land use

Floods are one of the natural phenomena that can cause significant damage to infrastructure, farmlands, and the environment. This phenomenon primarily occurs due to heavy rainfall, snowmelt, or a combination of these factors. Therefore, the aim of this study is to map flood hazard zones and examine their relationship with land use using the Analytic Network Process (ANP) model in the Razi Chay watershed in Ardabil Province.

In this study, data from Landsat 8 satellite imagery from 2022, a 30-meter ASTER DEM map, a 1:50,000 scale topographic map, a 1:250,000 scale geological map, and other detailed information of the studied watershed were utilized. Ten parameters influencing flood occurrence were analyzed, including elevation, slope, slope aspect, vegetation cover, geological formations, distance from the river, flow direction, land use, precipitation, and drainage density. The Analytic Network Process (ANP) model was employed to determine the importance of each variable.

Among the studied parameters, slope (with a weight of 30%), elevation (with a weight of 21%), and land use (with a weight of 17%) were assigned the highest weights, indicating their significant influence in controlling flood occurrence in the Razi Chay watershed. The results show that approximately 36% of the Razi Chay watershed falls within high-risk and very high-risk zones. These areas are typically located in the lower part of the watershed, often at the confluence of the two main streams. Considering the spatial distribution of settlements in the region, it can be concluded that most of the settlements in the lower part of the watershed are exposed to flood risks

By employing urban growth and development modeling, it is feasible to delineate a developmental trajectory that aligns with the specific circumstances of a city, considering environmental factors, natural elements, and population dynamics. The aim of this research is to propose an urban development model for Shushtar, which can serve as a valuable tool for analyzing the intricate processes of urban transformations. To accomplish this objective, two datasets were utilized: urban land use maps (including educational spaces, healthcare facilities, residential areas, etc.) and Landsat satellite imagery for key land uses such as rivers, barren lands, and forests, spanning three time periods: 1991, 2004, and 2014. These datasets were processed using GIS and MATLAB software. Existing urban land use maps were digitized and subsequently updated using Landsat satellite imagery. Subsequently, influential parameters in urban development were introduced as inputs to the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. After training the model for the years 1991 and 2004, the predicted results of urban development using the algorithm were compared with the actual situation in 2014, demonstrating a high accuracy of 93.7%. The land use change map, resulting from the change detection process, can be generated based on multi-temporal remote sensing images and their integration with urban land use maps, enabling an analysis of the associated consequences. The use of intelligent algorithms in this research has facilitated modeling with a high level of accuracy. The obtained results are deemed acceptable, and this development has also been predicted for the upcoming years.

Soil erosion is a spatiotemporal phenomenon influenced by variable processes, necessitating multiple observations over time and space. These measurements inherently contain a level of uncertainty and are costly and time-consuming. With the advancement of spatial technologies, Geographic Information Systems (GIS), interpolation methods, and the increasing range of environmental data and remote sensing, soil erosion models play a crucial role in designing and implementing soil conservation management and strategies. The most significant empirical models for estimating annual soil erosion losses include the USLE and its modified version, RUSLE, widely used globally for soil erosion estimation. These models consider factors such as raindrop erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, with most parameters now readily obtainable through high-quality remote sensing data.

The study area, the Alashtar watershed plain, spans 80305 hectares in the northern part of Lorestan province and Selseleh county. Geomorphologically, it lies within the elevated or folded Zagros unit, characterized by thrust faulting and multiple fractures. The watershed predominantly features a mountainous landscape, with hills and mountains covering 65.39% of the area. The minimum elevation is 1500 meters, the maximum is 3600 meters, and the average elevation is 2100 meters. According to the De Martonne method, the watershed has a Mediterranean climate with an average annual rainfall of 506 millimeters.The study area's aquifer is free-flowing, with all wells situated in the alluvial aquifer. In the mountainous part of the Alashtar watershed, carbonate formations, fracture systems, and weathering in the form of snow have developed karstic aquifers, which are the source of the Kehman River. The Kehman River, after originating from the southern heights of Green and hydrating the Alashtar plain, joins the Simreh and then the Karkheh rivers. The Alashtar plain, at the center of the watershed, is a graben surrounded by the Green, Varkhash, Mahab, Sarakheh, Darikanan, and Nashate heights, with geological formations dating back to the Mesozoic and Cenozoic eras. The predominant Jurassic-Cretaceous limestone rocks cover a significant portion of the Alashtar watershed, serving as the primary recharge units (karstic water sources). The soils of Alashtar belong to the brown soil group with a clay concentration horizon, characterized by very deep, dark brown to reddish-brown soils with a heavy texture, containing 3-15% coarse gravel in the surface layers and relatively high amounts of hard limestone grains in the sublayers.The LAMPT model is basically based on the global soil erosion equation, which calculates the net rate of soil erosion. The environmental parameters of the model, including climatic data, land cover and geomorphology, were obtained from meteorological departments, natural resources and Sentinel-2 sensor satellite images during the target year (2023). To complete the data, field surveys were conducted and training data samples were also selected to prepare the land use map. The basis of LAMPT model is RUSLE model, in this model Sediment delivery ratio index (SDR) is used to show the spatial patterns of distribution and performance of pure soil sediments at the basin level. This model integrates the general characteristics of the basin landscape (land use classes, landform parameters, soil types, land cover and management) and precipitation values to simulate the gross soil erosion rate, SDR and net sediment yield at the basin level.

The findings indicate that the average annual soil loss across the watershed is 9.42 tons per hectare, which is consistent with the sediment yield measured at the Sarab-e-Seyyed Ali station (watershed outlet) with an average annual rate of 10.1 tons. The model demonstrates adequate precision in estimating soil erosion and resulting sedimentation. An assessment of soil erosion losses across different land use classes, derived from Sentinel-2 satellite images, shows that 60% of the Alashtar watershed area comprises pastures. The soil erosion rates in pastures with weak, moderate, and dense canopy cover are 17.7, 11.3, and 9.1 tons per hectare per year, respectively. Given the extent of pastures in the Alashtar watershed and the high erosion rates, particularly in those with weak canopy cover, land use planning and optimal utilization of pastures are essential to reduce soil erosion rates in this area.

Conclussion:

In this research, the LAMPT model based on climate, land cover and geomorphological data was used to estimate the annual net rate of soil erosion in the Alashtar watershed. Estimating the net rate of soil erosion during 1402 using this model showed that the average annual soil loss at the basin level is 9.42 tons per hectare per year. The classification of the soil erosion map shows that 35% of the basin has an annual soil loss of more than 10 tons. In agricultural uses, rainfed fields have the highest rate of soil erosion during the year with the amount of 7.5 tons per hectare during the year. Considering the area of pastures in the Elashtar catchment area, the high rate of soil erosion in them, especially pastures with weak canopy, planning land use and optimal use of pastures is necessary to reduce the rate of soil erosion in this basin. The evaluation of the accuracy of the LAMPT model in comparison with the annual average sedimentation rate at the sediment measuring station of Sarab Said Ali (outlet of the basin) showed that the LAMPT model has a high accuracy in estimating the soil erosion and the sediment caused by it, and its difference with the ground data is small, so the use of This model is recommended to calculate soil erosion and its annual losses. According to the findings of the research, it is suggested that the northern and northeastern sub-basins of the basin should be prioritized for protection and watershed management measures. In addition to this, pastures with weak crown cover should be given special attention in terms of land use for watershed management and pasture management.

The environmental consequences of land subsidence are destructive, costly and irreparable, and include creating cracks on the surface of the earth, damaging human structures such as building foundations, streets, bridges, roads, and power transmission lines. Sewage is destruction of irrigation systems and fertile agricultural soils and damage to ancient sites. Remote sensing methods, unlike mapping data and topographical maps, which are in physical contact with terrestrial phenomena, are without the slightest interference on terrestrial phenomena, and measuring and evaluating changes in phenomena are evaluated from a distance. Short receiving time and high spatial accuracy of radar images have made it used as a general and widely used tool to estimate land subsidence. According to the statistics announced in the country of Iran, the adverse effects caused by land subsidence are not a low number and are rapidly developing and spreading in different regions of the country. Leave irreparable damage. Ardabil plain, with its rich underground water resources and good soil, has always been of interest in the last half century and has been a suitable place for providing drinking water and agriculture. With the boom of agriculture from the 1960s onwards and as a result the excessive harvest from the aforementioned table since 1363, the aforementioned source began to decline and the continuation of this situation in the following years caused this plain to become more critical.

In order to carry out this research, the data of 22 piezometric wells in the Ardabil plain have been used. The time period used in this research is a 7-year period from 1395 to 1402. The method used for the data of this section is the BRF method, as one of the methods of radial functions, which is used due to its low error value and high accuracy. SAR images of the European Space Agency's Sentinel 1 satellite in SLC format and with vv polarization have been used to find out the changes in land subsidence in the Ardabil plain. The images used by the Sentinel 1 satellite (in c-band with a wavelength of 5.6 cm) are in the group of sensors with medium resolution in terms of spatial resolution. The radar interferometry method provides the possibility of producing a digital model of the ground height, whose optimal height accuracy for c-band data with a wavelength of 5.6 cm is about 5 meters. This method is able to reveal surface changes in the ground in different intervals with millimeter accuracy by using at least 2 or more radar images. In this method, an artificial interferogram is produced with the help of digital elevation model of the earth and conversion of height into phase, and in this way, with the help of reverse DEM data, the phase effect caused by topography is calculated and removed from the phase difference values. The remaining phase difference belongs to the effect of surface displacement and atmosphere.

The results of the investigation of piezometric wells in the area of Ardabil Plain show that the maximum drop in the underground water level is 48.77 meters in the southeast of Ardabil Plain and the lowest drop in the underground water level is 1.57 meters in the north. Eastern Ardabil plain was calculated. The amount of fluctuations in the water level of piezometric wells shows that the highest amount of fluctuations was in the area of Pirqavam wells, Arallovi Bozor and Khalil Abad lands. The lowest fluctuation was also observed in the area of Agchechai wells, Nojedeh. In the studied time period, the water level of Khalilabad piezometric well in 2015 was 2.96 meters, while in 1402, the water level in this area reached 46.3 meters. During 7 years, the water level has dropped by 43 meters, which indicates a critical situation in this sector. The lowest fluctuation of the water level is also for the Aghche-chai well. The land subsidence map of Ardabil plain shows that: the south-eastern parts of Ardabil city and also to some extent in the southern part have suffered ground subsidence due to the extraction of underground water. In the next order, the western parts of Ardabil city are prone to land subsidence. Based on this map, the maximum amount of ground subsidence has been calculated at around 598 mm in the area of Khalil Abad well. In the area of Khalil Abad well, the situation of underground water level drop is very critical and it has dropped by 43 meters during 7 years from 1395 to 1402. The overlapping results of the underground water level and co-depth curves with the results of radar interferometry show the accuracy of the findings in this section. Overlapping the location of the historical sites with the land subsidence map shows that, first of all, Tapraqlu hill (first millennium BC) is in the area of land subsidence with a rate of 250 mm.

The results of the application of this method revealed a very high level of land subsidence for the Ardabil plain (598 mm during a 7-year period). The southeastern areas of Ardabil plain have found a critical situation in recent years due to unprincipled exploitation and lack of proper management. Overlapping the location of the historical sites with the land subsidence map showed that Taparaglu hill with a rate of 250 mm and Ozrik Tepe-si with a rate of 69 mm are in the area of land subsidence, respectively. The three historic sites of Qala-Bovini, Niar Tepesi and Tezre Tepesi are also located in the area prone to land subsidence with rates of 27 mm, 46 mm and 49 mm, respectively. The cause of land subsidence in the Ardabil plain, according to the studies conducted on the changes in the underground water level, is the excessive exploitation of the underground water resources for the cultivation of agricultural products and providing the possibility of compaction of the underlying layers. In general, it can be said that the research results indicate the involvement of historical sites in the area of land subsidence.

Salmas Plain is one of the important agricultural regions of West Azerbaijan province, playing a significant role in the region's agriculture and development. In recent decades, the plain has been severely impacted by the phenomenon of subsidence due to the over-extraction of groundwater from aquifers. Therefore, identifying areas prone to subsidence in the Salmas Plain is of particular importance for the management and control of this phenomenon. In this study, a fuzzy neural inference system model was used to predict subsidence-prone areas. To achieve this goal, seven important factors contributing to subsidence in the region were analyzed: land slope, digital elevation model, vegetation, groundwater depth, distance from roads, distance from rivers, and distance from piezometric wells. The information related to these factors was collected in ArcGIS software and transferred to MATLAB software for model implementation. Using the C-fold CV method, the data were randomly divided into three groups: 70% for training, 20% for testing, and 10% for validation. These data were introduced to MATLAB for training, testing, and validation. The data were accurately trained and validated, achieving a precision of 10⁻⁸. Several different membership functions, including trapezoidal, triangular, Gaussian, two-sided Gaussian, and bell curve functions, were tested in the model. The results showed that the trapezoidalmembership function, with a regression correlation coefficient of 0.86, and the Gaussian membership function, with a regression correlation coefficient of 0.81, performed best in identifying areas prone to subsidence.Based on the results, the eastern areas and the outlet of the plain have experienced significant drops in groundwater levels and subsidence. Statistical studies also revealed that groundwater levels have dropped by over 18 meters, averaging1.23 meters per year in recent years. The crisis has been exacerbated over the past two decades by over-extraction of groundwater for agriculture, combined with a storage-to-recharge ratio of 0.03% and unfavorable climatic conditions.The spatial pattern of land subsidence in Iran indicates that this phenomenon is strongly linked to the uncontrolled abstraction of groundwater resources, driven by increasing urban water consumption and low agricultural water efficiency. Drought conditions and excessive groundwater withdrawal have not only affected arid and central regions of the country but have also extended to semi-arid and humid areas in the northwest. Geological experts have reported that 300 out of 600 plains in the country are affected by subsidence. Due to the importance of this issue, many researchers have studied land subsidence. Their results indicate that the Salmas Plain, as part of the Lake Urmia catchment area, is experiencing a major subsidence crisis. This crisis has caused significant problems for infrastructure in many parts of the Salmas Plain, especially in the Qara Gheshlagh region. The main cause of subsidence is the over-extraction of water from wells constructed for agricultural purposes, which has led to a steady decline in the volume of aquifers.

To evaluate the susceptibility of the Salmas Plain to subsidence, the required data and statistics up to 2019 (1398 in the Iranian calendar) were collected as follows:Digital topographic maps (1:25,000 scale), a digital geological map (1:100,000 scale) of Salmas city, and thematic maps (e.g., slope, land use, geology, distance from wells, groundwater depth, distance from rivers, and distance from faults).GPS data, meteorological information, soil maps, and aerial photographs (1:20,000 scale).Digital Elevation Model (DEM) with a resolution of 30 x 30 meters.In this study, seven criteria were analyzed: vegetation type, groundwater depth, distance from roads, distance from rivers, distance from piezometric wells, digital elevation model, and slope. All criteria were first standardized in ArcGIS software and converted into an 800 x 800-meter grid. The data from each grid were then compiled into a matrix and transferred to MATLAB for analysis using the Adaptive Neuro-Fuzzy Inference System (ANFIS) model.To ensure accurate modeling, the satellite data were divided into three parts: 70% for training, 20% for testing, and 10% for validation. This process ensured precise measurement of modeling accuracy and error rates using MATLAB software.

Studies revealed that over the past 41 years, groundwater levels in the Salmas Plain have dropped by 18 meters, averaging 1.23 meters per year from 2014 to 2015 (93-94 in the Iranian calendar). The plain, characterized by clay and silty layers, has experienced significant aquifer compression and reduced porosity due to over-extraction of groundwater. This has resulted in land subsidence across the study area.The model results demonstrated that trapezoidal and triangular membership functions performed best, achieving error coefficients of 10⁻⁷ and 10⁻⁸, with mean error rates of 0.18 and 0.20, respectively. The regression correlation coefficients were 0.86 and 0.80, confirming the model's accuracy in identifying areas of high subsidence sensitivity. Approximately 8.9 and 6.3 square kilometers of the plain were classified as very high-risk areas, while 1.8 and 3.2 square kilometerswere classified as high-risk zones. These findings indicate that the proposed model can effectively predict subsidence with high accuracy and reliability.Furthermore, the results highlight that over-extraction of groundwater has caused aquifer compaction and subsidence. Linear regression analysis confirmed the model's accuracy, with a correlation coefficient of 0.86.

The Salmas Plain is a vital agricultural region where groundwater serves as the primary water source. Over 70% of extracted groundwater is used for agriculture. Irregular groundwater extraction and reduced rainfall have significantly lowered groundwater levels, exacerbating subsidence in the plain. Since 1993, the annual water table drop has increased to 1.23 meters.The fuzzy neural inference system model demonstrated that trapezoidal and triangular membership functions with regression correlations of 0.86 and 0.81 provided reliable results, predicting subsidence-prone areas effectively. The findings suggest that 8.6% and 6.3% of the study area are at very high risk of subsidence. Immediate measures are required to control groundwater extraction and manage the subsidence crisis in the Salmas Plain.

Land use changes and land surveying are one of the main topics of sustainable development. Land use change is an important example of human influence on the environment, which causes structural changes in social, economic and physical dimensions. In order to provide rational science for regional planning decisions and sustainable development, prediction models of land use patterns can be used. Therefore, land use map is one of the requirements of any national and regional development planning. the main purpose of this study is to monitor the evolution of the land use system and predict its changes using the Markov chain. Land use changes were evaluated using land use maps obtained in 2013, 2018, and 2023 using the change analysis tool (LCM) in TerrSet software. the changes in land use during the considered years show that man-made land use has changed from 9.81% in 2013 to 11.6% in 2018 and 12.73% in 2023, which indicates the growth and development of the city. On the other hand, the use of irrigated land has increased from 2.98% in 2013 to 1.03% in 2023, which can be attributed to urban development, drought and water shortage.

Land subsidence is a global geological hazard resulting from human activities and natural factors, requiring thorough investigation in many countries, including Iran, the United States, the United Kingdom, Australia, China, Egypt, France, Germany, India, Italy, Japan, Mexico, Poland, Saudi Arabia, Sweden, and the Netherlands. Iran is one of the most hazardous regions globally, experiencing numerous hazards each year. One of the risks that occurs due to climate change and population growth in plains is land subsidence. Reports indicate that 50% of Iran's plains (300 plains) are at risk of subsidence, causing significant problems for agricultural, residential, and transportation areas. While the consequences of land subsidence may not be as apparent as earthquakes or floods, its long-term and heightened impacts are more significant.In recent years, attention has been given to land subsidence in Iran, with numerous studies published on this topic. A preliminary review of the conducted research reveals their dispersion and lack of consistency. The importance of land subsidence and the abundance of scattered studies in this field prompted the authors to provide a systematic review of these studies. The systematic review approach aims to organize and integrate research findings from various studies in a specific domain to present new insights. Therefore, the main objective of this study is to comprehensively analyze the status of land subsidence in Iran using a systematic review approach. The systematic review approach is a research method that explores and extensively analyzes available resources in the studied field, providing reliable results.

The aim of the present study is to provide a comprehensive analysis of the status of land subsidence in Iran. To achieve this objective, a systematic review was conducted to assess the state of land subsidence in Iran. Systematic review is an essential tool for presenting evidence in a rigorous and reliable manner, making it suitable for gaining a comprehensive understanding of land subsidence. The literature for this study was selected based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. PRISMA is a validated method for guiding systematic reviews of academic literature.

The rate of land subsidence varies in different provinces and time periods. For example, in Azarshahr, it is 2.1 centimeters per year, in Marand plain, it is 2 centimeters per year, in Salmas plain, it is 11 centimeters in 2019, in Ardabil plain, it is 4.4 centimeters per year, in Meshkin Shahr plain, it is 9.35 centimeters per year, in Khorramdarreh, it is 4 centimeters per year, in Abhar, it is 4.3 centimeters per year, in Qorveh plain, the average is 11 centimeters per year, and in Mahidasht, it is 3 centimeters per year.The systematic review of research findings indicates that the subsidence rate in Tehran ranges from 3 to 43 centimeters. Additionally, subsidence rates of 11 to 27 centimeters have been reported in Shahriar and 20 centimeters in Varamin. In Ghorveh, the subsidence rate is 6.28 centimeters per year, in Dindarlu, it is 30 centimeters per year, in Marvdasht, it is 5.2 centimeters per year, in Noorabad, it is 4 centimeters per year, in Jiroft, it is 12 centimeters per year, in Kerman, it is 6 centimeters, and in Minab, it is 13 centimeters per year. In Mahyar plain, the subsidence rate ranges from a minimum of 4.6 to a maximum of 2.8 centimeters per year, in Najafabad, it is 7.7 centimeters per year, in Abarkuh, the range is from 5.5 to 12 centimeters per year, and in Dezful, it is 9.5 centimeters peryear. In Jooyin plain, the subsidence rate is 4.6 centimeters per year, in Sabzevar, it is 2 centimeters per year, in Mashhad, the range is from 14 to 23 centimeters per year, in Neyshabur, it is 10 centimeters per year, in Gorgan, it is approximately 5 centimeters per year, in Semnan, it ranges from 10 to 13 centimeters per year, and in Eyvanki, it is 11 centimeters per year. In Hashtgerd plain, the subsidence rate is 7.4 centimeters per year, in Qazvin, it is 3 centimeters per year, in Qom, it is 3 centimeters per year, in Aliabad, it is 16 centimeters per year, and in Shazand, it is 6 centimeters per year.

The deficiency of surface water in arid and semi-arid territories has exacerbated the dependence on groundwater resources, resulting in considerable reductions in groundwater levels. This phenomenon has been particularly pronounced in numerous plains throughout Iran, where the diminution has exacerbated issues related to land subsidence. A comprehensive understanding of groundwater level variations is imperative for enhancing water management strategies and alleviating the associated hazards. A range of statistical, mathematical, and machine-learning methodologies have been utilized to model the dynamics of groundwater aquifers. Recently, deep neural network algorithms have gained prominence in the investigation of surface and groundwater resources, particularly in light of the spatiotemporal characteristics inherent to groundwater. In the present investigation, a hybrid spatiotemporal data mining framework, denoted as Wavelet-PCA, was employed to analyze data acquired from 44 piezometric wells situated in the Qahavand plain over a span of three decades (1988-2018) for the purpose of elucidating temporal and spatial patterns associated with fluctuations in groundwater levels. Subsequently, a sophisticated deep recurrent neural network architecture incorporating Long Short-Term Memory (LSTM) was implemented to model the time series data resulting from the data mining procedure. Various degrees of wavelet transformation were applied to effectively capture the intricate trends in groundwater levels. The LSTM model exhibited a coefficient of determination (R²) of 0.85 for the training dataset while achieving an R² of 0.62 for the testing dataset. The research additionally examined regional patterns of land subsidence utilizing radar interferometry data obtained from the Sentinel-1 satellite during the period from 2014 to 2019. The results revealed an average maximum subsidence measurement of 9 centimeters, with the most pronounced subsidence noted in regions that are undergoing the most substantial declines in groundwater levels. This observed relationship between groundwater depletion and land subsidence underscores the necessity for judicious land use planning and the implementation of effective water resource management strategies in analogous regions.

In the current era, following the occurrence of environmental crises, the destruction of resources and the creation of obstacles on the way to sustainable development, it is necessary to carry out programs based on the recognition and assessment of ecological potential so that exploitation in It should be fed continuously from the environment and the natural values of the environment should be preserved. Therefore, after formulating development strategies and implementing social economic programs, geographical survey of the region and recognition of its environmental capabilities and then determining the ecological potential of the land for different uses with the aim of continuous exploitation with the least Destruction and preservation of the environment is an inevitable necessity. The ecological capacity of the land allows the planner to determine the direction and solutions of the region's development based on the region's capabilities, and provide a plan that fits the conditions of the region. Evaluation of the ecological potential is an effective step and a suitable tool to direct the current activities and uses in the land towards sustainable development. FAO defines "land suitability assessment" as an assessment process that considers the performance of land when used for a specific purpose. The assessment process is directed towards optimal land use by providing important information about the opportunities and constraints in using a given land.

According to the review of various library sources as well as the conditions of the region, 10 criteria of Elevation, slope, Aspect, distance to main roads, distance to waterways, distance to fault, distance to landslide points, distance to flood plains, Normalized Difference Vegetation Index (NDVI) and land use were investigated as different criteria for capacity measurement. To study the capacity of the studied area and to prepare suitable areas for the development of Ecotourism, the following steps were carried out; A): Assessment of land suitability to check the capacity of the region for tourism development using the MCE method First step) defining the set of evaluation criteria (information layers),  The second step) standardizing the criteria, The third step) defining the weights of each criterion, Fourth step) preparation of the final map: in the last step, the obtained weights were multiplied in the required layers and then added together. The fifth step) using the final desirability layer produced at the end of the fourth step, places that have a higher value than other areas, using the location method based on suitability and area (ZLS) as suitable places. They are selected for the development of nature tourism. B): Selection of the most important socio-economic parameters and ranking of the spots: the values of these indicators were extracted for the selected spots and at the end, the rank of each spot was determined using the TOPSIS method.

The results of the AHP questionnaire show that the Criteria of slope, Elevation and NDVI are the most important Criteria affecting the development of Ecotourism in this region. The results obtained for the development of Ecotourism in the studied area show that 21,269 (8.63%) hectares of the area are without capacity, and 6634 (2.70%) hectares are of the area with very high capacity. Due to the fact that a minimum area is required for the development of concentrated Ecotourism, in this study, seven areas with areas of at least 100 hectares were selected from the very high capacity category. Based on the results obtained from the AHP method, respectively, the indicators of the diversity of the area, the amount of pristine, the distance to road, the total population within 5 kilometers of the area and the average suitable map have the most importance on the selection of the best area for the development of Ecotourism. The results of the TOPSIS method show that region 2 is the best place for the development of Ecotourism in this watershed with a score of 0.8188. Then regions 5 and 1 were selected as the second and third regions with scores of 0.7868 and 0.6506 respectively. The places with the lowest rank are regions No. 4 and 3 with a score of 0.3197 and 0.0464 respectively.

Among the different capabilities of each region, tourist attractions are one of the most important natural resources in each region, and are important factors in attracting tourists and attracting capital, and thus are the main basis for sustainable development for the region. The results of the survey show that all three selected final regions are in the central part of Noor city and region 2 has the largest area (370.759 hectares) in terms of area. Also, according to the average map, it is equal to 0.928 and in terms of access, it is 144 meters away from the main road, and its virginity is 80%. Various studies (Balist et al., 2019), (Zarei et al., 2013), (Pour Ahmed et al., 2013) in the world show the superiority of this model (the combination of AHP method and TOPSIS method) over other models confirmed in their studies.

In Iran, specifically in Sari, neglecting the mutual relationship between public transportation and urban development has led to uneven development. The main objective of this study is to assess the possibility of implementing the TOD development model in Sari with future studies approach. This study uses a quantitative method to achieve its goal. Documentary, library, and field study methods were used to collect data. The data collection tool in this study was a questionnaire distributed among 50 experts in urban and transportation planning using cluster sampling. Hierarchical analysis, statistical analysis, and SWOT techniques were used to analyze the data. Initially, four bus stations on Keshavarz Boulevard, Pasdaran Boulevard, Azadi Boulevard, and Imam Reza Boulevard in Sari were selected. Based on public transportation-based development criteria, these four stations were compared, and using the AHP method, the Imam Reza Boulevard bus station was selected as the best station for public transportation-based development. Then, a SWOT analysis was conducted for it and the scenarios were developed based on a qualitative futures studies approach. The positive aspects of this station include suitable residential density, diverse bus stations within an 800-meter range, and the potential for increased density. Its weaknesses include a lack of parking around the station, a lack of green space, and the absence of pedestrian and vehicular separation. Its most important strategies include smartening the public transportation system, appropriate distribution and deployment of land uses, and improving the quality of public transportation.

In recent decades, land use and land cover changes, driven by various factors, have led to unprecedented degradation of ecosystems, particularly in peri-urban areas. The primary aim of this study is to assess land use changes with an emphasis on the peri-urban garden cities of Zanjan over the period from 1993 to 2023. This research utilizes satellite imagery from Landsat 5, 8, and 9 across the 30-year period and applies the fuzzy supervised classification method to categorize the satellite images. The results indicate that the highest growth in built-up areas occurred between 2003–2013, followed by a slowdown in recent years. Gardens initially expanded but began to decline from 2013 onward. Similarly, water resources and agricultural lands, after experiencing a period of decline, showed improvement by 2023. In contrast, rangelands exhibited a continuous downward trend. This study emphasizes the importance of ensuring the sustainability of peri-urban garden cities in Zanjan and recommends the adoption of entrepreneurial approaches to promote urban agriculture sustainability. The findings highlight that precise land use policies and management for garden cities around Zanjan can prevent irregular development and foster spatial equilibrium. In recent decades, land use and land cover changes, driven by various factors, have led to unprecedented degradation of ecosystems, particularly in peri-urban areas. The primary aim of this study is to assess land use changes with an emphasis on the peri-urban garden cities of Zanjan over the period from 1993 to 2023. This research utilizes satellite imagery from Landsat 5, 8, and 9 across the 30-year period and applies the fuzzy supervised classification method to categorize the satellite images. The results indicate that the highest growth in built-up areas occurred between 2003–2013, followed by a slowdown in recent years. Gardens initially expanded but began to decline from 2013 onward. Similarly, water resources and agricultural lands, after experiencing a period of decline, showed improvement by 2023. In contrast, rangelands exhibited a continuous downward trend. This study emphasizes the importance of ensuring the sustainability of peri-urban garden cities in Zanjan and recommends the adoption of entrepreneurial approaches to promote urban agriculture sustainability. The findings highlight that precise land use policies and management for garden cities around Zanjan can prevent irregular development and foster spatial equilibrium. In recent decades, land use and land cover changes, driven by various factors, have led to unprecedented degradation of ecosystems, particularly in peri-urban areas. The primary aim of this study is to assess land use changes with an emphasis on the peri-urban garden cities of Zanjan over the period from 1993 to 2023. This research utilizes satellite imagery from Landsat 5, 8, and 9 across the 30-year period and applies the fuzzy supervised classification method to categorize the satellite images. The results indicate that the highest growth in built-up areas occurred between 2003–2013, followed by a slowdown in recent years. Gardens initially expanded but began to decline from 2013 onward. Similarly, water resources and agricultural lands, after experiencing a period of decline, showed improvement by 2023. In contrast, rangelands exhibited a continuous downward trend. This study emphasizes the importance of ensuring the sustainability of peri-urban garden cities in Zanjan and recommends the adoption of entrepreneurial approaches to promote urban agriculture sustainability. The findings highlight that precise land use policies and management for garden cities around Zanjan can prevent irregular development and foster spatial equilibrium. In recent decades, land use and land cover changes, driven by various factors, have led to unprecedented degradation of ecosystems, particularly in peri-urban areas. The primary aim of this study is to assess land use changes with an emphasis on the peri-urban garden cities of Zanjan over the period from 1993 to 2023. This research utilizes satellite imagery from Landsat 5, 8, and 9 across the 30-year period and applies the fuzzy supervised classification method to categorize the satellite images. The results indicate that the highest growth in built-up areas occurred between 2003–2013, followed by a slowdown in recent years. Gardens initially expanded but began to decline from 2013 onward. Similarly, water resources and agricultural lands, after experiencing a period of decline, showed improvement by 2023. In contrast, rangelands exhibited a continuous downward trend. This study emphasizes the importance of ensuring the sustainability of peri-urban garden cities in Zanjan and recommends the adoption of entrepreneurial approaches to promote urban agriculture sustainability. The findings highlight that precise land use policies and management for garden cities around Zanjan can prevent irregular development and foster spatial equilibrium. In recent decades, land use and land cover changes, driven by various factors, have led to unprecedented degradation of ecosystems, particularly in peri-urban areas. The primary aim of this study is to assess land use changes with an emphasis on the peri-urban garden cities of Zanjan over the period from 1993 to 2023. This research utilizes satellite imagery from Landsat 5, 8, and 9 across the 30-year period and applies the fuzzy supervised classification method to categorize the satellite images. The results indicate that the highest growth in built-up areas occurred between 2003–2013, followed by a slowdown in recent years. Gardens initially expanded but began to decline from 2013 onward. Similarly, water resources and agricultural lands, after experiencing a period of decline, showed improvement by 2023. In contrast, rangelands exhibited a continuous downward trend. This study emphasizes the importance of ensuring the sustainability of peri-urban garden cities in Zanjan and recommends the adoption of entrepreneurial approaches to promote urban agriculture sustainability. The findings highlight that precise land use policies and management for garden cities around Zanjan can prevent irregular development and foster spatial equilibrium.

The Persepolis World Heritage Site, located in the northern part of Fars Province, is one of Iran’s most significant historical landmarks, dating back to the Achaemenid period (300–560 BC). Studies have shown that the surrounding areas are experiencing land subsidence due to prolonged drought, dam construction, and excessive groundwater extraction. Therefore, this study aims to investigate and map the stability/instability patterns of Persepolis from October 27, 2016, to February 22, 2020, using radar interferometry. To analyze the time series of subsidence in the study area, we applied Persepolis Time Series Analysis (PS-InSAR). The analysis was based on 180 SENTINEL-1A images, covering both ascending and descending orbits, to assess the rate of land deformation at the Persepolis archaeological site. Specifically, we focused on the Takht-e Jamshid region, located on the southern slope of Mount Ko Rahmat, and identified and measured land deformation phenomena there. Our results show that Takht-e Jamshid has not experienced significant subsidence during the observation period (2016–2020), with an estimated rate of -5 to -10 mm per year. This study provides evidence that the observed deformation at the site is not related to the subsidence of the Marvdasht aquifers, which are underlain by Quaternary sediments. These findings contribute to a deeper understanding of land subsidence dynamics in the Takht-e Jamshid region and underscore the need for continued monitoring and management of this invaluable archaeological site.

Land subsidence is a significant hazard affecting many plains of Iran, including the Eyvanakey Plain in Semnan Province. The Eyvanakey Plain has a high potential for subsidence due to the influence of climate and topography. Given the importance of this issue, the present research aims to evaluate land subsidence in the Eyvanakey Plain and analyze the factors contributing to its occurrence.In this study, Sentinel-1 radar images, Landsat satellite imagery, and data from piezometric wells in the region were utilized as primary research data. The key tools employed include GMT, ArcGIS, ENVI, and SPSS software. The study was conducted in two main phases: first, an assessment of land subsidence in the region from 2016 to 2022, and second, an analysis of the impact of groundwater depletion and land use changes on subsidence.The results indicate that the study area experienced subsidence ranging from 28 to 533 mm over the six-year period, which is a significant amount. Spatial analysis of the subsidence reveals that the highest rates occurred in the southern parts of the Eyvanakey Plain. These areas also experienced an annual drop in groundwater levels of approximately 2 meters, highlighting groundwater depletion as one of the primary causes of subsidence.Furthermore, based on the results, the extent of man-made areas and agricultural lands increased by 6.9 km² and 2.7 km², respectively, between 1992 and 2022. Given that the highest levels of subsidence were observed in these land use areas, the development of agricultural lands and man-made areas has been identified as the main cause of subsidence.

The rivers are the most important supplies of fresh and agricultural water in the cities and villages. The importance of chemical quality of waters is becoming increasingly important due to the increase and diversity of Anthropogenic activities in the urban and rural environments. Therefore, the current study aimed to investigate the trend of land use changes and the chemical quality of surface water in a period of twenty years in the Qaranquchai River in Hashtrud Ccounty. The Qaranquchai is one of the sub-basins of Qezel Ozen River in the northwest of the country. In this research, Land use maps for the years 2021 and 2001 were prepared and drawn using Sentinel 2 and Landsat 7 satellite images, and were examined their changes. The chemical quality data of surface water in the hydrometric stations of the Qaranquchai basin, including Mg, Ca, EC, TDS, Cl, HCO, SO4, K and Na were obtained from the Regional Water Organization of East Azarbaijan province from 2000 to 2020. Then, their changes over 20 years were analyzed using the Mann-Kendall test. The results of the analysis of land use changes showed that in 2001, the largest area of land use was pasture, while in 2021, rainfed land had the largest area in the Qaranquchai basin. In fact, during the studied years, with the increase of rainfed agricultural land from 38% to 53.1%, pasture land decreased from 60.17% to 42.3%. The results of the investigation of the changes of the chemical quality indicators of the water in the hydrometric stations in the Qarangochai river in the studied time period, showed that the trend of changes was increasing. The result of the increasing process of each of the anions and cations in the river water has caused its quality to decrease.

Rapid physical and population development and growth in recent decades has caused many problems in cities, and Ardabil city has not been exempted from this due to its historical antiquity and high population and physical growth. This research examines and evaluates the land suitability index per capita in Ardabil city using the overlap model and spatial analysis in the geographic information system (GIS) environment. The main purpose of this study is to determine the per capita suitability of land for different urban uses and provide solutions to improve planning and sustainable urban development. For this purpose, first, spatial and descriptive data related to urban land use were collected from different sources and then processed using spatial analysis tools in GIS. The research results show that the use of this model can help identify suitable areas for future developments and optimize existing uses. The findings of this research show that there is an inequality of compatibility among some urban uses such as residential areas with more than 42%, educational use with more than 35%, administrative use with 38%, medical use with 25% and sports use with 16%. They have neighboring uses. Tourist use with more than 80%, religious and cultural use with more than 75%, and commercial use with more than 70% are in completely compatible and relatively compatible conditions with their neighboring uses and have the least incompatibility with other uses. Therefore, urban planners and designers should pay special attention to the proper distribution of neighboring uses based on spatial-spatial components, while allocating the best space to the uses needed to solve the current shortage, in the programs related to future development. This study provides practical suggestions for improving urban planning policies and land management in Ardabil, which can be used as a model for other cities.

The purpose of this research is to investigate and analyze the physiographic characteristics and land cover of Koze Topraghi watershed in Ardabil province using geographic information system. In this regard, by using Landsat 9 satellite images, the topographic map of the area and the use of the digital elevation model of the land to prepare slope factors, slope direction, DEM, land use and vegetation. The results of the factors showed that the highest point of the studied area with a height of 2540 meters is located in the south of the studied area and the lowest point of the studied area with a height of about 1400 meters is located in the northern parts of the basin. In the study area, slopes of more than 70% are observed in the northeast, south, southeast elevations, and due to the lack of vegetation with low coverage, especially in the eastern parts, they are highly vulnerable to erosion. Low slope areas in the studied basin are mostly located in the central areas. The general direction of the slope in Kozetopraghi watershed is southeast and northeast. in the studied area, the slope decreases from east to west and from the heights to the center of the basin. On the other hand, in order to prepare and classify the land use map of the studied basin, the support vector machine method was used. The overall accuracy and Kappa coefficient for the land use map of the studied basin were equal to 99.93 and 0.99%, which had acceptable accuracy, in order to prepare the vegetation map for the year 1402, Landsat 9 satellite data was used and the factor map was prepared. the values of the vegetation factor in the basin ranged from -0.21 to 0.85, which often had more density in the southern parts.

Land is the basis of most human activities, production systems, and achieving sustainable development. On the other hand, with social and economic changes and unconventional resource exploitation, land instability conditions are intensifying. Measuring land use changes requires examining issues such as land use system, land cover, and monitoring patterns. Land use and land cover patterns, by providing monitoring and understanding the process of changes in the exploitation system through the use of remote sensing techniques, facilitate the possibility of reforming and changing policymaking, optimal management, and future planning of environmental resources. The aim of the present study is to monitor the changes in the LULC system in the Kuhpayeh-Segzi sub-basin using satellite images in the period 2000 to 2023 and update spatial information. The results of the evaluation of the land use and land cover maps using the Decision Tree Algorithm indicate an increase in the area of ​​more than 97% of built-up land, 173% of rangeland, 230% of irrigated areas, 72% of agricultural land, and a decrease in the area of ​​more than 14% of barren land. In this process, 913 hectares of barren land, 244 hectares of rangeland, 44 hectares of agricultural land, and 0.155 hectares of irrigated areas have been converted into built-up land. Sustainability of environmental resources, especially reducing the trend of land use and land cover change and stabilizing the exploitation system, requires the implementation of optimal resource management and the application of land protection models with the participation and empowerment of local rural communities.

In the research, the carbon storage capacity of Urmia's urban and suburb green infrastructures were analyzed. Urmia is characterized by a cold climate with a dense building structure with scattered green spaces, especially in the surrounding areas and neighborhoods of the city. The research was carried out using the carbon storage model available in the InVest software package. The results revealed that more than 57% of the study area has a carbon storage capacity of less than 2 tons, and less than 6 percent of the area has more than 30 tons per hectare, which is limited to the gardens and groves along the Shahrchai River. Soil carbon storage has the highest share with 2344.41 tons, and above biomass carbon storage followed closely with 1403.47 tons. The total amount of carbon storage in the studied area is 3900.9 tons per year. The carbon storage was highest in gardens and groves, followed by barren lands with sparse vegetation and rocky outcrops mountain’s without vegetation and soil cover had no carbon storage. Dense plants and trees have the highest storage capacity per unit area, However, the amount of storage depends on the season, so it is necessary to consider the selection and expanding the urban green spaces according to the types of plants and their compatibility with the climatic conditions and urban spaces of Urmia. As a result, it is necessary to create and develop urban green spaces at the same time as physical development, it should be the priority of Urmia's urban development plans.

Rivers besides the positive effects of the rivers in the areas, there are also risks that, if not paid attention to them, can cause a lot of damage. Accordingly, in this research, the geometric status of the river studied and the proper use of the marginal areas of the river are determined based on the morphology of the region. In this research, the aerial photographs of 1360 and satellite imagery of 1397 have been used to investigate river changes. In accordance with the objectives, using ARCGIS and Auto Cat software, the first step was to investigate the morphological status of the river and then using the fuzzy logic and AHP combination model to zoning the areas suitable for the development of residential, agricultural and green spaces. The results of the survey of the morphology of the river indicate that the second period is closer to the first period than the Meander River, and the river in general has tended towards the Meander over time. Also, the study of the displacement rate indicates that the first open with 32.66 km long has an average of 33.3 meters, and the second interval with 27.17 km long has an average of 17.2 meters displacement. In addition, the results of zoning of the marginal areas of the river indicate the proportion of the western margin of the river for the use of residential, agricultural lands and green spaces.