GIS
technical
Landform classification
using GIS
by Karsten Drescher, Terralogix Consulting, and Willem de Frey, Ekoinfo
Refining existing landform classifications using ESRI’s model builder.
L
andforms form an integral part
of the landscape; they reflect
the influence of geology and
climate [1] on a regional/broad scale.
The combination of landform and
climate influences the development
of soil conditions, which influence the
distribution and extent of certain plant
communities and associated animal
assemblages [2]. The significance of
landforms in terms of understanding
the potential and constraints within
the landscape associated with them,
is well documented [3]. Gauteng
Province Department of Agriculture,
Conservation and Environment
Directorate Nature Conservation’s
Ridges Policy [4] support this
statement.
On a large/fine scale the different
facets/units associated with landforms
such as crests, scarps, midslopes,
footslopes and valley bottoms
present habitat for flora and fauna.
The more complex the morphology
of the landform is, the higher its
potential to support a variety of
organisms at a variety of densities
[5]. Thus knowing the extent and
distribution of landforms, whether
complex such as ridges, tablelands,
hills and mountains or simple such as
highly productive plains and valleys
[6], is very important in terms of
environmental management to assess
the conservation significance and
potential of a portion of land within
the landscape to be developed or
affected by human activities.
From a geological and engineering
geological perspective, landforms are
of specific interest as the landforms
were created by geological processes.
The existing landforms also play
a significant role in the current
sedimentary processes.
Existing data
Existing morphology maps [7, 8],
which are part of the environmental
30 Fig. 1: Terrain morphology map of Southern Africa (after Kruger [9]).
Fig. 2: Terrain morphological divisions of South Africa (after Breedlove and Fraser [7, 8]).
potential atlas, are based on the
work done by Kruger [9] in 1983
(Fig. 1) and give the classification
of the morphological divisions in
South Africa (see Fig. 2 ) as well
as the finer detailed morphological
units on a provincial scale. Looking
at the map for Gauteng (Fig. 3), the
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technical
The calculations were done using
ESRI’s modelbuilder (ArcGIS 9.3
with 3D Analyst and Spatial Analyst
extensions).
Fig. 3: Terrain morphological units of Gauteng (after Breedlove and Fraser [7, 8]).
classifications are still fairly broad.
Kruger’s map was done at a scale of
1:8 000 000.
The Department of Agriculture,
Conservation and the Environment of
the Gauteng Provincial Government
has a policy on ridges [4] which is
based on the slope values derived
from a digital terrain model
(DTM) (Fig. 4).
Methodology and results
The landscape classification was done
similar to that done by Morgan and
Lesh [10].
Two landform classifications were
done. The first one was done for
the Gauteng province using a digital
terrain model (DTM) with a pixel
size of 30 m, derived from contour
lines with a height interval of 20 m.
Fig. 4: Ridges as defined by GDACE (after Pfab [4]).
Apart for classifying landforms with
more detail, the need was also
identified to define ridges using more
parameters than just the slope values.
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The second one for South Africa was
done using a DTM with a 200 m pixel
resolution, based on contour lines
with an interval of 100 m.
For Gauteng, the GIS process that
was followed is described in detail
by Morgan and Lesh [10]. The
most recent available boundary of
Gauteng as defined by the Municipal
Demarcation Board [11] was used and
projected to Lo 84/29 (WG29), and a
buffer of 5 km was added. Although
some of the boundary effects are
taken care of mathematically, any
remaining edge effects are minimised
using a 5 km buffer. A rectangle
covering the study area (Gauteng
plus buffer) was determined. Contour
lines with a 20 m interval were
merged and the dataset was projected
to the Lo 84/29 (WG29) projection
and clipped with the above mentioned
rectangle. It was discovered that
the dataset is too large for a straight
“Topo to Raster” (3D Analyst) process.
A TIN dataset was created using this
clipped contour dataset. The TIN
dataset was then converted to an
elevation raster dataset with a 30 m
pixel resolution.
The Gauteng-plus-buffer feature
was converted to a raster dataset
such that pixels inside the polygon
are assigned a value of one (1) and
the pixels outside the polygon are
assigned “No data”. Multiplying
this raster with the elevation raster
resulted in a DTM raster (see Fig. 5).
As the “No data” pixels are ignored
during the processing it speeds
up the processing which consists
of 37 calculation steps. As far as
computing time is concerned the
model builder runs the model within
3 hours on a 2,4 GHz quad processor
PC with 2 GB RAM. The resulting
Dikau [7] landforms are shown on
Fig. 6.
The results for the Gauteng dataset
were analysed further by comparing it
to the GDACE ridge dataset. For this
purpose the determined landforms
“Flat or nearly flat plains” and
“Smooth plains with some local relief”
were classified as “No ridges” while
all the other landforms were classified
as “Ridges” as shown on Fig. 7. The
result of the comparative study is
shown on Fig. 8.
For South Africa the above-mentioned
process was repeated with a DTM with
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a 200 m pixel size (Fig. 9) and all
the data was projected to the Albers
projection using the WGS84 datum.
The result of the model for South
Africa is shown in Fig. 10.
Fig. 5: DTM raster dataset for Gauteng.
Some problems were encountered
with the obtained version of the
publication in which the model is
described by Morgan and Lesh [10].
At one point in the model (“percent
of near level land”), the publication
states that for this parameter, the
focal statistics were calculated on a
20 pixel radius circular window for the
sum of all pixels and on a
1,5 km circular window (1,5 km with
30 m pixels is equivalent to a 50
pixel radius) for the sum of the slope
(slope less than 8%) pixels. During
this study, this led to some strange
results. The parameter should be
calculated by taking the sum of the
pixels with slope values of less than
8% within a 20 pixel circular window
and divide it by the sum of all pixels
within a 20 pixel circular window.
Furthermore, according to the
publication the “percent of near level
land” is reclassified as follows:
0 – 0,2:400
0,20 – 0,50:300
0,50 – 0,80:200
0,80 – 1,0:100
Fig. 6: Determined landforms for Gauteng.
The numbers 100 – 400 are part
of the Hammond’s terrain type
codes which are reclassified into
Dikau’s landform codes. Further
on in the publication, the code 411
for example denotes plains. Using
the classification as stated above,
code 411 would denote hills and
mountains. The above classification
should read:
0 – 0,2:100
0,20 – 0,50:200
0,50 – 0,80:300
0,80 – 1,0:400
Discussion
Fig. 7: Ridges determined by using landforms.
32 Looking at the Gauteng dataset,
Fig. 6, the determined landforms
are fairly similar to Breedlove and
Fraser’s morphological units (Fig. 3) but
have much finer detail. To validate
the model further a profile was
created using the Gauteng DTM and
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superimposed onto the landform
classification as shown in Fig. 11.
Furthermore, using the determined
landforms to define ridges compares
very well to GDACE’s ridge dataset
(Fig. 7).
For the South African dataset, Fig. 10,
the determined landforms compare
fairly well to Breedlove and Fraser’s
morphological divisions (Fig. 2), but
have much finer detail.
Comparing the landform classification
of Gauteng to the one for South
Africa (Fig. 6 and Fig. 10), there is
similarity between them but not an
exact match. This is to be expected
as the model uses an elevation range
from 940 to 1900 m above sea level
for the Gauteng dataset and an
elevation range of sea level to 3700
m above sea level for the South
African dataset. Furthermore the
focal statistics are determined over
the same number of pixels (20 pixel
circular window) but the pixels size of
the South African dataset is
200 m compared to the Gauteng
dataset where the pixel size is 30 m.
Fig. 8: Comparison between GDACE ridges and landform ridges.
Conclusions
The above mentioned results show
that the model appears to produce
acceptable results. It is however
important to note that the determined
landforms are not necessarily absolute
but relative to the dataset. This is
more prevalent on the mountainous
terrains: a plain on the Gauteng
dataset is also a plain on the national
dataset but a landform that is a high
hill compared to the rest of Gauteng
is not necessarily a high hill when
compared to the rest of South Africa.
Fig. 9: DTM for South Africa.
Geospace
¼
A further conclusion made is that the
ridges determined by the landforms
is mainly in agreement with the
GDACE ridge policy based on slopes
– the ridges policy can however be
somewhat refined.
? Irene size
References
[1] A N Strahler, & A H Strahler: Modern
Physical Geography Third Edition.
Wiley and Sons, New York, 1987.
[2] M G Barbour, J H Burk and W D Pitts:
Terrestrial Plant Ecology. Benjamin/
Cummings Publishing Company,
California, 1980.
[3] J A Wiens, M R Moss, M G Turner and
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Fig. 10: Determined landforms for South Africa.
33
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Fig. 11: Profile (vertically exaggerated) through the Gauteng DTM.
D J Mladenoff: Foundation Papers in
[7] G Breedlove and F Fraser: Environmental
[9] G P Kruger: Terrain morphology map
Landscape Ecology. Columbia University
Potential Atlas for South Africa: Terrain
of Southern Africa, Soil and Irrigation
Press, New York, 2006.
Morphological Divisions, online at
Research Institute, Department of
[4] M Pfab: Development Guidelines
www.environment.gov.za/Enviro-Info/
Agriculture, Pretoria, 1983.
for Ridges. Departmental Policy.
nat/images/mdiv.jpg, Department of
Department of Agriculture, Conservation,
Environmental Affairs and Tourism,
Landform Maps Using ESRI's
Environment and Land Affairs Directorate
University of Pretoria & GIS Business
Modelbuilder, online at
of Nature Conservation, 2001.
Solutions, 2000.
http://gis.esri.com/library/userconf/
[5] M G Turner, R H Gardner and R V O'Neill:
[8] G Breedlove and F Fraser: Environmental
[10] J M Morgan and A M Lesh: Developing
proc05/papers/pap2206.pdf, 2005.
Landscape Ecology in Theory and
Potential Atlas for Gauteng: Terrain
Practice Pattern and Process, Springer,
Morphological Units, online at
RSA_Prov.zip, online
USA, 2001.
www.environment.gov.za/Enviro-Info/
www.demarcation.org.za/, 2007.
[11] Municipal Demarcation Board.
[6] D B Lindenmayer and J Fischer: Habitat
prov/gt/gtmorp.jpg, Department of
Fragmentation and Landscape Change
Environmental Affairs and Tourism,
An Ecological And Conservation
University of Pretoria and GIS Business
Contact Karsten Drescher,
Terralogix Consulting,
012 803-8735,
Synthesis, Island Press, USA, 2006.
Solutions, 2000.
[email protected] 
34 PositionIT -Aug/Sept 2009