Figure 2 - available via license: CC BY-NC
Content may be subject to copyright.
Contour maps showing residuals ε of different methods of interpolation using GECO, from top left to bottom right: IDw, Kriging, MC, NN, RBF, MA.
Source publication
In this paper the accuracy of 6 spatial interpolation methods on geoid grid models are tested and compared: Inverse Distance Weighting, Kriging, Minimum Curvature, Natural Neighbour, Radial Basis Function and Moving Average. Grid models are derived from three global geopotential models: EGM2008, EIGEN-6C4 and GECO. Totally 18 geoid models in forms...
Contexts in source publication
Context 1
... sets of residuals also form grid models, as each of the residuals ε has a corresponding planar position of the node it is calculated from. Surfaces of 6 of those models made from GECO, one for each of the tested methods of interpolation, are shown in Figure 2. For the same reason as per Figure 1, contour maps of residuals for EGM2008 and EIGEN-6C4 aren't shown because of their barely noticeable differences from GECO residuals on Figure 2, at least in such a limited resolution. ...
Context 2
... of 6 of those models made from GECO, one for each of the tested methods of interpolation, are shown in Figure 2. For the same reason as per Figure 1, contour maps of residuals for EGM2008 and EIGEN-6C4 aren't shown because of their barely noticeable differences from GECO residuals on Figure 2, at least in such a limited resolution. Basic statistical indicators for all 18 sets of residuals are presented in Table 4. Figure 2 shows noticeable differences in spatial distribution of residuals for MC and MA in comparison with other methods of interpolation, which confirms previous assumptions. ...
Context 3
... the same reason as per Figure 1, contour maps of residuals for EGM2008 and EIGEN-6C4 aren't shown because of their barely noticeable differences from GECO residuals on Figure 2, at least in such a limited resolution. Basic statistical indicators for all 18 sets of residuals are presented in Table 4. Figure 2 shows noticeable differences in spatial distribution of residuals for MC and MA in comparison with other methods of interpolation, which confirms previous assumptions. Inspection of information provided in Table 4 also agrees with this. ...
Context 4
... for MC are also somewhat worse than the remaining 4 methods, RMSE and span of residuals being the highest and mean residual being furthermost away from zero. Although these indicators for MC aren't drastically lower than for example IDW, in combination with specific spatial distribution noticeable on Figure 2 they lead to the conclusion that method of MC isn't suitable for interpolation of geoid undulations from grid models. ...
Similar publications
In this paper, for design of large-scale electromagnetic problems, a novel robust global optimization algorithm based on surrogate models is presented. The proposed algorithm can automatically select a proper meta-model technique among multiple alternatives. In this paper, three representative meta-modeling techniques including ordinary Kriging, un...
Single and multiple surrogate models were compared for single-objective pumping optimization problems of a hypothetical and a real-world coastal aquifer. Different instances of radial basis functions and kriging surrogates were utilized to reduce the computational cost of direct optimization with variable density and salt transport models. An adapt...
The ability of a soil to provide the productivity service depends on the fulfillment of the functions that enable the realization of productivity service (PS). This study was conducted to determine and map the PS capacity of surface and subsurface soils in a 195-ha farmland located at Amasya province of Turkey. Functions that contribute to the prov...
![Plots of semivariograms (C0 [nugget], C [structured variance], A...](publication/340623257/figure/fig2/AS:11431281245885875@1716246117595/Plots-of-semivariograms-C0-nugget-C-structured-variance-A-range-and-RSS_Q320.jpg)
Due to limited in situ observations, prediction of large‐scale soil moisture content (SMC) for deep soil layers via interpolation is usually very challenging. This is especially true for regions with high spatial variations of terrain features. For precise prediction at a regional scale, SMC data for the 0‐ to 500‐cm soil profile across China's Loe...
Establishing a 3D orebody model is the foundation of digital mine and smart mine. In response to the phenomenon that the classical radial basis function surface reconstruction algorithm leads to surface boundary self-fitting and model discontinuity when the original data is sparse. This paper proposes a method of implicit automatic modeling of comp...
Citations
... In all these cases, an alternative remote sensing technology or geospatial technique could be used within a 95% confidence interval without significant changes in the elevation accuracy. Since these findings are in line with other studies, e.g., [23,[57][58][59][60], it can be stated that in specific cases it is possible to replace MLS technology with the less economically demanding ALS technology. This is particularly feasible if there is no need for forest road maps with a high level of detail. ...
Forest road maps are a fundamental source of information for the sustainable management, protection, and public utilization of forests. However, the precision of these maps is crucial to their use. In this context, we assessed and compared the elevation accuracy of terrain on three forest road surfaces (i.e., asphalt, concrete, and stone), which were derived based on data from three remote sensing technologies (i.e., aerial imaging, airborne laser scanning, and mobile laser scanning) using five geospatial techniques (i.e., inverse distance; natural neighbor; and conversion by average, maximal, and minimal elevation value). Specifically, the elevation accuracy was assessed based on 700 points at which elevation was measured in the field, and these elevations were extracted from fifteen derived forest road maps with a resolution of 0.5 m. The highest precision was found on asphalt roads derived from mobile laser scanning data (RMSE from ±0.01 m to ±0.04 m) and airborne laser scanning data (RMSE from ±0.03 m to ±0.04 m). On the other hand, the lowest precision was found on all roads derived from aerial imaging data (RMSE from ±0.11 m to ±0.23 m). Furthermore, we found significant differences in elevation between the measured and derived terrains. However, the differences in elevation between specific techniques, such as inverse distance, natural neighbor, and conversion by average, were mostly random. Moreover, we found that airborne and mobile laser scanning technologies provided terrain on concrete and stone roads with random elevation differences. In these cases, it is possible to replace a specific technique or technology with one that is similar without significantly decreasing the elevation accuracy (α = 0.05).
This study compares two interpolation methods in the problem of a local GNSS/levelling (quasi) geoid modelling. It uses raw data, no global geopotential model is involved. The methods differ as to the complexity of modelling procedure and theoretical background, they are ordinary kriging/least-squares collocation with constant trend and inverse distance weighting (IDW). The comparison itself was done through leave-one-out and random (Monte Carlo) cross-validation. Ordinary kriging and IDW performance was tested with a local (using limited number of data) and global (using all available data) neighbourhoods using various planar covariance function models in case of kriging and various exponents (power parameter) in case of IDW. For the study area both methods assure an overall accuracy level, measured by mean absolute error, root mean square error and median absolute error, of less than 1 cm. Although the method of IDW is much simpler, a suitably selected parameters (also trend removal) may contribute to differences between methods that are virtually negligible (fraction of a millimetre).
Providing geospatial data and information services like the Indonesian geoid model is one of the important things on implementation of The Regulation of Head of the Geospatial Information Agency (BIG) No. 15 of 2013 about Indonesia Geospatial Reference System 2013 (SRGI2013). The geospatial information service requires innovation to fulfill the needs of the user. The provision of Indonesian geoid model with various grid resolutions is one of the forms of renewal that designed to meet the needs of users based on the accuracy level that needed. Each geoid model with a different grid resolution will produce different geometries as well. Therefore, the value of geoid undulation on a certain coordinate will be different according to the grid resolution. Based on that case, this paper will analyze the accuracy of The Indonesian geoid model based on the various grid resolution. Grids resolution that used in this case is 36”, 1’, 2.5’, and 5’. The accuracy of the geoid model is obtained from the standard deviation of the difference between geometric undulation values and gravimetric undulation values in 186 control points. The result of this study shows that Indonesian geoid model with the grid resolution 36”, 1’, 2.5’, and 5’ have the geoid accuracy 5.129 cm, 6.472 cm, 9.584 cm, dan 13.942 cm sequentially. Based on that result, we can conclude that the less grid resolution of geoid model will produce a higher accuracy of the geoid model. In addition, this study could be a consideration for every user to use the Indonesian geoid model based on their need and specification.
