Application of Digital Photogrammetry to ... - Wiley Online Library

Photogrammetric Record, 16(95): 793–821 (April 2000)

APPLICATION OF DIGITAL PHOTOGRAMMETRY TO COMPLEX TOPOGRAPHY FOR GEOMORPHOLOGICAL RESEARCH By S. N. LANE*, T. D. JAMES* and M. D. CROWELL† University of Cambridge Abstract This paper is concerned with the application of automated digital photogrammetry, using 1:3000 scale photography, to complex, natural landform surfaces, of typical interest to geomorphologists. It assesses the quality of the results obtained using a relatively cheap and readily available area based stereomatching package, in terms of precision, accuracy and external reliability. Precision is investigated with reference to the confidence that can be placed in individual matches. Accuracy is evaluated using specially collected, independent datasets obtained from an area of complex topography in Glen Affric, Scotland. Data collection was stratified to areas of different surface roughness. External reliability is judged with respect to estimates of slope, a key parameter in geomorphological investigations. The results show that, whilst the effects of grid density and vegetation correction are the most important controls upon the accuracy and the external reliability of the photogrammetric results, collection parameters associated with the stereomatching process can also exert some control, particularly in areas of complex topography. It is impossible to generalize rules for choice of optimal collection parameters without careful consideration of the surface under investigation. Given that maximum grid densities are defined by the object space pixel resolution, the paper concludes that surface quality is largely governed by traditional controls upon photogrammetric data quality (camera calibration, base:distance ratio, ground control), combined with either scanning density or digital image resolution. However, over some surfaces, careful consideration has to be given to the effect of matching parameters. KEY WORDS: area based correlation, digital photogrammetry, geomorphology, landform, stereomatching

INTRODUCTION IMPROVEMENTS in measuring and monitoring terrain in three dimensions are critical to geomorphological studies. Photography has been used for some time as a source of data for mapping and interpreting landforms (Collin and Chisholm, 1991) and the *Now at University of Leeds. †Now at Department of Conservation, Hamilton, New Zealand.

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LANE et al. Digital photogrammetry for geomorphological research

extraction of quantitative elevation information from stereophotography under the umbrella term photogrammetry has been shown to have much potential for geomorphological study (Chandler and Cooper, 1988; Chandler and Moore, 1989; Kirby, 1991; Lane et al., 1993, 1994a; Dixon et al., 1998). Some of the earliest applications of photogrammetry were motivated by geomorphology; the past 30 years have seen increasing use of photogrammetry in geomorphological situations, with examples including slope stability (Wickens and Barton, 1971; Fraser, 1983; Chandler, 1989), glaciology (Small et al., 1984; Brecher, 1986), coastal studies (Kidson and Manton, 1973) and river channel studies (Welch and Jordan, 1983; Lane et al., 1994a). Concurrently, developments in photogrammetry, notably the shift to fully automated, digital methods (Brunsden and Chandler, 1996; Pyle et al., 1997; Butler et al., 1998) are making photogrammetry an increasingly cost-effective option (Chandler and Padfield, 1996). Indeed, growing numbers of geomorphologists are making use of these methods, often without the traditional collaboration with trained photogrammetrists which has characterized previous applications of photogrammetry to geomorphology (Small et al., 1984; Lane et al., 1994a; Chandler and Brunsden, 1995). However, as Cooper (1998) argues, the ease with which terrain data may be generated using these developments may have focused attention more on analysis and interpretation of the results acquired, than on issues of data quality. It is critical that geomorphologists give close attention to the various controls on the quality of the results that they obtain, with a view to optimizing the parameters over which they have control during data collection. This approach requires both careful consideration of conventional controls upon photogrammetric data quality (Cooper, 1998), as well as new research into controls upon automated generation of elevation information for geomorphological applications. This paper is concerned with the latter aspect. From the geomorphologist’s perspective, digital photogrammetry automates the final stages of data acquisition with two important consequences. First, numerical algorithms, rather than an operator, become responsible for identifying corresponding points on the stereo-images. The nature of the algorithm and the parameters used by the algorithm may exert an important control upon the quality of the information acquired (Butler et al., 1998). Whilst this is a general problem for all digital photogrammetric applications, it takes on special relevance for the complex terrain surfaces typical of geomorphological applications for two reasons. (1) Where the system is unable to match two points successfully, the software may interpolate. The probability of interpolation will decrease with an increase in surface texture and hence surface roughness. This effect may be countered by an increase in the difference between the view of a particular area recorded in each image, which is likely to increase with surface roughness, hence reducing the level of correlation between images and so increasing the probability of interpolation. However, the acceptability of interpolation for geomorphological research will also vary with roughness. In areas of greater roughness, interpolation will be less effective. As yet there has been little investigation of the interaction of these effects over rough, natural topography. (2) There is wide variety in the sophistication of the typical off-the-shelf software that geomorphologists are using. For instance, in the UK the ERDAS IMAGINE OrthoMAX software has been a popular choice for softcopy digital photogrammetric systems, mainly because the most basic version of the software is available cheaply to universities through the CHEST scheme. Until recently, this program contained a set of single default parameters, which were recommended as global for all surfaces. More recently, ERDAS 794



has provided a number of default parameter sets, with parameters that are optimized to suit particular image qualities and terrain types. More expensive versions of OrthoMAX, in common with other softcopy photogrammetric systems, offer much more sophisticated stereomatching algorithms. However, most geomorphologists are making use of the more basic systems, with area-based stereomatching and little guidance on how to select optimum parameter sets. Second, during manual data collection, the operator has control over the distribution of collected points and can thereby ensure that data coverage reflects the topography of the surface being measured. This facility is critical given that research has shown the importance of including features such as breaklines in representing landform surfaces (Lane et al., 1994a). The cheapest versions of the algorithms used for photogrammetric applications (for example, the CHEST version of the ERDAS system OrthoMAX) collect data over a grid, with a fixed spacing defined by the user. There is no possibility of increasing data collection density locally, nor means of controlling the exact location of points used for data collection, to allow optimal representation of complex topography. However, grid spacing has been shown to exert a significant control upon surface quality (McCullagh, 1998; Lane, 1998). It is also critical to know the maximum grid spacing that still provides adequate surface representation because finer grids are expensive in terms of computational and storage requirements (Zhang and Montgomery, 1994). The aim of this paper is to investigate the quality of a digital elevation model (DEM) obtained using the basic version of a softcopy digital photogrammetric system, in terms of precision, accuracy and external reliability. This research generates guidance for other geomorphologists who may wish to embark upon digital photogrammetric projects primarily using aerial photographs as a data source. The methodological approach employs an extensive benchmark dataset in order to explore the effects of digital photogrammetric controls upon the representation of the terrain surface.

DIGITAL PHOTOGRAMMETRIC CONTROLS ON DEM QUALITY Automation of the data acquisition stage introduces a number of controls on DEM quality that may affect the quality of terrain representation: (i) digital image creation; (ii) stereomatching performance; and (iii) feature representation. Creation of a digital image requires use either of a digital camera or of scanned images. Geomorphologists need to be aware of two main issues: firstly, digital cameras require careful calibration if they are to generate high quality information; and secondly, scanning must minimize image distortion, which inevitably means that expensive photogrammetric scanners are required. These are major concerns that are not considered in this paper, because the research was based upon a calibrated metric camera and images scanned by a high quality photogrammetric scanner. Rather, this paper focuses upon stereomatching performance and feature representation. Stereomatching involves the detection of homologous pixel pairs on two images (Dissart and Jamet, 1995) using either feature-based or area-based algorithms. Feature-based approaches achieve correspondence between structural information in the two images. The information is often extracted using an edge enhancement filter and results in optimal surface representation (for example, implicit inclusion of breaks of slope because they are often easily identifiable structural features) in the smallest amount of time (Kang et al., 1994), provided that the image contains sufficient structural features. Kang et al. showed that natural topography tended to have Photogrammetric Record, 16(95), 2000

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insufficient clearly defined edges for feature-based approaches to be effective. Areabased approaches provide gridded elevation maps to a user-specified density and based upon correlating small windows around each pixel on the grid over one image, with pixels on a second image. Thus, crucial to the matching process is sufficient texture. The process may also involve the epipolar constraint which uses the geometry imposed through the collinearity equations to constrain the position on the second image where the matching algorithm should look, given image co-ordinates of a pixel on the first image (Wolf, 1983). However, the matching procedure also introduces user-controlled parameters which may strongly influence co-ordinate determination. For instance, the user must specify the minimum acceptable correlation between two images for a point to be accepted, rather than ignored and interpolated. The greater the correlation, the higher the confidence that a match is successful. However, decisions over what is an acceptable minimum correlation will not be general, but will depend upon the acceptability of interpolation, which will be a function of surface roughness. In areas where inclusion of a particular point is important, it may be preferable to accept a match with a poorer correlation, rather than to interpolate, despite an increased probability of an incorrect match. Thus, optimum parameter sets need careful consideration when digital photogrammetry is applied to natural topography, and surface roughness is a critical issue. These factors lead into the third important control upon data quality, which concerns feature representation using digital methods. With both analogue and analytical photogrammetry, the operator chooses where to sample points, often aided by viewing a stereomodel of the two images. This method has the important property of allowing the operator to view the landscape in three dimensions, which significantly aids decisions over point location. Some uncertainties over surface representation may be introduced, in particular reproducibility by the same operator (Lane, 1998) or between different operators (Chandler, 1989). When automated digital photogrammetry is area based, with no possibility of representing structural features present in the landscape, point density will affect both surface quality and computational efficiency (Panuska et al., 1991). The density must be high enough to portray accurately the smallest terrain features in the area being modelled. However, the density of data will then be too high in those areas where there is relatively smooth terrain, implying unnecessary data redundancy (Peucker et al., 1978; Petrie and Kennie, 1987; Shearer, 1990; Moore et al., 1991) and computational efficiency. Issues of optimal grid density cannot be considered independently from the object space pixel dimensions, which will exert a lower limit upon the minimum acceptable grid spacing. With area based correlation, the minimum acceptable grid spacing is some multiple of the object space pixel dimensions, which for the algorithm used here is normally about 5 (ERDAS, personal communication). This figure is determined from the precision of the bundle adjustment, combined with some of the extraction parameters. The latter control the spatial extent of the correlation process, which in turn regulates the precision with which individual elevations may be estimated and hence the minimum acceptable grid spacing. The algorithm used in this study is based upon Reduced Resolution Data Sets (RRDSs). This system reproduces the observations of recent studies (Schenk et al., 1991) that human visual systems extract and match edges at various scales. Thus, the algorithm adopts a similar hierarchical approach, where matching is undertaken at progressively finer scales. This procedure reduces the probability of false matches at very disparate elevations and significantly reduces processing time. However, in this algorithm, the resolutions of each RRDS are determined by the object space pixel size, but the user is able to select the spacing at which a DEM is extracted. It follows that for a rough surface, progressive increases in grid spacing should smooth the topography because there is poorer surface representation. 796



METHODOLOGY Stereomatching and feature representation have yet to be investigated for the special case of DEM extraction from complicated geomorphological surfaces. With this in mind, this paper presents results from a field site chosen for this research, just west of Athnamulloch in Glen Affric, Scotland. The site comprised an area of complicated topography, with glacially eroded, Precambrian bedrock, partially buried by poorly drained Holocene peat soils, exceeding 1m in depth in places. The surface contained a range of geomorphologically interesting features, notably dissected peat channels, bedrock exposures, and slopes at angles varying from the horizontal to almost vertical. A DEM of the area was required as part of a wider project concerned with assessing the performance of Pinus sylvestris L. seedlings planted in 1992. The study required a number of parameters which could be derived from a digital elevation model, notably elevation (as a control on peat thickness), aspect (as an indication of exposure) and slope (as a result of its control on peat thickness and drainage). The 900 or so seedlings covering this area remain small (most are , 0·5 m) in height, with other vegetation comprising heather and grass. Assessment Data As a precursor to accepting the DEM, it was decided to investigate the quality of the results that were being generated. The following possibilities for quality assessment were identified: (a) analysis of the quality of the matching process in terms of the correlation statistics provided by the stereomatching algorithm which, because this relates to the confidence that a match is correct, is a measure of precision; (b) analysis of the accuracy of individual spot heights collected photogrammetrically, with reference to an independent dataset; and (c) analysis of the effects of different decisions controlling DEM extraction (that is, parameterization, grid density) upon the representation of key surface parameters (a measure of external reliability). Whilst (a) is the obvious focus of a study which has no data available for an accuracy assessment, the ultimate decisions concerning the design of a photogrammetric survey must focus upon surface accuracy (b) and the effect of operator decisions on the surface parameters required from the study (c). Possibility (b) needed an independent dataset of high quality. This requirement results in a problem common to any study of this kind, that the real surface cannot be known without some form of measurement, which means that the independent dataset may itself be in error. Ultimately, (c) is of most interest to the geomorphologist, because ideally the parameters derived for geomorphological analysis should be independent of decisions made during data collection. This study recognizes that these three issues are inter-related and should be considered collectively in contrast with previous approaches which have tended to view them as separate issues (Lane et al., 1994a). For instance, a critical question is the extent to which an increase in confidence that a successful match has been made translates into an increase in surface quality in terms of its accuracy; because an incorrect match will result in an inaccurate point, a higher probability that a match is correct will also result in a higher probability that the point is accurate. Thus, all three of the above possibilities were considered. For (a), matching parameters were recorded for each DEM collected. For (b), datasets were obtained using a total station and radial observations on to a roving pole from a network of four control points for two scales of analysis. Photogrammetric Record, 16(95), 2000

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(1) At the large scale, data points were obtained at the positions of 867 tagged seedlings. As it is impossible to assess the accuracy of these data points, and there remains the possibility that individual points are in error, these seedlings were surveyed twice, once in 1996 and once in 1997, to provide an estimate of data quality in terms of internal reliability. The results suggested a mean positioning error between the two surveys of 0·120 m and a standard deviation of 0·064 m. The mean error is commensurate with the best possible DEM resolution given the scale of the imagery in use (0·323 m, see below). (2) At the small scale, special high density datasets were collected in 1997 for the three sub-scenes. These datasets were chosen for areas with different topographical characteristics: (i) sub-scene 1: relatively steep topography of low roughness; (ii) sub-scene 2: relatively flat topography, but with channel networks dissected into the peat, and hence relatively rough and complicated topography; and (iii) sub-scene 3: relatively steep topography, but with a large number of bedrock outcrops, so making the surface relatively rough. For (c), the parameter “slope” was chosen, because it is a commonly derived geomorphological variable. DEM Generation DEM generation using digital photogrammetry was based upon 1:3000 scale photographs flown with a 60 per cent overlap using a Carl Zeiss Jena LMK metric camera and panchromatic film. The stereopair was scanned at 25 lm with 256 shades of grey, using a photogrammetric scanner, which resulted in an object space pixel dimension of 0·076 m. Errors introduced at this stage were minimized by the availability of an accurate camera calibration and scanning using a Helava DSW 100 workstation at City University designed for photogrammetric applications of this kind. Photocontrol was provided from a mixture of natural features and specially installed targets, that were located within the same local co-ordinate system used to obtain the data used for accuracy assessment. The fieldwork involved observations from four permanent control points to each photocontrol point; subsequent least squares adjustment was carried out to obtain optimal photocontrol point positions. This procedure resulted in standard errors of approximately 0·001 m, significantly lower than the best precision defined by the scale of the photography. The images were analysed using the ERDAS IMAGINE OrthoMAX module, one of the software systems in common use by geomorphologists. This system uses an area based correlator to identify corresponding points on the two images, from a search for pixels of corresponding contrast and brightness. The approach is hierarchical, with correlations performed at progressively higher resolutions, in which matching at one scale is used to constrain matching at the next scale. For a given resolution and a given point, accurate local models of the terrain produced at a coarser resolution are used to orthorectify patches at evenly spaced elevations above and below the predicted elevation of the point (ERDAS, 1995). At each elevation slice, patches are matched to determine levels of correlation, until a successful match is made. Table I describes and explains the parameters which control this matching process. The first stages of laboratory analysis repeat the parameters associated with conventional photogrammetry, but using scanned imagery. Camera configuration, basic image data and photocontrol data were entered into the program. Interior orientation was used to restore the internal geometry of the camera at the time of exposure using fiducial marks on the imagery. The results gave root mean square errors (RMSEs) of 3·4 lm and 4·1 lm with the two images used in this study, 798


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Parameter

Default value

Minimum threshold

0·6

Minimum correlation coefficient for point acceptance.

Noise threshold

0·4

Minimum correlation coefficient for point consideration.

Minimum precision

0·5

Minimum acceptable estimated precision (in pixels) for a point passing the minimum threshold test.

This parameter determines the precision label assigned to a successful match. A point with a lower value of precision in pixels is more likely to be a correct match. All points are labelled with reference to this parameter as poor, fair or good, with precision bandwidths of 1/3 of the minimum precision. Thus, with minimum precision set at 0·50, a poor point would be one with a precision of 0·33 to 0·50, a fair point would be 0·17 to 0·33 and a good point would be 0 to 0·17. Thus, changing this parameter changes the criterion used to accept points and not the precision of the match per se.

Y parallax allowance

0

Allows the correlator to move around when the bundle adjustment is poor.

If the bundle adjustment is poor, the epipolar constraint may be less effective at identifying search areas for stereomatching. This parameter allows the search window used for stereomatching to wander more, essentially using stereomatching to help to address the problem of a poor bundle adjustment.

Maximum parallax

5

Maximum search range (in pixels of x parallax) around a point.

The previous (coarser) resolution sets the expected elevation and this parameter sets the range of elevations (above and below) considered at this elevation, and hence the number of pixels used for the search for an optimal match.

Minimum template size

7

Smallest (initial) template size (in pixels) used by the area correlator.

Maximum template size

9

Largest (final) template size (in pixels) used by the area correlator.

These minimum and maximum size parameters refer to the size of the correlation window or template. In this algorithm, the template is square. Matching begins using the minimum template size and continues to larger templates if the matching fails for a given template. Smaller templates will increase the precision of the match, but also increase the probability of interpolation if matching is unsuccessful, particularly if the image content is low. Larger templates will tend to smooth topography.

Description

Explanation Minimum and noise threshold parameters define the criterion used in deciding whether or not to accept a match. The minimum threshold default value of 0·6 is thought to correspond to the average correlation for points matched by the human eye (Tateishi and Akutsu, 1992). Increasing the minimum threshold will result in fewer points being accepted and more interpolated. Thus decisions over the most appropriate value for this parameter need to be undertaken with reference to local topography and image quality. Where topographic quality is variable and image quality is poor, it may be sensible to lower these thresholds to increase the number of accepted matches.



TABLE I. Parameters which control stereomatching performance (ERDAS, 1995).

Parameter

Default value

Description

Explanation


Skip factor

2

Minimum spacing of points to accelerate dense collections.

This parameter is used to accelerate automatic collection by reducing the resolution of a given RRDS to reduce the number of correlations necessary.

Edge factor

2·5

Factor which helps to control non-unique correlations which may result in false fixes.

Non-unique correlations may arise where the decision to accept a match is overly based upon a particularly good correlation in one direction. The edge factor considers the error ellipse created by precision determined from multiple directions around a matched point. An elongated ellipse suggests overt dependence upon a single direction and the possibility of non-unique correlation. The edge factor is the ratio of the long and short axes of the ellipse.

Start RRDS

5

Initial image resolution in the hierarchical approach.

Photocontrol data are used to define the initial elevation of the model. Where the elevation range is large, this parameter should be increased.

End RRDS

0

Final image resolution in the hierarchical approach.

This parameter should be set to zero to obtain the highest precision from the process.

Resampling

On

On uses bilinear resampling during orthorectification of patches. Off uses nearest neighbour.

Post processing

On

On performs post processing. Off does not.

This parameter determines whether or not post-processing occurs, including both interpolation and blunder editing.

Rejection factor

1·5

Smoothing factor during post-processing used to reject abnormal spikes or dips in elevation.

This parameter controls blunder editing. During post-processing after each RRDS, each successfully matched point is compared with its nearest neighbours, in an attempt to remove false highs and lows in the dataset. A point elevation is rejected if its elevation value is more than the rejection factor times the standard deviation of neighbouring points different from the average value of surrounding points.


800 TABLE I (continued).


comparable with those obtained with analytical methods using non-scanned imagery (cf. Lane, 1994). Relative and absolute orientation were undertaken to establish the relationship between the two images at the time of exposure, and the image positions and orientations within the object space using a bundle adjustment. Again, the quality of the results obtained was comparable with that obtained using analytical methods. DEM collection was then undertaken using basic sensitivity analysis based upon factor perturbation, which is a standard scientific method (McCuen, 1973; Howes and Anderson, 1988; Lane et al., 1994b) used to explore the effects that decisions made during an analysis (the factors) have upon the results obtained (the estimates) by perturbing values of one factor at a time within the expected range of values of that factor. The factors perturbed are given in Table I, although minimum precision and the minimum and noise thresholds were not perturbed. There are important reasons for this decision. None of these three parameters controls performance of the stereomatching per se, but rather whether or not a point is considered and the matching class to which each considered point is attributed. Further, the y parallax parameter was not perturbed, which can significantly increase processing times, but is only necessary when the results of the bundle adjustment are poor. In this situation, the bundle adjustment results were good and so this parameter was not considered. In addition to the parameters described in Table I, the DEM was also collected at a range of spacings: 0·2 m, 0·323 m (default defined by image scale), 0·5 m, 0·7 m, 1·0 m, 1·3 m, 1·5 m and 2·0 m. DEM Analysis for Accuracy and Reliability Assessments In terms of the accuracy assessment, the closest DEM point to each assessment point was identified by plan form location. For the collection parameter variation, which used the default spacing, the maximum distance between DEM point and assessment point was 0·23 m. As the default spacing was increased to assess grid density effects, this distance increased. Once corresponding points were identified, error was defined as the difference in elevation for each point and this value was used to determine a mean error (ME) and standard deviation of error (SDE) for each surface collected. These parameters were chosen to reflect two different types of data quality. SDE is a measure of precision and is similar in representation to the RMSE, also often used to represent surface quality; both are based upon squared residuals and hence provide information concerning the distribution of residuals either side of a mean value.

O ((p 2 s ) 2 (p 2 s )) n

! O !

SDE 5

i51

n

RMSE 5

i

i

i

n

i

2

5

!

O (p 2 s ) n

i51

i

n

i

2

2 (pi 2 si)2 ,

(pi 2 si)2

i51

n

where pi 5 photogrammetrically acquired elevation and si 5 survey acquired elevation. ME is a measure of accuracy and provides an important indication of any systematic error that could be affecting the dataset. These two parameters were used both to visualize and to assess statistically the effects of collection parameter and grid density variation. A different methodology was required for the slope assessment. The accuracy assessment data are of too low a density to provide an adequate representation of slope. Because there was no knowledge of what the real slope was, the effects of Photogrammetric Record, 16(95), 2000

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changing both collection parameters and grid density were quantified with respect to the default DEM. Slope maps calculated with each collection parameter and grid density were used to calculate both the mean slope (MS) and the standard deviation of slope (SDS) for each sub-scene. In summary, the framework for analysis that is being adopted is shown in Table II. Central to this procedure (and presented in the results) is analysis by individual row to evaluate the effects of decisions made during data collection, broadly in terms of precision, accuracy and reliability. The discussion will evaluate links between rows in Table II, in order to consider: (i) the implications of these results for generating high quality DEMs of complicated terrain surfaces; and (ii) the methodologies suited to geomorphological assessment of surface quality. RESULTS The results are separated into five sections: (i) general DEM quality; (ii) exploration of the effects of parameter variation upon matching performance; (iii) investigation of surface accuracy in terms of both collection parameter and grid density effects; (iv) evaluation of the effects of collection parameter and grid density variation upon slope estimates; and (v) an exploration of whether or not surface quality can be improved by dealing with the vegetation error identified as being important in (i) and (iii). General DEM Quality Figs. 1(a), 1(b), 1(c) and 1(d) show the photogrammetrically acquired DEMs obtained for the fullfield site and the three sub-areas, collected with a 0·32 m spacing as defined by the default grid spacing. These DEMs are clearly encouraging because it is possible to identify geomorphological features such as dissected peat (Figs. 1(a) and 1(c)). Table III summarizes the quality of these DEMs in terms of both the accuracy assessment and the confidence that a particular match is indeed correct. Initial comparison (Fig. 2) suggested a remarkable level of agreement between photogrammetrically acquired and surveyed elevations. However, closer inspection of ME and SDE, often found to be more sensitive determinants of DEM quality (Li, 1992), challenges this conclusion. The full area and two of the sub-areas had consistent mean errors of between 0·3 m and 0·4 m, which are imperceptible with regression analysis in view of the total surface elevation range (30 m). The systematic nature of the ME almost certainly relates to the effects of vegetation height because the surveyed points were measured to the ground surface, whereas the photographs image the vegetation tops. The third sub-area comprised mixed bedrock exposure and vegetation, which explains the lower ME because the bedrock points have no systematic error due to vegetation. The SDE values (Table III) are consistent with the pixel resolution of the imagery. As noted above, the object space pixel resolution was approximately 0·076 m which, with regard to the bundle adjustment and the default collection parameters, suggests a best ground surface resolution of matched points of 0·323 m. Thus, there are basic limits on the quality of DEMs derived from this imagery, defined by the image scale and the scanning resolution. Table III also provides the distribution of points between matching categories (cf. Table I), the latter indicating the confidence that a particular point has been matched correctly. Thus, a “poor” match may be just as accurate as a “good” match, although there is a reduced probability that this is the case. It is clear from Table III that there is a relatively low percentage of “good” matches and hence a low percentage of points that it is possible to be confident are matched correctly. Sub-scenes 2 and 3 have much lower percentages of “good” matches and sub-scene 2 has a higher 802


LANE et al. Digital photogrammetry for geomorphological research TABLE II. The analytical framework adopted in this study in terms of the measures of DEM quality used and the questions that may be answered by using these measures. Controls on DEM quality Measures of DEM quality

Collection parameters

Statistics

Interpretation

Grid density

Matching performance

Distribution of points between matching categories

An assessment of the confidence in individual stereomatched. points (that is, precision).

Does collection parameter variation improve matching performance?

Not relevant.

Surface error

Mean and standard deviation of surface error judged with reference to independently acquired data.

An assessment of surface accuracy.

Does collection parameter variation improve surface accuracy?

Given that high densities are demanding of computer storage space and time, what effect does density variation have upon surface accuracy?

Effects on surface derivative (slope)

Mean and standard deviation of the differences in slope between the calculated and the default DEM.

An assessment of surface reliability from a geomorphological perspective.

What effect does parameter variation have upon surface derivatives?

What effect does grid density variation have upon surface derivatives?

percentage of interpolated points. The latter follows from the more complicated topographies in this area. For instance, the dissected peat channels here were up to 1·0 m deep and up to 0·5 m wide. Post-processing of a surface generated for this area could result in points on the channel margins being rejected, as controlled by the rejection factor (Table I). As the area around the peat channels was relatively flat, the standard deviation of elevations in this area is defined by the scale of the imagery and the averaging introduced during the stereomatching processes; in other words it is defined by the resolution used to collect the data (0·323 m). Thus, a point which happened to fall adjacent to the channel margin would be rejected and replaced by interpolation if its elevation was more different than 1·5 (the rejection factor) times this standard deviation. This value is about 0·45 m, which is less than typical peat channel depths. Immediately, this result suggests that the best possible precision defined by the object space pixel size is too great to allow accurate representation of dissected peat features. Either the scale of the photography or the scanning density needs to be increased. However, the low percentage of “good” matches reported in Table III, and the observation that the rejection factor could be having an important effect, suggests that there is a need to consider whether parameter variation can improve the likelihood that certain points are matched correctly and also improve surface accuracy. DEM Response to Perturbation of Matching Parameters Fig. 3 shows the response of matching quality statistics to changes in DEM parameters for the three sub-scenes. These graphs suggest that the parameter changes resulted in some differences in the distribution between quality classes, most notably Photogrammetric Record, 16(95), 2000

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(a)

(d) (b) (c) FIG. 1. Shaded relief models of the photogrammetrically-acquired DEM for: (a) the full study site; (b) sub-scene 1; (c) sub-scene 2; and (d) sub-scene 3.

804



FIG. 2. A comparison of independently observed survey elevations and DEM elevations estimated using digital photogrammetry.

for the nearest neighbour resampling, skip factor and template size parameters. First, nearest neighbour resampling resulted in a significant reduction (p , 0·05) in the numbers of interpolated points and poor matches with a corresponding increase in the number of fair matches. This effect is perhaps surprising because ERDAS (1995) suggests that use of nearest neighbour resampling as opposed to bilinear resampling should only make a difference where image content or quality is low. Second, increasing the skip factor resulted in an increase in the number of interpolated points, which is not surprising because increasing the skip factor is designed to increase the

TABLE III. Assessment of DEM quality in terms of accuracy and precision using default DEM.

Site

Adjusted R2

Mean error (m)

Standard deviation of error (m)

% of points “good“

% of points “fair”

% of points “poor”

% of points interpolated

Full site Sub-scene 1 Sub-scene 2 Sub-scene 3

99·96 98·32 92·73 99·87

0·320 0·310 0·364 0·187

0·154 0·165 0·187 0·193

16·7 25·7 17·9 15·2

40·9 27·9 32·0 42·2

31·6 35·2 35·4 31·8

10·8 11·2 14·7 10·8


805

LANE et al. Digital photogrammetry for geomorphological research (a)

(b)

(c)

FIG. 3. The response of matching quality statistics in response to changes in DEM collection parameters for (a) sub-scene 1; (b) sub-scene 2; and (c) sub-scene 3.

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speed of automatic data collection by reducing the resolution of the RRDS used in stereomatching (Table I). This procedure increases the possibility that points are incorrectly matched, do not meet the matching criterion for point selection, and are interpolated. Third, increasing template size results in a reduction in the number of fair matches, with large increases in the number of good matches, and smaller increases in the number of poor matches. Template size (Table I) controls the spatial extent of the area correlator. Larger search areas should result in increased confidence that a match is successful, so explaining the increase in the number of good matches. The increase in the number of poor matches is surprising, but perhaps explained by the nature of the topography in this study. Complicated topography will increase the potential for discrepancies in what the two images see and increasing template size may increase the potential that such discrepancies affect a match, so reducing matching quality. Surface Accuracy and Collection Parameter and Grid Density Variation Despite the changes in matching quality identified above, ME and SDE were found to be markedly insensitive to parameter variation. Only one parameter change (reducing the start RRDS) for one sub-scene (3) resulted in a statistically significant change in ME (p , 0·05). All other parameter changes for all other sub-scenes resulted in very small changes in ME ( 1 2 0·02 m). This observation was further supported by consideration of the accuracy associated with each matching class for DEMs acquired using the default matching parameters. If matching class was associated with statistically distinguishable values of ME or SDE, then changes in matching class should result in changes in ME. However, for both the large scale dataset and the three sub-scene datasets, it was not possible to identify significant differences (p 5 0·05) between MEs or SDEs for points in different matching classes. This was not the case for grid density variation. Fig. 4 shows the response of ME and SDE to changes in spacing. Whilst it is clear that there is no consistent change in mean error with grid spacing (Fig. 4(a)), there is a consistent change in the standard deviation of error (SDE) (Fig. 4(b)), which increases with grid spacing. Between 0·32 m and 0·20 m, as expected, there is no reduction of SDE, because the grid spacing has reached the limits imposed by the combined effects of object space pixel size and area based correlation. Above 0·32 m, there is a slow increase in SDE, much greater for sub-scene 3 than either sub-scenes 1 or 2, and reflecting the greater topographic variability associated with this area. The major increase in SDE occurs between 1·5 m and 2·0 m for sub-scenes 1 and 3, but sub-scene 2 shows a rather anomalous reduction in SDE. The data upon which this point is based were checked and no mistakes identified. It is clear from this analysis that progressive increases in grid spacing result in a progressive degradation of DEM quality, although this effect is largely expressed as increases in SDE rather than changes in ME. The reason for this result may be the dominant effect of the vegetation error upon surface accuracy statistics. External Reliability and Collection Parameter and Grid Density Variation One of the most effective means of addressing problems of DEM error is to consider its propagation through to estimates of parameters derived from the DEM (Wise, 1998). Fig. 5 shows the mean (MS) and standard deviation (SDS) of slope estimates in response to perturbation of DEM collection parameters for each of the three sub-scenes. With the exception of the post-processing parameter, Fig. 5 suggests relatively little response of either MS or SDS to parameter perturbation. However, statistical tests suggest that it is possible to distinguish (at p , 0·05) between the default MS and the MS of some of the surfaces for some of the sub-scenes after a Photogrammetric Record, 16(95), 2000

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(b)

FIG. 4. The response of mean error (ME) and standard deviation of error (SDE) to changes in grid spacing.

parameter change. In sub-scene 1, this is the case for: nearest neighbour resampling; reduction in the rejection factor; increase in the skip factor; and removal of postprocessing. In sub-scene 2, this is the case for all of these procedures plus: increasing the template size; decreasing the maximum parallax; and increasing the rejection factor. In sub-scene 3, this is the case for reduction in the rejection factor and removal of post-processing only. Further, the MS and SDS parameters hide the large number of points whose slopes do change in response to matching parameter perturbation. Fig. 6 is based upon an assumption that slopes should be accurate to within 1 2 10 per cent. Somewhat surprisingly in view of Fig. 5, sub-scene 3 witnesses larger percentages of points changing by 1 2 10 per cent, reaching almost 25 per cent when postprocessing is switched off. In all sub-scenes, changes in template size, use of nearest neighbour resampling and increasing the skip factor also seem to have important effects. A number of points of interest follow from these observations. First, removal of post-processing has a significant effect upon slope estimates for all sub-scenes, but 808



FIG. 5. The response of mean slope (MS) and standard deviation of slope (SDS) to parameter perturbation, for sub-scenes 1, 2 and 3.

most notably sub-scene 3. This is the sub-scene with a large number of bedrock pixels adjacent to vegetation which, in an uncorrected DEM, could produce large elevation changes between adjacent pixels. Post-processing could result in replacement of these pixel elevations with interpolated ones, which could produce large slope changes. This response might also be expected to be manifest when the rejection factor is decreased, but this does not seem to be the case. Second, there seems to be some

FIG. 6. The percentage of points showing a slope change greater than 1 2 10 per cent in response to parameter perturbation, for sub-scenes 1, 2 and 3.


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difference in the response of areas with different surface characteristics to perturbation of matching parameters. From considering Figs. 5 and 6, sub-scene 1 witnesses small differences in slope as compared to sub-scenes 2 and 3. Sub-scene 2 does not seem to be very different from sub-scene 1 in Fig. 6, even though there were more parameter changes that resulted in significant differences in MS with respect to the default MS. This response suggests that sub-scene 2 was dominated by a larger number of relatively small slope changes. From Fig. 6, sub-scene 3 is associated with relatively larger slope changes than sub-scene 2, even though fewer MS differences were significant. This result suggests that parameter changes in sub-scene 3 tended to be dominated by larger slope changes, albeit to a relatively smaller number of points. The two sub-scenes seem to be responding in different ways to parameter changes and, again, consideration of the special nature of the topography associated with each area helps to understand these responses. For instance, in sub-scene 2, which contained the dissected peat topography, inspection of the spatial pattern of slope changes that are greater than 1 2 10 per cent shows how these changes are almost entirely constrained to the dissected peat network. This effect is illustrated in Fig. 7, which shows the distribution of 1 2 10 per cent slope changes when the template size is increased to 9/11. The result makes sense. Increasing this parameter should smooth the topography (Table I) and such smoothing will have greatest effect where topographic variability is greatest, close to the peat networks. One recommendation that might follow from this result is that the template size should be as low as possible

FIG. 7. The distribution of points in sub-scene 2, with a slope change 1 2 10 per cent, when the template size is increased to a minimum of 9 and a maximum of 11.

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over this sort of complicated topography and reduction of the template size to 6/8 (that is, lower than the default) does increase MS (Fig. 5), if not significantly. However, as the precision of the match will generally be lower with smaller template sizes (Table I), reducing this value will also increase the probability that a match is declared as unacceptable and is interpolated. In an area dominated by dissected peat features, interpolation is problematic because it will also have the effect of smoothing the topography. Thus, reducing template size has contradictory effects, dependent upon the exact spatial distribution of point elevations in this surface. Two points of interest follow from this specific case. Firstly, joint variation of collection parameters will be necessary to investigate this effect further. Secondly, very careful evaluations of the effects of parameter variation are required, with respect to the parameter being varied, characteristics of the surface being considered and the effects of this response upon external parameters (for example, slope) which seem much more sensitive to parameter changes than ME or SDE. Optimal parameter sets, if they exist, will be surface specific, and involve multiple parameter sets for optimal surface representation. As with ME and SDE, the effects of changes in grid density upon slope estimates are significantly greater than the effects of matching parameter variation. Fig. 8 shows changes in MS, SDS and maximum and minimum values of slope for different grid densities for the three sub-scenes. All grid densities are significantly different (p , 0·05) from the default collection density. Similarly, Fig. 8 shows how the effect of grid density changes is progressively to suppress the variance of the slope estimates, reflected in a declining standard deviation (Fig. 8(a)) and even more rapid decline in maximum slope value for each sub-scene (Fig. 8(b)). These changes occur systematically in all sub-scenes. However, it is notable in Fig. 8(a), that the roughest surface (sub-scene 2) sees the steepest and largest reductions in both MS and SDS. Compared with the effects of matching parameter changes, reduction in grid density seems to have a much greater effect upon surface representation, perhaps with the exception of the effects of post-processing. Vegetation Correction and DEM Quality The observations above suggest that there are fundamental limitations upon surface quality due to the effects of vegetation. If vegetation height was uniform everywhere, parameters derived from the DEM would not be affected. Unfortunately, this is not the case. In this study site there were areas of unvegetated peat and bedrock, which have the potential significantly to distort estimates of slope at boundaries between vegetated and unvegetated areas. The typical vegetation height measured in the field was about 0·30 m, of a comparable magnitude to the minimum possible grid spacing (0·32 m). For bedrock pixels adjacent to vegetation pixels where the real ground surface is horizontal, slope value estimates could be as high as 45°, even where the actual ground surface slope is zero, although it should be minimized by the effects of post-processing and the rejection factor. Turning post-processing off, or increasing the rejection factor, should result in the retention of this sort of variability (Table I). From a geomorphological perspective, this type of error, if it is not removed by post-processing, is unacceptable. With this consideration in mind, point specific vegetation height was measured during collection of the survey data from sub-scene 3 and was added to each of the total station survey point elevations. When these corrected elevations were compared with the photogrammetrically acquired DEM, a significant reduction (p , 0·05) in mean error (0·187 m to – 0·030 m) resulted and no significant change (p 5 0·05) in standard deviation of error (from 0·193 to 0·177). This result confirms the conclusion made above that a vegetation effect explains most of the mean error. The slightly Photogrammetric Record, 16(95), 2000

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(b)

FIG. 8. Changes in mean (MS) and standard deviation of slope (SDS) (a) and maximum slope (b) in sub-scenes 1, 2 and 3 in response to changes in DEM spacing.

negative mean error implies that what the images view is slightly below the top of the vegetation canopy, as would be expected with regard to its variable cover and density within a typical object space pixel resolution (0·07 m). One of the advantages of digital methods is that they may provide a basic way of correcting the photogrammetrically acquired DEM using image analysis. For this purpose, a supervised, binary classification of vegetated (1) and unvegetated (0) areas was undertaken using an orthorectified image (Fig. 9). The classification was multiplied by the mean vegetation height estimated from distributed measurements made in the field and this vegetation height surface was then subtracted from the original photogrammetrically acquired DEM. This process resulted in a reduction of the ME for the full study site from 0·32 m to 0·18 m. It does not seem to reduce to zero, no doubt because the vegetation cover characteristics of sub-scene 3 are different from 812



FIG. 9. The binary classification of the orthophotograph into vegetated (black) and unvegetated (white) areas.

the vegetation as a whole. It is possible to investigate the effects of this process upon slope estimates. The largest change in slope due to a vegetation correction was 17° and, as Fig. 10 shows, the frequency distributions of slopes for corrected and uncorrected DEMs are relatively similar. This result is not surprising because, in the uncorrected DEM, post-processing may have eliminated most of the erroneous slopes introduced at vegetation/bedrock boundaries, such that vegetation correction effects Photogrammetric Record, 16(95), 2000

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FIG. 10. Plot of frequency distributions for slopes for the uncorrected and vegetation corrected DEMs.

are suppressed. Indeed, there was a strong association (p , 0·05) between those points on vegetation bedrock boundaries and those points that were interpolated. DISCUSSION These results have made some progress towards identifying key controls upon DEM quality; summary answers to the questions raised in Table II are shown in Table IV. Five key points of interest can be identified. First, whilst DEM collection parameters do control the confidence that can be placed in individual matches, parameter variation seems to have much less effect upon surface accuracy compared with grid density variation and vegetation correction. This finding has important methodological implications. It questions the use of internal stereomatching quality parameters per se for formulating decisions over DEM quality. Independent measures of surface accuracy suggested that whilst some statistics (for example, correlation and regression statistics) were not useful for DEM evaluation, the mean and standard deviation of error were useful, both for DEM evaluation and for identifying the procedures necessary to improve DEM quality. ME and SDE provided different types of information. Both are properties of the surface, as opposed to individual points, and correspond to surface accuracy and surface precision respectively. Thus, they are different in nature to the internal stereomatching quality results which refer to individual point precisions. This analysis suggests that whilst it may be possible to be more confident about a point whose match is classified as “good”, this increase in confidence does not manifest itself at the level of the surface in terms of an improvement in surface accuracy. Thus, whilst it is important to attempt to maximize the number of “good” matches, as there is a higher probability that a “good” match is a correct match, evaluations of surface quality need to make direct reference to surface accuracy. Second, the key control upon surface accuracy is the design of the photogrammetric survey (camera calibration, scanning resolution and quality, image scale, photocontrol) and not procedures associated with the stereomatching aspects of data collection. This conclusion is reflected in this study in SDE values of 0·15 m to 0·20 m, which could not be reduced through parameter variation, use of higher grid 814


LANE et al. Digital photogrammetry for geomorphological research TABLE IV. A summary of the results obtained with reference to the questions asked in Table II. Controls on DEM quality Measures of DEM quality

Interpretation

Collection parameters

Grid density

Matching performance

Precision.

Collection parameter variation does result in some changes in matching performance.

Not relevant.

Surface error

An assessment of surface accuracy.

Surface accuracy is relatively insensitive to collection parameter variation.

Mean surface error is insensitive to changes in grid density, largely due to the over-arching importance of other controls (for example, vegetation), but the standard deviation of error does decrease for higher grid densities reflecting a progressive smoothing of topography.

Effects on surface derivative (slope)

An assessment of surface reliability.

Consideration of surface reliability suggests that matching parameters do have important effects, but these may be local, which is why surface accuracy, based upon datasets of a relatively small sample size, is not a useful indicator of their effects and is specific to the geomorphological characteristics of particular surfaces.

Slope parameters were particularly sensitive to changes in grid density.

densities or vegetation correction. The SDE will be most closely related to the object space pixel resolution, and hence image scale and scanning resolution. The effects of changes in slope parameters in response to grid density variation, which were considerable, even with small changes in grid density (Fig. 8) further support this conclusion. Moreover, this investigation emphasizes the importance of effective survey design which, in combination with scanning resolution, determines the object space pixel size and hence the highest density at which a DEM can be extracted. From basic analysis of the object space pixel resolution, it is possible to obtain a clear recommendation for the best possible data resolution and, if data are collected at this density, a basic estimate of probable surface accuracy. This facility is true provided that a camera calibration is available, the images are scanned using a high quality scanner, appropriate photocontrol is available, and the internal and external orientation stages are successful. Some collection parameter variation may be useful to increase the number of “good” matches, but this procedure is unlikely to affect surface quality significantly. The fact that this conclusion is valid for three sub-scenes with different surface roughnesses helps to affirm its generality for the complicated, natural topography typical of geomorphological investigations. These evaluations also have implications for the analysis of archival imagery. Image scale and maximum scanning densities (limited by data handling problems) will define the quality of the information acquired, even if it is possible to measure appropriate photocontrol, engage in camera self-calibration and use a high quality photogrammetric scanner. Photogrammetric Record, 16(95), 2000

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Third, the above findings may only be valid for accuracy when it is judged at the level of the whole surface. For instance, there was no evidence of changes in surface accuracy in response to changing the percentage of interpolated points, as might be expected for the roughest surface (sub-scene 2), where interpolation is likely to be most serious; Fig. 3(b) shows a significant reduction in the percentage of interpolated points for this sub-scene, but no statistically significant changes in ME or SDE. However, when considering slope estimates, the conclusion is somewhat different. Evaluation of slope estimates involved consideration of all pixels within the DEM, rather than the sub-set used for accuracy assessment, which was defined by field sampling. Thus, whilst the full area DEM comprised 460 382 pixel elevations, the dataset used for accuracy assessment comprised only 634 points or 0·14 per cent of these pixels. In view of the results shown in Fig. 6 which suggested that a small number of points may be associated with quite large slope or elevation changes, the probability of detecting these changes in the accuracy assessment data is low. If the accuracy assessment points are randomly distributed across the study area, and if 5 per cent of points show slope changes greater than 1 2 10 per cent, only about 30 of the accuracy assessment points should show large elevation changes. Without more advanced means of acquiring high quality data at a much greater density, surface accuracy assessment may be of little use in judging collection parameter effects. Fourth, and in marked contrast to the conclusions reached about surface accuracy effects, consideration of how slope estimates respond to collection parameter variation showed how certain parameters could result in quite major changes to local slope values. There was clear evidence of different types of surface behaviour in relation to different parameter changes and different surface roughnesses. Whilst there was some association between changes in matching category and slope estimates in response to collection parameter perturbation, recommendations for optimal parameter sets had to be made with respect to characteristics of the specific surface under consideration. Further, optimizing the number of good matches could actually downgrade surface representation. This effect was illustrated by increasing the template size parameter, which resulted in a large increase in the number of “good” matches in sub-scene 2. However, because this sub-scene was an area with sharp breaks of slope associated with the dissected peat network, and taking account of the fact that the slope estimates changed significantly along the breaks of slope in response to increase of this parameter, the extent to which this increase in good matches is an optimal solution can be questioned. Increasing template size increases the spatial extent over which the area correlator is applied and hence over which averaging takes place. As noted above, smaller template sizes may produce a better estimation of pixel elevations in the vicinity of breaks of slope as a result of a reduction in the area over which correlation takes place, even if this will necessarily downgrade the precision of those estimations. This result suggests that parameter changes need to be undertaken with care, giving close consideration to their meaning as part of the stereomatching process, as well as with reference to the particular surface being studied. Where surface roughness is spatially variable, parameter changes may need to reflect this variation. Automation of this type of procedure could be achieved, through the use of surface analysis to vary parameter changes, and this possibility should be the subject of further research. Joint, simultaneous parameter changes may also be required, a subject that merits further investigation. In the meantime, it may be necessary to return to the more traditional approach of using stereovision to view stereopairs and to assess individual points in the critical areas of topography which have the most complex nature. More advanced (and expensive) versions of the ERDAS software provide a facility for this form of blunder editing. Finally, the vegetation classification suggested that basic improvements in DEM accuracy and the reliability of parameters estimated from the surface may follow from 816



a simple procedure that combined image classification with additional field knowledge. In this study, there was a relatively uniform and low level vegetation cover. Unfortunately, the potential for vegetation correction may be limited in geomorphological applications that do not have these ideal situations. This conclusion in turn links into questions about the best time to acquire imagery (the low growth period of the year), as well as the optimal density of data collection. Maximum possible grid densities may be limited by the error that results from vegetation effects, rather than the maximum possible density defined by the object space pixel size. This finding was reflected by the insensitivity of ME to changes in grid spacing and emphasizes the important limitations that surface cover can have upon surface accuracy. This effect is particularly strong where the cover is variable, because it can significantly distort parameters that are estimated from the DEM. It was also clear that there were interactions between collection parameter variation and slope estimates. In particular, it was quite surprising to find that slope estimates were relatively insensitive to vegetation correction; this effect was shown to be the result of post-processing which, as a result of defining points at bedrock/vegetation boundaries to be incorrect and replacing them by interpolated points, had removed these largely erroneous slopes. In this situation, interpolation enhances DEM quality. CONCLUSION The results from this research are particularly encouraging in terms of the use of a basic off-the-shelf digital photogrammetric system for geomorphological research. They suggest that provided proper attention is given to design of the photogrammetric survey, the accuracy of the results obtained is limited by image scale and scanning density, and it is relatively predictable. Automation of the data collection process introduces new uncertainties and initial consideration suggested that these have little effect upon surface accuracy. However, there was evidence that surface accuracy statistics were not necessarily reliable bases upon which to reach this conclusion, because they were derived from a relatively small database with respect to the density of the generated DEM. Alternative measures of DEM quality and, notably, consideration of the reliability of an external parameter (slope), provided a much more effective way of assessing DEM quality. This comment does not challenge the basic conclusion that proper photogrammetric design, as with traditional photogrammetry, is the key control upon the quality of the data that is obtained. However, it does suggest that the introduction of automated matching procedures can create major uncertainties over surface representation in areas of more complicated topography; whilst matching parameter changes had little effect upon derived parameters in areas of relatively smooth topography, this was not the case over more complicated topography. In such areas, it was not possible to endorse the recommendation that optimization of the number of matches deemed “good” in terms of precision was a sensible collection strategy. Rather, parameter perturbation needs to be undertaken carefully and with respect to the surface being analysed. Further research is required for a range of different landform surfaces, at a number of different spatial scales, to investigate whether or not it is possible to produce optimal parameter sets for particular types of topographic characteristics. Such an approach might use image analysis to identify the type of surface that is being studied and hence the optimal approach to DEM extraction. Similarly, more sophisticated software (for example, PCI Geomatics) already allows combined feature based and area based matching, a procedure that will almost certainly improve surface representation in the same way that traditional photogrammetric data collection methods often combined collection of low density grids with surface breaklines to achieve an optimal surface representation (Lane, 1998). Photogrammetric Record, 16(95), 2000

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Decisions over an acceptable grid density had to be made with reference to the precision defined by the scale of photography and image resolution, the topographic variability of the surface being mapped, computational (time and space) efficiency, and the intended use for the data. One of the real advantages of geomorphologists becoming increasingly involved in digital photogrammetric methods is that they have much more control over the parameters used, so increasing the ease with which data quality can match demands of the data. As Fryer et al. (1994), McCullagh (1998) and Wise (1998) show, there remain considerable problems with the use of digital data supplied by organizations when those data were not necessarily intended for the purposes to which they are being put. In some senses, the results in this paper are encouraging, suggesting that it is possible to obtain high quality DEM representations with little need for concern over new issues introduced by the stereomatching process. However, for rough natural topography, further research is required to improve understanding of how area-based correlators work, notably in the vicinity of breaks of slope. ACKNOWLEDGEMENTS This research was supported by the Association of Commonwealth Universities and a grant from the 20th International Geographical Congress Fund. The Forestry Authority kindly provided vehicle access. J. H. Chandler and M. J. Gooch (Loughborough University) and an anonymous reviewer provided constructive comments upon an earlier draft of this paper. REFERENCES BRECHER, H. H., 1986. Surface velocity determination on large polar glaciers by aerial photogrammetry. Annals of Glaciology, 8: 22–26. BRUNSDEN, D. and CHANDLER, J. H., 1996. Development of an episodic landform change model based upon the Black Ven mudslide, 1946–1995. Chapter 40 in Advances in hillslope processes (Eds. M. G. Anderson and S. M. Brooks). Wiley, Chichester. 1306 pages: 869–896. BUTLER, J. B., LANE, S. N. and CHANDLER, J. H., 1998. Assessment of DEM quality characterizing surface roughness using close range digital photogrammetry. Photogrammetric Record, 16(92): 271–291. CHANDLER, J. H., 1989. The acquisition of spatial data from archival photographs and their application to geomorphology. Unpublished PhD. thesis, City University, London. 300 pages. CHANDLER, J. and COOPER, M., 1988. Monitoring the development of landslides using archival photography and analytical photogrammetry. Land and Minerals Surveying, 6(11): 576–584. CHANDLER, J. H. and MOORE, R., 1989. Analytical photogrammetry: a method for monitoring slope instability. Quarterly Journal Engineering Geology, 22(2): 97–110. CHANDLER, J. H. and BRUNSDEN, D., 1995. Steady state behaviour of the Black Ven mudslide: the application of archival analytical photogrammetry to studies of landform change. Earth Surface Processes and Landforms, 20(3): 255–275. CHANDLER, J. H. and PADFIELD, C. J., 1996. Automated digital photogrammetry on a shoestring. Photogrammetric Record, 15(88): 545–560. COLLIN, R. L. and CHISHOLM, N. W. T., 1991. Geomorphological photogrammetry. Ibid., 13(78): 845–854. COOPER, M. A. R., 1998. Datums, coordinates and differences. Chapter 2 in Landform monitoring, modelling and analysis (Eds. S. N. Lane, K. S. Richards and J. H. Chandler). Wiley, Chichester. 454 pages: 21–36. DISSART, O. and JAMET, O., 1995. 3D reconstruction of buildings from stereo-images using both monocular analysis and stereomatching: an assessment within the context of cartographic production. Proceedings of the Society of Photo-Optical Instrument Engineers, 2486: 255–266. DIXON, L. F. J., BARKER, R., BRAY, M., FARRES, P., HOOKE, J., INKPEN, R., MEREL, A., PAYNE, D. and SHELFORD, A., 1998. Analytical photogrammetry for geomorphological research. Chapter 4 in Landform monitoring, modelling and analysis (Eds. S. N. Lane, K. S. Richards and J. H. Chandler). Wiley, Chichester. 454 pages: 63–94. ERDAS, 1995. IMAGINE Version 8.2: OrthoMAX user’s guide. Vision International and Manchester Computing. FRASER, C. S., 1983. Photogrammetric monitoring of Turtle Mountain: a feasibility study. Photogrammetric Engineering & Remote Sensing, 49(11): 1551–1559.

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Re´sume´ Cet article traite de l’application de la photogramme´trie nume´rique automatique a` l’e´tude de la surface de formes naturelles complexes qui inte´ressent particulie`rement les geómorphologues, en utilisant des photographies a` l’ećhelle de 1:3000. On y e´value la qualite´ des re´sultats obtenus, en termes de prećision, d’exactitude et de fiabilite´, en utilisant un ensemble de logiciels relativement bon marche´, aise´ment disponibles et base´s sur l’appariement ste´reóscopique des surfaces. On estime la prećision en se re´fe´rant a` la confiance que l’on peut placer dans les appariements individuels. On e´value l’exactitude en se servant d’un jeu de donneés saisies spećialement et de façon inde´pendante, tireés d’une zone du Glen Affric, en Ecosse, dont la topographie est complexe. On a stratifie´ la saisie des donneés en diffe´rentes zones, selon leur rugosite´ de surface. On a pu juger de la fiabilite´ en se re´fe´rant aux estimations de la pente, parame`tre-cle´ dans les recherches geómorphologiques. Les re´sultats montrent que, si la densite´ de la grille et la correction de la ve´ge´tation restent les points les plus importants qui influent sur l’exactitude et la fiabilite´ des re´sultats photogramme´triques, les parame`tres de saisie associe´s au processus d’appariement ste´reóscopique peuvent exercer e´galement quelque influence, notamment dans les zones a` topographie complexe. Il est impossible de geńe´raliser des re`gles dans le choix des parame`tres optimaux de saisie, sans tenir compte attentivement de la nature de la surface objet de l’e´tude. Etant donne´ que les densite´s maximales de grille sont de´finies par la tache`le (re´solution en pixel dans l’espace objet), on conclut dans cet article que la qualite´ de l’e´tude de la surface est en grande partie de´termineé par les parame`tres classiques qui re´gissent la qualite´ des donneés photogramme´triques (e´talonnage de la came´ra, rapport base/hauteur, canevas d’appui au sol), associe´s soit a` la densite´ du balayage du cliche´, soit a` la re´solution de l’image nume´rique. Toutefois, sur certaines surfaces, une attention particulie`re doit eˆtre donneé aux effets des parame`tres d’appariement. Zusammenfassung Dieser Beitrag bescha¨ftigt sich mit der Anwendung der automatisierten Digitalphotogrammetrie, wobei Bilder im Maßstab 1: 3000 fu¨r komplexe, natu¨rliche Gela¨ndeoberfla¨chen, die fu¨r Geomorphologen von typischem Interesse sind, verwendet werden. Die Qualita¨t der gewonnenen Ergebnisse wird unter Verwendung eines relativ billigen und leicht verfu¨gbaren Programmes zur Stereokorrelation von Fla¨chen bewertet, wobei Pra¨zision, Genauigkeit und externe Zuverla¨ssigkeit als Testparameter dienen. Die Pra¨zision wird mit Hilfe der Konfidenz fu¨r einzelne Korrelationen bewertet. Die Genauigkeit wird unter Benutzung speziell gewonnener unabha¨ngiger Datensa¨tze von einem Gebiet mit komplexer Topographie in Glen Affric, Schottland, abgescha¨tzt. Die Datenerfassung wurde nach Gebieten unterschiedlicher Oberfla¨chenrauheit sortiert. Die externe Zuverla¨ssigkeit wird unter Beru¨cksichtigung von Scha¨tzungen der Gela¨ndeneigung, die fu¨r geomorphologische Untersuchungen ein Schlu¨sselparameter ist, bewertet. Die Ergebnisse zeigen, dass die Erfassungsparameter, die mit der Stereokorrelation verbunden sind, auch einen Einfluss ausu¨ben ko¨nnen, besonders in Gebieten mit komplexer Topographie, wenn auch die Einflu¨sse von Gitterdichte und Vegetationskorrektur die wichtigsten Einflussgro¨ßen 820



auf Genauigkeit und externe Zuverla¨ssigkeit der photogrammetrischen Ergebnisse sind. Es ist unmo¨glich, Regeln zur Auswahl optimaler Erfassungsparameter zu verallgemeinern, ohne die zu untersuchende Oberfla¨che sorgfa¨ltig zu beachten. Nimmt man als gegeben an, dass die maximalen Gitterdichten von der Pixelauflo¨sung im Objektraum definiert werden, so wird im Artikel geschlussfolgert, dass die Oberfla¨chenqualita¨t hauptsa¨chlich von den traditionellen Einflu¨ssen auf die photogrammetrische Datenqualita¨t abha¨ngen (Kammerkalibrierung, Basisverha¨ltnis, Gela¨ndepasspunkte), und zwar in Kombination mit entweder der Abtastdichte oder der digitalen Bildauflo¨sung. Jedoch sollte bei einigen Oberfla¨chen dem Einfluss der Korrelationsparameter sorgfa¨ltige Beachtung geschenkt werden.


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