Introduction
The pedogenesis of the soils is determined by the climate (Cerdà, 1998a),
the parent material (Prosdocimi et al., 2016) and human management
(Debolini et al., 2015; Yan et al., 2015; Zhao et al., 2015; Cerdà et
al., 2016), however also as a consequence of the landforms and processes that
act on them. Soil features are largely controlled by the landforms on which
they are developed. The physiographic penetration on soil properties is
recognised based on the progress of the soil–landform relationship (Ali and
Moghanm, 2013). The landforms formed by the same geomorphic processes are the
main key feature because they can easily be identified, and were responsible
for producing the undercoat material of the soils (Park and Burt, 2002;
Henderson et al., 2005; Mini et al., 2007; Poelking et al., 2015). Previous
studies have shown that there is a clear relationship between landform and
soils, in that landforms and soil both control hydrological erosional,
biological and geochemical cycles. Based on the type of landform, other
parameters of watersheds can be predicted, such as soil, erosion, biological
parameters and so on (Berendse et al., 2015; Brevik et al., 2015; Decock et al., 2015;
Keesstra et al., 2012; Adugna et al., 2015; Ochoa-Cueva et al., 2015; Smith
et al., 2015).
A geographical information system (GIS), with features such as the
ability to acquire and exchange many different sources, organisation,
retrieval and display of data, analysis of numerous data and possibility to
provide multiple services, has been introduced as an efficient tool in
planning. Combining a GIS with fuzzy logic provides a comparatively new land
evaluation method (Badenko and Kurtener, 2004;
Oinam et al., 2014; Wang et al., 2015). Incorporating both of these methods is more flexible, and
reflects human creativeness and understanding in making decisions. Fuzzy
inference is considered as a deduction for mathematical modelling in
imprecise and vague processes, i.e. uncertainty about data, and thus creates a
context for modelling uncertainty (Kurtener, 2005).
Ali and Moghanm (2013) studied the variation of soil properties over the
landforms around Idku Lake, Egypt, with the spatial distribution of
CaCO3, electrical conductivity (EC), organic matter (OM), pH, nitrogen (N), phosphorus (P),
potassium (K), iron (Fe), manganese (Mn), copper (Cu) and zinc (Zn) over the
various landforms discussed in detail. The results showed that the changes
of CaCO3, EC and OM are minimal in the landforms of sand sheets,
hammocks, sabkhas, clay flats and former lake bed.
Aliabadi and Soltanifard (2014) apply a GIS and fuzzy inference for
determination of the impact of water and soil EC and calcium carbonate on
wheat crops. Regarding the results of the fuzzy inference system, 76 % accuracy was
achieved using the Mamdani's method and 52 % of accuracy was achieved for the Sugeno
technique.
In addition, El-Keblawy et al. (2015) investigated relationships between
landforms, soil characteristics and dominant xerophytes in the northern
United Arab Emirates. Soil texture, electrical conductivity (EC) and pH were
determined in each sample point. The results showed that
soil and landforms also control the geomorphological and hydrological
processes (Cerdà and García-Fayos, 1997; Cerdà, 1998b; Dai et
al., 2015; Nadal-Romero et al., 2015).
One of the largest wheat-producing regions in Iran is located in the Shiraz
Plain, Fars province (Bijanzadeh et al., 2014). The aim of this study is to
investigate the relationship between landform classes and EC of water and
soil in this area using a combination of a GIS and a fuzzy model. The
methodology employed in this study is summarised in Fig. 1.
Flowchart of the methodology employed to investigate the
relationship between landform classification, and soil and water EC.
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Location of the study area (DEM with spatial resolution of 30 m)
(source: http://earthexplorer.usgs.gov).
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Material and methods
The study area has an area of 3909 km2 and is located at a longitude of
29∘06–29∘43 N and a latitude of 52∘18 to
53∘28 E (Fig. 2). The altitude of the study area ranges from the
lowest at 1433 m to the highest at 3083 m. The region is located in the
north of the Fars province, which has cold winters and hot summers. The
average temperature for the area is 16.8 ∘C, ranging between 4.7
and 29.2 ∘C (Soufi, 2004). The research area demonstrates a biodiversity of
mountains, relief and lithology, and geological characteristics such as, for
instance, sedimentary basin and elevated reliefs (Soufi, 2004). The main
agricultural produce consists of grain, fruit and vegetables, while the
partly wooded mountains are used for pasture. The main land use types of the
region are agriculture, range land, farming and forests.
In terms of geology, the Precambrian Hormoz series and the Quaternary units
are the oldest and youngest rocks in the basin, respectively. Spans of
outcropped rocks, covering from the Cretaceous to Quaternary, are carbonate
sediments of deep to shallow marine facies. These sedimentary sequences
include large and small stratigraphic gaps in the form of disconformity and
sometimes nonconformity (Khaksar et al., 2006).
The area is situated in an arid and semi-arid region. Rainfall varies from
150 mm on the plains to 650 mm on the high mountains, with an average of 350 mm.
The rainfall is concentrated in cold seasons, while the precipitation is
very low from June to October (Sigaroodi et al., 2014).
During winter, several migratory bird species from north of Caspian Sea,
flamingos (Phoenicopterus roseus), common shelducks (Tadorna tadorna) and
mallards (Anas platyrhynchos) spend 4 months in the area feeding on brine
shrimp (Artemia franciscana). Thus, the lake has important ecological value
(Sigaroodi et al., 2014).
Inverse distance weighted (IDW) model
An IDW model was used for interpolating the EC properties. IDW interpolation
explicitly implements the assumption that things that are close to one
another are more alike than those that are farther apart. To predict a value
for any unmeasured location, IDW will be used to measure neighbourhood
values in the predicted location. Assumed value of an attribute f at any
unsampled point is an average of distance-weighted sampled points lying
within a defined neighbourhood around that unsampled point. Basically, it is a
weighted moving average (Burrough et al., 1998):
f^(x0)=∑i=1nf(xi)dij-r∑i=1ndij-r,
where x0 is the estimation point and xi are the data points within a
chosen surrounding. The weights (r) are related to distance by dij.
Membership functions.
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Position of sample points for (a) water and (b) soil EC.
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Classification of water EC values (Kumar et al., 2003).
Class
EC (ds/m)
Low
< 0.25
Moderate
0.25–0.75
High
0.75–2.25
Very high
> 2.25
Classification of soil EC values (Mokarram et al., 2010).
Class
EC (ds/m)
Low
< 8
Moderate
8–12
High
12–16
Very high
> 16
Fuzzy method
In research, model functions are accustomed to computing membership
function (MF), as described in Fig. 3 (Burrough and McDonnell, 1998). According to Fig. 3, an asymmetric function needs to be applied (Models 1 and 2)
(Fig. 3). If MF(xi) shows individual membership value for ith land
property x, then in the computation process these model functions (Models 1
to 2) show the following form.
For asymmetric left (Model 1),
MF(xi)=[1/(1+{(xi-ai-b1)/b1}2)]ifxi<(a1+b1).
For asymmetric right (Model 2),
MF(xi)=[1/(1+{(xi-a2+b2)/b2}2)]ifxi>(a2-b2).
In this study, in order to define fuzzy-rule-based membership functions, the
categories shown in Tables 1 and 2 are used.
Landform classification
The Topographic Position Index (TPI) (Weiss, 2001) compares the elevation of each cell in a digital
elevation model (DEM) to the mean elevation of a specified neighbourhood around that cell. Positive TPI (Eq. 4) compares the elevation of each cell in a DEM to the mean
elevation of a defined neighbourhood around that cell. Mean elevation is
subtracted from the elevation value at the centre (Weiss, 2001):
TPIi=Z0-∑n-1Zn/n,
where Z0 is the elevation of the model point under
evaluation, Zn is the elevation of grid and n is the total number of surrounding points employed in the evaluation.
Incorporating TPI at small and large scales permits a number of nested
landforms to be distinguished (Table 3). The actual breakpoints among
classes can be selected to optimise the classification for a specific
landscape. As in slope position classifications, additional topographic
metrics, such as, for example, differences of elevation, slope or aspect
within the neighbourhoods, can help delineate landforms more accurately
(Weiss, 2001).
Topographic Position Index (TPI) thresholds for small and large
neighbourhoods used to define landscape feature classes.
Landform
TPI
Small neighbourhood
Large neighbourhood
Plains
-1 < TPI < 1
-1<TPI < 1*
Open slopes
-1 < TPI < 1
-1 < TPI < 1**
U-shaped valleys
-1 < TPI < 1
TPI < -1
Mountain tops/high ridges
TPI > 1
TPI > 1
Upper slopes/mesas
-1 < TPI < 1
TPI > 1
Mid-slope drainages/shallow valleys
TPI < -1
-1 < TPI < 1
Canyons/deeply incised streams
TPI < -1
TPI < -1
Mid-slope ridges/small hills in plains
TPI > 1
-1 < TPI < 1
Upland drainages/headwaters
TPI < -1
TPI > 1
Local ridges/hills in valleys
TPI > 1
TPI < -1
* Plain
landform class required a slope of < 0.5. ** Open slopes landform class required a
slope of > 0.5
Additionally, the classes of canyons, deeply incised streams, mid-slope and upland
drainages and shallow valleys tend to have strongly negative plane form
curvature values. On the other hand, local ridges/hills in valleys,
mid-slope ridges, small hills in plains and mountain tops and high ridges
have strongly positive plane form curvature values.
Results and discussion
Inverse distance weighted (IDW) interpolation
IDW interpolation was used to produce the prediction of soil and water EC,
as shown in Fig. 4. According to Fig. 4, sample points were selected
randomly in the study area. These data were prepared by the Organization of
Agriculture Jahad Fars province in 2012. The lowest and highest output for
IDW were 0.016 and 14.48 respectively for water EC, while the lowest and
highest soil EC was 0 and 34.5 respectively. The interpolation maps for
soil and water EC are shown in Fig. 5. The statistical properties of the
interpolated soil and water EC are shown in Table 4.
Descriptive statistics of the water and soil EC.
Statistic parameter
Water EC
Soil EC
(ds/m)
(ds/m)
Maximum
14.48
28.25
Minimum
0.016
0.78
Average
3.80
3.91
Standard deviation
6.13
3.82
Skewness
6.54
3.09
Kurtosis
62.97
15.46
Interpolated maps of study area for (a) water and (b) soil EC.
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Fuzzy method
Fuzzy maps were prepared for soil and water EC, as shown in Fig. 6. The
fuzzy values were classified into four classes. EC < 0.25, EC
between 0.25 and 0.5, EC between 0.5 and 0.75 and EC > 0.75 are in the
classes of low, moderate, high and very high respectively (Shobha et al.,
2014). The areas of the classes for soil and water EC are shown in Table 5.
Fuzzy maps of the study area for (a) soil and (b) water EC.
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Areas of the classes for water and soil EC.
Class
Area (%)
Area (km2)
Water EC
Soil EC
Water EC
Soil EC
Low
0.00
24.31
0.11
950.23
Moderate
36.60
11.78
1430.87
460.63
High
31.69
25.74
1238.91
1006.27
Very high
31.65
38.16
1237.10
1491.86
For water EC, the fuzzy model showed that 36.6 % of the land was in the
moderate class; high, 31.69 %; and very high, 31.65 %. In comparison,
the results of the fuzzy model for soil EC showed that 24.31 % of the land
was in the low class; moderate, 11.78 %; high, 25.74 %; and very high,
38.16 %. Based on the results obtained, the land suitable for wheat
agriculture is located in the north and northeast in the study area.
Landform classification
In order to determine of relationship between landform classification, and
soil and water EC, the landform map of the study area was prepared. Using the TPI, the landform classification map of the study area was generated. The
TPI maps generated using small and large neighbourhoods are shown in Fig. 7. The TPI is between -106 to 130 and -334 to 533 for 3 and 45 cells for small
and large neighbourhoods respectively (Fig. 8). The landform maps generated
based on the TPI values are shown in Fig. 8. The classification has 10
classes: high ridges, mid-slope ridges, upland drainage, upper slopes, open
slopes, plains, valleys, local ridges, mid-slope drainage and streams. The
areas of the landform classes are shown in Fig. 9. It is observed that the
largest landform is streams, while the smallest is plains.
TPI maps generated using (a) small (3 cells) and (b) large (45
cells) neighbourhood.
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Landform classification using the TPI method.
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The average EC for each landform class was determined, and the relationship
between EC and landform was prepared. According to Fig. 9, the EC of water
is high for the valley class while the high EC of soil is in the upland drainage
class. The lowest EC levels for soil and water are in the plains class.
Relationship between landform classes.
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Dazzi and Monteleone (2001) investigated the relationship between soil
properties and landform in Italy. The results show that in plains, the EC
value is greater than the other landform types that are similar to results of
the study area. Ali and Moghanm (2013), who investigated relationship between
soil properties and landform classes in Idku Lake, Egypt, also found that the
lowest EC was in plain class. In fact, there is a relationship between soil
parameters and land use (Wasak and Drewnik, 2015; Kukal and Bawa
Debasish-Saha, 2014). Yu et al. (2014) showed that there is relationship
between soil parameters (such as soil organic carbon (SOC), soil total
nitrogen (STN)) and types of land cover (grassland, farmland, swampland). Niu
et al. (2015) and Yu et al. (2015) investigated the relationship between land
use and soil moisture. The results provided an insight into the significance
for land use and farming water management in this area. Saha and Kukal (2015)
found that there is a relationship between soil structural stability and land
use. The results indicated the degradation of soil physical attributes due to
the conversion of natural ecosystems to farming system and increased erosion
hazards. In fact the landforms are located at high elevation such as in
mountains; the leaching process is high, while in landforms which are located
at low elevation such as plains, the accumulation process is evident.
Therefore, in the study area and similar research, the EC value was recorded
high in lower topographical positions (Walia and Chamuah, 1994; Singh and
Rathore, 2015). In fact EC and other soil properties can be estimated easily
and without measuring salinity in the laboratory using satellite data such as
from a digital elevation model (DEM) that save time and money.