Nicholas J. Tate

Causes and consequences of error in digital elevation models

All digital data contain error and many are uncertain. Digital models of elevation surfaces consist of files containing large numbers of measurements representing the height of the surface of the earth, and therefore a proportion of those measurements are very likely to be subject to some level of error and uncertainty. The collection and handling of such data and their associated uncertainties has been a subject of considerable research, which has focused largely upon the description of the effects of interpolation and resolution uncertainties, as well as modelling the occurrence of errors. However, digital models of elevation derived from new technologies employing active methods of laser and radar ranging are becoming more widespread, and past research will need to be re-evaluated in the near future to accommodate such new data products. In this paper we review the source and nature of errors in digital models of elevation, and in the derivatives of such models. We examine the correction of errors and assessment of fitness for use, and finally we identify some priorities for future research.

Spatial Scale Problems and Geostatistical Solutions: A Review

The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re-scale data prior to subsequent integration, but few tools with which to implement the re-scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re-scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re-scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re-scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized.

Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data.

A terrestrial laser scanner was used to take four scans of an area of trees, approximately 480 m2 in area, within a coniferous tree stand situated in Leicestershire, UK. A number of measurements were extracted from the point cloud and compared with field measurements. Automatic stem recognition was achieved for all stems except those at the edge of the study plot. From the locations of detected stems, diameter at breast height (DBH) was measured with two least-squares shape-fitting algorithms and a circular Hough transformation method; the results were then compared with field measurements. The root mean squared error (RMSE) for DBH measurement from the laser scanner was found to be in the range 0.019–0.037 m, using three measures. Stem density (1031 stems ha−1) and basal area (73 m2 ha−1) were also measured with reasonable accuracy. Estimation of tree volume was not as successful, in contradiction to previous research, as upper diameters and heights of trees could not be measured. This was probably a result of previous research being focused on low-density forest stands. This study presents an assessment of laser scanning capabilities in a forest environment with high (1000 stems ha−1) stand density, and finds automation of the analysis to yield some important tree and stand variables to be very effective.

Refining Population Surface Models: Experiments with Northern Ireland Census Data

This paper compares and contrasts alternative methods for the construction of discontinuous population surface models based on the census and remotely sensed data from Northern Ireland. Two main methods of population distribution are employed: (1) a method based on redistribution from enumeration district (ED) and postcode centroids, and (2) a method based on dasymetric redistribution of ED population counts to suitable land cover zones from classified remotely sensed imagery. Refinements have been made to the centroid redistribution algorithm to accommodate an empirical measure of dispersion, and to allow redistribution in an anisotropic form. These refinements are evaluated against each other and the dasymetric method. The results suggest that all of the methods perform best in urban areas, and that while the refinements may improve the statistical performance of the models, this is at the expense of reduced spatial detail. In general, the techniques are highly sensitive to the spatial and population resolution of the input data.

A fuzzy c-means classification of elevation derivatives to extract the morphometric classification of landforms in Snowdonia, Wales

The increasing global coverage of high resolution/large-scale digital elevation data has allowed the study of geomorphological form to receive renewed attention by providing accessible datasets for the characterisation and quantification of land surfaces. Digital elevation models (DEMs) provide quantitative elevation data, but it is the characterisation and extraction of geomorphologically significant measures (morphometric indices) from these raw data that form more informative and useful datasets. Common to many geographical measures, morphometric measures derived from DEMs are dependent on the scale of observation. This paper reports results of employing a fuzzy c-means classification for a sample DEM from Snowdonia, Wales, with a number of morphometric measures at different resolutions as input, and morphometric classification of landforms at each resolution as output. The classifications reveal that different landscape components or morphometric classes are important at different resolutions, and that morphometric classes exhibit resolution dependency in their geographical extents. Examination of the scale dependency and behaviour of morphometric classifications of landforms at different resolutions provides a fuller and more holistic view of the classes present than a single-scale analysis.

Manual mapping of drumlins in synthetic landscapes to assess operator effectiveness

Mapped topographic features are important for understanding processes that sculpt the Earths surface. This paper presents maps that are the primary product of an exercise that brought together 27 researchers with an interest in landform mapping wherein the efficacy and causes of variation in mapping were tested using novel synthetic DEMs containing drumlins. The variation between interpreters (e.g. mapping philosophy, experience) and across the study region (e.g. woodland prevalence) opens these factors up to assessment. A priori known answers in the synthetics increase the number and strength of conclusions that may be drawn with respect to a traditional comparative study. Initial results suggest that overall detection rates are relatively low (34–40%), but reliability of mapping is higher (72–86%). The maps form a reference dataset.

Teaching GIS&T

This special issue contains a series of short papers on teaching with and about geographic information science and technology (GIS&T) in a variety of educational contexts, with contributors originating from both sides of the Atlantic. Although geographical information systems (GIS) already has an edited volume that purports to outline its history (Foresman, 1998), we have yet to put the entire phenomenon into its proper perspective, evenmore so into its pedagogic context. Briefly, most authors cite the Canada system of the 1960s as the first ‘GIS’, but the term did not gain much currency until the mid-1970s, when the first academic, research-oriented meetings were held. The late 1980s and early 1990s saw a proliferation of programmes designed to teach the technology and, more importantly, what Goodchild (1992) called the ‘geographic information science’ (GISc) that underpins it. It seems to us that both the content of courses in ‘GIS’, with or without the ‘c’, and the ways bywhich they have been delivered, reflect an interplay between the available technology, the GIS industry and the academy. For better or worse the educational agenda has followed a technology-driven set of imperatives. Sometimes it also pays to revisit things one did and wrote some time ago and in looking at these stages we revisit the chapter ‘Enabling progress in GIS and education’ that one of us co-authored a decade ago for the second edition of the ‘big book of GIS’ (Forer & Unwin, 1999). Although much of the content of that chapter now has a vaguely antique flavour and the perspective reflected that of two geographers working in the context of academic geography, five ‘dilemmas’ that at the time seemed important for educators were listed and described:

Improving user assessment of error implications in digital elevation models

A digital representation of a terrain surface is an approximation of reality and is inherently prone to some degree of error and uncertainty. Research in uncertainty analysis has produced a vast range of methods for investigating error and its propagation. However, the complex and varied methods proposed by researchers and academics create ambiguity for the dataset user. In this study, existing methods are combined and simplified to present a prototype tool to enable any digital elevation model (DEM) user to access and apply uncertainty analysis. The effect of correlated gridded DEM error is investigated, using stochastic conditional simulation to generate multiple equally likely representations of an actual terrain surface. Propagation of data uncertainty to the slope derivative, and the impact on a landslide susceptibility model are assessed. Two frameworks are developed to examine the probable and possible uncertainties in classifying the landslide hazard: probabilistic and fuzzy. The entire procedure is automated using publicly available software and user requirements are minimised. A case study example shows the resultant code can be used to quantify, visualise and demonstrate the propagation of error in a DEM. As a tool for uncertainty analysis the method can improve user assessment of error and its implications.

Smoothing/filtering LiDAR digital surface models. Experiments with loess regression and discrete wavelets

This paper reports on the smoothing/filtering analysis of a digital surface model (DSM) derived from LiDAR altimetry for part of the River Coquet, Northumberland, UK using loess regression and the 2D discrete wavelet transform (DWT) implemented in the S-PLUS and R statistical packages. The chosen method of analysis employs a simple method to generate ‘noise’ which is then added to a smooth sample of LiDAR data; loess regression and wavelet methods are then used to smooth/filter this data and compare with the original ‘smooth’ sample in terms of RMSE. Various combinations of functions and parameters were chosen for both methods. Although wavelet analysis was effective in filtering the noise from the data, loess regression employing a quadratic parametric function produced the lowest RMSE and was the most effective.

An approach to classification of airborne laser scanning point cloud data in an urban environment

Digital topographic data, including detailed maps required for urban planning, are still unavailable in many parts of the world. Airborne laser scanning (ALS) has the unique ability to provide geo-referenced three-dimensional data useful for the mapping of urban features. This article examines the performance of decision tree classifiers on two ALS data sets, collected in different seasons from different flying heights with different scanners using laser beams at different wavelengths – 1550 and 1064 nm – for the same study area. Classification was undertaken on the point clouds based on attributes derived from the triangulated irregular network (TIN) triangles attached to a point, as well as attributes of the individual points. Classification accuracies of 0.68 and 0.92 (kappa coefficient) could be achieved for the two data sets. Decision tree seems to be a classification method that is particularly suitable for geographic information system (GIS), as it can be converted to ‘if–then’ rules that can be implemented fully within a GIS environment. Grass and paved areas could be distinguished better using intensity from one data set than the other, which could be related to the wavelengths of the lasers, and need to be explored further.