Scholarly article on topic 'Analysis of urban growth and sprawl from remote sensing data: Case of Fez, Morocco'

Analysis of urban growth and sprawl from remote sensing data: Case of Fez, Morocco Academic research paper on "Earth and related environmental sciences"

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Abstract of research paper on Earth and related environmental sciences, author of scientific article — Abdelkader El Garouani, David J. Mulla, Said El Garouani, Joseph Knight

Abstract Fez is the most ancient of the imperial cities of Morocco. In Fez the rate of population growth has been spectacular in recent times (484,300 inhabitants in 1982 and 1,129,768 in 2014). The accelerated rate of population growth has generated a large urban sprawl in all its forms and serious environmental problems. In this research, we have analyzed the relationship between urbanization and land use changes and their impact on cityscape in Fez and the importance of the increase in impervious surface areas. Satellite imageries and census data have been used to identify different patterns of land use change and growth of the city for the period 1984–2013. Classification and analysis of the satellite imageries were performed using Erdas imagine and ArcGIS Software. Urban sprawl in Fez was assessed over 29years (1984–2013). The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 78% to 87%. The maps showed that between 1984 and 2013 the amount of urban or developed land increased by about 121%, while rural cover by agriculture and forest decreased respectively by 11% and 3%.

Academic research paper on topic "Analysis of urban growth and sprawl from remote sensing data: Case of Fez, Morocco"

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Original Article/Research

Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case of Fez, Morocco

Abdelkader El Garouani, David J. Mulla, Said El Garouani, Joseph Knight

PII: DOI:

Reference:

S2212-6090(16)30066-8 http://dx.doi.Org/10.1016/j.ijsbe.2017.02.003 IJSBE 153

To appear in: International Journal of Sustainable Built Environ-

Received Date: Revised Date: Accepted Date:

21 April 2016 17 January 2017 10 February 2017

Please cite this article as: A. El Garouani, D.J. Mulla, S. El Garouani, J. Knight, Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case of Fez, Morocco, International Journal of Sustainable Built Environment (2017), doi: http://dx.doi.org/10.1016/j.ijsbe.2017.02.003

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Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case

of Fez, Morocco

Abdelkader EL GAROUANf, David J. MULLAb, Said EL GAROUANIc & Joseph KNIGHT

a Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, Route d'Imouzzer, BP. 2202, Fez

30060, Morocco, e-mail: el_garouani@yahoo.fr

b Department of Soil, Water, and Climate, University of Minnesota, 1991 Upper Buford Circle, 439 Borlaug Hall, Minneapolis, Minnesota, USA, e-mail: mulla003@umn.edu

c Informatic Department, Faculty of Sciences, Abdelmalek Essaadi University, B.P. 2121, Tetouan, 93002, Morocco,

e-mail: saidelgarouani@yahoo.fr

d Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota, 1991 Upper Buford Circle, 439 Borlaug Hall, Minneapolis, Minnesota, USA, e-mail: jknight@umn.edu

Abstract:

Fez is the most ancient of the imperial cities of Morocco. In Fez the rate of population growth has been spectacular in recent times (484 300 inhabitants in 1982 and 1 129 768 in 2014). The accelerated rate of population growth has generated a large urban sprawl in all its forms and serious environmental problems. In this research, we have analyzed the relationship between urbanization and land use changes and their impact on cityscape in Fez and the importance of the increase in impervious surface areas. Satellite imageries and census data have been used to identify different patterns of land use change and growth of the city for the period 1984-2013. Classification and analysis of the satellite imageries were performed using Erdas imagine and ArcGIS Software. Urban sprawl in Fez was assessed over 29 years (1984-2013). The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 78% to 87%. The maps showed that between 1984 and 2013 the amount of urban or developed land increased by about 121%, while rural cover by agriculture and forest decreased respectively by 11% and 3%.

Keywords: Urban expansion, classification, GIS, Remote Sensing, Fez, Morocco

I- Introduction

Urbanization that is considered as a positive process linked to modernization, industrialization and global integration has economically benefitted only a minority of the urban population (Bhatta, 2010; Sharma, 1985). During the last century, Moroccan society was increasingly urban (Fig. 1). The accelerated rate of urbanization in all forms and the population growth in Morocco has been generating serious environmental problems and concern for both the government and interested stakeholders (Lehzam, 2012).

The amount of impervious surface in a landscape is an important indicator of environmental quality. Impervious surfaces are defined as any surface which water

cannot infiltrate and are primarily associated with transportation and building rooftops (Bauer et al., 2007). Imperviousness increases water runoff, and hence, is a primary determinant of runoff volumes in urbanized areas. The impervious surface area provides a measure of land use that is closely correlated with these impacts (Arnold and Gibbons, 1996). It therefore follows that impervious cover information is fundamental to assess flooding risks and flood management in the city.

Economic development demands sustainable land management. Spatial information on land use/land cover types and their change detection in time series are important means for city planning and new development activities (Ewing et al. 2002). The present research is undertaken in that

spirit. It will analyze the relationship between urban growth and land use changes and their impact on the Fez cityscape. This information is an essential tool in decisionmaking and management policy of the city by the local authority and for ensuring sustainable urban growth and development in the study area. The period of focus is from 1984 to 2013. Topographical maps, highresolution satellite imageries and other necessary data have been used to detect land use/land cover changes in the study area.

Figure 1: Population growth in urban and rural areas in Morocco (HCP, 2015)

Numerous researchers, including Arvind et al. (2006), Lunetta and Balogh (1999), Yuan et al. 2005, Zubair (2006) and others have demonstrated the value of multitemporal satellite imagery for classification of land cover. The strong development of remote sensing and GIS technology has helped us to study the urban space development.

II- Study Area

Fez is located on the northern part of Morocco (Fig. 2). The urban community of Fez accounted for 1 129 768 inhabitants in 2014 and the city has about 30 km2 (El Garouani et al., 2011). Founded in 789 by Moulay Idriss 1st and home to the oldest university in the world (Quaraouiyne University built in 857). Fez reached its height in the 13th-14th centuries under the

Merinids, when it replaced Marrakesh as the capital of the kingdom. Although the political capital of Morocco was transferred to Rabat in 1912, Fez has retained its status as the country's cultural and spiritual center (Aouni et al. 1992). Fez is a religious, touristic and academic center. Due to its importance, the historic Medina of Fez was added to the UNESCO World Heritage List in 1981. Over the past 20 years, there were many problems and challenges posed by the rapid growth of Fez just like every other city in Morocco.

In fact, the demand for infrastructure, basic services and housing in expanding urban area in Fez are on the increase. Moreover, provision of education, health, transportation, water and sanitation services should be accelerated in urban centers.

III- Methodology

Present study is based on spatial remote sensing data as well as non-spatial data available from various sources for different periods. Urban development has led to expansion of the cityscape of Fez, leading to changes in land use. The study specifically focuses on interpreting the city's land use change patterns and growth based on satellite and demographic data.

Our approach combined spring and summer images. In the summer image, the Fez urban area appears unvegetated and is

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distinguishable from forests and orchards. However, the spring image is needed to separate vegetated areas from urban areas with significant amounts of asphalt and concrete and other impervious surfaces that are spectrally similar to bare soil in a summer image. The importance of multitemporal imagery was confirmed by determining the transformed divergences for the dataset (Two images in 1984 and two images in 2013). Compared to the single dates, both the average and the minimum separability of classes were increased by the combination of spring and summer images.

This research uses Landsat images to calculate VSW (Vegetation-Soil-Water) index images, that distinguish clearly between Vegetation, Soil and Water elements on the image. This VSW index image is the basis for detecting clearly urban areas (My Vo Chi et al., 2009). The VSW index is calculated by the scatter plot of red band versus near-infrared band, which are common in almost of all types of satellite imagery (Fig. 3). That consist to transform or analyze a feature space mathematically to isolate groups of pixels that may be related. Each axis represents DNs from one satellite band and plot each pixel in the feature space from an image using its DNs.

Image processing software has been used for geometric correction of satellite data, supervised classification, accuracy assessment of classification, land use maps (1984 & 2013), change detection, final output maps etc.

■ 2.45

Red reflectance

Figure 3: Scatterplot of TM4 (Y-axis) and TM3 (X-axis)

GIS software has been used for the digitization, integration, overlay and presentation of the spatial and non-spatial data of land use change in the city. Field surveys were performed throughout the study area using Global Positioning System

(GPS) to obtain accurate location point data for each land use class included in the classification scheme. Figure 4 presents the methodology used to produce maps of land use change and urban expansion. Table 1 presents the classification units of land use identified in the study area.

Figure 4: Flowchart of spatial and temporal changes in urban land cover

Table 1: Land cover classification scheme

Level -I Level -II

Building Residential and Industrial area

Agriculture Land Crop Land, Olive trees, Orchard

Forest Forest

Natural land Rangeland

Water bodies River, Lakes, Dam

3.1. Satellite Imagery

To accomplish the objectives of the present study, four available satellite images were obtained from the United States Geological Survey (USGS) databases online resources:

It was important to utilize images covering the summer season to ensure that agricultural land surrounding Fez are fully assessed.

Landsat Thematic Mapper (TM) and Operational Land Imager-Thermal Infrared Sensor (OLI_TIRS) data have several advantages for this application: synoptic view, digital, GIS compatible data, availability of data since 1984, and economical costs. This paper extends the methods and results of our previous works (El Garouani et al., 2012, 2014 and 2015) and of the present contribution.

Data used in the research include the multitemporal dataset and topograp hic maps:

• Landsat TM and OLI-TIRS images (For 0818-1984, 04-15-1985, 06-15-2013 and 0818-2013) - Google earth images (09-122013).

• Aerial photographs - Topographic maps.

PDemographics and census data (1982, 1994, 2004 and 2014).

• Urban development plan of Fez - Field observations, etc.

Landsat images are described below:

The Landsat Thematic Mapper (TM) sensor was carried on Landsat 4 and Landsat 5, and images consist of seven spectral bands with a spatial resolution of 30 meters for Bands 1 to 5 and 7 (Table 2).

Table 2: Spectral bands description of Landsat TM

Bands Wavelength

Band 1 - Blue 0.45-0.52

Band 2 - Green 0.52-0.60

Band 3 - Red 0.63-0.69

Band 4 - Near Infrared 0.76-0.90

Band 5 - Shortwave Infrared 1 1.55-1.75

Band 6 - Thermal 10.40-12.50

Band 7 - Shortwave Infrared 2 2.08-2.35

Landsat 8 Operational Land Imager (OLI) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. The ultra-blue Band 1 is useful for coastal and aerosol studies. Band 9 is useful for cirrus cloud detection. The resolution for Band 8 (panchromatic) is 15 meters (Table 3).

Table 3: Spectral bands description of Landsat OLI

Before analysis, the images were geometrically corrected. During geometric correction, control points are detected on the topographic maps and the satellite images with RMS errors that are estimated below 0.5 pixel. After that, the images for 1984 and 2013 were registered on Lambert Conform Conic Projection, datum Merchich, zone I (North Morocco).

A subset image was created from each Landsat image for subsequent treatment and classification (Fig. 5).

3.2. Image classification

Image classification is a conventional change detection method. The advantage of image classification is the ability to create a series of land cover maps. We applied the maximum likelihood supervised classification (MLC) method for time series of Landsat bands and VSW index.

The maximum likelihood algorithm is one of the most widely used in the classification of satellite imagery. The method is based on the likelihood that each pixel belongs to a particular class. The basic theory assumes that these likelihoods are equal for all classes and that input bands are uniformly distributed. The method requires a significant calculation time and is based on a normal distribution of the data in each band in the classification. It tends to over-classify signatures with relatively large values in the covariance matrix (Vorovencii and Muntean, 2013).

MLC is performed according to the following steps (Richards and Jia, 1993). The method consisted in choosing the training samples

for each of the desired classes from the color composite image. During the training phase, 40 training sites were selected by onscreen digitization of specific polygons (5 training samples by thematic class). The files obtained were saved and used for the images classification. Each training field was assigned a number from 1 to 8 representing land cover classes including: water, urban, industrial area, rangeland, olive trees, orchard, forest and agriculture.

By using GIS technique (co nvert vector, overlay and calculate), the urban expansion information (areas, the replacement of land covers to urban area) was assessed over the study periods. The areas identified as urban in 2013, but not developed in 1984 had a high greenness value (due to vegetative cover) in the 1984 imagery and thus had low to no impervious surface in the 1984 time period.

IV- Results and Discussion 4.1. Land use analysis

Urbanization is a major cause of land use changes and land conversions. It makes unpredictable and long lasting changes on the landscape. An important aspect of change detection is to determine what is actually changing to what i.e. which land use class is changing to the other. Analyzing the spatial and temporal changes in land use and land cover is one of the effective ways to understand the current environmental status of an area and ongoing changes (Arvind, 2006, Yuan et al. 2005 and Zubair, 2006). The land use maps of two points in time, that is, 1984 and 2013 based on automatic classification and visual interpretation respectively depict land use categories changes such as residential, agriculture, industrial, water body, forest, etc (Fig. 6).The growth of urban area and accompanying increases in amount of impervious surface area are readily apparent.

Bands Wavelength

Band 1 - Ultra Blue (coastal/aerosol) 0.43 - 0.45

Band 2 - Blue 0.45 - 0.51

Band 3 - Green 0.53 - 0.59

Band 4 - Red 0.64 - 0.67

Band 5 - Near Infrared (NIR) 0.85 - 0.88

Band 6 - Shortwave Infraredl 1.57 - 1.65

Band 7 - Shortwave Infrared2 2.11 - 2.29

Band 8 - Panchromatic 0.50 - 0.68

Band 9 - Cirrus 1.36 - 1.38

08-18-1984

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Figure 5: False color composites of the Landsat and Google Earth images showing Fez in 1984 and 2013.

An independent sample of an average of 30 polygons, with about 250 pixels for each selected polygon, was randomly selected from each classification to assess classification accuracies. Error matrices as cross-tabulations of the mapped class vs. the reference class were used to assess

classification accuracy (Congalton & Green, 1999). Overall accuracy, user's and producer's accuracies, and the Kappa statistic were then derived from the error matrices (Table 4).

Figure 6: The land cover maps of Fez in 1984 and 2013.

Table 4: Error Matrix Analysis of field sites (columns) against Landsat classification (rows)

a-1984

1 2 3 4 5 6 7 8 Total ErrorC Accuracy

Water 1 130 0 0 0 0 3 0 0 133 0.02 97.74

Urban 2 0 230 9 0 0 0 0 0 239 0.04 96.23

Industrial area 3 0 0 99 0 0 2 0 0 101 0.02 98.02

Rangeland 4 5 20 16 600 0 13 0 0 654 0.08 91.74

Olive trees 5 15 1 0 0 115 13 0 9 153 0.25 75.16

Orchard 6 0 0 38 0 0 165 0 0 203 0.19 81.28

Forest 7 0 0 0 0 2 0 70 0 72 0.03 97.22

Agriculture 8 3 0 0 14 1 27 58 220 323 0.32 68.11

Total 153 251 162 614 118 223 128 229 1878

ErrorO 0.15 0.09 0.39 0.02 0.03 0.26 0.45 0.04

Accuracy 84.97 91.63 61.11 97.72 97.46 73.99 54.69 96.07

b-2013

1 2 3 4 5 6 7 8 Total ErrorC Accuray

Water 1 284 0 0 6 20 1 0 0 311 0.10 91.32

Urban 2 0 68 14 0 0 4 0 0 86 0.21 79.07

Industrial area 3 0 0 140 0 0 8 0 0 148 0.06 94.59

Rangeland 4 40 0 4 207 0 25 0 0 276 0.33 84.42

Olive trees 5 2 0 0 0 233 2 1 10 248 0.07 93.95

Orchard 6 18 0 30 0 2 162 0 3 215 0.37 66.05

Forest 7 0 0 0 0 0 22 172 0 194 0.11 88.66

Agriculture 8 23 7 0 58 46 50 16 242 442 0.45 54.75

Total 367 75 188 271 301 274 189 255 1920

ErrorO 0.28 0.09 0.26 0.24 0.33 0.41 0.09 0.05

Accuray 77.38 90.67 74.47 76.38 77.41 59.12 91.01 94.90

ErrorO = Errors of Omission (expressed as proportions) - ErrorC = Errors of Commission (expressed as proportions)

4.2. Change detection

Following the classification of imagery from the individual years, a multi-date postclassification comparison change detection algorithm was used to determine changes in land cover in the interval; 1984-2013. According to the results from 1984 to 2013, the urban change was large, urban areas increased from 2041 ha (1984) to 4503 ha (2013) (Table 5).

Error matrices were used to assess classification accuracy and are summarized in Table 4. The overall accuracies for 1984 and 2013 were, respectively, 87% and 78.5%. User's (Field) and producer's (Landsat classification) accuracies of individual classes were consistently high, ranging from 55% to 98%.

Classification maps were generated for two years (Fig. 6) and the individual class area and change statistics are summarized in Table 5. From 1984 to 2013, urban area

increased approximately 2462 ha (121%) and olive trees increased 981 ha (89%) while non-orchard agriculture decreased 4124 ha (11%) and forest decreased 19 ha (3%). For the water body surfaces there is an increase of 247%. This large increase can be explained by the construction of two dams (El Gaada in the East and Dhar Mehrez in the South) and, on the other hand, by the heavy rainfall in 2013, which allowed the emergence of wetlands in the NW (Table5).

The relationship between population growth and growth in urban land area as determined from the Landsat-derived change maps was also examined. Development patterns of Fez reflect the distribution of population and households because residential land uses take over all the land that is developed (HCP, 2015). The average annual growth in urban area determined from the Landsat change detection was 4.2 % from 1984 to 2013. This compares to an annual population growth

rate of approximately 3.9 % from 1982 to 2014. So the growth in urban area is relatively higher than the population growth rate. Population and urban expansion data were also tabulated (Table 6). An urban sprawl index (USI) was calculated as the ratio of urban expansion to population increase (OECD, 2013). The urban sprawl index measures the growth in urban area over time adjusted for the growth in population. When the population changes, the index measures the increase in the urban area over time relative to a benchmark where the built-up area would have increased in line with population. The index is equal to zero when both population and urban area are stable over time. It is larger than zero when the growth of the urban area is greater than the growth of population, i.e. the density of the metropolitan area has decreased.

In Fez, the USI = 4.2 / 3.9 = 1.08. It is slightly higher than zero, so the urban area is slightly higher than the population growth. This index provides a way to assess the degree of sprawl for each region.

To further evaluate the results of land cover conversions, a matrix of land cover changes from 1984 to 2013 was created (Table 7). In the table, unchanged pixels are located along the major diagonal of the matrix. These results indicate that increases in urban areas mainly came from conversion of agricultural, rangeland and orchard land to urban uses during the period, 1984-2013. Of the 2 462 ha of total growth in urban land use, 22 65 ha was converted from agricultural land, 82 ha from orchard and 70 ha from rangeland. We note that 70 ha of rangeland was converted to urban between 1984 and 2013, while at the same time, 5 ha of urban was converted to rangeland. These changes may be classification errors. Classification errors may also cause other unusual changes. For example, 288 ha of agricultural land changed to rangeland and 42 ha of rangeland changed to agricultural land. These changes are most likely

associated with omission and commission errors in the Landsat classifications change map. Registration errors and edge effects can also cause apparent errors in the determination of change vs. no-change.

This comparative approach has demonstrated how landscape changes can be derived from satellite imagery in the urban spatial structure. Interpretation of Fez's growth over a period of 29 years allows a deeper understanding of growth mechanisms, underlying drivers of urban expansion, and their effects on local livelihoods.

According to our observations, urban sprawl has a negative impact on infrastructure and the sustainability of Fez. The information on land use change reveals both the desirable and undesirable changes and classes that are "relatively" stable overtime. This information serves as a vital tool in decisions making and policy formulation by the local authority. For example, due to urban expansion, Fez lacks vegetation cover. Needless to say that vegetation and open green spaces (parks) are the most important parameters of quality of urban environment assessment. Hence, a vigorous focus needs to be given to grow more trees and also develop green belts that can reduce a city's ecological footprint and carbon emissions significantly. A suitable strategy to reclaim industrial wastelands is also required.

On the other hand, as the city grows in size and population, harmony among the spatial, social and environmental aspects of a city and between its inhabitants becomes of paramount importance. Urban development should be guided by a sustainable planning and management vision that promotes interconnected green space, a multi-modal transportation system, and mixed-use development. Diverse public and private partnerships should be used to create sustainable and livable communities that protect historic, cultural, and environmental resources. In addition, policymakers, regulators and developers should support

sustainable site planning and construction techniques that reduce pollution and create a balance between built and natural systems. So, what innovative approaches can be taken up to achieve this goal? This work will help to find answers to this question. In study area, some strategies should be adopted by the local authorities and stakeholders including:

- Provision of adequate and affordable housing for all.

Table 5: Land use change between 1984 and 2013 (hectares)

- Ensuring environmental sustainability.

- Good governance and enhanced urban development.

- Planning for flood mitigation.

In this work, we try to produce the necessary data and information for adaptation and implementation of these strategies in Fez context.

ion of

Land use 08-18-1984 08-18-2013 Change (2013-1984) Percent Change %

1 Water 98 339 241 247

2 Urban 2 041 4 503 2 462 121

3 Industrial area 116 167 51 43

4 Rangeland 3 023 3 140 117 4

5 Olive trees 1 100 2 081 981 89

6 Orchard 1 211 1 502 291 24

7 Forest 641 622 -19 -3

8 Agriculture 38 761 34 637 -4 124 -11

Table 6: Comparison of urban area estimates from Landsat classifications and the demographics and census data

Percent Change (%) Annual growth (%)

Year 1 982 1 994 2 004 2014 2014-1982

Population 494 300 770 200 951871 1 112 072 617 772 125 3.9

Year 1984 2013 2013-1984 Percent Annual growth

Change (%) (%)

Urbanisation (ha) 2 041 4 503 2 462 121 4.2

Table 7: Matrix of land cover and changes (ha) from 1984 to 2013

Water Urban Industrial area Rangeland Olive trees Orchard Forest Agriculture 2013 Total

Water 95 0 0 5 1 119 0 120 339

Urban 0 2 027 1 70 47 82 10 2 265 4 503

Industrial area 0 6 115 4 5 6 0 32 167

Rangeland 1 5 1 2 826 5 9 5 288 3 140

Olive trees 0 1 0 49 1 016 23 6 985 2 081

Orchard 1 2 0 24 4 966 0 506 1 502

Forest 0 0 0 3 0 1 616 1 622

Agriculture 1 0 0 42 22 5 3 34 563 34 637

1984 Total 98 2 041 116 3 023 1 100 1 211 641 38 761 46 990

V- Conclusion

Through this research, the urban expansion of the Fez study area over different periods using multi-temporal satellite images (Landsat - Google Earth) was achieved. The classification and VSW Index was able to delineate soil, water, vegetation and urban clearly. The main direction of urban expansion in Fez is expansion and increased construction on the West and South of the city. The consistent and high quality impervious surface data provided the Landsat classifications is critical to developing new flood management strategies for protection as well as for rehabilitation. Information from remote sensing data can play a significant role in quantifying and understanding the nature of changes in land cover and where they are occurring. Such information is essential to planning for urban growth and development.

General patterns and trends of land use change in Fez were evaluated by classifying the amount of land area that was converted from agricultural, forest and rangeland use to urban use (impervious area) during the period from 1984 to 2013; comparing the results of Landsat-derived statistics to estimates from other inventories; quantitatively assessing the accuracy of change detection maps; and analyzing the major urban land use change patterns in relation to population growth.

Acknowledgment:

The authors wish to thank the Sidi Mohamed Ibn Abdellah University (Fez, Morocco) and the Fulbright Foreign Scholarship Program for the funding of this research conducted at the University of Minnesota, Minneapolis, Minnesota, USA and at the Faculty of Science and Techniques (Fez, Morocco). The authors also thank Mr. I. Laacouri for his support.

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