An indirect interpolation model and its application for digital elevation model generation
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RESEARCH ARTICLE
An indirect interpolation model and its application for digital elevation model generation Mingwei Zhao 1,2 Received: 27 January 2020 / Accepted: 17 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Spatial interpolation is an important facet of generating digital elevation models (DEMs). When faced with geographical big data, a DEM with high accuracy is required in many types of investigations and applications. This requirement has encouraged researchers to enhance current interpolation methods and improve DEM construction as much as possible. Currently, almost all spatial interpolation methods use given sampling points as inputs and then calculate the missing sampling points individually. All of them consider terrain similarities in local areas but fail to consider the influence of the varying tendency in the overall terrain. In this paper, we propose a new concept for spatial interpolation that also considers local terrain similarity. However, in our approach, the unknown elevation points are expressed using both sampling points and unknown neighboring elevation points. In this way, the unknown elevation points of the entire computed region are connected formulaically, which introduces the trend features of terrain changes into the DEM construction process. We select a typical experimental area and conduct a DEM experiment based on elevation data sources. The experiment reveals that compared with traditional methods, the new method constructs a more accurate DEM, with morphological characteristics that are more consistent with the real terrain surface. Keywords DEM . Indirect interpolation . Accuracy . Surface
Introduction To meet natural resource and ecology needs, geoscientists have expanded time series analysis to include twodimensional and multidimensional analyses, and many studies on the problem of spatial interpolation have been conducted since the early twenty-first century. A series of modeling methods, such as the adjacent method, inverse distance weighting (IDW) method, spline function method, trend surface method, and multiple regression method, were established during this period. When studying global changes, regional-, and global-scale ecosystem models, such as the mountain climate simulator (MT-CLIM) model and general forest ecosystem model (FOREST-BGC), all require environmental factors, such as temperature, precipitation, and solar
* Mingwei Zhao [email protected] 1
College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
2
State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
radiation, as input parameters (Grafius and Malanson 2009; Running and Gower 1991). However, the values of these environmental factors are difficult to obtain via satellite remote sensing. Thus, it is necessary to use site data and interpolation methods to generate values that are usable in the geographic information system (GIS) environment. The accuracy of the adopted interpolation method wil
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