Abstract
User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The spatial resolution of UDTM was calculated by combining two variants of the point pattern analysis method and was found to be 19 m. On the other hand, the complexity of terrain method was used to define the spatial resolution of HDTM (3 m). HDTM achieved better results than UDTM in all aspects of accuracy assessment. The exterior accuracy of HDTM was better by 1.483 m (RMSE). Finally, the results of the applied DNA concept showed that a stream generated from HDTM had a 2.587 m lower horizontal root mean square error (HRMSE) than the stream generated from UDTM.
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Notes
The average nearest neighbor ratio is calculated as the observed average distance divided by the expected average distance. The expected distance is the average distance between neighbors in a hypothetical random distribution. If the index is less than 1, the pattern exhibits clustering; if the index is greater than 1, the trend is toward dispersion or competition. The average nearest neighbor method is very sensitive to the area value. The ANN tool is most effective for comparing different features in a fixed study area (ESRI 2016).
Autocorrelation is a mathematical representation of the degree of similarity between data at a given time and space (URL 8 2018).
A variogram is a description of the spatial continuity of the data. Experimental variogram is a discrete function calculated using a measure of variability between pairs of points at various distances.
A tool that automates the whole process was created in ModelBuilder.
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Šiljeg, A., Barada, M., Marić, I. et al. The effect of user-defined parameters on DTM accuracy—development of a hybrid model. Appl Geomat 11, 81–96 (2019). https://doi.org/10.1007/s12518-018-0243-1
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DOI: https://doi.org/10.1007/s12518-018-0243-1