Ijciet 10 01_014

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 1, January 2019, pp.143–154, Article ID: IJCIET_10_01_014
Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
©IAEME Publication Scopus Indexed
DETECTION WETLAND DEHYDRATION
EXTENT WITH MULTI-TEMPORAL
REMOTELY SENSED DATA USING REMOTE
SENSING ANALYSIS AND GIS TECHNIQUES
Hayder Dibs
Hydraulic Structures Department, Water resources Engineering Faculty,
AL-Qasim Green University, Babylon, Iraq
Suhad AL-Hedny
Department of Environment, Faculty of Environmental Science,
Al-Qasim Green University, Babylon, Iraq
ABSTRACT
To prevent losing water resources and wetlands, and conserve existing wetlands
ecosystem for ecosystem and biodiversity services, good, wetlands habitats forstart
any sustainable development programs, it is necessary to detect, monitor and
inventory water resources and their surround uplands. Recently, AL-Razaza Lake
suffer from a critical situation because of the decreasing in the water level and
increase a salinity. We have propose a method to monitor and model the spatial and
multi-temporal changes of AL-Razaza Lake in the period 1992–2018. This study
includes pre-processing, processing and post-processing stages. In Addition, a
supervised classification was used to classify the satellite images. Validation result
reveals that the overall accuracies and kappa coefficients of the supervised
classifications were 88, 90.79, 95.94 and 87.67 respectively, and 82%, 86%, 93% and
79% respectively. The results showed that the percentage change was significant
during this period, such that the decreased surface area was from 1313.87 km2
in
1992 to 224.85 km2
in 2018.The noticeable results show the rapidly decreasing in the
Lake area by 82.8% with area about 1089.02 km2
over the last three decades. All the
dehydration extended area of the Lake was replaced by soil.
Keywords: Wetland Change Detection, Dehydration Extent, Multispectral Image
Classification, Landsat Satellite Images, and Supervised Classification.

Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote
Sensing Analysis and GIS Techniques
Cite this Article: Hayder Dibs and Suhad AL-Hedny, Detection Wetland Dehydration
Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis
and GIS Techniques, International Journal of Civil Engineering and Technology
(IJCIET), 10 (1), 2018, pp. 143–154.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
1. INTRODUCTION
Study the water resources degradation and/ or dehydration extent play a big role to investigate
and analysis wetland global change to monitor and manage the water resources around the
world (Dibs, 2018). Non-organize the international water resources policies, Global warming,
Land use-Land cover (LU-LC), low education and human modifications paly a big role in
wetland changing and lead to loss the biodiversity, increase of environmental problems and
reveal of different types of natural disaster such as desertification, loss of natural resources,
wildlife habitats, biodiversity and water resources (Dwivedi et al., 2005; Abd and Husam,
2013). Al-Razaza Lake provides many services including control the flood water retention of
Euphrates river flood, maintenance of water quality, wildlife habitat, and control of soils
erosion, and it is also important for human beings for food crops, and balance of the
ecosystems (Atasoy et al., 2011; Sun et al., 2012). Its water level was started to decrease
from the 1980s and accelerated since 1990s due to many reasons as climate change, rising
atmospheric temperatures and continuing evaporation process during Iraq’s dry in hot
summers and decrease the water levels in the Euphrates River, which is the most important
main water sources of AL-Razaza lake, all these factors together led to a decrease in the water
elevation and the lake surface area, revealing and increasing soil salinity that become a real
disaster (Nawal et al., 2012and K.N.Kadhim, 2018). The lake water elevations have been
decreased to be only (5-10) m deep were reported by local reports (Atasoy et al., 2011). Both
remote sensing and geographical information system (GIS) are powerful tools to obtain
accurate and update to day information on the distribution of water resources degradation over
large areas (Brisco et al., 2013and K.N.Kadhim&Noor S.,2018). Remotely sensed imagery is
very important data resources to analysis and process through the GIS, It uses for conduct
object recognition (Hayder et al., 2014). Many studies studied and performed towards the
determination of the water surface area extraction, wetland change, water body detection,
degradation and mapping (Demir et al., 2013; Dibs et al., 2015; Dibs, 2016;Dibs et al., 2017;
Dibs et al., 2018a; Dibs et al., 2018b), disaster monitoring (Volpi et al., 2013; Brisco et al.,
2013), forest and vegetation change(Markogianni et al., 2013), urban sprawl (Raja et al.,
2013), and hydrology (Zhu et al., 2011).Remote sensing degradation algorithms based on
imagery differencing that including transformations like (1) principal component analysis, (2)
modeling of spectral mixture, (2) using different vegetation indices, and (3) change vector
analysis have accurate result for mapping earth’ surface changes (Dahl, 2006; Selçuk, 2008;
Lu et al., 2004; Hayder, 2018). Water resources detection is well represented (Abd et al.,
2011; Liya and Karsten, 2015). This study propose a technique for investigating the
spatiotemporal degradation changes and dehydration extent of Al-Razaza Lake for the period
time 1990 up to 2018 using digital image processing and GIS analysis on satellite imagery.
Recommendations are made in the context of the water surface extraction, monitor and
manage of water bodies, surface water dehydration extent, Wetland change, desertification,
natural resources loss, water resources loss, water quality maintenance, wildlife habitat, and
soil erosion control

Hayder Dibs and Suhad AL-Hedny
2. MATERIAL AND METHODS
Extraction and detection technique of water resources was initially tested to map the spatial
and temporal water body changes (dehydration extent) of the Al-Razaza Lake. For the
purpose of this study, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus
(ETM+) and Landsat 8 operation land imager (OLI) data acquired from 1992 to 2018 were
used. In this research, supervised classification (Mahalanobis distance algorithm) were
applied on the four collected Landsat images of 1992, 2001, 2010, and 2018 for water
resources degradation. The accuracy assessment, and a statistical analysis of the expected
results were conducted to get an accurate spatiotemporal degradation and dehydration extent
of Al-Razaza Lake. Figure 1 shows the overall adopted methodology to perform this study.
2.1. Study area description
Al-Razaza Lake was generated to divert the annual floodwaters of the Euphrates River into a
desert to prevent flooding across southern Iraq side. It is a few miles from North West of
Karbala province and north east of Al-Anbar province, Iraq as shown in Figure 2. It is
bounded by 33°10′N to 32°20′N and 43°55′ E to 43°15′ E (Nawal et al., 2012). Al-Razaza
Lake is a part of a wide valley that consists of AL-Tharthar, AL-Habbaniya, Al-Razzaza and
Bahr Al-Najaf (Atasoy et al., 2011). The water surface area of this lake about 1810 km2
when
the water level at 40m above sea level. It holds about of 26 billion m3
of water with length
(Nawal et al., 2012). The lake is supplied by different water sources, including the Euphrates
River; Lake Habaniya; Rashidiya; groundwater springs; rainwater and seasonal flows. The
climate of the lake basin is characterized by cold winters and dry and hot summers, being
influenced by the desert surrounding the lake (Nawal et al., 2012). Figure 2 describes the
study area location.
2.2. Satellite Data
Four Landsat satellite images were applied to conduct this study from 1992 to 2018. Two
images downloaded from TM sensor of Landsat 4 and 5 in August 1992 and February 2010,
respectively. The third imagery was obtained from ETM+ sensor in January 2001, and the last
image of OLI sensor acquired in May 2018.The TM scenes consist of seven 7 bands with 30m
as a spatial resolution for bands (1 – 5) and 7. Band 6 spatial resolution is 120m. The ETM+
imagery consist of 8 spectral bands with 30m as a spatial resolution for bands (1 – 5) and 7.
Band 6 spatial resolution is 60 m. However, the newly satellite Landsat 8 OLI launched (on
11 February 2013) has 9 spectral bands and 2 thermal bands. All spectral its bands have
spatial resolution equal to 30m, and the thermal bands (10 & 11) are obtained at 100m spatial
resolution and then resampled to 30m. All of the obtained Landsat images of 1992, 2001,
2010 and 2018 were collected from the path 169 and row 37. These time series of Landsat
satellite images are freely downloaded from the website of United States geological survey
(USGS) (http://glovis.usgs.gov).

Figure 1. Flowchart showing the overall methods adopted in the study area.
2.3. Image Pre-Processing
To make the input satellite images ready for further analyzing and processing, the following
pre-processing steps were performed; radiometric calibration, atmospheric correction,
geometric correction, co-registration, and resampling. As reported by Song et al., (2001)
atmospheric correction of satellite image might not be needed in case of using a single image
for image classification. However, when multi-temporal or multi-sensor images are used, the
atmospheric correction become a mandatory (Lu and Weng, 2007). So, radiometric and
atmospheric correction were performed as reported by (Schroeder et al., 2006). The Landsat
calibration perform using ENVI v 5.0 software. A pre-georeferenced was performed to UTM
zone 38 north projection using WGS-84 datum. The geometric correction of satellite images
was performed to a reference image (Landsat 8 OLI image 2018) to register the all other
images, with using the root mean square error (RMSE) < 0.5 pixel from using the image to
image co-registration technique, the Arc map software has been used for performing
geometric and co-registration. The 2018 image had previously been georeferenced from using
ground control points (GCPs) and topographic map (RMSE < 0.4 pixel). Around 22 control
points (CPs) were used for co-registration all the images to the reference image. Finally, the
images were resampled using the nearest neighbor method. Figure 3 (a) indicates the Landsat
time series images after radiometric calibration, atmospheric correction, geometric correction,
co-registration, and resampling were performed.
Ye
Pre-processing stage for 1992, 2001, 2010 & 2018
Landsat MSS
1992
Landsat7
ETM+2001
Landsat7
ETM+2010
Landsat 8
OLI 2018
Radiometric
Correction
Geometric
Correction
Images
Co-registration
Images
Resampling
Wetland Change Detection 1992,
2001, 2010 & 2018
Image Classification (Mahalanobis Distance) for
1992, 2001, 2010 & 2018
Selecting Training Sites
Thematic Maps of 1992, 2001,
2010 & 2018
Accuracy assessment of 1992, 2001,
2010 & 2018 classifications
Collecting
Testing sites
Visual
Interpretation
Processing stage for 1992, 2001,
2010 & 2018
N
Images Layer
stacking
Images
Sub-setting

Figure 2 Location map of the study area in the western part of Iraq
2.4. Image Processing
For further satellite images processing and analysis stages, the following processing steps were
performed: (1) Layers stacking, and (2) Images resizing. Layer stacking was the next step of
processing stage, it is applied to all of the Landsat images using the Layer Stacking tool under
Envi v 5.0 software. After that, the satellite scenes were resized. The steps of images Layer
stacking and resizing were conducted in order to saving time when processing and analyzing
data and reduce the storage size (Abd, 2013; Dibs et al., 2015). Figure 3 (b) indicts the final time
series images of Landsat that ready to further analysis to identify the water dehydration extent of
Al- Razaza Lake after performed the layer stacking and resizing.
2.5. Multi-Temporal Supervised Classifications
The classification techniques and approaches are developed by scientists and practitioners for
increase the accuracy of classification (San et al., 1997). The classification of remote sensing
data is very attractive to researchers who deals with applications of environment or
socioeconomic (Dibs et al., 2016). In this study totally, four classes were established as water
bodies, shallow water, soil-1, and soil-2. Description of these classes are presented in Table 1.
Table 1. Land cover classification
Land cover
classes
Description
Soil-1 Areas with no vegetation cover, stony area
Soil-2 Areas with no vegetation cover, sandy area
Water bodies Deep water of Al-Razaza lake
Shallow water Coastal and the shallow water areas surrounding the of Al-Razaza
lake
‫ݐݏ݅ܦ‬ = ටሺܺ − ‫ܯ‬௜ሻ୘ ∗ V௜
ିଵ
∗ ሺܺ − ‫ܯ‬௜ሻ

Where (X) is the input variable vector and (Mi) represent the class (i) mean vector for
over all pixels, (Vi) is the (variance/covariance) matrix for class (i), and (T) is the matrix
transpose. Ranging from (0 - 1), the MDC typicality identify the absolute strength of class
membership (San et al., 1997; Eastman et al., 2005).
Figure 3. (a) Landsat satellite images with all corrections, (b) Landsat satellite images after
performing layer stacking and resizing
2.5.1. Training and testing sites
Training site another step was performed to this research over the study area. The training
sites of each class is selected by using a topographic map, and visual image interpretation.
The training sets size has a great effect on the result of classification accuracy (Mather and
Koch, 2010). By following the recommendation of (Foody et al., 2006; Gao& Liu, 2010), the
size of each training sets should not select to be fewer than 10-30 observations for each class.
Training and testing sites were selected for each class by delimiting polygons around
representative site. From using this pixels enclose by these polygons, we obtained spectral
signature for the four classes type and recorded by the satellite images. Then we entered the
spectral signatures into the processing of classification. Regarding to the reference image and
visual image interpretation, the interesting regions (ROI) were generated for each class of the
study area. The samples screening were conducted for each class type in the ENVI v 5.0
software. The principle is to get the evenly distributed training sites over the catchment area
and to keep the size to be similar as possible for both the Landsat 8 image and the to the series
of images. In this paper, the size of the training sites of water bodies, shallow water, soil-1,
and soil-2 categories were set to be greater than 1500 pixels. However, only 200 pixels for the
water body class. Figure 4 demonstrates the thematic maps of time series of Landsat images
from 1992-2018.

2.6. Water dehydration extent detection
After the thematic maps generated for each individual satellite image, a statistical comparison
was performed to determine the dehydration of Al-Razaza Lake. This method is perhaps the
most common technique to use for change detection (Jensen, 2004), and has been used by
Yang (2002) to monitor water resources change in the Atlanta. Figure 4 shows the thematic
maps and the spatial distribution of their classes. Figure 4 (a) demonstrates the four classes
distribution in 1992. The images classification results are summarized for all images, whereas
Tables 3 and 4 respectively display the percentage, relative change and area in square
kilometer of the four classes for the period from 1992 to 2018. In 1992 the dominant class is
soil (soil-1 & soil-2) as indicates in Table 3 & 4, which represent approximately 67.40 % of
the study area, with area was 3157.03 km2
(1534.01 km2
for soil1-1 and 1623.02 km2
for soil-
2). In other hand, area of soil-1 class is bigger than area of soil-2 class, and the percentage
32.75 % for soil-1 and 34.65 % for soil-2 in 1992. But, in 2001 the total soil classes represent
approximately 74.65 % (49.05 % for soil-1 and 25.60 % for soil-2) of the study area or 3496.6
km2
(2297.50 km2
for soil-1 and 1199.10 km2 for soil-2), and that indicate the dominant class
is also soil, the soil class area in increasing. In contrast, the shallow water class decreased and
covered the smallest area of approximately 213.10 km2
, which represents 4.55 % of the study
area in1992, while the shallow water class in 2001 decreased to cover the smallest area of
approximately 138.18 km2
, which represents 2.95 % of the study area. The last class was the
water bodies, it has the second highest percentage of the study area after soil class at
approximately 28.05% or 1313.87 km2
in 1992. However, the water bodies is the second most
prevalent class in the study area and its area decreased by 2001 to become 22.40 % or 1049.22
km2
. The changes in land cover classes for the period 1992 to 2001 as seen in Figure 4 ( a &
b) and Table 3 & 4.
For the 2010 image classification, the thematic map indicates in Figure 4 (c) and Table 3
& 4 indicate the statistical results. For 2010 still the dominant class is also soil class, which
represents approximately 78.5 % (36.30 % for soil-1 and 42.20 % for soil-2) of the study area
or 3676.96 km2
(1700.30 km2
for soil-1 and 1976.66 km2
for soil-2). The shallow water is
became the second most prevalent class in the study area, covering 12.00 % or 257.60 km2.
The water bodies’ class covers the smallest area of approximately 224.85 km2, which
represents 9.5 % of the study area. However, the statistical result of image classification in
2018 as seen in Table 2 and its spatial distribution in Figure 4 (d) indicate increase the soil
class as which represents approximately 89.70 % (46.60 % for soil-1 and 43.10 % for soil-2)
of the study area, which represents area of 4201.55 km2
(2182.75 km2
for soil-1 and 2018.80
km2
for soil-2). Whereas the shallow water is became the second most prevalent class in the
study area, covering 05.50%, which covers about 257.60 km2
. The water bodies’ class
decreased to cover the smallest area of approximately 224.85 km2
, which represents 04.80 %
of the study area. Table 3 indicates the statistical results in course of time (1992 to 2018), the
soil -1 and soil-2 classes increase by +13.85 and 8.45 respectively, with area represents
approximately 0648.74 km2
and + 0395.78 km2 respectively. In the other hand, area of
shallow water decreased for the same course of time from percentage of 5.5 % in to become
4.55 %, with relative change equal to 0.95% with relative change of area equal to 044.5 in of
the study area. In contrast, the area of water bodies class has dramatically decreasing and the
dehydration extent increase. The Al-Razaza lake water surface area start to decrease for the
three interval of time 1992 - 2001, 2001 - 2010 and 2010 - 2018 to have 264.65 km2
, 604.24
km2
and 220.13 km2
, respectively. The lake surface area change of water bodies decreased
with relative change percentage of 23.25 %, with relative change in area approximately to
1089.02 km2
of the study area as indicate in Table 3. The statistical results indicate rapidly
decreasing trend in Al-Razaza Lake surface area in the period of 1992–2018, this change
equal to 1089.02 km2
and that means the lake lose around 82.8% of its total area in very short

time (over lost than three decades) as indicates in Table 4. The dehydration extent increase
dramatically and it has negative impact on the climate of surrounding area, rising atmospheric
temperature, and inadequate water contained the lake, biodiversity and the ecosystem.
Table 2 Comparison of classification classes area and percent.
1992 2001 2010 2018
Class Area in Km2
(%) Area in Km2
(%) Area in Km2
(%) Area in Km2
(%)
Soil-1 1534.01 32.75 2297.50 49.05 1700.30 36.30 2182.75 46.60
Soil-2 1623.02 34.65 1199.10 25.60 1976.66 42.20 2018.80 43.10
Water bodies 1313.87 28.05 1049.22 22.40 0444.98 09.50 0224.85 04.80
Shallow water 0213.10 04.55 0138.18 02.95 0562.06 12.00 0257.60 05.50
Total area in
Km2 Total pixels of image = 5204474 5204474 pixels * 30m *30m/1000000 = 4,684.0266
Figure 4. Four classified thematic maps of Landsat satellite images of the study area: (a) The
classified image in 1992; (b) the classified image in 2001; (c) The classified image in 2010; (d) The
classified image in 2018.
Table 3. Statistical summary of Landsat classification area for 1992 and 2018
Classes 2018 (%) 1992 (%)
Relative change
(2018 -1992) (%)
Relative change of
Area in Km2
Soil-1 46.60 32.75 + 13.85 + 0648.74
Soil-2 43.10 34.65 + 08.45 + 0395.78
Water bodies 04.80 28.05 - 23.25 - 1089.02
Shallow water 05.50 04.55 + 00.95 + 0044.5

Table 4. Statistic result of Al-Razaza lake surface water area change.
2.7. Classification accuracy assessment
Accuracy assessment is critical for any generated map from remote sensing data (Hayder and
Thulfekar, 2018. Error matrix is commonly method to present the classification accuracy
(Jensen, 2004). Overall’s, user’s and producer’s accuracies, and the Kappa statistic were
calculated from the error matrices as reported by (Yuan et al., 2005). An independent sample
of an average of 275 polygons, with about 90 pixels randomly selected for each polygon, for
each classification to calculate the classification accuracies. Error matrix as cross tabulations
of the generated class vs. the corresponding class were used to obtain classification accuracy
(Congalton& Green, 1999). Table 5 summaries the accuracy assessment of all the classified
images in the course of time 1992 - 2018 using ENVI v 5.0software. The overall accuracy
assessment, kappa coefficient, producer’s and user’s accuracies are illustrated in Table 5.Post
classification were applied to reduce the classification errors caused due to the similarity in
the spectral responses of classes such as soil, shallow water and water body.
Table 5 Summary of Lands at classification accuracies for 1992, 2001, 2010, and 2018
1992 2001 2010 2018
Land cover class Producer’s User’s Producer’s User’s Producer’s User’s Producer’s User’s
Soil-1 85.30 75.72 85.69 81.93 96.30 95.55 90.40 83.23
Soil-2 80.87 89.26 80.50 86.60 94.55 98.16 87.34 91.74
Water bodies 99.77 99.10 99.98 99.88 100.00 99.99 80.43 100.00
Shallow water 79.99 81.10 92.59 76.49 99.97 79.66 76.96 76.68
Overall accuracy 88.00 90.79 95.94 87.67
Kappa coefficient 00.82 00.86 00.93 00.79
3. CONCLUSION
Mapping water resources changes at regional scales is very important for different of
applications including desertification, loss of natural resources, wildlife habitats, land
planning, and increase the global warming. Al-Razaza suffered from dehydration extent from
1980s till recent years due to decreasing surface water and international and national water
policies. This study aimed to model the spatial and temporal changes of surface water area of
AL-Razaza Lake for 1992, 2001, 2010 and 2018 by performing a time series of supervised
classification on Landsat satellite images. Validation of this study conducted by calculation
the accuracy assessment of each image classification. The statistical results of validation for
this method reveals that the overall accuracies and kappa coefficients of the supervised
classifications were 88, 90.79, 95.94 and 87.67 respectively, and 82%, 86%, 93% and 79%
respectively. The results demonstrated an intense decreasing and rapidly changing trend in the
Year Lake surface area
(km2)
Lake surface area change
(km2)
Total change area in
(km2)
1992 1313.87
-1089.02
- 264.65
2001 1049.22
- 604.24
2010 0444.98
- 220.13
2018 0224.85

lake surface area in the period 1992–2018, the Lake lost 82.8% of its total area 1089.02 km2
compare to Lake surface area in 1992. If such a trend in AL-Razaza Lake continues, it is very
likely the lake will lose its entire water in the near future.
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