Next Article in Journal
Combining 2D Mapping and Low Density Elevation Data in a GIS for GNSS Shadow Prediction
Next Article in Special Issue
A Geosimulation Approach for Data Scarce Environments: Modeling Dynamics of Forest Insect Infestation across Different Landscapes
Previous Article in Journal
Fractal Characterization of Settlement Patterns and Their Spatial Determinants in Coastal Zones
Previous Article in Special Issue
Collaborative Strategies for Sustainable EU Flood Risk Management: FOSS and Geospatial Tools—Challenges and Opportunities for Operative Risk Analysis
Open AccessArticle

Spatial Sampling Strategies for the Effect of Interpolation Accuracy

by 1, 1,*,†, 1,† and 2,*
School of Environment Science and Spatial Information, China University of Mining and Technology, Daxue Road No.1, Xuzhou 221116, China
School of Geodesy and Geometrics, Jiangsu Normal University, Shanghai Road No.101, Xuzhou 221116, China
Authors to whom correspondence should be addressed.
These authors contributed equally to this work
Academic Editors: Songnian Li, Suzana Dragicevic, Xiaohua Tong and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2742-2768;
Received: 28 August 2015 / Revised: 21 November 2015 / Accepted: 30 November 2015 / Published: 8 December 2015
(This article belongs to the Special Issue Bridging the Gap between Geospatial Theory and Technology)
Spatial interpolation methods are widely used in various fields and have been studied by several scholars with one or a few specific sampling datasets that do not reflect the complexity of the spatial characteristics and lead to conclusions that cannot be widely applied. In this paper, three factors that affect the accuracy of interpolation have been considered, i.e., sampling density, sampling mode, and sampling location. We studied the inverse distance weighted (IDW), regular spline (RS), and ordinary kriging (OK) interpolation methods using 162 DEM datasets considering six sampling densities, nine terrain complexities, and three sampling modes. The experimental results show that, in selective sampling and combined sampling, the maximum absolute errors of interpolation methods rapidly increase and the estimated values are overestimated. In regular-grid sampling, the RS method has the highest interpolation accuracy, and IDW has the lowest interpolation accuracy. However, in both selective and combined sampling, the accuracy of the IDW method is significantly improved and the RS method performs worse. The OK method does not significantly change between the three sampling modes. The following conclusion can be obtained from the above analysis: the combined sampling mode is recommended for sampling, and more sampling points should be added in the ridges, valleys, and other complex terrain. The IDW method should not be used in the regular-grid sampling mode, but it has good performance in the selective sampling mode and combined sampling mode. However, the RS method shows the opposite phenomenon. The sampling dataset should be analyzed before using the OK method, which can select suitable models based on the analysis results of the sampling dataset. View Full-Text
Keywords: spatial interpolation; terrain complexity; sampling density; sampling mode spatial interpolation; terrain complexity; sampling density; sampling mode
Show Figures

Figure 1

MDPI and ACS Style

Zhang, H.; Lu, L.; Liu, Y.; Liu, W. Spatial Sampling Strategies for the Effect of Interpolation Accuracy. ISPRS Int. J. Geo-Inf. 2015, 4, 2742-2768.

Show more citation formats Show less citations formats

Article Access Map

Back to TopTop