Geomorphometry from SRTM

Guth, Peter L.

doi:10.14358/pers.72.3.269

Cited by 125 publications

(72 citation statements)

References 16 publications

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“…Rivers present only a minor portion of the cells in a DEM, but this portion is particularly prone to certain types of errors (Guth, 2006;McMaster, 2002). Elevation anomalies along the valley bottoms of the Pieniny Mountains in Poland were found to range from −39 to +145 m in the X-band SRTM and in the ASTER GDEM from −52 to +88 m (Czubski et al, 2013;Ludwig and Schneider, 2006), thus vastly exceed-ing reported DEM accuracies (Jarvis et al, 2008;Tachikawa et al, 2011).…”

Section: Dem Artifacts and The Longitudinal River Profilementioning

confidence: 99%

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

2017

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Abstract. The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithmsquantile carving and the CRS algorithm -that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12 m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.

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Section: Dem Artifacts and The Longitudinal River Profilementioning

confidence: 99%

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

2017

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“…The original 1" SRTM data follows the observations made by Guth (2006), and plots almost atop the curve for the simulated 2" NED, while the original 1" NED plots far to right (higher mean slopes). The curve for 3" SRTM data plots far to the left (lower mean slopes), accompanied by the interpolated 1"-IDW data, which shows that inverse distance weighted resulted in an underestimation of slopes, thus it should not be used to resample DEMs to higher resolutions.…”

mentioning

confidence: 51%

“…This parameter not only influences the smoothness of the kriged surface, but also strongly determines the computational time involved, since the size of covariance matrices grows up exponentially. According to Guth (2006), SRTM 1" data compares much more closely with simulated 2" NED than with 1" NED. Following his methodology, we created a simulated 2" NED DEM by decimating the 1" data and used a plot of mean slope value versus elevation to compare the DEMs in this study.…”

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confidence: 94%

SRTM resample with short distance‐low nugget kriging

Grohmann

Steiner

2008

International Journal of Geographical Information Science

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Abstract. The Shuttle Radar Topography Mission (SRTM), was flow on Space Shuttle Endeavour in February 2000, with the objective of acquire a digital elevation model of all land between 60 • north latitude and 56 • south latitude, using Interferometric Synthetic Aperture Radar (InSAR) techniques. SRTM data is distributed at horizontal resolution of 1 arc-second (aprox. 30m) for areas within the USA and at 3 arc-second (aprox. 90m) resolution for the rest of the world. A resolution of 90m can be considered suitable for small or medium-scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it won't increase the level of detail of the original DEM, but it will lead to a surface where there is coherence of angular properties (i.e., slope, aspect) between neighbouring pixels, an important characteristic when dealing with terrain analysis. This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3" to 1". We present for a test area in western USA, which include different adjustment schemes (changes in nugget effect value and in the interpolation radius) as well as comparisons with the original 1" model of the area, with the National Elevation Dataset DEMs, and with other interpolation methods (splines and IDW). The basic concepts for using kriging to resample terrain data are: 1) to work only with the immediate neighbourhood of the predicted point, due the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; 2) add a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and 3) use a small value of nugget effect, to avoid smoothing that can obliterate terrain features. Drainages derived from the surfaces interpolated by kriging and by splines have good agreement with streams derived from the 1" NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1" surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline-interpolated surface still presented some noise and linear artifacts, which were removed by kriging.

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“…The exceptional value of SRTM3 is due to great geographical coverage, consistency and detail. This 3-s collection is based on the original 1-s terrain model (GUTH 2006). When the SRTM data set was created, there already existed a less detailed model GTO-PO30, which was developed primarily on the basis of maps (detail of 30 s or 1 km).…”

Section: Source Datamentioning

confidence: 99%

Towards a More Realistic Depiction of the Earth’s Surface on Maps

Drachal

Dębowska

2013

Pure Appl. Geophys.

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Abstract-In 2000, the shuttle radar topography mission (SRTM) produced the most complete, highest resolution digital elevation model (DEM) of the Earth. These data were used to create global 3 00 DEM and to correct 30 00 DEM which are both available on the internet. After a careful survey in the Institute of Geodesy and Cartography, Poland, these elevation data were recognized as extremely valuable and worth developing a unique form of visualization. As a result, a new design of a physical map of Europe at scale of 1:10 million was developed. For depicting the shape of the terrain, an original modification of combined shaded relief was employed, to reveal all the nuances of elevation data. True colors of the Earth's surface represented on the map originated from MODIS satellite image. The combination of true colors and terrain features made a realistic map, showing the landscapes as if from a point above the Earth. The image of the terrain is extremely detailed as it is based on the abundance of data defining the elevation of each point of land.

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Geomorphometry from SRTM

Cited by 125 publications

References 16 publications

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

SRTM resample with short distance‐low nugget kriging

Towards a More Realistic Depiction of the Earth’s Surface on Maps

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