Glacier mapping of the Illecillewaet ice® eld, British

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int. j. remote sensing, 1999, vol. 20, no. 2, 273 ± 284

Glacier mapping of the Illecillewaet ice® eld, British Columbia,

Canada, using Landsat TM and digital elevation data

R. W. SIDJAK and R. D. WHEATEGeography Program, Faculty of Natural Resources and Environmental Studies,University of Northern British Columbia, 3333 University Way, Prince George,BC, V2N 4Z9, Canada; e-mail: [email protected], [email protected]

Abstract. Glacier inventory is important to provide estimates of freshwaterstorage and as an indicator of climate variability. The methodology for glacierinventory in Canada has been based on manual interpretation of aerial photo-graphs. Digital methods using Landsat Thematic Mapper (TM) satellite imageryand terrain models o� er improved e� ciency and repeatability, while retainingsu� cient accuracy and precision. Supervised maximium likelihood classi® cationtrials using di� erent input bands were assessed for accuracy of mapping glacierextent and discriminating glacier zones at Illecillewaet Ice® eld, Glacier NationalPark, British Columbia. Results were compared with visual image interpretation,with the best results obtained using the combination of principal componentstwo, three and four of the masked glacier area, the ratio TM-4/TM-5, and theNormalized Di� erence Snow Index (NDSI). This method avoids problems withsensor saturation, shadowed areas, and discriminates debris mantled ice and ice-marginal water bodies. Combining the thematic map with a high-resolution digitalelevation model allows derivation of glacier inventory attributes.

1. Introduction

The glaciers of the Columbia Mountains represent a signi® cant water reservoirin the Columbia River basin, which depends on runo� from these glaciers, particularlyduring dry periods. Long term change in glacier extent and volume is considered ane� ective index of climate change. Periodic glacier inventory provides information onmass balance trends and changes in areal extent and volume necessary for watermanagement and climate monitoring. Important attributes of glacier inventoryinclude areal extent, equilibrium line altitude (ELA) and accumulation area ratio(AAR).

Glacier mapping and inventory e� orts in Canada since the 1950s have relied onaerial photograph interpretation (Ommanney 1986). This method is expensive andlaborious, and problems with distortion due to high relief and low platform heighthave been recognised (Champoux and Ommanney 1986 a). Previous investigationsof the utility of satellite imagery for glacier inventory and monitoring have beenhampered by the poor spatial resolution of Landsat MSS data (80m), as well asunder-utilised multispectral image processing techniques (é strem 1975, Champouxand Ommanney 1986 b, Howarth and Ommanney 1986). A single Landsat TMscene captures an area covered by hundreds of aerial photographs, minimises relief

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online Ñ 1999 Taylor & Francis Ltd

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R. W. Sidjak and R. D. W heate274

displacement, and provides su� cient spatial resolution (30 m) to discriminate featuresof interest.

This project aims at demonstrating the applicability of combining classi® edLandsat TM imagery with high resolution digital elevation models (DEM) formapping glacier extent in a format compatible with the existing Canadian GlacierInventory. The study involves data integration, image processing, image classi® cationand the creation of map and tabular products.

2. Study area and history

The Illecillewaet Ice® eld area (® gure 1) was selected due to its century-long recordof ground observations (Champoux and Ommanney 1986 b) and present researchand monitoring activity. The area, centred at 51ß 15 ¾ N, 117ß 30 ¾ W is considered tobe representative of alpine glaciers in the Canadian Cordillera. The IllecillewaetGlacier terminus position was surveyed and annually photographed by the Vauxfamily between 1887 and 1912. Subsequent monitoring was conducted by the WaterSurvey of Canada and Parks Canada. A previous inventory of the region’s glacierswas made by Parks Canada from aerial photographs acquired in 1951± 52 and 1978(Champoux and Ommanney 1986 a). The historic record shows the terminus of theIllecillewaet Glacier retreated more than 1000 m from 1887± 1962, and advancedabout 100 m between 1962± 84 (Champoux and Ommanney 1986 b). Since 1984 theglacier has resumed its retreat.

3. Data sources

1 Landsat TM quad scene recorded on 18 August 1994, was acquired from theNational Hydrology Research Institute. Requirements for the scene were minimalcloud cover and a date late in the ablation season for minimal snow. Digital elevationdata are available in 1 : 20 000 scale map sheets covering 0.2ß longitude Ö 0.1ß latitude

Figure 1. Location map of study site.

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Fourth Circumpolar Remote Sensing Symposium 275

from the Province of British Columbia Terrain Resource Inventory Mapping (TRIM)digital mapping program. Elevation data are represented as vector points, breaklinesand feature outlines derived from analytical stereoplotting. In areas where automatedstereoplotting cannot adequately resolve the surface, such as snow and ice surfaces,points are manually digitised from ancillary sources of elevation data, resulting in alower density and reliability of elevation points. Four map sheets were combined forthe Illecillewaet sub-scene, representing 27.5 km Ö 22.5 km.

4. Data integration

Data from the vector and raster formats were integrated into a coherent, georefer-enced database to create a continuous raster image digital elevation model (DEM)from the planimetrically correct TRIM data. The TM subscene was then registeredto this dataset.

The DEM creation involved (1) the selection of compatible vector data from theTRIM ® les, (2) importation into the PCI image processing package, (3) rasterizationof the vector points, and (4) interpolation into a continuous image surface. Severaltrials were attempted using di� erent combinations of the vector data types andinterpolation algorithms. The best results were achieved by using all available pointelevation data and none of the line data as input to the conic interpolation algorithmresident in PCI. The conic interpolator identi® es the spatial relation of each pixelwith a morphological feature such as a slope, depression or peak and assigns itsvalue according to this spatial context (PCI User’s Manual 1996). Brugman et al.(1996) discuss the advantages of this data source and interpolation method. TheDEM noise’ was ® ltered out with a single pass of a 3 Ö 3 median ® lter.

The geo-correction of the TM sub-scene was accomplished through an identi® ca-tion of 25 ground control points on both the TM image and in the planimetricallycorrected shaded relief image created from the DEM overlaid with TRIM vectorsdepicting hydrography and roads. The TM image was resampled to 25 m pixelspacing using a second order cubic convolution where the root mean square errorfor the correction was minimised to less than one pixel.

5. Image processing

The image processing e� orts were directed toward producing input data for ane� ective and reproducible supervised classi® cation of glacier extent and zone discrim-ination. The principal components analysis (PCA) was employed to reduce dataredundancy due to correlation between the TM bands and to enhance contrast inthe features of interest (Orheim and Luccitta 1987). PCA is a multi-spectral techniquewhich transforms data values by rotating the co-ordinate axes, resulting in a reducednumber of signi® cant data channels.

Initially, PCA was performed for the entire sub-scene containing the IllecillewaetGlacier. The ® rst principal component (PC1) represents a weighted average of allthe bands, usually referred to as brightness’. In this case, the loadings indicate thatthe PC1 is dominated by the visible and near-infrared bands (table 1), probably asa result of limited vegetation cover, compared to a forested region with no glaciers.In contrast, the PC2 (® gure 2 (a)) depicts the in¯ uence of the short-wave infraredbands 5 and 7 and cleanly isolates glacier from non-glacier surfaces as a result ofthe low re¯ ection of snow and ice at the longer wavelengths (band 5 digital numbersfor ice/snow here are 10± 15, band 7: 2± 6). From the loadings, the PC3 is clearlydominated by the thermal band 6, with little glacier information, whereas the PC4

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Table 1. Principal Component factor loadingsÐ TM sub-scene.

PC1 PC2 PC3 PC4 PC5 PC6 PC7

TM-1 0.984 Õ 0.036 0.037 Õ 0.155 Õ 0.072 Õ 0.022 Õ 0.004TM-2 0.975 Õ 0.168 Õ 0.019 0.076 0.115 Õ 0.049 Õ 0.016TM-3 0.988 Õ 0.116 Õ 0.078 Õ 0.034 0.033 0.049 0.007TM-4 0.906 0.108 0.125 0.383 Õ 0.071 Õ 0.002 0.013TM-5 0.281 0.984 Õ 0.190 0.017 Õ 0.001 0.011 Õ 0.031TM-6 0.229 0.568 0.779 Õ 0.116 0.068 0.015 0.007TM-7 0.333 0.872 Õ 0.315 Õ 0.113 0.058 Õ 0.057 0.098

Percentage of sub-scene variance accounted for by component.


% Variance 75.24 16.73 4.84 2.56 0.45 0.12 0.05

(a) (b)

Figure 2. Principal components PC2 and PC4 based on analysis of sub-scene.

( ® gure 2 (b)) displays more details on the glacier surface perhaps related to the snowgrain size (Hall et al. 1988, Brugman et al. 1996). The remaining components aredominated by visual noise and account for only 0.5 per cent of the total scenevariance.

Much of the information derived from this analysis was strongly in¯ uenced bythe non-glaciated areas surrounding the glaciers, which in this study were notrelevant. In an e� ort to reduce this e� ect and to minimise any scene speci® c nature

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of the principal components, further analysis was applied under a mask of theglaciated area of the scene, a procedure also suggested by BoresjoÈ -Bronge and Bronge(1996). The mask was created from a threshold of the second principal componentof an unmasked PCA where glaciated areas strongly contrast with all other areas inthe scene. This mask was then used to create new principal components based solelyon glacier areas, for which the loadings are shown in table 2.

Pixel saturation is typical over glaciated and snow covered areas, particularly inthe visible bands: TM-1, -2 and -3 (Hall et al. 1988). The PCA reduced this saturationby identifying most of the scene brightness variance and thus the saturation, withinthe ® rst principal component. Subsequent principal components, especially thesecond, third and fourth, were found to depict strong, unsaturated contrast over theglaciated areas, enhancing surface features (® gure 3).

Table 2 indicates an in¯ uence of the middle infra-red bands TM-5 and TM-7 incomponents 5, 6 and 7, which results from the low variance of digital numbers inthese bands for glacier surfaces. The patterns depicted appear to be related totopographic elements of the glacier surface, representing remnants not seen in thehigher components. Continued research is required to fully assess the potential forutilising these lower components.

Further image processing involved band ratioing and the Normalized Di� erenceSnow Index (NDSI). The ratio TM-4/TM-5 has been shown to e� ectively separateice and snow zones over glacier surfaces, particularly in areas containing shadow(Hall et al. 1987 ) and to enhance contrast in the snow zones (Williams et al. 1991).The NDSI has been e� ective in distinguishing snow from similarly bright soil,vegetation and rock, as well as from clouds in TM imagery (Dozier 1989, Hall et al.1995 a):

NDSI= (TM2 Õ TM5)/(TM2+TM5) (1)

This is based on the di� erence between strong re¯ ection of visible radiation andnear total absorption of middle infrared wavelengths by snow (Hall et al. 1995 a).Its e� ectiveness in mapping snow cover over rugged terrain has been demonstratedin Hall et al. (1995 b). A simple cosine correction for radiometric normalization ofthe topographic e� ect (Civco 1989) yielded no signi® cant improvement in uniformitywithin the spectral classes of interest. This may be due to an insu� cient DEM and

Table 2. Principal Component factor loadingsÐ glaciated areas.


TM-1 0.986 0.162 0.013 Õ 0.028 0.005 0 0TM-2 0.983 Õ 0.173 Õ 0.052 Õ 0.015 Õ 0.019 0.018 0TM-3 0.997 Õ 0.031 0.063 0.032 Õ 0.006 0.001 0TM-4 0.978 Õ 0.201 Õ 0.038 Õ 0.020 0.026 Õ 0.022 0TM-5 0.871 0.151 Õ 0.102 0.155 0.380 0.190 Õ 0.049TM-6 0.923 0.337 Õ 0.173 0.070 Õ 0.015 Õ 0.008 0TM-7 0.763 0.171 Õ 0.101 0.190 0.474 0.253 0.226

Percentage of scene variance accounted for by component.


% Variance 6.92 2.59 0.34 0.09 0.05 0.02 0.00

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(a) (b)

Figure 3. Principal components PC3 and PC4 based on analysis under a mask isolatingglacier surfaces.

data registration accuracy for illumination modelling (Dozier and Marks 1987,Brugman et al. 1996), and the anisotropic re¯ ectance of old snow (Hall et al. 1988,Brugman et al. 1996).

6. Image classi® cation

Challenges facing mapping of glacier areas from satellite imagery include thediscrimination of ice from snow under both direct and shadowed illumination, icefrom marginal water bodies, and the identi® cation of debri-covered ice. Glaciersurfaces are fundamentally divided into an ice and a snow facies (Williams et al.1991), with the transient snowline dividing them. Late in the mass-balance year thetransient snowline approximates the location of the equilibrium line on temperateglaciers. The snowline can usually be identi® ed in satellite images (é strem 1975,Williams et al. 1991). Further discrimination of a wet-snow surfaces, including aslush zone, a percolation zone, and a dry-snow zone above the snowline fromLandsat imagery is outlined in Williams et al. (1991 ). However, the di� erence betweenslush, wet snow and ice is di� cult to identify, owing to varying physical andradiometric conditions through a scene, which makes the location of the snowlineuncertain. A thin debris cover can signi® cantly alter the spectral response of ice,while a thicker cover prevents discrimination of the boundary between ice andadjacent moraines. Sediment laden lakes were found to have a spectral signaturesimilar to that of ice, leading to confusion in some trials.

Supervised maximum likelihood classi® cation trials were conducted using di� er-

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ent combinations of input bands. Qualitative assessment of classi® cation results wasguided by a visual interpretation of the image. Training areas were established forthirteen separate classes: snow, wet snow/ ® rn, ice, debris-covered ice, bedrock, andmoraineÐ each under both direct and shadowed illuminationÐ and water bodies.An e� ort was made to sample the full range of spectral variation within each class.Separate training areas were not established for vegetation or clouds.

Classi® cation trials were performed with the following band combinations:

(1) TM bands 3, 4, and 5(2) Band ratio TM4/TM5 and NDSI(3) Masked principal components 1± 4(4) Masked principal components 2± 4(5) Masked principal components 1± 4+band ratio TM-4/TM-5+NDSI(6) Masked principal components 2± 4+band ratio TM-4/TM-5+NDSI

The theme map products were 3 Ö 3 mode ® ltered and shadowed and illuminatedclasses were aggregated. Shaded relief was incorporated into the RGB-coded thememap by inverting the cosine correction procedure given in Civco (1989 ). This isaccomplished by creating a shaded relief/illumination model for the desired illumina-tion conditions from the DEM, and then applying the following linear transformationto each of the RGB channels:

dDN ij = DN ij Õ (DN ij Ö Amk Õ X ij

mk B (2)

where:DN ij = the product digital number for pixel ij in the shaded imageDN ij = the digital number for pixel ij in the raw imagemk= the mean value for the entire shaded relief/illumination model

X ij = the value of pixel ij in the illumination model

7. Results and discussion

Results of the classi® cation trials are summarized in table 3. A discussion of theclassi® cation performance refers to the thematic map product of Trial 5 (® gure 5)and a corresponding TM-5-4-3 colour composite image (® gure 4). Classidenti® cation is described in the legend.

Classi® cation of the snow/accumulation area was relatively simple. However,snow slopes oriented directly towards the incident illumination were excluded fromthe snow class in all other trials, despite adequate sampling of these areas duringthe training. Shadows on snow, cast either by topography or clouds, were partiallyresponsible for errors in the ® rn class in all trials (see 1 and 2, ® gure 5). Visual imageinterpretation lead to mapping of the transient snowline within the ® rn class, usuallynearer the ® rn-ice margin than the snow-® rn margin. However, a rigorous evaluationof the accuracy of snowline mapping is not possible without further ground truth.The ablation area/bare-ice class was easily mapped, except under heavy cloudshadows (see 3, ® gure 5), where Trials 1± 4 did not successfully discriminate ice fromnunatak and medial moraine. Highly fractured ice in crevassed ® elds and icefallswas partially mapped as ® rn in all trials. Ice-marginal water bodies (see 4, ® gure 5)were mapped as ice in most trials, but correctly mapped in Trial 5.

Glacier areas subject to topographic shadows resulting from the high relief ofthe area (>2000 m) were trained and assessed. Trials 1± 4 performed very poorly

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Figure 4. Landsat TM-5± 4-3 colour composite image.

under these conditions, usually overestimating large areas to the glacier class. Asuccessfully classi® ed shadow area is marked S ’, while an erroneously committedarea of shadow without glacier is marked X’.

Mapping of debris covered ice has been recognised as a problem in glacierinventory (Whalley and Martin 1986). The spectral signature of the class is relatedto the combination of re¯ ections from both debris and ice. Ice that is completelycovered with debris cannot be spectrally distinguished from adjacent non-glaciatedareas, while the signature of areas of more dispersed cover is very broad and poorlyde® ned, due to variable debris material type, morphology and quantity. Trial 5 wasfound to produce very good results in this class, without signi® cant errors due toomission or addition. Mixed pixels of ice and adjacent rock or debris were alsomapped in this class, appearing as a margin of red around glacier termini.

8.1 ConclusionsFor glacier inventory purposes, supervised classi® cation of Landsat TM scenes

in the mapping of glacier extent appears to be a reasonable method which may haveimpact in detection of global change. Principal components analysis, image ratioing

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Figure 5. Theme map produced from classi® ed image (see text for explanation of identi® edpoints).

and image di� erencing produce superior classi® cation input channels compared tothe original TM bands. A secondary set of components with loadings and generatedimages based on glacier surfaces alone provides the most useful information forclassi® cation, highlighting local variations and evaluating the in¯ uence of the sur-rounding terrain. The information contained in lower-order components appeared

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to be related to glacier surface topography. Identi® cation of the transient snowlineunder varying radiometric conditions is di� cult, but may be improved by re® nementof the discrimination between ® rn and wet snow classes.

Acknowledgments

The study was carried out in collaboration with Drs Brugman and Pietroniro ofthe National Hydrology Research Institute and the cryospheric systems researchinitiative (CRYSYS), a Canadian Department of Environment and the UniversityNorthern British Columbia as a contribution to the NASA Earth Observing System(EOS) program. The authors wish to acknowledge CRYSYS for providing theoperating funds for this study and the anonymous reviewers for helpful and criticalcomments.

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Documents

Glacier mapping of the Illecillewaet ice® eld, British