Ice, Snow and Environment Over High Asia Zone II Versions EN2 Vol 3 (4) 2018
A dataset of glacier elevation changes in the Kangri Karpo Mountains during 1980 – 2014
: 2018 - 04 - 18
: 2018 - 04 - 28
: 2018 - 10 - 30
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Abstract & Keywords
Abstract: Due to the influence of the Indian monsoon, the Kangri Karpo Mountains, located in southeast Tibet, is the most humid and maritime glacier-concentrated region on the Tibetan Plateau. Glacier mass loss in Kangri Karpo importantly contributes to the rise of global mean sea level, the adjustment of run-off distribution, and the increase of glacial lake outburst floods (GLOFs). Based on topographic maps (1980), SRTM (2000), and TerraSAR-X/TanDEM-X (2014), the study obtained the latest glacier elevation changes on the Kangri Karpo Mountains. With a spatial extent of 29°N – 30°N, 96°E – 98°E totaling an area of 3600 km2, the Mountains cover the region south of the Purlung Tsangpo River and north of the Gongri–Gabo River. The dataset consists of two subsets: ① 2014 digital elevation model (DEM) data obtained from TerraSAR-X/TanDEM-X, and ② raster data of glacier elevation changes during 1980 – 2014 based on topographic maps, SRTM and TerraSAR-X/TanDEM-X acquisitions. To mitigate influences of cloud cover on optical images, Differential Interferometric Synthetic Aperture Radar (DInSAR) was used to extract DEM data and acquire glacier elevation changes. Compared with ICESat-GLAS, TerraSAR/TanDEM achieved a vertical precision of 2.65 m. Glacier mass balance resulting from glacier elevation changes showed a high consistency with field measurements. The dataset records the latest glacier elevation changes in the Kangri Karpo Mountains, with minimum influences of cloud and seasonal snow cover on the glacier maps. It can be used as a fundamental dataset in future glacier-related studies.
Keywords: DEM; glacier elevation change; Kangri Karpo; DInSAR; TerraSAR/TanDEM
Dataset Profile
Chinese title1980–2014 年岗日嘎布地区冰川高程变化数据集
English titleA dataset of glacier elevation changes in the Kangri Karpo Mountains during 1980 – 2014
Data authorsWu Kunpeng, Liu Shiyin, Jiang Zongli, Wei Junfeng
Data corresponding authorsWu Kunpeng (, Liu Shiyin (liusy@
Time range1980 – 2014
Geographical scopeA spatial extent of 28°54′16″N – 29°31′42″N, 96°22′59″E – 97°8′23″E, which covers the region south of the Purlung Tsangpo River and north of the Gongri–Gabo River.
Spatial resolution10 m
Data volume142 MB
Data format*.tif (Geo TIFF, 32 bit float)
Data service system<>
Sources of fundingNational Natural Science Foundation of China (Grant Nos. 41801031, 41471067), Fundamental Programme of the Ministry of Science and Technology of China (MOST) (Grant No. 2013FY111400), Talent Introduction Program of Yunnan University (YJRC3201702), and International Collaboration Project (131C11KYSB20160061-4).
Dataset compositionThe dataset consists of two subsets: 2014 digital elevation model (DEM) data, and raster data of glacier elevation changes during 1980 – 2014.
1.   Introduction
Glaciers are key components in the cryosphere system,1 and their change is a useful indicator of climate variability. 2 Recent glacial retreat brought by global warming3-4 has an important impact on regional water resources5-6 and induces geological hazards that threatens human lives and properties in the downstream.7-8 During the building of the Second Glacier Inventory of China,9-10 abundant outcomes have been achieved in the glacier area and length changes.11-15 However, it is difficult to understand glacier variation only in terms of area change, which makes glacier elevation change and mass balance the subject of much current research.16-24 Though glaciological, hydrological and geodetic methods have been used to determine the mass balance of a glacier, the high altitude and harsh climatic conditions make fieldwork very difficult. In this context, satellite remote sensing becomes a promising alternative for assessing multiple glaciers at one time, even in remote mountainous terrain. These include stereo photogrammetry, laser altimetry from the ICESat-GLAS (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System).16-21,23-24 One critical point in stereo photogrammetry is to obtain cloud-free images at the end of ablation season (with minimal seasonal snow cover).25 However, it is still challenging for remote sensing to obtain glacier mass balance over areas with rugged terrain, frequent clouds, and/or with optically thick debris cover. It is particularly difficult to acquire optical images of higher quality for the Kangri Karpo Mountains on the southeastern Tibet Plateau, due to its rich precipitation, frequent clouds, seasonal snow and debris cover caused by the Indian monsoon. While previous studies agreed that glaciers in the Kangri Karpo were losing mass, their results differed from each other.17,19,26-27 Varied results of mass balances can result in large uncertainties in subsequent applications, such as glacier change studies, hydrologic modeling, and information generalization from the local to regional scale.
With the development of remote sensing technology, synthetic aperture radar (SAR) has been multi-band, multi-polar and multi-mode, able to obtain radar data reflected from the ground independent of sunlight and weather conditions.28 SAR has been applied in glaciological research since its emergence. SAR interference includes repeated observation and bistatic interference. Bistatic interferograms contain both ground and topographic phases from which glacier-elevation changes can be derived, such as SRTM DEM and TanDEM. Based on TerraSAR-X/TanDEM-X, reliable outcomes have been achieved on the change of glacier surface elevation in Tibetan Plateau and its surrounding area.29-32 Most glaciers have been now been mapped from aerial photographs taken in October 1980 and subsequently by X-band SAR interferometry (InSAR) in February 2000 (during the SRTM), resulting in a digital elevation model (DEM). Single-pass X-band InSAR from TerraSAR-X on 18 February 2014 and 13 March 2014, together with TanDEM-X digital elevation measurements, provided the basis for another map. In this study, differential synthetic aperture radar interferometry (DInSAR) was used to estimate glacier mass balance in the Kangri Karpo.
2.   Data collection and processing
2.1   Data collection
Four types of data were used to compile the dataset of glacier surface elevation change: topographic maps based on aerial photos taken in 1980 (TOPO DEM), DEM from the Shuttle Radar Topography Mission (SRTM DEM), TerraSAR-X/TanDEM-X acquired in bistatic InSAR stripmap mode and the Chinese Glacier Inventory (Table 1 & Figure 1).

Table 1   List of the topographic maps, SRTM DEM and TerraSAR-X/TanDEM-X images used to generate the dataset

Fig.1   Study area and coverage of the TOPO DEMs, TerraSAR-X/TanDEM-X images
The study used twelve 1: 50000 topographic maps compiled from aerial photos taken in October 1980 by the Chinese Military Geodetic Service. Using a seven-parameter transformation method, these maps were georeferenced into the 1954 Beijing Geodetic Coordinate System (BJ54: geoid datum level is Yellow Sea mean sea level at Qingdao Tidal Observatory in 1956) and re-projected into the World Geodetic System 1984 (WGS1984)/Earth Gravity Model 1996 (EGM96). Contours were digitized manually and then converted into a raster DEM (TOPO DEM) with a 30 m grid cell using the Thiessen polygon method. According to the photogrammetric Chinese National Standard33 issued by the Standardization Administration of the People’s Republic of China, the nominal vertical accuracy of these topographic maps was within 3 – 5 m for flat and hilly areas (with slopes of <2° and 2 – 6° respectively) and within 8 – 14 m for the mountainsides and high mountain areas (with slopes of 6 – 25° and >25°). Since the slopes of the most of the glacierized areas in Kangri Karpo were gentle (~ 19°), the vertical accuracy of the TOPO DEM on the glaciers is better than 9 m.
The SRTM acquired interferometric synthetic aperture radar (InSAR) data simultaneously in both the C- and X-band frequencies from 11 to 22 February 2000. The “unfilled” SRTM C-band DEM, with a swath width of 225 km and a 1 arc-second resolution (approximately 30 m) in WGS84/EGM96, is available at: <>. The accuracy of SRTM1 is specified as 16 m with a 90% confidence level which varies according to region.34
TerraSAR-X was launched in June 2007 by the German Aerospace Center (DLR). TerraSAR-X and its add-on for digital elevation measurements (TanDEM-X) are flying in a close orbit formation to act as a flexible single-pass SAR interferometer. Interferometric data were acquired in the pursuit monostatic mode, the bistatic mode and the alternating bistatic mode. The current baseline for operational DEM generation was the bistatic mode which minimized temporal decorrelation and made efficient use of the transmit power. This study employed the experimental Co-registered Single look Slant range Complex (CoSSC) files, acquired in bistatic InSAR stripmap mode on 18 February 2014 and 13 March 2014. The main goal of the TanDEM-X mission was to generate a global consistent DEM with a 12Í12 m grid posting and a vertical accuracy of <2 m.28
The Chinese Glacier Inventory is available at West Data Centre (, which was used to divide glacier area and off-glacier area. To ensure the integrity of the glacier surface elevation change, we used a combination of the first Chinese Glacier Inventory, the second Chinese Glacier Inventory and glacier inventory from Landsat images acquired in 2000. Besides, in order to assess the accuracy of the DEMs, laser altimetry from the ICESat-GLAS (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System) were obtained from the US National Snow and Ice Data Center (NSIDC, release 634, product GLA 14, (Figure 1).
2.2   Data processing
We adopted the data processing method of Wu et al.22 There are three parts for compiling the dataset of glacier surface elevation change, including: to generate the DEM TerraSAR-X/TanDEM-X acquisitions, to acquire glacier surface elevation changes during 1980 – 2014 and 2000 – 2014, and to acquire glacier surface elevation changes during 1980 – 2000. GAMMA SAR and interferometric processing software was employed for interferometric processing of the CoSSC files, while ArcGIS software was employed for data analysis. Figure 2 shows the flow chart of major data processing steps.

Fig.2   Schematic workflow for compiling the dataset of glacier surface elevation change
The TerraSAR-X/TanDEM-X acquisitions were processed by differential SAR interferometry (DInSAR) using GAMMA SAR and interferometric processing software. In the DInSAR method, most parts of the topographic phase were simulated and removed and the reliability of phase unwrapping increased by the smaller phase gradients, so the topographic residual phase can be transformed directly to an elevation change. To improve the phase-unwrapping procedure and minimize errors, the unfilled, finished, SRTM C-band DEM was employed. Before generating the differential interferogram, precise horizontal offset registration and fitting between the SRTM C-band DEM and the TerraSAR-X/TanDEM-X acquisitions is required. Based on the relation between the map coordinates of the SRTM C-band DEM segment covering the TerraSAR-X/TanDEM-X master file, and the SAR geometry of the respective master file, an initial lookup table was calculated. While the areas of radar shadows and layover in the TerraSAR-X/TanDEM-X interferogram would introduce gaps in the lookup table, a method of linear interpolation between the gap edges in each line of the lookup table was used to fill these gaps. The offsets between the master scene and the simulated intensity of the SRTM C-band DEM, were calculated using cross-correlation optimization of the simulated SAR images employing GAMMA’s offset_pwrm module. The horizontal registration and geocoding lookup table were refined by these offsets. The SRTM C-band DEM was translated from geographic coordinates into SAR coordinates via the refined geocoding lookup table, and conversely, the final difference map was translated from SAR coordinates into geographic coordinates. Then a differential interferogram was generated by the TerraSAR-X/TanDEM-X interferogram and the simulated phase of the co-registered SRTM C-band DEM. An adaptive filtering approach was used to filter the differential interferogram. GAMMA’s minimum cost flow (MCF) algorithm was then employed to unwrap the flattened differential interferogram. According to the computed phase-to-height sensitivity and select ground-control points (GCPs) from respective off-glacier pixel locations in the SRTM C-band DEM, the unwrapped differential phase was converted to absolute differential heights. While, a residual not covered by the baseline refinement would exist it can be regarded as a linear trend estimated by a two-dimensional first-order polynomial fit in off-glacier regions. The linear trend and a constant vertical offset were removed from the maps of absolute differential heights. Finally, the resulting datasets were translated to a metric cartographic coordinate system with 30 m × 30 m pixel spacing. The same DInSAR method was employed to acquire the glacier elevation change from 1980-2014 with the data from the TOPO DEM and TerraSAR-X/TanDEM-X acquisitions.
For changes in glacier elevation from 1980–1999, common DEM differencing with the TOPO and SRTM C-band DEMs was used to construct a difference map. Relative horizontal and vertical distortions between the two datasets can be corrected with statistical approaches based on the relationship between elevation difference, slope and aspect:
\[\frac{dh}{\mathrm{tan}\left(\alpha \right)}=a\mathrm{ }\mathrm{cos}\left(b-\phi \right) +\mathrm{ }\frac{\stackrel{-}{dh}}{\mathrm{tan}\left(\alpha \right)}\]
where dh is elevation difference of each pixel between different DEM data, a is the magnitude of the shift vector, b is the direction of the shift vector, α is slope and φ is aspect, \(\stackrel{-}{dh}\) is the overall elevation differences between different DEM data (the magnitude of the vertical vector). Elevation differences in off-glacier regions were used to analyze the consistency of the TOPO and SRTM C-band DEMs. After co-registration, histogram statistics of the elevation differences for off-glacier regions showed that elevation difference in off-glacier regions concentrated on the mean elevation difference from 4.94 m to 0.67 m. It is concluded that elevation difference in off-glacier regions have stabilized, the pre-processed DEMs were acceptable and suitable for the estimation of changes in glaciers mass balance. Outliers of elevation differences with values exceeding ±100 m, usually around data gaps and near DEM edges, were omitted. The vertical biases and horizontal displacements could be adjusted simultaneously using the substantial cosinusoidal relationship between standardized vertical bias and topographical parameters (slope and aspect). Biases, caused by different spatial resolutions between the DEMs, could be adjusted by the relationship between elevation differences and maximum curvatures.
3.   Sample description
The dataset of glacier surface elevation change in Kangri Karpo consists of two subsets: ① DEM data named TanDEM.tif, acquired by TerraSAR-X/TanDEM-X on 18 February 2014 and 13 March 2014, and stored in folder of Digital Elevation Model; ②data of glacier surface elevation change named SRTM_TOPO_DIFF_1980_2000.tif, TDX_SRTM_DIFF_2000_2014.tif and TDX_TOPO_DIFF_1980_2014.tif, derived from topographic maps, SRTM and TerraSAR-X/TanDEM-X acquisitions, and stored in the folder of Glacier Surface Elevation Change. Figure 3 shows the two subsets.

Fig.3   The DEM and glacier surface elevation changes in Kangri Karpo
Notes: (a) DEM data of TerraSAR-X/TanDEM-X acquisitions; (b) glacier surface elevation change from 1980 – 2000; (c) glacier surface elevation change from 2000 – 2014; (d) glacier surface elevation change from 1980 – 2014.
4.   Quality control and assessment
TerraSAR-X/TanDEM-X acquisitions are X-band data which have lesser penetration depth into snow and ice. Affected by geometric distortion including foreshortening, layover and shadows, regions in the bistatic TerraSAR-X/TanDEM-X interferograms cannot be used because of their poor measurement quality. In the process of phase unwrapping, geometric distortions were masked out, so that data voids existed in DEM. As the area of data voids was too small to significantly affect the mass balance, they could be neglected. The error of image registration algorithm was controlled within 0.01 pixels in GAMMA SAR and interferometric processing software.
Elevations from the ICESat Geoscience Laser Altimeter System (GLAS) were used to assess the accuracy of the TOPO and SRTM C-band DEMs. These data were obtained from the National Snow and Ice Data Center (NSIDC) (release 634; product GLA 14). Because of the effect of clouds, some GLAS data could not represent the true altitude of the ground. Outliers of elevation differences between GLA 14 and multi-source DEMs in off-glacier regions, with values exceeding ±100 m, were removed. Comparisons between the GLAS and the TOPO and SRTM C-band DEMs elevation data yielded a mean and SD of 2.74 ± 1.73 m and 2.65 ± 1.48 m respectively. The GCPs used to convert the unwrapped TerraSAR-X/ TanDEM-X interferogram into absolute heights from off-glacier pixel locations revealed that the vertical biases of the TerraSARX/TanDEM-X DEM and GLA 14 were similar to those of the SRTM C-band DEM and GLA 14.
The penetration depth of the SRTM C-band radar beam into snow and ice needs to be considered when assessing glacier elevation changes. The penetration depth can range from 0 to 10m depending on a variety of parameters such as snow temperature, density and water content. As a first approximation, because the penetration depth of the SRTM X-band radar beam is much smaller than the C-band, the elevation difference between these two values can be considered to be the SRTM C-band radar beam penetration into snow and ice. Differences between the SRTM C and X-bands showed an average 1.24m C-band penetration depth in the Kangri Karpo. Due to the non void-filled SRTM C-band DEM, data voids exist in the accumulation area. In this study, information on elevation change exists for all altitudinal zones, but the area of data voids was too small to affect the mass balance significantly (0.7% of the area above 6000 m a.s.l.), so it could be neglected.
To estimate the errors of the derived surface-elevation changes, the residual elevation differences in off-glacier regions were estimated assuming that heights in these areas did not change from 1980 – 2014 and that elevations should be equal in TOPO and SRTM C-band DEMs and the TerraSAR-X/TanDEM-X DEM (Figure 4). Because averaging over larger regions reduces the error, the standard deviation (STDV) over off-glacier regions will probably overestimate the uncertainty of the larger sample, so the uncertainty can be estimated by the standard error of the mean (SE):
\[\sigma =\sqrt{{MED}^{2}+{SE}^{2}}\]
where N is the number of the included pixels. To avoid the effect of autocorrelation, choosing a de-correlation length based on the spatial resolution is recommended. De-correlation length of 600 m and 400 m were chosen for DEMs with the spatial resolution of 30 m and 10 – 20 m, respectively.16 The resolutions of DEMs have a relatively large range in this study, a de-correlation length of 600 m and 200 m was employed for difference maps derived by common DEM differencing and DInSAR. The overall errors of the derived surface-elevation changes can then be estimated using SE and MED from off-glacier regions:
After error correction, mean elevation difference (MED) decreased to the range of −0.53 – 0.67 m, and the overall errors between TOPO DEM, SRTM and TerraSAR-X/TanDEM-X at the range of 0.43 – 0.77 m (Table 2).

Fig.4   Estimated elevation differences between SRTM and TOPO DEMs in off-glacier area before (a) and after (b) co-registration
Table 2   Statistics of vertical errors between the TOPO, SRTM and TSX/TDX
ItemMED (m)STDV (m)NSE (m)\(\mathrm{\sigma }\) (m)
SRTM - TOPO0.6716.4118290.380.77
TanDEM - SRTM–0.429.93163560.080.43
TanDEM - TOPO–0.535.36163560.040.53
Notes: MED is mean elevation difference, STDV is standard deviation, N is the number of considered pixels, SE is standard error and \(\mathbf{\sigma }\) is the overall error of the derived surface elevation change.
5.   Value and significance
A comparison showed significant differences in the changes of glacier thickness in eastern Nyainqentanglha Mountains. Using SRTM and SPOT5 DEMs (24 November 2011), this study shows that glaciers experienced a mean thinning of 0.39 ± 0.16 m a-1.17 Kääb et al.,27 Neckel et al.19 and Gardner et al.26 acquired different results over the Kangri Karpo based on ICESat and SRTM, concluding a glacier thinning of 1.34 ± 0.29 m a-1, 0.81 ± 0.32 m a-1 and 0.30 ± 0.13 m a-1 during 2003 and 2009, respectively. Using SRTM DEM and TerraSAR-X/TanDEM-X acquisitions (18 February 2014 and 13 March 2014), glaciers were found to have experienced a mean thinning of 0.79 ± 0.11 m a-1 in the Kangri Karpo. This result concurred with Neckel et al.,19 but significantly differed from Kääb et al.27 This discrepancy can be attributed to the estimation variations of SRTM C-band penetration. We obtained an average SRTM C-band penetration of 1.24 m for Kangri Karpo, estimated as the difference between SRTM C- and X-band DEMs. Kääb et al.27 employed an average penetration of 8 – 10 m for eastern Nyainqentanglha Mountains; 7 – 9 m based on winter trends that reflected February conditions. When compared with previous findings that estimated a SRTM C-band penetration of 1.24 – 2.5 m for Himalaya and Nyainqentanglha,17-18, 29-30 the penetration of 1.24 m for Kangri Karpo, which has like characters, is hence reliable.
As field measurement of mass balance is the best indicator of glacier change, monitoring has been carried out on Parlung No. 4 Glacier (5O282B0004/G096920E29228N) and Parlung No. 10 Glacier (5O282B0010/G096904E29286N), both on the northern slope of Kangri Karpo.4, 35 Large ice deficits were found on them, at rates of -0.71 m w.e. a-1 from May 2006 to May 2007 and -0.78 m w.e. a-1 for 2005 – 2009, respectively. To convert surface-elevation changes into glacier mass balance, the ice/snow density was considered. A value of 900 kg m-3 was applied to assess the water equivalent (w.e.) of mass changes from elevation differences, with an ice density uncertainty of 17 kg m-3 introduced.26 Based on SRTM DEM and TerraSAR-X/TanDEM-X acquisitions (18 February 2014), the two glaciers were found to have experienced substantial downwasting from 2000 to 2014, with mean mass deficits of 0.65 ± 0.22 m w.e. a-1 and 0.67 ± 0.22 m w.e. a-1. A comparison between field measurements and remote sensing data showed a high consistency in the mass deficits for the Parlung No. 4 and No. 10 Glaciers.
The dataset of glacier surface elevation change provides important basic data for studying glacier mass balance, glacier velocity and the characteristics of regional hydrological change. When used together with climate and hydrological data, this dataset has great significance for the research of water resources, the analysis of characteristics and trends of geographical environment change, among others.
6.   Data usage and recommendations
The DEM and glacier surface elevation change data can be downloaded from Science Data Bank. This dataset provides 32-bit floating-point GeoTIFF grid data produced by using ArcGIS software. These include data in *.tif, *.tfw and *.tfw.aux.xml formats. It could be read and processed by GIS software, such as ArcGIS, SuperMap, and also Matlab software.
We would like to thank DLR for providing TerraSAR-X/TanDEM-X data, USGS for providing free access to SRTM C- and X-band and Landsat data, and WestDC for providing glacier inventory.
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Data citation
1. Wu K, Liu S, Jiang Z et al. A dataset of glacier elevation changes in the Kangri Karpo Mountains during 1980 – 2014. Science Data Bank. DOI: 10.11922/sciencedb.574 (2018).
Article and author information
How to cite this article
Wu K, Liu S, Jiang Z et al. A dataset of glacier elevation changes in the Kangri Karpo Mountains during 1980 – 2014. China Scientific Data 3 (2018). DOI: 10.11922/csdata.2018.0011.zh
Wu Kunpeng
data processing, data production and manuscript writing.
PhD, Lecturer; research area: glacier remote sensing.
Liu Shiyin
design and implementation of data products collection.
PhD, Professor; research area: glacier change.
Jiang Zongli
design of SAR data processing.
PhD, Associate Professor; research area: glacier change.
Wei Junfeng
design of DEM correction.
PhD, Lecturer; research area: glacier mass balance.
We would like to thank DLR for providing TerraSAR-X/TanDEM-X data, USGS for providing free access to SRTM C- and X-band and Landsat data, and WestDC for providing glacier inventory.
Publication records
Published: Oct. 30, 2018 ( VersionsEN2
Released: April 28, 2018 ( VersionsZH4
Published: Oct. 30, 2018 ( VersionsZH5