Propagation of vertical and horizontal source data errors into a TIN with linear interpolation

Fan, Lei; Smethurst, Joel; Atkinson, Peter M.; Powrie, W.

doi:10.1080/13658816.2014.889299

Cited by 18 publications

(5 citation statements)

References 39 publications

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“…The random error in height (s z ), attributed to the positioning of the antenna, is typically 2-3 cm (HMK 2017). Thus, according to (2), the estimated total random error in d (both for rut depth and logging residue depth) was 4.0 cm, assuming errors of 2.5 cm for s TIN , s z and s p and a value of 0.5 for the location parameter m, which is 1 near the points and 0.3 in the middle (Fan et al 2014).…”

Section: Gnss Methodsmentioning

confidence: 99%

Logging Mats and Logging Residue as Ground Protection during Forwarder Traffic along Till Hillslopes

Ring

Andersson

Hansson

et al. 2021

Croat. j. for. eng. (Online)

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Forest soils in Northern Europe are generally trafficked by forest machinery on several occasions during a forest rotation. This may create ruts (wheel tracks), which could increase sediment transport to nearby surface water, reduce recreational value, and affect tree growth. It is therefore important to reduce soil disturbance during off-road forest transportation. In this study, rut depth was measured following forwarder traffic on study plots located along four harvested till hillslopes in Northern Sweden with drier soil conditions uphill and wet conditions downhill. The treatments included driving 1) using no ground protection, 2) on logging residue (on average, 38–50 kg m–2) and 3) on logging mats measuring 5×1×0.2 m. The hillslopes contain areas with a high content of boulders, stones, and gravel as well as areas with a significant content of silt. Six passes with a laden forwarder with four bogie tracks were performed. On the plots with ground protection, the application of logging residue and the application and removal of logging mats necessitated additional passes. Rut depth was measured using two methods: 1) as the difference in elevation between the interpolated original soil surface and the surface of the rut using GNSS positioning (Global Navigation Satellite Systems), and 2) manually with a folding rule from an aluminium profile, placed across the rut, to the bottom of the rut. The two methods generally gave similar results. Driving without ground protection in the upper part of the hillslopes generated ruts with depths <0.2 m. Here, the rut depth was probably modified by the high content of boulders and stones in the upper soil and drier soil conditions. In the lower part of the hillslopes, the mean rut depth ranged from 0.21 to 0.34 m. With a few exceptions, driving on logging residue or logging mats prevented exposure of mineral soil along the entire hillslope. Soil disturbance can thus be reduced by acknowledging the onsite variability in ground conditions and considering the need for ground protection when planning forest operations.

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Section: Gnss Methodsmentioning

confidence: 99%

Logging Mats and Logging Residue as Ground Protection during Forwarder Traffic along Till Hillslopes

Ring

Andersson

Hansson

et al. 2021

Croat. j. for. eng. (Online)

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“…Examples of the Monte Carlo method employed in the analysis of error propagation in GIS include an application in flood management (Qi et al 2013), a GIS-based assessment of seismic risk (Emmi and Horton 1995), potential slope failures (Zhou et al 2003), natural resource analysis (Davis and Keller 1997), interpolation of DEMs (Fan et al 2014), analysis of highway maintenance (Hong and Vonderohe 2014), evaluation of the accuracy of agricultural land valuation using land use and soil information (Fisher 1991), and the computation of building volumes (Biljecki et al 2014a).…”

Section: Related Work and Research Opportunitiesmentioning

confidence: 99%

Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs

Biljecki

Heuvelink

Ledoux

et al. 2015

International Journal of Geographical Information Science

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While error propagation in GIS is a topic that has received a lot of attention, it has not been researched with 3D GIS data. We extend error propagation to 3D city models using a Monte Carlo simulation on a use case of annual solar irradiation estimation of building rooftops for assessing the efficiency of installing solar panels. Besides investigating the extension of the theory of error propagation in GIS from 2D to 3D, this paper presents the following contributions. We (1) introduce varying XY/Z accuracy levels of the geometry to reflect actual acquisition outcomes; (2) run experiments on multiple accuracy classes (121 in total); (3) implement an uncertainty engine for simulating acquisition positional errors to procedurally modelled (synthetic) buildings; (4) perform the uncertainty propagation analysis on multiple levels of detail (LODs); and (5) implement Solar3Dcity -a CityGML-compliant software for estimating the solar irradiation of roofs, which we use in our experiments. The results show that in the case of the city of Delft in the Netherlands, a 0.3/0.6 m positional uncertainty yields an error of 68 kWh/m 2 /year (10%) in solar irradiation estimation. Furthermore, the results indicate that the planar and vertical uncertainties have a different influence on the estimations, and that the results are comparable between LODs. In the experiments we use procedural models, implying that analyses are carried out in a controlled environment where results can be validated. Our uncertainty propagation method and the framework are applicable to other 3D GIS operations and/ or use cases. We released Solar3Dcity as open-source software to support related research efforts in the future.

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“…To keep the consistency of the comparisons between the roughness indices, gridded DEM maps were used to calculate local surface roughness in this study. The triangulation with a liner interpolation method [17] was used to produce those DEM maps of a spatial grid resolution of 1 m. The DEM maps constructed using the detrended point cloud data are shown in Fig. 2.…”

Section: B Dem Mapsmentioning

confidence: 99%

Comparisons of five indices for estimating local terrain surface roughness using LiDAR point clouds

Li¹

2022

2022 29th International Conference on Geoinformatics

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Terrain surface roughness is an abstract concept, and its quantitative description is often vague. As such, there are various roughness indices used in the literature, the selection of which is often challenging in applications. This study compared the terrain surface roughness maps quantified by five commonly used roughness indices, and explored their correlations for four terrain surfaces of distinct surface complexities. These surfaces were represented by digital elevation models (DEMs) constructed using airborne LiDAR (Light Detection and Ranging) data. The results of this study reveal the similarity in the global patterns of the local surface roughness maps derived, and the distinctions in their local patterns. The latter suggests the importance of considering multiple indices in the studies where local roughness values are the critical inputs to subsequent analyses.

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Propagation of vertical and horizontal source data errors into a TIN with linear interpolation

Cited by 18 publications

References 39 publications

Logging Mats and Logging Residue as Ground Protection during Forwarder Traffic along Till Hillslopes

Logging Mats and Logging Residue as Ground Protection during Forwarder Traffic along Till Hillslopes

Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs

Comparisons of five indices for estimating local terrain surface roughness using LiDAR point clouds

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