DEM quality assessment for quantification of glacier surface change

Abstract. In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average ∼ 3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

To improve our understanding of Svalbard-type polythermal glacier drainage, hydraulic geometry models of the subglacial hydrology of two contrasting glaciers in Svalbard have been constructed. The models are tested against a uniquely long and rich set of field observations spanning 45 years. Digital elevation models (DEMs) were constructed from bedrock data measured with ground penetrating radar and surface data of two medium-sized polythermal glaciers, Hansbreen and Werenskioldbreen, in south-west Spitsbergen. Hansbreen has a low angle bed with over-deepenings and a calving front, while Werenskioldbreen has steeper bed and terminates on land. Together they are representative of many Svalbard glaciers. The DEMs were used to derive maps of hydraulic potential and subglacial drainage networks. Validation of the models was done using field observations including location mapping and speleological exploration of active moulins, positions of main river outflows, dyetracing and water chemistry studies, and observations of water pressure inside moulins. Results suggest that the water pressure is generally close to ice overburden pressure but varies greatly depending on local conditions such as bed location, the thickness of cold ice layer, the thickness of the glacier and seasonal changes in meltwater input.

Abstract. Increased rates of glacier retreat and thinning need accurate local estimates of glacier elevation change to predict future changes in glacier runoff and their contribution to sea level rise. Glacier elevation change is typically derived from digital elevation models (DEMs) tied to surface change analysis from satellite imagery. Yet, the rugged topography in mountain regions can cast shadows onto glacier surfaces, making it difficult to detect local glacier elevation changes in remote areas. A rather untapped resource comprises precise, time-stamped metadata on the solar position and angle in satellite images. These data are useful for simulating shadows from a given DEM. Accordingly, any differences in shadow length between simulated and mapped shadows in satellite images could indicate a change in glacier elevation relative to the acquisition date of the DEM. We tested this hypothesis at five selected glaciers with long-term monitoring programmes. For each glacier, we projected cast shadows onto the glacier surface from freely available DEMs and compared simulated shadows to cast shadows mapped from ∼40 years of Landsat images. We validated the relative differences with geodetic measurements of glacier elevation change where these shadows occurred. We find that shadow-derived glacier elevation changes are consistent with independent photogrammetric and geodetic surveys in shaded areas. Accordingly, a shadow cast on Baltoro Glacier (the Karakoram, Pakistan) suggests no changes in elevation between 1987 and 2020, while shadows on Great Aletsch Glacier (Switzerland) point to negative thinning rates of about 1 m yr−1 in our sample. Our estimates of glacier elevation change are tied to occurrence of mountain shadows and may help complement field campaigns in regions that are difficult to access. This information can be vital to quantify possibly varying elevation-dependent changes in the accumulation or ablation zone of a given glacier. Shadow-based retrieval of glacier elevation changes hinges on the precision of the DEM as the geometry of ridges and peaks constrains the shadow that we cast on the glacier surface. Future generations of DEMs with higher resolution and accuracy will improve our method, enriching the toolbox for tracking historical glacier mass balances from satellite and aerial images.

<p>Spaceborne digital elevation models (DEMs) are fundamental data for mapping and analyzing geomorphic features at regional and continental scale, but are limited by both their spatial resolution and accuracy. Typically, accuracy is measured using point- or profile-based geodetic measurements (e.g., sparse GNSS). We develop new methods to quantify the vertical uncertainty in spaceborne DEMs relevant to geomorphic analysis, focusing on the pixel-to-pixel variability internal to a given DEM, which we term the <em>inter-pixel consistency</em>. Importantly, the methods we develop are not based on external, geodetic measurements. Our codes are published open-source (https://github.com/UP-RS-ESP/DEM-Consistency-Metrics), and we particularly highlight a novel sun-angle rotation and hillshade-filtering approach that is based on the visual, qualitative assessment of DEM hillshades. Since our study area is in the arid Central Andes and contains diverse steep (volcano) and flat (salar) features, the environment is ideal for vegetation-free assessments of DEM quality across a range of topographic settings. We compare global 1 arcsec (~30 m) resolution DEMs (SRTM, ASTER, ALOS, TanDEM-X, Copernicus), and find high quality (high inter-pixel consistency) of the newest Copernicus DEM. At higher spatial resolution, we also seek to improve the stereo-processing of 3 m SPOT6 optical DEMs using the open-source AMES Stereo-Pipeline. This includes optimizing key parameters and processing steps, as well as developing metrics for DEM uncertainty masks based on the underlying image texture of the optical satellite scenes used to triangulate elevations. Although higher resolution spaceborne DEMs like SPOT6 are only available for limited spatial areas (depending on funds and processing power), the improvement in geomorphic feature identification and quantification at the hillslope scale is significant compared to 30 m datasets. Improved DEM quality metrics provide useful constraints on hazard assessment and geomorphic analysis for the Earth and other planetary bodies.</p>

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

Nowadays, digital elevation models (DEMs), with their simple data structure and widespread availability, became the main source for representing Earth’s surface and have been an integral part of any geospatial analysis, in particular, geomorphometric research. Spaceborne photogrammetry remains an important technique for obtaining high-resolution elevation data, especially over large or/and inaccessible areas. However, DEMs acquired directly through remote sensing based instruments; such as photogrammetric DEMs, are prone to a severe amount of uncertainty. The aim of this research is to provide a relatively comprehensive understanding of high-resolution Spaceborne photogrammetric DEM generation and enhancement for geomorphometric studies over regions with relatively low relief such as Sahel-Doukkala. To perform such analysis, a total of five interpolation algorithms available in most GIS software packages and commonly used in geomorphological studies; namely ANUDEM, IDW, OK, MQ and TPS, were selected and compared with respect to different spatial resolution, viz. 5 m, 10 m, 20 m and 30 m. Moreover, the impact of DEM filtering algorithms on introduced errors were examined in order to find the optimal strategy for removing errors and enhancing the modeled topographic surface. Results have been revealed that ANUDEM algorithm performs well when compared to other algorithms, in terms of accuracy as well as the natural look of the modeled surface. In addition, investigations demonstrate that spatial resolution of 10 m shows a satisfactory compromise between reducing errors and keeping topographic details. Furthermore, a combination of three digital filters which are Gaussian filter, median filter, and slope based filter, were able to reduce different sources of errors and to improve the elevation accuracy of DEM by 5% as so as the derived geomorphometric parameters.

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

Satellite images can be rectified and adapted to map representation also without information about viewing geometry of the sensor. Polynomial Functions (PFs) or Rational Polynomial Functions (RPFs) can be applied for this purpose, both requiring Ground Control Points (GCPs), of which the positions in the image as well as in the real world must be known. Typically for PFs only planimetric (X, Y) positions of GCPs are used while for RPFs 3D coordinates (X, Y, Z) of them as well as a DEM (Digital Elevation Model) of the entire scene are required. Check Points (CPs) with the same characteristics of GCPs (but not coincident with them) are used to better verify the accuracy of the product. Not only topographic survey, but also maps or ortho-photos with adequate resolution supply the coordinates of GCPs as well as CPs. This paper is aimed to compare methods to rectify IKONOS images based on PFs or RPFs applications, considering the positional accuracy of the results as index for performance evaluation. Tests were executed on IKONOS panchromatic image of an area of the Cilento and Vallo di Diano National Park, in Campania Region (Italy): ortho-photos (scale 1:10,000) were used for GCPs and CPs planimetric position in the real world while for RPFs applications also DEM (cell size 20 m) was considered as source of 3D information. To compare the selected methods, differences (residuals) between the X, Y coordinates of GCPs (but also of the CPs) on the ortho-photos and corresponding values in the rectified image were calculated and evaluated. The positional accuracy of the resulting products in relation to the method as well as to the number of GCPs was analyzed; also the implications of the calculation of Rational Polynomial Coefficients (RPCs) in alternative to the use of the values supplied for them by the image provider were investigated.

Determination of long-term erosion rates in vineyards of Navarre (Spain) using botanical benchmarks

Multi-temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)-borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi-temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi-temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV-borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf-off conditions (e.g., cross-flight strategy) turned out to be sufficient for our geomorphic research purposes.

A proper understanding of the occurrence of depressions is necessary to understand how they affect the processing of a Digital Elevation Model (DEM) for hydrological analysis. While the effect of DEM cell resolution on common terrain derivatives has been well established, this is not well understood for depressions. The more widespread availability of high resolution DEMs derived through Light Detection and Ranging (LIDAR) technologies presents new challenges and opportunities for the characterization of depressions. A 6-meter LIDAR DEM for a study watershed in North Carolina was used to determine the effect of DEM cell resolution on the occurrence of depressions. The number of depressions was found to increase with smaller cell sizes, following an inverse power relationship. Scale-dependency was also found for the average depression surface area, average depression volume, total depression area and total depression volume. Results indicate that for this study area the amount of depressions in terms of surface area and volume is at a minimum for cell sizes around 30 to 61 meters. In this resolution range there will still be many artificial depressions, but their presence is less than at lower or higher resolutions. At finer scales, the (small) vertical error of the LIDAR DEM needs to be considered and introduces a large number of small and shallow artificial depressions. At coarser scales, the terrain variability is no longer reliably represented and a substantial number of large and sometimes deep artificial depressions is created. The results presented here support the conclusion that the use of the highest resolution and most accurate data, such as LIDAR-derived DEMs, may not result in the most reliable estimates of terrain derrivatives unless proper consideration is given to the scale-dependency of the parameters being studied.

This paper presents the output of a research work undertaken to assess the quality of digital elevation models (DEMs) derived from the 1:100,000 scale topographic maps produced by the Sudan National Survey Department and British Ordnance Survey in 1970’s/ 1980’s. Despite the increasing concern for understanding andworking with errors existing in DEMs, knowledge about DEM quality is still at an early stage, especially in the developing countries. This paper presents the results of the accuracy tests which were performed for different types of topography. The overall TOPOMAP-DEM RMSE was found to be about ±7.066 m. A key outcome of this research is a mathematical model for TOPOMAP-DEM contours quality assessment and a defined procedure for producing a comprehensive DEM quality report. Also it provides the knowledge and the means for incorporating considerations of DEM quality. Additionally, it revealed the possibility of producing DEMs from topographic maps, with reasonable accuracy, using appropriate and unsophisticated interpolation methods such as the natural neighbour (NN) interpolator.

Abstract. Recently, with InSAR data provided by the German TanDEM-X mission, a new global, high-resolution Digital Elevation Model (DEM) has been produced by the German Aerospace Center (DLR) with unprecedented height accuracy. However, due to SAR-inherent sensor specifics, its quality decreases over urban areas, making additional improvement necessary. On the other hand, DEMs derived from optical remote sensing imagery, such as Cartosat-1 data, have an apparently greater resolution in urban areas, making their fusion with TanDEM-X elevation data a promising perspective. The objective of this paper is two-fold: First, the height accuracies of TanDEM-X and Cartosat-1 elevation data over different land types are empirically evaluated in order to analyze the potential of TanDEM-XCartosat- 1 DEM data fusion. After the quality assessment, urban DEM fusion using weighted averaging is investigated. In this experiment, both weight maps derived from the height error maps delivered with the DEM data, as well as more sophisticated weight maps predicted by a procedure based on artificial neural networks (ANNs) are compared. The ANN framework employs several features that can describe the height residual performance to predict the weights used in the subsequent fusion step. The results demonstrate that especially the ANN-based framework is able to improve the quality of the final DEM through data fusion.

The main objective of the study is Digital Terrain Model (DTM) generation from aerial photogrammetric data and identify and map the potential avalanche prone zones in Manali region. Avalanche is a dynamic hazardous phenomenon in the snow-bound mountainous terrain. Mapping of avalanche prone terrain is crucial to minimize the avalanche hazard. Nowadays, airborne data capturing technology, such as large-format Photogrammetry, has opened new vistas for the mapping of complex and inaccessible mountainous areas. In the present study, large format digital Photogrammetry data of 20 cm ground sample distance (GSD) have been used to generate high-resolution and accurate Digital Elevation Model and ortho-images. Digital terrain model along with its derivative terrain products and land cover map generated from land cover classification of derived ortho-photo is analyzed to locate the probable avalanche zone. The terrain characteristics, snow-pack condition and prevailing meteorological conditions are the groups of variables that influence the occurrence of avalanche. Amongst these, the terrain characteristics is the most influencing factor, and easier to map due to its stable nature along the time. Therefore advanced geo-informatics techniques have been used by mixing terrain property, Digital Elevation Model (DEM) and satellite imagery to determine the different geographical factors that affect the avalanche triggering. Also the derived information was combined in Analytic Hierarchy Process to extract a map of the avalanche prone zones in the study area standard mapping techniques as coarse-resolution data are not very appropriate for such studies.

Abstract. Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.