Performance Assessment of TanDEM-X DEM for Mountain Glacier Elevation Change Detection

Podgórski, Julian; Kinnard, Christophe; Pętlicki, Michał; Urrutia, Roberto

doi:10.3390/rs11020187

Cited by 29 publications

(24 citation statements)

References 48 publications

(71 reference statements)

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“…The same applies to the SRTM data even though the extreme differences are much lower. These results confirm the findings of Podgórski et al [31] on poor performance of TanDEM-X data on very steep slopes. The rest of the area shows a close vertical correspondence with the reference data.…”

Section: Final Dem and Comparison With Other Dem Data Sourcessupporting

confidence: 92%

“…This outcome hampers the applicability of the SRTM and TanDEM-X data in very detailed geomorphological research of the study area. Similar accuracy issues were observed in other areas with extremely rugged terrain [31,67].…”

Section: Discussionsupporting

confidence: 81%

“…However, for pronounced rugged alpine areas, the spatial resolution and measurement accuracy of the existing digital elevation datasets can be still insufficient for specific tasks requiring very high accuracy and spatial resolutions. For example, Podgórski et al [31] report that steep slopes in alpine areas introduce large errors in the TanDEM-X DEM dataset. For this reason, custom remote sensing data need to be acquired, mostly with UAV-SfM or TLS.…”

Section: Introductionmentioning

confidence: 99%

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Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

et al. 2019

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Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.The general advantage of laser scanning over photogrammetry is in the ability of sampling several kinds of surfaces (e.g., top of vegetation canopy, inter-canopy surfaces, and ground) which are in the line of sight of the laser beam until impermeable surface restrains further penetration of the laser energy. UAV-SfM has become a low-cost alternative to TLS, generating point clouds with comparable accuracies to TLS albeit with the limitation of sampling the top surface in the field of view. Either way, in cases where TLS or UAV-SfM are used separately, data shadows originate in areas which are obscured in the sensor's field of view [23,29] (Figure 1). Complementing the unsampled areas with the surface altitude measurements is possible by sensing the area from multiple positions and different viewing perspectives by changing the location of the sensor. Zhang and Lin [30] provide a systematic overview of the lidar and photogrammetric data fusion. Cawood et al. [7] showed there is no exclusive method for capturing complex 3D geometry in the case study of 3D modelling a large boulder with distinct geological structural features. Combination of TLS and UAV-SfM provided the most satisfying results to capture the geometric complexity of the object of interest for structural analysis. A similar approach of combining different viewing geometry of TLS and photogrammetry was demonstrated in 3D modelling of a historical city by [23]. Planning the data collection in the mentioned studies concerned environments with a relatively easy access for the technology and the surveying pe...

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Section: Final Dem and Comparison With Other Dem Data Sourcessupporting

confidence: 92%

Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

et al. 2019

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“…Bilinear interpolation has commonly been used for resampling DEM data with different spatial resolutions [63,68]. However, the accuracy of the DEM data is degraded by downsampling to a lower resolution, regardless of the interpolation method [102]. Moreover, the TanDEM-X DEM was produced by averaging multi-year DEMs over 2010-2015.…”

Section: Uncertainty Of Glacier Inventory Datamentioning

confidence: 99%

“…Moreover, the TanDEM-X DEM was produced by averaging multi-year DEMs over 2010-2015. Using a multi-year averaged DEM for elevation change detection might result in an average divergence on the dynamic parts of the scene compared to a single DEM acquired at a given time [102]. The impact of pulse penetration into snow on the SRTM DTM data could cause higher uncertainties in the snow accumulation areas [69].…”

Section: Uncertainty Of Glacier Inventory Datamentioning

confidence: 99%

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Zhang

Menenti

et al. 2019

Remote Sensing

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Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide the boundary of clean ice and debris-covered glacier facies, while debris-covered glacier facies play a key role in mass balance research. The aim of this study was to develop an automatic algorithm to distinguish ice cover types based on multi-temporal satellite data, and the algorithm was implemented in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. The classification method was built upon an automated machine learning approach: Random Forest in combination with the analysis of topographic and textural features based on Landsat-8 imagery and multiple digital elevation model (DEM) data. Very high spatial resolution Gao Fen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) imagery was used to select training samples and validate the classification results. In this study, all of the land cover types were classified with overall good performance using the proposed method. The results indicated that fully debris-covered glaciers accounted for approximately 20.7% of the total glacier area in this region and were mainly distributed at elevations between 4600 m and 4800 m above sea level (a.s.l.). Additionally, an analysis of the results clearly revealed that the proportion of small size glaciers (<1 km 2 ) were 88.3% distributed at lower elevations compared to larger size glaciers (≥1 km 2 ). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20 • and 30 • , and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin.In the past few decades, much work has been accomplished to map the extent of clean glacial ice and to quantify changes over time using satellite image data [6]. Methods applied range from visual interpretation [7] to segmentation of band ratio or spectral indices (e.g., the Normalized Difference Snow Index) images [8] and different unsupervised (e.g., the Iterative Self Organizing Data Analysis Techniques Algorithm, ISODATA) [9] and supervised (e.g., the Maximum Likelihood algorithm) classification [10] and decision tree methods [5,11]. For extracting debris-covered glaciers using multispectral imagery, fully manual onscreen digitizing is widely considered to be a common classification approach [12]. However, the accuracy of results using manual approaches depends greatly on the researcher's experience. Due to the laborious work of manual delineation, many researchers have further proposed semi-automated methods to extract the debris-covered glacial surface [13,14]. The use of Unmanned Aerial Vehicles (UAVs) and terrestrial remote sensing techniques offers new ways to monitor the debris-covered glaciers on a detailed spatial scale [15,16].Nevertheless, the spatial heterogeneity of the g...

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Assessing the Accuracy of Digital Elevation Models for Darjeeling‐Sikkim Himalayas

Mandal¹,

Sarkar²

2022

Advances in Remote Sensing Technology and the Three Poles

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Performance Assessment of TanDEM-X DEM for Mountain Glacier Elevation Change Detection

Cited by 29 publications

References 48 publications

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Assessing the Accuracy of Digital Elevation Models for Darjeeling‐Sikkim Himalayas

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