A review of landform classification methods · A review of landform classiﬁcation methods Marzieh...

A review of landform classification methods

Marzieh Mokarram1• Dinesh Sathyamoorthy2

Received: 16 May 2018 / Revised: 13 August 2018 / Accepted: 14 August 2018 / Published online: 17 August 2018

� Korean Spatial Information Society 2018

Abstract The study of landform can be used to predict

specific solid rock and moisture conditions that exist in that

landform. Landforms are of significance in engineering

because they influence the quality and type of grading, pre-

determine the drainage requirements, and the soil or rock

conditions. They have added significance because there are

an infinite number of duplicates of each type. Given that

each of the landform units have different characteristics

(soil, slope, elevation, vegetation etc.), classification of

landforms is importance. Therefore, landforms are widely

recognized as natural objects that partition the earth’s

surface into essential spatial entities. Landform entities

differ from one another in terms of specification such as

shape, size, orientation, relief and contextual position.

They also differ in terms of the physical processes that

were involved in their formation and that continue to

operate within them at the present time. This paper aims at

providing a review of the landform characteristics and

classification. Various definitions and attributes of land-

forms which focus on aspects of morphometry, geomor-

phometric context, terrain positions and scale are discussed

as well. In addition, the methods developed for the land-

form classification are assessed.

Keywords Landform � Geomorphometric � Classification

1 Introduction

As there is a wide variety of landforms on the earth, it is

useful to classify the earth surface complexity to easily

identify and manage them. Hence, the description of the

landform characteristics has received significant attention

from the earth scientists for decades. Landform is a specific

geomorphic feature on the surface of the earth, ranging

from different scale features such as plains, plateaus and

mountains to hills, valleys or alluvial fans.

There is no unique and universal definition in the views,

applications and definitions of the landform classification

worldwide. Scientists have brought this concept into con-

servation planning by identifying land facets [1], abiotic

units [2], geodiversity [3], geophysical stages [4], and ge-

omorphometric units [3]. Therefore, researchers decided to

cooperate with users in different contexts and solve this

problem [5]. As a land surface morphology is based on the

landforms structures, it forms the basis for geomorphology

maps and other earth sciences [6].

Air-photo interpretation and field studies are traditional

methods of identifying landforms that are limited by the

efficiency of an individual performing the interpretation

[7]. Therefore, the processes of identifying and determin-

ing landforms were initially difficult and time consuming.

Until recently, there have been published researches on the

general geomorphometry regarding principles of measuring

a shape and surface. These studies can be applied to

landforms with some modifications to materialize these

methods. For example, a set of 15 generic landforms based

on geometric features, such as altitude, slope and curvature,

were defined by Dikau [8]. However, there still is a place

& Marzieh Mokarram

[email protected]

Dinesh Sathyamoorthy

[email protected]

1 Department of Rangeland and Watershed, Agriculture

College and Natural Resources of Darab, Shiraz University,

Shiraz 71946-84471, Iran

2 Science and Technology Research Institute for Defence

(STRIDE), Ministry of Defence, Kuala Lumpur, Malaysia

123

Spat. Inf. Res. (2018) 26(6):647–660

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for developing methods and general types of classification

models which represent topography better. For example

MacMillan et al. [9] (segmenting landforms using fuzzy

method), Burrough et al. [10] (using fuzzy k-means for

high resolution landform classification), Schmidt and

Hewitt [11] (Fuzzy land element classification from

DTMs), Saadat et al. [12] (landform classification using

10-m resolution digital elevation model (DEM)), Jasie-

wicz, and Stepinski [13] (using DEM with high resolution),

Jasiewicz et al. [14] (using DEM and GeoPAT toolbox)

and Zawawi et al. [15] (using DTM with a spatial scale of

10 9 10 m) used automated methods of landform classi-

fication that improve the previous methods with specific

scale, scope, and spatial resolution. These researches of the

landform classification used digital elevation models

(DEMs) and new algorithms, such as the artificial neural

network (ANN) and self-organizing maps (SOM).

This paper is aimed at providing a review of landform

characteristics and classifications in different scale. The

various definitions and characteristics of landforms,

focusing on aspects of morphometry, geomorphometric

context, terrain positions and scale, will be discussed. In

addition, the methods developed for landform classification

will be assessed.

2 Landforms

Characteristics and recognizable shapes of landforms

define the physical features of the earth’s surface [16]. A

subjective semantic definition of landforms in the field of

specific geomorphometry is a terrain unit created by natural

processes [17]. However, many geomorphologists prefer a

definition which includes the recognition of artificial

landforms, such as quarries and waste heaps [18].

Dimensions such as length, width and height, and the sta-

tistical frequency of principal geomorphic attributes are

used to distinguish a landform type [18].

In addition, landform types have been referred to as

relief forms with meso-form associations [19] and land-

form patterns [20, 21]. In fact, plains, hills, mountains and

valleys which can be observed from multiple scales are

examples of landform types. These names are used for

larger landscapes in geography. A landform element is a

sub-part of a landform type that is hierarchical to landform

type.

2.1 Landform definitions

Landforms are defined as specific geomorphic features on

the surface of the earth, ranging from large-scale features

such as plains and mountain to minor features such as

individual hills and valleys. In addition, studies of

landforms involve two major interrelated conceptual

frameworks; functional and historical [21]. Processes

operative in the fields of geomorphology, hydrology,

ecology, forest, soil, pedology, geology, cartography and

others can be defined by landforms [15, 19, 22]. For

example, the separate and distinct soil characteristics,

topography, rock materials, and groundwater conditions

can be presented for each landform [23].

Some researchers view landform units as terrain features

created by natural processes with dependable information

concerning their own structure [24]. Landforms are also

defined by their surface shapes and location (can be per-

ceived as a small peak) in relation to other landforms,

underlying geologic materials and soil attributes [25].

Evans [26] described the size and space of landforms

clustered around the space available at characteristic

scales. The surface shape of landforms has been consis-

tently used to interpret or infer hill slope forming pro-

cesses, such as erosion, denudation, accumulation and

deposition, and other geomorphic processes. In addition,

Pike et al. [27] suggested that a landform has morphologic

features such as a watershed, which is interrelated to a part

of the land surface formed by specific geomorphological

process. Each landform may be combined of several

landform elements which have relatively stable morpho-

metric properties [28].

Landform units have been used as basic landform

descriptors in vegetation mapping [29], soil mapping [30]

and landscape ecology [31]. Terms such as ridge, hollow

and foot slope are used in many mapping legends to define

essential terrain units at the hill slope scale [7]. However,

the rationale behind these land units is only weakly defined

and differs in different legends.

Various studies have suggested notations and descrip-

tions of land elements [30]. For example, Milne et al. [31]

defined 77 land units at different scales, while Speight [30]

described 70 different landform elements by using the

attributes of slope, morphological type, dimensions and

geomorphological activity.

2.2 Landform characteristics

2.2.1 Morphometry

Simple morphometric features, such as saddles, peaks,

channels, ridges and planes, are identified based on the

predefined rules in geomorphometry, [32, 33]. Bolongaro-

Crevennaa et al. [34] showed that the morphometric fea-

tures proposed by Wood [35] can characterize each DEM

element but they cannot fully describe a group of more

complex forms of actual landforms. They demonstrated

that a strict relationship exists between morphometric

features and geomorphologic processes. To elaborate their

123

648 M. Mokarram, D. Sathyamoorthy

demonstration, they suggested that the morphometric

parameters can be used to characterize selected numeric

elementary forms associated with landforms.

Terrain form is categorized into ‘general geomor-

phometry’ and ‘specific geomorphometry’ [36]. General

geomorphometry involves the quantitative analysis of

landforms characteristics which are applicable to any

continuous surface. Specific geomorphometry is restricted

to specific concepts which may not be continuous for the

whole surface.

For general geomorphometry, the best known classifi-

cation design was proposed by Peucker and Douglas [37] in

six classes; ridge, peak, pass, channel, pit and planar

(Fig. 1; Table 1). This classification is based on a 3 9 3

window for evaluating DEMs. This design has been

employed by many authors [32, 34, 38].

Dikau [7] proposed a classification design for 15 classes

based on possible combinations of a land slope and a

curvature. For tangential curvature (slope area) these

classes can only define sloping elements of a surface

(Fig. 2) and do not have any class features such as chan-

nels, ridges and peaks. While for maximum curvature (flat

area) the classes of features consist of peak, ridge, plain,

saddle, channel and pit. As it includes, all possible forms

combinations are very appropriate for general geomor-

phometric classification. It has also been employed by

many recent authors [11, 39].

A more expanded design that defines a complete system

of the landform classification concerning the previous

classifications of Gauss [40] and Troeh [41] was presented

by Shary et al. [42] (Fig. 3) that is based on tangential

profile, mean difference and total Gaussian curvatures.

According to the scheme, form facets are defined with their

homogeneous ‘slope’ and ‘aspect’, whereas form elements

are described by their curvature and slope. This

scheme might remain theoretical rather than applicable as

it is not practically effective to extract these components

from DEMs and label them by using a generic nomencla-

ture in the landform classification context. Moreover, a set

of useful DEMs must be applicable to be related to land-

form classes. Then, a threshold is needed to judge if the

slope of an image element is satisfactory to be classified as

a sloping element or if the extent to which an element is

convex enough to be classified into a class or not (for

example, peak).

2.2.2 Geomorphometric context

Geomorphometry is a science devoted directly to quanti-

tative analysis of the earth’s surface. Nowadays, it is highly

regarded as a separate discipline. It provides qualitative

and quantitative descriptions and measurements of land-

forms [43]. An essential principle of geomorphometry

asserts that there exists a relationship between a relief form

and the numerical parameters used to describe it. Likewise,

a relief form relates to the processes related to its genesis

and evolution. One aim of geomorphologists working with

landform models is to obtain the best representation of a

physical reality [34].

The interaction between form and process is the center

of geomorphology [44] and form characteristics are con-

sidered as key components of geomorphological systems

[45]. A different method was built based on physiographic

aspects of land surfaces by Christian and Stewart [46].

Geomorphometric characteristics have been measured

manually for decades [47] with some methods which

involve derivation making from topographic maps that was

time consuming and labor intensive. To this end, Evans

[36] defined an integrated system of geomorphometry.

Since then, important progress has been achieved in

improving DTM accuracies [10–12], and developing new

algorithms and software to calculate terrain derivatives

[48].

Fig. 1 Six landform classes

proposed by Peucker and

Douglas [37]. a Ridge,

b channel, c plane, d peak,

e pass and f pit

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A review of landform classification methods 649

2.2.3 Terrain positions

Terrain positions depict geographic meanings and spatial

processes. They are usually labeled as ridge tops, shoul-

ders, back slopes, foot slopes, etc. Locations with the same

morphometric properties might belong to different slope

positions and are associated with different geomorphic

processes [49].

The system proposed by Gauss [40] described four field-

invariant geometric forms defined by total Gaussian signs

and mean curvatures (Fig. 4). This system, as expressed by

Troeh [41, 50], partitioned land surfaces into four gravity-

specific classes in order to determine two relative accu-

mulation mechanisms based on the consideration of tan-

gential and profile curvatures signs (Fig. 5).

Dalrymple et al. [51] proposed a nine-unit slope model,

while Conacher and Dalrymple [52] described each unit by

taking the best advantages of position, slope, profile cur-

vature and actual processes (Table 2). In addition, the four-

Table 1 Definitions of the xix landform classes proposed by Peucker and Douglas [37]

Feature name Description

Peak Point that lies on a local convexity in all directions (all neighbors lower)

Ridge Point that lies on a local convexity that is orthogonal to a line with no convexity/concavity

Pass Point that lies on a local convexity that is orthogonal to a local concavity

Plane Points that do not lie on any surface concavity or convexity

Channel Point that lies in a local concavity that is orthogonal to a line with no concavity/convexity

Pit Point that lies in a local concavity in all directions (all neighbors higher)

Fig. 2 The 15 form elements proposed by Dikau [8]. a Nose, b shoulder slope, c hollow shoulder, d super, e planar slope, f hollow, g super foot,

h foot slope, i hollow foot, j peak, k ridge, l plain, m saddle, n channel and o pit

Fig. 3 The landform classification scheme proposed by Shary et al.

[42]. a Total Gaussian curvature, b difference curvature, c total

Gaussian curvature, d mean curvature, e profile curvature and

f tangential curvature

123


unit slope model proposed by Wood [53] (Fig. 6) delin-

eates slope profiles that represent typical examples of

classifying landscape into relative positions.

Ruhe and Walker [54] separated hill slopes into five

segments; summit, shoulder, back slope, foot slope, toe

slope and alluvium by using a slope length and width

(Fig. 7). They also recognized geomorphic units of head,

nose and side slopes which add divergent, convergent and

linear possibilities of plan curvature to their

scheme (Fig. 7).

Pennock et al. [55] used data published by Young [56]

to categorize nine 3D landform elements by applying

measures of plan, profile curvatures and slope (Fig. 8). The

Fig. 4 Illustration of the four landform classes defined by Gauss [40]

based on total Gaussian and mean curvature [42]. a C-depressions,

b C-hills, d C-saddles and e C-saddles

Fig. 5 Illustration of the four landform classes defined by Troeh

[41, 50]. a Transit 1, b relative deflection zones, c relative accumu-

lation zones and d transit 2

Table 2 Landform units defined by Conacher and Dalrymple [52]

Land-surface unit Characteristics

1 Interfluve Predominant pedomorphologic processes caused by vertical (up and down) soil–water movements; 0�–1� slopegradient

2 Seepage slope Upland area where responses to mechanical and chemical eluviation by lateral subsurface soil–water movements

predominate

3 Convex creep slope Convex slope element where soil creep is the predominant process, producing lateral movement of soil materials

4 Fall face Areas with gradients greater than 45�, characterized by the process of fall and rockslide

5 Transportational

midslope

Inclined surfaces with 1�–45� gradients and responses to transport of large amounts of material downslope by flow,

slump, slide, erosion and cultivation

6 Colluvial footslope Concave areas with responses to colluvial redeposition from upslope

7 Alluvial toeslope Areas with responses to redeposition from upvalley alluvial materials; 0�–4� gradient8 Channel wall A channel wall distinguished by lateral corrosion by stream action

9 Channel bed A stream channel bed with transportation of material downvalley by stream action as the predominant process

Fig. 6 Slope models proposed by Wood [53]. a Upper convex

segment, b cliff face, c straight segment and d lower concave segment

123


elements were described with slope rate change in both

vertical and horizontal planes. This scheme is also similar

to the Richter [57] and Dikau [8] schemes.

In another study, three hierarchical terrain entities

classes and terrain and soil components were identified by

Zinck and Valenzuela [58]. They attempted to automati-

cally classify landforms to entities which are approxi-

mately equivalent to one, and only occasionally both of

two main hierarchical levels of landform patterns or land-

form facets (Fig. 9).

A two-level descriptive procedure of systematic and

parametric landforms description into landform patterns

and elements was proposed by Speight [20, 30]. Table 3

shows the classification system of profiles across the terrain

divided into morphological types of landform elements as

classified by Speight [30]. Skidmore et al. [59] related a

slope position to implied processes of the hill slope and soil

formation (Fig. 10). Upper slope positions were regarded

as zones of active removal and transporting materials while

lower slope positions were clearly identified as zones of

deposition and accumulation of materials. The proposed

method of Dikau et al. [19, 60] follows Hammond method

in defining four classes of a gentle slopes proportion, six

classes of a relative relief and four classes of a profile type

(Table 4) which, when combined together, result in 96

landform sub-classes [61]. MacMillan et al. [62] follows

Pennock’s modified original rules to include the consider-

ation of the relative slope position measured to the total

elevation difference from channel to divide or pit to the

peak (Fig. 11).

Most of the above discussed methods combine the

environmental meanings of terrain shape and position [63]

and have been attractive in representing specific charac-

teristics of topography [64]. Some of the indices that are

used to classify terrains (landforms) into positions gener-

ated from DEMs are the topographic position index (TPI)

[65], local elevation [66] and relative hill slope position

[67].

2.2.4 Scale

Most classifications of landforms are specific to a particular

scale. Landforms have been widely defined to occur across

a hierarchy of scales which assert that a geomorphic system

must be viewed in its complex, hierarchical context

[7, 32, 68, 69]. Etzelmuller and Sulebak [32] who assert

that a geomorphic system must be viewed in its complex,

hierarchical context.

Some characterizations of landforms are basically scale-

free and define the same invariant spatial entities regardless

of the scale [42]. Most either implicitly or explicitly define

a hierarchy with different sizes of landforms occurring at

different scales. Dikau [70] who follows Kugler [71],

illustrated how different landforms have been conceptual-

ized to occur over different scales. This hierarchical con-

ceptualization includes relief units ranging from relatively

homogeneous form facets to more complex associations or

patterns which consist of assemblages of lower level forms.

Hierarchical conceptualization was considered to make

form units (e.g. form facets) easier to be defined at micro-

scales by inherent shape-based attributes [70].

Dikau [70] suggested that most efforts to classify land-

forms correspond to the micro (1–100 m) and meso

(100 m–10 km) scales. Most scales/scholars also typically

focus on levels of complexity on the offered range of form

facets to form associations.

Many authors have examined the effects of varying grid

resolution on the values and accuracies of land-surface

characteristics and objects derived from elevation data sets

[72]. Ideally, each application scale in the wide range of

Fig. 7 Geomorphic units proposed by Ruhe and Walker [54].

a Summit, b shoulder, c backslope, d foot slope, e toe slope and

f alluvium

Fig. 8 Geomorphic units proposed by Pennock et al. [55]. a Diver-

gent, b planar, c convergent, d shoulder, e backslope and f footslope

Fig. 9 Illustration of conceptual differences between repeating

landform types and facets [42]. a Lower relief (undulating) and

b high relief (undulating)

123


use refers to multiple concepts; thus, it is a vague term.

Scales are attributed to the following [49] concepts:

(i) Cartographic scale: Depicted size of a feature on a

map relative to its actual size in the world;

(ii) Generalization: Level of abstraction;

(iii) Extent of the area;

(iv) Phenomenon scale: Depicted size at which human

or physical earth structures or processes exist

(v) Analysis scale: Size of the unit at which some

problem is analyzed

Scale is the window of perception, and hence, it has a direct

influence on the landform type on a specific location

[49, 73]. For instance, as shown in Fig. 12, at the finer scale

topography, a location can be perceived as a small peak,

but when the window is magnified, it becomes part of a

planar slope, and at a coarser scale, even a channel. If a

morphometric class is found to be persistent in the same

area of geographical space at different spatial resolutions, it

is defined as scale-independent [74].

A recent study for classification with different scales

was done by Kramm et al. [75]. The results showed that

increasing resolution should extract more details from the

study area. Generally, most experiences indicate that DEM

pixel resolutions between 5 and 30 m are most suitable for

landform classifications [76].

DEMs of the Shuttle Radar Topography Mission

(SRTM) and Advanced Spaceborne Thermal Emission and

Reflection Radiometer (ASTER) (30 m pixel resolution)

and two DEMs (5 and 10 m pixel resolution) use for

landform classification commonly. Based on the presented

method by Dikau et al. [19], Weiss [77], Jasiewicz and

Stepinski [13], Dragu and Blaschke [39], landforms were

classified in four different ways for each digital elevation

model.

Table 3 Topographic position classes proposed by Speight [30]

Name Topographic position classes

Crest Area high in the landscape having positive plan and/or profile curvature

Depression (open,

closed)

Area low in the landscape having negative plan and/or profile curvature, closed: minimum local elevation; open: extends

at same or lower elevation

Flat Areas having a slope\ 3%

Slope Planar element with an average slope[ 1%, subclassified by relative position

Simple slope Adjacent below a crest or flat and adjacent above a flat or depression

Upper slope Adjacent below a crest or flat but not adjacent above a flat or depression

Mid slope Not adjacent below a crest or flat and not adjacent above a flat or depression

Lower slope Not adjacent below a crest or flat but adjacent above a flat or depression

Hillock Compound element where short slope elements meet at a narrow crest\ 40 m

Ridge Compound element where short slope elements meet at a narrow crest[ 40 m

Fig. 10 Illustration of the inferred relationship between slope

position and geomorphic processes inherent in the approach of

Skidmore et al. [59]. a Stable residual, b degraded transportational

and c aggraded depositional

Table 4 Classification criteria of the method proposed by Dikau et al. [60]

Distribution of gentle slopes Local relief Profile type

(A) More than 80% of the area is gently sloping (1) 0–30 m (a) More than 75% of gentle slope is lowland

(B) 50–80% of the area is gently sloping (2) 30–91 m (b) 50–75% of gentle slope is lowland

(C) 20–50% of the area is gently sloping (3) 91–152 m (c) 50–75% of gentle slope is upland

(D) Less than 20% of the area is gently sloping (4) 152–305 m

(5) 305–915 m

(6)[ 915 m

(d) More than 75% gentle slope is upland

123


3 Landform classification

The landform classification has a wide range of application

domains, including mapping geomorphologic units [78],

mapping lithology [79], predicting soil properties [80], a

landscape ecology [81], vegetation mapping [82], precision

agriculture [83] and landslide mapping [84]. The landform

classification also constitutes a basic research topic in

geomorphometry [43]. Up until the 1950s, poor qualitative

descriptions of landforms had been issued due to the lack

of quantitative methods [85]. Traditionally, studies have

been performed by using a manual overlay process that

relies on hand-drawn maps on the transparent vellum.

Lately, GIS permits the geographic data merging by using

computers. The knowledge-based process that combines

existing geographic data to generate new information is

generally known as a cartographic modeling. The compo-

nent maps that show the geographic distribution of a single

category of parameters are known as geographic themes

[21].

Therefore, the landform classification is described as the

science of land definition which focuses on the extraction

of land-surface parameters and objects from DEMs and

DTMs [27]. Since GIS tools and digital elevation data

become easily accessible, more research subjects are in

geomorphometry and neighboring fields that focus on the

landform classification [5].

3.1 Brief history

For a long time, the landform mapping was based on the

visual expression of stereo aerial photographs and topo-

graphic maps supported with field surveys [47]. The visual

expression and landforms extraction was tedious; on the

other hand, the measurement of parameters from topo-

graphic maps was labor intensive and time consuming. In

addition, the classes of landforms relied heavily on the

expert’s experience and conceptual model [86] which is

materialized implicitly onto maps.

By appearing computers in the 1950s, the availability of

DEMs and derived data sets, such as slope, aspect, shaded

relief and hydrographical pattern, technology was used to

examine terrains [87]. Furthermore, the advent of GIS

technology and digital image processing techniques have

led to significant advances in the terrain parameterization

[32], specifically for the development of the automated

landform classification [10, 11]. There are many methods

in digital geomorphology to delineate watershed catch-

ments and sub-catchments with standard procedures [32].

With the development of science, different methods

were proposed for the extraction and classification of

landforms. Some of these methods were designed for

identifying certain features types like the linear or circular

forms [88], or specific forms such as mountains [89], hill

tops [90], landslides or strike ridges [91] or other features

[92]. Many approaches aim at characterizing hill slope

forms [10, 69] as it is widely pronounced in hydrological

studies. Concerning hydrological responses, the systematic

down slope variations are shown due to the water, soil and

nutrient movement controlled by surface forms along the

hill slope. Ruhe and Walker [54] identified geomorphic

units that divide hill slopes into five segments; summit,

shoulder, back slope, foot slope, toe slope, alluvium using

slope, and slope length and width. Landform elements can

Fig. 11 Illustration of landform elements extracted from land-surface

parameters for a 64 ha site in Alberta, Canada [9]. a Level crest,

b divergent shoulder, c upper depression, d backslope, e divergent

backslope, f convergent backslope, g terrace, h saddle, i midslope

depression, j footslope, k toeslope, l fan, m lower slope mound, n level

lower slope and o depression

Fig. 12 Changing morphometric classes with scale [73]. a Morpho-

metric class, b, c channel, d planar and e peak

123


be extracted from DEMs by applying various approaches

such as the examples given in Table 5.

3.2 Classification approaches

3.2.1 Classification based on general geomorphometry

General geomorphometry created a basis for the quantita-

tive comparison of different landscapes, as well as suiting

methods of the surface analysis provided outside geomor-

phology [27]. Before the advent of computers, the surface

analysis was acutely difficult; however, recently, with

increasing processing capabilities and great availability of

DEMs, it has become widespread and has many applica-

tions in digital terrain modeling [43]. The landform clas-

sification based on the general geomorphometry deals with

elementary forms and is less common as compared to a

wide range of application domains of the specific geo-

morphometry [27]. General geomorphometry is relatively

universal; it passes problems of landform subjective defi-

nition to an extent. Subjective ideas and operator biases are

reduced drastically though they are not totally eliminated

[93]. These biases appear objective in case that they have

choice of the data source, resolution and algorithms for

interpolation, smoothing and derivative calculation.

One significant characteristic and at the same time

drawback of the landform classification based on the gen-

eral geomorphometry is that it lacks accuracy whereas the

specific geomorphometry is precise, however, the specific

geomorphometry can only be applied in a specific context

[32]. General and specific geomorphometries are not

completely separated because some specific landforms like

hill slopes and drainage networks are so widespread on the

earth that their specific geomorphometry gets a general

importance and because some techniques of general geo-

morphometry can be applied to specific landforms as well

[94].

Geomorphons were introduced by Stepinski and Jasie-

wicz [95], and Jasiewicz and Stepinski [13] to automati-

cally classify pixels based on the local gradients (reduced

to the sign -, 0, ?), and computed according to the line-of-

sight principle. This way, 498 different geomorphon results

are produced (6561 if the orientation is taken into account)

in which the terrain setting may be categorized. Camiz and

Poscolieri [96] described in different ways to classify the

geomorphometry structures of the terrain under study and

provide appropriate graphical representations.

3.2.2 Classification based on specific geomorphometry

Specific geomorphometry involves the detection of discrete

features such as drumlins, sand dunes, alluvial fans, land-

slides, etc. [27]. The definitions of these specific geomor-

phologic features require operational determinations and

precise thresholds of parameters to ensure their proper

characterization [97]. The specific geomorphometry relates

to domain use such as the ecological, surface hydrology,

mapping and climate studies since these fields extract

specific landforms relevant to their interest using terrain

parameters. This type of classification covers a very great

part of the landform classification studies.

3.3 Automated landform classification

Due to the taxonomic designs of landforms like their

provenances, compositions and functions, these features

are hard to map and quantify by applying manual methods

merely. The automatic classification of geomorphological

land units mainly focuses on morphometric parameters

[89]; this can describe the form of a land surface in relation

to landform formation processes. Automated methods of

segmenting landforms into landform elements or facets

have been defined by different authors [39].

Consequently, researchers have been developed routines

procedures of the automatic landform extraction and clas-

sification for a variety of applications. Barbanente et al.

[98] expanded routines procedures of automatically iden-

tifying ravines and cliffs which are not categorized features

that can be justifiably included in a general landscape

classification methodology. Noticeably, several research

groups have developed methodologies to extract terrain

features from DTMs [91]. Dikau [8] expanded a method to

identify convex scarps, straight front and concave foot

slopes, scarp forelands, cuesta scarps, valleys, small drai-

nage ways and crests.

The automated classification of landform elements that

use object-based image analysis (OBIA) was proposed by

Dragut and Blaschke [39]. The OBIA are classified as

landform elements that apply a relative classification model

and built on both surface shape and altitudinal position of

objects. The classification has nine classes: peaks and toe

slopes (defined by an altitudinal position or degree of

dominance), shoulders and negative contacts (defined by

profile curvatures), head, side and nose slopes (defined by

Table 5 Examples of landform classification

Methods Authors

Combination of morphometric parameters [8, 28]

Fuzzy logic, unsupervised classification [10]

Supervised classification [106]

Probabilistic clustering algorithms [92]

Multivariate descriptive statistics [5, 8]

Double ternary diagram classification [34]

123


plan curvatures) and steep slopes and flat/gentle slopes

(defined by slope gradients). Classes are defined by taking

the best advantages of flexible fuzzy membership func-

tions. The mentioned method is directly applicable to

precision farming in Alberta, Canada. The model uses

derivatives computed from DEMs and the fuzzy logic rules

to identify up to 15 morphologically defined landform

facets.

3.4 Fuzzy versus crisp classifications

Methods of the landform classification can be sub-divided

according to whether they generate continuous (fuzzy) or

they generate rigid (crisp) classifications [99]. Each of

these classes may be further sub-divided according to

whether the classification rules are based on an expert

judgment or they are based on a statistical analysis.

The fuzzy classification is referred to a continuous

classification which takes the natural class overlap into

account to allow individual spatial entities to have mem-

berships (partial belongings) in multiple classes, and to

allow the existence of overlapped classes which have

gradual transitions from one another [100]. The degree to

which an entity belongs to a given class is not expressed in

terms of Boolean set theory (0 or 1), but in the fuzzy logic,

this theory is set based on membership function. Crisp

methods are increasingly being replaced by the fuzzy

methods classification because they allow a class overlap

which is very common for natural groups in terrain phe-

nomena [49].

Morphometric classes derived from mixtures of these

derivatives are more informative and useful if fuzzy sets

are used. In the context of analyzing DEMs, any given cell

may in fact contain elements of a number of different

morphometric classes. This is represented by the degree of

membership or belonging (range from 0 to 1). A mem-

bership grade of 1 would be associated with a cell that

exactly meets the ‘ideal’ attribute values of a particular

morphometric class, while a value of 0 would show that the

cell has no similarity or membership to that morphometric

class.

There are two methods of defining the membership

values of the fuzzy sets: ‘the semantic import model’ and

‘the similarity relation model’ [9]. The similarity relation

model uses surface derivatives as an input to a multivariate

fuzzy classification with a training set to define its ‘class

central concepts’ and yields the class membership values

for each case for instance a raster cell [9, 10]. The semantic

import (SI) model permits a formal identification and

incorporation of imprecise and overlapping semantics used

to describe or classify data [9]. In the case of terrain

classification, the SI model permits experts to identify

conceptual landform elements and to make class definitions

by using imprecise semantics [11].

Fuzzy c-means is an unsupervised technique which

identifies natural clusters and input data groupings in

geographical space [101]. It uses same methods of identi-

fying clusters, but it assigns each data point with a mem-

bership to each cluster representing its degree of

membership or similarity to that cluster [10]. In addition,

fuzzy k-means those algorithms which provide uncertainty

measures for the delineated land elements [10]. These

techniques are based on the fact that the geomorphometric

signatures of different landform units are related to similar

terrain parameters. The only difference between fuzzy c-

and k-means is the clusters model. Fuzzy c-mean uses

reciprocal distance to compute fuzzy weights while fuzzy

k-means algorithm is applied to solve clustering problems

[99].

3.5 Other methods

There are also other different methods of the landform

classification. The support vector machine (SVM) is a

group of theoretically superior machine learning algo-

rithms. It was developed to be competitive with the best

available machine learning algorithms in classifying high-

dimensional data sets [102]. For example, Stepinski et al.

[103] used SVMs for a test site on Mars to produce the

most accurate results as compared to other conventional

techniques of classifying topographic objects. Mangai et al.

[102] used SVMs to classify landforms and to identify a

wide variety of landforms in the subcontinent of India. In

addition, the artificial neural network (ANN) employs

learning algorithms to supervise the landforms classifica-

tion; consequently, it is applied to the landform classifi-

cation studies with specific aims [104].

There are semi-automatic methods to classify morpho-

metric features (landform elements) in a wide variety of

areas that have been developed by utilizing self-organizing

maps (SOM) [105]. It is an unsupervised ANN algorithm

which is used to cluster or visualize high dimensional input

vectors into two dimensional output based on the existing

regularities and correlations among them [105]. SVMs

have been used in a wide variety of areas such as the

classification of remote sensing data [106], the information

visualization and the knowledge discovery [107].

Mokarram et al. [108, 109], Mokarram and Hojati [110],

Shaygan and Mokarram [111] were used new methods such

as fuzzy and neural network for landform classification.

123


4 Conclusion

This paper provided a background on the theoretical basis

of landform characteristics and classification. It is impor-

tant to distinguish between objective classification

approaches which are primarily based on the surface shape

consideration and are typically applicable across all scales,

and subjective classifications that are typically applicable

to a specific scale or range of scales that tend to use a

variety of local and regional land-surface parameters as

inputs of the classification process.

In the past, the landform classification was performed by

using manual overlay processes which relied on hand-

drawn maps on the transparent vellum. Currently, GIS

permits the integration of geographic data by taking the

best advantages of computers. Over time and with the

creation of new methods such fuzzy methods, SVM and

ANN, the landforms classification have become easier and

more accurate. By using defined classes, users can deter-

mine natural phenomena such as a climate change, the type

of land and even disease distribution based on the landform

class type.

Using new methods such as genetic algorithm, PSO,

subpixel and etc. should increase resolution and extract

more details from input data for landform classification. So

we suggest to use the new algorithm form other fields for

landform classification.

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