Accuracy Evaluation of Four Greenland Digital Elevation Models (DEMs) and Assessment of River Network Extraction

Digital Elevation Models (DEMs) of Greenland provide the basic data for studying the Greenland ice sheet (GrIS), but little research quantitatively evaluates and compares the accuracy of various Greenland DEMs. This study uses IceBridge elevation data to evaluate the accuracies of the the Greenland Ice Map Project (GIMP)1 DEM, GIMP2 DEM, TanDEM-X, and ArcticDEM in their corresponding time ranges. This study also analyzes the impact of DEM accuracy and resolution on the accuracy of river network extraction. The results show that (1) within the time range covered by each DEM, TanDEM-X with an RMSE of 5.60 m has higher accuracy than the other DEMs in terms of absolute height accuracy, while GIMP1 has the lowest accuracy among the four Greenland DEMs, with an RMSE of 14.34 m. (2) Greenland DEMs are affected by regional errors and interannual changes. The accuracy in areas with elevations above 2000 m is higher than that in areas with elevations below 2000 m, and better accuracy is observed in the north than in the south. The stability of the ArcticDEM product is higher than those of the other three DEM products, and its RMSE standard deviation over multiple years is only 0.14 m. Therefore, the errors caused by the applications of DEMs with longer time spans are smaller. GIMP1 performs in an opposite manner, with a standard deviation of 2.39 m. (3) The river network extracted from TanDEM-X is close to the real river network digitized from remote sensing images, with an accuracy of 50.78%. The river network extracted from GIMP1 exhibits the largest errors, with an accuracy of only 8.83%. This study calculates and compares the accuracy of four Greenland DEMs and indicates that TanDEM-X has the highest accuracy, adding quantitative studies on the accuracy evaluation of various Greenland DEMs. This study also compares the results of different DEM river network extractions, verifies the impact of DEM accuracy on the accuracy of the river network extraction results, and provides an explorable direction for the hydrological analysis of Greenland as a whole.

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Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030186 ◽

2021 ◽

Vol 10 (3) ◽

pp. 186

Author(s):

HuiHui Zhang ◽

Hugo A. Loáiciga ◽

LuWei Feng ◽

Jing He ◽

QingYun Du

Keyword(s):

Spatial Scales ◽

Digital Elevation Models ◽

Regional Scale ◽

Drainage Density ◽

River Network ◽

Landscape Patterns ◽

Estimation Methods ◽

River Networks ◽

Flow Accumulation ◽

Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

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Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

Remote Sensing ◽

10.3390/rs12183016 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3016

Author(s):

Ignacio Borlaf-Mena ◽

Maurizio Santoro ◽

Ludovic Villard ◽

Ovidiu Badea ◽

Mihai Andrei Tanase

Keyword(s):

Remote Sensing ◽

Laser Scanning ◽

Digital Elevation Models ◽

Shuttle Radar Topography Mission ◽

Synthetic Aperture ◽

Mountainous Regions ◽

Aerial Laser Scanning ◽

Digital Elevation ◽

The Difference ◽

The Impact

Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and can capture unique information about its targets. However, SAR observations are affected by the coupled effect of viewing geometry and terrain topography. The study aims to assess the impact of global digital elevation models (DEMs) on the normalization of Sentinel-1 backscattered intensity and interferometric coherence. For each DEM, we analyzed the difference between orbit tracks, the difference with results obtained with a high-resolution local DEM, and the impact on land cover classification. Tests were carried out at two sites located in mountainous regions in Romania and Spain using the SRTM (Shuttle Radar Topography Mission, 30 m), AW3D (ALOS (Advanced Land Observation Satellite) World 3D, 30 m), TanDEM-X (12.5, 30, 90 m), and Spain national ALS (aerial laser scanning) based DEM (5 m resolution). The TanDEM-X DEM was the global DEM most suitable for topographic normalization, since it provided the smallest differences between orbital tracks, up to 3.5 dB smaller than with other DEMs for peak landform, and 1.4–1.9 dB for pit and valley landforms.

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Abrasive Surfaces Measured by Digital Optical Stereopsis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.76-78.465 ◽

2009 ◽

Vol 76-78 ◽

pp. 465-470

Author(s):

Dennis V. de Pellegrin ◽

Andrew Torrance

Keyword(s):

Failure Analysis ◽

Digital Elevation Models ◽

Optical Microscope ◽

The Other ◽

Digital Elevation ◽

Microscope Imaging

The principle of stereopsis involves measuring an object’s geometry from a pair of images taken at slightly different viewing positions. This technique is frequently used for geographical mapping in satellite-based reconnaissance, however, the same practice has not been reliably applied at the other end of the scale spectrum: i.e. optical microscope imaging. The impediments have been identified and addressed in this work, concluding that optical stereopsis can be applied to microscopical surface examinations, and that the resulting digital elevation models can be of particular use in tribological investigations for performance and failure analysis.

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Unusual surface morphology from digital elevation models of the Greenland Ice Sheet

Geophysical Research Letters ◽

10.1029/98gl02589 ◽

1998 ◽

Vol 25 (19) ◽

pp. 3623-3626 ◽

Cited By ~ 9

Author(s):

Simon Ekholm ◽

Kristian Keller ◽

Jonathan L. Bamber ◽

S. Prasad Gogineni

Keyword(s):

Surface Morphology ◽

Digital Elevation Models ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Digital Elevation

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Do the Innovation Index of the Regions and the Sectors of Belonging Affect the Performance of the ASOs?

International Business Research ◽

10.5539/ibr.v13n9p113 ◽

2020 ◽

Vol 13 (9) ◽

pp. 113

Author(s):

Margaret Antonicelli ◽

Ivano De Turi

Keyword(s):

Fixed Effects ◽

Success Factors ◽

The Other ◽

Time Range ◽

Dual Purpose ◽

Panel Model ◽

Unbalanced Dataset ◽

Economic Contribution ◽

The One ◽

The Impact

The considerable economic contribution of academic spin-offs (ASOs) has drawn numerous scholars’ attention to explore the factors that influence their development (Hossinger, 2020). The growing attention on these issues has led researchers to investigate the drivers and success factors that have the greatest impact on the performance of ASOs.This paper has been designed with a dual purpose. On the one hand, the document aims to examine how much a region's innovation index reflects positively on the performance of ASOs. On the other hand, the paper also examines in which sectors the impact of innovation on the performance of ASOs emerges most. The research hypotheses of these paintings were explored in an empirical study of 1,007 Italian spin-offs over a time range from 2010 to 2019. To carry out the analysis, a panel model with fixed effects was used, with an unbalanced dataset.

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Assessing the Impact of Uncertainties of Digital Elevation Models on Hydro-Geomorphological Analysis Using Gaussian White Noise

10.5194/egusphere-egu2020-1347 ◽

2020 ◽

Author(s):

Lukas Graf ◽

Mariano Moreno-de-las-Heras ◽

Joan Estrany

Keyword(s):

White Noise ◽

Imperfect Information ◽

Digital Elevation Models ◽

Gaussian White Noise ◽

Terrain Analysis ◽

Stream Network ◽

Digital Terrain ◽

Digital Elevation ◽

The Impact ◽

Geomorphological Analysis

Digital elevation models (DEM) are mathematical representations of the Earth's bare surface in computer-readable format. The underlying measurements are often obtained by remote sensing and photogrammetry methods and processed into continuous raster data. Each of these data sources, however, provides imperfect information, and further processing steps often increase the degree of imperfection. Consequently, the process of DEM generation cumulates in uncertainty, which affects subsequent hydro- and geomorphological analyses and modelling (e.g., stream network delineation, flowpath distribution, erosion modelling). In many DEM-based studies, however, the aspect of uncertainty related to the DEM data source has been neglected. Therefore, we propose a new approach for quantifying the effects of DEM uncertainty on hydro-geomorphological modelling based on Gaussian white noise, a concept widely used in signal processing to map noise in signals and extract the actual message context. The basic idea is to add noise to the original DEM values by means of a Gaussian distribution whose parameters are determined from the mean value of the elevation values in a moving window and the device-specific properties (precision and accuracy). We postulate that such an approach can be used to determine uncertainties and their effect on subsequent analysis steps of hydro-geomorphological modelling. It is conceivable to create DEM ensembles depending on known parameters such as the accuracy and precision of the measuring instrument, as is used operationally in weather forecasting. Using such ensembles, probability ranges for terrain and catchment hydro-geomorphological properties can be determined and uncertainty ranges can be specified. Thus, the currently mostly deterministic approach of digital terrain modelling will be replaced by a more probabilistic understanding. Overall, our approach will help decision-makers and scientists to better assess the results of digital terrain analysis. Furthermore, it will also facilitate determining whether a result of DEM-based hydro-geomorphological analysis is sufficiently certain to answer specific research questions.

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Assessing the impact of sea level rise on port operability using LiDAR-derived digital elevation models

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111318 ◽

2019 ◽

Vol 232 ◽

pp. 111318 ◽

Cited By ~ 5

Author(s):

Vicente Gracia ◽

Joan Pau Sierra ◽

Marta Gómez ◽

Mónica Pedrol ◽

Sara Sampé ◽

...

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Digital Elevation Models ◽

Digital Elevation ◽

The Impact

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Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120545 ◽

2019 ◽

Vol 8 (12) ◽

pp. 545 ◽

Cited By ~ 8

Author(s):

Nayyer Saleem ◽

Md. Enamul Huq ◽

Nana Yaw Danquah Twumasi ◽

Akib Javed ◽

Asif Sajjad

Keyword(s):

Risk Assessment ◽

Landslide Susceptibility ◽

3D Visualization ◽

Digital Elevation Models ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Landslide Risk ◽

Conditioning Factors ◽

Digital Elevation ◽

The Impact

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.

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Validation of digital elevation models (DEMs) and comparison of geomorphic metrics on the southern Central Andean Plateau

Earth Surface Dynamics ◽

10.5194/esurf-5-211-2017 ◽

2017 ◽

Vol 5 (2) ◽

pp. 211-237 ◽

Cited By ~ 48

Author(s):

Benjamin Purinton ◽

Bodo Bookhagen

Keyword(s):

Standard Deviation ◽

Profile Analysis ◽

Digital Elevation Models ◽

Frequency Noise ◽

Differential Gps ◽

Fourier Frequency ◽

Digital Elevation ◽

Southern Central ◽

Standard Deviations ◽

Terrain Elevation

Abstract. In this study, we validate and compare elevation accuracy and geomorphic metrics of satellite-derived digital elevation models (DEMs) on the southern Central Andean Plateau. The plateau has an average elevation of 3.7 km and is characterized by diverse topography and relief, lack of vegetation, and clear skies that create ideal conditions for remote sensing. At 30 m resolution, SRTM-C, ASTER GDEM2, stacked ASTER L1A stereopair DEM, ALOS World 3D, and TanDEM-X have been analyzed. The higher-resolution datasets include 12 m TanDEM-X, 10 m single-CoSSC TerraSAR-X/TanDEM-X DEMs, and 5 m ALOS World 3D. These DEMs are state of the art for optical (ASTER and ALOS) and radar (SRTM-C and TanDEM-X) spaceborne sensors. We assessed vertical accuracy by comparing standard deviations of the DEM elevation versus 307 509 differential GPS measurements across 4000 m of elevation. For the 30 m DEMs, the ASTER datasets had the highest vertical standard deviation at > 6.5 m, whereas the SRTM-C, ALOS World 3D, and TanDEM-X were all < 3.5 m. Higher-resolution DEMs generally had lower uncertainty, with both the 12 m TanDEM-X and 5 m ALOS World 3D having < 2 m vertical standard deviation. Analysis of vertical uncertainty with respect to terrain elevation, slope, and aspect revealed the low uncertainty across these attributes for SRTM-C (30 m), TanDEM-X (12–30 m), and ALOS World 3D (5–30 m). Single-CoSSC TerraSAR-X/TanDEM-X 10 m DEMs and the 30 m ASTER GDEM2 displayed slight aspect biases, which were removed in their stacked counterparts (TanDEM-X and ASTER Stack). Based on low vertical standard deviations and visual inspection alongside optical satellite data, we selected the 30 m SRTM-C, 12–30 m TanDEM-X, 10 m single-CoSSC TerraSAR-X/TanDEM-X, and 5 m ALOS World 3D for geomorphic metric comparison in a 66 km2 catchment with a distinct river knickpoint. Consistent m∕n values were found using chi plot channel profile analysis, regardless of DEM type and spatial resolution. Slope, curvature, and drainage area were calculated and plotting schemes were used to assess basin-wide differences in the hillslope-to-valley transition related to the knickpoint. While slope and hillslope length measurements vary little between datasets, curvature displays higher magnitude measurements with fining resolution. This is especially true for the optical 5 m ALOS World 3D DEM, which demonstrated high-frequency noise in 2–8 pixel steps through a Fourier frequency analysis. The improvements in accurate space-radar DEMs (e.g., TanDEM-X) for geomorphometry are promising, but airborne or terrestrial data are still necessary for meter-scale analysis.

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Analysis of Surface Rolling Process Impact on Crankshaft Fatigue Strength

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.488-489.236 ◽

2012 ◽

Vol 488-489 ◽

pp. 236-240

Author(s):

Xiao Ping Chen ◽

Ru Fu Hu

Keyword(s):

Standard Deviation ◽

Fatigue Strength ◽

Rolling Process ◽

Comparative Test ◽

The Other ◽

Test Results ◽

The Impact ◽

Effect Of Surface

In this paper, the fatigue comparative test includes two groups of crankshafts, one group with surface rolling process and the other not, has been finished. And the test results are analyzed. The parent standard deviation test shows that there is not effect of surface rolling process impact on the uniformity of crankshaft fatigue strength. But it is sure that there is the impact of surface rolling process impact on the average of crankshaft fatigue strength by the average maternal test. So the surface rolling process can be used to improve crankshaft fatigue strength.

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