Glacier mapping of the Illecillewaet ice eld, British Columbia, Canada, using Landsat TM and digital elevation data

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1 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. WHEATE Geography Program, Faculty of Natural Resources and Environmental Studies, University of Northern British Columbia, 3333 University Way, Prince George, BC, V2N 4Z9, Canada; Abstract. Glacier inventory is important to provide estimates of freshwater storage and as an indicator of climate variability. The methodology for glacier inventory in Canada has been based on manual interpretation of aerial photographs. Digital methods using Landsat Thematic Mapper (TM) satellite imagery and terrain models o er improved e ciency and repeatability, while retaining su cient accuracy and precision. Supervised maximium likelihood classi cation trials using di erent input bands were assessed for accuracy of mapping glacier extent and discriminating glacier zones at Illecillewaet Ice eld, Glacier National Park, British Columbia. Results were compared with visual image interpretation, with the best results obtained using the combination of principal components two, three and four of the masked glacier area, the ratio TM-4/TM-5, and the Normalized Di erence Snow Index (NDSI). This method avoids problems with sensor saturation, shadowed areas, and discriminates debris mantled ice and icemarginal water bodies. Combining the thematic map with a high-resolution digital elevation model allows derivation of glacier inventory attributes. 1. Introduction The glaciers of the Columbia Mountains represent a signi cant water reservoir in the Columbia River basin, which depends on runo from these glaciers, particularly during dry periods. Long term change in glacier extent and volume is considered an e ective index of climate change. Periodic glacier inventory provides information on mass balance trends and changes in areal extent and volume necessary for water management and climate monitoring. Important attributes of glacier inventory include areal extent, equilibrium line altitude (ELA) and accumulation area ratio (AAR). Glacier mapping and inventory e orts in Canada since the 1950s have relied on aerial photograph interpretation (Ommanney 1986). This method is expensive and laborious, and problems with distortion due to high relief and low platform height have been recognised (Champoux and Ommanney 1986 a). Previous investigations of the utility of satellite imagery for glacier inventory and monitoring have been hampered by the poor spatial resolution of Landsat MSS data (80 m), as well as under-utilised multispectral image processing techniques (é strem 1975, Champoux and Ommanney 1986 b, Howarth and Ommanney 1986). A single Landsat TM scene captures an area covered by hundreds of aerial photographs, minimises relief International Journal of Remote Sensing ISSN print/issn online Ñ 1999 Taylor & Francis Ltd

2 274 R. W. Sidjak and R. D. W heate displacement, and provides su cient spatial resolution (30 m) to discriminate features of interest. This project aims at demonstrating the applicability of combining classi ed Landsat TM imagery with high resolution digital elevation models (DEM) for mapping glacier extent in a format compatible with the existing Canadian Glacier Inventory. The study involves data integration, image processing, image classi cation and 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 record of ground observations (Champoux and Ommanney 1986 b) and present research and monitoring activity. The area, centred at 51ß 15¾ N, 117ß 30¾ W is considered to be representative of alpine glaciers in the Canadian Cordillera. The Illecillewaet Glacier terminus position was surveyed and annually photographed by the Vaux family between 1887 and Subsequent monitoring was conducted by the Water Survey of Canada and Parks Canada. A previous inventory of the region s glaciers was 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 the Illecillewaet Glacier retreated more than 1000 m from 1887± 1962, and advanced about 100 m between 1962± 84 (Champoux and Ommanney 1986 b). Since 1984 the glacier has resumed its retreat. 3. Data sources 1 Landsat TM quad scene recorded on 18 August 1994, was acquired from the National Hydrology Research Institute. Requirements for the scene were minimal cloud cover and a date late in the ablation season for minimal snow. Digital elevation data are available in 1 : scale map sheets covering 0.2ß longitude Ö 0.1ß latitude Figure 1. Location map of study site.

3 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, breaklines and feature outlines derived from analytical stereoplotting. In areas where automated stereoplotting cannot adequately resolve the surface, such as snow and ice surfaces, points are manually digitised from ancillary sources of elevation data, resulting in a lower density and reliability of elevation points. Four map sheets were combined for the Illecillewaet sub-scene, representing 27.5 kmö 22.5 km. 4. Data integration Data from the vector and raster formats were integrated into a coherent, georeferenced database to create a continuous raster image digital elevation model (DEM) from the planimetrically correct TRIM data. The TM subscene was then registered to this dataset. The DEM creation involved (1) the selection of compatible vector data from the TRIM les, (2) importation into the PCI image processing package, (3) rasterization of the vector points, and (4) interpolation into a continuous image surface. Several trials were attempted using di erent combinations of the vector data types and interpolation algorithms. The best results were achieved by using all available point elevation data and none of the line data as input to the conic interpolation algorithm resident in PCI. The conic interpolator identi es the spatial relation of each pixel with a morphological feature such as a slope, depression or peak and assigns its value according to this spatial context (PCI User s Manual 1996). Brugman et al. (1996) discuss the advantages of this data source and interpolation method. The DEM `noise was ltered out with a single pass of a 3Ö 3 median lter. The geo-correction of the TM sub-scene was accomplished through an identi cation of 25 ground control points on both the TM image and in the planimetrically corrected shaded relief image created from the DEM overlaid with TRIM vectors depicting hydrography and roads. The TM image was resampled to 25 m pixel spacing using a second order cubic convolution where the root mean square error for the correction was minimised to less than one pixel. 5. Image processing The image processing e orts were directed toward producing input data for an e ective and reproducible supervised classi cation of glacier extent and zone discrimination. The principal components analysis (PCA) was employed to reduce data redundancy due to correlation between the TM bands and to enhance contrast in the features of interest (Orheim and Luccitta 1987). PCA is a multi-spectral technique which transforms data values by rotating the co-ordinate axes, resulting in a reduced number of signi cant data channels. Initially, PCA was performed for the entire sub-scene containing the Illecillewaet Glacier. The rst principal component (PC1) represents a weighted average of all the bands, usually referred to as `brightness. In this case, the loadings indicate that the PC1 is dominated by the visible and near-infrared bands (table 1), probably as a 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 infrared bands 5 and 7 and cleanly isolates glacier from non-glacier surfaces as a result of the low re ection of snow and ice at the longer wavelengths (band 5 digital numbers for ice/snow here are 10± 15, band 7: 2± 6). From the loadings, the PC3 is clearly dominated by the thermal band 6, with little glacier information, whereas the PC4

4 276 R. W. Sidjak and R. D. W heate Table 1. Principal Component factor loadingsð TM sub-scene. PC1 PC2 PC3 PC4 PC5 PC6 PC7 TM Õ Õ Õ Õ Õ TM Õ Õ Õ Õ TM Õ Õ Õ TM Õ Õ TM Õ Õ Õ TM Õ TM Õ Õ Õ Percentage of sub-scene variance accounted for by component. PC1 PC2 PC3 PC4 PC5 PC6 PC7 % Variance (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 snow grain size (Hall et al. 1988, Brugman et al. 1996). The remaining components are dominated by visual noise and account for only 0.5 per cent of the total scene variance. Much of the information derived from this analysis was strongly in uenced by the non-glaciated areas surrounding the glaciers, which in this study were not relevant. In an e ort to reduce this e ect and to minimise any scene speci c nature

5 Fourth Circumpolar Remote Sensing Symposium 277 of the principal components, further analysis was applied under a mask of the glaciated 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 component of an unmasked PCA where glaciated areas strongly contrast with all other areas in the scene. This mask was then used to create new principal components based solely on glacier areas, for which the loadings are shown in table 2. Pixel saturation is typical over glaciated and snow covered areas, particularly in the visible bands: TM-1, -2 and -3 (Hall et al. 1988). The PCA reduced this saturation by identifying most of the scene brightness variance and thus the saturation, within the rst principal component. Subsequent principal components, especially the second, third and fourth, were found to depict strong, unsaturated contrast over the glaciated areas, enhancing surface features ( gure 3). Table 2 indicates an in uence of the middle infra-red bands TM-5 and TM-7 in components 5, 6 and 7, which results from the low variance of digital numbers in these bands for glacier surfaces. The patterns depicted appear to be related to topographic elements of the glacier surface, representing remnants not seen in the higher components. Continued research is required to fully assess the potential for utilising these lower components. Further image processing involved band ratioing and the Normalized Di erence Snow Index (NDSI). The ratio TM-4/TM-5 has been shown to e ectively separate ice 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 a): NDSI= (TM2Õ TM5)/(TM2+ TM5) (1) This is based on the di erence between strong re ection of visible radiation and near total absorption of middle infrared wavelengths by snow (Hall et al a). Its e ectiveness in mapping snow cover over rugged terrain has been demonstrated in Hall et al. (1995 b). A simple cosine correction for radiometric normalization of the topographic e ect (Civco 1989) yielded no signi cant improvement in uniformity within the spectral classes of interest. This may be due to an insu cient DEM and Table 2. Principal Component factor loadingsð glaciated areas. PC1 PC2 PC3 PC4 PC5 PC6 PC7 TM Õ TM Õ Õ Õ Õ TM Õ Õ TM Õ Õ Õ Õ TM Õ Õ TM Õ Õ Õ TM Õ Percentage of scene variance accounted for by component. PC1 PC2 PC3 PC4 PC5 PC6 PC7 % Variance

6 278 R. W. Sidjak and R. D. W heate (a) (b) Figure 3. Principal components PC3 and PC4 based on analysis under a mask isolating glacier 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 the discrimination of ice from snow under both direct and shadowed illumination, ice from marginal water bodies, and the identi cation of debri-covered ice. Glacier surfaces 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 the transient snowline approximates the location of the equilibrium line on temperate glaciers. 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 a slush zone, a percolation zone, and a dry-snow zone above the snowline from Landsat imagery is outlined in Williams et al. (1991). However, the di erence between slush, wet snow and ice is di cult to identify, owing to varying physical and radiometric conditions through a scene, which makes the location of the snowline uncertain. A thin debris cover can signi cantly alter the spectral response of ice, while a thicker cover prevents discrimination of the boundary between ice and adjacent moraines. Sediment laden lakes were found to have a spectral signature similar to that of ice, leading to confusion in some trials. Supervised maximum likelihood classi cation trials were conducted using di er-

7 Fourth Circumpolar Remote Sensing Symposium 279 ent combinations of input bands. Qualitative assessment of classi cation results was guided by a visual interpretation of the image. Training areas were established for thirteen separate classes: snow, wet snow/ rn, ice, debris-covered ice, bedrock, and moraineð 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 3Ö 3 mode ltered and shadowed and illuminated classes were aggregated. Shaded relief was incorporated into the RGB-coded theme map by inverting the cosine correction procedure given in Civco (1989). This is accomplished by creating a shaded relief/illumination model for the desired illumination conditions from the DEM, and then applying the following linear transformation to each of the RGB channels: ddn ij = DN ij Õ (DN ij Ö A mk Õ X ij (2) m k B where: DN ij = the product digital number for pixel ij in the shaded image DN ij = the digital number for pixel ij in the raw image m k = 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 the classi cation performance refers to the thematic map product of Trial 5 ( gure 5) and a corresponding TM colour composite image ( gure 4). Class identi 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 from the snow class in all other trials, despite adequate sampling of these areas during the training. Shadows on snow, cast either by topography or clouds, were partially responsible for errors in the rn class in all trials (see 1 and 2, gure 5). Visual image interpretation lead to mapping of the transient snowline within the rn class, usually nearer the rn-ice margin than the snow- rn margin. However, a rigorous evaluation of the accuracy of snowline mapping is not possible without further ground truth. The ablation area/bare-ice class was easily mapped, except under heavy cloud shadows (see 3, gure 5), where Trials 1± 4 did not successfully discriminate ice from nunatak and medial moraine. Highly fractured ice in crevassed elds and icefalls was 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 of the area (>2000 m) were trained and assessed. Trials 1± 4 performed very poorly

8 280 R. W. Sidjak and R. D. W heate Table 3. Results of the classi cation trials. Trial Comments and interpretation Trial 1 Areas with low brightness values were typically classi ed as `shadowed glacier. Large areas of the scene (TM-3,-4,-5) erroneously committed as glacier. Very bright, snow covered slopes facing the sun omitted from the snow class. These results interpreted to be from the large variance in overall scene brightness dominating the classi cation procedure. Trial 2 Poor discrimination of glacier facies and misclassi ed shadow areas. Interpreted to be due to the loss of spectral (TM-4/TM-5+ NDSI) resolution with compression of the pixel value range associated with ratio and di erence images. Trial 3 Produced markedly better results, however, mis-identi cation within shadowed areas persisted. There was strong (PC 1± 4) over-representation of the `debris covered ice class, likely due to its large and relatively poorly de ned spectral footprint. Principal component 1 may allow overall brightness to dominate, losing classi cation resolution in poorly illuminated areas. Trial 4 Produced improved results in shadowed areas and water bodies compared to Trial 3. However, the overall (PC 2± 4) glacier area was slightly under-represented. Trial 5 Virtually all glacier area was correctly identi ed. Resolution through shadow was unsurpassed. Nunataks, (PC2± 4+ TM4/ TM5+NDSI) medial and dispersed supraglacial moraine, and ice-marginal water bodies were correctly mapped.

9 Fourth Circumpolar Remote Sensing Symposium 281 Figure 4. Landsat TM-5± 4-3 colour composite image. under these conditions, usually overestimating large areas to the glacier class. A successfully classi ed shadow area is marked `S, while an erroneously committed area of shadow without glacier is marked `X. Mapping of debris covered ice has been recognised as a problem in glacier inventory (Whalley and Martin 1986). The spectral signature of the class is related to the combination of re ections from both debris and ice. Ice that is completely covered with debris cannot be spectrally distinguished from adjacent non-glaciated areas, while the signature of areas of more dispersed cover is very broad and poorly de ned, due to variable debris material type, morphology and quantity. Trial 5 was found to produce very good results in this class, without signi cant errors due to omission or addition. Mixed pixels of ice and adjacent rock or debris were also mapped in this class, appearing as a margin of red around glacier termini. 8.1 Conclusions For glacier inventory purposes, supervised classi cation of Landsat TM scenes in the mapping of glacier extent appears to be a reasonable method which may have impact in detection of global change. Principal components analysis, image ratioing

10 282 R. W. Sidjak and R. D. W heate Figure 5. Theme map produced from classi ed image (see text for explanation of identi ed points). and image di erencing produce superior classi cation input channels compared to the original TM bands. A secondary set of components with loadings and generated images based on glacier surfaces alone provides the most useful information for classi cation, highlighting local variations and evaluating the in uence of the surrounding terrain. The information contained in lower-order components appeared

11 Fourth Circumpolar Remote Sensing Symposium 283 to be related to glacier surface topography. Identi cation of the transient snowline under varying radiometric conditions is di cult, but may be improved by re nement of the discrimination between rn and wet snow classes. Acknowledgments The study was carried out in collaboration with Drs Brugman and Pietroniro of the National Hydrology Research Institute and the cryospheric systems research initiative (CRYSYS), a Canadian Department of Environment and the University Northern British Columbia as a contribution to the NASA Earth Observing System (EOS) program. The authors wish to acknowledge CRYSYS for providing the operating funds for this study and the anonymous reviewers for helpful and critical comments. References Adam, S., 1996, Snowline mapping using radar imagery, Place Glacier, B.C.. M.Sc, University of Saskatchewan, Canada. Boresjö-Bronge, L., and Bronge, C., 1996, Landsat TM data and ground radiometer measurements for snow and ice type classi cation in the Vestfold Hills, East Antarctica. In Proceedings of the Fourth Circumpolar Symposium on Remote Sensing of the Polar Environments, held in L yngby, Denmark, on 29 April± 1 May, 1996, SP-391 (Paris/ Noordwijk: European Space Agency), pp. 71± 80. A more comprehensive edition is included in this volume. Brugman, M. M., Pietroniro, A., and Shi, J., 1996, Mapping alpine snow and ice using Landsat TM and SAR imagery at Wapta Ice eld. Canadian Journal of Remote Sensing, 22, 127± 136. Champoux, A., and Ommanney, C. S. L., 1986 a, Photo-interpretation, digital mapping, and the evolution of glaciers in Glacier National Park, B.C. Annals of Glaciology, 8, 27± 30. Champoux, A., and Ommanney, C. S. L., 1986 b, Evolution of the Illecillewaet Glacier, Glacier National Park, B.C., using historical data, aerial photography, and satellite image analysis. Annals of Glaciology, 8, 31± 33. Civco, D. L., 1989, Topographic normalization of Landsat Thematic Mapper digital imagery. Photogrammetric Engineering and Remote Sensing, 55, 1303± Dozier, J., 1989, Spectral signature of Alpine snow cover from the Landsat Thematic Mapper. Remote Sensing of Environment, 28, 9± 22. Dozier, J., and Marks, D., 1987, Snow mapping and classi cation from Landsat Thematic Mapper data. Annals of Glaciology, 9, 97± 103. Hall, D. K., Ormsby, J. P., Bindschadler, R. A., and Siddalingaiah, H., 1987, Characterization of snow and ice re ectance zones on glaciers using Landsat TM data. Annals of Glaciology, 9, 104± 108. Hall, D. K., Chang, A. T. C., and Siddalingaiah, H., 1988, Re ectances of glaciers as calculated using Landsat-5 TM data. Remote Sensing of Environment, 25, 311± 321. Hall, D. K., Riggs, G. A., and Salomonson, V. V., 1995 a, Development of methods for mapping global snow cover using Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 54, 127± 140. Hall, D. K., Foster, J. L., Chein, J. Y. L., and Riggs, G. A., 1995 b, Determination of actual snow covered area using Landsat TM and digital elevation model data in Glacier National Park, Montana. Polar Record, 31, 191± 198. Howarth, P. J., and Ommanney, C. S. L., 1986, The use of Landsat digital data for glacier inventories. Annals of Glaciology, 8, 90± 92. Ommanney, C. S. L., 1986, Mapping Canada s glaciers since Annals of Glaciology, 8, 132± 134. Orheim, O., and Luccitta, B. K., 1987, Numerical analysis of Landsat Thematic Mapper images of Antarctica: surface temperatures and physical properties. Annals of Glaciology, 9, 109± 120. é strem, G., 1975, ERTS data in glaciologyð An e ort to monitor glacier mass balance from satellite imagery. Journal of Glaciology, 15, 403± 415.

12 284 Fourth Circumpolar Remote Sensing Symposium PCI, 1996, PCI Users Manual, (Toronto: PCI Inc.). Whalley, W. B., and Martin, H. E., 1986, The problem of hidden ice in glacier mapping. Annals of Glaciology, 8, 181± 183. Williams, R. S., Hall, D. K., and Benson, C. S., 1991, Analysis of glacier facies using satellite techniques. Journal of Glaciology, 37, 120± 128.