imagery acquired from different sensors were tested for classification accuracy. The image chan-

nel combinations are listed in Table 3. The Radarsat chan-are combined at the pixel level, it is very important that

Table 2. The RMS Residual Errors of the Ground Control Points (GCPs)as Calculated by the Satellite Model

RMS Residual Error (m)Number ofScene Date GCPs collected X Y Total

Radarsat (S2) 17 May 1998 15 5.13 4.41 6.77Radarsat (S1) 21 July 1998 13 3.13 3.14 3.72Radarsat (S7) 22 July 1998 14 5.45 6.25 8.29SPOT 16 May 1998 12 6.56 5.86 8.80SPOT 26 July 1998 12 5.47 5.39 7.68

166 Toyra et al.

Table 3. The May 1998 and July 1998 Image Channel CombinationsClassified and Examined for Classification Accuracya

Image Channel Combination

Date Image Channels 1 2 3 4 5 6 7 8 9 10

May 1998 Radarsat (S2) channel X XAll three SPOT channels X X

July 1998 Radarsat (S7) channel X X X XRadarsat (S1) channel X X X XAll three SPOT channels X X X X

a X indicates which image channels were used in a combination. In total, three image channelcombinations were classified for May 1998 and seven for July 1998.

nel and the SPOT channels were combined using the aug- diate return values for dry land, and very low returns formented vector approach (Schistad Solberg et al., 1994). open water (Adam et al., 1998). Figure 2 provides examples

of Radarsat backscatter characteristics for different landClassification Accuracy Assessment cover classes in the May 1998 scene, and Figure 3a shows

the mean radar backscatter value for each classificationThe accuracy of the classification results was estimated bytraining area, or subclass. The relatively low incidencesampling 100 random pixels per class (300 pixels in total)angle of the S2 beam makes the Radarsat sensor sensitivefrom areas with existing reference data. This was accom-to wave action on the large lakes, which increases the radarplished by creating a mask that covered areas with refer-backscatter of the open water to that of non-flooded landence data. The mask was superimposed on one of the(see Fig. 3a). As indicated by Table 4, the Radarsat channelclassified images, and 100 pixels were sampled randomlyalso had some difficulties with separating between floodedfrom each class within the mask. Separate sets of 300 pixelsvegetation and nonflooded land. Flooded willows couldwere sampled for May 1998 and July 1998. The sampledeasily be discriminated using the Radarsat channel becausepixels were compared against reference data and the resultsof the characteristically high radar backscatter (see Fig. 2).were plotted in confusion matrices, which compare theAll of the coniferous vegetation and most of the denserelationship between the classification results and the ref-deciduous vegetation were dry in May 1998. Some of theerence data. The Kappa coefficient, which indicates if theareas with dense deciduous vegetation had high radar re-results presented in the confusion matrix are significantlyturn values relative to other dry vegetation and were, there-better than randomly generated results, was used as afore, classified as flooded (see Fig. 3a). The deciduousmeasure of accuracy. The Kappa coefficient has been rec-vegetation within these areas was almost fully foliated,ommended as a suitable measure of accuracy as it accounts

for all elements in the confusion matrix rather than just which may have increased the return signal due to the highthe diagonal elements (Rosenfield and Fitzpatrick-Lins, water content in the leaves, or there could have been a1986). A test of significance was used for comparison of high moisture content in the underlying vegetation or soil.classification results. The test evaluates if two Kappa coeffi- The deciduous and mixed vegetation along the levees didcients are significantly different from each other according not have the increased backscatter. The cause of the in-to Cohen (1960) [Eq. (1)]: creased return signal needs further investigation.

Areas with very dense emergent vegetation and areasZ5(k12k2)/√(v12v2), (1) with sparse emergent vegetation were difficult to separate

from dry vegetation using the Radarsat S2 channel alonewhere v is the Kappa variance, which is calculated ac-(see Figs. 2 and 3a). The dense layers of brown emergentcording to Fleiss et al. (1969). If the Z-value exceeds 1.96,

the two classification results are significantly different at vegetation, or thatch, were bent over at the time of thethe 95% confidence level. overpasses, which could have prevented the double bounce

effect to take place. A possible explanation for why thesparse emergent vegetation showed intermediate returnRESULTS AND DISCUSSIONvalues and was confused with dry upland vegetation may

May 1998 Imagery be that the presence of the sparse vegetation only slightlyincreased the signature of the otherwise open water be-Three May 1998 image channel combinations were classi-cause of reduced opportunity for double bounce effect.fied using the Mahalanobis distance classification. The re-A medium dense layer of emergent vegetation had thesulting classification statistics are provided in Table 4. Clas-characteristic high returns and could be separated fromsification of the Radarsat S2 channel alone resulted indry land. The transition zones between open water andan overall Kappa coefficient of 0.76060.001 at the 95%flooded vegetation exhibit intermediate return values andconfidence level. The Radarsat signal has typically high

radar return values in areas of flooded vegetation, interme- were, therefore, classified as dry vegetation.


Table 4. The Confusion Matrices for Classification of the May 1998 Image Channel Combinations Using the MahalanobisDistance Algorithma (Toyra et al., in press)

Radarsat SPOT Radarsat and SPOT

OW FV NFL OW FV NFL OW FV NFL

OWb 94.8 0.0 1.0 OWb 100.0 10.6 0.0 OWb 100.0 2.9 0.0FVb 0.0 83.7 25.3 FVb 0.0 75.0 14.1 FVb 0.0 90.4 6.1NFLb 5.2 16.3 73.7 NFLb 0.0 14.4 85.9 NFLb 0.0 6.7 93.9

Kappa50.76060.001 Kappa50.80060.001 Kappa50.92060.000

a The values represent percentage of randomly sampled pixels. The columns represent the verification data and the rows show how the pixels wereclassified. The Kappa coefficient at the 95% confidence level is also provided.

b Number of randomly sampled pixels for each class: OW (open water)597; FV (flooded vegetation)5104; NFL (nonflooded land)599.

The overall visual appearance of the classified Radarsat According to the Z-test, classification of the Radarsatimage was relatively poor. Although a Kuan filter was used image in combination with the SPOT image achieved sig-to reduce speckle in the original Radarsat image, the re- nificantly better results compared to when the two imagemaining image speckle was sufficient to give the classified types were classified separately (see Table 5). This combi-image a noisy appearance. nation of channels could better discriminate between the

Classification of the SPOT channels resulted in an flooded vegetation and the dry upland areas and resultedoverall Kappa coefficient of 0.80060.001 at the 95% confi- in an overall Kappa coefficient of 0.92060.000 at the 95%dence level. The Z-test indicated that there was no signifi- confidence level. The SPOT channels could separate thecant difference between the Kappa coefficients of the clas- dry deciduous vegetation from dense flooded grasses andsified SPOT image and the classified Radarsat image. The the Radarsat channel could distinguish between floodedresults from the Z-test are listed in Table 5. About 10% and nonflooded willows and grasses. A visual comparisonof the flooded vegetation pixels, representing areas with of the classified scenes also confirmed that the overallsparse emergent vegetation, were classified as open water. appearance was much improved. However, some of theIn addition, the SPOT channels had difficulties with dis- sparse emergent vegetation was still confused with non-criminating dry grass meadows from flooded grasses and flooded land and a few pixels in the transition zones be-sedges. The SPOT scene also had some problems with tween open water and flooded vegetation were classifiedseparating flooded dense willows from the nonflooded de- as nonflooded land. The thematic map derived by classify-ciduous and mixed vegetation along the levees. However, ing the Radarsat channel in combination with the SPOTthe dry deciduous vegetation, which was problematic for channels is illustrated in Figure 4.the Radarsat channel, were straightforward to classify using No conclusions could be drawn about the Radarsatthe SPOT channels since the forest type had distinct reflec- and SPOT sensor’s ability to detect standing water beneathtance characteristics and because most of these areas were a coniferous forest and a dense deciduous forest since alldry. The overall appearance of the classified SPOT scene of these areas were nonflooded. However, the forestedwas better than that of the classified Radarsat scene. The areas often occur in nonactive parts of the delta and areclassified image was not noisy and, therefore, easier to in- usually not flooded, or are only flooded at the time ofterpret. overbank flow after which they drain relatively rapidly.

Figure 2. Examples of land cover classes identified on a subset July 1998 Imageryof the Radarsat S2 scene from May 1998.

Seven different combinations of the Radarsat S1 channel,Radarsat S7 channel and the SPOT channels from July1998 were classified using the Mahalanobis distance classi-fication. The resulting classification statistics are providedin Table 6. Classification of the Radarsat S7 channel aloneresulted in an overall Kappa coefficient of 0.42460.001 atthe 95% confidence level. Because of the high incidenceangle of the S7 beam, wave action did not cause increasedbackscatter on the lakes and the open water category wasclassified with very high accuracy. Figure 3b provides themean Radarsat S7 backscatter value for each classificationtraining area, or subclass. The Radarsat S7 image couldnot discriminate between flooded and nonflooded vegeta-tion as the two categories had similar radar backscattercharacteristics (see Fig. 3b). The high incidence angle

168 Toyra et al.

Figure 3. The mean radar backscatter value for each clas-sification training area, or subclass, in the a) May 1998Radarsat S2 scene, b) July 1998 Radarsat S7 scene, and c)July 1998 Radarsat S1 scene.

lengthened the distance that the radar signal had to travel lems with the dry deciduous vegetation as the May 1998Radarsat S2 channel (see Fig. 3c). The radar backscatterthrough the canopy layer, resulting in increased canopy

attenuation, which prevented the radar signal from pene- values of some areas of deciduous vegetation were highrelative to other dry vegetation. These areas of dry decidu-trating the vegetation and detecting the standing water.

The classified Radarsat S1 channel had an overall ous vegetation had similar backscatter values to very denseflooded grasses and sedges, resulting in confusion betweenKappa coefficient of 0.56460.001 at the 95% confidence

level. The Z-test indicated that the Radarsat S1 image ob- the flooded vegetation and the nonflooded land classes(see Table 6). However, the Radarsat S1 channel couldtained significantly better results compared to the Radarsat

S7 image, although the accuracy is still poor. The results discriminate flooded willows from dry vegetation becauseof the strong return signal.from the Z-test are provided in Table 7. In Figure 5, the

main land covers are identified on a portion of the Radarsat As opposed to in May of 1998, flooded grasses andsedges achieved strong return signals in July and wereS1 scene. The mean radar backscatter value for each classi-

fication training area, or subclass, is provided in Figure 3c. straightforward to classify. The difference in flooded grassbackscatter between the May scene and the July scene isBecause of the low incidence angle of the S1 beam, the

radar signal was scattered by the waves on the lakes and apparent when Figure 2 is compared to Figure 5. Mostgrasses and sedges were green and oriented vertically inthe strength of the return signal was increased to that of

nonflooded land. As a result, 15% of the open water pixels July. The increase in wet biomass probably increased theoverall backscatter of grasses and sedges, while the verticalwere confused with nonflooded land, and 10% of the non-

flooded land pixels were confused with open water. orientation created a double bounce effect between thestanding water and the stems. Very sparse emergent vege-The Radarsat S1 channel experienced similar prob-

Table 5. Significance Test (Z-Test) for Comparing Two Classification Resultsfrom May 1998a

Channel Combinations Kappa1 vs. SignificantBeing Compared Kappa2 Z-Value Difference

Radarsat and SPOT vs. Radarsat 0.92 vs 0.76 5.06 YesRadarsat and SPOT vs. SPOT 0.92 vs 0.80 3.80 YesRadarsat vs. SPOT 0.76 vs 0.80 0.89 No

a A Z-value over 1.96 indicates that there is a significant difference at 95% confidence level.


confidence level. The combination of Radarsat channelsexperienced the same problems with the nonflooded landand flooded vegetation classes as the Radarsat S1 channelhad alone. The Radarsat S7 channel did not contributemuch information to aid in the discrimination of floodedvegetation. However, the Radarsat S7 channel did improvethe classification of open water, which is the main reasonfor the increased classification accuracy.

The classified SPOT channels achieved an overallKappa coefficient of 0.67460.001 at the 95% confidencelevel. The results obtained from the SPOT channels weresignificantly better than those obtained from the RadarsatS7 channel or the Radarsat S1 channel, whereas there wasno significant difference between the accuracies of theclassified SPOT image and the classified Radarsat S1 andS7 image. The SPOT channels could separate open waterfrom nonflooded land, although there was some difficultiesin distinguishing between flooded dead willows and sparseemergent vegetation from open water. In addition, theSPOT channels could not discriminate between flooded

Figure 4. A combination of the May 1998 Radarsat S2 and dry willows, grasses or sedges. There were also prob-channel and the May 1998 SPOT channels classified lems with separating flooded willows from dry deciduousinto open water, flooded vegetation, and nonflooded and mixed vegetation along the levees. The areas of dryland using a Mahalanobis distance classifier (after

deciduous and coniferous vegetation, which were problem-Toyra et al., in press).atic for the Radarsat S1 channel, had very distinct reflec-tance values in the SPOT channels and were, therefore, not

tation was still classified as nonflooded vegetation. The confused with flooded vegetation. Visually, the classifiedappearance of the classified image was poor and very noisy. SPOT image had a better overall appearance than the

When the Radarsat S1 and S7 channels were com- classified Radarsat S1 and S7 image.bined, the classification accuracy increased significantly to Classification of the Radarsat S7 channel in combina-

tion with the SPOT channels resulted in an overall Kappaan overall Kappa-coefficient of 0.6960.001 at the 95%

Table 6. The Confusion Matrices for Classification of the July 1998 Image Channel Combinations Using the MahalanobisDistance Algorithma

Radarsat S7 Radarsat S1 Radarsat S1 and S7


OW 93.4 0.0 0.0 OW 84.9 8.5 11.0 OW 91.5 0.0 0.0FV 5.7 74.5 85.0 FV 0.0 59.6 22.0 FV 6.6 76.6 31.0NFL 0.9 25.5 15.0 NFL 15.1 31.9 67.0 NFL 1.9 23.4 69.0


SPOT Radarsat S7 and SPOT Radarsat S1 and SPOT


OW 96.2 12.8 0.0 OW 94.3 4.3 0.0 OW 97.2 6.4 0.0FV 1.0 68.1 31.0 FV 3.8 79.8 28.0 FV 0.9 83.0 16.0NFL 2.8 19.1 69.0 NFL 1.9 16.0 72.0 NFL 1.9 10.6 84.0


Radarsat S1, S7, and SPOT

OW FV NFL

OW 94.3 0.0 0.0FV 3.8 88.3 14.0NFL 1.9 11.7 86.0

Kappa50.84560.001

a The values represent percentage of randomly sampled pixels. The columns represent the verification data and the rows show how the pixels wereclassified. The Kappa coefficient at the 95% confidence level is also provided.

b Number of randomly sampled pixels for each class: OW (open water)5106; FV (flooded vegetation)594; NFL (nonflooded land)5100.

170 Toyra et al.

Table 7. Significance Test (Z-Test) for Comparing Two Classification Results from July 1998a

Kappa1 vs. SignificantChannel Combinations Being Compared Kappa2 Z-Value Difference

Radarsat S1, S7 & SPOT vs. Radarsat S1 & SPOT 0.85 vs 0.83 0.45 NoRadarsat S1, S7 & SPOT vs. Radarsat S7 & SPOT 0.85 vs 0.74 2.46 YesRadarsat S1, S7 & SPOT vs. SPOT 0.85 vs 0.67 3.83 YesRadarsat S1, S7 & SPOT vs. Radarsat S1 & Radarsat S7 0.85 vs 0.69 3.47 YesRadarsat S1, S7 & SPOT vs. Radarsat S1 0.85 vs 0.56 6.29 YesRadarsat S1, S7 & SPOT vs. Radarsat S7 0.85 vs 0.42 9.42 YesRadarsat S1 & SPOT vs. Radarsat S7 & SPOT 0.83 vs 0.74 2.01 YesRadarsat S1 & SPOT vs. SPOT 0.83 vs 0.67 3.38 YesRadarsat S1 & SPOT vs. Radarsat S1 & Radarsat S7 0.83 vs 0.69 3.02 YesRadarsat S1 & SPOT vs. Radarsat S1 0.83 vs 0.56 5.84 YesRadarsat S1 & SPOT vs. Radarsat S7 0.83 vs 0.42 8.97 YesRadarsat S7 & SPOT vs. SPOT 0.74 vs 0.67 1.36 NoRadarsat S7 & SPOT vs. Radarsat S1 & Radarsat S7 0.74 vs 0.69 1.01 NoRadarsat S7 & SPOT vs. Radarsat S1 0.74 vs 0.56 3.83 YesRadarsat S7 & SPOT vs. Radarsat S7 0.74 vs 0.42 6.96 YesSPOT vs. Radarsat S1 & Radarsat S7 0.67 vs 0.69 0.36 NoSPOT vs. Radarsat S1 0.67 vs 0.56 2.46 YesSPOT vs. Radarsat S7 0.67 vs 0.42 5.59 YesRadarsat S1 & Radarsat S7 vs. Radarsat S1 0.69 vs 0.56 2.82 YesRadarsat S1 & Radarsat S7 vs. Radarsat S7 0.69 vs 0.42 5.95 YesRadarsat S1 vs. Radarsat S7 0.56 vs 0.42 3.13 Yes

a A Z-value over 1.96 indicates that there is a significant difference at the 95% confidence level.

coefficient of 0.73560.001 at the 95% confidence level. The open water category was classified with a high accuracyas the SPOT channels could discriminate between waterThe results were not significantly different from those ob-

tained from the SPOT channels or from the Radarsat S1 and nonflooded land, while the Radarsat S1 channel couldseparate flooded dead willows from open water. Floodedand S7 channels. However, the combination of Radarsat

S7 and SPOT classified the open water category with higher vegetation was also more straightforward to separate fromnonflooded land due to the complementing informationaccuracy than the SPOT channels as the Radarsat S7 chan-

nel could discriminate between flooded dead willows and obtained from the two image types. The Radarsat S1 chan-nel had difficulties with distinguishing the relatively highopen water.

Significantly better results, compared to all previous backscatter in the dry deciduous vegetation from that ofdense flooded grasses and sedges, while the dry deciduouschannel combinations, were achieved when the Radarsat

S1 channel was combined with the SPOT channels. This vegetation had a distinct reflectance in the SPOT channels.Similarly, flooded willows, grasses and sedges had almostchannel combination could better separate flooded vegeta-

tion from dry vegetation and obtained an overall Kappa the same spectral reflectance as dry vegetation in the SPOTchannels, while they had very characteristic backscatter incoefficient of 0.82560.001 at the 95% confidence level.the Radarsat S1 channel. Some confusion still occurredbetween flooded dense willows and dry deciduous vegeta-tion. A visual inspection of the classified image, providedin Figure 6, confirmed that the classification was improved.

The Radarsat S1, Radarsat S7 and SPOT combinationresulted in an overall Kappa coefficient of 0.84560.001at the 95% confidence level, which was not significantlydifferent from the results obtained by the Radarsat S1 andSPOT combination. The Radarsat S7 channel contributedto an improvement in the segmentation of the open watercategory, but did not otherwise have a noticeable effecton the classification. Given the additional cost and com-puter time required for purchasing and processing an addi-tional image channel and the insignificant amount of addi-tional information contributed by the Radarsat S7 scene, acombination of Radarsat S1 and SPOT data was consideredsufficient for the delineation of surface water.

Figure 5. Examples of land cover classes identified on a subsetof the Radarsat S1 scene from July 1998.

Much like May of 1998, all of the areas vegetated


requires further investigation. In addition, the remainingimage speckle in the Radarsat imagery resulted in classifiedimages with a very noisy appearance.

Very little additional information was obtained by com-bining Radarsat scenes acquired at a low and a high inci-dence angle. The flooded vegetation and the nonfloodedland classes were separated based on the information inthe low angle Radarsat image. However, the high angleRadarsat channel did improve the classification of the openwater class.

SPOT scenes alone achieved slightly better classifica-tion results compared to the Radarsat scenes, although thedifference was only significant in the summer period. Theareas of dry deciduous vegetation, which was problematicfor the Radarsat channels, had very distinct reflectancevalues in the SPOT channels. As a result, the SPOT scenescould distinguish the dry deciduous vegetation fromflooded vegetation. However, the SPOT scenes could not

Figure 6. A combination of the July 1998 Radarsat S1 discriminate between flooded and dry willows, grasses orchannel and the July 1998 SPOT channels classified

sedges when the vegetation layer was relatively dense.into open water, flooded vegetation, and nonfloodedResults show that classification of the Radarsat channelland using a Mahalanobis distance classifier.

in combination with the SPOT channels achieves signifi-cantly better results compared to when the two image typesare classified separately. However, it is important to useby conifers and most of the areas with dense deciduous

vegetation were dry in July of 1998, which means that the Radarsat imagery acquired at low incidence angles. TheSAR and VIR combination resulted in an overall Kappa-ability of the Radarsat channels or SPOT channels to detect

standing water in forested areas could not be determined. coefficient of 0.92060.000 (a50.05) and 0.82560.001(a50.05) for May 1998 and July 1998, respectively.

These results clearly demonstrate the utility of aCONCLUSIONS multisensor approach to environmental monitoring. Futureresearch will require validation in terms of the approachThis study has demonstrated that the extent of standing

water in the Peace-Athabasca Delta can be mapped during presented along with derived time series of spatial floodextent. It is hoped that these dates will provide invaluablespring and summer conditions using a combination of Ra-

darsat and SPOT imagery. Radarsat and SPOT imagery hydrological information required for ecological monitor-ing of the sensitive ecosystem.from May and July of 1998 were classified into open water,

flooded vegetation, and nonflooded land using the Mahala-nobis distance classification algorithm. The Radarsat signal The authors of this paper would like to acknowledge Wood Buffalocould penetrate vegetation and detect standing water be- National Park and National Water Research Institute (NWRI)

for partial funding, Terry Pultz (Canada Centre for Remote Sens-neath a layer of willows, grasses or sedges at incidenceing) for providing the RADARSAT imagery, and Paris Vachonangles between 208 and 318. Canopy attenuation on the(Canada Centre for Remote Sensing) for calibrating the RA-radar signal was higher during the summer period when DARSAT data. The authors would also like to thank Wood Buffalo

the vegetation layer was dense. When the incidence angle National Park, Tom Carter (NWRI), Kelly Best (NWRI), Steveincreased to 458, the path length through the canopy cover Adam (University of Calgary), and Robert Grandjambe (Fort

Chipewyan) for field support.increased, and the canopy attenuation prevented the signalfrom penetrating the vegetation. Classification of Radarsatimagery for the purpose of flood mapping achieved rela- REFERENCEStively poor results, especially during the summer period.In the spring, the Radarsat signal could not penetrate dense

Ackleson, S. G., Klemas, V., McKim, H. L., and Merry, C. J.layers of grasses and sedges, as they were bent over and(1985), A comparison of SPOT simulator data with LANDSATlying down on the ground or over a water surface. The MSS imagery for delineating water masses in Delaware Bay,

flooded, dense grasses and sedges were, therefore, con- Broadkill River, and adjacent wetlands. Photogramm. Eng.fused with dry upland vegetation. However, the vegetation Remote Sens. 51:1123–1129.layer could easily be penetrated by the Radarsat signal Adam, S., Wiebe, J., Collins, M., and Pietroniro, A. (1998),during the summer when grasses and sedges were standing RADARSAT flood mapping in the Peace-Athabasca Delta,upright. There were also problems with discriminating dry Canada. Can. J. Remote Sens. 24:69–79.

Bennett, M. W. A. (1987), Rapid monitoring of wetland waterdeciduous vegetation with Radarsat alone. This problem

172 Toyra et al.

status using density slicing. In Proc. Fourth Australasian Re- Pohl, C., and Van Genderen, J. L. (1998), Multisensor imagefusion in remote sensing: concepts, methods and applications.mote Sensing Conf., Australasian Remote Sensing Conference

Committee, Adelaide, pp. 682–691. Int. J. Remote Sens. 19:823–854.Prowse, T. D., Conly, M., and Lalonde, V. (1996), Hydrometeoro-Bruniquel, J., and Lopes, A. (1997), Multi-variate optimal speckle

reduction in SAR imagery. Int. J. Remote Sens. 18:603–627. logical Conditions Controlling Ice-Jam Floods, Peace RiverNear the Peace-Athabasca Delta, Northern River Basin StudyCarter, V. (1982), Applications of remote sensing to wetlands. In

Remote Sensing for Resource Management (C. J. Johannsen Project (Report No. 103), NRBS, Edmonton, Canada.Pultz, T. J., Leconte, R., St-Laurent, L., and Peters, L. (1991),and J. L. Sanders, Eds.), Soil Conservation Society of America,

Ankeny, pp. 284–300. Flood mapping with airborne SAR imagery. Can. Water Re-sour. J. 16:173–189.Cohen, J. (1960), A coefficient of agreement for nominal scales.

Ed. Psychol. Measure. 20:37–46. Richards, J. A., Woodgate, P. W., and Skidmore, A. K. (1987),An explanation of enhanced radar backscattering from floodedCrevier, Y., and Pultz, T. J. (1996), Analysis of C-Band SIR-C

radar backscatter over a flooded environment, Red River, Fort forests. Int. J. Remote Sens. 8:1093–1100.Rose, P. W., and Rosendahl, P. C. (1983), Classification of LAND-Smith, Manitoba, Canada. In Applications of Remote Sensing

in Hydrology, Proc. Third Int. Workshop, NHRI Symp. No. SAT data for hydrologic application, Everglades National Park.Photogramm. Eng. Remote Sens. 49:505–511.17, National Hydrology Research Institute, Saskatoon, Can-

ada, pp. 47–60. Rosenfield, G. H., and Fitzpatrick-Lins, K. (1986), A coefficientof agreement as a measure of thematic classification accuracy.Fleiss, J. L., Cohen, J., and Everitt, B. S. (1969), Large sample

standard errors of Kappa and weighted Kappa. Psychol. Photogramm. Eng. Remote Sens. 52:223–227.Saper, R., Pultz, T. J., Bowering, R. J., Crevier, Y., and McLaurin,Bull. 72:323–327.

Hall, D. K. (1996), Remote sensing applications to hydrology: I. (1996), Demonstration of RADARSAT potential for floodmonitoring—the 1996 Manitoba spring floods. In ApplicationsImaging radar. Hydrol. Sci. 41:609–624.

Hess, L., Melack, J. M., and Simonett, D. S. (1990), Radar detec- of Remote Sensing in Hydrology. Proc. Third Int. Workshop,NHRI Symp. No. 17, National Hydrology Research Institute,tion of flooding beneath the forest canopy. Int. J. Remote

Sens. 11:1313–1325. Saskatoon, Canada, pp. 313–321.Schistad Solberg, A. H., Jin, A. K., and Taxt, T. (1994), Multi-Jaques, D. R. (1989), Topographic Mapping and Drying Trends

in the Peace-Athabasca Delta, Alberta, Using Landsat MSS source classification of remotely sensed data: fusion of LandsatTM and SAR images. IEEE Trans. Geosci. Remote Sens. 32:Imagery, Parks Canada, Wood Buffalo National Parks, Fort

Smith, Canada. 768–778.Schott, J. R. (1997), Remote Sensing: The Image Chain Approach,Jensen, J. R. (1996), Introductory Digital Image Processing: A

Remote Sensing Perspective, Prentice-Hall, Englewood Oxford University Press, New York.Skidmore, A. K., Woodgate, P. W., and Richards, J. A. (1986),Cliffs, NJ.

Kaiser, C. (1997), Digitized 1:250 000 NTS map sheets (84I and Classification of the Riviera Forest of south eastern Australiausing co-registered Landsat MSS and SIR-B radar data. In74L), unpublished database, Parks Canada, Fort Smith.

Kuan, D. T., Sawchuck, A. A., Strand, T. C., and Chavel, P. Proc. Symp. Remote Sens. for Resources Development andEnv. Management, A. A. Balkema, Rotterdam, 1986, pp.(1987), Adaptive restoration of images with speckle. IEEE

Trans. Acoust. Speech Signal Proc. 35:373–383. 517–519.Srivastava, S. (1998), Extraction of Beta Nought and Sigma NoughtLeconte, R., and Pultz, T. J. (1991), Evaluation of RADARSAT

for flood mapping using simulated satellite SAR imagery. Can. from RADARSAT CDPF Products, Report No. AS97–5001,Canadian Space Agency, Ottawa, Ontario.J. Remote Sens. 17:241–249.

Lee, K. (1975), Ground investigations in support of remote sens- Srivastava, S. K., Banik, B., Adamovic, M., et al. (1998), Radarsatimage quality and calibration performance—update. 1998 Int.ing. In Manual of Remote Sensing, Vol. 1 (R. G. Reeves,

Ed.), American Society of Photogrammetry, Falls Church, VA, Geosci. Remote Sens. Symp. (IGARSS ’98), Institute of Electri-cal and Electronics Engineers, Piscataway, pp. 2454–2458.pp. 805–856.

Li, C., Brisco, B., Brown, R. J., Wang, J., and McDonald, J. Swain, P. H., and Davis, S. M. (Eds.). (1978), Remote Sensing:The Quantitative Approach, McGraw-Hill, New York.(1995), SAR/TM wetland identification in the Lake St. Clair

Delta. In Proc. 17th Can. Symp. Remote Sens., Canadian Timoney, K. (1996), Vegetation monitoring program. Task E.2—vegetation monitoring. In Peace-Athabasca Delta TechnicalRemote Sensing Society, Ottawa, pp. 60–64.

Lillesand, T. M., and Kiefer, R. W. (1994), Remote Sensing and Studies—Appendix 1, Task Reports: Understanding the Eco-system (Peace-Athabasca Delta Technical Studies, Fort Chi-Image Interpretation, Wiley, New York.

Mitsch, W. J., and Gosselink, J. G. (1986), Wetlands, Van Nos- pewyan, Canada).Toutin, Th. (1995), Multisource data fusion with an integratedtrand Reinhold, New York.

Moore, G. K., and North, G. W. (1974), Flood inundation in the and unified geometric modelling. EARSeL J., Adv. RemoteSens. 4:118–129.southeastern United States from aircraft and satellite imagery.

Water Resour. Bull. 10:1082–1096. Toyra, J. (1999), Flood mapping in the Peace-Athabasca Delta,Canada, using Radarsat and SPOT imagery, Master’s Thesis,Pietroniro, A., and Prowse, T. D. (1996), Environmental monitor-

ing of the Peace-Athabasca Delta using multiple satellite data University of Saskatchewan, Saskatoon, Saskatchewan, Can-ada (unpublished).sources. In Applications of Remote Sensing in Hydrology.

Proc. Third Int. Workshop, NHRI Symp. No. 17, National Toyra, J, Pietroniro, A., Martz, L. W., and Prowse, T. D. (inpress), A multi-sensor approach to wetland flood monitoring.Hydrology Research Institute, Saskatoon, Canada, pp.

237–251. Hydrol. Process.


Ustin, S. L., Wessman, C. A., Curtiss, B., Kasischke, E., Way, J., Geomatics in the Era of RADARSAT, Int. Symp. Natural Re-and Vanderbilt, V. (1991), Opportunities for using the EOS sources Canada, Ottawa, p. 1–9.imaging spectrometers and synthetic aperture radar in ecologi- Wang, Y., Hess, L. L., Filoso, S., and Melack, J. M. (1995),cal models. Ecology 72:1934–1945. Understanding the radar backscattering from flooded and non-

Vachon, P. W., Gray, A. L., Livingstone, C. E., and Luscombe, flooded amazonian forests: results from canopy backscatterA. P. (1997), Adaptive compensation of RADARSAT SAR ana- modeling. Remote Sens. Environ. 54:324–332.logue-to-digital converter saturation power loss. In GER97-

Multisensor Hydrologic Assessment of a Freshwater Wetland

Documents

Transcript of Multisensor Hydrologic Assessment of a Freshwater Wetland