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Comparison of data reduction algorithms for Li DAR ‐derived digital terrain model generalisation
Author(s) -
Yilmaz M,
Uysal M
Publication year - 2016
Publication title -
area
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 82
eISSN - 1475-4762
pISSN - 0004-0894
DOI - 10.1111/area.12276
Subject(s) - digital elevation model , terrain , interpolation (computer graphics) , elevation (ballistics) , mean squared error , remote sensing , algorithm , reduction (mathematics) , ranging , root mean square , triangulation , data reduction , computer science , approximation error , interferometric synthetic aperture radar , geodesy , mathematics , geology , artificial intelligence , geography , synthetic aperture radar , statistics , geometry , data mining , engineering , motion (physics) , cartography , electrical engineering
A digital terrain model ( DTM ) is a three‐dimensional representation of the terrain relief created from discrete points related to each other through their elevations. New technologies such as satellite remote sensing, airborne laser scanning and radar interferometry are efficient methods for constructing high‐quality DTM s in a cost‐effective manner. The accuracy of a DTM is influenced by a number of factors, including the accuracy, density and spatial distribution of elevation points, the terrain surface characteristics, etc. In this paper, direct comparisons of absolute and relative vertical accuracies are made between data reduction algorithms for the generalisation of DTM extracted from airborne Light Detection and Ranging (Li DAR ) data. The absolute vertical accuracies are presented in terms of the mean error ( ME ), the mean absolute error ( MAE ) and the root mean square error ( RMSE ) and the relative vertical accuracies are characterised as per cent slope over Mount St Helens in southwest Washington State. The results show that Li DAR datasets can be reduced to 50 per cent density level by a uniform data reduction algorithm using triangulation with a linear interpolation method for the generalisation of DTM while still maintaining the quality of the original data.