Error in a USGS 30-meter digital elevation model and its impact on terrain modeling

Holmes, Karen; Chadwick, Oliver A.; Kyriakidis, Phaedon C.

doi:10.1016/s0022-1694(00)00229-8

Cited by 261 publications

(177 citation statements)

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“…Holmes et al (2000) showed that a small amount of elevation error in the DEM can severely affect the TA derived from the model, such as the index of topographic moisture, slope, flow accumulation and all interpretations depending on that information.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Moura-Bueno

Dalmolin

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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ABSTRACT:Terrain attributes (TAs) derived from digital elevation models (DEMs) are frequently used in digital soil mapping (DSM) as auxiliary covariates in the construction of prediction models. The DEMs and information extracted from it may be limited with regard to the spatial resolution and error magnitude, and can differ in the behavior of terrain features. The objective of this study was to evaluate the quality and limitations of free DEM data and to evaluate a topographic survey (TS) underlying the choice of a more appropriate model, for use in DSMs at a scale of 1:10,000. The study was conducted in an area of 937 ha in the watershed of Lajeado Giruá, in southern Brazil. The DEMs: DEM-TS, DEM-Topographic Map (TM), DEM-ASTER, DEM-SRTM, and DEM-TOPODATA were evaluated with regard to the precision elevation by statistical tests based on field reference points, the root mean square error (RMSE), identification of the number and size of spurious depressions, and the application of the Brazilian Cartographic Accuracy Standards Law (BCASL) to define the scale of each DEM. In addition, the TA derived from each DEM was compared with the TA from DEM-TS, considered to be terrain reality. The results showed that the elevation data of DEM-TS had the best quality (RMSE = 1.93 m), followed by DEM-SRTM (RMSE = 5.95 m), DEM-Topographic Map (RMSE = 8.28 m), DEM-TOPODATA (RMSE = 9.78 m) and DEM-ASTER (RMSE = 15.57 m). The DEM-TS was well-represented at a 1:10,000 scale, while the DEM-Topographic Map and DEM-SRTM fitted 1:50,000, the DEM-TOPODATA 1:50,000 and the DEM-ASTER a 1:100,000 scale. The results of DEM-SRTM and DEM-TOPODATA were closest to terrain reality (DEM-TS) and had the lowest number of spurious depressions and RMSE values for each evaluated attribute, but were inadequate for not fitting detailed scales compatible with small areas. The techniques for the acquisition of elevation data of each DEM and mainly the flat to gently undulating topography were factors that influenced the results. For a DSM at a scale of 1:10,000 in similar areas, the most appropriate model is DEM-TS.

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Section: Introductionmentioning

confidence: 99%

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Moura-Bueno

Dalmolin

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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“…The elevations measured at control points are used to assess the errors. The point map of DEM errors can then be used to generate equiprobable error surfaces, which are then added to the original DEM to produce an equiprobable realization of a DEM (Hunter and Goodchild, 1997;Holmes et al, 2000;Endreny and Wood, 2001;Temme et al, 2008). This technique is based on the following theory.…”

Section: Theorymentioning

confidence: 99%

“…An obvious problem with this approach is that the deterministic DEM (Z Ã ðs j Þ) is usually not available, so that the input DEM is in fact used to generate simulations, which leads to (see, e.g., Holmes et al, 2000;Temme et al, 2008):…”

Section: Theorymentioning

confidence: 99%

“…Typically, a DEM is a raster map (an image or an elevation array) that, like many other spatial features, can be efficiently modeled using geostatistics. The geostatistical concepts were introduced in geomorphometry by Fisher (1992Fisher ( , 1998 and Wood and Fisher (1993), then further elaborated by Kyriakidis et al (1999), Holmes et al (2000) and Oksanen (2006b). The methodological developments and future trends of geostatistical modeling of topography can be followed in the Ph.D. thesis of Oksanen (2006a).…”

Section: Introductionmentioning

confidence: 99%

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Geostatistical modeling of topography using auxiliary maps

Hengl

Bajat²,

Blagojević³

et al. 2008

Computers & Geosciences

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a b s t r a c tThis paper recommends computational procedures for employing auxiliary maps, such as maps of drainage patterns, land cover and remote-sensing-based indices, directly in the geostatistical modeling of topography. The methodology is based on the regressionkriging technique, as implemented in the R package gstat. The computational procedures are illustrated using a case study in the south-west part of Serbia. Two point data sets were used for geostatistical modeling: (1) 2051 elevation points were used to generate DEMs and (2) an independent error assessment data set (1020 points) was used to assess errors in the topo-DEM and the SRTM-DEM. Four auxiliary maps were used to improve generation of DEMs from point data: (1) distance to streams, (2) terrain complexity measured by standard deviation filter, (3) analytical hillshading map and (4) NDVI map derived from the Landsat image. The auxiliary predictors were significantly correlated with elevations ðadj:R 2 ¼ 0:20) and DEM errors ðadj:R 2 ¼ 0:27Þ. By including auxiliary maps in the geostatistical modeling of topography, realizations of DEMs can be generated that represent geomorphology of a terrain more accurately. In addition, downscaling of a coarse 3 arcsec SRTM DEM using auxiliary maps and regression-kriging is demonstrated using the same case study. A methodological advantage of regression-kriging, compared to splines, is the possibility to automate the data processing and incorporate multiple auxiliary predictors. The remaining open issues are computational efficiency, application of local regression-kriging algorithms and preparation of suitable auxiliary data layers for such analyses.

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“…However, according to Wechsler (2007) the RMSE method sometimes does not calculate an accurate assessment of how precisely each grid in a DEM represents topographical features. To solve this issue, number of researchers proposed spatial simulation methods for assessing the uncertainty of elevation estimates in each DEMs grid (Holmes et al, 2000;Carlisle, 2005;Wechsler and Kroll, 2006;Abd Aziz et al, 2012). The spatial simulation process analyses the spatial correlation in data to produce equiprobable estimates (realizations) of each particular grid in the DEMs.…”

Section: Introdctionmentioning

confidence: 99%

Assessing uncertainties associated with digital elevation models for object based landslide delination

Feizizadeh¹,

Blaschke²

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:Digital elevation models (DEMs) are representations of topography with inherent errors that constitute uncertainty. DEMs data are often used in object based analyses without quantifying the effects of these errors. The main objective of this research is to establish a semi-automated object-based image analysis (OBIA) methodology for modelling uncertainty associated with DEMs when applied for locating landslides. In order to assess the uncertainty of DEMs, the Monte Carlo Simulation methodology was employed for evaluation of the effects of uncertainty on elevation and derived topographic parameters. The effect of DEM error is investigated, using stochastic conditional simulation to generate multiple equally likely representations of an actual terrain surface. Accordingly, distributional measures including accuracy surfaces, spatial autocorrelation indices, and variograms were also employed to quantify the magnitude and spatial pattern of the uncertainty. The semi-automated object based rule-sets for landslide delineation were developed based on two approaches a): without uncertainty assessing of DEMs and b): under applying uncertainty assessing of DEMs to examine the probable and possible uncertainties in delineating the landslides and measuring the improved accuracy after minimizing these associated uncertainties. The results of two approaches were validated using a landslide inventory database and very accurate GPS dataset. Results indicated very significant improvement in accuracy of results (> 28 %) when employing DEMs under uncertainty assessment. This research demonstrates how application of this methodology can address DEMs uncertainty, contributing to more responsible use of elevation and derived topographic parameters, and ultimately results obtained from their use.

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Error in a USGS 30-meter digital elevation model and its impact on terrain modeling

Cited by 261 publications

References 10 publications

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Geostatistical modeling of topography using auxiliary maps

Assessing uncertainties associated with digital elevation models for object based landslide delination

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