DOI: https://dx.doi.org/10.12795/rea.2025.i49.11

Formato de cita / Citation: Atik, M., Sebbab, M.M., Ousbih, M., & El Ouahidi, A. (2025). Integrated GIS and remote sensing tools approach for the management of peripheral areas in Maghreb cities from 1988-2005. Case study: Greater Agadir (Morocco) and the city of Algiers (Algeria). Revista de Estudios Andaluces,(49), 211-227. https://dx.doi.org/10.12795/rea.2025.i49.11

Correspondencia autores: m.atik@uiz.ac.ma (Mohamed Atik)

CC BY-NC-SA 4.0.

Integrated GIS and remote sensing tools approach for the management of peripheral areas in Maghreb cities from 1988-2005. Case study: Greater Agadir (Morocco) and the city of Algiers (Algeria)

Enfoque integrado de SIG y herramientas de teledetección para la gestión de las zonas periféricas en las ciudades del Magreb desde 1988 hasta 2005. Caso de estudio: el Gran Agadir (Marruecos) y la ciudad de Argel (Argelia)

Mohamed Atik

m.atik@uiz.ac.ma 0009-0006-3066-9647

Spaces, Societies, Environment, Planning and Development Laboratory (SSEPD), Department of Geography and Planning, Faculty of Languages, Arts and Human Sciences, Ibnou Zohr University, Ait Melloul. Route Nationale N°10, Azrou City, next to the Hassan II Agronomic and Veterinary Institute, Ait Melloul, Morocco.

Mohamed Mahmoud Sebbab

mohamedmahmoud.sebbab@edu.uiz.ac.ma 0000-0001-6520-9129

Spaces, Societies, Environment, Planning and Development Laboratory. Department of Geography and Planning, Faculty of Languages, Arts and Human Sciences- Ait Melloul, Ibnou Zohr University, Ait Melloul. Route Nationale N°10 cite d’Azrou à côté de l’institut Agronomique et Vétérinaire Hassan II Ait Melloul (Morocco).

Mehdi Ousbih

mehdi.ousbih@edu.uiz.ac.ma 0000-0002-3312-614X

Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, Agadir (Morocco) BP. 8106, Cité Dakhla. 80000 Agadir. Souss-Massa. Morocco.

Abdelhadi El Ouahidi

a.elouahidi@uiz.ac.ma 0009-0008-9407-4508

Spaces, Societies, Environment, Planning and Development Laboratory. Department of Geography and Planning,
Faculty of Languages, Arts and Human Sciences- Ait Melloul, Ibnou Zohr University, Ait Melloul

Route Nationale N°10 cite d’Azrou à côté de l’institut Agronomique et Vétérinaire Hassan II Ait Melloul (Morocco).

1. INTRODUCTION

Urban sprawl in the Maghreb region of North Africa is being accelerated as cities expand, taking up traditionally agricultural land (Zhang et al., 2024, 2023; Chapman et al., 2017; Hornsby & Yuan, 2008; Aroua, 2022; Mansour et al., 2023). Patronize the changes because of population explosion, opportunities for economic gains and a lack of proper planning in urban centers is not something that only happens in developed nations(Abou Rayan & Djebedjian, 2016; Silva, 2016; Madbouly, 2009). The bulk of the impact is not only to the urban infrastructure needs and services(Arimah, 2017; Bongwa & Dijk, 2021; El-bouayady & Radoine, 2023), but also on the lives of local communities that would be disturbed due to it, as well as ravaging of valuable agriculture lands (Mun Bbun & Thornton, 2013) leading toward an even more surged food insecurity in their region(Gomiero, 2016; Haber, 2007; Skilbeck, 2021) despite a legislative system put in place after independence to preserve the agricultural heritage.

The issue of land use must now be placed at the heart of the debate on spatial planning(Luo et al., 2023; Ramírez et al., 2023; Li et al., 2022; Hersperger et al., 2018; Koomen et al., 2008; Ligtenberg et al., 2001; Fürst et al., 2017) for at least three reasons which are the high population growth (Ncube et al., 2014; Waha et al., 2017), the considerable reduction of agricultural land (Bertini & Zouache, 2021; Devkota et al., 2022) and the continuous emergence of large urban complexes (Maksudyan, 2018; Hadjri & Osmani, 2004) that meet neither sustainable development standards nor the expectations of citizens whose habit of living has completely changed over the last fifteen years.

Explaining the patterns of urban expansion and its consequences on agricultural land utilization is an important area where scientific research may be practiced (Basu et al., 2023; Elangovan & Krishnaraaju, 2023; Frimpong et al., 2023; Slimani & Raham, 2023). The rapid urbanization of the cities in Maghreb is driving a very high demand for housing, infrastructure and services, leading to significant land pressure on surrounding areas. This paper attempts to tackle this central problem by providing empirical information on the pace of urbanization, the magnitude of land use changes, and the dynamics between urban growth and agricultural expansion in some of these fast-evolving urban settings(Atik & Abdellaoui, 2013).

The study is a part of the vast majority of studies based on GIS and remote sensing in urban research (Belal & Moghanm, 2011; Jat et al., 2008; Mundia & Aniya, 2005; Rahman et al., 2011; Weng, 2001). Working with analytical methods, this study creates urban sprawl maps and land use conversions from agriculture into urban use in order to investigate their driving forces. Although there have been many research contributions on the types of urban expansion in different regions(Das & Angadi, 2022; Dhanaraj & Angadi, 2022; Koko et al., 2021; Manesha et al., 2021; Roy & Kasemi, 2021; Hegazy & Kaloop, 2015; Xiao et al., 2006; Weng, 2001), this paper builds upon these existing findings and provides an extra layer by exploring the literature that focuses on Maghribian cities. The specific socio-economic conditions in the region like population growth, migration trends and cultural factors play an influential role on urbanization and present special planning issues that need to be addressed.

In this context, the article investigates dimensions of complexity driving urban expansion and explore the challenges and consequences stemming from this transformative process in Maghreb cities by using GIS (Geographic Information Systems) and remote sensing approach to monitor urban dynamics, detect changes, and estimate socio-spatial mutations based on two illustrative case studies: Greater Agadir (Morocco) and the eastern periphery of Algiers (Algeria). It is a diachronic study of supervised classifications (Bhaskaran et al., 2010; Forget et al., 2018; Lynch et al., 2020; Salata, 2021; Sun et al., 2022; Xu et al., 2023) and grid methods (Kosyakov & Sadykov, 2015; Liu et al., 2009; Musiaka & Nalej, 2021) to identifies the spatiotemporal dynamics of the urban space(Atik & Abdellaoui, 2013).

To fulfill this aim, we used satellite imagery «Landsat-TM and ETM» from 1988-2005 and thematic maps and aerial photos. This processing allowed us to produce maps that spatially illustrate the distribution of forms of urban development and the impact of sprawl on agricultural land. These analyses are supplemented by field observations, survey results, and various data (sociodemographic, urban, economic, etc.).

2. STUDY AREA

To assess the potential offered by the integration of GIS and remote sensing for the mapping of changes in the landscapes of the outskirts of large Maghreb cities, we have chosen two representative sites:

2.1. Grand Agadir (Morocco)

Greater Agadir is a large urban agglomeration located in the northwestern part of the Souss Plain on the edge of the Atlantic Ocean and bordered to the north by the High Atlas massif (Agnaou et al., 2023; Wang et al., 2023). Thanks to its pivotal geographical position, the accumulation constitutes a crossroads and an obligatory crossing between Morocco’s North and the South. Its latitude is 30° 25’ north, and its longitude is 9° 36’ west (figure 1). It is part of the Souss-Massa region, which covers an area of 53 789 km²; it represents about 7.6% of the total area of Morocco; this region includes two prefectures (Agadir Ida-Outanane, Inezgane-Ait Melloul) and four provinces (Chtouka-Ait Baha, Tiznit, Taroudant, Tata).

Greater Agadir covers an area of nearly 145 km², from Anza in the north to Aït Melloul in the south, including Tikiouine in the east. Its structure is composed of several urban units promoted over the previous decades to the rank of municipalities, thus sharing the territorial management of this urban nebula. It currently includes Agadir, the central city and regional metropolis of southern Morocco, and the enlarged urban commune which consists of four cities (Agadir, Anza, Bensergao, Tikiouine); Inezgane, the commercial city par excellence, located 10 km south of Agadir, is a municipality to which two dormitory centers, Tarrast and Jorf, are annexed; Dcheira and Aït Melloul represent two large municipalities. The first is located at the edge of the main road 32; it plays the role of dormitory city; the second is located on the left bank of the Oued Souss, located at the point where the roads of Tiznit and Taroudant intersect. These municipalities have an urbanized area of 3,000 hectares out of a total area of 20,516 hectares. They concern a population of about 67 859 614 according to the national census of 2004, with a growth rate that has been around 10% since 1960 (Atik & Abdellaoui, 2013).

2.2. Eastern periphery of Algiers (Algeria)

We chose three communes in the eastern outskirts of Algiers (Bordj el Kiffan, Bab-Ezzouar and Dar el Beida) in an area of 63 km2; figure 2 shows the location of the study area (Bellout et al., 2020). This choice was based on the rapid urban growth in this area characterized by exceedances and transgressions of laws, non-compliance with urban planning rules, and high consumption of agricultural land. The population densities are, for the three communes of Bordj el Kiffan, Bab Ezzouar, and Dar el Beida, respectively 7002, 11737, and 2404 inhabitants/km2; these values, extracted from the RGPH 2008, are indeed particularly high and infrequent.

3. MATERIALS AND METHODS

3.1. Data and methodologies

Our work focused on a thematic approach for detecting change in urban space. In this study, different data were used:

• TM and ETM satellite images of LANDSAT of 09 February 1988, 19 March 2002, and 12 April 2005 for the case of Greater Agadir and TM satellite images of 21 June 1984, 16 April 2001, and 21 June 2005 or the case of the eastern periphery of Algiers. These images show poor contrast (difficulty differentiating between urban and non-urban).
• The case of Greater Agadir: aerial photographs (black and white) at a scale of 1:30 000 and 1:17 500 dating from 1994 were used as a field reality to assess change detection accuracy. Topographical maps at a scale of 1:50 000 dating from 2003 and 2005 were also used, as well as socio-demographic, urban, and economic data from geographical databases developed during the 2008 thesis work;
• The case of the eastern periphery of Algiers: aerial photos respectively of 1992 and 1998 at the scale of 1:40 000, four topographic maps at 1/25 000 (Algiers East (13-4-31), Algiers West (14-4-31), Rouiba East (31-4-31) and Rouïba West (32-431)), the map of administrative boundaries by MapInfo software, population census data (Kouaouci & Rabah, 2013) (RGPH 1987, 1998 and 2008) and urban plans (Algiers PDAU). Since aerial images and photos are taken at different times of the year, it is necessary to apply adequate preprocessing to make them comparable (Jensen, 1996). In addition, we have chosen training sites in the area. Subsequently, the change detection is based on a supervised classification of the images and a comparison of the results to highlight the variations from one date to another (Belal et al., 2013). The advantages of this approach not only provide a complete picture of the study area at different dates and identify all pixels that changed state between the two dates, but also the nature of these changes.

A variety of tools and applications were employed in this paper. These included MICMAC, an open-source software distributed under a free license, and CeCCIL-B and ToolBox, a free and multi-platform project developed by the National Centre for Space Studies (CNES). Further, other open-source software packages were used to process high-resolution optical and multispectral imagery.

Figure 3 summarizes the processing chain we followed.

3.2. Methods for classifying maximum likelihood of urban areas

3.2.1. Preprocessing

It is usually necessary to pre-process the data before analyzing it. The aim is to improve the quality of the images by making them suitable for interpretation. In this context, we have carried out a classic processing chain:

• Resampling: The images used are of different resolutions (Landsat TM with a resolution of 30 m for the 1988 image and Landsat ETM+ with a resolution of 28.50 m for the 2002 and 2005 images). For an average and homogeneous spatial resolution of 28.5 m using the cubic convolution technique. This sampling function, which uses a bicubic approximation of 16 nearest neighbors of the pixel concerned, makes it possible to model the image locally by a polynomial surface (Abdellaoui & Rougab, 1997). At the end, we obtain spectral planes that will coincide perfectly at the level of each pixel with the reference image (images of 2005). The accuracy of these transformations varies according to the pictures, in our case, the operation produced an average calibration error (Root Mean Square Error) of 0.258, which is acceptable (Moller-Jensen, 1990).
• Enhancing and improving dynamics: The initial image is difficult to read because it is not very contrasted; most pixels are within a small range of the coding scale (0 to 255). Spreading the dynamics already makes it possible to make the different channels more readable and recognize the main themes present in the study scene.

Extraction of the study area and other basic processing: After extracting the area covering Greater Agadir and its surroundings, we delimited the urban footprint by transforming our basic images into binary images by codifying their pixels with values 1 for the urban footprint and 0 for others. A threshold was carried out to isolate the urban unit because it is beneficial to exclude vegetation and water (sea and river) from urban areas in order to reduce the calculation time and avoid any confusion between bare ground vegetation and buildings.

3.2.2. Supervised classification

Classifying urban areas on satellite images is complex, as these images do not exhibit a unique and distinguishable spectral response (Weber, 1998). Many authors have already presented methods to improve the classification of urban areas using data obtained by remote sensing. These methods, based on pixels or objects, are very diverse and vary according to the data used and the study areas to which they are applied. They are also difficult to categorize because they are a mixture of methods of extracting information from images. These approaches include, in the classification process, the incorporation of ancillary information such as spatial data (Harris & Ventura, 1995; Zhang et al., 2008), contextual data (Cabral, 2007; Gluch, 2002; Gong & Howarth, 1990; Shaban & Dikshit, 2001; Weber, 1998). Other authors suggest the use of indices (Abdellaoui, 2007; Abdellaoui & Benkouider, 2009; Biraud-Burot, 2005; Zha et al., 2003), the analysis of mixed spectra (Lu & Weng, 2004; Phinn et al., 2002), the use of expert systems or the use of neural networks (Civco & Hurd, 1997; Zhang & Foody, 2001). Some results show that extracting information using objects is an exciting alternative for classifying images. The incorporation of contextual information that integrates texture, shape, hierarchical relationships, and pixel proximity can lead to a significant improvement in classification results.

The maximum likelihood classification method we used in this work proved to be the most effective in distinguishing between urban and non-urban areas using statistics to assess the quality of the classification. This method presupposes the definition of learning areas for each reference class with a Gaussian distribution (Jensen, 1996). The minimum number of pixels that must compose the learning areas for each class must be more significant than 10 times the number of channels used in the classification (Jensen, 1996). The choice of these areas may involve a certain degree of subjectivity since the analyst determines the learning areas and decides on their number, location, and size. To respect the normal distribution, the groups obtained in the classification that were not supervised by the ISOCLUST algorithm were used to select the learning areas. Thus, we guarantee the selection of homogeneous zones. These thematic classes (urban, bare soil, and vegetation) were defined to assess the types of land use affected by urban growth. Also, fieldwork and a geographic database strongly supported the training plots. These training plots are used to obtain the spectral signatures of the classes(Atik & Abdellaoui, 2013).

The visual analysis of the classifications relating to the different dates detected specific indications of the change from the building to the unbuilt that did not correspond to the reality of Greater Agadir. Similarly, we noted confusion between buildings and bare soils due to the approximation of radiometric values between these two classes. Some apparent changes were produced by spectral overlap between classes or boundary pixels. In this sense, the road network, in particular the roads that separate the blocks of urban extension, has created a large number of confusions with certain building blocks. For example, in the classification of the 2003 image, these roads were assigned to the building class, whereas in the 1988 image, they were omitted from the same class (perhaps the vegetation effect). Other changes were produced by significant changes in vegetation cover, namely differences in phenological stages between dates. Based on these confusions, the rule of maximum likelihood has been reiterated several times in order to improve and validate the results and isolate the built and unbuilt. The training plots were modified during iterations to obtain a sufficient satisfaction threshold (90% good classification and a Kappa coefficient greater than or equal to 0.8). The selection of channels used allows the exclusion of redundant spectral information from the classification process and thus achieves better class discrimination (Jensen, 1996). The transformed divergence statistic (Jensen, 1996; Swain et al., 1978) was used to evaluate the best combination of channels. We obtained the maximum spectral separation value using all six channels simultaneously. The number of channels was then reduced by retaining the maximum value of the transformed divergence statistic to reduce information redundancy.

This maximum value was maintained intact until the use of four channels with 15 distinct combinations. After trying these 15 combinations with the overall accuracy and kappa statistics, we found that the combination of channels (1, 4, 5, 7) leads to the best results for the urban class (Atik & Abdellaoui, 2013) (figure 4).

To eliminate noise and roads, we applied a variant of the sequential alternating filter (Serendero, 1989; Serra and Soille, 1994; Soille, 2004); A succession of closures and openings of increasing size forms this operator. This filter, which replaces linearity with the growth criterion, is better adapted to visual perception structure, simplifies an image without smoothing it, and thus preserves contrasts (Abdellaoui et al., 2006; Gadal, 2004). The processing resulted in the urban right-of-way being isolated for each date.

3.3. Grid analysis method: the case of the eastern periphery of Algiers

Several authors have used the grid method successfully (Huzui et al., 2011; Mazurek & Dayre, 1988). This work has highlighted some specific advantages of the method, in particular, the possibility of highlighting:

• The respective position of the various elements in the landscape;
• The weight of these elements in the landscape;
• The development of an evolutionary process.

Moreover, Antony (2003), in a modeling study of the dynamics of urban sprawl by grid applied in Belfort, showed that this method offers a double possibility of reading:

• A vertical reading of cells that takes into account their evolution over time;
• A horizontal reading to take into account their evolution in space.

Finally, a study of the viability of the landscape of the Prahova Valley in Romania (Abdellaoui et al., 2010) showed how several parameters (geology, slope, land use, vegetation, and building index) can be combined to analyze the viability of the area studied by gridding. Thus, the grid helps create spatial units of equal size to record and geocode information.

The method allows for evaluating and comparing data on different dates (Silva & Rosa, 1989). The grid placed on its georeferenced information allows for considering it in space and time (Antony, 2003).

Our approach is to superimpose a transparent surface grid on the satellite image and then select all the meshes representing the frame in the three images (1987, 2001, 2005) (figure 5). The grids we have developed cover the entire area studied with a surface of 150m x 150m per mesh, representing 22500m2 or 2.25 ha. The total number of meshes is 2967.

Each grid is assigned a value corresponding to one of the components of the landscape classes previously defined on themes that are related to the research’s purpose (Mazurek, 1983). The information in the images is then transformed by the grid into a matrix in which the data appears in the form of a table that allows its matrix storage (Antony, 2003). Thus, each theme of the landscape is encoded by a binary value (absence or presence) (figure 4); this coding is used for the study of the urban dynamics of the city of Buzau in Romania (Hachemi et al., 2010). These grids can be differentiated by colors. By superimposing them, we can quickly obtain the different changes in land use and quantify the urbanized agricultural areas (Belal et al., 2013).

The grid was validated by referring to several essential documents, such as aerial photos, topographic maps, and urban planning documents (figure 5).

Using this method of analysis, we encountered difficulties:

• To evaluate objects representing the building and their proportion in the grid and to remedy this problem, we worked with meshes evaluated at 25%, 50%, 80%, and 100% built. These proportions can be easily identified by their color differences;
• The 30 m resolution of satellite images presents a significant challenge, as it hinders precise discrimination of classes. This often leads to confusion between bare soil and buildings, greenhouse crops and buildings, etc. Overcoming this challenge requires the analyst’s expertise and deep knowledge of the study area.
• Evaluation of spectral signatures representing the frame and their proportion in each grid mesh.

To overcome these difficulties related to coding in relative presence (Antoni, 2003) proposes a method of hierarchical coding of categories by which we can discard all the less important elements in the grid. To remedy this problem, we worked with meshes evaluated at 25%, 50%, 80%, and 100% built according to the coding method using the relative presence of landscape themes in the grid, inspired by the process resulting from the principle of presence-absence (Antoni, 2003). Their color differences can easily spot these proportions (figure 4). We validated the grid by referring to several basic documents, such as aerial photos, a topographic map, and the PDAU of Algiers (Belal et al., 2013).

The grid method ensures good control of the information assigned to each mesh because the filling is done manually. This offers the possibility of overcoming the administrative limits (Lajoie, 1992) or respecting them, as was the case in our study, to derive the results of each municipality.

Note that the results of a classification by grid method can be corrected manually without resorting to a new classification procedure, as in automatic classifications.

One of the main limitations of grid analysis is the time it consumes. Manual grid filling, especially in large study areas, can be a time-intensive process. In contrast, automatic classification is simpler and can be completed in a relatively short time, regardless of the surface studied.

The grid method’s ability to update the database is very limited, unlike automatic classification.

4. RESULTS AND DISCUSSION

4.1. Supervised classification methods: The case of Greater Agadir

The various processing carried out made it possible to establish a map of the evolution of the city’s right-of-way and its periphery (figure 6).

The final result aligns with the reality observed on the ground and the fundamental references (topographic maps, aerial photographs, etc.). The colors white, red, and yellow symbolize the evolution of the three dates: 1988, 2002, and 2005. However, several precautions must be taken for other colors (green, purple, orange, and blue). The interpretation is based on a detailed examination of all these new objects that appear (purple) or disappear (blue and orange). It may be either new constructions (they stand out well if they are contiguous and numerous, less well if they are buildings or isolated houses), disappeared constructions, or a building whose roof may have merged on an image, with the road or bare ground (the case is not uncommon if it is a question of dirt tracks and houses with a roof of sheet metal covered with dust or slums) and which suddenly appears more reflective because it has been renewed, or washed by the rain. It should also be noted that the green color, which symbolizes the objects lost in 2002 and 2005, presents, in most cases, the constructions of precarious housing that have been eradicated as part of programs to reduce slums or change the forms and development of plots. But also, by the launch of the subdivision program (the case of the Hay El Mohammady subdivision) or the appearance of certain buildings on the site of old demolished buildings. The areas of uncertainty are located in the residential Talborjt neighborhoods north of the town of Agadir, down the mountain, where trees cover scattered houses. These elements were verified and resolved by field observation and the use of other techniques presented above (Atik & Abdellaoui, 2013).

The comparison of the classified images and their representation on a final space map (figure 6) allowed the highlighting of urban dynamics. The urbanized area (all built-up areas consisting of dense buildings, residential buildings, large buildings, and urban construction sites) amounted to 100919 ha in 1988 and 206625 ha in 2005. Between the two dates, the built-up areas increased by 145706 ha, a rate of increase of 60% in 17 years. The area covered by the urban area studied for the period 1988-2002 is about 196105 ha, and for the period 2002-2005, it is about 10520 ha. These figures, calculated from digital processing, correspond to 90% of the official figures of the Regional Inspectorate of Housing, Urban Planning and Spatial Planning (IRHUAE) and the development holding Al Omrane in the Souss Massa region (Atik & Abdellaoui, 2013).

The urban development in the Greater Agadir is done in a linear way along the road axis port–airport on about 30 square kilometers on a total area of 159 square kilometers. It brings together five autonomous and individualized urban entities (Agadir, Tikiouine, Inezgane, Dcheira, Aït-Melloul), of varying size, which are articulated in relation to each other according to their economic and urban vocation as well as their location along the local road network, especially along the RN 10 and RN 1. This development is determined according to two important elements: on the one hand, by its physical and natural environment (blocked to the North and East by the natural barrier of the mountain and by the Souss Massa National Park to the South, the agglomeration is now developing on the plain on both sides of the Souss River) and on the other hand, by its economic and demographic dynamics. The profound changes have taken place in peripheral localities (Tikiouine, Inezgane, Dcheira, Aït-Melloul), which have been subjected to particularly significant urban growth: Tikiouine (95.15%), Aït Melloul (85.13%) and Bensergao (70.63%). With the exception of the city of Agadir, where urban development has been done in planned mode (launch of several housing estates and urban projects), peripheral localities are developing in a spectacular, anarchic way (Atik & Abdellaoui, 2013).

Its rapid evolution and design based on strong segregation of functions have produced a fragmented space and a poorly connected agglomeration, which suffers from the absence of centrality and attractive places of conviviality.

The analysis of the spatial representation of cities according to their rate of population growth between 1960 and 2004 makes it possible to distinguish two urban forms:

• Landscaped urban space: planned and developed urban area (city of Agadir) with a concentration of equipment, services, and jobs in well-defined regions of the town of Agadir at the expense of the rest of the agglomeration. The diversified housing offer (subdivision, residences...);
• Non-regulatory extensions that cover a variety of forms, from slums to non-regulatory housing or scattered constructions contributing to the sprawl of agricultural space. Each form is part of a specific production process and involves actors who act according to their logic. Non-regulatory housing represents by far the most dominant form, the most dynamic in its forms of growth and redeployment, and the most problematic for regularization and urban integration interventions. It infects the peripheral dormitory centers (Inezgane, Dcheira, Tikiouine...), which are isolated and poorly equipped (poor equipment, insufficient infrastructure).

4.2. Grid analysis method: the case of the eastern periphery of Algiers

The processing carried out by the Grid Analysis Method made it possible to establish a mapping of the spatio-temporal evolution of the city’s footprint and its periphery. The results (figure 7) give us an overview of changes in land cover in the study area. The extension of buildings to the detriment of arable agricultural soils is very worrying during the period 1987-2005; the built-up areas increased from 2009 ha in 1987 to 3181 ha in 2005, an increase of 1172 ha at the expense of the agricultural area, which is constantly decreasing (Belal et al., 2013).

We found that urbanized areas are fallow (land that is not cultivated permanently), and their areas have decreased from 1989 ha in 1987 to 696 ha in 2005 depending on the results of treatments; These data reveal the increase in the consumption of agricultural land in the three municipalities studied during the period (1987-2005).

Urban sprawl has encroached on 1100 ha of cultivable land in 18 years or 61ha per year.

Thus, this area has lost more than half of its agricultural area to the benefit of uncontrolled and poorly controlled urbanization; the local authorities of these municipalities have never considered that its agricultural spaces converted into urbanized spaces are land with high agricultural potential and that they are now not compensable.

The municipality of Bordj El Kiffan, which was most marked by illegal constructions, lost 639.67ha, or 54% of its agricultural area, and almost half of the urbanized area in the study area (figure 7).

Most of its losses occurred between 1990 and 1995, a period of security instability in Algeria, according to statements by agricultural specialists in the commune.

The municipality of Dar El Beida has recorded a decrease in its agricultural area of 443 ha or 37% of its agricultural potential, despite the protected areas around Houari Boumediene airport.

Bab Ezzouar consumes 200 ha of agricultural land, which, compared to other municipalities, represents only 15.44% of the total urbanized area in the study area. However, this town has lost all its agricultural land, which was intended for the realization of ZUHN to meet the growing housing needs of the Algerian population (Belal et al., 2013).

The rate of artificialization of agricultural land is worrying in the area studied. Each year, urban sprawl consumes more than 60ha of arable land. At this rate, all the remaining agricultural area will inevitably be consumed in the space of 20 years, which calls on the public authorities to take firm decisions to save the agricultural heritage on the outskirts of Algiers (Belal et al., 2013).

5. CONCLUSIONS

Despite the limitations mentioned in the two processing methods related to the subjectivity of the interpretation and the resolution of satellite images, they have made it possible to obtain critical quantitative data on the spatiotemporal evolution of land cover and the evaluation of the consumption of agricultural land. The results showed that the study areas lost more than 12000ha of land with high agricultural potential in 18 years at a rate of 700ha per year. Poorly controlled urbanization encroached on half of the agricultural area from 1987 to 2005.

To meet the housing needs of the population, several programs of collective housing and individual housing estates have been built to the detriment of the country’s most fertile agricultural land without considering urban plans and laws that prohibit attacks on agricultural land.

The results of this study show that the agricultural space in these study areas is threatened by abusive, rapid, and uncontrolled urbanization. If the artificialization of arable land continues at the current rate, agricultural land will be consumed entirely after less than twenty years.

Our study showed that multi-date satellite images can analyze the city’s evolution over time (diachronic analysis). A study in progress aims to prove the potential of this method for analyzing the city’s evolution in space (forms and dynamics of sprawl) and simulating its development in the future.

From this study, some insights can be derived on the way authorities in cities of the Maghreb region are working towards managing a combination of urban growth and protecting their agriculture lands. It is a goldmine for policymakers, urban planners and decision-makers who are grappling with these challenges. These results shed light on a path forward for sustainable development and suggest urban planners and land use regulations should be more anticipatory. They were arguing for a partnership in terms of regulating some economic growth to balance with protecting of the environment and the sustainability in agriculture.

Notably, this study underscores the necessity of adopting nuanced approaches grounded in both facts and anecdotal evidence. The study is a brilliant example of unifying GIS and remote sensing methods to more traditional ethnographic fieldwork, providing multiple perspectives to the issue of urbanization. It calls attention to the loss of productivity that may be due not only to near term problems with burgeoning population sizes, but also with anticipation to land degradation inherent at times in the longer-term scales of ecological sustainability and agricultural productivity. Thus, such an all-inclusive strategy would actually be beneficial for a better comprehension of the continuous dynamism in urban areas among different parts of the world.

Acknowledgements

The authors are deeply grateful to the anonymous reviewers and Prof. Rosa María Jordá Borrell (Editors-in-Chief, Revista de Estudios Andaluces) for suggestions and comments.

Responsibilities and conflicts of interest

The authors declare that there is no conflict of interest in relation to the publication of this article and that: a) All authors have been equally involved in all stages of the research as well as in both the drafting of the content and the manuscript revision process b) they have the publishing rights to all tables and graphic ma-terial that are part of the manuscript.

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