Geomorphology 58 (2004) 357 – 370
www.elsevier.com/locate/geomorph
Computer-assisted discrimination of morphological units on
north-central Crete (Greece) by applying multivariate statistics
to local relief gradients
Ayodele Oluwatomi Adediran a,*, Issaak Parcharidis b,
Maurizio Poscolieri c,1, Kosmas Pavlopoulos d,2
a
Department of Environmental Management, Mediterranean Agronomic Institute of Chania (Maich), Chania, Crete, Greece
b
Department of Geophysics-Geothermic, Faculty of Geology, University of Athens, Greece
c
CNR Istituto di Astrofisica Spaziale e Fisica Cosmica, via del Fosso del Cavaliere 100, 00133 Rome, Italy
d
Department of Geography, Harokopio University of Athens 70 El. Venizelou str, Athens 17671, Greece
Received 28 August 2001; received in revised form 15 March 2003; accepted 17 July 2003
Abstract
Traditional manual methods have been employed for decades to measure geomorphometric properties from topographic
maps. Such measurement techniques tend to be tedious and time-consuming and the designated landform elements cannot be
easily overlaid on any digital map and imagery for further applied research. This study deals with a new quantitative
geomorphometric procedure, based on the multivariate statistical analysis of local topographic gradients within a part of northcentral Crete. This method employs sets of computer algorithms that automatically extract and classify geomorphometric
properties from Digital Elevation Models (DEMs). This was done by evaluating the morphological setting around each pixel of
the DEM along the eight azimuth directions. ISODATA unsupervised classification was implemented to generate 10
morphometric classes showing the spatial distribution of areas with a similar geomorphic scenario.
Results revealed that this approach permitted a quick estimation of the spatial distribution of morphologically homogeneous
terrain units. It also demonstrated the ability of the delineated landform elements to be superimposed on any digital map and
imagery for further investigation. This became apparent during the examination of the relationship between the
geomorphological units and the land-cover/land-use types in the study area. Both relative association and the dominant land
cover/land use types in relation to geomorphological units are presented.
D 2003 Elsevier B.V. All rights reserved.
Keywords: Geomorphometry; DEM; Morphological; Remote sensing; Land cover/land use types
* Corresponding author. Present address: Department of Geography, University of Saskatchewan, 9 Campus Drive, Saskatoon,
Saskatchewan, Canada S7N 5A5. Tel.: +1-306-966-5632; fax: +1-306-966-5680.
E-mail addresses: [email protected] (A.O. Adediran), [email protected] (I. Parcharidis), [email protected]
(M. Poscolieri), [email protected] (K. Pavlopoulos).
1
Fax: +39-06-20660188.
2
Fax: +30-3-210-9514759.
0169-555X/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.geomorph.2003.07.024
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A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
1. Introduction
Recently, the rapidly increasing availability of
digital elevation data has promoted the development
of sophisticated computer algorithms for the calculation and discrimination of geomorphometric properties of the Earth’s surface. Geomorphometric
properties have been measured manually for decades
(Horton, 1945; Miller, 1953; Coates, 1958), but measurement of such properties from topographic maps
can be labour intensive and time consuming.
Since the end of the 1970s, Digital Elevation
Models (DEMs) (Mark, 1978) and derived data sets,
such as slope, aspect, hydrographical pattern and
shaded relief have been exploited to examine the
geomorphological and geological settings of a region
(Pike and Wilson, 1971; Evans, 1972; Mark, 1975;
Pike and Rozema, 1975; Carrara et al., 1977). During
the past 20 years the available digital data have been
exploited by other investigators for geomorphologic
and geomorphometrical studies (Franklin, 1987; Morris and Heerdegen, 1988; Skidmore, 1989; Ventura
and Irvin, 2000). Moreover, morphometric parameters, obtained from the DEMs, have been used to
compare quantitatively terrain units (Onorati and
Poscolieri, 1988; Onorati et al., 1992; Hutchinson
and Gallant, 2000). Technical advances in computing
have further made the quantitative modelling of landforms a routine task. Other works that have successfully employed available DEMs for automated
determination and classification of slope steepness
and slope directions include Band (1986), Skidmore
(1990), Martz and De Jong (1991), Mackay et al.
(1992), Dymond et al. (1995), and Giles and Franklin
(1998).
Geomorphometry, or simply morphometry (Pike
and Dikau, 1995), is the numerical representation of
topography and is a combination of mathematics,
engineering and, more recently, computer science. In
the past, attempts at defining geomorphometry have
concentrated on the geometry of the landscape, but
new technical advances in electronic computers, analytical algorithms, input/output devices and large sets
of topographic data have reoriented geomorphometry
(Pike, 1993, 1999). The computer implementation of
morphometry provides geomorphologists with a digital representation of landforms that is now essential
to process modeling (Dikau, 1998; Dehn et al., 2001)
at all levels of generalization. Computer morphometry
contributes to various synoptic attempts at integrating
land-surface form with remotely sensed spectral and
other environmental data to facilitate a broad-scale
explanation of physical processes.
This study is an attempt to contribute to the
available procedures on quantitative description and
discrimination of forms and patterns on the Earth’s
surface, thereby overcoming the inherent shortcoming
of studies that focus mainly on a qualitative approach
to terrain analysis. It also demonstrates the value of
overlaying delineated landform elements on digital
maps and imagery for further applied research.
The main objectives of this work are to automatically extract and classify the morpho-units of the
north-central part of the island of Crete by applying
advanced statistics and image processing algorithms
(Parcharidis et al., 2001) to local geomorphometric
properties of DEMs. The emphasis here is on the
morphologically homogeneous terrain units, characterized mostly by quite similar sloping settings. This
approach permits a quick estimation of the spatial
distribution of the different type of slope steepness
and, at the same time, shows the impact of the
lithological and tectonic structure on the overall
relief.
The investigation is also addressed to the relationships between the geomorphology and the land cover
types in the study area; the focus is on the inference of
the relative occurrence of land cover types within the
geomorphological units.
2. Area description/study area
The study area is situated in North-central Crete
between 35j25 V51WN and 35j09 V47WN latitudes,
24j32 V07WE and 24j54 V42WE longitudes. It faces
the Cretan Sea and is located between the towns of
Rethimno and Iraklieo (Fig. 1), including the settlements of Perama, Anogia, Vrisses, Loutra, Tilisos,
Platanos and Gergeri (see Fig. 2).
2.1. Geomorphological setting
Generally, the geomorphology of Crete reflects
Alpine and post-Alpine tectonic activity. The highland
and midland zones exhibit high relief with east to west
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
359
Fig. 1. Map of Crete Island showing the location of the study area (inset).
and in some places, north to south (e.g. the peninsula
of Gramvoussa) orientations.
The study area includes a large part of the mountain range of Idi and of the mountain range of Talleon
along the Northern coast of the island. It is dominated
by the ‘‘Plattenkalk’’ formations, which originated
during the Mesozoic and Tertiary periods and occupy
most of the mountainous portion of the region. Limestone and dolomite, formed during the Jurassic to
Miocene periods, constitute the dominant bedrock,
although small outcrops of sandstone, schist and
low-grade metamorphic rocks also occur. The mountain range of Idi exhibits a WNW –ESE orientation:
the maximum observed height is 2456 m a.s.l., and the
average range elevation approaches 2000 m. Tectonic
basins that separate the existing mountains in general
follow the ranges’ orientation, while smaller tectonic
grabens often occur.
The carbonate rocks cropping out in the study area
did not permit the development of well-defined hydrographic networks, among which the watershed
related to the Idi range is outstanding in central Crete.
The karst features of the Idi mountains vary in terms
of occurrence and type. The degree of karstification
allows distinction of two systems: the eastern and the
western; The eastern system is more complex and is
dominated by features where all the types of karstic
phenomena are observed; in the western system the
phenomena are gentler. The mountain range of Tallea,
extending north of the Idi range, along a E – W
orientation, is characterized by smoother topographic
relief and a number of karst basins, mainly dolines.
The coasts of the island form numerous capes, peninsulas and bays; they are generally steep due to the
occurrence of fracture zones characterized by vertical
movements.
2.2. Geological setting
The Alpine basement rocks of Crete are composed
of a stacked series of highly heterogeneous tectonic
nappes exposed in uplifted blocks, which are bounded
by normal faults forming the Neogene and Quaternary
basins (Fytrolakis, 1980; Ten Veen, 1998). The nappe
pile has been described by Bonneau (1976), and
Bonneau et al. (1977) as including the eight following
nappes: (1) Autochthon or Ida zone, (2) Phyllites
nappe, (3) Tripolis nappe s.s, (4) Pindos-Ethia nappe,
(5) Miamou and Vatos nappes, (6) Arvi nappe, (7)
Asteroussia mappe and (8) Ophiolites nappe.
A different view was proposed by Seidel et al.
(1977) who consider the following five nappes: (1)
nappe of the Ophiolites and ophiolitic melange, (2)
Pindos nappe, (3) Tripolis nappe, (4) nappe of the
Phyllites-Quarzites and Trypali and (5) Plattenkalk
unit. According to Fassoulas (1999), the nappes are
divided into two major groups: upper and lower,
separated by a major detachment fault. The total
thickness of the nappes pile does not exceed 4– 5 km.
After the emplacement of the Alpine nappes,
Crete formed part of the so-called Southern Aegean
landmass (Meulenkamp, 1971). The post-orogenic
period was characterized by erosion of the preNeogene’s nappe pile. Locally, in many areas of
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Fig. 2. Geological map of the study area.
Crete these series of Neogene’s clastics unconformly
cover the Alpine basement. The Neogene deposits,
mainly limestone breccias, reflect the impact of
differential vertical movements along East – West
and North –South trending structures. The stretching
is associated with uplift due to the underthrusting
and folding in the deeper parts of the nappes (Ten
Veen, 1998). During the Eocene/Lower Miocene
periods, the nappes moved from North to South at
speed of about 1 –2 cm/year (Baumann et al., 1976).
This displacement is a form of creeping due to
gravity or gravitational strain caused by the diapir-
ism observed in the Cyclades area, which in the last
10 million years has been uplifted about 10 km
(Makris, 1976).
In the middle – late Miocene a transition is marked
by a break-up of southern Aegean landmass and of the
basin bordering it to the South. According to Le
Pichon and Angelier (1979), this event was related
to an extension in the Cretan Sea caused by the
inception of the roll-back process of the Hellenic
Trench system. Extension resulted in the break-up of
the landmass illustrated by the horst and graben
morphology of Crete.
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
In Early Tortonian the sea invaded Crete exploiting the complex horst/graben morphology with consequent deposition of a thick sequence of mainly
marine sediments. During this period, subsidence
phenomena could be recognized all over Crete (Drooger and Meulenkamp, 1973). Locally, tilting and
erosion of the older parts of the Neogene’s sequence
took place during a period of tectonic instability,
which caused the changes in basin configuration and
sedimentation at the Tortonian – Messinian time-interval boundary.
Meulenkamp (1979) proposed a late Messinian
rejuvenation of the relief resulting in the deposition
of coarse-conglomeratic, non-marine successions and
of fluvio-lacustrine to the lagoon’s sediments. At the
end of the early Pliocene, the island underwent an
overall regression, which caused the emergence of
almost the entire island.
During the Pleistocene Crete achieved its present
shape. Strong differential movements were the dominant tectonic processes controlling the Quaternary
landscape evolution (Drooger and Meulenkamp,
1973).The geological map of the study area, with
the corresponding legend derived from the geological
map on a scale of 1:200,000 by Creutzburg et al.
(1977), is depicted in Fig. 2. In its southwestern part
there are faults with North – South orientations, while
in the central part (Idi/Psilorites mountain) they
follow mainly Northwest – Southeast and North –
South orientations. In the central coastal zone, the
faults present mainly East – West orientations parallel
to the coastline.
2.3. Description of land cover units
Field surveys revealed that the land cover is
extremely heterogeneous due to the large variety of
adjacent topographic features. For instance, cultivated
areas alternate with non-cultivated and built-up areas.
Small areas of vineyards, olive groves, orchards or
greenhouses are mixed with residences or other buildings, as well as natural land interspersed with crop
cultivation and buildings. According to Triantophyllidou-Baladie (1990), cereals, vines and olive trees
have been the main crops cultivated in Crete generally, and in Psilorities specifically, through the centuries. The dominant natural vegetation is phryganic
intermixed with herbaceous plants. A review of the
361
historical changes of Crete’s vegetation can be found
in Rackham (1990). He traces the changes in the
Cretan landscape and vegetation over the last 3000
years, focusing on the effects of climate changes and
the impact of human activity. Rackham and Moody
(1996) also provide information on the history of the
Cretan vegetation.
3. Data processing techniques
3.1. Data set
The data set used in this study consists of: (a) a
DEM obtained from standard SPOT stereo pairs
(ground resolution of 20 m/pixel) to accomplish geomorphometric classification; (b) a SPOT4 Xi multispectral scene, recorded on 30 September 1999.
Moreover, ancillary data have been taken into account
for the land-cover classification, such as topographic
maps from the Greek Military Geographic Service on
a scale of 1:50,000, orthophotos from the Greek
Ministry of Agriculture on a scale of 1:25,000 and a
geological map on a scale of 1:200,000.
3.2. Land cover classification methods
The SPOT multispectral imagery, before applying
any classification techniques, has undergone pre-processing procedures such as radiometric calibration and
geometric correction.
Furthermore, field surveys were carried out to
identify peculiar spectral signatures, ground truthing
and for the definition of sampling areas; however, prior
to the fieldwork, unsupervised classification was produced for the SPOT4 Xi imagery. The result was used
in conjunction with field investigations and other
ancillary data to aid identification of suitable categories for subsequent land-cover mapping of the study
area, by confirming which land-cover classes could or
could not be spectrally separated, and by determining
the level of the detail that could be reasonably
expected from analysis of SPOT4 Xi bands.
The ground truth data collected during the field
observations were used for the construction of the
spectral signatures library as input to the supervised
classification. This one was based on the standard
nomenclature of the CORINE (COoRdination of IN-
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formation on the Environment) program, which consists of forty-four items structured at three levels
(European Environment Agency, 1997). The Bayesian
technique of Maximum Likelihood (Jensen, 1996;
Lillesland and Kieffer, 2000) was applied and 11
classes, on the whole, were identified in the classified
map (Fig. 3). A diagonal line occurring across Fig. 3
is an artefact due to the mosaicking of the two SPOT
scenes used for the land-cover classification. For the
purpose of this study, this edge effect did not pose any
concerns.
3.3. Morphometric units discrimination methods
3.3.1. Classical methods of morphometric analysis
In order to define quantitatively the geomorph
characteristics of the study area, a procedure based on
the analysis of local morphological settings was implemented. Starting from the DEM, digital morphometric
maps of the area were computed: they are shaded relief,
slope and aspect maps (Fig. 4). The shaded relief image
(Imhof, 1982; Drury, 1978) better emphasizes subtle
morphological features; it was created by calculating
Fig. 3. Landcover/landuse type classification map. For color see online version.
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
363
Fig. 4. Morphometric maps derived from the DEM, (A) grey-level representation of the DEM, (B) shaded relief, (C) slope map, (D) aspect map.
the reflected sunlight over the artificially illuminated
surface of the DEM, given an azimuth from NW and an
altitude with respect to horizon of 45j.
Slope and aspect or, in other words, the magnitude
and direction of the vector tangent to the topographic
surface pointing downhill at a point, can be measured
in the field, determined from a topographic map and
computed from a DEM (Onorati et al., 1992; Guth,
1995 ). Slope shows changes in elevation over distance and identifies the maximum rate of change in
value from each cell to its neighbours, while an aspect
map shows the prevailing direction that a slope faces
at each pixel. Aspect identifies the down-slope direction of the maximum rate of change in value from
each cell to its neighbours. For this study Fig. 4 was
employed mainly for the accuracy assessment of the
classified morphometric map (Fig. 6).
3.3.2. Application of kernel
The specific methods used for this study started
with the application of specially designed algorithms
(filtering kernel) to the North-Central Crete DEM.
Computer-based operations can regionalize parameters derived locally from DEMs within a moving
Fig. 5. Sketch of the basic concept for local morphometric analysis.
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A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
window (Nogami, 1995) by filtering techniques or by
aggregating neighbouring parameter values or landform elements.
In this study, a user-defined 3 3 directional filter
was applied to the DEM in order to evaluate the
topographic gradient of each pixel along the eight
main azimuth directions. For this purpose, starting
from the NW corner and moving clockwise, elevation
differences between the central pixel and all neighbours were calculated (see Fig. 5). The resulting
Fig. 6. Morphometric ISODATA classification map. For color see online version.
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
values for any pixel represent local morphological
variations, reflecting changes in shape, orientation and
steepness, and extend from one to eight layers the data
set as input for the application of multispectral classification techniques (Negroni et al., 2000; Parcharidis
et al., 2001; Cavalli et al., 2002).
3.3.3. Application of the isodata unsupervised method
An unsupervised cluster analysis technique, the
ISODATA algorithm, was applied to the eight resultant layers (now referred to as morpho-bands) after
they had been layer-stacked. ISODATA represents a
flexible iterative partitioning method used extensively
in engineering (Hall and Khanna, 1977) and nowadays implemented in many image processing software
packages (ERDAS, ENVI, ER-MAPPER). It is based
upon estimating some reasonable assignment of the
pixel vectors into candidates and then moving them
from one cluster to another in such a way that the sum
of squared error (SSE) measure of the preceding
section is reduced.
ISODATA stands for ‘‘Iterative Self-Organizing
Data Analysis Technique’’ where the ‘‘Self-Organizing’’ refers to the way in which it locates the clusters
that are inherent in the data. The ISODATA clustering
method uses the minimum spectral distance formula
to form clusters; it begins with either arbitrary cluster
means or means of an existing signature set, and each
time the clustering repeats, the means of these clusters
are shifted. The new cluster means are used for the
next iteration. The ISODATA utility repeats the clustering of the image until either a maximum number of
iterations have been performed, or a maximum percentage of unchanged pixels have been reached between two iterations. The ISODATA procedure has
also been applied to landform classification from
digital terrain data using as input morpho-attributes
such as elevation, slope, profile and tangent curvature,
wetness index and solar incident radiation (Irvin et al.,
1997).
The output of the classification is presented as a
digital thematic map (Fig. 6), showing the spatial
distribution of areas with similar characteristics (in
our application the geomorphologic setting), and
allowing an easier comparison among different geographical sites. The mean elevation differences of
neighbourhood with respect to the central pixel for
the 10 classes, resulting from this unsupervised clas-
365
Table 1
Mean values and morphostructural interpretation of the elevation
differences between each central pixel and the eight neighbours for
the 10 classes obtained by applying ISODATA (Fig. 6) clustering
method to Northern-central Crete’s DEM
Color
1
White
(15.10%)
2
Red
(11.53%)
3
Maroon
(21.70%)
4
Olive Green
(11.28%)
5
Light Green
(6.58%)
6
Yellow
(5.84%)
7
Cyan
(2.41%)
8
Purple
(2.47%)
9
Blue
(5.77%)
10 Violet
(2.00%)
Elevation differences
0.36
+ 2.00
+ 4.00
7.94
4.83
1.56
+ 2.50
+ 0.82
1.25
0.09
3.37
6.90
+ 9.37
+ 2.04
5.34
+ 3.30
6.24
14.10
+ 16.40
+ 8.69
+ 1.48
+ 10.80
+ 10.52
+ 10.35
0.39
+ 6.65
+ 13.57
8.18
+ 4.15
+ 20.21
2.32
+ 1.89
3.44
+ 3.44
+ 1.58
2.00
+ 3.36
3.69
+ 7.30
7.21
+ 9.98
8.52
+ 8.00
6.78
+ 0.49
+ 0.04
6.81
+ 6.94
13.09
+ 16.75
Interpretation
4.56
2.36
0.48
+ 1.38
+ 5.00
+ 8.45
+ 0.32
1.23
3.09
+ 6.61
+ 3.13
0.67
+ 5.14
1.98
8.88
+ 17.15
+ 7.47
1.50
+ 0.10
7.72
14.04
9.06
9.56
9.41
12.76
6.32
+ 0.48
15.56
2.56
+ 13.36
Gently
sloping areas
facing SW
Average
sloping areas
facing SE
Gently
sloping areas
facing NNW
Gently/average
sloping areas
facing NE
Average
sloping areas
facing NNW
Steeply
sloping areas
facing NE
Steeply
sloping areas
facing NW
Steeply/average
sloping areas
facing W
Steeply/average
slopingareas
facing SW
Steeply
sloping areas
facing SSW
sification, are shown in Table 1. To apply the ISODATA method to North-Central Crete differential data
sets, the following parameters were chosen: 10 classes, a change threshold percent of 5 and a maximum
iteration of 3.
3.3.4. Accuracy assessment
The DEM, aspect, slope (Fig. 4) and geological
maps of the study area (Fig. 2) were employed for the
purpose of accuracy assessment and the interpretation
of the obtained classes. The pixel-by-pixel digital
number observation shows the corresponding relationships between the slope (steepness), aspect (direction),
DEM (elevation) and the results of the classified map.
In fact, for these classes, mean and standard deviation
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Table 2
Summary of the mean statistical values obtained from the terrain
derivatives (elevation DEM, aspect and slope)
Geomorphological
units (classes)
Mean
aspect values
(degrees)
Mean
slope values
(degrees)
Mean
elevation
values (m)
1
2
3
4
5
6
7
8
9
10
220
125
220
50
264
54
309
269
221
191
10
18
8
14
21
31
29
26
27
38
637
736
509
656
750
895
718
793
907
914
values (see Table 2) of the DEM and of its derived
thematic layers (slope and aspect maps) were also
obtained for further quantitative comparison with the
classified map. This approach gave the opportunity to
obtain the statistics of each pixel and of comparing
them with their corresponding class in the classification map.
Fieldwork was conducted within the study area
with the specific aim of assessing the accuracy of the
maps produced and comparing the land-cover types of
the area with the geomorphic settings.
4. Results and discussion
4.1. Statistics of morphometric classes and relative
interpretation
The ISODATA unsupervised classification produced 10 classes, with different degrees of morphographic representation. Fig. 6 presents the resultant
classification map where each class is represented by
a given color in order to facilitate the interpretation. In
Table 1, for every one of the ten classes, mean
elevation differences between the central pixel and
the eight surrounding ones are shown with their
corresponding morphographic interpretation.
The classification differentiated features based on
elevation, aspect and slope; for instance, gently sloping
areas with southwest (SW) aspect were in a different
class from steeply sloping areas facing south-southwest (SSW). Class centroid means of terrain deriva-
tives (elevation, aspect and slope) were further used to
interpret similarities and differences between classes
(see Table 2). For example, class ten with a mean slope
of 38j and a mean elevation value of 914 m a.s.l.
exhibited a higher and steeper relief than class three
with a gentle mean slope of 7.9j and a relatively lower
mean elevation of about 509 m a.s.l. Also, in contrast to
class seven with a mean aspect value of 308j (NW
azimuth orientation), class ten had a mean aspect value
of 191j (SSW azimuth orientation).
4.2. Geomorphologic interpretation of the classification map
The analysis of the morphometric maps and the
geological settings of the study area distinguished
seven different units (Fig. 6).
In Unit one (I), class 3 (with gently sloping areas
facing NNW) is dominant, while class 1 (with gently
sloping areas facing SW), and class 2 (with average
sloping areas facing SE) are less represented. This Unit
corresponds to an area of smooth relief with low to
medium morphological slope rate (2– 15%) and low
elevations. The geological formations prevailing in this
unit are Mio-Pliocene deposits of marls, marly limestones with evaporates diapirism and Quaternary formations of alluvial deposits and fans. The main
landforms appearing here are valleys filled with fluvial –torrential deposits, in many of which high downcutting erosion is evident. The orientation of the main
flows of drainage networks in this Unit (Í) is SE to NW.
In the southern part of the Unit, along its boundaries
with units ÍÍÍ, ÍV and V, at elevations of approximately
600 – 700 m, karstified surfaces occur in carbonate
rocks (crystalline limestone of Plattenkalk unit and
limestones of Trypali unit) with dolines and uvalas.
Unit II, located north of Talea Mountains shows
areas with different characteristics. The borders of
these areas have a N to S orientation. At the western
edge of Unit II, class 7 (with steeply sloping areas
facing NW) predominates, while the eastern area is
dominated by class 6 (with steeply sloping areas facing
NE). Alternations of the aforementioned areas continue
up to the easternmost border of this unit. The main
geomorphologic characteristics of the unit are rough
relief and high slope rates, with slopes facing NW up to
NE. The presence of these alternations is probably
linked to the intense Neo-tectonic activity affecting
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
the NW part of Crete. This Neo-tectonic activity is
pointed out by faults with prevailing E –W orientation.
Furthermore, the main characteristic of this Unit, especially in its south-eastern part, is the presence of karst
landforms such as sinkholes, poljes and uvalas. They
are developed at the contact of dolomites of Triassic –
Jurassic age with crystalline limestone formation.
Unit III, situated in the southern part of unit II, is
dominated by class 1 (with gently sloping areas facing
SW), class 2 (with average sloping areas facing SE),
class 3 (with gently sloping areas facing NNW), class
4 (with gently/average sloping areas facing NE) and
class 5 (with average sloping areas facing NNW).
This unit corresponds to areas of relatively gentle
relief including areas of rough relief with low to
medium slope rates, as indicated by the high number
of encompassed ISODATA classes. It consists of
crystalline limestones and phyllites that belong to
the Plattenkalk geotectonic zone.
Unit IV corresponds to the northern slopes of Idi
Mountain and is characterized by the predominance of
class 6 (with steeply sloping areas facing NE) and
class 2 (with average sloping areas facing SE). The
relief of this Unit is rough and intense, characterized
by steep slopes. Class 1 (with gently sloping areas
facing SW) and class 2 (with average sloping areas
facing SE areas) correspond to karst solution surfaces
with numerous karst landforms (sinkholesc, uvalas).
The elongated shape as well as the ESE – WNW
orientation of this Unit is identified by the matching
tectonic features orientation of the geotectonic zone
that develops in the Northern segment of the Idi
Mountains. The southern, northern and central parts
consist of Platenkalk geotectonic zone formations
while the northern part consists of Tripoli geotectonic
unit. The drainage networks of the unit flow northwards, in the areas of tectonic uplift that show intense
down-cutting and gorge formation.
Unit V is evident at the southern slopes of Idi
Mountains and is dominated by class 8 (with steeply/
average sloping areas facing W), class 9 (with steeply/
average sloping areas facing SW) and class 10 (with
steeply sloping areas facing SSW), corresponding to a
rough relief with steep slopes, locally controlled by
faulting. Areas dominated by class 1 (with gently
sloping areas facing SW) and class 2 (with average
sloping areas facing SE), are karstified with karst
planation surfaces with numerous karst landforms
367
(sinkholes, uvalas) reaching elevations of 1500 –
1700 m. Drainage networks in this unit flow southwards, exhibiting V-shaped channels with locally
intense down-cutting.
In Unit VI, which occurs SW of Units I and V, class
2 (with average sloping areas facing SE), class 4 (with
gently/average sloping areas facing NE), class 5 (with
average sloping areas facing NNW) and class 7 (with
steeply sloping areas facing NW) are evident, indicating a rough relief northwards. This area consists of
limestones and dolomites while a gentler relief is
developed on phyllites, flysch (formations of Pindus
and Tripoli geotectonic zones) and locally Mio-Pliocene deposits towards the south. Drainage networks in
this unit flow towards the northwest forming alluvial
plains and alluvial fans.
Unit VII is dominated by class 1 (with gently
sloping areas facing SW) and corresponds to areas
that consist of Quaternary deposits (alluvial cones,
fans, etc.) as well as Mio-Pliocene deposits. Planation
surfaces, evident in sections of this unit, are developed
on carbonate formations.
On the whole, two interesting occurrences can be
observed: (i) the linear character of the sixth and ninth
classes, following a NW –SE trend in the southern
boundary between Talleon and Idi, coincide with a
NW –SE oriented fault; (ii) abrupt interruptions and
contacts between the classes correspond to fault zones.
4.3. Comparison between land cover types and
morphometric units
Fig. 3 shows the map of classified land cover-types,
with its corresponding legend showing the 11 landcover/land-use classes identified in the study area. The
classification reveals that the dominant land-cover
type in the area is olive followed by natural grassland
and sparsely vegetated area, respectively.
In order to reveal the relationship between geomorphometric units and land-cover/land-use types, a
statistical analysis was performed. The results are
shown in Tables 3 and 4.
Table 3 summarizes the correspondence between
the geomorphometric units and main land cover
types, depicting the relative distribution of each
land-cover/land-use type within the geomorphometric
units. For instance, it was observed that in Geomorphometric unit 1 (gently sloping areas facing SW) the
368
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
Table 3
Distribution of land cover/land use types in relation to geomorphological units
Geomorphological units
Land cover/land
use types classes
(Corine nomenclature)
1. Gently sloping
areas facing SW
2. Average sloping
areas facing SE
3. Gently sloping
areas facing NNW
4. Gently/average sloping
areas facing NE
5. Average sloping
areas facing NNW
6. Steeply sloping
areas facing NE
7. Steeply sloping
areas facing NW
8. Steeply/average
sloping areas facing W
9. Steeply/average
sloping areas facing SW
10. Steeply sloping
areas facing SSW
Complex cultivation
patterns (9)
Sclerophyllous
vegetation (2)
Bare rocks (1)
(natural grassland). This was highly consistent with
field trip observations in the study area.
Specifically, Table 4 reveals a close spatial association between steeply sloping areas facing NE and
sparsely vegetated areas, with this land cover accounting for 24.6% of the morphometric unit total coverage.
A similar pattern could also be discerned between
coniferous forest and steeply/average sloping areas
facing West, where coniferous forest accounted for
15.8%. Bare rocks and artificial surfaces were poorly
represented within the study area; each of these two
classes accounted for less than 1% in all the geomorphometric units.
Broad leaved
forest (10)
Transitional
woodland/shrub (5)
Sparsely vegetated
areas (3)
Transitional
woodland/shrub (5)
Coniferous forest (11)
5. Conclusions
The results of the classification of geomorphometric
units in north-central Crete have further demonstrated
the possibility of an automated extraction of geomorphometrical properties from a DEM (Pike, 1993,
1999). Generally, in order to draw conclusions regarding the relief evolution we have to classify the geomorphic characteristics of an area by taking into
consideration geotectonic, climatic and hydrological
parameters together with geomorphologic indexes
(drainage density, drainage frequency, basin elongation, etc.). Our investigations reveal that the combination of two relief morphology characteristics such as
slopes and aspect can be combined with the geomorphologic features of a certain area and categorized into
Units. The application of the applied geomorphometric
method for the evaluation of analogous morphological
Mixed forest (7)
Mixed forest (7)
most abundant land cover type is class 9 (complex
cultivation patterns), while in geomorphometric unit 4
(gently/average sloping areas facing NE) the dominant
land cover type is class 10 (broad-leaved forest).
Table 4 shows the results of inspection of the
dominant land cover/land use type in all morphological units. As can be seen from the table, land cover
class 8 (olive) was the most abundant in all the geomorphometric units, followed by land cover class 6
Table 4
Spatial distribution and relationship between the geomorphological units and percent landcover/land use types
Landcover types
Bare rocks
Sclerophyllous vegetations
Sparsely vegetated areas
Artificial surfaces
Transitional woodland/shrub
Natural grassland
Mixed forest
Olive
Complex cultivation patterns
Broad leaves forest
Coniferous forest
Geomorphological units
1
2
3
4
5
6
7
8
9
10
0.01
0.46
10.19
0.08
5.75
17.19
0.45
47.45
8.10
0.45
9.82
0.00
0.98
15.82
0.06
5.00
17.45
0.41
43.05
10.08
0.97
6.16
0.22
0.45
9.90
0.15
7.26
17.45
0.20
48.61
5.97
0.93
8.84
0.00
0.92
9.70
0.03
7.11
17.68
0.29
49.19
7.44
1.49
6.14
0.03
0.58
7.31
0.01
13.25
21.70
0.43
41.27
4.11
1.25
10.06
0.00
0.45
24.58
0.00
11.34
25.21
0.24
27.84
3.32
0.71
6.29
0.01
0.22
5.71
0.00
18.97
20.56
1.12
33.34
2.11
0.47
17.46
0.05
0.27
9.13
0.02
12.03
19.13
0.47
38.69
3.33
1.07
15.79
0.00
0.36
16.03
0.09
9.83
19.13
2.34
37.35
3.98
0.39
10.47
0.00
0.39
21.05
0.00
10.71
17.01
1.90
38.94
2.40
0.27
7.26
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
units assisted in highlighting the spatial distribution of
geomorphologic features and their degree of intensity.
Our study also highlighted the increasing importance of satellite-derived digital data in the evaluation
of the land surface processes by overlaying the landcover and the morphometric maps of the study area.
The combined use of digital data, GIS and remote
sensing techniques also facilitated the implementation
of statistical analysis on the classified maps, especially in evaluating the spatial relationship between the
morphometric and the land-cover maps. Although
Pickup and Chewings (1996) and Hoersch et al.
(2002) demonstrated that a strong link exists between
vegetation and the underlying morphology, it is not
the purpose of this paper to examine this assertion in
detail. Our main goal in that direction was to demonstrate that this could be accomplished with the application of GIS and multivariate statistical analysis.
This study has demonstrated a valuable information source that can be used in geomorphological
applications. It has also provided precise information
about topography, and served both basic and applied
ends. Moreover, the study has demonstrated the ability of the delineated landform elements to be overlaid
on any digital map and imagery for further applied
research. An interesting development of the presented
approach could be, for instance, the investigation of
interrelationships between geomorphology, vegetation
distribution and human impact, framed also into a
natural hazard context.
Acknowledgements
The authors gratefully acknowledge the contribution of the Department of Environmental Management,
Mediterranean Agronomic Institute, Chania, Crete,
Greece for making available the data set for this study.
References
Band, L.E., 1986. Topographic partition of watersheds with digital
elevation models. Water Resour. Res. 22 (1), 15 – 24.
Baumann, A., Best, G., Gwosdz, W., Wachendorf, H., 1976. The
nappe pile of eastern Crete. Tectonophysics 30, 33 – 40.
Bonneau, M., 1976. Esquisse structurale de la Crete alpine. Bull.
Soc. Geol. Fr. 18 (2), 351 – 353.
Bonneau, M., Angelier, J., Epting, M., 1977. Reunion extraordin-
369
aire de la societée geologique de France en Crete. Bull. Soc.
Geol. Fr. 19, 87 – 102.
Carrara, A., Pugliese-Carratelli, E., Merenda, L., 1977. Computer
based data banks and statistical analysis for slope instability
phenomena. Z. Geormorphol. N.F. 21 (2), 187 – 222.
Cavalli, R.M., Fusilli, L., Pascucci, S., Pignatti, S., Poscolieri, M.,
2002. Relationships between morphological units and vegetation categories of Soratte Mount (Italy) as inferred by processing
elevation and MIVIS hyperspectral data. In: Benes, T. (Ed.),
Proc. Of the 22nd EARSeL Symposium and General Assembly,
Prague (Cech Rep.), 4 – 6 June 2002. Millpress, Rotterdam, The
Netherlands, pp. 573 – 579.
Coates, D.R., 1958. Quantitative Geomorphology of Small Drainage Basins in Southern Indiana. Columbia University, Dept.
Geology, New York.
Creutzburg, N., Drooger, C.W., Meulenkamp, J.E., 1977. General
Geological Map of Greece, Crete Island. Geological Mapping
Section Institute of Geological and Mining Research (I.G.M.R),
Athens, Greece.
Dehn, M., Gärtner, H., Dikau, R., 2001. Principles of semantic
modeling of landform structures. Comput. Geosci. 27 (8),
1005 – 1010.
Dikau, R., 1998. The need for field evidence in modelling landform
evolution. In: Hergarten, S., Neugebauer, H. (Eds.), Process
Modelling and Landform Evolution, Lecture Notes in Earth
Sciences, vol. 78. Springer, Heidelberg, pp. 3 – 12.
Drooger, C.W., Meulenkamp, J.E., 1973. Stratigraphic contributions to the geodynamic in the Mediterranean area: crete as a
case history. Bull. Geol. Soc. Greece 10, 193 – 200.
Drury, S.A., 1978. Image Interpretation in Geology. Allen and Unwin, London.
Dymond, J.R., DeRose, R.C., Harmsworth, G.R., 1995. Automated
mapping of land components from digital elevation data. Earth
Surf. Process. Landf. 20, 131 – 137.
European Environment Agency, 1997. Technical and Methodological Guide for Updating the CORINE Land Cover Database
(EUR17288EN), Commission of the European Communities,
Brussel, Belgium.
Evans, I.S., 1972. General geomorphometry, derivatives of altitude,
and descriptive statistics. In: Chorley, R.J. (Ed.), Spatial Analysis in Geomorphology. Methuen, London, pp. 17 – 90.
Fassoulas, C., 1999. The structural evolution of central Crete: insight into the tectonic evolution of the south Aegean (Greece).
Geodynamics 27, 23 – 43.
Franklin, S.E., 1987. Geomorphometric processing of digital elevation models. Comput. Geosci. 13 (6), 603 – 609.
Fytrolakis, N. (Ed.), 1980. The Geology of Crete Island. National
Technical University of Athens, Athens, Greece, p. 80.
Giles, P.T., Franklin, S.E., 1998. An automated approach to the
classification of the slope units using digital data. Geomorphology 21, 251 – 264.
Guth, P.L., 1995. Slope and aspect calculations on gridded digital
elevation models: examples from a geomorphometric toolbox
for personal computers. Z. Geomorphol., Suppl.Bd 101, 31 – 52.
Hall, D.J., Khanna, D., 1977. Statistical Methods for Digital Computers. Wiley, New York.
Hoersch, B., Braun, G., Schmidt, U., 2002. Relation between land-
370
A.O. Adediran et al. / Geomorphology 58 (2004) 357–370
form and vegetation in Alpine regions of Wallis, Switzerland. A
multiscale remote sensing and GIS approach. Comput. Environ.
Urban Syst. 26, 113 – 139.
Horton, R.E., 1945. Erosional development of streams and their
drainage basins; hydrophysical approach to quantitative morphology. Geol. Soc. Amer. Bull. 56, 275 – 370.
Hutchinson, M.F., Gallant, J.C., 2000. Digital elevation models and
representation of terrain shape. In: Wilson, J.P., Gallant, J.C.
(Eds.), Terrain Analysis: Principles and Applications. Wiley,
New York, pp. 29 – 49.
Imhof, E., 1982. Cartographic Relief Presentation, vol. XVIII.
Gruyter, Berlin, pp. 1 – 389.
Irvin, B.J., Ventura, S.J., Slater, B.K., 1997. Fuzzy and isodata
classification of landform elements from digital terrain data in
Pleasant Valley, Wisconsin. Geoderma 77, 137 – 154.
Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed. Prentice Hall, Englewood
Cliffs, NJ. 316 pp.
Le Pichon, X., Angelier, J., 1979. The Hellenic arc and trench
system: a key to the neotectonic evolution of the Eastern Mediterranean area. Tectonophysics 60, 1 – 42.
Lillesland, T.M., Kieffer, R.W., 2000. Remote Sensing and Image
Interpretation, 4th ed. Wiley, New York, p. 724.
Mackay, D.S., Robinson, V.B., Band, L.E., 1992. Classification of
higher order topographic objects on digital terrain data. Comput.
Environ. Urban Syst. 16, 473 – 496.
Makris, J., 1976. A dynamic model of the Hellenic Arc deduced
from geophysical data. Tectonophysics 36, 339 – 346.
Mark, D.M., 1975. Geomorphometric parameters: a review and
evaluation. Geogr. Ann. 57j, 165 – 177.
Mark, D.M., 1978. Concepts of data structure for Digital Elevation
Models. Proc. of the Am. Soc. of Photogrammetry, Digital Terrain Models Symposium, St. Louis, Missouri, USA, May 9 – 11,
pp. 24 – 31.
Martz, L.W., De Jong, E., 1991. Using Cesium-137 and landform
classification to develop a net soil erosion budget for a small
Canadian prairie watershed. Catena 18, 289 – 308.
Meulenkamp, J.E., 1971. The neogene in the southern aegean area.
In: Strid, A. (Ed.), Evolution in the Aegean. Opera Botanica,
vol. 30, pp. 5 – 12.
Meulenkamp, J.E., 1979. Field guide to the neogene of Crete. Publ.
Dep. Geol. Paleontol. Univ. Athens, Ser. A 32, 1 – 31.
Miller, J.C., 1953. A quantitative geomorphic study of drainage
basin characteristics in the Cinch Mountain Area, Virginia and
Tennessee, Tech. Rep. 3.
Morris, D.G., Heerdegen, R.C., 1988. Automatically derived catchments boundaries and channel networks and their hydrological
applications. Geomorphology 1, 131 – 141.
Negroni, C.N., Parmegiani, N., Poscolieri, M., 2000. Analisi degli
sconvolgimenti morfologici di un abitato del Bronzo in Etruria
Meridionale (in Italian). In: Lollino, G. (Ed.), CNR GNDCI
Pub. No. 2133. Proc. of the Conference GeoBen2000, Torino
(It), 7 – 9 June 2000, pp. 681 – 688.
Nogami, M., 1995. Geomorphometric measures for digital elevation
models. Z. Geomorphol., N.F. Suppl.Bd 101, 53 – 67.
Onorati, G., Poscolieri, M., 1988. The Italian mean heights archive,
a digital data set useful for thematic mapping and I units analysis. Proc. of the 8th Symposium EARSeL, Capri (Italy), 17 – 20
May, Commission of the European Communities, Luxembourg,
EURIISSIEN, pp. 451 – 466.
Onorati, G., Poscolieri, M., Ventura, R., Chiarini, V., Crucilla, U.,
1992. Analysis of the digital elevation model of Italy for quantitative geomorphology and structural geology. Catena 19,
147 – 178.
Parcharidis, I., Pavlopoulos, A., Poscolieri, M., 2001. Geomorphometric analysis of the Vulcano and Nysiros island: clues of the
definitions of their volcanic landforms. In: Giovannelli, F. (Ed.),
Proc. of the International Workshop ‘‘ The Bridge Between Big
Bang and Biology’’, Stromboli (Messina, It), 13 – 17 September
1999, CNR President’s Bureau of the National Research Council pub., Special Volume, pp. 310 – 320.
Pickup, G., Chewings, V.H., 1996. Correlations between DEM-derived topographic indices and remotely sensed vegetation cover
in rangelands. Earth Surf. Processes Landf. 21, 517 – 529.
Pike, R.J., 1993. A bibliography of geomorphometry with a topical
key to the literature and an introduction to the numerical characterization of topographic form.-U.S. Geological Survey Openfile report 93-262A.
Pike, R.J., 1999. A Bibliography of Geomorphometry, the Quantitative Representation of Topography-Supplement 3 (Open-File
Report 99-140), U.S. Geological Survey.
Pike, R., Dikau, R., 1995. Advances in geomorphometry. Z. Geomorphol., N.F. Suppl.Bd 101, 238.
Pike, R.J., Rozema, W.J., 1975. Spectral analysis of landforms.
Ann. Assoc. Am. Geogr. 65, 499 – 516.
Pike, R.J., Wilson, S.E., 1971. Elevation – relief ratio, hypsometric
integral, and geomorphic area-altitude analysis. Geol. Soc.
Amer. Bull. 82, 1079 – 1084.
Rackham, O., 1990. Vegetation History of Crete. Petromarula I.
Corpus Christi College, Cambridge, pp. 29 – 39.
Rackham, O., Moody, J., 1996. The Making of the Cretan Landscape. Manchester University Press, Manchester, p. 237.
Seidel, E., Kreuzer, H., Harre, H., 1977. K/Ar Dates of the Cretan
Phyllite-Quartzite Series. VI Coll. Geol, vol. I. Aegean Region,
Athens, pp. 121 – 127.
Skidmore, A.K., 1989. A comparison technique for calculating gradient and aspect from a gridded digital elevation model. Int. J.
Geogr. Inf. Sys. 3, 323 – 334.
Skidmore, A.K., 1990. Terrain position as mapped from a gridded
digital elevation model. Int. J. Geogr. Inf. Syst. 4, 33 – 49.
Ten Veen, J., 1998. Neogene Outer-Arc Evolution in the Cretan
Segment of the Hellenic Arc: Tectonic, Sedimentary and Geodynamic Reconstructions, vol. 160. PhD University of Utrecht,
p. 191.
Triantophyllidou-Baladie, V., 1990. The Cretan rural landscape and
its changes in the late medieval and modern times. Stability and
Change in the Cretan Landscape. Petromarule, vol. 1. Corpus
Christi College, Cambridge, England, pp. 47 – 51.
Ventura, S.J., Irvin, B.J., 2000. Automated landform classification
methods for soil-landscape studies. In: Wilson, J.P., Gallant, J.C.
(Eds.), Terrain Analysis: Principles and Applications. Wiley,
New York, pp. 267 – 290.