Investigation of Some Methodologies for Gully Erosion Mapping
Ali Mohammadi Torkashvand
The aim of this study is to investigate the accuracy, error and precision
of some models for providing erosion types map in Jajrood sub-basin, north-east
Tehran, Iran. Seven models were applied for providing working units maps
that four models were prepared by the integration of different data layers.
Other models were photomorphic units map derived from processing satellite
images, land units map and rocks erodibility map. Gully erosion intensity
in 314 spots was investigated to provide the ground truth map. This map
was crossed by the different working units` maps. Results indicated that
the integration of land use, rocks erodibility and land units layers is
a better model than other models of data layers integration from an economic
and executive regards. Accuracy for this model and photomorphic units
map was 89.8 and 89.0%, respectively. The least coefficient of variation
(14.1%), consequently, the highest precision was related to photomorphic
units` map. Land units and rocks erodibility models, because of their
low accuracy and precision, are not suitable for providing gully erosion
map. That is why the best model is photomorphic units map.
Gully erosion is a serious geo-environmental issue causing land
degradation. It causes damages to vulnerable agricultural lands, water
pollution by soil particles and chemicals and mudflows which may affect
urban areas (Poesen and Hooke, 1977). In contrast to the effort during
the last decades to investigate sheet (inter-rill) and rill soil erosion
processes, relatively few studies have been focused on quantifying and/or
predicting gully erosion (Martinez Casasnovas, 2003). Gullies can develop
as enlarged rills, but their genesis is generally more complex, involving
sub-surface flows and sidewall processes (Bocco, 1991). The geographic
distribution of gullies is one of the most important information required
for soil conservation. Large gully systems have received much attention
from researchers using modern geospatial analysis (Tabatabaei, 2000; Zinck
et al., 2001; Martinez Casasnovas, 2003; Martinez Casasnovas
et al., 2004).
The possibility to use the aerial photographs for soil mapping has been
known for a long time (Goosen, 1967). 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 gully erosion
mapping at the national scale is time consuming and expensive (Raoofi
et al., 2004). 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 gully erosion mapping (Martinez
Casasnovas, 2003) that is economical due to low costs as well as quickness
(Raoofi et al., 2004).
Gully erosion is a serious problem in many parts of the world and particularly
in Iran, because of climate, lithology, soils, relief and land use/cover
characteristics. Iran Watershed Evaluation and Studies Office (2000) prepared
a design for providing erosion types map at the national level (scale-1:250000).
They integrated data layers of soil, slope, lithology, land type and land
use for providing working units map, but field investigations had indicated
that this way is impossible for the total area of Iran because of time
and costs. In Isfahan province as a pilot design, Rahnama (2003) investigated
the possibility of preparation of soil erosion features map by aerial
photograph interpretation and concluded similar results. He recommended
satellite imagery and GIS for this aim.
Sirvio et al. (2004) have investigated gully erosion hazard
assessments in Taita Hills, SE-Kenya, applying airborne digital camera
orthomosaics and GIS for small-scale studies and field measurements for
large-scale studies. Detection of distribution and intensity of gully
erosion and main factors affecting gully erosion were investigated within
Taita Hills and changes during the last 50 years. Raoofi et al.
(2004) categorized rill and gully erosions in Taleghan basin-Tehran province
by using visual interpretation of images derived from the fusion of ETM+
bands and Cosmos image. Also a map of ground truth from eroded regions
was provided by using Cosmos image as well as visual interpretation and
field observations. Measurements had indicated an approximate 80% accuracy
for the categorization. Allan James et al. (2007) investigated
the ability of the ALS (Airborne Laser-Scanning) baselineographic data to identify
headwater channels and gullies for two branching gully system in frosted
areas and to extract gully morphologic information. Regarding results,
at the gully network scale, ALS data had provided accurate maps-the best
available-with robust detection of small gullies except where they are
narrow or parallel and closely spaced.
Most of erosion and sediment studies have been carried out to provide
a quantitative erosion map (Singh et al., 1992; Martinez-Casanovas,
2003; Ygarden, 2003; Sidorchuk et al., 2003) and less to
preparing an erosion features map. 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). It appears
that the distinct methodology for providing gully erosion map and its
intensities with regards to statistics factors such as accuracy, precision
and error, have not been done. The aim of this research is to develop
a methodology based on data layers integration in GIS and satellite images
processing for providing gully erosion map at the national scale (1:250000).
MATERIALS AND METHODS
The Jajrood sub-basin with 162,558 ha located between 51 ° 34 ´
E and 52 ° 6 ´ E, 35 ° 13 ´ N and 35 ° 48 ´ N, was
considered for the investigation of erosion features. It extends from
northeast to southeast Tehran province, Iran. The highest and the lowest
height of the basin are 3000 and 867 above msl, respectively. The Jajrood
river originating in the northern Miegoon region and in the northern Varamin
entered in alluvial plains. Land covers were rangeland, badland, sand
borrow, agriculture land and urban regions. Basin lithology include pyroclastic
stones, tuffs, andesite, shale, conglomerate, gypsum and limestone. Also,
Quaternary deposits have covered in the major part of the southern basin
particularly in the Varamin plain (47.8% of area basin). The majority
of rain and snow (75-85%) falls between November and April and the rest
corresponds to autumn and winter storms and spring showers.
Necessary maps such as baselineographic, geology, plant cover type and land
units were scanned and georeferenced. Digital Elevation Model (DEM) was
prepared by 1:50,000 baselineographic digital data, classified slope map-the
DEM-derived slope map was classified into eight slope (percent) classes
0-2, 2-5, 5-8, 8-12, 12-20, 20-40, 40-70 and >70 based on Mahler (1979)
classification, land use was derived using ETM + satellite image
and rocks erodibility layer based on Feiznia (1995). According to their
sensitivity to erosion, the rocks were categorized into the following
five classes: very sensitive, sensitive, moderately sensitive, resistant
and very resistant.
Seven methods were used to prepare working units` maps of which four
methods were to integrate different data layers including (a) plant cover
type, geology and slope, (b) land use, geology and slope, (c) land use,
rocks sensitivity to erosion and slope and (d) land use, rocks sensitivity
to erosion and land units` layers. The other three methods were based
on (e) land units (f) sensitivity of rocks to erosion and (g) image photomorphic
unit maps. Selection of the data layers was carried out through exploratory
studies in Kan sub-basin (Mohammadi-Torkashvand et al.,
2005). Slope, plant cover type, geology, land use and land unit are the
important factors in the formation of the soil-water erosion features.
Image processing included radiometric correction, selecting best bands
for making colour composite with regards to the OIF, making principal
components 1, 2 and 3, resampling spectral bands and principal components
to the panchromatic bands, georeferencing by the nearest neighbour method,
making different colour composites using the spectral bands and linear
stretching and filtering in different stages for preparation of colour
Finally, all colour composites were compared and the best color image
was selected for the distinction of erosion features. From DEM, a hill
shade layer was prepared and overlayed on a color composite that obtained
3-D view possibility. Regarding the lack of visual distinction of small
and moderate gully erosions on the satellite image, photomorphic units
with attention to color, tone, texture, drainage pattern and other images
characteristics, were differentiated on color composite by the screen
digitizing methods (Daeles and Antrop, 1977).
Different methods were incorporated for classification of gully erosion
severity such as Flugel et al. (1999), Refahi (2000), Boardman
et al. (2003) and Sirvio et al. (2004) and
the series of changes are based on experience and expertise considerations
(Mohammadi-Torkashvand et al., 2005). A total of 314 points
has been considered on color composite images (for field investigation)
by classified random sampling. Figure 1 shows the positions
of these points.
A primary polygon was determined for each control point regarding image
||The position of ground control points (+) in Jajrood
||Classification of gully erosion intensity
*: Sustainable gullies that erosional activity exist
at the less than 10% of its length; **: Gullies that have erosional
activity at the 10-50% of its length; ***: More than 50% of gullies
length exist erosional activity
The intensity of gully erosion was investigated in these ground control points and then
frontiers of each polygon were corrected with due attention to field views
(Table 1). Modified polygons were marked with regards
to the intensity of gully erosion in field. Polygons with same the intensity
were combined together and ground truth map of gully erosions was prepared.
This map has been crossed by working units` maps to investigate the ability
each of models on separating gully erosion intensities. Equation
1 was used for investigating each of models accuracy.
where, A is model 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 comparison to the actual
conditions (per cent). Root mean squared error of working units` accuracy
(RMSE) was computed by Eq. 2.
where, Z(xi) is working units` area (ha) that is uniform in actual
condition. The precision each of models was obtained by Eq.
where, CV and S are coefficient of variation and standard deviation of
working units` accuracies, respectively and is method accuracy (A in Eq.
In layers integration models, the most and the least numbers of
working units are related to models a and d, respectively. Most polygons
of models a, b and c have a small area which is not possible to be presented
in the maps 1:250,000 due to cartographic limitations. Least numbers of
units` is related to rocks erodibility and land units maps, but the units`
area is great (Table 2). In all models, less than 10%
of working units area exist in the accuracy less than 50%. In the high
accuracy (more than 90%), the greatest area of working units is related
to model a. Least area of working units in the accuracy more than 90%
is related to model f. Only 54.0% of working units area in model e i.e.,
photomorphic units map, have the accuracy more than 90% that is less than
data layers integration models (Table 3).
The greatest and the least accuracy are related to models a and b that
is 68.3 and 53.4%, respectively. The difference of accuracy between models
a, b, c, d and e is not considerable, but it is significant with models
f and g. Cross of seven working units` maps and ground truth map with
the view of root mean squared error are shown at Table 4.
Results indicated the greatest error is related to model g that RMSE is
9480.8 ha with very considerable difference as compared with other methods.
In data layers integration models, RMSE is least for model a and then
model d. Also, model f has a high error (2466.1 ha).
Table 4 shows the coefficient of variations of working units`
accuracies (CV), that a high coefficient of variation i.e., low precision. The
greatest precision is related to model e that had obtained from satellite images
interpretation. Least precision is also related to models f and g. In data layers
integration methods, model c had the least precision.
||Number of units in the working units` maps of the Jajrood
||Percentage of working units` area compared with the
basin area in different accuraciesAccuracy
||Accuracy, error and precision in different methods
In addition to accuracy and precision, economic and executive matters
are very important factors in preparing soil erosion features map in the
national scale (Rahnama, 2003). 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) and also, in providing qualitative
erosion maps such as land slide map (Bayramin et al., 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, unit`s replication and increasing field control points
are the most important factors affecting on the map preparation expenses.
On the other hand, it is natural that the small units have more uniformity
in comparison with large ones causing more accuracy in models a and b
as compared to other models, although this difference is not considerable
between models a, b, c, d and e. Off course, models that derived from
layers integration have same precision, approximately.
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., 2005). Therefore, pay attention to low difference
accuracy and precision between layers integration models and also economic
and executive matters importance, map d has been distinguished as the
better working units map for providing erosion gully map in 1:250000 scales
as compared with other data layers integration methods.
Rocks erodibility and land units maps are having large units, but those
are not homogenous with the view of gully erosion intensity. Increasing
units` area is caused to increase the diversity of gully erosion intensity
due to more variables affect on this erosion feature. Consequently, accuracy,
error and precision of these maps reduce that this subject is more for
rocks erodibility map. Using these two maps, by Mohammadi-Torkashvand
and Nikkami (2006) for providing erosion features map, suitable methods
have not also been distinguished. Therefore, not only economic and executive
regards are important for providing erosion features mapping, but also,
accuracy and precision have importance.
Previously, it had been talked that processing ETM + images
for distinguishing gully erosion intensities were done, but this processing
was not caused distinction of small and medium gullies. Hajigholizadeh
(2005) also for providing surface, rill and gully erosion maps in five
basins in Tehran province, Iran, by using images visual interpretation,
concluded that recognition of surface, rill and small gully erosion is
very difficult with due attention to images resolution. For large gullies
in Central Brazil, Vrieling and Rodrigues (2004) found that optical ASTER
imagery provided better description of gully shape than ENVISAT ASAR data,
when compared to QuickBird image. With the current availability of high-resolution
satellites such as IKONOS and QuickBird options for detecting and monitoring
individual small-scale features have increased, although not yet reported
in literature. The visual interpretation provided usually good results
and despite of intensive development of numerical interpretation approaches,
it is still popular. It is used mainly for erosion mapping of large areas
in third world countries (Tripathi and Rao, 2001; Sujatha et al.,
2000). Raoofi et al. (2004) distinguished that gully erosion
map derived from visual interpretation of Cosmos images with ground truth
map had 80% conformity.
Using photomorphic units derived from visual interpretation of satellite
images with due attention color, tone, texture, drainage pattern and other
images characteristics, is a suitable method for studying surface features
(Alavipanah, 2004). Therefore, regarding drainage pattern, physiographic
and other properties on color composite image, was prepared the photomorphic
units map. This provides homogeneous data over large regions with a regular
revisit capability and can therefore greatly contribute to regional erosion
assessment (King and Delpont, 1993; Siakeu and Oguchi, 2000). Investigations
showed that photomorphic unit`s map had the great conformity as compared
with gully erosion truth map.
In the 1:250,000 scales, representation of small working units is
difficult and results in map confusion, color eating piecemeal and low
quality. Therefore, when the slope layer is used for providing erosion
features map in four models, it established the high number of units with
the small area. High numbers of working units, unit`s replication and
increasing field control points are the most important factors affecting
on the map preparation expenses. The model derived from the integration
of rocks erodibility, land use and land units layers were better than
Comparison of accuracy, error and precision of this model with the land
units and rock sensitivity maps and photomorphic units` map showed that
differentiating photomorphic units in satellite imagery makes more uniform
units for using as working units in gully erosion studies. As the second
precise method, data layers integration, with same accuracy (89%) is applicable
in providing gully erosion map. It is suggested that integration of other
layers and satellite images with higher resolution were investigated for
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