Introduction

Since the twentieth century, urbanization has become one of the most significant impacts of human activities on the earth, affecting the quality of life and sustainability of ecosystems (Trinder and Liu 2020; Shao et al. 2021). While urban areas occupy only 3% of the earth’s surface (Sarvestani et al. 2011), they are growing at an unimaginable rate and scale in many countries around the globe (Sun et al. 2013). This situation has led to critical conditions such as urban sprawl, especially in developing countries with high urban development rates (Vermeiren et al. 2012). Urban sprawl is an inevitable byproduct outside of dense urban centers (Xu et al. 2019; Cobbinah and Darkwah 2016) and is a scattering of physical development toward peripheral areas of the city (Tewolde and Cabral 2011; Galster et al. 2001). In developing countries, urban sprawl and its subsequent effects on the landscape are influenced by complex interactions of structural factors related to population growth, policy, and economic development (PoojaSonde et al. 2020; Tang et al. 2008). In the last decade, world academics and researchers have studied the effects of urban development on green cover and ecosystems using various methods (Wu et al. 2019; Nguyen et al. 2020). They found that the correlation between urban green space and urban development is complicated and depends on different forces (Quigley 2002; Syphard et al. 2011). Depending on the intensity of the pressures and the changes in the urban ecosystem, these changes may cause landscape transformation over time (Gökyer 2013; Mosammam et al. 2017). Thus, studying the green cover changes spatial patterns is one of the urban sustainability aspects (Nguyen et al. 2020) and can help planners and stakeholders recognize drivers of land use changes and take required actions (Alizadeh Zenouzi et al. 2022).

The results of a study by de Barros Ferraz et al. (2005), which investigated the process of forest conversion to urban areas using landscape metrics, indicated the destruction of all forest lands in Rondônia in Brazil if the trend of the changes will continue in the future. Also, Kushwaha et al.’s (2021) study indicated that economic, geopolitical, and urban functions had influenced mainly the urban areas of Delhi in India. Also, using the Markov chain model, they concluded that if the current trend continues, the city will experience sprawl toward the surrounding area. However, Wei et al. (2020) believe that urban sustainability can be enhanced with long-term urban green space planning, land use management, and preventing agricultural fragmentation in peripheral areas.

According to the Forman model, an ecological city benefits from the suitable distribution, composition, continuity, and extent of green and open spaces as structural components of the urban landscape (Forman 1995). Therefore, preserving natural green space is a vital factor in establishing the sustainability of communities’ ecological services and the health and well-being of urban life (Hodson and Sander 2021; Vargas-Hernández and Zdunek-Wielgołaska 2021). Forman and Gordon (1986) also outlined the main characteristics of a landscape matrix, including relative area, connectivity, and control over dynamics (Ingegnoli 2002). Therefore, spatial metrics can characterize landscape structure and recognize urban growth patterns. Also, based on spatial properties, it can evaluate possible urban growth options (Aguilera et al. 2011). Metrics can quantify spatial landscapes’ characteristics (Uuemaa et al. 2009; Li and Wu 2004). Landscape metrics are connected to ecological operations in studies related to environmental landscapes (DiBari 2007; Luck and Wu 2002). In this regard, spatial metrics are a helpful tool for planners who need to reliably characterize the processes of urban development and their effects (Kim and Ellis 2009). Landscape ecology emphasizes the structural analysis of the landscape, the spatial changes, and the consequences of ecological processes in these landscapes (Botequilha-Leitão et al. 2006). Landscape ecology and spatial planning converge in the same workspace because of their precise spatial dimension (Antrop 2001). Therefore, landscape ecology has been continuously used in spatial planning (Steinitz et al. 2003; Corry and Nassauer 2005). Regular monitoring of urban physical development and its impact on different urban ecosystems prevents decreasing the ecosystems’ capacity to support urban life. The nature, pattern, and intensity of urban growth and their effect on ecosystems can be explored via land cover maps (Martínez-Harms and Balvanera 2012). In this regard, remote sensing is one of the most valuable and powerful tools (Chuvieco 2008). This technique helps the researchers visualize past trends and predict future urban growth for proper urban development planning (Das and Angadi 2021). It has many benefits, including using different scales, collecting data in non-visible zones, and homogeneous, high-frequency, and cost-effective data with historical records (Chuvieco 2008; Pôças et al. 2011). According to previous studies, satellite remote sensing data have successfully monitored urban growth and its impacts on urban land cover. As such, it is one of the primary sources for extracting land cover maps (Fenta et al. 2017; Weng 2012). Urban growth is dynamic and happens in different processes globally. Therefore, urban growth impacts affect various landscapes differently (Zubair 2021). Landsat 7 and 8 images have been widely used to recognize green cover and their changes (Townshend et al. 2012; Estoque & Murayama 2015; Mancino et al. 2020; Tan et al. 2010). Identifying contributors to land cover change and urban sprawl is vital for sustainable land use planning (Magidi & Ahmed 2019). It requires analyzing the urban development process and determining suitable change detection algorithms that apply the trends of urban sprawl measurement. Urban sprawl monitoring via GIS and remote sensing techniques can be reliable and cost-effective tools that have empowered urban researchers and planners to detect urban land use change from a historical perspective, too (Araya & Cabral 2010; Sudhira & Ramachandra 2007). These tools are also helpful during the decision-making process (Hegazy & Kaloop 2015; Sudhira et al. 2005). However, when combined with landscape metrics, remote sensing can fully describe the fundamental urban land alteration process and patterns (McGarigal 2002; Mallupattu and Sreenivasula Reddy 2013). Landscape metrics can quantitatively describe and measure the basic spatiotemporal patterns and structures of landscapes (Abebe 2013; Aguilera et al. 2011; Liu and Yang 2015).

Maragheh City, one of the medium cities in the northwest of Iran, has faced many ecological problems, including losing lots of green areas. Therefore, the quantitative and qualitative changes in the Maragheh City landscape and the continuation of this process and resulting problems led to the formation of this research problem. The findings and results of this study could be beneficial for controlling the current and ongoing environmental challenges in the studied area. Therefore, in this study, the development pattern of Maragheh City during the last years and its impact on the urban ecological landscape was analyzed by remote sensing and landscape metrics.

The key contributions of this research are using a combination of descriptive tools, including Landsat satellite imagery, and spatial analysis tools, including GIS, FRAGSTATS, and IDRISI, to achieve the research objectives.

The main aim of the present study is to investigate land use changes and their impact on the structure of the landscape of Maragheh.

Therefore, the objectives of this study include the following:

  • To discover the physical development process of the city in the last years,

  • Investigating the impacts of urban development on the landscape of the city, and

  • Simulation of the land use changes by 2030.

This study is necessary to have a clear insight into the past land use changes process and prevent the destruction of green areas to support the landscape ecology of Maragheh. Therefore, implementing this study would benefit the resident and natural habitats of the studied area by helping preserve the ecological balance of the area by considering green spaces in the planning programs. The innovation of this study is conducting the combination methods of satellite image data, landscape metrics, and simulation models to measure, evaluate, analyze, and predict the quantity and quality of land use changes. Therefore, the prediction of land use changes to identify possible ecological damages and preparing necessary measures for the proper urban and surrounding areas’ land use planning to have ecologically balanced urban areas is another scope of this study.

Therefore, this study tries to answer the following questions:

What was the development process of Maragheh City in the past decades?

What effects had the development process of Maragheh on the city’s ecological landscape?

How will land use changes process in the future years?

Methodology

Study area

The city of Maragheh, with geographical coordinates of 37° and 21 min to 37° and 25 min north longitude and 46° and 12 min to 46° and 17 min longitude, is located in the northwest corner of Iran (Shorabeh et al. 2022). Maragheh City, with 30,417 ha of area, has a 175,600 population (Statistics Center of Iran, General Population and Housing Census 2020). The population of Maragheh has increased 1.7 times in the last 30 years (1990–2020). However, the city area has grown faster than its population and caused the structure of Maragheh City with unrestrained growth has been disturbed. The destruction of ecological elements like green spaces and arboretums has happened during rapid urban development.

Figure 1 shows Maragheh’s city location. This city leads to Tabriz City from the north; Bostan Abad, Hashtrood, and Charavimaq cities from the east; Oscou, Ajab Shir, Bonab, and Malekan cities from the west; and is connected to the province of West Azerbaijan from the south. The city expanded from the central part to the outer parts of the surrounding highways, especially the northeastern parts (Valiasr town) and the west of the Sufi Chai River. Also, the rustic texture and villages in the southeast and southwest of the city have developed towards Maragheh with irregular and illegal construction.

Fig. 1
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The study area

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In the past, the imbalanced growth of the area and the Maragheh population has caused the sprawl and scattered spatial pattern of urban development. The horizontal growth of the city has destroyed natural landscapes and replaced them with residential neighborhoods. In such a way that under the influence of construction development, the city of Maragheh structure has become disturbed and disordered, and most importantly, environmental challenges have been imposed on this city.

The city of Maragheh has a semi-cold to cold climate. The city of Maragheh has a colder climate compared to other parts of the country. The primary core of Maragheh City was developed in the valley of the Sufi Chai river and the flat plain at altitude levels of 1400 to 1500 m. The average height of Maragheh City is about 1495.5 m above sea level. The city of Maragheh is located in the Sufi Chai river basin, the region’s most critical surface stream.

The structural form of Maragheh City is almost checkered, mainly seen in the recent development. In general, from 1986 to 2016, the city’s physical development was very slow towards the west, but the city had a sudden development towards the east. This trend has continued until now (Ghorbani et al. 2021).

The methods

The present study is applied research with a descriptive-analytical method intended to investigate and analyze the urban growth and development trend of Maragheh City. Landscape metrics were conducted in this study to explore the development pattern of Maragheh City. This method helps to show the mosaic pattern of urban land uses and its changes in urban development processes. Landscape metrics can be used in decisions related to urban growth, land use planning, and urban green space development planning (Botequilha and Ahren 2002). These metrics are essential for bringing the language of urban planners and ecologists closer together. Metrics define the shape, geometry, and nature of a landscape’s distribution of structural components and compare them quantitatively (Herzog and Lausch 2001). Researchers use landscape metrics modeling in different cities worldwide to study urban structures (Ramachandra et al. 2012; Akın and Erdoğan 2020). Thus, landscape ecology can describe the systems quantitatively that is required for the study of landscape change and function (McGarigal and Marks 1995).

The process of doing this study is shown in Fig. 2. In this study, Landsat satellite images for 1986, 1996, 2006, 2016, and 2020 were prepared to explore the land use changes in the studied area. After preprocessing operations, land use maps are monitored based on the classification method and using an artificial neural network by ENVI software in five classes (constructed lands, barren lands, agricultural lands, water, and green lands). A random sampling method was used as terrestrial reality points (control points) to check the accuracy of the maps obtained from the classification of satellite images. Then, with the control points collected through Google Earth images, land use maps, and visual interpretation, the classification accuracy was calculated using the error matrix and statistical parameters (overall accuracy and kappa coefficient). The generated maps for measuring land use metrics patterns in the study area were entered into FRAGSTATS software (McGarigal 2002) for analysis and calculation.

Fig. 2
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The process of exploring the development pattern of Maragheh

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The characteristics of satellite images used in this study can be seen in Table 1.

Table 1 Characteristics of satellite images used in this research
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As it is demonstrated in Table 2, different metrics have been used to analyze the satellite image information.

Table 2 Metrics used in research
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After identifying the landscape changes in Maragheh, land use simulation for the future years has been done using the Markov chain and cellular automata models in IDRISI Selva software. The Markov chain analysis is one of the most common methods used in simulation and modeling processes (Mondal et al. 2020). The estimated values in 2030 are based on 1986–2020, using the Markov chain model in IDRISI.

Results and discussion

In this part, land use patterns of Maragheh have been investigated using ENVI 5.3 and ArcMap software, and land use metrics have been analyzed by FRAGSTATS software. Also, land use changes simulation has been simulated with the Markov chain model and the Markov chain combination model with cellular automata to find out the possible changes in the land uses in Maragheh City.

Land use changes process in Maragheh

To explore the land use changes from 1986 to 2020, first, radiometric and atmospheric corrections were done on the images in the study area. Then, five land use classes, including barren land, constructed lands, agricultural land, gardens, green spaces, and water surfaces, were selected according to land use distributions in the city of Maragheh regarding land cover. In the next step, a maximum of 3 × 3 was processed on the final images using the monitored neural network and filtering method. Finally, land use maps were extracted.

The error matrix was first formed for classification accuracy, and then two indicators of total precision and kappa coefficient were used. The error matrix is shown in Table 3.

Table 3 Kappa coefficient and total image accuracy
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After analyzing the land use change maps in GIS, tables for the land use and land cover change were obtained based on the classified images of consecutive years in the area. Land use changes in the city show, from 1986 to 2020, barren land in the city decreased from 17,096 ha in 1986 to 14,646 ha in 2020. The constructed land, which was about 526 ha in 1986, increased to 2395 ha at the end of the period. However, green lands with an area of 6484 ha in 1986 decreased to 6140 ha in 2020. Agricultural land, with 8472 ha in 1986, increased to 9118 ha in 2020. Finally, water areas from 115 ha in 1986 increased to 394 ha in 2020 due to the construction of dams (Table 4).

Table 4 Land use changes in Maragheh in 1986–2020 (per hectare)
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Figure 3 shows the changes in each land use rate from 1986 to 2020. The classified information layers regarding land use from 1986 to 2020 have overlapped in these maps.

Fig. 3
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Land use changes in 1986, 1996, 2006, 2016, and 2020

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According to Fig. 4, constructed lands between 1986 and 2020 increased by about 2156 ha, gardens and green space decreased by about 443 ha, barren lands decreased by about 2444 ha, and agricultural lands increased by about 506. Finally, water surfaces have increased by 225 ha.

Fig. 4
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Changes in land use classes to another class from 1986 to 2020

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According to Fig. 5, constructed lands more than other land uses are spread green spaces around the city. Therefore, constructed lands, gardens and green spaces, water surfaces, agricultural lands, and barren lands have experienced changes during the study period in Maragheh City, respectively.

Fig. 5
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Changes in land use classes per hectare between 1986 and 2020

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The trend of expansion of Maragheh City shows that with the creep of the city and spatial development on the surrounding rural areas, urban issues in all economic, social, cultural, and spatial dimensions have been mixed with rural structures and caused rural land use changes, mainly agricultural and natural resources.

Land use metrics of Maragheh

After classifying land use in Maragheh City, images were entered into the FRAGSTATS software to calculate the land use metrics. A set of metrics were analyzed to understand better and describe the ecosystem dynamics and landscape structure. Therefore, the most relevant landscape metrics and the categories with the most changes were studied for the study area. Therefore, the results of comparing landscape metrics from 1986 to 2020 were presented. According to Table 5, the CA metric shows that constructed lands have increased from 526 ha in 1986 to 2683 ha in 2020. There is a significant decrease in gardens and green spaces from 6484 ha in 1986 to 6041 ha in 2020. Also, based on the NP metric, which determines the number of spots in a particular class and is used to measure the degree of fragmentation of metric classes, from Table 5, it can be seen that the number of land use spots has increased. In other words, the more spots, the less the continuity of the categories. In this study, the number of urban spots has increased; consequently, their continuity has decreased.

Table 5 CA and NP metrics from 1986 to 2020
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The PLAND metric is the base unit for showing the composition of a landscape. It shows how a landscape is composed of one type of spot. According to Table 6, the percentage of constructed land area has increased from 1.61 to 8.21%. Also, gardens and green spaces have increased from 19.834 to 24.02% in 2020. The LPI metric represents the percentage of the total area occupied by the most prominent spot. Table 6 shows that the predominance of spots in 1986 and 2020 is related to barren lands. Also, the trend of changes in this metric in the studied periods is that the predominance of the constructed land class spots has increased, and the size of the gardens and green spaces spot has decreased. Therefore, the destruction in the integrity of land cover that can be seen for gardens, green spaces, and agricultural land classes has been happening.

Table 6 PLAND and LPI metrics from 1986 to 2020
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As can be seen in Table 7, according to the LSI metric, when its value equals 1, the landscape contains a spot with maximum compaction. However, as the complexity of the shape of the spots increases, its value increases, and the corresponding class becomes more scattered.

Table 7 LSI and E.D. metrics from 1986 to 2020
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The rate of change of this metric shows that the shape of the landscape has an increasing trend for all land uses, and the spots have become more complex. This trend, especially for construction use, shows a 24% dispersion. The metric changes in E.D. margin density show that the margin density of the construction spot has increased by 19%, which indicates an increase in the influence of the constructed land use.

MPS metric shows changes in mean spot levels between 1986 and 2020 that average spot levels decreased the most in garden and green space and became more fragmented. After that, there are barren lands, and in other words, in all classes, we face a significant decrease in the average spot level, with an increase in fragmentation.

The MNN metric also shows the average Euclidean distance from the calculated metrics for the separation and proximity of land features. As shown in Table 8, the values of this metric have increased in the garden and green space class, indicating an increase in distance and scattering of spots in this class and a decrease in their ecological relationship and fragmentation in the garden and green space class. However, the value of this metric has decreased in the constructed land class, which indicates an increase in integrity.

Table 8 MPS and MNN metrics from 1986 to 2020
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As can be seen in Table 9, the IJI metric shows the scatter and proximity index of the relative scatter of each type of land use. This index can be used to indicate urban creep, where urban development has changed land uses and land cover. The study of this index shows that the highest value of this metric in 1986 is related to constructed lands, i.e., the constructed lands are more scattered and in 2020 is connected to gardens land. The trend of metric changes in MESH (continuity of spots) during these periods indicates an increase in disintegration and a decrease in continuity in gardens and green space. The lack of proper connection and stability between natural land uses is due to new and irregular constructions in the city. The lack of paths and green roads prevents the relationship of green spaces. The new constructions have caused the disintegration of natural lands, which will have irreparable effects on the city’s ecological sustainability in the long run.

Table 9 IJI and MESH metrics from 1986 to 2020
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Table 10 shows a large range of changes in the SHEI index between 0.6811 and 0.8001, indicating a greater diversity of spots in the landscape. SIEI index changes range between 0.7748 and 0.8582, indicating an increase in Simpson’s variation of spots in the landscape. There has been a decrease in the CONTAG (spot integrity) metric from 53.8538 to 48.2647%, which indicates a decrease in the degree of cohesion and an increase in fragmentation throughout the landscape. The SHDI metric (Shannon’s variation) indicates the relative diversity of spots on the land. This metric is against the continuity of the landscape. In the present study, the diversity of land appearance has reached from 1.962 to 1.2877, which shows a decreasing trend. If the results of Shannon’s diversity metrics are more than 1, the distribution is clumped, and if it is equal to 1, they are random, and if it is less than 1, they are evenly distributed. This index is more than 1 in Maragheh, which indicates the clumped distribution.

Table 10 SHDI, SHEI, SIEI, and CONTAG metrics from 1986 to 2020
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Simulation of land use change with the Markov chain model

In the Markov chain method, satellite imagery of land cover is analyzed based on the probability change matrix. The Markov chain method analyzes land use zoning images and presents output in the form of a possible change matrix and an output image of a possible change matrix for the desired year (Ahadnejad and Rabet 2010). The change probability matrix shows the probability of changing each class from the classified land use to another one. In the present study, to model land use change using the Markov chain model, the land use map of 1986 was used as a base map and 2020 as a follow-up map to predict changes until 2030. According to the results of the Markov chain probability matrix (Table 11), it can be said that the probability of land use change to public gardens and green space in 2030 is 86.39, which is the highest probability of change among land uses.

Table 11 Probability of land use change up to 2030 horizon using the Markov chain model
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The Markov chain combination model with cellular automata

In this study, the mixed cellular automata-Markov chain model was conducted to simulate land use change. For the 2030 horizon, the land use map of 1986 and 2020 was selected as the model input using the Markov chain method. Ten years have been considered for predicting changes until 2030 to create a matrix of the probability of land use changes. Then, the Markov chain model results were used as input data to perform the cellular automata-Markov chain model. As seen in Table 12 and Fig. 6, the results of predicting changes show that changes in gardens and green spaces will increase from 6041 ha in 2020 to 7771 ha in 2030, which total will increase by 1730 ha. Also, construction lands will increase from 2683 ha in 2020 to 3304 ha in 2030.

Table 12 Prediction of Maragheh land use share for 2030 with cellular automata-Markov chain model
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Fig. 6
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Prediction map of land use changes using cellular automata-Markov chain model from 2030 to 2020

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To explore the physical development pattern of Maragheh City with the overlap of the constructed land class during 2020–2030 on the digital elevation model (Fig. 7), the Maragheh expansion trend was visually extracted. According to Fig. 7, it can be seen that the growth of 621 ha of constructed lands in the Maragheh region causes the intensity of urban physical development and irregular growth of the rural areas toward the agricultural, gardens, and green lands.

Fig. 7
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Land use level changes made to show the intensity of urban dispersion

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Discussion

Rapid urban development and land use changes, especially urban green space, put ecological challenges to sustainable development (Alizadeh Zenouzi et al. 2022). These alterations in the urban environment have motivated scientists to draw the attention of decision-makers to the need to protect and develop ecological elements such as green spaces and farmlands in the cities, particularly in developing countries and/or cities (Niemelä et al. 2010; McPhearson et al. 2016). In this regard, analyzing urban growth by considering the development pattern and process dimensions helps to understand how urban landscapes change over time (Viana et al. 2019). Therefore, understanding the complex urban systems dynamics and evaluating the effects of urban development on urban ecology are among the issues that researchers are considering for environmental management and protection (Shi et al. 2021; Mörtberg et al. 2013; Wu 2014).

The findings of this study indicated that rapid urbanization in recent years has led to the occupation of green spaces with different urban constructions in Maragheh City. Also, the urban development pattern has been scattered and creeping. This situation impacts the ecological balance of the area. Thus, many socioeconomic challenges and environmental degradation happens with imbalanced ecosystems (Atasoy 2018). The physical development of cities has disturbed the balance of built-up lands and green spaces, particularly in developing countries (Arshad et al. 2022). In these cities, urban green space planning has been formed based on one-dimensional quantitative approaches that focus only on increasing the area of green spaces (Wang et al. 2020). According to this study, the density and the average size of green spots have decreased in the urban construction process.

Consequently, the fragmentation of urban green space imposes ecosystem challenges, affects the quality of living environments, and threatens urban ecological sustainability. Therefore, optimal planning and ecological measures are required to organize the landscape ecology and control urban sprawl. The results of Zhang et al (2020) study in the Three Gorges Reservoir Area, China, indicated the main factors for the landscape pattern changing to support urban ecological systems in the region. They include the construction of natural reserves, returning agricultural land to the forest project, and the development of urbanization according to the environmental capacities.

Physical cohesion, continuity, and extension of nature surrounding the city may be achieved by measures such as green belts, national parks, or natural corridors such as river valleys. A cohesive green network can be created by connecting minor green spots in the structure of cities by natural and artificial corridors and preventing the interruption of corridors by extending them towards green spots (Chicago Metropolitan Agency 2016). Green space planning using the green network connection theory can improve the current green space system (Kong et al. 2010). Maragheh City, as a sample of medium size city in a developing country, requires a comprehensive re-planning program for urban development and ecological landscape improvement with considering the agricultural lands and green and natural spaces conservation.

Conclusion

This study has explored the relationships between urban development patterns and landscape ecology by analyzing land use changes. Due to population growth and spatial development, the city of Maragheh is constantly experiencing land use changes, especially in its peripheral areas. The land use alteration commonly caused destroying gardens and agricultural lands, expansion of slums, and land use changes in the suburbs and surrounding rural areas. Studies of satellite imagery show that constructed land use from 1986 to 2020 had the most changes and increased by 2157 ha. Gardens and green spaces decreased by about 443 ha. This condition indicates the development of constructed lands on gardens and green lands. Therefore, constructed lands are the driving force of these changes. Also, the spatial distribution of the metrics shows a reduction in landscape coherence, an increase in landscape fragmentation, and a decrease in green spot density. Thus, the results confirm that the landscape of Maragheh City has become more complex and geometrically irregular over time and has become more fragmented. However, suitable planning programs should be considered for Maragheh City to reduce the fragmentation of the landscape and control indiscriminate and scattered development. In general, planning a green space ecological network, including the green space spots, size, shape, connections, and quality, can significantly improve the quantity and quality of the green space system in Maragheh. Planning for green areas can also promote the environmental and social benefits of green spaces and enhance the ecological sustainability of the city. Therefore, highlighting the role of the green space ecological network has particular importance for urban green space planning. In other words, planning an urban green space network is mainly a response to urban sprawl and the significant loss of natural spaces (Ignatieva et al. 2011; Kong et al. 2010).

Emphasizing green space planning and considering landscape cohesion in urban development programs to prevent urban sprawl and the consequent environmental problem is the significant contribution of this study to the relevant literature.

Overall, the present study recommends solutions to improve the city of Maragheh’s development pattern to protect landscape coherence. Following the endogenous development pattern, green spaces planning and designing for physical cohesion in harmony with various urban land uses, protection of gardens and green lands around the city, the revival of ecological networks, and prevention of urban sprawl would improve the cohesion of the studied area. Also, improving the environmental structure of Maragheh City can create cohesion, identity, and integration of residential areas and consequently contribute to sustainable urban development.

This study investigated urban development and its impacts on landscape ecology. However, more study is required to identify the critical factors for land use alteration, green space reduction, urban sprawl formation, and consequent urban ecological challenges.