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Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models
Author(s) -
Smith S. L.,
Holland D. A.,
Longley P. A.
Publication year - 2005
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2005.00636.x
Subject(s) - point cloud , interpolation (computer graphics) , range (aeronautics) , computer science , laser scanning , grid , multivariate interpolation , scale (ratio) , remote sensing , sample (material) , lidar , surface (topology) , algorithm , geography , laser , mathematics , artificial intelligence , computer vision , optics , image (mathematics) , cartography , geometry , geodesy , chemistry , materials science , physics , chromatography , composite material , bilinear interpolation
Airborne laser scanning (ALS) is becoming an increasingly popular data capture technique for a variety of applications in urban surface modeling. Raw ALS data are captured and supplied as a 3D point cloud. Many applications require that these data are interpolated onto a regular grid in order that they may be processed. In this article, we identify and analyze the magnitudes and spatial patterning of residuals from ALS models of urban surfaces, at a range of different scales. Previous research has demonstrated the effects of interpolation method and scale upon the nature of error in digital surface models (DSMs), but the size and spatial patterning of such errors have not hitherto been investigated for urban surfaces. The contribution of this analysis is thus to investigate the ways in which different methods may introduce error, and to understand the uncertainty that characterizes urban surface models that are devised for a wide range of applications. The importance of the research is shown using examples of how the different methods may introduce different amounts of error and how the uncertainty information may benefit users of ALS height models. Our analysis uses a range of validation techniques, including split‐sample, cross‐validation, and jackknifing, to estimate the error created in DSMs of urban areas.