Scientific African 14 (2021) e01032
Contents lists available at ScienceDirect
Scientific African
journal homepage: www.elsevier.com/locate/sciaf
Geospatial modelling of landslide susceptibility in Cross River
State of Nigeria
Joel Efiong a,∗, Devalsam Imoke Eni a, Josiah Nwabueze Obiefuna b,
Sylvia James Etu c
a
Department of Environmental Resource Management, University of Calabar, P.M.B. 1115, Calabar, Nigeria
Department of Geography and Environmental Science, University of Calabar, P.M.B. 1115, Calabar, Nigeria
c
Department of Geology, University of Calabar, P.M.B. 1115, Calabar, Nigeria
b
a r t i c l e
i n f o
Article history:
Received 31 May 2021
Revised 17 September 2021
Accepted 25 October 2021
Editor DR B Gyampoh
Keywords:
Geospatial modelling
Landslide
Landslide Susceptibility mapping
Cross River State
Nigeria
Frequency ratio
∗
a b s t r a c t
Landslides have continued to wreck its havoc in many parts of the globe; comprehensive
studies of landslide susceptibilities of many of these areas are either lacking or inadequate.
Hence, this study was aimed at modelling landslide susceptibility in Cross River State of
Nigeria, using geospatial approach. Frequency ratio (FR) model was adopted in this study.
In adopting this approach, a landslide inventory map was developed using 72 landslide
locations identified during fieldwork combined with other relevant data sources. Using appropriate geostatistical analyst tools within a geographical information environment, the
landslide locations were randomly divided into two parts in the ratio of 7:3 for the training and validation processes respectively. A total of 12 landslide causing factors, such as;
elevation, slope, aspect, profile curvature, plan curvature, topographic position index, topographic wetness index, stream power index, land use/land cover, geology, distance to
waterbody and distance to major roads, were selected and used in the spatial relationship
analysis of the factors influencing landslide occurrences in the study area. FR model was
then developed using the training sample of the landslide to investigate landslide susceptibility in Cross River State which was subsequently validated. It was found out that the
distribution of landslides in Cross River State of Nigeria was largely controlled by a combined effect of geo-environmental factors such as elevation of 250 – 500 m, slope gradient
of >35°, slopes facing the southwest direction, decreasing degree of both positive and negative curvatures, increasing values of topographic position index, fragile sands, sparse vegetation, especially in settlement and bare surfaces areas, distance to waterbody and major
road of < 500 m. About 46% of the mapped area was found to be at landslide susceptibility risk zones, ranging from moderate – very high levels. The susceptibility model was
validated with 90.90% accuracy. This study has shown a comprehensive investigation of
landslide susceptibility in Cross River State which will be useful in land use planning and
mitigation measures against landslide induced vulnerability in the study area including extrapolation of the findings to proffer solutions to other areas with similar environmental
conditions.
© 2021 The Author(s). Published by Elsevier B.V. on behalf of African Institute of
Mathematical Sciences / Next Einstein Initiative.
This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Corresponding author.
E-mail addresses: joelefiong@unical.edu.ng (J. Efiong), devalsamimoke@unical.edu.ng (D.I. Eni), joeobiefuna@unical.edu.ng (J.N. Obiefuna).
https://doi.org/10.1016/j.sciaf.2021.e01032
2468-2276/© 2021 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. This is an
open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
Introduction
Landslides are a major category of mass movement involving relatively rapid and perceptible downslope movements,
sliding or falling of relatively dry, weathered debris as a result of slope failure [1]. Unlike other categories of mass movements like the creep, landslides take place characteristically on discrete surfaces, which sharply define the movement. They
include rock slides, debris slides, mudflows, bog flows, which could be translational slides, rotational slips, falls and subaqueous slides. It has been proved that landslides usually occur under different geo-environmental conditions based on the
nature of the area under study [2]. The distribution of landslides is controlled by some localized physical factors such as,
slope, topography, land use and geology. Predicting how these factors trigger landslide occurrence is a major step towards
managing landslide hazard, therefore, making development of landslides maps to be location specific.
The effect of landslides can be long-lasting on the environment causing topographic changes, changes in the river course
and pattern, destruction of forest and wildlife habitat, removal of agricultural soils from slopes and disruption of road traffic [3]. Generally, landslides have environmental and socioeconomic costs implications on human populations. There is an
increasing loss of life and property due to landslides at a global scale. It is reported that landslides accounted for about 9
per cent of global total natural disasters with over 44,0 0 0 deaths between 2002 and 2007 [4]. In Nigeria, landslides appear
to be more common in the southeastern part of the country including Cross River State, which is the study area where the
negative impacts are enormous. For instance, in 1988 a major landslide occurred in Nanka area in Anambra State leading to
the relocation of over fifty families [5].
Landslides have continued to have devastating effects in the last few years in Cross River State of Nigeria. For instance, in
2013, it was reported that landslides killed 9 people in Calabar, (News Agency of Nigeria, 9 September 2013) [6]. In the same
year another landslide occurred at the Obudu Mountain Resort, affecting traffic and leaving several persons abandoned at the
Resort [7]. Earlier on in 2012, the Kache Bano Canopy Walkaway in the Drill Ranch Afi Mountain in Boki Local Government
Area of Cross River State was considerably damaged by a massive mud and landslide down the Bano stream drainage [8].
Moreover, over 50 houses in Agwagune community in Biase local government area of Cross River State were swept by a
landslide which hit the area in January 2017 [9]. This same community was earlier hit by landslides in 1992 and 1997.
On October 17, 2018, over sixty houses and historical sites were damaged by landslide at Anderson and Marina Streets in
Calabar, the Cross River State capital [10]. The Palace of the Obong of Calabar, the Primary Health Centre, and the Europeans’
Cemetery amongst others, located within the Anderson axis were on the verge of being destroyed by landslides [10]. Several
other locations in the state, including Ekenkpon in Odukpani Local Government Area have not been spared the detrimental
effects of landslide. Surprisingly, no comprehensive investigation has been carried out to map landslide prone areas in the
state, in spite of its propensity. There is currently no landslide database (inventory) for Cross River State; only scanty studies
have been done in some segments of Cross River State, using other approaches. For example, literature revealed that two
investigations were carried out on landslides in the Obudu axis of the study area using geotechnical methods [11–12]. In
addition, a landslide susceptibility assessment of Calabar, the Cross River State capital, was conducted using geographical
information systems (GIS) [13]. However, the input map layers and map output of this study have distorted and undefined
map projection without showing precise locations of the of landslide susceptibility areas.
Several geospatial approaches and models have been used for landslide susceptibility mapping. These models include
statistical index, index of entropy, frequency ratio, spatial prediction model, machine learning techniques, conditional probability model, analytical hierarchical process, artificial neural network, logarithmic regression models, and others. Some of
these models were used singly or in combinations with other similar models [14–20]. Hence, there is no dispute on the
capabilities of GIS in landslide susceptibility mapping. Moreover, none of these methods can be said to be better than the
other, except in terms of the purpose of the study.
Landslide susceptibility maps have remained useful tools in landslide hazard management since they show the degree
of susceptibility of areas to landslide occurrence. They become decision making instruments for planning purposes and
emergency management. In generating landslide susceptibility maps, it is hypothesized that future landslides will occur
under same conditions as in the previous ones [21]. Hence, the susceptibility of a particular area can be investigated by
evaluating the spatial relationship between a set of landslide-enabling factors and past occurrences of landslide. This has led
to the generation of landslide susceptibility maps for many regions of the world using geospatial techniques. The objectives
of this study were therefore to develop an inventory of landslides in the study area and model a landslide susceptibility
map for Cross River State of Nigeria using geospatial technique.
Description of the study area
The study area is Cross River State, located in the Southeastern flank of Nigeria. It lies within Longitudes 08° 00 E and
09 o 25 E of the Greenwich Meridian and Latitudes 04 o 00 N and 06° 45 N of the Equator (Fig. 1). It is bounded by the
Republic of Cameroon in the East and the Cross River estuary in the South. Cross River State is further bounded by four
other states in Nigeria, including Akwa Ibom in the Southwest, Abia and Ebonyi in the West, and Benue in the North. The
study area is approximately 21,167.9 km2 . Topographically, elevation values in the area ranges from 0 to 1880 m above mean
sea level. The geology of the area is made up of sedimentary basin and basement complex of metamorphic and igneous
[22].
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J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
Fig. 1. Cross River State of Nigeria.
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Scientific African 14 (2021) e01032
Fig. 2. Flowchart of Methodology.
The study area is regarded as humid tropical environment and area is characterized by two climatic seasons (rainy and
dry seasons). Although rainfall occurs all through the year, displaying a double-maxima pattern with peaks in June/July
and September, the area has average annual rainfall in excess of 1800 mm [23]. This makes the study area to be highly
susceptible to landslides (Figure SM1) and other processes of running water. The area also records high temperature values
with average daily maximum above 24 °C and a range of 6 °C. Cross River State has the richest forest reserve in Nigeria.
However, in most places the original vegetation cover has been removed through agricultural activities, quarrying and rock
blasting, tree logging and urban expansion [24]. These activities could also exacerbate geo-environmental hazards, including
landslides in the study area.
Methodology
The six methodological steps adopted in this study are: (1) Data collection, (2) Development of landslide inventory map,
(3) Determination of the landslide conditioning factors, (4) Model construction and spatial relationships, (5) Development of
landslide susceptibility map, and (6) Model validation. These steps are illustrated in Fig. 2.
Data collection
The data for the development of landslide susceptibility map were collected and extracted from the Aster Digital Elevation Model (DEM), Landsat 8 imageries, and the Cross River State Geographic Information Agency (CRSGIA). The Aster DEM
was obtained in tiles which were first and foremost mosaicked and the area of interest that is the study area was then
clipped for this study. The choice of Aster Dem was because it is readily available free of charge [25]. Moreover, it has a
moderate spatial resolution of approximately 30 m. This was considered appropriate due to the large size of the study area.
Again, the interest in this baseline study was to indicate problematic areas with the view of generating awareness
amongst policy makers and the public. Moreover, landslide susceptibility maps produced using Aster imagery has been
found to be of good accuracy [26]. Landslide conditional factors such as elevation, slope gradient, aspect, curvatures (profile
and plan), Topographic Wetness Index (TWI), Topographic Position Index (TPI) and Stream Power Index (SPI) were extracted
from the Digital Elevation Model (DEM).
Landsat 8 imageries of December 2019 and January 2020, a period with the highest chance of obtaining cloud-free imageries covering the entire study area was used to classify the land cover. On board of Landsat 8 are two sensors: the
Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI generates 9 spectral bands which are: band
1- Deep blue (wavelength: 0.433–0.450), band 2-Visible blue (wavelength: 0.450–0.515),band 3 – Visible green (wavelength:
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J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
0.525–0.600), band 4 - Visible red (wavelength: 0.630–0.680), band 5 – Near infrared (wavelength: 0.845–0.885), band 6
– Short wavelength infrared 2 (wavelength: 1.560–1.660), band 7- Short wavelength infrared 3 (wavelength: 2.10 0–2.30 0),
band 8 – Panchromatic (wavelength: 0.500 −0.680) and band 9 – Short wavelength infrared 1 (wavelength: 1.360 – 1.390).
The OLI images have capacity to discriminate vegetation types, cultural features, biomass and vigour [27]. For this study,
band 9 was excluded since it is primarily used for cirrus cloud detection. The first eight bands were stacked and the area
of interest which is Cross River State of Nigeria was extracted. Data on water bodies and road networks were obtained from
the Cross River State Geographic Information Agency, while data on geology were collected from the Department of Geology,
University of Calabar, Calabar.
Landslide data were collected from relevant published documents and fieldwork. Intensive fieldwork was embarked upon
across the study area, particularly to places where information existed on the occurrence of landslides from 1990 – 2019.
In some of the locations, it was discovered that landslides have occurred repeatedly in recent years. A total of 72 landslide
occurrences typified by rock falls, soil falls, climatic mudflows and soil creep, were identified and mapped (Table SM1). The
coordinates of the landslide locations were obtained as point data using Geographical Positioning System (GPS).
Landslide inventory map
From the 72 landslide locations that were identified and mapped during the fieldwork, a database was developed for
historical landslides in Cross River State. a landslide inventory (LI) map was developed in the GIS (Figure SM2). From here,
70 per cent of landslide locations were used to train the model (Figure SM3), while the remaining 30 per cent were used
for testing (Figure SM4). The training and testing datasets were statistically determined from the inventory of landslides in
the study area using the geostatistical analyst tool in ArcGIS 10.3.1 (Figure SM5).
Factors influencing landslide occurrence
Twelve factors (elevation, slope, aspect, profile curvature, plan curvature, TPI, TWI, SPI land cover, geology, distance to
water body and distance to major roads) influencing the occurrence of landslides were used in modelling susceptibility to
landslides in the study area. The selection of these factors was based on knowledge from relevant literatures [28].
Elevation
Elevation is simply the height above or below a fixed reference point of a geographic location. Elevation in this study as
expressed in the DEM, was measured relative to the mean sea level. The importance of elevation in hydrological and slope
stability studies cannot be under estimated. Elevation sets environmental control and provides the basis for spatial variation
in hydrological conditions and stability of slopes in a particular region [29]. Elevation in Cross River State ranges from sea
level (0) to 1880 m (Figure SM6).
Slope
Slope as the first derivative of the elevation was obtained to quantify the variation in elevation over a distance. It is an
important parameter in hydro-geomorphological studies including landslide feasibility and water flow management. While
there are many methods for determining slope from a DEM, the third order finite difference [29], where slope is calculated
based on the elevation of a point and its eight neighbours was used in this study. The slope algorithm is expressed as Eqn 1:
Slope = arctan
x2 + y2
(1)
where x and y are the sizes of cells in the x and y directions, respectively.
Generally, slope value decreases with the flattering of the terrain and vice versa. Studies have shown that the shear stress
in the residual soils, caused by gravitation, increases with increasing slope angle [30]. Slope in the study area was extracted
from Aster DEM using the Surface tool in the spatial analyst toolbox of ArcGIS 10.3.1. Slope in the study area ranges between
0 and 69.68°, with about 60 per cent of the area having slopes of less than 10° (Figure SM7).
Aspect
Aspect, which is another first derivative of elevation, was determined as the angle between the y-axis and the direction
in which the slope is steepest in the clockwise direction [31]. Aspect ranges vary from 0 to 360 and has direct influence
on rainfall intensity, soil moisture, and exposure of slope deposits to sunlight and wind. Aspect algorithm is expressed as
Eqn 2:
Aspect = arctan(slope in x/slope in y )
(2)
In the present study, aspect ranges from −1 to 359. 833 (Figure SM8).
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Curvature (Profile and plan)
Curvature is simply described as the "slope of the slope" of a surface [31]. It has been used by several scientists to predict
areas that are susceptible to future landslide occurrence [32]. Curvature has been expressed either as the standard curvature,
profile or/and plan curvature [31]. While profile curvature measures the flow acceleration/deceleration, plan curvature relates
with convergence/divergence of flow across the surface.
Curvature was obtained by fitting a fourth-order polynomial to a 3 × 3 window of a DEM [33], expressed as Eqn 3:
Z = Ax2 y2 + Bx2 y + Cxy2 + Dx2 + E y2 + F xy + Gx + Hy + I
(3)
where A, B, C, D, E, F, G, H and I were determined from the surface as coefficients. The coefficients for every cell on the
surface relate to the nine elevation values. Curvature, profile curvature, and plan curvature were then estimated using the
surface tool in the Spatial Analyst toolbox.
The value of profile curvature in Cross River State, Nigeria, ranges from −21.9242 to 17.2208 (Figure SM9). Similarly, the
value of plan curvature ranges from −17.9835 to 21.2401 (Figure SM10).
Topographic position index (TPI)
The topographic position index (TPI) is a reflection of the difference between the focal cell elevation and the mean elevation of all cells in its neighbourhood [34]. TPI, together with lithology, is an effective factor for debris flow. The consideration
of TPI as a landslide conditioning factor and its extensive usage in landslide susceptibility studies is based on the argument
that landslide events usually take place on the ridges [35]. Figure SM11 is the land facet extension window for the TPI
extraction for Cross River State. The TPI values in the study area range from – 172.284 to 242.858 (Figure SM12).
Topographic wetness index (TWI)
Topography plays an important role in landslide hazards and therefore indices related with it, particularly the TWI, have
always been included in susceptibility investigations. TWI depends largely on slope and proves very informative in the analysis of the effect of topographic control on hydrologic response within a watershed [36]. TWI for this study was calculated
as Eqn 4 and Eqn 5:
TWI = ln
= ln
α
(4)
tanβ + C
(α + 1 )∗Cell size
,
tan(β ∗ π /180 ) + C
(5)
where α is the flow accumulation, β is the slope (in rad), π /180 is taken as 0.017453 and C (constant term) is equal to
0.001. TWI layer of Cross River State ranges in values from 2.45156 to 20.3617 (Figure SM13).
Stream power index (SPI)
The stream power index (SPI) as a landslide conditioning variable extracted from DEM has been used extensively in
landslide susceptibility models [34]. It is used to indicate the erosive power of running water. Cases where SPI has been
used in landslide susceptibility mapping abound in literature [34,35]. SPI for Cross River State was computed as Eqn 6:
SPI = (α ∗ cell size )∗tan(β ∗
π /180 )
(6)
where α is the flow accumulation, β is the slope (in rad) and π /180 is calculated as 0.017453.
SPI layer of Cross River State has values ranging from 0 to 2399.24 (Figure SM14).
Land use/ land cover (LULC)
Land use describes how a particular portion of the earth’s surface has been used by man (such as for residence, industry,
agriculture, etc.); whereas land cover describes the materials which are present on the surface (such as vegetation, soil-rock,
water, and others). For planning purposes however, the expression “land cover” comprises both land use and land cover [37].
For the present study, LULC was used to describe the material that is on the surface of the earth.
The material on the surface of the earth no doubt has significant effect on landslides. Hence, land use/land cover is a
major landslide conditioning factor. Several studies, particularly in recent times, have included LULC in landslide susceptibility index (LSI) and zonation mapping as one of the conditioning factors See for example: [23,24,35]. It has been shown that
built-up areas and other land portion mostly bare surfaces are more susceptible to landslides than areas that have dense
vegetation cover [26].
In the present study, the land cover map was determined from Landsat 8 data using the supervised classifier algorithm
in Erdas Imagine 2014. Six (6) land cover classes were extracted: undisturbed forest, disturbed forest, grassland, cropland,
settlements /bare surfaces, and water body (Figure SM15, Table SM2). The result reveals that 41.03 per cent of the land cover
is “undisturbed forest” while 25.01 per cent constitutes “disturbed forest”. About 28 per cent of the land is covered with
crops while 3.43 per cent constitutes settlements and bare surfaces. Also, waterbody covers 1.16 per cent of the land surface
while grassland is 0.93 per cent. However, with increase population and other activities placing demand on land, land cover
change would likely exacerbate landslides in the study area.
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Geology
Studies had shown that geology has significant control on landforms [38], and essentially influences slope stability. Many
studies have shown that rock hardness, type of rock, and structure of the interlayer are strong indicators for landslide susceptibility of an area [19]. The generalized geology map of Cross River State was obtained from the Department of Geology,
University of Calabar, and digitized into GIS database created for this project. Four (4) main categories of rocks are identified
and described in Cross River State (Figure SM16, Table SM3). These are the basement complex rocks of Precambrian age in
the Oban Massif and Obudu Plateau, the Cretaceous sedimentary rocks, the Tertiary Sands and Igneous intrusions.
Lithological description of the Oban Massif and Obudu Plateau revealed a composition of Precambrian rocks which include migmatites, gneisses, charnockite, granulite, amphibolite, schists and phyllites intruded by acidic, intermediate, basic
and ultrabasic rocks. The Cretaceous sediments consist of shale, limestone, consolidated sandstone, mudstone and marl deposits. The Tertiary deposit in the study area is primarily unconsolidated sandstone of Benin Formation. Igneous rocks in
the area are basically dolerite and pegmatite intrusions [21].
Distance to water body
Several studies, including [2,24] used distance to water body or drainage as one of the conditioning factors for landslide
susceptibility. The major river in the study area is the Cross River, from where the state derives its name. The Cross River has
its origin from the neighbouring Cameroon Republic and enters the Cross River State in an east-westerly direction, before
flowing down and empties into the Atlantic Ocean through the Cross River estuary in the south. This main river is however
served by several estuaries including the Aya and Calabar rivers. Water bodies like rivers, streams, and lakes have significant
influence on slope stability; hence distance to water bodies was included as a conditioning factor in this study. Distance to
water body layer of Cross River State was developed and used as one of the conditioning factors (Figure SM17).
Distance to major roads
Roads are basically human constructions on the earth’s surface with the basic aim of providing movement corridors
for goods and services from one place to another. Road construction interferes with earth materials which could result in
slopes being unstable [35, 23]. Moreover, fieldwork revealed that many landslides in the study area occurred very close to
road arteries, such that distance to major roads was considered a conditioning factor of landslide in the study area and
major road layer of Cross River State was developed (Figure SM18).
Model construction and spatial relationships
Several GIS-based landslide susceptibility mapping approaches, including statistical index (SI), frequency ratio (FR), analytical hierarchy process (AHP), and artificial neural networks (ANN) have been discussed in literature [14–21]. While none
of these methods is yet to be considered the most suitable for DEM-based landslide conditioning parameters for landslide
susceptibility mapping, the frequency model appears to be in common usage [35,36], therefore, it was adopted to evaluate landslide susceptibility in this study. Moreover, frequency ratio model is an observation-based approach that relates the
distribution of landslides with each of the landslide conditioning factor [14].
The frequency ratio (FR) model is mathematically expressed as Eqn 7[14]:
FR =
Nip /N
Nil p /N l
,
(7)
p
where Ni is the number of pixels in each landslide conditioning factor class, N is the number of all pixels in the study area,
lp
Ni is
the number of landslides in each landslide conditioning factor class, and N l is the total number of landslides in the
study area.
The landslide conditioning factors layers and training dataset were used for the construction of the frequency ratio model.
To allow for this, landslide conditioning factors raster maps were resampled to a common pixel size of 30 m grid. Where
conditioning factor data was in vector format, it was first of all rasterized before resampling. All conditioning factors with
continuous data were resampled using the cubic convolution technique while those with discrete data were resampled using
nearest-neighbour approach. The 12 landslide conditioning factors were then reclassified into the appropriate classes for the
model development. In adopting the FR in this study, values greater than 1 indicated higher significance of correlation
between landslide and certain attributes or class of the conditioning factor and vice versa. Generally, the higher the value
of the ratio, the more significant the factor’s attribute or class is to the relationship [36].
Spatial relationships between the occurrence of landslides and conditioning factors in Cross River State, Nigeria, are presented in Table 1. The visualization of the relationships for the training data set is shown in Figure SM19.
From the results in Table 1, 74 per cent of landslides occurred in elevation range of < 100 m, with no landslides occurrence at elevation > 500 m above mean sea level. However, class 250 – 500 m had the highest FR of 2.20. Also, except for
slope gradient of < 3°, landslides occurred in all other slope classes, with the highest percentage (32 per cent) occurring in
the class of 15 – 25°, followed by 26 per cent in class 5 – 10 and 14 per cent each in classes 10 – 15 and 25 – 35. Meanwhile, slope class number 6 (>35°) had the highest FR of 6.96. Table 1 further reveals that 66 per cent of the landslides
occurred in slopes facing the south, southwest, and west, with the rest occurring in other classes. The highest FR was 2.08,
corresponding to slopes facing the southwest direction.
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Table 1
Landslide conditioning factors, classes and frequency ratio values.
Data Layer
Class
number
Class value (range)
No of pixels
in class
% of pixels
in class
No of landslides
in class
% of landslides
in class
FR
PR∗
Elevation (m)
1
2
3
4
5
1
11,613,860
9,213,187
1,711,117
733,805
248,010
23,519,979
2,037,261
49.38
39.17
7.28
3.12
1.05
100
8.66
37
5
8
0
0
50
0
74
10
16
0
0
100
0
1.50
0.26
2.20
0
0
3.95
0
2.58
Slope (in
degrees)
< 100
100 – 250
250 – 500
500 – 1000
> 1000
Total
<3
3–5
5 – 10
10 – 15
15 – 25
25 – 35
> 35
Total
Flat (- 1)
N (0 – 22.5)
NE (22.5 – 67. 5)
E (67.5 – 112. 5)
SE (112.5 – 157.5)
S (157.7 – 202.5)
SW (202.5 – 247.5)
W (247.5 – 292.5)
NW (292.5 – 337.5)
N (337.5 – 360)
Total
< −0.7
6,483,491
8,194,228
3,545,431
2,316,594
740,310
202,664
23,519,979
492,242
1,295,334
2,929,083
2,941,095
2,865,568
2,812,553
2,992,825
3,008,499
2,899,925
1,282,855
23,519,979
469,556
27.57
34.84
15.05
9.85
3.15
0.86
100
2.09
5.51
12.45
12.50
12.18
11.96
12.72
12.79
12.33
5.45
100
1.10
4
13
7
16
7
3
50
0
3
4
1
5
8
13
12
1
3
50
2
8
26
14
32
14
6
100
0
6
8
2
10
16
26
24
2
6
100
4
0.29
0.75
0.93
3.25
4.45
6.96
16.63
0
0.49
0.64
0.16
1.82
0.17
2.08
1.87
0.17
1.10
9.63
2.00
−0.7 - −0.2
−0.2 – 0.2
0.2 – 0.7
> 0.7
Total
< −0.6
−0.6 - −0.2
−0.2 – 0.2
0.2 – 0.6
> 0.6
Total
< −40
- 40 - −25
−25 - −8
−8 – 7
7 – 23
23 – 40
> 40
Total
<3
3–5
5–7
7–9
9 – 11
11 – 13
>13
Total
< 20
20 – 40
40 – 60
60 – 80
80 – 100
100 – 120
> 120
Total
Igneous intrusions
Tertiary sands
Basement Complex
Cretaceous sediments
Total
2,684,877
17,057,021
2,837,932
470,593
23,519,979
1,544,051
1,323,056
17,633,586
1,351,822
1,667,464
23,519,979
509,075
2,910,767
9,841,087
7,538,229
2,037,232
515,718
167,871
23,519,979
4,472,146
7,222,747
5,937,640
3,470,421
1,644,388
542,409
230,228
23,519,979
5065
23,497,415
15,310
1744
345
85
15
23,519,979
454,474
1,514,303
12,163,463
9,408,778
23,541,018
11.41
72.52
12.07
2.00
100
6.56
5.63
74.97
5.75
7.09
100
2.16
12.38
41.84
32.05
8.66
2.19
0.71
100
19.01
30.71
25.25
14.76
6.99
2.31
0.98
100
0.02
99.90
0.07
0.01
0.001
0.0003
0.00006
100
1.93
6.43
51.67
39.97
100
16
7
20
5
50
3
19
6
14
8
50
1
5
11
14
13
5
1
50
23
13
9
1
3
0
1
50
0
48
2
0
0
0
0
50
0
15
16
19
50
32
14
40
10
100
6
38
12
28
16
100
2
10
22
28
26
10
2
100
46
26
18
2
6
0
2
100
0
96
4
0
0
0
0
100
0
30
32
38
100
2.80
0.19
3.32
5.00
13.31
0.91
6.76
0.16
4.87
2.26
14.96
0.92
0.81
0.53
0.87
3.00
4.56
2.80
13.50
2.42
0.85
0.71
0.14
0.86
0
2.04
7.02
0
0.96
61.45
0
0
0
0
62.41
0
4.66
0.62
0.95
6.23
2
3
4
5
6
7
Aspect
1
2
3
4
5
6
7
8
9
10
Profile
curvature
1
2
3
4
5
Plan curvature
1
2
3
4
5
TPI
1
2
3
4
5
6
7
TWI
1
2
3
4
5
6
7
SPI
1
2
3
4
5
6
7
Geology
1
2
3
4
5
1.94
1.00
1.18
1.17
1.38
1.60
4.56
3.46
(continued on next page)
8
J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
Table 1 (continued)
Data Layer
Class
number
Class value (range)
No of pixels
in class
% of pixels
in class
No of landslides
in class
% of landslides
in class
FR
PR∗
Land Use/Land
Cover
1
Undisturbed forest
9,621,296
41.03
6
12
0.29
1.33
2
3
4
5
6
Disturbed forest
Cropland
Grassland
Water body
Settlement/Bare surfaces
Total
< 500
5,864,986
218,863
6,667,711
803,020
272,670
23,448,546
3,625,567
25.01
0.93
28.44
3.42
1.16
100
15.41
9
8
15
0
12
50
19
18
16
30
0
24
100
38
0.72
17.14
1.06
0
20.63
39.85
2.47
1.35
500 – 1000
1000 – 1500
1500 – 2000
> 2000
Total
< 500
2,651,040
2,298,448
2,022,745
12,922,179
23,519,979
2,753,588
11.27
9.77
8.60
54.94
100
11.71
9
6
5
11
50
13
18
12
10
22
100
26
1.60
1.23
1.16
0.40
6.85
2.22
1.02
500 – 1000
1000 – 1500
1500 – 2000
> 2000
Total
2,349,301
2,028,232
1,750,305
14,638,553
23,519,979
9.99
8.62
7.44
62.24
100
11
5
8
13
50
22
10
16
26
100
2.20
1.16
2.15
0.42
8.15
Distance to
water body (m)
1
2
3
4
5
Distance to
major road (m)
1
2
3
4
5
∗
Computation of PR is shown in Table SM4.
Profile curvature result reveals that the highest percentage (40 per cent) of landslides occurred in the profile curvature
class number 4 (class range: 0.2 – 0.7), followed by class number 2 (class range: −0.7 - −0.2) with 32 per cent. However,
landslide frequency ratio was generally higher in concave slopes with the value of 5.00 recorded where curvature value
was> 0.7. Table 1 again clearly shows that the highest percentage of landslides occurred in the plan curvature class number
2 (class range: −0.6 to - 0.2) with FR value of 6.76, followed by class number 4 (class range: 0.2 to 0.6). Class range of −0.2
to 0.2 had the least FR value of 0.16.
The highest FR ratio amongst land use /land cover classes of 20.63 was recorded for “settlement /bare surfaces”, followed
by 17.14 for “cropland”. On the other hand, “water body” had the least FR of 0, followed by “undisturbed forest” (0.29),
“disturbed forest” (0.72), and “grassland” (1.06). With respect to geology, the Tertiary Sands appeared to be more unstable
with FR value of 4.66. The results in Table 1 further indicated that with a distance of 500 m to water body, FR was 2.47,
and continued to decrease as distance to waterbody increases. At distance 20 0 0 m, FR was< 1.
Further, it was observed that FR tends to decrease as distance to major road increases. For example, the highest FR value
of 2.22 relates with distance < 500 m while at distances greater than 20 0 0 m, FR value was computed as 0.42. Again, TPI
values in the positive range had significant FR values of > 1. However, this accounted for only about 38 per cent of landslide
occurrence. The class of 23 – 40 in TPI values had the highest FR value of 4.56 while the class range of −2 to −8 had the
least FR value of 0.53. Forty-six (46) per cent of landslides occurred in TWI class of 3. This class also accounted for the
highest FR value of 2.46 in this conditioning factor. Except for TWI class range 13 with the next highest FR value of 2.04,
no other class had an FR value that was>1. Similarly, more than 96 per cent of landslides occurred in SP1 range of 20 –
40 with the remaining 4 per cent occurring with 40 – 60. The highest FR of 61.45 was obtained for SPI class 40 – 60. Flow
erosion may, therefore, be very significant in the study area. This finding is however, different from that of the study carried
out by [36], where over 95 per cent of the landslides occurred in SPI range of < 20. A summary of classes with the highest
frequency ratio in the respective conditioning factors is presented in Table SM4.
Landslide susceptibility map
Landslide susceptibility map was developed by calculating and classifying Landslide Susceptibility Indices (LSI) for Cross
River State. Using the prediction rates of the individual conditioning factors obtained during the training process (Table 1),
the LSI was obtained using Eqn 8
LSI = (Elevation ∗2.58 ) + (Slope∗1.94 ) + (Aspect∗1.0 ) + (Profile curvature∗1.18 ) + (Plan curvature∗1.17 )
+ (TPI∗1.38 ) + (TWI∗1.60 ) + (SPI∗4.56 ) + (Geology∗3.46 ) + (LULC∗1.33 ) + (Distance to waterbody∗1.35 )
+ (Distance to major roads∗1.02 )
(8)
Eq. (8) consists of the summation of 12 conditioning factors of landslides, (elevation, slope, aspect, profile curvature, plan
curvature, TPI, TWI, SPI, geology, land use/landcover, distance to waterbody, and distance to major roads). The calculated
LSI values ranged from 61.007954 – 1072.1932898, classified using the Natural breaks (Jenks) into fives class (Figure SM20).
9
J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
Fig. 3. Landslide susceptibility index (LSI) map.
10
J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
Fig. 4. ROC curve.
Table 2
Prediction ability analysis of the model.
Landslide Susceptibility level
Class
LS1
% of pixels
I
Very Low
1
Low
1
Moderate
High
9
Very High
61.01 – 179.90
4.55
179.90 – 243.42
4.55
243.42 – 303.90
303.90 – 406.00
40.90
406.00 – 1072.19
29.11
0.16
28.73
0.16
25.37
10.53
3.88
6.26
II
III
IV
V
No. of landslides
% of landslides
Landslide density (LD) (/km2)
4
18.18
0.72
7
31.82
5.08
This resulted in the LSI map in Fig. 3. The five classes of the susceptibility map were interpreted as very low (LSI: 61.01 –
179.90), low (LSI: 179.90 – 243.42), moderate (LSI: 243.42 - 303.90), high (LSI: 303.90 – 406.00) and very high (LSI: 406.00
– 1072.19).
Validation of the model
Landslide density index
The Landslide Density Index (LDI), the ratio of the percentage of landslides to the percentage of pixels in each class on
landslide susceptibility map was used to evaluate the performance of the frequency model. Several authors have used the
LDI approach [2,14,19,36]. Firstly, landslide locations which were not used for the model development were considered as
the future landslide locations [36]. In this study, recall that all landslide points were divided into two parts (70 per cent for
training of the model and 30 per cent for testing) using automated procedure in the GIS (section 3.2). Table 2 shows the
results of the LDI computation for the test data set.
From Table 2, the total percentage of landslides distributed in the ‘very low’ and ‘low’ susceptibility areas is less than
10 per cent while the ‘moderate’, ‘high’ and ‘very high’ susceptibility areas witnessed 90.90 per cent of total landslide
distribution in the study area. The LD for levels I and II are very low (0.16 landslides/km2 each). Thereafter, landslide densities
increased very drastically from 0.72 in the moderate class to 5.08 in the very high class. This agrees with the findings of
[36]. This result can be very effective in predicting future landslides in the study area.
The ROC curve
The receiver operating characteristic (ROC) curve which is a comprehensive indicator of response sensitivity and specific
variable was used to test the sensitivity of the prediction model. In adopting this approach, in landslide risk assessment, the
x-axis of the ROC curve represents specificity, which is the probability of misprediction of the non-landslide points, while
the y-axis highlights sensitivity which relates with the success rate of the disaster point [39]). The size of the area enclosed
by the curve and the abscissa represents the prediction accuracy of the model. Generally, a model with high sensitivity
and high specificity will have a ROC curve that hugs the top left corner of the plot. A model with low sensitivity and low
specificity will have a curve that is close to the 45-degree diagonal line. Hence, the closer the curve to the upper left corner,
11
J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
the he higher the accuracy of the model. AUC has values ranging from 0 to 1, with values less than or equal to 5 considered
to represent an unsatisfactory or a poor model. This can also be expressed as percentage.
In the present study, the ROC was implemented and found to be 0.5836 or 58.36 per cent (Fig. 4). We can see that the
ROC curve (the orange line) has a measurable level sensitivity and specificity in predicting the occurrence of landslides in
the study area
Discussion
The major objective of this study was to model landslide susceptibility in Cross River State of Nigeria using geospatial
approach. Seventy-two (72) landslide locations were mapped and used in preparing the landslide inventory map of the
study area. For the first time landslide inventory was produced for Cross River State in Nigeria, which is a confirmation of
milestone in landslide susceptibility studies [14,20,36]. While most developed countries of the world have a readily available
landslide database [20], such is not available in most developing countries, including Nigeria.
Based on the spatial relationship between elevation and landslide occurrence, it was revealed in this study that landslides
did not occur at elevation > 500 m with class 250 – 500 m having the highest FR. Available literature [36], revealed landslide
occurrence with the highest FR recorded for elevation class range of 200 – 400 m. Moreover, landslides rarely occur with
elevation > 700 m due to the fact that higher elevations are characterized by rocky materials, mostly made up of basement
complex rock with very thin overburden [36]. Hence, the finding of the present study corroborates previous finding.
The FR in slope factor was observed to increase with increase in slope gradient. This implies that low slope gradients are
less susceptible to landslide occurrence. It has also been reported that landslides occurred with angles > 30° but < 40 o in
the Obudu mountain region of the study area [12]. This finding is in agreement with those of previous studies [16,20,36].
The result of this study showed that about two-third (66 per cent) of the landslides occurred in slopes facing the South,
Southwest, and West, with the rest occurring in other classes. This may be due to the high rainfall amount experienced
in the study area that is caused by the SW – NE wind blowing across the area from the Atlantic Ocean. The Southwest
class alone accounted for 26% of the total landslide occurrences and also had the highest FR. A similar finding of rainfall
facing-aspect having influence on the occurrence of landslides has been reported in relevant literatures [36]. Early rains at
the beginning of the rainy season have been adjudged to be responsible for most landslides in some parts of Southeastern
Nigeria while heavy rains during the peak periods induces mass movements with destructive effects in the mountainous
part of the study area [12].
The spatial analysis with respect to profile curvature revealed that FR is generally higher in concave slopes, implying that
high slope gradient and erosion process prevail as profile curvature increases [36]. Like the profile curvature, the more the
value draws towards the positive, the higher the probability of occurrence of landslide while the lower the probability of
occurrence when value tends towards the negative value [36]. However, the finding further disagrees with the argument
[40] that the more convex or concave a slope is, the lesser the probability of landslides occurrence.
With respect to land use and land cover, it was found out that “settlement /bare surfaces” recorded the highest FR and
decreases in a pattern that seem to follow the level of disturbance meted on the original land cover with the undisturbed
forest having the least FR, except for waterbody. These results imply that the more the landcover of an area is disturbed by
human activities, the higher it’s susceptibility to landslide occurrence. Thus, as vegetation cover increases, the probability of
slope failure decreases [36]. Also, the nature of the tertiary sand deposits makes it to be the most susceptible of all geologic
types in the study area.
Similarly, the present study revealed that the highest FR was recorded for class with distance of < 500 m to water body,
and continues to decrease as distance to water body increases. As the distance to water body decreases, the influence of
stream water in causing slope instability increases. Hence, more landslides are expected to occur closer to stream courses
than further from it.
In the examination of spatial relationship between distance to major roads and landslide occurrence, it was found that FR
tends to decrease as distance to major road increases. This means that locations nearer to the main roads tend to be more
susceptible to landslide occurrence than those far away. Two major cases of occurrence of landslides along major roads in
the study area have been reported [11–12]. Also, it was reported that landslides in Obudu killed several people and many
more were trapped on the road on the 14th of October 2013 [12].
From Table 2, it could be affirmed that the distribution of landslides in Cross River State, Nigeria, is largely controlled
by a combination of geo-environmental factors, such as elevation of 250m- 500 m, slope gradient of >35°, slopes facing
southwest direction, and increasing degree of both positive and negative profile and plan curvatures. Also, the distribution
of landslides follows an increasing value of TPI, TWI of < 3, SPI of 40 – 60, fragile sands, sparse vegetation especially
settlement/bare surfaces, distance to water body of < 500 m, and distance to major road of <500 m.
In view of the main objective of this study, the landslide susceptibility map of Cross River State of Nigeria has been
produced (Fig. 3). Here, the map has been classified into five regions, based on susceptibility index. It was found that 29.11
per cent of the total map area had very low susceptibility index, while 28.73 per cent has low susceptibility index. However,
25.37 per cent of the total map area was classified as moderate, 10.53 per cent as high, and 6.26 per cent as very high
susceptibility. This implies that about 46 per cent of the map area is at high risk of landslide susceptibility. Most of these
portions are within the southern part of the State, comprising Calabar, the capital city. This area is also witnessing large
urban expansion which could further increase the risk factor.
12
J. Efiong, D.I. Eni, J.N. Obiefuna et al.
Scientific African 14 (2021) e01032
The validation of this susceptibility model shows 90.90 per cent accuracy making this study promising as a screening
tool that can be utilized at a regional scale and can be adopted in other areas with similar terrain. This study can be used
in addition to site-specific susceptibility analysis as done in some of the landslide locations [12]. However, the sensitivity
analysis using the ROC revealed an AUC of 58.36 per cent which is considered to be fair in predicting landslide occurrence
in the study area. Some factors which could be responsible for this low value of AUC include the size of the study area
which is considerable large when compared with the occurrence of landslide. It could also be that some of the landslide
conditioning factors which could have significant control on the final susceptibility index. In any case, the model that have
been developed in this study has to be able to predict landslide that this regional scales.
Conclusion
Landslide susceptibility of Cross River State has been modelled in this study from 72 landslide occurrence from 1990 2019. For the first time a comprehensive landslide susceptibility map of Cross River State of Nigeria was developed. The
geospatial techniques of Geographical Information Systems, remote sensing and Global Positioning System were used in the
quantitative analysis of the susceptibility of the study area to landslide and have proven to be very effective in terms of time,
cost, and accuracy of the results. Other advantages of the GIS include the ability to integrate data from different sources and
layers at the same time of the analysis while remote sensing enables the collection of data from environment without
physical contact especially in inaccessible areas in some of the study locations while GPS was used to precisely locate exact
site of landslide occurrence. The frequency ratio model as a statistical technique, aided in eliminating human bias in the data
analytical procedure, unlike some other traditional methods like index method, expert scoring and analytical hierarchical
process Five classes of landslide susceptibility have been developed. About 90% of landsides occurred in the moderate, high,
and very high LSI. The study area is regional in nature and size, and so the susceptibility map is intended to be a foundation
for guidance and prediction of areas that are liable to landslide occurrence in the State. The result of the analysis had been
validated for general land use planning and landslide susceptibility mitigation strategies. This study will be a motivation for
further studies in similar hydro-geomorphic environment that would need emergency response to landslide susceptibility.
However, this study contributes to the understanding of landslide occurrence in Cross River State of Nigeria and can serve
as a country wide tool in urban and regional planning strategies by taken into consideration susceptibility classification
schemes developed in this study and will be a vital database in decision making processes concerning geo-environmental
hazards.
Declaration of Competing Interest
None.
Acknowledgments
The authors would like to thank the Tertiary Education Trust Fund (TETFUND) for providing the grant that made this
research possible. We also thank the University of Calabar whose platform in the TETFUND was used to obtain the grant.
We thank members of the screening team for finding our proposal worthy for sponsorship. We also thank everyone who
assisted in one way or another for the success of the research, particularly, those who works provided the foundation for
this present study; our gratitude also goes to the field assistants.
Funding
This research was supported by the Tertiary Education Trust Fund (TETFUND): Year 2015–2017 (Merged) TETFUND RESEARCH PROJECT (RP) INTERVENTION for University of Calabar, Calabar, Cross River State.
Authors’ Contributions
Conceptualization, Joel Efiong; methodology, Joel Efiong and Josiah Obiefuna; Software, Joel Efiong; data and resources,
Joel Efiong, Devalsam Eni, Josiah Obiefuna and Sylvia Etu; writing- original draft preparation, Joel Efiong and Devalsam Eni;
writing – review and editing, Josiah Obiefuna and Sylvia Etu; supervision and project administration, Joel Efiong; funding
acquisition, Joel Efiong, Devalsam Eni, Josiah Obiefuna and Sylvia Etu.
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