Geographic Information System and Remote Sensing: Proposing a Model for Providing Erosion Features Map in Iran at the National Scale
Ali Mohammadi Torkashvand
This study in Jajrood sub-basin, North East Tehran, was conducted to
investigate some methods for water-soil erosion types mapping by GIS.
Four models were used for providing working units` maps by integration
of data layers including A. plant cover, geology and slope B. land use,
geology and slope C. land use, rocks erodibility and slope and D. land
use, rocks erodibility and land units. The surface, rill and gully erosion
intensity in the 314 spot were controlled to provide ground truth map
from each of these erosion features. Soil erosion type`s map obtained
from the integration of these truth maps and then this map was crossed
by the maps A to D. Results indicated that the integration of land use,
rocks erodibility and land units layers is a better model for providing
erosion types map than other models from an economic and executive regards.
The cross of the map D with the ground truth maps of surface, rill and
gully erosions showed the greatest and least accuracy are related to providing
gully erosion and erosion features maps, respectively. The greatest precision
of model was related to providing gully erosion map (with coefficient
of variation 17.8%) and the least precision (with 40.5% coefficient of
variations) was related to providing erosion features map. In conclusion,
model D is suitable for preparation of gully erosion map no erosion features
map. It is recommended that the same study is done in another basin with
the different climate and geology.
Soil erosion is a serious geo-environmental issue causing land degradation
in sub-humid to arid Mediterranean countries (Bou Kheir et al.,
2006) including parts of Iran. It has both direct and indirect negative
impacts: loss of soil, loss of green cover, deterioration of agriculture,
decertification and of course, economic reverberations (Khawlie et
al., 2002). Some agricultural and rangeland areas have already declined
due to soil erosion. It is necessary to establish soil conservation measures
which can reduce land degradation and assure a sustainable management
of soil resources. The implementation of effective soil conservation measures
has to be preceded by a spatially distributed erosion risk assessment
(Moussa et al., 2002; Souchere et al., 2005) and erosion
features assessment and its intensities (Mohammadi Torkashvand et al.,
The possibility to use the aerial photographs for soil mapping has been
known for a long time. Commonly they were used to support conventional
geomorphological methods (Stromquist, 1990) and also for direct identification
of sheet, rill and gully erosion (Frazier et al., 1983; Stromquist
et al., 1985). But it should be regarded that field survey and
photo-interpretation for erosion types mapping at the national scale is
time consuming and expensive (Raoofi et al., 2004). In Isfahan
province, Rahnama (2003) also concluded similar results in providing erosion
types map by aerial photograph interpretation. He recommended satellite
imagery and GIS for this aim. The extension of the use of modern spatial
information technologies, such as Geographical Information Systems (GIS),
Digital Elevation Modeling (DEM) and remote sensing, have created new
possibilities for research as a key for erosion types mapping (Martinez
Casasnovas, 2003) that is economical due to low costs as well as quickness
(Raoofi et al., 2004).
Numerous studies have been conducted for providing quantitative erosion
maps (Singh et al., 1992; Ygarden, 2003; Martinez-Casasnovas,
2003; Sidorchuk et al., 2003), but has less been regarded to erosion
types mapping. Qualitative erosion mapping approaches are adapted to regional
characteristics and data availability. Resulting maps usually depict classes
ranging from very low to very high erosion or erosion risk. There is no
standard method for qualitative data integration and consequently many
different methods exist (Vrieling, 2006).
Noble and Fletcher (1984) provided New Zealand erosion features map in
1:250000 scale. Working units had been established by integration of lithology,
soil, slope, erosion, plant cover, climate and land use layers and then
units regarding to erosion intensities of sheet, rill, gully, tunnel gully,
stream bank, Massive and etc. were investigated and labeled by field observations.
Khawlie et al. (2002) for providing a risk map of soil erosion
in Eastern Mediterranean rugged mountainous areas, Lebanon, applied remote
sensing and GIS. With steep slopes, torrential rain, improper human interference,
run-off is high and water-soil erosion is continuously deteriorating the
land cover. Remote sensing can facilitate studying the factors enhancing
the process, such as soil type, slope gradient, drainage, geology and
land cover. Digital elevation models created from SAR imagery (Shi and
Dozier, 1997) contribute significantly to assessing vulnerability of hydric-soil
erosion over such a difficult terrain. GIS layers of the above factors
are integrated with erosional criteria to produce a risk map of soil erosion.
Results indicated that 36% of the Lebanese terrain is under threat of
high-level erosion and 52% of that is concentrated in the rugged mountainous
regions. Bou Kheir et al. (2006) integrated two data layers including
erodibility of rocks and soil and potential sensitivity to erosion as
a model for providing risk map of soil erosion. The risk map corresponds
well to field observations on the occurrence of rills and gullies. In
recent researches, integration of data layers has been used in erosion
and sediment different studies (Feoli et al., 2002; Navas and Machin,
1997; Bayramin et al., 2003).
Navas et al. (2005) used GIS to integrate the information derived
from an automated land evaluation system that, in turn, identified the
erosion risk of areas by combining data on various soil properties and
physiographic and bioclimatic factors. According to the map of erosion
risks generated for the Arnás catchments, southern Pyrenees, there
were three distinct areas with different soil erosion features where fallout
137Cs was used to assess the soil redistribution pattern. Therefore,
in this research, methodologies of preparing this map are investigated
by integrating effective data layers in the environment of Geographic
Information Systems (GIS). Based on the information source of Lansat TM
data, colour aerial photographs and ground investigation data, Yuliang
and Yun (2002) used remote sensing and GIS techniques for the task of
soil erosion types and intensity classification in Shanxi province during
May-July. The research reflects the advantages of integration of RS and
GIS techniques, which is worth popularizing and applying. Shrimail
et al. (2001) prioritized erosion-prone areas in hills using remote
sensing and GIS as a case study of the sukhna lake catchment, northern
India. The study indicated that 1. IRS ID LISSIII data can be used for
land use/land cover mapping with a reasonably good (83%) classification
accuracy for hydrological and erosion assessment applications and 2. That
a simple index-based approach using three main causative factors, i.e.,
slope, soil and land use/land cover, can give fairly good delineation
of erosion-prone areas for prioritizing.
The aim of this study is to develop a methodology based on data layers
integration in GIS for providing water-soil erosion types map and its
intensities at the national scale (1:250000).
MATERIALS AND METHODS
Jajrood sub-basin has been considered as study basin with 162558 ha area
that its location is 51°34´ and 52°6´ eastern longitude
and 35°13´ and 35°48´ northern latitude, in the north-east
Tehran province, Iran, during years 2004-2005. Land covers were rangeland,
badland, sand borrow, agriculture land and urban regions. In basin, different
lithic units exist including pyroclastic stones, tuffs, andesite, shale,
conglomerate, gypsum and limestone. Also, quaternary deposits have covered
in the vast part of south basin particularly in Varamin plain. Climate
according to Demartonne method (Scientific Bulletin of Climatological
Research Institute, 2004) is sub-humid in the narrow ribbon of basin north
and semi-arid in the parts of north and central of basin. From central
parts to south of basin, climate is arid.
Preparation of data layers: Maps of land use, geology, plant cover,
topography and land units (at the 1: 250000 scales) were scanned and georeferenced.
From topographic layer, at first, digital elevation model and then slope
layer was provided according to Mahler (1979) classification. Rocks erodibility
layer base on Feiznia (1995) classification from geology layer was prepared.
Preparation of working units maps: Four models applied for preparation
of working units maps that derived from integration of data layers including:
||Plants cover, lithology and slope layers
||Land use, lithology and slope layers
||Land use, rocks erodibility and slope layers
||Land use, rocks erodibility and land units layers
Selection of data layers was regarding exploratory studies in Kan sub-basin
(Mohammadi Torkashvand et al., 2005). After this, these models
are called models A, B, C and D (Fig. 1).
Processing the remote sensing data (ETM+ Satellite Images, path 164-35,
2002) was done by ILWIS 3.2 Academic software that briefly including:
selection of best bands for making color composite with regards to OIF,
making principal components 1, 2 and 3, resampling spectral bands and
principal components to georeference of panchromatic band by nearest neighbor
method, making different color composite by using spectral bands, linear
stretching and filtering in different stages for preparation of color
composite. Finally, all color composites were compared and the best color
image was selected for distinction of erosion features. From DEM, hill
shade layer was prepared and overlayed on color composite to derive 3-D
||Integrated data layers and working units maps A, B, C and D
||The position of ground control points (+) in Jajrood sub-basin
Field practices: Different methods were incorporated for classification
of soil erosion types severities (Flugel et al., 1999; Refahi, 2000;
Boardman et al., 2003; Sirvio et al., 2004) and experiences and
expertise considerations (Mohammadi Torkashvand et al., 2005). Numbers
of 314 points have been considered on color composite image for field investigations
(Fig. 2). With regards to lack visual distinction of surface
and rill erosions, small and medium gullies on satellite images, a photomorphic
unit with attention to drainage patterns and also other characteristics such
as color, tone and texture, were differentiated on color composite by screen
digitizing method (Daeles and Antrop, 1977) as a polygon was determined for
each of control point. The intensities each of erosion types was investigated
in these ground control points and then frontiers each of polygon were corrected
with due attention to field views for every one of the surface, rill and gully
erosions. Modified polygons were marked with regards to intensities each of
erosion features in field. Polygons that had same intensity were together combined
and ground truth maps of surface, rill and gully erosions were prepared. Erosion
types map derived from integration of surface, rill and gully erosions maps.
This map was crossed by working units maps to investigate the ability each
of method on separating soil erosion types intensities. Equation
1 was used for investigating methods accuracy.
That A is map accuracy or map conformity with actual conditions (percent),
Z*(xi) is working units area (ha) and Ci is maximum
area of each working unit that is uniform in compared to actual conditions
(percent). Root mean squared error of working units accuracy was computed
by Eq. 2.
That Z(xi) is working units area (ha) that is uniform in
actual conditions. The precision each of method by Eq. 3
That CV and S are coefficient of variation and standard deviation of
working units accuracy, respectively and
is method accuracy (A in Eq. 1).
In layers integration models, the most and least numbers of working
units are related to models A and D, respectively (Table
1). Most polygons of models A, B and C have small area which is not
possible to be presented in the maps 1:250,000 maps due to cartographic
limitations. Table 2 presents the accuracy and error
of data layers integration models in distinguishing soil erosion types
intensities. According to this Table 2, the highest
and the least accuracy belong to models A and C that is 68.3 and 53.4%,
respectively. The accuracy difference between models A, B and D is not
considerable, but it is significant with model c. It should be regarded
that model c has the greatest precision (high coefficient of variation).
With regards to the results of data layers integration, model d has suitable
been distinguished than models A, B and C Totally, regarding results and
economic and executive regards, integration of land use, rocks sensitivity
to erosion and land units as a method as working units map applied for
preparing of surface, rill and gully erosion features maps.
Accuracy: Figure 3 shows the accuracy of model
d in preparing erosion features maps. The greatest and least accuracy
is related to preparation of gully erosion map that working units have
89% conformity with field actual conditions. Accuracy is approximately
similar for providing surface and rill erosions map. The least accuracy
is related to providing erosion features map.
||Number of units in working units maps
||Accuracy, coefficient of variation (precision) and RMSE of crossed
layers as working units maps
Working units area (in terms of percent as compared with basin area)
in different accuracies for providing different erosions maps
||The accuracy of model D in providing different erosions maps
||The root mean squared error of model D in providing different erosions
It can approximately tell that working units had not the accuracy less
than 50% for preparing rill erosion map, but the greatest area is related
to providing erosion features map (39.9%). In providing gully erosion
map, 72.3% area of working units had the accuracy more than 90%. Least
area of working units in accuracy more than 90% is related to providing
erosion features map (Table 3).
Root mean squared error: Results that are related to root mean
squared error of model d are shown in Fig. 4. This index
also shows that model has the least error for preparing gully erosion
map as compared with other erosions. RMSE trend for providing different
erosions map is following: Gully < rill < surface< Erosion features.
Therefore, model has the greatest error in preparing erosion features
map that RMSE is 1732.5 ha.
||The coefficient of variation (precision) of model D in providing
different erosions maps
Precision: The greatest precision of model is related to providing
gully erosion map, because have the least coefficient of variations (Fig.
5). Least precision of model with 40.5% coefficient of variations
is related to providing erosion features map. Precision trend approximately
is same with accuracy trend, with this difference that providing rill
erosion map has a few more precision as compared with surface erosion
Comparison of the four models a, b, c and d indicated that three
models a, b and d have the nearly same accuracy, but the model d has a
less precision as compared with the models a and b. In the models a, b
and c, slope layer has applied. In the different studies, the slope layer
is an important data layer in the integration of data layers. For providing
quantitative erosion maps, slope layer is used as a basic layer (Singh
et al., 1992; Feoli et al., 2002; Essa, 2004) and also,
in providing qualitative erosion maps such as land slide map (Bayramin
et al., 2003; Esmali and Ahmadi, 2003) and erosion risk map (Khawlie
et al., 2002). But it should be regarded when the slope layer was
used for providing erosion features map, it establish the high number
of units with the small area. High numbers of working units, units replication
and increasing field control points are the most important factors affecting
on the map preparation expenses. In the 1:250,000 scales, representation
of small working units is difficult and results in map confusion, color
eating piecemeal and low quality (Mohammadi Torkashvand et al.,
In addition to accuracy and precision, therefore, economic and executive
matters are the very important factors in preparing erosion features map
in the national scale (Rahnama, 2003). On the other hand, it is natural
that the small units have more uniformity in compared with large ones
causing more accuracy in maps a and b as compared to map D, although this
difference was not considerable.
Therefore, pay attention to low difference accuracy between layers integration
models and also economic and executive matters importance, model d has
been distinguished as the better working units map for providing water-soil
erosion map in 1:250000 scales in compared to other data layers integration
models. In this model from land units layers was used instead of the
slope layer for the integration with the rocks erodibility and land use
layers. Bou Kheir et al. (2006) also for providing risk map of
soil erosion in Lebanon applied two data layers including erodibility
of rock and soil and potential sensitivity to erosion. Shrimail et
al. (2001) for prioritizing erosion-prone areas in hills, a cumulative
erosion index computed from the rating given to the some main causative
factors among them soil erodibility and land cover.
It seems when one of the erosion features has been considered, alone,
accuracy is more as compared with erosion features. It is natural to increase
the diversity in erosion types intensities, consequently, decrease accuracy.
Results indicated that the integration of land units, land use and rocks
sensitivity layers establish working units with more uniformity with the
view of gully erosion than surface and rill erosions. Model precision
was low for preparing erosion features map. Surface and rill erosions.
In conclusion, it seems that the model d is suitable for preparation
of gully erosion map but had low accuracy and precision in providing erosion
features map, consequently, it is not proposed. It is proposed to investigate
the other method of data layers integration. It is also recommended to
evaluate the different methods in a basin with climate, geology and various
land use to differ from Jajrood sub-basin.
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