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GIS Guide to Good Practice
Section 4: Structuring, Organising, and Maintaining Information

4.4 Derived Data

You will often be using data derived from other sources when creating or managing a GIS data set. There are often important considerations in documenting derived data sets, as discussed in Section 5. When deriving data from another source, or when making use of derived data, it is the responsibility of the data user to ensure that any intellectual property rights belonging to the initial data creator(s) are respected. In some cases this may simply be a requirement to acknowledge the originating source, in other cases a royalty payment may be due for some part of the data to be used. Be sure to check out the situation in advance.

4.4.1 The Digital Elevation Model

One of the core components within many GIS databases is the Digital Elevation Model or DEM. This sub-section will look briefly at the principal pathways you can take to create a DEM, special issues that practitioners should be aware of relating to accuracy and integrity, and the specialised process-related metadata that should be recorded.

DEM vs DTM. There is considerable confusion between these two terms - which many people use interchangeably. Some people also use the term Digital Surface Model (DSM). DSM appears to be a synomym for DEM, but with the added possibility of being a component of a stack of surface models, for example modelling atmospheric or subsurface layers. DSMs are not yet in common usage.

Strictly speaking, the term DTM, Digital Terrain Model, should be reserved for those models of reality which include information relating to surface texture, etc., in addition to information regarding elevation. The term Digital Elevation Model, DEM, should be reserved for representations of altitude alone.

There are few genuine DTMs around yet - the concept is established but the tools to display, or visualise, information of this nature are not yet fully/widely available.

A DEM normally consists of a regular matrix of elevation values, from which altitude functions such as slope and aspect can be calculated, and which may be rendered for visualisation as isolines (contours), perspective or panoramic views, etc. A DEM is typically described in terms of its horizontal resolution. Resolution, for a DEM, defines the horizontal and vertical precision by which the information is recorded. A typical example might define the horizontal grid to be fifty metres - by which it is meant that the information in the DEM is arranged with one value every fifty metres in each of the co-ordinate directions. The resolution of the vertical element of the information will indicate whether the value represents the computed average elevation for that, say, fifty metre cell, of the elevation at the mid point as well as defining the range of accuracy to be expected of the elevation values, possibly plus or minus two or three metres.

Sometimes the surface elevation information does not form a regular matrix, but rather comprises a collection of measured locations with altitude. A DEM constructed from such data may be interpolated to form a regular matrix, or the surface may be represented by linking the measured points within a Triangulated Irregular Network (TIN). The TIN structure, of triangular facets each with a measureable slope and direction, is an efficient storage mechanism from which a regular matrix can readily be derived when necessary.

TINs have another useful property: they can be stacked. This means that they can be used to represent layers of information, for example atmospheric layers, subsurface archaeological or geological stratigraphy, etc.

Resolution, in the context of a TIN, is a simpler concept than for DEMs formed from a regular matrix of values. The vertical resolution remains the same but the horizontal resolution is a function of the precision of the co-ordinates defining the data points and the number and distribution of the data points with respect to the surface morphology. A primarily flat surface will require fewer points than a rugged or undulating surface.

Contour lines are not a good form in which to store elevation data. Contours are derived data, data interpolated from information of altitude at known points, and in themselves offer no information about the surface morphology between them. Contours may be an effective way of illustrating the third dimension (altitude or depth) on two dimensional paper, but are a poor technology for storing altitude information that may be used analytically.

A DEM held as a regular matrix suffers the same disadvantages due to size as does the raster data model. Although the co-ordinates of each cell in the data set can be derived from the co-ordinates of the origin and from the number of cells in each direction and their separation, a value must still be stored for every cell. The computer file size for such a data set is thus the product of the number of rows and the number of columns. For example, an OS LandForm Panorama tile, 20km square and with a 50m cell separation, holds 401 rows and columns - 160,801 cells. The same area in the LandForm Profile data set, with a cell separation of 10m, comprises 402,002,500 cells. In computer storage terms, the coarser data set would require around 629Kbytes; the finer resolution data set requires some 1.57Gbytes (note difference in units). If the elevation data is held in floating point form (fractional numbers) then the storage requirements may be doubled, depending upon the individual computer system. It is less easy to give guidelines on the space requirements of TIN data sets ... a rough guide might be (number of points times 6) times 4 bytes. Thus a TIN with 900 data points would require 21600 bytes (21Kbytes). This figure may vary according to the implementation of the TIN structures.

There are several potential sources of elevation data:

  • Primary Survey. EDM, theodolite, etc., measurements. These data are typically irregular in their ground coverage and are thus ideal for TIN data structures

  • Photogrammetry. Conventional photogrammetry using stereo images, generates isolines, contours. These must be converted into digital elevation data, typically via the TIN data structures
    Digital photogrammetry uses stereo digital images and typically produces a regular matrix of values, a DEM

  • Synthetic Aperture Radar (SAR) altitude mesurements. Satellite radar imagery which generates a regular matrix of altitude values directly, a DEM

Several times in the above discussion the terms interpolation or interpolated have been used. In the context of DEMs these refer to the technique used to approximate the altitude of points for which there is no measured data. The purpose of interpolation is to attempt to regain a representation of the actual surface morphology.

There are perhaps three techniques commonly used in GIS to perform such a task: each has specific capabilities dependent upon the nature of the data. These three techniques are Linear interpolation, which effectively runs a straight line between the points with altitude values, the Cubic Spline, which interpolates a smooth curve through the given data points, and Statistical interpolation using Kriging semi-variograms. For further discussion see Chapter 8 of Burrough (1986).


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