Skip to main content Accessibility help
×
×
Home

Information:

  • Access
  • Cited by 72
  • Cited by
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Gardelle, Julie Berthier, Etienne and Arnaud, Yves 2012. Slight mass gain of Karakoram glaciers in the early twenty-first century. Nature Geoscience, Vol. 5, Issue. 5, p. 322.

    Cook, A. J. Murray, T. Luckman, A. Vaughan, D. G. and Barrand, N. E. 2012. A new 100-m Digital Elevation Model of the Antarctic Peninsula derived from ASTER Global DEM: methods and accuracy assessment. Earth System Science Data, Vol. 4, Issue. 1, p. 129.

    Huss, M. 2012. Extrapolating glacier mass balance to the mountain-range scale: the European Alps 1900–2100. The Cryosphere, Vol. 6, Issue. 4, p. 713.

    Gardner, A. Moholdt, G. Arendt, A. and Wouters, B. 2012. Accelerated contributions of Canada's Baffin and Bylot Island glaciers to sea level rise over the past half century. The Cryosphere, Vol. 6, Issue. 5, p. 1103.

    Cogley, Graham 2012. No ice lost in the Karakoram. Nature Geoscience, Vol. 5, Issue. 5, p. 305.

    Willis, Michael J. Melkonian, Andrew K. Pritchard, Matthew E. and Rivera, Andrés 2012. Ice loss from the Southern Patagonian Ice Field, South America, between 2000 and 2012. Geophysical Research Letters, Vol. 39, Issue. 17, p. n/a.

    Xu, Junli Liu, Shiyin Zhang, Shiqiang Guo, Wanqin Wang, Jian and Sun, Qinghua 2013. Recent Changes in Glacial Area and Volume on Tuanjiefeng Peak Region of Qilian Mountains, China. PLoS ONE, Vol. 8, Issue. 8, p. e70574.

    Tennant, Christina and Menounos, Brian 2013. Glacier change of the Columbia Icefield, Canadian Rocky Mountains, 1919–2009. Journal of Glaciology, Vol. 59, Issue. 216, p. 671.

    Melkonian, A. K. Willis, M. J. Pritchard, M. E. Rivera, A. Bown, F. and Bernstein, S. A. 2013. Satellite-derived volume loss rates and glacier speeds for the Cordillera Darwin Icefield, Chile. The Cryosphere, Vol. 7, Issue. 3, p. 823.

    Pieczonka, Tino Bolch, Tobias Junfeng, Wei and Shiyin, Liu 2013. Heterogeneous mass loss of glaciers in the Aksu-Tarim Catchment (Central Tien Shan) revealed by 1976 KH-9 Hexagon and 2009 SPOT-5 stereo imagery. Remote Sensing of Environment, Vol. 130, Issue. , p. 233.

    Gardelle, J. Berthier, E. Arnaud, Y. and Kääb, A. 2013. Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011. The Cryosphere, Vol. 7, Issue. 4, p. 1263.

    Rabatel, A. Letréguilly, A. Dedieu, J.-P. and Eckert, N. 2013. Changes in glacier equilibrium-line altitude in the western Alps from 1984 to 2010: evaluation by remote sensing and modeling of the morpho-topographic and climate controls. The Cryosphere, Vol. 7, Issue. 5, p. 1455.

    Neckel, N Kropáček, J Bolch, T and Hochschild, V 2014. Glacier mass changes on the Tibetan Plateau 2003–2009 derived from ICESat laser altimetry measurements. Environmental Research Letters, Vol. 9, Issue. 1, p. 014009.

    Pope, Allen Rees, W. Fox, Adrian and Fleming, Andrew 2014. Open Access Data in Polar and Cryospheric Remote Sensing. Remote Sensing, Vol. 6, Issue. 7, p. 6183.

    Ramachandran, Bhaskar Dwyer, John Raup, Bruce H. and Kargel, Jeffrey S. 2014. Global Land Ice Measurements from Space. p. 145.

    Farajzadeh, Manuchehr and Karimi, Neamat 2014. Evidence for accelerating glacier ice loss in the Takht’e Solaiman Mountains of Iran from 1955 to 2010. Journal of Mountain Science, Vol. 11, Issue. 1, p. 215.

    Kropáček, Jan Neckel, Niklas and Bauder, Andreas 2014. Estimation of Mass Balance of the Grosser Aletschgletscher, Swiss Alps, from ICESat Laser Altimetry Data and Digital Elevation Models. Remote Sensing, Vol. 6, Issue. 6, p. 5614.

    Lambrecht, Astrid Mayer, Christoph Aizen, Vladimir Floricioiu, Dana and Surazakov, Arzhan 2014. The evolution of Fedchenko glacier in the Pamir, Tajikistan, during the past eight decades. Journal of Glaciology, Vol. 60, Issue. 220, p. 233.

    Melkonian, Andrew K. Willis, Michael J. and Pritchard, Matthew E. 2014. Satellite-derived volume loss rates and glacier speeds for the Juneau Icefield, Alaska. Journal of Glaciology, Vol. 60, Issue. 222, p. 743.

    Farhan, Suhaib Bin Zhang, Yinsheng Ma, Yingzhao Guo, Yanhong and Ma, Ning 2015. Hydrological regimes under the conjunction of westerly and monsoon climates: a case investigation in the Astore Basin, Northwestern Himalaya. Climate Dynamics, Vol. 44, Issue. 11-12, p. 3015.

    ×

Actions:

      • Send article to Kindle

        To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

        Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing
        Available formats
        ×

        Send article to Dropbox

        To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

        Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing
        Available formats
        ×

        Send article to Google Drive

        To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

        Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing
        Available formats
        ×
Export citation

Digital elevation models (DEMs) are frequently used to calculate elevation changes and regional mass balances of glaciers, for example in the European Alps (Berthier and others, 2004; Paul and Haeberli, 2008; Huss and others, 2010), Alaska and Canada (Larsen and others, 2007; Schiefer and others, 2007; Berthier and others, 2010), Patagonia (Rignot and others, 2003) and High Mountain Asia (Suraza-kov and Aizen, 2006; Berthier and others, 2007; Bolch and others, 2011). Geodetic mass-balance measurements from space-borne imagery can be used to assess glacier changes in remote or inaccessible areas and, thus, better constrain glacier losses and contribution to sea-level rise (Cogley, 2009). Previous studies have shown that a thorough processing strategy (i.e. proper horizontal and vertical adjustments of the two DEMs to be differenced) is needed to compute unbiased elevation changes from multitemporal space-borne DEMs (e.g. Nuth and Kääb, 2011).

The 90m freely available SRTM (Shuttle Radar Topography Mission) DEM was derived from C-band synthetic aperture radar (SAR) imagery acquired in February 2000 and, due to its wide coverage, is often used in regional mass- balance studies. It is suspected to cause an altitude- dependent vertical bias in elevation changes when differentiated with other DEMs derived from aerial photographs or satellite optical imagery (Berthier and others, 2006). Paul (2008) attributed this bias to a difference in the original spatial resolution of the DEMs and concluded that this bias, estimated on the surrounding ice-free terrain, should not be directly applied to glaciers. However, it is still under debate whether this bias should be corrected or not, and whether it is similar on and off glaciers (Berthier and others, 2006; Larsen and others, 2007; Möller and others, 2007; Schiefer and others, 2007; Huss and others, 2010; Möller and Schneider, 2010). In addition, the C-band radar penetration of SRTM can reach up to 10m in snow and ice (Dall and others, 2001; Rignot and others, 2001). Thus, the SRTM DEM (or any other DEM derived from C-band radar data) may actually map a surface that is below the real surface, especially in accumulation areas (Langley and others, 2008), leading to a biased estimate of glacier elevation changes (Berthier and others, 2006, Fig. 4).

To explore the impact of DEM resolutions and radar penetration, we used three different DEMs over the Karakoram (Pakistan and China) region: a 40 m SPOT5 DEM of December 2008 derived from optical imagery (Korona and others, 2009), a 90 m SRTM DEM acquired in C-band (Rodríguez and others, 2006) and a 30 m SRTM DEM acquired in X-band, both in February 2000. In addition, we produced a glacier inventory based on Landsat 5 imagery from August 1998. Our Karakoram study site (35°08’- 36°55’ N, 75°02’-76°29’ E) exhibits one of the steepest reliefs on Earth and includes 5615 km2 of glaciers with altitude ranges from 2700 to 7900ma.s.l.

Impact of DEM Resolution

Following the experiment suggested by Paul (2008), we resampled the 90 m SRTM C-band DEM to 40 m, resampled it back to 90 m and subtracted it from the original (90 m) DEM. Elevation changes were averaged in 100 m altitude bins, and a bias with altitude similar to Paul (2008) was found on and off glaciers, with higher values off glaciers (not shown here). Paul (2008) suggested that terrain slope determines the amplitude of the elevation differences and that plan curvature (the second derivative of the topography) determines the sign of this difference. Here we build on these previous findings but show that terrain maximum curvature can be used to correct those elevation biases (Fig. 1a and b). The curvature used in this study is computed with a 5 × 5 kernel size (in this regard, it is only a local derivative of the slope) and defined as the maximum curvature in any plane intersecting the surface (Wood, 1996). The computation was done with the IDL/ENVI (Research Systems Inc., Boulder, CO) software, but is also implanted in the open-source GRASS GIS software.

Fig. 1. Relation between terrain slope (a) and maximum curvature (b) and the elevation differences computed from the original 90m C-band SRTM and the one resampled from 40 m. Grey triangles represent glacier pixels, black circles non-glacier pixels.

The link between the maximum curvature and the elevation bias can be qualitatively illustrated if we consider how slopes are represented by low- and high-resolution DEMs. A slope that is constant over numerous continuous coarse pixels (i.e. low curvature values in the direction of the slope) will be equally represented by the low- and high- resolution DEMs. By contrast, a slope that is constant only over a few high-resolution pixels (i.e. high curvature values in the direction of the slope) will be better estimated by the high-resolution than by the coarser DEM (Kervyn and others, 2008).

The relation between elevation differences and the terrain maximum curvature from our experiment (Fig. 1b) is similar over and outside glaciers, a key condition for using it confidently to correct the elevation differences on glaciers. Conversely, elevation differences as a function of altitude (not shown here) or as a function of slope (Fig. 1a) differ strongly on and off glaciers, because of different curvature distributions. We also tested other definitions of curvature for correcting this bias, which revealed that the plan curvature also has a clear relationship with the elevation difference, but does not fully compensate the bias, as the maximum curvature does (squares in Fig. 2).

Fig. 2. Elevation differences off glaciers between SPOT5 and SRTM C-band DEMs as a function of altitude. Filled circles represent raw elevation differences, open circles elevation differences after correction based on terrain maximum curvature, and squares those after a correction based on terrain plan curvature.

This bias due to DEMs of different resolutions can thus be corrected on and off glaciers, based on the maximum curvature of each pixel (taken from the high-resolution DEM) and using the relation between elevation differences and maximum curvature established off glaciers. As the curvature varies within a glacier, the correction will introduce local modifications of elevation changes. How this correction will influence the individual glacier (or glacier- complex) mass balance will depend on the curvature distribution, which varies from one glacier to another.

This validity of the curvature correction has been tested in a real case study involving a SPOT5 DEM from December 2008 and the SRTM C-band DEM from February 2000 over Karakoram. The raw elevation differences (SPOT5-SRTMC-band) off glaciers exhibit a positive elevation bias above 5000 m that reaches 11 m at 6000 m (black circles in Fig. 2). The relation between maximum curvature and the elevation differences has been computed on ice- free terrain and used to correct the elevation bias. The SPOT5-SRTMC-band differences after correction show that the elevation bias has been properly removed (open circles in Fig. 2). A correction based on plan curvature has also been tested (squares in Fig. 2). Part of the bias is corrected, but the adjustment is not as good, with a 5 m bias remaining at 6000 m.

Accounting for Radar Penetration

Another factor that can affect the glacier elevation changes computed with at least one radar DEM (e.g. SRTM C-band) is the penetration of the radar signal into snow and/or ice. Comparison of the SRTM X-band (9.7GHz) and C-band (5.7GHz) DEMs allows us to quantify this penetration, as it is much smaller in the X-band than in the C-band. These two DEMs were acquired simultaneously, so no ‘glaciological’ elevation change is expected. However, the coverage of the SRTM X-band DEM is not continuous. The swath width is narrower (~50 km), and the resulting DEM is available only along selected strips.

The C-band DEM has been resampled to 30 m and subtracted from the X-band DEM. First, because of the difference in the original resolution of the DEMs, a correction according to the terrain maximum curvature was applied using the above-described method. Then, in addition to the glacier inventory, we identified the snow- covered areas (both on and off glaciers) on a Landsat image from 24 February 2000, a few days after the acquisition of both SRTM DEMs. The SRTMX-band-SRTMC-band differences as a function of altitude (Fig. 3) show no significant bias over debris-covered parts of glaciers or bare ice-free terrain, confirming the efficiency of the curvature correction.

Fig. 3. Elevation differences between SRTMX-band and SRTMC-band as a function of altitude.

The snow-covered ice-free terrain exhibits a strong bias with altitude above 5000 m that reaches ~5 m at 6000 m. The bias on snow-covered glacier parts is similar but is ~2m higher at all altitudes. We interpret the bias above 5000 m as a differential penetration in snow between C-band and X-band radar. The 2 m systematic bias on snow-covered glaciers is attributed to the differential penetration in ice, C-band having a larger penetration than X-band. Little is known about the absolute value of X-band penetration into snow and glacier ice, but it decreases as temperature and water content rise (Ulaby and others, 1986; Surdyk, 2002). In Antarctica, Davis and Poznyak (1993) measured penetration depths at 10GHz from 2.1 to 4.7 m, and Surdyk (2002) reported a 4m penetration depth at 10.7GHz at -8°C. However, ice and snow conditions in winter in Karakoram are not identical to those in Antarctica: temperature is probably higher, snow is wetter and ice is dirtier (presence of debris), so penetration depth in the X-band should be less than the values found in the literature. As a first approximation, we assume no penetration of the X-band signal and consider that the SRTMX-band-SRTMC-band value corresponds to the C-band penetration. Clearly, this hypothesis requires further validation, especially by the German Aerospace Center (DLR) TanDEM-X mission, which will release high- resolution DEMs from X-band radar data.

Comparing C-band and X-band DEMs makes it possible to better take into account and correct for SRTM penetration into snow and ice. We stress that the amplitude of penetration will likely be peculiar to each region as snowfall seasonality and snowpack characteristics were different among glacierized areas in February 2000, when the SRTM was flown. One limitation of the methodology proposed to account for C-band penetration is the limited coverage of the X-band DEM, restricted to some stripes.

Conclusion

We have analyzed, and proposed corrections for, two issues that can lead to altitude-dependent bias between DEMs and, thus, erroneous measurements of glacier elevation and volume changes. The first issue concerns the comparison of DEMs with different original resolutions. Building on previous findings (Paul, 2008), we found that these biases are directly explained by variations in terrain maximum curvature and that, importantly, the relationship is unchanged on and off glaciers. Thus, the relationship between elevation differences and maximum curvature can be established off glaciers and applied to the ice-covered areas. The second issue is related to the penetration of the radar signal into ice and snow, a penetration that can reach several meters in the case of the SRTM C-band DEM. We have shown that analyzing the elevation differences between the two SRTM DEMs (30 m X-band and 90 m C-band acquired simultaneously in February 2000) can provide a first-order estimate of C-band penetration. A 3m average penetration of the SRTM C-band signal was found for Karakoram but must be recalculated for each ice-covered region.

It is known that DEMs should be corrected prior to comparison, but we stress here that the elevation differences on the ice-free terrain cannot readily be used to correct the ice/snow elevation changes. Together with other processing steps (Nuth and Kääb, 2011), particular attention should be paid to the two additional corrections proposed in this correspondence when the forthcoming 30m global Tan- DEM-X DEM is compared to the SRTM 90 m C-band DEM or any other earlier DEMs acquired with different resolution and/or in a different wavelength of the electromagnetic spectrum. In addition, we recommend investigation of the potentially strong impact of such corrections on previously published mass-balance estimates (Surazakov and Aizen, 2006; Berthier and others, 2007; Larsen and others, 2007; Möller and others, 2007; Schiefer and others, 2007; Paul and Haeberli, 2008).

Acknowledgements

We thank F. Paul and N. Barrand for excellent reviews which helped to improve the quality of this correspondence. J. Gardelle acknowledges a PhD fellowship from the French Space Agency (CNES) and the French National Research Center (CNRS). E. Berthier acknowledges support from CNES through the TOSCA and ISIS program and from the Programme National de Télédétection Spatiale. We thank the US Geological Survey for allowing free access to their Landsat archive, the International Center for Tropical Agriculture (CIAT) for SRTM C-band data, and the German Aerospace Research Center (DLR) for SRTM X-band data. This work was supported by Agence Nationale de la Recherche (reference ANR-09-320 CEP-005-01/PAPRIKA).

14 January 2012

References

Berthier, E, Arnaud, Y, Baratoux, D, Vincent, C and Rémy, F (2004) Recent rapid thinning of the Mer de Glace glacier derived from satellite optical images. Geophys. Res. Lett., 31(17), L17401 (doi: 10.1029/2004GL020706)
Berthier, E, Arnaud, Y, Vincent, C and Rémy, F (2006) Biases of SRTM in high-mountain areas: implications for the monitoring of glacier volume changes. Geophys. Res. Lett., 33(8), L08502 (doi: 10.1029/2006GL025862)
Berthier, E, Arnaud, Y, Kumar, R, Ahmad, S, Wagnon, P and Chevallier, P (2007) Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India). Remote Sens. Environ., 108(3), 327-338 (doi: 10.1016/ j.rse.2006.11.017)
Berthier, E, Schiefer, E, Clarke, GKC, Menounos, B and Remy, F (2010) Contribution of Alaskan glaciers to sea-level rise derived from satellite imagery. Nature Geosci., 3(2), 92-95 (doi: 10.1038/ ngeo737)
Bolch, T, Pieczonka, T and Benn, DI (2011) Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) derived from stereo imagery. Cryosphere, 5(2), 349-358 (doi: 10.5194/tc-5- 349-2011)
Cogley, JG (2009) Geodetic and direct mass-balance measurements: comparison and joint analysis. Ann. Glaciol., 50(50), 96-100 (doi: 10.3189/172756409787769744)
Dall, J, Madsen, S, Keller, K and Forsberg, R (2001) Topography and penetration of the Greenland ice sheet measured with airborne SAR interferometry. Geophys. Res. Lett., 28(9), 1703-1706 (doi: 10.1029/2000GL011787)
Davis, CH and Poznyak, VI (1993) The depth of penetration in Antarctic firn at 10 GHz. IEEE Trans. Geosci. Remote Sens., 31(5), 1107-1111
Huss, M, Usselmann, S, Farinotti, D and Bauder, A (2010) Glacier mass balance in the south-eastern Swiss Alps since 1900 and perspectives for the future. Erdkunde, 64(2), 119-140 (doi: 10.3112/erdkunde.2010.02.02)
Kervyn, M, Ernst, GGJ, Goossens, R and Jacobs, P (2008) Mapping volcano topography with remote sensing: ASTER vs. SRTM. Int. J. Remote Sens., 29(22), 6515-6538 (doi: 10.1080/01431160802167949)
Korona, J, Berthier, E, Bernard, M, Remy, F and Thouvenot, E (2009) SPIRIT. SPOT 5 stereoscopic survey of Polar Ice: reference images and topographies during the fourth International Polar Year (2007-2009). ISPRS J. Photogramm. Rem. Sens, 64(2), 204-212 (doi: 10.1016/j.isprsjprs.2008.10.005)
Langley, K and 6 others (2008) From glacier facies to SAR backscatter zones via GPR. IEEE Trans. Geosci. Remote Sens., 46(9), 2506-2516 (doi: 10.1109/TGRS.2008.918648)
Larsen, CF, Motyka, RJ, Arendt, AA, Echelmeyer, KA and Geissler, PE (2007) Glacier changes in southeast Alaska and northwest British Columbia and contribution to sea level rise. J. Geophys. Res., 112(F1), F01007 (doi: 10.1029/2006JF000586)
Möller, M and Schneider, C (2010) Correspondence. Volume change at Gran Campo Nevado, Patagonia, 1984-2000: a reassessment based on new findings. J. Glaciol., 56(196), 363-365 (doi: 10.3189/002214310791968458)
Möller, M, Schneider, C and Kilian, R (2007) Glacier change and climate forcing in recent decades at Gran Campo Nevado, southernmost Patagonia. Ann. Glaciol., 46, 136-144 (doi: 10.3189/172756407782871530)
Nuth, C and Kääb, A (2011) Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change. Cryosphere, 5(1), 271-290 (doi: 10.5194/ tc-5-271-2011)
Paul, F (2008) Correspondence. Calculation of glacier elevation changes with SRTM: is there an elevation- dependent bias? J. Glaciol., 54(188), 945-946 (doi: 10.3189/ 002214308787779960)
Paul, F and Haeberli, W (2008) Spatial variability of glacier elevation changes in the Swiss Alps obtained from two digital elevation models. Geophys. Res. Lett., 35(21), L21502 (doi: 10.1029/ 2008GL034718)
Rignot, E, Echelmeyer, K and Krabill, W (2001) Penetration depth of interferometric synthetic-aperture radar signals in snow and ice. Geophys. Res. Lett., 28(18), 3501-3504 (doi: 10.1029/ 2000GL012484)
Rignot, E, Rivera, A and Casassa, G (2003) Contribution of the Patagonian icefields of South America to sea level rise. Science, 302(5644), 434-437 (doi: 10.1126/science.1087393)
Rodríguez, E, Morris, CS and Belz, JE (2006) A global assessment of the SRTM performance. Photogramm. Eng. Remote Sens., 72(3), 249-260
Schiefer, E, Menounos, B and Wheate, R (2007) Recent volume loss of British Columbian glaciers, Canada. Geophys. Res. Lett., 34(16), L16503 (doi: 10.1029/2007GL030780)
Surazakov, AB and Aizen, VB (2006) Estimating volume change of mountain glaciers using SRTM and map-based topographic data. IEEE Trans. Geosci. Remote Sens., 44(10), 2991-2995 (doi: 10.1109/TGRS.2006.875357)
Surdyk, S (2002) Using microwave brightness temperature to detect short-term surface air temperature changes in Antarctica: an analytical approach. Remote Sens. Environ., 80(2), 256-271 (doi: 10.1016/S0034-4257(01)00308-X)
Ulaby, FT, Moore, RK and Fung, AK (1986) Microwave remote sensing, active and passive. Vol. 3. From theory to applications. Addison-Wesley, Reading, MA
Wood, JD (1996) The geomorphological characterisation of digital elevation models. (PhD thesis, University of Leicester)