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2. REMOTELY SENSED DATA 
2.1 Data Acquisition 
Techniques have been developed for position-based 
mapping of surface cover using Landsat digital data 
and implemented for a study area in the upper 
Kananaskis Valley of southwestern Alberta, located 
in part of the Rocky Mountains (see Figure 1) 
(Lodwick, 1981; Paine, 1984). The research involved 
approximately one-twentieth of an image of the 
Calgary scene, taken on September 20, 1975, and 
comprised natural areas of woodland and rock. Input 
is in the form of digital computer-compatible tapes 
of Landsat multispectral scanner data supplied by 
the Canada Centre for Remote Sensing in Ottawa. The 
output products are maps at scales and accuracies 
suitable for a wide range of environmental 
applications. 
Scale in km 
Figure 1. The Kananaskis Valley in Alberta 
The analysis procedure comprised four major stages 
to preprocess, enhance, position and classify the 
data. Firstly, data from the computer-compatible 
tapes were reformatted and radiometrically corrected 
to remove errors in video response values introduced 
during image acquisition. Secondly, the data were 
enhanced for investigation and interpretation using 
principal components analysis. The advantage of 
this technique is that the original information can 
be defined in one, or at most two variables, which 
are readily interpretable in terms of natural 
surface phenomena. Thirdly, the image was 
geometrically adjusted, using a second order 
polynomial model and ground control points to 
spatially resect it to the earth's surface. 
Individual pixel values were then resampled to a UTM 
grid of 50 m spacing using the nearest-neighbour 
technique. Finally, interpretation of the data was 
carried out with a supervised classification scheme 
using a parallellepiped technique. 
The results indicated that the first and second 
principal components contained over 99 percent of 
the information in the four original data sets. 
Classification using these data resulted in accurate 
definition of the surface cover of the test area. 
The rectification established the positioning 
accuracy of these data with an RMS error of less 
than 50 m, which is suitable for mapping at a scale 
of 1:50,000. The resulting map contained ten 
distinct surface cover classes, which were used as 
one input data set for the LRIS. The significant 
advantage of this approach is that sequential 
Landsat imagery enables updating on a regular basis. 
2.2 Format Conversion (Raster to Vector) 
Remotely sensed data are usually collected in a 
raster format and the decision about storage, 
manipulation and retrieval of these data often 
hinges on the question of raster versus vector 
format. Broadly, the methods of describing the 
positional extent (or coding) of spatial entities 
are either vector-based, using coordinates, or 
raster-based, using scan lines. Arguments have been 
made in favour of each of the raster and vector 
formats. Generally, particular formats and format 
conversions are not of concern to the system user 
but of the information system itself (Haralick. 
1980). 
Using vector format, polygons are described by a 
series of lines or points, lines are also described 
by a series of lines or points (Peucker and 
Chrisman, 1975; Edson, 1975) and points are 
described either by absolute coordinates (relative 
to the coordinate system origin) or by relative 
coordinates (relative to a previous point) (Baxter, 
1976; Burton, 1979). 
Using raster format, polygons, lines and points 
are all represented by those parts of an overlain 
grid matrix, or system of scan lines, which cover 
them. A raster matrix, or grid map may be full, 
where all cells or pixels are stored, or sparse, 
where only significant cells of each scan line are 
stored (Miller, 1980; Barber, 1982). The usual 
comment made, when discussing the relative merits of 
vector and raster formats, is that vectors are more 
efficient in terms of storage space, and rasters are 
more efficient in terms of computation time (Barber, 
1982). 
With Landsat data, storage in raster format is an 
appropriate approach. However, when dealing with an 
LRIS, where survey data as points and lines are to 
be stored, positional accuracy is an important 
consideration. Also efficiency considerations 
favour storage by polygons (vectors) (Mepham and 
Paine, 1986). To polygonize the remote sensing data 
set required that the data be smoothed to a level of 
generalization which would provide a more efficient 
storage format. This smoothing/filtering process 
requires careful selection of a spatial filter which 
will not degrade the data (Paine and Mepham, 1986). 
This resulted in a data set represented by polygons 
formed from line segments based on unique points. 
3. CONVENTIONAL MAP DATA 
3.1 Selection of Data Types 
One of the requirements of this prototype LRIS was 
for test data that represented a broad range of 
spatial land information. Thus, three basic data 
types were selected: 1) primary data (positional), 
2) secondary data (thematic), and 3) digital 
elevation model (DEM) data. 
The first two types are fairly standard and have 
accepted definitions. Primary land-related informa 
tion comprises three main components: 1) geographic 
positioning systems established with a range of 
survey techniques, 2) mapping systems established 
from the basic survey data, and 3) land registries 
containing crown land and land titles information 
(LRIS, 1981). 
Secondary land-related information can loosely be 
defined as thematic data (Kozak, 1980). It is 
usually represented graphically as a map with the 
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