A comparative test of photogrammetrically sampled digital elevation models

Torlegård, Kennert; Östman, Anders; Lindgren, Ralf

doi:10.1016/0031-8663(86)90002-5

Cited by 36 publications

(18 citation statements)

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“…We continue until 15 per cent of the DEM elevation values have been corrected or confirmed. According to Torlegård et al (1986) gross errors account for less than 3 per cent of the population, so the 15 per cent limit is well within either the systematic (as defined by Thapa et al 1992) or the random error set, provided the first 3 per cent were really gross errors.…”

Section: Discussionmentioning

confidence: 91%

“…DEM producers might go back and make another measurement, but this might become a boring task if new values do not differ substantially from the old ones. Secondly, according to Torlegård et al (1986) blunders typically account for less than 3 per cent of the dataset, 0.5 per cent being a median value. Thus pursuing the task over such limit might be misleading, because the methods have been designed for finding gross errors only.…”

Section: Resultsmentioning

confidence: 99%

“…It has been a considered an important problem which led to collective efforts like the one summarized by Torlegård et al (1986). They reported the results of DEM derived independently by a number of organizations working on the same set of aerial photographs.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Elevation Accuracy of Digital Elevation Models: A Comparison of Some Error Detection Procedures

López

2000

Transactions in GIS

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Abstract:The widespread availability of powerful desktop computers, easy-to-use software tools and geographic datasets have raised the quality problem of input data to be a crucial one. Even though accuracy has been a concern in every serious application, there are no general tools for its improvement. Some particular ones exist however, and we are reporting here results for a particular case of quantitative raster data: Digital Elevation Models (DEM). We tested two procedures designed to detect anomalous values (also named gross errors, outliers or blunders) in DEM, but valid also for other quantitative raster datasets. A DEM with elevations varying from 181 to 1044 m derived from SPOT data has been used as a contaminated sample, while a manually derived DEM obtained from aerial photogrammetry has been regarded as the ground truth. That allows a direct performance comparison for the methods with real errors. We assumed that once an outlier location is suggested, a "better" value can be measured or obtained through some methodology. The options are different depending upon the user (end users might only interpolate, while DEM producers might go to the original data and make another reading). In this experiment we simply put the ground truth value. Preliminary results show that for the available dataset, the accuracy might be improved to some extent with very little effort. Effort is defined here as the percentage of points suggested by de methodology in relation with its total number: thus 100 per cent effort implies that all points have been checked. The method proposed by López (1997) gave poor results, because it has been designed for errors with low spatial correlation (which is not the case here). A modified version has been designed and compared also against the method suggested by Felicísimo (1994). The three procedures can be applied both for error detection during the DEM generation and by end users, and they might be of use for other quantitative raster data. The choice of the best methodology is different depending on the effort involved.

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Section: Discussionmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

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Improving the Elevation Accuracy of Digital Elevation Models: A Comparison of Some Error Detection Procedures

López

2000

Transactions in GIS

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“…Kidner [32] and Torlegård, et al [33] reported two major research areas: (1) developing new interpolation methods and (2) optimising the selection of existing interpolation methods. There are a number of existing geographic data interpolation methods with various different approaches and uses [21,23].…”

Section: Interpolation Methods and Parametersmentioning

confidence: 99%

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

Salekin

Burgess

Morgenroth

et al. 2018

IJGI

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Abstract:It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m −2 ), 50% (0.259 points m −2 ), and 75% (0.129 points m −2 ) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m −2 ). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m −2 ). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

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“…Secondly, the DEM error is thought to be as small as not affecting the outputs of the analyses using a DEM input. Last but not least, DEMs are assumed and used as error-free models of reality, even though the existence of elevation uncertainty and gross errors are widely recognized [38], [19]. In the last decades, geomorphometry based on fine topscale DEMs have become popular in environmental science [35].…”

Section: Introductionmentioning

confidence: 99%

Modelling the Uncertainty of Slope Estimation from a Lidar-Derived Dem: a Case Study from a Large-Scale Area in the Czech Republic / Modelovanie Neistoty Vo Výpočte Sklonov Z Lidarových Dmr; Prípadová Štúdia Vybraného Malého Územia V Čr

Mudron¹,

Podhoranyi²,

Cirbus³

et al. 2013

GeoScience Engineering

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This paper summarizes the methods and results of error modelling and propagation analyses in the Olše and Stonávka confluence area. In terrain analyses, the outputs of the aforementioned analysis are always a function of input. Two approaches according to the input data were used to generate field elevation errors which subsequently entered the error propagation analysis. The main goal solved in this research was to show the importance of input data in slope estimation and to estimate the elevation error propagation as well as to identify DEM errors and their consequences. Dependencies were investigated as well to achieve a better prediction of slope errors. Four different digital elevation model (DEM) resolutions (0.5, 1, 5 and 10 meters) were examined with the Root Mean Square Error (RMSE) rating up to 0.317 meters (10 m DEM). They all originated from a LIDAR survey. In the analyses, a stochastic Monte Carlo simulation was performed with 250 iterations. The article focuses on the error propagation in a large-scale area using high quality input DEM and Monte Carlo methods. The DEM uncertainty (RMSE) was obtained by sampling and ground research (RTK GPS) and from subtraction of two DEMs. According to empirical error distribution a semivariogram was used to model spatially autocorrelated uncertainty in elevation. The second procedure modelled the uncertainty without autocorrelation using a random N(0,RMSE) error generator. Statistical summaries were drawn to investigate the expected hypothesis. As expected, the error in slopes increases with the increasing vertical error in the input DEM. According to similar studies the use of different DEM input data, high quality LIDAR input data decreases the output uncertainty. Errors modelled without spatial autocorrelation do not result in a greater variance in the resulting slope error. In this case, although the slope error results (comparing random uncorrelated and empirical autocorrelated error fields) did not show any statistical significant difference, the input elevation error pattern was not normally distributed and therefore the random error generator realization is not a suitable interpretation of the true state of elevation errors. The normal distribution was rejected because of the high kurtosis and extreme values (outliners). On the other hand, it can show an important insight into the expected elevation and slope errors. Geology does not influence the slope error in the study area. AbstraktTáto práca zhŕňa metódu a výsledky modelovania chýb a analýzu šírenia chýb vo výpočte sklonov z DMR získaných LIDAR-om v skúmanej lokalite okolia sútoku riek Olše a Stonávka. V terénnych analýzach výstupy uvedenej analýzy sú vždy funkciou vstupu. Na generovania pola výškových chýb boli použité dve rozdielne metódy podľa vstupných dát. Modelované chyby v nadmorských výškach následne vstupovali do analýzy šírenia chýb. Hlavným cieľom práce bolo tak ako aj poukázanie na význam kvality vstupných dát vo výpočte sklonov a odhad šírenej chyby z nadmorských výšok v sklonoc...

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A comparative test of photogrammetrically sampled digital elevation models

Cited by 36 publications

References 0 publications

Improving the Elevation Accuracy of Digital Elevation Models: A Comparison of Some Error Detection Procedures

Improving the Elevation Accuracy of Digital Elevation Models: A Comparison of Some Error Detection Procedures

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

Modelling the Uncertainty of Slope Estimation from a Lidar-Derived Dem: a Case Study from a Large-Scale Area in the Czech Republic / Modelovanie Neistoty Vo Výpočte Sklonov Z Lidarových Dmr; Prípadová Štúdia Vybraného Malého Územia V Čr

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