Photomorphic Units Map Derived from Processing Satellite Images
as a Model in Providing Erosion Types Maps
A. Mohammadi Torkasvand
In this study, the possibility using ETM+
satellite images for providing erosion types maps was investigated. With
regards to the lack of visual distinction of surface, rill and small gully
erosions on the satellite image after processing, photomorphic units with
attention to color, tone, texture, drainage pattern and other image characteristics,
were differentiated on color composite by the screen digitizing methods.
Photomorphic units map as a model was crossed by ground truth maps of
surface, rill and gully erosion; and erosion features map. Results indicated
that the greatest accuracy and precision of model is related to providing
gully erosion map, although it also is suitable in providing surface and
rill erosion maps.
Soil erosion is a serious geo-environmental issue led to 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,
desertification and of course, economic reverberations (Khawlie
et al., 2002). 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 and Nikkami, 2006).
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). Rahnama (2003) investigated
the possibility of preparation of soil erosion features map in the parts of
Isfahan Province, Iran, by aerial photograph interpretation and concluded that
this way is impossible for the total area of Iran because of time and costs.
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).
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) and less to preparing erosion
features map (Mohammadi-Torkashvand, 2008). 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. They provided the reliable data and maps
in time to National Water Conservancy Ministry. 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.
Khawlie et al. (2002) for providing a risk map
of soil erosion in eastern Mediterranean rugged mountainous areas, Lebanon,
applied remote sensing and GIS. They indicated that 36% of the Lebanese terrain
is under threat of high-level erosion and 52% of that has been concentrated
in the rugged mountainous regions. By applying airborne digital camera orthomosaics
and GIS for small-scale studies and field measurements for large-scale studies,
Sirvio et al. (2004) have studied gully erosion
hazard assessments in the Taita Hills, SE-Kenya. They investigated distribution
and intensity of gully erosion and the main factors affecting gully erosion
and its changes during the last 50 years within the Taita Hills.
Hajigholizadeh (2005) used the ETM+ satellite
images interpretation method for providing erosion features maps of five basins
in Tehran Province, Iran. Results of this research showed that the recognition
of surface and rill erosions is very difficult due to image resolution. Therefore,
they differentiated gully erosion polygons with low, moderate and high intensity
on images and polygons were controlled and corrected by field studies. The synergy
of Landsat TM and JERS-1 data by Metternicht and Zinck (1998)
studies provided a unique combination that allowed more accurate identification
of badlands, slightly eroded areas, miscellaneous land, fallow land and moderately
eroded areas, as compared to the results obtained by Landsat TM alone.
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 erosion maps
and its intensities with regards to statistics factors has not been done; therefore,
the aim of this study is to develop a methodology based on satellite images
processing with the view of the accuracy, error and precision of erosion types
mapping at the national scale (1:250000).
MATERIALS AND METHODS
ETM+ satellite images (path 165-34) relate to 2002 year were
used for the investigation of erosion features at the Jajrood sub-basin
with 162,558 ha located between 51Â°34` E, 52Â°6` E, 35Â°13 N
and 35Â°48` N. This basin extends from northeast to southeast Tehran
Province, Iran. The highest and the lowest height of basin are 3000 and
867 m, 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.
Basic land units in the great parts of basin are 1.1, 1.6, 2.7, 4.27,
6.5, 8.1 and 9.7. Within the basin, different lithic units 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). Climate according
to the Demartonne method is sub-humid, semi-arid and arid in the northern,
central and southern regions, respectively. 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.
Image processing included radiometric correction, selecting best bands
for making color composite with regard to the Optimum Index Factor, 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
Finally, all color composites were compared and the best color image was selected
for the distinction of erosion features. For this mean; principal components
2 and 3 with panchromatic band were combined by HIS method to differentiate
green, red and blue wavelengths, then from those a color composite was created
by RGB method. This color composite and also another color composite of RGB5-3-1
were used in providing photomorphic units map. Regarding the lack of visual
distinction of surface, rill and small 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 Antrope, 1977),
consequently, 76 photomorphic units as working units were differentiated. Off
course, from DEM, a hill shade layer was prepared and overlayed on a color composite
that obtained 3-D view possibility.
In this study, erosion features are soil-water erosion types including surface,
rill and gully erosions. Different methods were incorporated for classification
of surface, rill and gully erosion severity such as Flugel
et al. (2003), 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). The magnitude
of erosion in each erosion feature was investigated in 314 ground control points
and with due attention to the field views for every one of the surface, rill
and gully erosions for every ground control point, a polygon was determined.
Then, polygons with regard to the intensity of each erosion features in the
field, were marked. Polygons with same the intensity were combined together
and ground truth maps of surface, rill and gully erosions were prepared. Figure
1 indicates the position of ground control points with land uses in Jajrood
basin. Figure 2 also shows the truth map of rill erosion.
|| Land Uses in Jajrood basin and the positions of ground
||Truth map of rill erosion in Jajrood basin
The erosion features map obtained from integration of the surface, rill
and gully erosions maps. Erosion types maps and erosion features map were
crossed by photomorphic units map to investigate the ability of this map
(as a model) on separating erosion features. Equation 1
was used for investigating model accuracy:
where, A is model accuracy or map conformity with actual condition (percent),
Z*(xi) is working units` area (ha) and Ci is maximum
area of each working unit that is uniform compared to actual conditions
(percent). Root Mean Squared Errors of (RMSE) working units accuracy were
computed by Eq. 2.
where, RMSE is Root Mean Squared Error of working units` accuracy and
Zxi is working units` area (ha) that is uniform in actual condition.
The precision of method was investigated by applying the working units
accuracy coefficient of variation (Eq. 3).
where, S is working units accuracy standard deviation and
the model accuracy.
RESULTS AND DISCUSSION
Table 1 indicates the results of the photomorphic units
map cross by erosion types maps. The greatest accuracy of model (photomorphic
units` map) is related to providing gully erosion map that is approximately
90%. Accuracy is 86.4 and 81.0% in preparation of the surface and rill
erosions maps, respectively. The lowest accuracy of model, also, is related
to providing erosion features map (72%).
While accuracy difference of model in providing surface and rill erosion
maps was 5.4%, model precision difference (working units` accuracy coefficient
of variation) in preparing two above erosion types is only 0.5%. Thus,
RMSE of model for providing surface and rill erosions maps is 652.0 and
1019.5 ha, respectively. This point is important that the model accuracy
and precision in providing gully erosion map, respectively 3.4 and 5.8%
is more than surface erosion map, but RMSE of model in preparing surface
erosion map is greater than gully erosion map. The highest CV and error
(RMSE) is related to providing erosion features map.
The working units` area percentage in comparison with the basin area
in different accuracies is calculated and shown in Table
2. Any area of working units in rill erosion map has accuracy less
than 50%. The greatest areas of working units with the accuracy more than
90% are related to surface erosion map (61.0%) and gully erosion map (54.0%).
The greatest and the least areas of working units respectively with accuracy
less than 50% and more than 90% is in providing erosion features map.
Accuracy and precision of model (photomorphic units map) in providing erosion
features was less than when use this model for preparation of surface, rill
or gully erosions map, alone. This can be due to increase in diversity of erosion
types intensity led to decrease the accuracy and precision of model. This diversity
is also caused that the more areas of working units have accuracy less than
50%. Mohammadi-Torkashvand (2008) applied some models
of data layers integration in GIS for providing erosion types maps and concluded
that models have a more accuracy and precision in providing surface, rill and
gully erosion maps than the preparation of erosion features map. In data layers
integration, accuracy was 66.6%, while in this study is 72.0% by using photomorphic
It seems differentiating photomorphic units with regards to the different factors
such as drainage pattern led to increase in accuracy and precision of model
in providing gully erosion map. In differentiating photomorphic units, off course,
only large gullies were detectable, but this subject in differentiating photomorhic
units increases the accuracy of model. Allan James et
al. (2007) investigated the ability of the ALS (Airborne Laser-Scanning)
topographic 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. For large gullies in Central
Brazil, Vrieling and Rodriguez (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).
||Accuracy, coefficient of variation (precision) and root
mean squared error of model in providing erosion types maps
||Working units area (in terms of percent as compared
with basin area) in different accuracies for providing different erosions
For providing gully erosion map, coefficient of variations also is low i.e.,
accuracy variations of working units is more uniform than surface and rill erosion
map and erosion features map. Both surface erosion and rill erosion weren`t
detectable on satellite images, but photomorphic units map has a great accuracy
in providing these erosion maps particularly surface erosion map. 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. Direct detection of surface and rill erosion
with regards to ETM+ resolution is not possible. In Nejabat
(2003) studies, indirect detection of surface erosion on TM satellite images
in the part of Fars Province, Iran, was investigated. He calculated 68% accuracy
when the ground truth map of surface erosion was compared with photomorphic
units` map. In the Taleghan basin in Tehran Province, Iran, a gully and rill
erosion map (direct detection on image obtained from the fusion of ETM+
bands and Cosmos image) was compared with a ground truth map indicated an approximate
80 percent accuracy (Raoofi et al., 2004).
Mohammadi-Torkashvand (2008) reported that the integration
of land use, land units and rocks erodibility layers as a model in providing
surface, rill and gully erosion maps had accuracy 78.9, 78.4 and 89.0%, respectively.
Accuracy difference between data layers integration model and photomorphic units
model in providing gully erosion map is low (89.0 versus 89.8%), but it is considerable
for rill erosion map and especially surface erosion map. This can be due to
more dependency of gully erosion to land use, land units and rocks erodibility
than surface and rill erosion those are surface features of soil erosion. 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 (Alavi
Panah, 2004). This provides homogeneous data over large regions with a regular
revisit capability and can therefore greatly contribute to regional erosion
In general, it seems that this model (photomorphic units map) is suitable in
providing gully erosion map. Thus, regarding earlier studies (Mohammadi-Torkashvand
et al., 2005; Mohammadi-Torkashvand, 2008)
in this basin, using photomorphic map as a model is better than data layers
integration in preparation of the surface, rill erosion maps; and erosion features
map. It is proposed that satellite images with higher resolution were investigated
for this aim.
This study was supported by the grant of soil conservation and watershed
management research institute, Tehran, fund. The author would like to
thank from this institute and particularly Dr. Davood Nikkami, Dr. Hakhimkhani
and Mr. Bayat for their aid and use of equipment and facilities.
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