Robust methods for assessing the accuracy of linear interpolated DEM |
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| Institution: | 1. Technical University of Crete (TUC), Department of Environmental Engineering, Chania, Greece;2. Marine Sciences Department, School of Environment, University of the Aegean, Greece;3. Department of Environment and Hydrology, Region of Peloponnesus, Regional Unit of Lakonia, Sparta, Greece;1. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;2. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;3. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China;4. Department of Geography, University of California, Los Angeles, Los Angeles, CA 90095, USA;5. Anhui Center for Collaborative Innovation in Geographical Information Integration and Application, Chuzhou University, Chuzhou 239000, China;6. Upper and Middle Reaches of the Yellow River Administrative Bureau of Yellow River Conservancy Commission, Xi''an 710021, China |
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| Abstract: | Methods for assessing the accuracy of a digital elevation model (DEM) with emphasis on robust methods have been studied in this paper. Based on the squared DEM residual population generated by the bi-linear interpolation method, three average-error statistics including (a) mean, (b) median, and (c) M-estimator are thoroughly investigated for measuring the interpolated DEM accuracy. Correspondingly, their confidence intervals are also constructed for each average error statistic to further evaluate the DEM quality. The first method mainly utilizes the student distribution while the second and third are derived from the robust theories. These innovative robust methods possess the capability of counteracting the outlier effects or even the skew distributed residuals in DEM accuracy assessment. Experimental studies using Monte Carlo simulation have commendably investigated the asymptotic convergence behavior of confidence intervals constructed by these three methods with the increase of sample size. It is demonstrated that the robust methods can produce more reliable DEM accuracy assessment results compared with those by the classical t-distribution-based method. Consequently, these proposed robust methods are strongly recommended for assessing DEM accuracy, particularly for those cases where the DEM residual population is evidently non-normal or heavily contaminated with outliers. |
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| Keywords: | DEM accuracy Interpolation residuals Robust estimation Confidence interval Monte Carlo simulation |
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