Abstract
Geographic Information System (GIS) uses geospatial databases as a model of the real world. Since we are speaking of the real world this entails that in many cases the information about the Earth’s surface is highly important. Therefore, the generation of a surface model is significant. Basically, the quality of the Digital Elevation Model (DEM) depends on the source data or techniques used to obtain them. However, different spatial interpolation methods used for the same data may provide different results. This paper compares the accuracy of different spatial interpolation methods such as IDW, Kriging, Natural Neighbor and Spline. Since interpolation is essential in DEM generation, then is important to do a comparative analysis of such methods to find out which one provides more accurate results. The DEM data set used is from an aero photogrammetric surveying. According to this data set, three scenarios are performed for each of the methods. Selected random control points are derived from the base data set. The first example includes 10% of randomly selected control points, the second example includes 20%, and the third example includes 30%. The Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) are calculated. We find out that results do not have much difference; however, the most accurate results are derived from the Spline and Kriging interpolation methods.
Highlights
The usage of Geographic Information System (GIS) tools facilitates a more efficient decisionmaking process because of their spatial analysis capabilities
To compare different interpolation methods, we examined the difference between the known data and the predicted data using the mean absolute error and root mean squared error, equation as below (Johnston et al, 2001; Webster & Oliver, 2001; Kravchenko & Bullock, 1999)
As introduced in this paper, there are several spatial interpolation methods that can be used by applying GIS software
Summary
The usage of GIS tools facilitates a more efficient decisionmaking process because of their spatial analysis capabilities. The utility of the decision-making process, in operational terms, will be significantly improved when surface models are considered. GIS software packages offer several methods to create reliable surface models. Once we have obtained sample points from certain techniques, by interpolation methods we can create a data structure for the entire area. Interpolation is a crucial operation in GIS. It can help the visualization process and a better understanding of the dataset for the respective areas. The main objective of this paper is to assess the impact of different interpolation methods on the accuracy of DEM generation. The quality of an interpolation method varies in relation with the data sample size and landform types, but in this case the research was carried out considering the study area as a whole
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
