Assessment of Dem Quality for Characterizing Surface Roughness Using Close Range Digital Photogrammetry

This paper presents a procedure for assessing the quality of a digital elevation model (DEM) which has been applied to the output of a normalized cross correlation based stereomatching algorithm. Using semimetric photography of natural gravel river bed surfaces acquired in the field, digital photogrammetry was used to extract DEMs automatically for use in characterizing surface roughness properties. The procedure for assessing DEM quality involves examination of (i) ortho-images, to provide a qualitative check on stereomatching performance; (ii) DEM collection statistics which quantify the percentage of correctly matched pixels as a function of those interpolated; and (iii) height differences between check points, measured using independent field survey, and corresponding DEM points. The concepts of precision, accuracy and reliability are defined in the context of DEM quality assessment and methods are outlined which can be used to assess these variables. The assessment is conducted for two adjacent stereopairs with similar characteristics, considering the effects of both DEM collection parameters and different lens models upon DEM quality. Results show that digital photogrammetry, in conjunction with independent field survey, can be used successfully for extracting high resolution, small scale DEMs from natural gravel surfaces. Components (i) and (ii) of the quality assessment suggest the need to optimize DEM collection parameters, although the effects of not using a properly specified lens model were minimal at this scale. Method (iii) showed that increasing stereomatching success does not necessarily lead to more accurately estimated DEM points. However, the use of method (iii) remained difficult because of the scale of the photogrammetric application being used; check point positioning within the photogrammetric co-ordinate system was only possible to 2 10 mm which, for a gravel bed surface, was associated with elevation variance of a similar, sometimes greater, magnitude. The next stage of this research will require the use of higher quality check data, possibly from laser profiling.