جستجوی مقالات مرتبط با کلیدواژه "تغییرات کاربری اراضی" در نشریات گروه "جغرافیا"

 Land subsidence is an invisible danger that has faced many cities in Iran, including Pishva, with a serious challenge. The importance of subsidence has led to the evaluation of the amount of subsidence in the urban area and outskirts of Pishva using radar images and the SBAS time series method (2020-2022) in this research.

 In this research, Landsat satellite images, Sentinel 1 radar images, and library information were used as research data to achieve the desired goals. The working method has been that first, using satellite images, the trend of land use changes in the region has been evaluated, and then, based on the available information, the state of the decline of underground water resources in the region has been investigated. In the following, the subsidence rate of the region has been calculated using the SBAS time series method.

 The results of this research have shown that the studied area has subsided between 107 and 411 mm during a 2-year period (2020 to 2022), based on which it can be said that the maximum annual subsidence of the area is more than 20 cm.  The results of the evaluation of land use changes in the region have shown that during the 30 years (1991 to 2021), the land use of manufactured areas has increased from 2.2 to 9.6 square kilometers. Also, the level of piezometric wells in the region has faced an annual drop of more than 1 meter.

 Considering that the development of manufactured uses is associated with an increase in the pressure on the earth's surface, and also the development of residential and industrial areas is associated with an increase in the use of underground water resources, the changes in land uses during recent years, the context has provided for subsidence in the region.Innovation: The method's high precision allows the research results to be used for various construction planning.

Karst landscapes are of great importance due to their role in providing vital water resources. However, these areas face a high risk of resource pollution, which is a significant concern. This study aimed to identify pollution-prone zones within the Tekab Catchment. The research utilized topographic maps (1:50,000 scale), a 1:100,000 geological map, and a 12.5-m digital elevation model derived from Landsat satellite imagery. Key analytical tools included ArcGIS and ENVI software. The study was conducted in two stages. First, the (Concentration, Overlapping, and Protection (COP) method was employed to delineate areas vulnerable to pollution. Second, the trend of residential expansion towards these vulnerable zones was assessed. The COP model results indicated that approximately 33% of the catchment area, primarily the northwestern and southern regions, exhibited high to very high pollution vulnerability. This was attributed to the underlying lithology, land cover type, hydroclimatic conditions, and geomorphological characteristics. Furthermore, evaluation of land use changes revealed that the built-up area had increased from 8 km2 in 1990 to 19 km2in 2019. Consequently, the southern and northwestern parts of the Tekab Catchment were facing heightened vulnerability due to both their inherent pollution potential and the ongoing expansion of human settlements. These findings underscored the critical need for targeted management strategies to protect the valuable water resources within the Tekab Catchment, particularly in the identified high-risk zones, and ensure the long-term sustainability of this important karst landscape.

The water level reduction in Lake Urmia and its effects on the surrounding environment has been among the important national and international challenges in the past two decades. Therefore, this study aimed to investigate the current status and predict the future state of land use in the Gadarchai watershed, located in West Azerbaijan Province, which is one of the important sub-watersheds of the Lake Urmia basin. For this purpose, Sentinel-2 satellite images for 2016, 2020, and 2022 were obtained from the European Union's Copernicus website. Then, preprocessing methods were applied in various software environments, and the relevant images were sent to the eCognition software environment. In this environment, using various basic and object-oriented methods (especially segmentation and production of different coefficient layers), the nearest neighbor classification method was implemented, and land use maps were produced. Finally, the Markov chain model was used to predict changes in land use in future years. To verify the accuracy of the Markov chain model, the predicted land use change map for 2022 was compared with the 2022 classification map. The research results showed that with the application of basic methods, especially nearest neighbor classification, it is possible to produce land use maps with high accuracy (90% kappa coefficient). Also, by applying the Markov model, land use change maps with an acceptable accuracy level (around 80%) are possible. The final results indicate that by the year 2028, agricultural land use (13.89%), dry farming (14.1%), residential areas (0.33%), and salt pans of Lake Urmia (26%) will increase. It should be noted that the soil use class will decrease by 10.26%, and pastures will decrease by 5.35%. Overall, the final models demonstrate the high accuracy of basic and object-oriented methods and the suitable performance of the Markov model in the process of studying land use changes.

 The amim od this research is to investigate the effects of land use changes on vegetation in Mian-ab watershed  in Shushtar city  from 2000 to 2020.

 In this research, Landsat 5 and 8 satellite images for 2000 and 2020, respectively, were downloaded from the United States Geological Survey (USGS) website. After extracting the images, necessary pre-processing, including atmospheric, radiometric, and geometric corrections, was done on them in the Envi-5.3 software environment. To prepare the land use map, supervised classification algorithms, including maximum likelihood, minimum distance from the mean, parallel surfaces, and Mahalanoi distance, were used.

  The results show, in the period of 2020, changes in agricultural indicators (46.1%), range land (1.14%), water areas (1.54%), and a decreasing trend in rainfed lands (1.94%) and residential areas (3.63%) have an increasing trend compared to 2000. Dams, industrial centers, polluting factories, and temperature increases have caused most areas of good condition of vegetation indicators to become average.

 Based on the research findings, residential areas are rarely observed where the basin has a steep and slippery slope (15-30%) since leveling operations are not carried out in them. The natural situation and fluctuations in the height of the Shushtar watershed have produced a variety of plants.

Innovation:

 The results of the conversion of agricultural and barren lands and lands that are already part of the river bed were applied, and the percentage of changes in the area of the index (SAVI) was lower than other indices, which is why the soil surface moisture of this index is affected by crop irrigation.

Over the past two decades, the expansion of Shahrebeez to the surrounding areas has caused the destruction of resources and environmental problems, including the disruption of the ecological balance, the increase in service costs, the construction of unsuitable land, the aggravation of air pollution, and the lack of attention to existing worn-out structures and progressing trends. Previously, environmental quality has been reduced. The purpose of this study was to analyze the changes in urban land use of Tabriz with satellite images to determine the nature and intensity of these changes and in which directions and in which cases they are more so that a solution to these problems can be considered. In this research, land use changes in Tabriz city in three periods (2000, 2005, 2010, 2015, 2020) were investigated using multi-temporal data of Landsat satellite images in ENVI 5.8 software, and the results were analyzed in Arc GIS 10.2 software. And its results were produced in the form of a map. The obtained results show that the lands built during the period of 20 cases of Matala, from 30% in 2000 to 0.53 in 2020, which has increased by 23.0% in this 2020 year. Undeveloped land has also reached 0.42 in 2020 from 62.00 in 2000, which has gone through a significant decrease and changes in the last 20 years and has decreased by 0.19. Vegetation uses have decreased from 11.0% during the studied 20-year period to 4% in 2020.

Land use and examining its changes is one of the most basic needs of organizations for planning urban development and other construction projects such as urban green space development.In this research, the classification and identification of the changes in the built-up areas of Ardabil during the years 2013-2021 have been investigated with the help of remote sensing and the use of Landsat 8 satellite images. First, the desired satellite image and the studied area were determined in Google Earth system, then using it, the urban coverage map was prepared. The images presented in this study were corrected atmospherically, radiometrically and geometrically and were ready for further processing. For this purpose, the investigated area was divided into 5 separate uses of agricultural land, barren land, orchards, built-up areas and water. After classifying the images, in order to evaluate their accuracy, ground survey data and error matrix have been used, based on which the Landsat 8 images of 2013 and 2021 have an overall accuracy of 82 and 80 respectively, as well as a Kappa coefficient of 78. 0.0 and 0.72% are ranked first and second in terms of image classification. The trending results of the changes in uses show the upward trend of the area of built-up land (4.96 square kilometers) and agricultural lands (1.54 square kilometers), as well as the downward trend of the area of barren lands (6.58 square kilometers) and gardens (0.24). square kilometers). Based on this, it can be concluded that during the studied period, Ardabil city, like most parts of the country, has been faced with the development of urbanization in the past years, which was the result of the conversion of barren lands and gardens into residential lands. Finally, it has caused an increase in the area of built land in the past year.

Understanding the trend of land use change and its influencing factors stands as a paramount concern in contemporary urban studies. The vicinities surrounding cities bear the impact of urban growth, triggering alterations in the roles, functions, and configurations of these ecological complexes. Tabriz, being a pivotal metropolis in the country, has undergone notable changes in land use in recent years. The urban expansion has transformed the surrounding lands, including gardens and agricultural areas. This study aims to scrutinize the patterns of land use change in Tabriz from 1986 to 2016. Employing a descriptive-analytical research approach with documentary and field data collection methods, Landsat satellite images, ENVI 4.8 software, and Arc GIS 10.2 geographic information system were utilized to analyze the changes in Tabriz's urban lands over three decades. The findings reveal that Tabriz, in pursuit of its development, has predominantly encroached upon green spaces and agricultural lands, leading to a decline in their share over time.

In recent years, land use changes in Isfahan city and its surroundings have been increasing, and these changes have been caused by factors such as population growth, economic interests, and changes in society's needs. The current research was conducted with the aim of investigating the temperature changes of Isfahan city and its surroundings in a forty-year period (1979-2018). The current research is based on the purpose of applied research and its method is descriptive-analytical according to the subject of the research and its nature, and to investigate the temperature changes affected by the changes in land use and urban development of Isfahan city, temperature data from The city of Isfahan and the area of ​​50 kilometers around the city of Isfahan have been collected from the database of the European Center for Medium-Range Weather Forecasts (ECMWF). To verify the accuracy, the data of meteorological stations of Isfahan, Isfahan Airport and Najaf-Abad have been compared and evaluated. became. The results of the research show that in this 40-year period, we have seen a significant increase in the average annual temperature from 0.06 degrees in the eastern area of ​​Isfahan city to 0.11 degrees in the western area. This means that the average temperature of Isfahan city and its surrounding areas has increased between 2.5 degrees and 4 degrees during a period of 40 years.

Considering the extensive changes in land use and the necessity of providing awareness to managers and planners about the nature of these transformations for policy-making and problem-solving, the detection of land use changes through land cover analysis has become essential. Therefore, land use mapping is a requirement for any national and regional development planning. It empowers managers, planners, and experts to design and implement necessary actions by identifying current conditions, comparing capabilities and potentials, and addressing current and future needs and issues. Due to the high costs associated with traditional manual methods of land use mapping, remote sensing can offer engineers a faster and more accurate means for land use mapping and subsequent assessment of changes in an area. The present research aimed to investigate the land use changes in the city of Rasht from 1990 to 2020 using Landsat satellite images. Data analysis was conducted using Google Earth Engine, and the results were further analyzed and visualized using ArcGIS software. The findings indicated that over the 30-year study period, the percentage of forest and agricultural land decreased, while built-up areas, croplands, and pastures increased. The highest increase was observed in residential (urban and rural) land use, encompassing 8,080 hectares, followed by a 12,690-hectare expansion in pastureland.

In this research, the images of Landsat satellite 5, 7 and 8 (in the years 1987, 2001 and 2014) are used respectively to examine the process of the changes in urban green space of Gorgan City. After doing the necessary pre-processing operations in the images, the maps of land use/cover for each year were provided in three classes including built-up, barren land and green spaces by using supervised classification method. The accuracy of the classification process was assessed later. The findings showed that the green space has decreased about 4.55 km2 during 1987 - 2001. In addition, built areas has increased during the years of 2001–2014 at the rate of 15.39% equivalent to 5.48 km2, while green space has decreased 1.98 km2. The finding resulted from examining the process of changes during the 27 years reveal that green space has decreased significantly despite the physical expansion of the city, so that it has reduced from 16.40 km2 in 1987 to 9.87 km2 in 2014. The findings of this research can be considered as an effective step towards better management of Gorgan’s urban green space.

Over the past few decades, problems such as the unplanned development of urban areas and land use changes have been clearly seen in the area of suburban villages, which in turn will have physical, economic, social and environmental effects on rural areas. In this regard, the aim of the current research is to analyse the criteria of the influencing factors on land use change in the villages on the around of Khash city. The current research is descriptive-analytical in terms of research method and quantitative-qualitative in terms of nature and applied-developmental in terms of purpose.The data collection tool is library and field (interview and questionnaire). According to the nature of this research,Delphi methods and structural analysis using MICMAC software were used. The statistical population of the research includes all the professors, managers and experts of Khash city, and the statistical sample size was determined by using the Delphi method of 20 people. First, by using the Delphi method, a set of criteria affecting land use change in the villages on the around of Khash city were identified, including 5 criteria (economic, physical, environmental, political-management and social) and 49 indicators. Among the examined criteria, the economic criterion was chosen as the first and most important factor affecting land use changes in the villages on the around of Khash city.Finally, 10 indicators (economic policies and urban economic reconstruction, development of border bazaar infrastructure, development of local markets, investment attraction, development of the tourism industry, maintaining the activities of small industries and improving them, changing urban performance (prominence of the service sector), expansion of production sectors and industrial, job creation and entrepreneurship, followed by the reduction of unemployment and the expansion of transformation and service industries) were selected as indicators influencing land use change in the villages on the around of Khash city.

Water is one of the critical needs of human life and living things. Therefore, proper planning is important for its consumption all over the world, especially in Iran. This study was conducted to evaluate the impacts of land use change on water yield in the Teraz watershed, Khuzestan province, Iran, from 1990 to 2050. Water yield was evaluated using integrated valuation of ecosystem services and tradeoffs (InVEST) tool and land use was mapped using the maximum likelihood classification in the ENVI 5.3, and CA-Markov in the TerrSet environment. Moreover, the relationship between water yield and landscape metrics, including the number of patches (NP), patch density (PD), landscape shape index (LSI), and Largest Patch Index (LPI), was considered based on the Geographically Weighted Regression (GWR) method in the Arc GIS 10.5. The results showed that the area of ​​forest and rangeland in the studied area decreased in the last 30 years, by 3199 and 1611 ha, respectively, and the area of ​​agriculture and construction land uses increased by 4388 and 387 ha, respectively. It is predicted that in the next 30 years, 2442 ha of forests will decrease and the area of ​​agriculture, rangeland, and construction land uses will increase by 1651, 687, and 102 ha, respectively. It was found that the total volume of available water yield in the region is equal to 26.5 Mm3 in 2020, on average of 857 m3 ha-1, and based on that, the ranking of land uses is as follows: 1. Construction, 2. Rangeland, 3. Forest, 4. agriculture. The results of GWR confirmed that water yield had a significant and negative spatial relationships with the NP, PD, and LSI metrics (R2>=0.83, p-value>0.05), while its relationship with the LPI metric was a significant and positive relationship (R2>0.84, p-value>0.05). It can be concluded that the landscape features and land use pattern can determine the production and yield of water in the study area.

Water provision service is one of the most valuable ecosystem services that is important as a key service for the healthfulness and management of water resources. In this study, using the Water Yield model of InVEST software, the water supply and demand in Ilam watershed was modeled. The data related to average annual precipitation, annual evaporation and transpiration, root limiting depth, water available for plants,  land use map, water consumption and estimation of water provision quantity, basin boundary, and subbasins of the watershed were the inputs of this model. The results of model application showed that in Ilam watershed, 45 million cubic meters of water provided annually, with the highest and lowest water provision volumes being in Arghavan (7 million cubic meters) and Chalimar (802 thousand cubic meters) subbasins, respectively. According to results, there are great differences in the water supply and demand in the subbasins of this watershed. Therefore, planner and policymakers should pay attention to this important issue in the layout of land uses and foresee sustainable use of rich forests in the high yield subbasins such as Arghavan subbasin. The results provided in this study, along with showing the importance of modeling surface water demand and its used in macro-policies of water allocation, can function as a guideline and help the managers and planners of Ilam city to adopt reasonable decisions in managing ecosystem and correctly using land in this area.

Detecting land use changes is essential for managers and planners because land use has changed widely. Being aware of these changes can help the policy-making process and solving existing problems. Therefore, preparing a land use map is one of the requirements of any development planning at the national and regional level. Such maps enable managers, planners and experts to design and implement the necessary measures in the field of solving problems and meeting present and future needs by identifying the current situation and comparing capabilities and potentials. Nowadays, due to the high cost of preparing land use maps with traditional and manual methods, remote sensing can help engineers with more accuracy and speed in preparing land use maps and evaluating changes in the region. The aim of the current research is to prepare land use maps of Zabarkhan district and monitoring land use changes in this area from 1996 to 2019 using Landsat 5 and 8 satellite images related to TM and OLI sensors. According to the findings, the uses of agricultural lands, built lands and pastures have increased during this period, while barren lands and gardens have decreased. Built-up lands include urban, rural, tourism (wildlife park, ecotourism residence), industrial, workshop, facilities, communication (regional and local roads), military, mining and water storage resources for agriculture. The highest rate of increase is related to urban and rural uses with 461.43 hectares, equivalent to 54.01% of all uses.

Today Over the past few decades, the growth of urban societies and population growth have led to the spread of distorted cities into separate regions with its original texture and its development towards agricultural land and its surroundings. This unplanned development leads to land use changes in the periphery of the city. In the past few decades, the city of Qorveh has encountered with the development of urban and peri-urban settlements, which caused changes in the land use of the city of Qorveh and its surrounding areas. Analysis of land use change in Qorveh city can be effective in planning for future development of the city. Therefore, to attempted to estimate and determine land use change trends, Landsat satellite images with TM, ETM and OLI sensors were used for the three time periods of 1986, 2000 and 2018. These images were categorized, validated and detected by the Neural Network method to five floors; urban lands, arable lands, dry lands, water zones and Bayer lands. Finally, Markov Autoclave was used to calculate land use changes in Qorveh city for 2031. The results indicate that, continued land use change would result in the degradation of agricultural land use.

Nowadays, following the technological advances and increased accuracy of remote sensing and satellite imagery capabilities, it seems that the combination of these capabilities with the available information of changes or destruction of the resources in the past and present, and base on the modeling will be able to predict the land cover changes in the natural and anthropogenic areas such as protected area. One of the most important ways to obtain this object is the spatial modeling of land cover changes using various strategies. Therefore, the present study was to evaluate the ability of Ca-Markov model to estimate the land cover changes in protected area located in the Bijar state from 1987 to 2013 period and also modeling of this changes and predict of them for 2026. For this purpose, Landsat images of 1987, 2000, and 2013 were used to extraction of land cover maps. Validation of the model was taken by comparing the predicted map of 2013 using model and land cover map for the same year. The rangelands and agriculture lands use in 2013 were about 21180.65 and 10273.53 hectares which prediction result of the model showed 21935.97 and 9631.62 hectares, respectively. This result indicates the high accuracy of the Ca-Markov model to predict the land cover changes in 2013. The results showed that from 1987 to 2000, about 12 percent, and from 2000 to 2013, about 10 percent of the rangelands converted to agriculture lands. The prediction result of Ca-Markov model shows that if the current trend continues, between 2013 and 2026, about 13 percent of the rangelands will converted to agriculture land.

awareness of the type and percentage of land use and cover types is a fundamental requirement for understanding and management of a region. The growth of urbanization and increasing use of resources due to the development of the required facilities in urban life has changed the pattern of demand for resources and lands, changing the nature and quality of agricultural land, Historical and natural landscapes and surrounding urban areas through the transformation of these lands into residential areas. In recent decades, the suburban lands of cities have changed their use due to the urbanization process and the need of citizens for new residential areas and the surrounding lands, which are often high quality agricultural lands and gardens. This, along with things like industrialization and changing rainfall patterns, has destroyed the cover and natural environment of cities, and thus has posed many social and environmental challenges and endangered sustainable urban development, and as a result of this process, a lot of ecological pressure has been imposed on the natural ecosystem of the region. These changes are considered as one of the important and effective factors of social and environmental challenges. Today, remote sensing technology and GIS due to capabilities such as high monitoring power and resolution, frequent images, cost reduction, etc., To effectively identify and quantify land use changes and their effects on the environment and monitoring And rapid management of the growth and development of cities are used. In the present study, the aim is to evaluate the urban development of Ardabil in the last 30 years using remote sensing technology and satellite images.

Landsat satellite imagery was used to prepare land use maps for 1987, 2000 and 2017. In order to ensure the quality of data and bands, the images used in this research were first corrected for radiometric errors in ENVI 5.3 software environment. Then RVI, SAVI, NDVI, BI and IPVI indices were extracted. In the next step, maps related to filter texture, vegetation delineation and tasseled cap were prepared. At the end of this step, all the extracted layers were merged with the corrected image bands. Then satellite imagery using support vector machine algorithms, maximum similarity and artificial neural network with acceptable accuracy in six user classes (residential areas, covered agricultural lands, fallow, barren lands, urban forest and water) floor were classified. Then, to evaluate the classification accuracy, the overall accuracy and kappa coefficient were calculated for each of the maps.

According to the values of overall accuracy and kappa coefficient, which in 1987 for the support vector machine algorithm were 90% and 0.86, respectively, the maximum likelihood was 84.5% and 0.78, and the neural net was 90.5% and 0.87, respectively, in 2000. Overall accuracy and kappa coefficient for support vector machine algorithm 92% and 0.90, maximum likelihood 92.5% and 0.90 and neural net 92.6% and 0.90, and in 2017 overall accuracy and kappa coefficient for backup vector machine algorithm 90.6% and 0.88, maximum likelihood of 82.8% and 0.78 and for neural net were 88% and 0.85, it was found that the support vector machine algorithm has the highest accuracy compared to the other two algorithms. According to the results obtained from the study of satellite images classified by the support vector machine algorithm, the area of land built in Ardabil has increased from 20.023 square kilometers in 1987 to 41.554 square kilometers in 2017.

In general, it can be concluded that to evaluate the trend of urban sprawl and awareness of land use change patterns for optimal management and planning of cities, the use of satellite images, especially Landsat images is a suitable and low cost option. The results also showed that the rate of land use change to land uses is increasing and since land is the main element in urban development, so control how to use it and also calculate the real need of the city for land, to In order to provide different uses is effective. As a result, according to the findings of this study, in the absence of proper planning for this city due to favorable lands for urban development around the city, in the not too distant future, witness the destruction of agricultural lands around the city of Ardabil and conversion they will be residential areas.

In recent years, the phenomenon of drought has affected many watersheds in Iran and causes many changes in land use and lake area every year. This research tries to investigate the drought situation and its relationship with land use changes in the Abkhiz basin of Maharlo lake in the south of Iran by using the drought remote sensing indicators in the years 2000, 2010 and 2020. In this way, the drought condition was determined using EVI, NDVI, and VCI indicators, then land use maps for the years 2000 and 2020 were prepared using Landsat ETM+8 satellite imagery. In the following, using the regression method, the most important effective drought indicators for land uses were determined. Also, using Markov and Komarkov chains, the state of drought and land use indicators for 2040 was predicted. According to the results of the indicators, it was found that the southern parts are more at risk of drought than other parts. The results of the survey of land use changes showed that in 2020, compared to 2000, more lands were placed in the salty and barren classes. The results showed that according to the regression method, the most important indicators in relation to land use is the NDVI index. The results of Markov and Komarkov chain for predicting indicators showed that this method has acceptable accuracy. Thus, the Kappa coefficient for the VCI index is 0.98, which indicates the high accuracy of the model in predicting drought. Also, the maps predicted by this method showed that in 2040, the values of this index will decrease, which will indicate more drought in the region.

The rapid expansion of urbanization along with irregular changes in land use and Landscape System of Tehran has disrupted the composition and distribution pattern of urban green infrastructure. The present study seeks to analyse the spatial-temporal changes of urban green infrastructure in Tehran Landscape System affected by the spatial processes of land use changes in the statistical period (4 decades of 1990 to 2030). To reach this aim, the present study has identified (1) the effect of spatial processes on the changing landscape pattern and (2) the relationship between the spatial pattern and ecological processes of landscape and its influence on the capacities and constraints of green urban infrastructure.

The present study has focused on the landscape system of Tehran and its 22 districts as the study area. The descriptive-analytical study consists of following stages: 1) Classifying urban land uses in1990-2000, 2000-2010 and 2010-2020 statistical periods using Landsat satellite images: (in Envi 5.3, Google Earth and Arc GIS 10.2 software), 2) Modelling and forecasting land use changes in 2030 using integrated model of Markov chain, automated cells (CA-MARKOV) and TerrSetsoftware), 3) Determining spatial processes of landscape changes via decision tree algorithm. 4) Quantifying landscape metrics of composition and configuration of landscape pattern (green, open & built patches) at both class and landscape levels in the mentioned periods (in Fragstate 4.2 software).
 

Many environmental decisions presume that some types or composition of land use are preferred to others. It is assumed that the spatial arrangement of elements in a land-space mosaic controls its ecological processes. This proposition is known as the pattern/ process paradigm, and forms the central hypothesis of landscape ecology (a branch of science developed to study ecological processes in their spatial context). Ten spatial landscape processes are considered to reflect changes in various patterns of landscapes (aggregation, attrition, creation, deformation, dissection, enlargement, fragmentation, perforation, shift, and shrinkage). These processes actually change the spatial structure of urban landscape and affect the quality of ecological processes in Tehran Landscape System. To identify the spatial processes responsible for landscape pattern changes during a defined period of time, a decision tree algorithm was developed. Decision tree required the following input: area or size (a), the perimeter or edge length (p), and number of patches (n) in each land-cover class. The decision tree algorithm applied on Tehran Landscape System has indicated that spatial processes of 'attrition' and 'fragmentation' have led to a decrease in the integration of green and open patches in this landscape system. Measuring LSI and IJI metrics in 1990-2030 statistical period at the class level has also proved the previously mentioned finding. Increased ENN-MN and decreased PLAND of open and green patches during two periods of 1990-2000 and 2010-2020 due to the spatial process of 'attrition' have also showed this decreased integrity over time. These conditions have reduced the resilience of Tehran atmosphere and its capability to absorb air pollution and also have resulted in the recent development of thermal islands in different urban areas. Moreover, the COHESION metric has reduced in green and open patches due to the spatial processes of 'attrition' and 'fragmentation' at the class level. At the landscape level, the value of SIDI metric has also decreased from 1990 to 2020 and the same trend will continue according to 2030 forecast. Spatial process of 'aggregation' in constructed patches has resulted in a decrease of NP and PD at landscape level during 1990-2000 and 2010-2020. Findings indicate the effect of spatial process of aggregation on constructed lands (high-rise buildings) in the northern (such as District 1) and western parts of the city (such as District 22) which has interrupted wind movement and air purification in Tehran. The values of LSI and ED has also decreased at the landscape level due to the 'attrition' of open and green patches leading to a reduction in the heterogeneity order of urban landscape system. On the other hand, increased IJI value in 2020 and 2030 indicates increased turbulence in distribution and also increased fineness index of open and green patches in the landscape system of Tehran.

Findings indicate that spatial processes of 'attrition' and 'fragmentation' have resulted in a reduction in the number and area of green and open patches in the composition pattern and also decreased coherence at class level from 1990 to 2020. This has resulted in an unbalanced distribution of the patches in the configuration pattern of green urban infrastructure in Tehran. The spatial process of 'aggregation' has been repeated during the statistical period in the constructed patches. Data forecasted for 2030 shows the impact of 'attrition' on changes occurring in both green and open land use. The landscape is also getting more simplified due to the dominance of constructed land uses. Findings can be applied to determine a roadmap and plan the spatial pattern of urban green infrastructure.

The purpose of this study was to model and predict temporal and spatial patterns of land-use change in the Zayandehrud basin. In this research, the CA-Markov prediction model was used to simulate and predict land-use change. First, the land-use changes from 1996 to 2018 were studied and then the future changes for 2030 and 2050 were simulated. Afterward, the future land-use scenarios were designed. The model was validated by comparing the simulated map of 2018 with the real map, and the kappa coefficient of 94 % was utilized to evaluate the model. Based on the results, the Built-up land-use was altered from 13016 hectares in 1996 to 154194 hectares in 2050. This outcome necessitates the management of the future development of the city. Furthermore, the amount of agricultural land was varied from 177067 hectares in 1996 to 40,000 hectares in 2050. Among all the changes, agricultural lands attracted the most attention and concerns. The results indicated the land-use changes in the form of urban areas and reducing area of agricultural lands. Such alterations were taken place in two distinct stages: urban lands have been developing since 2013, with a direct impact on the reduction of vegetation due to the conversion of agricultural lands into other land-uses. The dynamic trend of changes has also been confirmed and intensified since 1996. In 2018, a significant area of agricultural lands was converted into urban and industrial areas. In addition, the agricultural and orchard lands were 74057 hectares in 2018 and can be reduced to 40,000 hectares by 2050. It revealed 34057 hectares lost as compared to the agricultural and orchard lands in 2018. The present study depicts that the expansion of urban and industrial activities and reducing the level of agricultural land in the region requires more attention and care in land management.