Mainland Norway glacier products
Climate change has profound effects on glaciers world-wide and glaciers are also considered as a sensitive indicator of climate change (Lemke et al., 2007). Throughout the 20th century the glaciers in mainland Norway have retreated, although several periods of advances have occurred, the latest in the 1990s after a period of mass surplus on coastal glaciers (Andreassen et al., 2005). When glaciers retreat, new glacier lakes may form that cause concern and may represent hazard potential. Glaciers cover about one percent of the land area in Norway.
Satellite sensors enable (near) global coverage of glaciers and gives possibilities for semi-automated mapping of glacier area outlines (GAO) and glacier lake outlines (GLO). Within the CryoClim project we have derived glacier inventories for two periods, 1988-1997 and 1999-2006, using Landsat TM/ETM+ imagery and GIS techniques.
Selection of Landsat TM/ETM+ imagery
Effective glacier observations depend on the spatial resolution, region covered (swath width), spectral resolution (position of spectral bands) and the data cost of the satellite images. Previous studies have shown that the Landsat TM/ETM+ sensors are well suited for mapping glacier extent and monitoring changes even for small alpine glaciers (e.g. Paul et al., 2002; Kääb et al., 2002). Landsat is a multispectral satellite sensor with medium high spatial resolution of 30 metres and a large swath width of 185 km. For efficient observation of GAO and GLO products, NVE chose to use Landsat imagery in the CryoClim project. One of the major challenges was to find suitable Landsat scenes as few appropriate scenes are available due to seasonal snow and clouds. After careful inspection 12 Landsat scenes for the period 1999-2006 were selected. In addition 9 Landsat satellite scenes were selected for the 1988-1997 period (see figure).
Glacier Area Outline (GAO) and Glacier-dammed Lake Outline (GLO)
Glacier outlines can be obtained from thresholded multispectral band ratios (Bayr et al., 1994; Jacobs et al., 1997; Sidjak and Wheate, 1999). The band ratio method is robust and accurate for GAO extraction for debris-free glaciers (Albert, 2002; Paul et al., 2003), and includes different thresholds of Landsat TM3 (Red)/TM5 (Mid-IR). An additional threshold in Landsat TM 1 (Blue) can be used to improve the mapping of glacier tongues in cast shadow (Paul and Kääb, 2005). The ratio uses the spectral differences between the visible (high reflection of glacier ice) and the mid-infrared (high absorption of glacier ice) spectrum.
In a pilot study in a test region in Norway the applicability of standard glacier mapping methods were tested using segmentation of ratio images computed from the raw digital numbers for Landsat TM (Andreassen et al., 2008). The results confirmed that the applied method was robust and highly accurate for extracting glacier outlines in the test area. This method was therefore chosen in CryoClim for mapping GAO of all glaciers in mainland Norway. This choice is also in agreement with recent guidelines and recommendations (Paul et al., 2009; Racoviteanu, 2009).
Previous studies have shown that the Normalized Difference Water Index (NDWI) can be used for semi-automatic glacier lake detection (Huggel, 1998; Huggel et al. 2002; Paul, 2007). In the CryoClim project phase 2 we therefore have chosen to use the NDWI method to detect and map the extent of the glacier lakes from Landsat imagery. If other lakes are present in the mid-1980s and not found in the phase 2 product (1999-2006) it is little risk that they will be hazardous anymore, and lakes are not included in the phase 3 delivery. The method for deriving GLO is the same as for phase 2. However in many occasions it is actually fastest to do a manual digitization of the lakes directly from the Landsat satellite image (Bolch et al., 2010), and this method was mostly used in the phase 3 GLO product.
The CryoClim product processing chain for GAO and GLO covering mainland Norway is hosted by NVE and consists of several steps, from finding satellite imagery to product delivery. The first step in the processing chain is to find suitable satellite imagery for glacier mapping. It is important that the imagery is cloud free and acquired at the end of the ablation season when little seasonal snow is remaining. Raw imagery needs to be orthorectified, and imagery that is already orthorectified (e.g. delivered by Norsk Satelittdataarkiv) has to be validated. Glacier products are then derived from the imagery using standard glacier mapping algorithms, visual inspection of glacier lakes and NDWI. Some manual editing of the GAO and GLO products is needed in areas with debris cover, interference between glaciers and lakes, cast shadow and for glacier lakes with different spectral characteristics due to varying turbidity. In this step the products will also have an on-the-fly validation. The figure shows a subset of the Folgefonn ice cap and surrounding areas, illustrating GAO and GLO products and historical occations of Glacier Lake Outburst Floods (GLOFs) - also called jøkulhlaups.
The figure below shows preliminary results in glacier area from the Seiland and Øksfjord area in western-Finnmark. It shows difference in glacier area between phase 2 (1988 and 1990) and phase 3 (2006). Both layers include Landsat satellite images listed in the section above (Ø=Øksfjordjøkelen, Sv=Svartfjelljøkelen, L=Langfjordjøkelen, Se = Seilandsjøkelen and N=Nordmannsjøkelen).
Glacier Periodic Photo series (GPP)
Glacier Periodic Photo series will be available for a selection of glaciers. NVE has devloped an application for viewing the products: http://glacier.nve.no/viewer/GPP/en/cc/ Here a selection of photos of the terminus of Austdalsbreen (outlet of Jostedalsbreen in southern Norway) in 2005, 2006, 2007, and 2008 is shown. The photos are taken by NVE personnel.
Climate change indicator products
The advances and retreats of mountains glaciers are a visible sign of the effects of climate change. The climate change indicator products of glaciers in mainland Norway include:
-surface mass balance (from NVE’s field observations) -length change (from NVE’s field observations) -area changes (from satellite imagery and topographical maps) The products are available for a selection of individual glaciers with long time series. Glacier surface mass balance and glacier length change are retrieved directly from NVE’s databases, whereas the area product is made from available remote sensing observations.
Link to Climate Indicator application for mainland Norway: http://glacier.nve.no/viewer/CI/en/cc/
Albert, T. 2002. Evaluation of remote sensing techniques for ice-area classification applied to the tropical Quelccaya ice cap, Peru. Polar Geography, 26, 210-226.
Andreassen, L.M. , F. Paul, A. Kääb, and J.E. Hausberg. 2008. Landsat-derived glacier inventory for Jotunheimen, Norway, and deduced glacier changes since the 1930s. The Cryosphere, 2, 131-145.
Andreassen, L.M., Elvehøy, H., Kjøllmoen, B., Engeset, R.V., and Haakensen, N. 2005. Glacier mass balance and length variations in Norway, Ann. Glaciol., 42, 317–325.
Bayr, K.J., Hall, D.K., and Kovalick W.M. 1994. Observations on glaciers in the Eastern Austrian Alps using satellite data. International Journal of Remote Sensing 15, 1733-1742.
Bolch, T., Menounos, B. and Wheate, R. (2010) Landsat-based inventory of glaciers in western Canada, 1985-2005. Remote sensing of environment 114:127-137.
Huggel, C. 1998. Periglaziale Seen im Luft- und Satellitenbild. Diploma thesis, Department of Geography, University of Zurich.
Huggel, C., Käab, A., Haeberli, W., Teysseire, P., and Paul, F. 2002. Remote sensing based assessment of hazards from glacier lake outbursts: a case study in the Swiss Alps. Can. Geotech. J., 39, 316-330.
Jacobs, J.D., Simms, E.L., and Simms, A. 1997. Recession of the southern part of Barnes Ice Cap, Baffin Island, Canada, between 1961 and 1993, determined from digital mapping of Landsat TM. Journal of Glaciology, 43, 98–102.
Kääb, A., Paul, F., Maisch, M., Hoelzle, M., and Haeberli, W. 2002. The new remote-sensing-derived Swiss glacier inventory: II. First results. Ann. Glaciol., 34, 362–366.
Lemke, P., J. Ren, R.B. Alley, I. Allison, J. Carrasco, G. Flato, Y. Fujii, G. Kaser, P. Mote, R.H. Thomas, and T. Zhang, 2007. Observations: Changes in Snow, Ice and Frozen Ground. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Paul, F. 2007. The New Swiss Glacier Inventory 2000 - Application of Remote Sensing and GIS. Schriftenreihe Physische Geographie, Universität Zürich, 52, 210 pp.
Paul, F., Barry,R., Cogley, G., Frey, H., Haeberli, W, Ohmura, A., Ommanney, S., Raup, B., Rivera, A., and Zemp, M. 2009. Recommendations for the compilation of glacier inventory data from digital sources. Annals of Glaciology, 50, 119-126.
Paul, F., Huggel, C., Kääb, A., and Kellenberger, T. 2003. Comparison of TM-derived glacier areas with higher resolution data sets, EARSeL Workshop on Remote Sensing of Land Ice and Snow, Bern, 11.–13.3.2002, EARSeL eProceedings, 2, 15–21.
Paul, F., and Kääb, A. 2005. Perspectives on the production of a glacier inventory from multispectral satellite data in the Canadian Arctic: Cumberland Peninsula, Baffin Island. Annals of Glaciology, 42, 59-66.
Paul, F., Kääb, A., Maisch, M., Kellenberger, T.W., and Haeberli, W. 2002. The new remote sensing-derived Swiss glacier inventory: I. Methods. Annals of Glaciology, 34, 355-361.
Paul, F., Winsvold, S., Kääb, A., Nagler, T. and Schwaizer, G.2016. Glacier Remote Sensing Using Sentinel-2. Part II: Mapping Glacier Extents and Surface Facies, and Comparison to Landsat 8. Remote Sensing, 8(7), 575, doi:10.3390/rs8070575.
Racoviteanu, A.E., Paul, F., Raup, B., Khalsa, S.J.S., and Armstrong, R. 2009. Challenges and recommendations in mapping of glacier parameters from space: results of the 2008 Global Land Ice Measurements from Space (GLIMS) workshop, Boulder, Colorado, USA. Annals of Glaciology 50, 2009.
Sidjak, R.W., and Wheate, R.D. 1999. Glacier mapping of the Illecillewaet icefield, British Columbia, Canada, using Landsat TM and digital elevation data. International Journal of Remote Sensing, 20, 273-284.
Winsvold, S. H., Andreassen, L. M. and Kienholz, C. 2014. Glacier area and length changes in Norway from repeat inventories, The Cryosphere, 8, 1885–1903.