Modelling vertical error in LiDAR-derived digital elevation models

Aguilar, Fernando J.; Mills, J. P.; Delgado, Jorge; Aguilar, Manuel A.; Negreiros, João; Pérez, José Luís

doi:10.1016/j.isprsjprs.2009.09.003

Cited by 110 publications

(105 citation statements)

References 31 publications

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“…The variance r 2 k depends on the local DEM accuracy, assessed for each k-th habitat class (Table 2). It can be expected that errors in DEM elevations are spatially dependent (Hunter and Goodchild 1997;Aguilar et al 2010). However, as the identification of the errors' autocorrelation is a difficult task and requires many measurement locations, the assumption of the independent disturbance term used here is common in similar studies (e.g., Liu et al 2012).…”

Section: Uncertainty Assessment Approachmentioning

confidence: 99%

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

Mirosław‐Świątek

Kiczko

Szporak-Wasilewska

et al. 2017

Wetlands Ecol Manage

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Modelling groundwater depths in floodplains and peatlands remains a basic approach to assessing hydrological conditions of habitats. Groundwater flow models used to compute groundwater heads are known for their uncertainties, and the calibration of these models and the uncertainty assessments of parameters remain fundamental steps in providing reliable data. However, the elevation data used to determine the geometry of model domains are frequently considered deterministic and hence are seldom considered a source of uncertainty in model-based groundwater level estimations. Knowing that even the cutting-edge laser-scanning-based digital elevation models have errors due to vegetation effects and scanning procedure failures, we provide an assessment of uncertainty of water level estimations that remain basic data for wetland ecosystem assessment and management. We found that the uncertainty of the digital elevation model (DEM) significantly influenced the results of the assessment of the habitat's hydrological conditions expressed as groundwater depths. In extreme cases, although the average habitat suitability index (HSI) assessed in a deterministic manner was defined as 'unsuitable', in a probabilistic approach (grid-cell-scale estimation), it reached a value of 40% probability, signifying 'optimum' or 'tolerant'. For the 24 habitats analysed, we revealed vast differences between HSI scores calculated for individual grid cells of the model and HSI scores computed as average values from the set of grid cells located within the habitat patches. We conclude that groundwater-modelling-based decision support approaches to wetland assessment can result in incorrect management if the quality of DEM has not been addressed in studies referring to groundwater depths.

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Section: Uncertainty Assessment Approachmentioning

confidence: 99%

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

Mirosław‐Świątek

Kiczko

Szporak-Wasilewska

et al. 2017

Wetlands Ecol Manage

View full text Add to dashboard Cite

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“…This random error is approached by a Gaussian distribution N (0 , σ 2 s), being important to highlight that σs may take different values for any zk depending on local terrain complexity, point density, land cover, pulse penetration, etc. (Hodgson and Bresnahan, 2004;Aguilar et al, 2010;Su and Bork, 2006;Montealegre et al, 2015a). A simple probabilistic approach provides the mathematical framework (conditional pdf) for each observation factor in the graphical model through the following expression:…”

Section: Observation Factorsmentioning

confidence: 99%

“…Regarding observed elevations error (σs) in terrain mapping, a well-known characteristic is its relationship with terrain slope, especially in the case of DSM generation by means of laser scanning, where planimetric error may be relatively high and also may be directly translated to vertical error on sloping surfaces (Aguilar et al, 2010). Bearing in mind that LiDAR point density is also a critical influencing factor, every observed elevation was assigned an uncertainty (standard deviation) given by the empirical equation proposed by Karel and Kraus (2006):…”

Section: Dsm Accuracy Assessmentmentioning

confidence: 99%

“…In any case, and once the earth points have been collected, it is necessary to carry out a filtering process to label, at least, ground points and non-ground points in order to produce high quality DTMs (Aguilar et al, 2010). This step is not required when it comes to producing DSMs, but it is usual to count on the two kinds of surface models just to compute the so-called normalize DSM (nDSM = DSM -DTM), which can be very useful as an additional input layer to extract man-made features in land cover classification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis and Validation of Grid Dem Generation Based on Gaussian Markov Random Field

Aguilar¹,

Aguilar²,

Blanco³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m 2 ) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

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“…Papers dealing with first interpolation and adopting the VAN concept Kraus et al (2004Kraus et al ( , 2006 and Aguilar et al (2010) deal with various aspects of first interpolation, the process used for DTM creation. These topics are not related to our paper, which deals with second interpolation instead, but the above-listed papers are referenced here because they adopt the accuracy at the nodes definition.…”

Section: Guidelines and Their Prescriptionsmentioning

confidence: 99%

Standardization of figures and assessment procedures for DTM verticalaccuracy

Casella

Franzini

2014

Geomatics, Natural Hazards and Risk

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Digital Terrain Models (DTMs) are widely used in many sectors. They play a key role in hydrological risk prevention, risk mitigation and numeric simulations. This paper deals with two questions: (i) when it is stated that a DTM has a given vertical accuracy, is this assertion univocal? (ii) when DTM vertical accuracy is assessed by means of checkpoints, does their location influence results? First, the paper illustrates that two vertical accuracy definitions are conceivable: Vertical Accuracy at the Nodes (VAN, the average vertical distance between the model and the terrain, evaluated at the DTM's nodes) and Vertical Accuracy at the interpolated Points (VAP, in which the vertical distance is evaluated at the generic points). These two quantities are not coincident and, when they are calculated for the same DTM, different numeric values are reached. Unfortunately, the two quantities are often interchanged, but this is misleading. Second, the paper shows a simulated example of a DTM vertical accuracy assessment, highlighting that the checkpoints' location plays a key role: when checkpoints coincide with the DTM nodes, VAN is estimated; when checkpoints are randomly located, VAP is estimated, instead. Third, an in-depth, theoretical characterization of the two considered quantities is performed, based on symbolic computation, and suitable standardization coefficients are proposed. Finally, our discussion has a welldefined frame: it doesn't deal with all the items of the DTM vertical accuracy budget, which would require a much longer essay, but only with one, usually called fundamental vertical accuracy.

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Modelling vertical error in LiDAR-derived digital elevation models

Cited by 110 publications

References 31 publications

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

Analysis and Validation of Grid Dem Generation Based on Gaussian Markov Random Field

Standardization of figures and assessment procedures for DTM verticalaccuracy

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