نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 120 (زمستان 1400)

DEMs (digital elevation models) are of critical importance in different areas such as land use planning, infrastructural project management, soil science, hydrology and flow direction studies. Across greater spatial scales, their usage is the key for contouring topographic and relief maps. A DEM represents the bare surface, eliminating all natural and artificial features, while the digital surface model (DSM) captures both natural and artificial features of the environment. DSM is of significant interest for applications such as environmental planning, map updating, or building detection. Ground filtering is the removal of the points belonging to the above-ground objects in order to retrieve ground points to be used in generating DEM. DEM can be effectively obtained from LIDAR or digital photogrammetry. Lidar point clouds have great success in representing the objects they belong to; but since the Lidar data acquisition is still a costly process, using point clouds generated by the photogrammetric process to produce DSM is a reasonable alternative. Since DSM represents the information of surface of the land objects and is also affected by ground slope, it cannot be useful lonely for interpreting the data; therefore, to make optimal use of it, a distinction is required between the land and non-land pixels. On this basis, due to the large volume of the high-resolution images and with regard to complex urban structure, a fast yet simple and accurate method is desirable.

Based on the filtering algorithms, the provided digital surface model is classified into ground and off-ground pixels. For all the off-ground pixels, the closest ground point is assumed to be the relevant low point, thus, through the height difference of the off-ground point with the assigned ground point, the so-called normalized height is computed. However, most of the filtering algorithms are mainly developed to filter Lidar data and will require the adjustment of a number of complex parameters to achieve high accuracy. At the same time, the processing time, degree of effectiveness in different scenes, and degree of automation of these methods are also important. Scene details and topographical complexity, for example in urban areas, make the filtering process even more challenging. For optimal results, users should try to adjust various parameters until they find the desired filtering result, which is a time-consuming and costly process. Due to the lack of a comprehensive study on the efficiency, automation, and computational complexity of different filtering methods on the points cloud obtained from photogrammetry, in this study, different and most widely used algorithms in this field of study were compared with each other. The studied methods were analyzed in terms of class filtering quality, processing time (execution time), scene complexity, and number of algorithm parameters (indicating the degree of user involvement in data processing to determine the amount of automation). Results of this analysis can be useful in order to better understanding the performance of filtering methods on the DSM obtained from high resolution images (dense point clouds from aerial and UAV images). In addition, it can be suitable for different users according to the parameters of time, hardware, scene type, and output accuracy.

Ground filtering is essential for DEM generation. In this paper, for ground filtering, at first, a suitable algorithm was selected and, after setting the initial parameters, they were applied to the point clouds. Comparing the obtained results, it can be seen that in the building class with sloping roofs, Morph and ATIN methods performed better, but in buildings with flat roofs, only Morph method had good accuracy. In the mono-tree class, the Morph and ATIN methods in Metashape software were able to perform the separation well, and in the tree row class, both methods performed well. The ATIN method in Metashape software was able to differentiate the road class more accurately than other methods. It also performed well in the river class. Therefore, according to the results of this study, if the goal is to identify high tolls in urban areas, due to the lower computational cost of the Morph method than the ATIN method, the Morph method is recommended. But if the goal is to produce good quality DTM, the ATIN method will be the priority.

In this research, ATIN, ETEW, MLS, MORPH1D, and MORPH2D algorithms for land extraction were evaluated. Thus, first the algorithms were examined on the test data and, then, the results were analyzed with the ground true images. In this study, five filtering methods were examined and compared on three images of urban areas, which included various natural and human-made features, including streets, trees, and buildings. The data were related to the digital aerial imagery taken by Intergraph/ZI DMC sensor in Vaihingen city, Germany. DSM data sets were defined on the grid with the ground resolution of 9°cm. Comparing the results of all the three data sets, it can be seen that the difference in accuracy between the one- and two-dimensional morphology algorithms was very small and they had similar performance. In terms of processing time, the ATIN method had longer execution time than other methods and the ETEW method had shorter execution time than other algorithms. Also, the number of algorithm parameters indicated the degree of user participation in data processing. Therefore, due to the point that the ETEW algorithm had fewer parameters, its degree of automation was higher than other algorithms. Comparing and reviewing the results obtained from the test data demonstrated that MLS and ETEW algorithms had the lowest efficiency in the urban area. On the other hand, in features such as buildings with sloping roofs, single trees, and tree rows, two ATIN and Morph algorithms provided favorable results. According to the obtained results, the suitable algorithm was Morph algorithm for flat-roofed buildings and ATIN algorithm for road and parking. In general, it is recommended to use the Morph algorithm for urban and small areas due to time savings and less effective parameters.

In recent years, data has become the life-giving force of developing innovations in smart cities all around the world. The up-to-date, availability, and freeness of this data are the deciding factors in their frequent use in smart city projects. Today, different sources of information on city-related issues are available. They are crucial for driving towards “Smart Cities”. Among these sources is the Open Street Map (OSM) project, which is a free and open-source information repository used in many urban and non-urban-related applications. At present, OSM is used for a wide range of applications, for example, navigation, location-based services, construction of 3D city models, and traffic simulation. In the meantime, building blocks are among the OSM data that plays a key role in urban-related studies. These studies include constructing 3D building models, modeling urban energy systems, and land-use management in smart cities. Regarding the importance of completeness in the quality of spatial data, this study will assess the historical course of building blocks data completeness in OSM.

The 20 districts of the Tehran metropolis have been selected as the study area. This city, with an area of 730 square kilometers and a population of around 8 million people covers the center of Tehran. The main purpose of this study is to present an analysis of the completeness of building block data in the OSM for the Tehran metropolis in 10 years (between 2011 and 2020). To reach this aim, an object-based approach based on object matching was used to assess the completeness parameter.

The findings of this study demonstrate that during the recent two years, OSM building block data in Tehran increased in terms of the number of features and the completeness of geometric information considerably. The number of data increased from 300 features in 2011 to 40.138 features in 2020, as well as the number of features edited and added to the OSM dataset increased from 38 and 194 in 2011 to 28680 and 10705 in the end of 2020, respectively. The completeness of OSM building block data in Tehran has increased from 0.18% in 2011 to 2.7% in 2020. Moreover, the evaluation of the completeness of OSM data in different regions of Tehran shows that the completeness of all regions of Tehran was less than 1% from 2011 to 2014, and in the last two years, for 12 of 20 regions of Tehran, the completeness is still less than 1%, but for the other eight regions (i.e., the regions no. 1, 2, 4, 5, 11, 15, and 20), which are mostly located in the northern part of Tehran, the completeness has increased. However, the data have many weaknesses in terms of the attribute information completeness.

This study has provided a clear view of OSM building block status in Tehran. In addition, it has provided a better view of OSM data in different regions of Tehran. The insights gained from this study can lead toward creating the awareness required to use of these data in various fields of application. It can also assist local and national managers and related organizations to support active regions and encourage inactive regions. This paper represents a potential starting point for many possible future research directions in smart cities, especially in Tehran. Smart cities can conduct similar studies to understand the state of OSM data in their regions, make plans based on the findings, and manage their space more efficiently. To conduct future research, we evaluate the factors affecting the growth and development of OSM data and the efficiency of the OSM data in some smart city applications.

Remote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), object-based methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the time-series images. In such a situation, the use of the Google Earth Engine (GEE) as a web-based RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semi-automatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel-1 and sentinel-2 data, which belongs to the European Space Agency (ESA), has been utilized.
 

2-1- Study Area The study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- Data Various data sources have been used in this study, including Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using High-Resolution Google Earth Imagery (GEI) in two classes: urban-residential (buildings) and non-residential areas (vegetation, soil, road, water and etc.).
2-3-

The goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and pre-processing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and High-Potential Buildings (HPB) was produced from Sentinel-1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Built-up Index (NDBI), and Build-up Extraction Index (BUEI) were extracted from Sentinel-2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and non-residential areas. The samples were selected randomly and 400 points were collected for each residential and non-residential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used.

According to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance.

Due to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semi-automatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel-1 and Sentinel-2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE Web-Based platform has a very high potential in distinguishing features and building mapping.

Mining (especially surface) is one of the major causes of land and environmental degradation globally. Environmental impacts such as deforestation, landscape degradation, alteration of stream and river morphology, widespread environmental pollution, siltation of water bodies, biodiversity loss, etc., have been noted to be associated with mining. Surface deformation is the biggest problem in open cast mines and their surrounding areas due to mining activities.  Surveying engineers study the amount of displacement in open pit mines by using leveling to calculate the amount of displacement and determine it. These methods are expensive and time consuming. Satellite images are considered as an important tool for land resource management due to the wide view that provide of an area and also due to its regular repetitive coverage. Interferometric Synthetic Aperture Radar (InSAR) is a useful tool in the study of surface displacements. The SAR interferometry concept has been introduced in the last 1980s.The objective of this study as an academic research is monitoring deformation using Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) method for managing a very rich iron ore resource in the eastern part of Iran named Sangan, near the Afghanistan boundary.

In this paper, surface deformation calculation based on the processing of PS-InSAR technique (Persistent Scatterers SAR Interferometry) have been carried out. For this study, according to the availability of data for study area 47 SLC images of Sentinel-1A covering the study area during the period of October 7, 2014 –July 7, 2020 are downloaded from European Space Agency website. Sentinel-1A acquired images with a swath width of 250 by 180, with revisiting time 12 days within the IW data acquisition mode, it is reduced to six days if the images acquired by the Sentinel-1B satellite are available. Sentinel-1 has launched on 4th April 2014 by ESA. PS includes following steps:Master image selection, Co-registration data, Reflectivity map generation Amplitude stability index, Persistent Scatterers Candidate selection (PSC), PS point selection, Multi-image sparse grid phase unwrapping, Atmospheric phase screen estimation Removal and PS phased reading Displacement estimation. Study area Sangan Iron Ore Complex (SIOC) is located at latitude N 34°24’ to 34°55’   longitude E 60°16’ to 60°55’ in the Khorasan-e-Razavi Province, North-Eastern Iran. The iron ore deposit is about 20 km Northeast of Sangan town at about 1650 meters above sea level. Sangan Iron Ore Mines (SIOM) is one of the largest mineral areas in Iran, and also considered to be one of the Middle East’s richest deposits which are located in a rectangular area with 26km length and 8km width.

In this paper, the 47 scenes of IW SLC Sentinel-1A images, spanning the period from October 7, 2014–July 7, 2020 are accumulated displacement map and the time series of the deformation derived. The PS were selected on the basis of the ASI threshold value of 0.7, which signifies the stability of target points. The LOS displacement was improved by using APS and atmospheric phase delay correction. Later, the LOS displacement velocity on PS locations was estimated. The temporal coherence of all the selected PS was also tested. The PS points having ASI value of 0.7 and above, and temporal coherence of 0.9 and above, gave a relatively stable estimation of LOS velocity. We have identified 215377 Scatterers points. By imposing the standard threshold of 0.7 on ensemble coherence value, this amount decreased dramatically to 52449 PS points.  These factors make the chosen technique suitable for studies of surface deformations. The results showed that the deformation velocity in this area is -4.8 mm/yrs and maximum displacement-30mm. In order to verify the results, we collected the Total Station data and PS data for analysis and comparison. Due to the lack of data in the plain, the Total Station data is related to downslope areas and as a result, uplift of area has been used to validation the results. It has been observed that for the same area the Total Station value shows good agreement with the PS- InSAR result. However, there may be some errors due to the fact that the data are not synchronous and that the nature of the impression is different.

In the present study, PS-InSAR technique and C-band sentinel-1 data have been used for surface deformation monitoring in open cast mines of Sangan-Khaf, Khorasan Razavi. It can be concluded that monitoring the deformation of mined surfaces using traditional monitoring techniques such as field surveys and using Total Station, especially in large study areas, is time consuming. Since in using the interferometry methods in the study of open pit mines, the area covered by SAR images is much larger, so the use of this method will reduce costs. The results were assessed and validated using leavening data has been observed that, for the same area, the levelling value shows good agreement with the PS- InSAR result.

Various religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the lunar crescent can be seen with the naked eye under two conditions at sunset. First, the moon is older than 24 hours, and the moon's lag time is greater than 48 minutes. Fotheringham and Maunder developed standards for the seeing of the crescent moon at the beginning of the nineteenth century, and Bruin used his own criteria in 1977. Schaefer recently addressed crescent visibility extensively and integrated weather conditions into his work. Yallop then utilized the same database that Shaffer developed in 1997, but he overhauled some of the observation records extensively. Furthermore, many Muslim astronomers had developed their own criteria and published them in their literature. Despite the fact that different study organizations have created different criteria, there are still mistakes in the best time to forecast the crescent moon sighting. The use of old and conventional observations in modeling is one of these limitations, as is the use of non-uniform and heterogeneous observations. The Yallop criterion, for example, forecasts the visibility of the crescent moon for older crescents pessimistically. The Odeh criterion, on the other hand, forecasts young crescents with optimism. New Iranian criteria, such as the phase and altitude criteria (Mirsaeed criterion) and the triangular model (Iran criterion), have been presented in Iran. The goal of these criteria is to find the best timing between sunset and the first sighting of the crescent moon. Bruin, Schaefer, and Yallop have spent the last four decades developing the notion of the best moment. Because, after sunset, the sky darkens and the conditions for seeing the narrow crescent improve, while the moon approaches the horizon and the conditions for viewing the crescent moon worsen. Because the thickness of the atmosphere along the horizon is 3.7 times more than that of the zenith, the moonlight travels a greater distance than it did just a few minutes before. As a result, the sky towards the horizon is red or orange, and the crescent is not visible in this part of the sky.

The objective of this study is to verify the rate of sky darkening in various regions and its influence on modeling the crescent visibility parameters of the moon, as well as to identify the best time to find out. To that end, 268 observational reports gathered from different divisions of Iran during the previous 20 years (2000-2021) were used to model the lunar crescent sighting. The proposed models are based not only on an examination of 20-year data to provide all effective tidal frequencies of the moon (the minimum period of moon’s notation motion is 18.61 years), but also on the use of sky-changing parameters such as local darkening rate and local sun occultation epoch time, the effect of the moon's distance from Earth, and the altitude of the moon from the horizon. The darkening rate of the sky factor was confirmed using various parameters and variables such as each point's geodetic latitude. Furthermore, unlike prior studies, the proposed models are developed using categorized observational reports with the least amount of error and can forecast the crescent sighting time in the presence of the sun (daylight time). The statistical correlation between the waiting time of each observation and the effective parameters in the lunar crescent visibility was studied in the first step. Following that, the parameters with the highest correlation values were chosen as the key quantities for modeling. After that, 17 alternative mathematical models with 2, 3, 4, and 5 parameters were implemented and tested, and the coefficients of the final two models (two and five parameter models) were determined using the least squares method as the suggested models.

As a simple model, the two-parameter model can forecast crescent visibility with an average root-mean-square error (RMSE) of 4.7 minutes. The five-parameter model, on the other hand, was a more full and accurate model than the prior model, which was tested in two separate situations. They were evaluated over data for perigee distances of moon orbit (less than 375 thousand km) and observations for apogee distances of moon orbit (distance more than 390 thousand km) in the first and second cases, respectively. The findings of the 5-parameter model revealed that the first and second forms of the model had an average RMSE of 3.6 and 4.0 minutes to forecast the best time to see the crescent moon with the naked eye, respectively.

The results revealed that the best period to observe the crescent moon is from 32 minutes after sunset to 12 minutes earlier than sunset owing to the angular separation of the moon from the sun (10 to 20 degrees) and the difference in the altitude of the moon from the sun (5 to 20 degrees). When a result, as the local darkening epoch time increases, so does the waiting epoch time. In other words, the lunar crescent appears earlier in the northern part of Iran than in the southern half.

In recent years, protecting infrastructure, especially critical infrastructure, has become increasingly important because the economy of a region and the well-being of its inhabitants depend on the continuous and reliable operation of its infrastructure. These infrastructures are like arteries for survival of urbanism damaging .Some of infrastructures can have devastating effects on security, economy, and society at the regional and national levels. There are different systems and infrastructures in different countries, including Communication, electricity, gas and oil, banking and finance, transportation, water supply and government services infrastructure, which are critical infrastructures. A review of various types of infrastructures shows that energy infrastructure is more important and plays a more significant role in comparing with other types of infrastructure.Maintaining the security of this infrastructure against attacks and threats is one of the priorities of securing a country. One way to ensure security is to measure the spatial vulnerability of infrastructure. This article assesses the capacity of Yazd province against the vulnerability of energy infrastructure.

The information for this research has been extracted by documentary methods (including books, scientific articles, reports, etc.) as well as using the country's infrastructure database. Then, GIS layers of the energy infrastructure of Yazd province, including electric transmission network, electric plant, gas transmission lines, gas pressure regulation stations, oil transmission lines, oil products transmission lines, oil and gas storage tank and gas stations were examined.The next step was ranking the importance of infrastructure elements with the DEMATEL model. Then, the infrastructure elements of Yazd province were prioritized with the analytic network process(ANP) model. The next step was to prepare maps and GIS layers for each of the infrastructure elements ,by preparing them in Arc GIS and the priorities of the network analysis process model ;sothe final vulnerability map of the province was prepared.

After calculations of supermatrix coefficients, the results show the importance of these infrastructures in providing services to people and other infrastructures, as well astheattractiveness for each infrastructure element. Gas transmission network with the value of 0.1003, oilproducts transmission lines with the value of 0.0988, oil and gas tank with the value of 0.0995, have the most weight and importance, and gas stations with the value of 0.0485 has the least importance in comparing to other energy infrastructures in the Yazd province.The results show that the central part of Yazd province is more vulnerable thanthe other part of province, because moreenergy infrastructuresareestablished inthe central part of Yazd province. Examination of the results on a smaller scale show thatthe vulnerability of energy network infrastructure inYazd,Meybod, Mehriz and Sadooghis high,butinBahabad, Khatam, and Abarkoohis low.

The results show that distribution of infrastructure in the Yazd province has not beenin a good model. The central part of the province is more vulnerable than the peripheralareas so that more than half of the infrastructure of the energy network (55%) is in very vulnerable zone and 18% of the infrastructure is in highly vulnerable zone;thus, observing the teachings of passive defense in the province deserves more importance.

The third millennium has been called the age of urbanization and the urban population is expected to reach about 6.25 billion by 2050. The United Nations also estimates that the urban population of developing regions will grow at an average annual rate of about 2.02 percent, from 2.67 billion in 2011 to 3.92 billion in 2030. Indeed, the urbanization process is a phenomenon that has become increasingly concentrated in developing countries in recent decades. Although the pace of change varies considerably between countries and regions, in fact all developing countries are becoming increasingly urbanized. The increase in urbanization has caused many problems in urban areas. This has led to the fact that today land use management of urban infrastructure has become the main challenge of many planners and city managers. Accordingly, this study seeks to investigate the scattering around the Tehran-Eyvanekey communication axis, so Pakdasht cities with about 210 thousand people , Sharifabad with about 12,000 people and Eyvanekey with about 12,000 people, make it one of the busiest axes in the metropolitan area of Tehran.

The main purpose of this study is to analyze the process of space expansion and modeling in the axis of Tehran Eyvanekey between 1985 and 2020 using remote sensing data and GIS. To have a comprehensive study of spatial organization of this metropolis, a deductive or inductive approach with a practical nature has been used. The basis of the study is based on using the satellite data and images (Landsat multi-time images) related to different years. Using IDRISI, GIS and GOOGLE EARTH softwares and Fuzzy Artmap LCM, MARKOV and CA models.

In this study, in order to evaluate the pattern of expansion of built areas in the corridor of Tehran to Eyvanekey, TM and ETM + images of Landsat satellite related to the years 1985, 2000, 2011, and 2020 have been used. Based on this, the amount of land use changes in the four periods is as follows: The most expansion of practical surfaces in the axis of Tehran-Eyvanekey with an area of 223250 hectares, dedicated to built areas with an increase of 30,495 hectares over the last 35 years. After identifying the urban expansion pattern of Tehran-Eyvanekey corridor, in the next stage, in order to simulate how land use changes in the axis of Tehran-Eyvanekey for the year 2031, the method of automatic cells and chains has been used. For this purpose, to simulate land use changes in the axis of Tehran Eyvanekey in 2031, land use maps in 1985 and 2020 were used. The results show that according to the trend of urban growth in the region in 2031, the land area will reach more than 50,000 hectares. Also, according to the growth rate of urban areas in this region, it can be seen that during different periods, we see a kind of exponential growth in the study area, so that for the period 1985 to 2000, about 240 hectares per year have been built. This trend of growth has expanded and in the next period, ie 2000 to 2011, this number has reached about 580 hectares, and finally in the last period, ie 2011 to 2020, we have witnessed the expansion of about 2251 hectares per year in the built lands, which can be signs of accelerative urbanization. Therefore, the strategy of increasing physical density and using related methods to guide the development of the city towards greater sustainability, should be on the agenda of planners and those in charge of urban affairs.

Modeling land use changes is an effective way to obtain information about how land use changes over time as well as the factors affect it. So, in order to analyze the process of space expansion and modeling in the axis of Tehran-Eyvanekey, it was modeled over a period of 35 years. The results showed that most of the land use changes during this period are related to the built lands, which due to the location of the built areas along the main arteries has a northwest-southeast pattern that is affected by urban growth in the metropolis of Tehran. As a result, they live in these areas, which are either engaged in the urban industries of these areas or use the satellite cities in this corridor as dormitory cities. Interestingly, as we move away from the main center, the metropolis of Tehran, the rate of urban land expansion decreases, which indicates that due to the low cost of housing in satellite cities, this area is a dormitory for the metropolis of Tehran.

The Earth's atmosphere (atmosphere) is divided into concentric layers with different chemical and physical properties. To study wave propagation, two layers called the troposphere and ionosphere are considered. The troposphere is the lowest part of the Earth's atmosphere and extends from the Earth's surface to about 40 kilometers above it. In this layer, wave propagation is mainly dependent on water vapor and temperature. Unlike the ionosphere, the troposphere is not a dispersive medium for GPS signals (seeber, 2003). As a result, the propagation of waves in this layer of the atmosphere does not depend on the frequency of the signals. The delay caused by the troposphere can be divided into two parts of hydrostatic delay and wet delay. The hydrostatic component of the tropospheric delay is due to the dry gases in this layer. In contrast, the wet component of tropospheric refraction is caused by water vapor (WV) in the troposphere. The study of atmospheric water vapor is important in two ways: First, short-term climate change is highly dependent on the amount of atmospheric water vapor. Water vapor has temporal and spatial variations that affect the climate of different regions. Second, long-term climate variation is reflected in the amount of water vapor. Obtaining water vapor using direct measurements and water vapor measuring devices is a difficult task. Radiosonde and radiometers are used to directly measure atmospheric water vapor, but the use of these devices will have problems and limitations, for example, the maintenance cost of these devices is expensive and also these devices do not have a suitable station cover. The best way to get information about water vapor changes indirectly is to use GPS measurements. GPS meteorological technology can provide continuous and almost instantaneous observations of the amount of water vapor around a GPS station.Estimation of precipitable water vapor (PWV) and water vapor density using voxel-based tomography method has disadvantages. The coefficient matrix of tomography method has a rank deficiency. Initial value of water vapor must be available to eliminate it. Also, the amount of WV inside each voxel is considered constant, if this parameter has many spatial and temporal variations. In this method, the number of unknowns is very high and it is computationally difficult to estimate (Haji Aghajany et al., 2020). To overcome these limitations, this paper presents the idea of using learning-based models. To do this, in this paper, 3 models of artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression model (SVR) have been used.

Due to the availability of a complete set of observations of GPS stations, radiosonde and meteorological stations in the north-west of Iran, the study and evaluation of the proposed models of the paper is done in this area. Observations of 23 GPS stations were prepared in 2011 for days of year 300 to 305 by the national cartographic center (NCC) of Iran. Out of 23 stations, observations of 21 stations are used to training of models and observations of the KLBR and GGSH stations are used to test the results of the models. In the first step, the observations of 21 GPS stations that are for training are processed in Bernese GPS software (Dach et al., 2007) and the total delay of the troposphere in the zenith direction (ZTD) is calculated. It should be noted that for every 15 minutes, a value for ZTD is calculated using the observations of each station. In the second step, the zenith hydrostatic delay (ZHD) is calculated. By subtracting ZHD from ZTD, zenith wet delay (ZWD) are obtained. ZWD values are converted to PWV values. The obtained PWV values are considered as the optimal output of all three models ANN, ANFIS and SVR. Also, the input observations of all three models will be the latitude and longitude values of each GPS station, day of the year and time.

After the training and achievement of the minimum cost function value for all three models, the PWV value is estimated by the trained models and compared at the location of the radiosonde station as well as the test stations. The mean correlation coefficient for the three models ANN, ANFIS and SVR in 6 days was 0.85, 0.88 and 0.89, respectively. Also, the average RMSE of the three models in these 6 days was to 2.17, 1.90 and 1.77 mm, respectively. The results of comparing the statistical indices of correlation coefficient and RMSE of the three models at the location of the radiosonde station show that the SVR model has a higher accuracy than the other two models. The average relative error of ANN, ANFIS and SVR models in KLBR test station was 14.52%, 11.67% and 10.24%, respectively. Also, the average relative error of all three models in the GGSH test station was calculated to be 13.91%, 12.48% and 10.96%, respectively. The results obtained from the two test stations show that the relative error of the SVR model is less than the other two models in both test stations.

The results of this paper showed that learning-based models have a very high capability and accuracy in estimating temporal and spatial variations in the amount of precipitable water vapor. Also, the analyzes showed that the SVR model is more accurate than the two models ANN and ANFIS. By estimating the exact amount of PWV, the amount of surface precipitation can be predicted. The results of this paper can be used to generate an instantaneous surface precipitation warning system if the GPS station data is available online.

Lack of uniform light radiation on the objects, reduces the amount of contrast in the images and makes it difficult to extract image features. This problem destroys information about the behavior, shape, size, pattern, texture, and tone of the effects, and compresses the image histogram in one or more specific areas. UAV images have been widely used in recent years due to their extensive coverage, high operating speed, use in hard-to-reach areas and up-to-date equipment. If drone images are correctly taken and pre-processed, they provide good accuracy for a variety of applications. The preprocessing is important since the image acquisition conditions cannot be changed in most cases so that the acquired images are contaminated with some distortions or errors which must be removed or their effect reduced to a minimum before any process. Improving the exposure in the image, which increases the amplitude of the histogram, can highlight features with similar gray-scale values, and this is useful in identification.

In this study, two aerial images have been used with a variety of vegetation, soil and man-made features using Storm 2 hexacopter drone in Simorgh city (Kiakla) in Mazandaran province with longitude and latitude 52⸰ 54' 1'' and 36⸰ 35' 49''.  At first the SMQT algorithm is applied to the input images. So the bits number of the input image is calculated to determine the number of transmission levels. Then with rgb2gray command creates a gray image of the original image. The overall average of the image is calculated and the DN of each pixel is compared to the average. If the DN is greater than the pixel value, the number 1 is assigned to the pixel, otherwise the number zero in another image is assigned to the pixel. The average calculation and segmentation of pixels based on the number of bits continues, each segmentation is called a transfer. Then, by converting the data from these divisions into values in the spectral range of the image, a new image is created. This image has higher radiometric resolution than the original input image but lower spectral resolution. For this reason, the image is fused. Global gamma correction is applied to the fused image. Finding gamma in the image, especially local gamma is time consuming and complex for programming and computing. Therefore, to increase the computing speed, a local gamma of 0.7 was applied to the whole image and then the first step processes are applied again and finally, the SSIM index is checked for image enhancement.

The SSIM value for input image 1 and 2 is 0.8372 and 0.8401 while this value before processing was 0.4352 and 0.4161. Examining the histogram of the images before and after processing, in all three bands R, G and B, shows the stretch of the image histogram in the range of 0 to 255. There is a decrease in the number of peaks and valleys in the histogram of the processed images. The density function for input and processed images shows that the more homogeneous the number of effects in the image, the greater the slope of the function graph. The value of the density function has increased after processing, which is due to the stretching of the image histogram. SSIM is used to validate the results in this study. The images have been visually improved significantly, but this is not enough for verification. The goal of quantitative quality recognition is to design computational methods that can accurately and automatically express image quality, which affects all the image pixels in the same way. The SSIM range is between (+1 and 0). The closer the measured value for an image to one, the better image quality will be. SMQT also has less computational complexity and less configuration. If the image of a light object is formed in a completely dark background (such as night shooting), this algorithm does not work in the background pixels. Examining the image samples taken from a complication at night, it was found that the black pixels changed color to purple after fusion. In order to optimize the algorithm, it is suggested to increase the efficiency of the algorithm by examining the spectral behavior of different features in different color spaces and integrating their effective components in image or feature highlighting or the use of plant or soil indicators. The fuzzy method can also be used for semi-shady areas. These improvements should also prevent complexity of computing by increasing efficiency.

Floods are natural disasters which occurrence causes annual great damage to people and environment around the world. So, specifying flood susceptible land is a necessity to reduce and control destructive impacts. Watershed management implementations could affect runoff volume and flood occurrence. The goal of this study is to apply the combination of Curve Number method and AHP in Arc-GIS to prepare flood susceptibility map and to investigate the role of biological measures in flood susceptibility of the region through this method and statistical tests.

For this purpose, Pardisan watershed located in the southern part of Qom city was selected. Ten factors layers viz. drainage density, slope, annual rainfall, distance from river, elevation, flow accumulation, SCS Curve Number, geo infiltration, geomorphology and previous floods were prepared and classified based on flood susceptibility in different scales. Then future Curve Number was determine with assuming the implementation of biological watershed management in different land uses such as rangeland, agriculture, garden and badland. In this study, AHP method in Arc-GIS was used to calculate pairwise comparison and determine the weight of each factor. Overlaying current and future Curve Number layers with nine layers using the weights obtained from the hierarchical analysis method led to the preparation of flood susceptibility maps for pre and post watershed management implementation.

Geo infiltration map showed the proportion area of “low”, “and “very low” infiltration classes were 4.46% and 16.87%, respectively while moderate and high infiltration classes were 39.75% and 38.92%. Slope map indicates that 0-2%, 2-5%, 5-15%, 15-35% and 35-60% classes comprise 29.87%, 35%, 30.11%, 4.88% and 0.14% of the studied area, respectively. In this region, South parts were steep whereas; north parts were mild. Distance to river is another factor classified in to four groups of 0-500, 500-1000, 1000-3000 and 3000-6500 meter with 38.86%, 24.32%, 29.63% and 7.19% of the region, respectively. Elevation classified map revealed 45.1% of the region were in 900-1200 meter range whereas; 36.4%, 14.8%, 3.6% and 0.1% were in 1200-1500,1500-1800,1800-2100 and 2100-2400 meter classes, respectively. As can be seen in rainfall map, 25.57% of the region was categorized in 140-160 mm rainfall class while 35.41%, 20.59% and 18.43% of the whole area were classified in 160-180,180-200 and 200-250mm groups. In the region, South parts have more rainfall volume than north. Also, flow accumulation map indicated that 96.5%, 1.97%, 1.07%, 0.24% and 0.22% were classified as 0-1500, 1500-5000, 5000-15000, 15000-25000, 25000-100000 values which high flow accumulation pixel range show high flood susceptibility. Drainage density map represents 10.38%, 14.36%, 56.88% and 18.38% of the studied area were grouped in 0-0.05, 0.05-0.07, 0.07-0.09 and 0.09-0.12 classes. Also, Curve Number (SCS) map for garden, cultivated lands, rangelands and badlands shows that 25.54% of the study area was classified as 15-35 CN value while 36.14%, 0.9% and 37.42% were categorized in 35-50, 50-65 and 65-80 classes before performing biological measures. After biological measures in different uses, 15-35 Curve Number values are observed in 36.6% of the area and 35-50, 50-65, 65-80 classes comprise 32.05%, 29% and 2.35% of the study area, respectively. The geomorphological map shows that the class with the highest score is visible in 68.96% of the area, while the classes with the lower scores are observed in 3.07, 18.34, 9.37, and 0.26% of the region, respectively. The past flood zoning map of the region also shows that 22.41% of the region exist in low susceptibility class, 36.15% of the region locates in the medium susceptibility class and 41.44% is in the high sensitivity class. For AHP approach, the calculated consistency ratio of this study was less than 0.1. Therefore; the compatibility between ten selected factors was acceptable. AHP results showed that the Curve Number factor has the highest weight percentage (27.44) whereas; the geo-infiltration has the lowest weight percentage (3.20). Comparison of flooding classes for pre and post water management implementation shows that high and medium flooding classes will decrease by 7.3 and 39.7% and low and very low susceptibility classes will increase by 22.18 and 24.82 %, respectively due to the implementation of biological watershed management measures. Also, Sign and Wilcoxon statistical tests indicated the existence of significance difference in flood classes’ for pre and after implementing biological watershed management.

Flood susceptibility map provision is a necessity in arid and semi-arid regions due to insufficient vegetation cover. The results of this study indicate positive effects of biological watershed management in decreasing flood vulnerability. These findings can be considered for future planning of the region and help watershed managers for optimal utilization of water and soil resources and reduction of flood damage.

The tropopause is a thin layer separating the stratosphere from the troposphere and is often characterized by a large change in the thermal, mass and chemical structure of the atmosphere.Compared to global studies on the tropopause and its various features, studies conducted in Iran are very few and the methods used are often less inclusive or the length of the statistical period is limited. For this reason, and considering the importance of the tropopause and its effect on exchanges between the troposphere and the stratosphere, and also due to the lack of information about it in Iran, accurate knowledge of the height of the tropopause in the country using more reliable data sources is a fundamental necessity. To calculate the tropopause, we used daily temperatures of ECMWF reanalysis datasets from January 1979 until December 2018. Gridded data witha spatial resolution of 0.25*0.25 were used. In vertical levels, we used 10 standard isobaric surfaces from 700 to 50 hPa.

The location of the tropopause thermally and dynamically was defined. According to the WMO (World Meteorological Organization), the tropopause is defined as the lowest level at which the lapse rate decreases to 2°C/km or less, provided that the average lapse rate between this level and all higher levels within 2 km does not exceed 2°C/km.In this study, this index was used to identify the tropopause.In this study, to identify the factors affecting the tropopause, the relationship between the tropopause and spatial variables (latitude and longitude) and altitude was evaluated by general and partial correlations.

The results of this study showed that in the months of cold season, the tropopause pressure level on Iran is followed by latitude, and the tropopause height decreases with increasing latitude, but in the months of the warm season (June, July, and August), the tropopause pressure level is different from the months of the winter season.In these months, the changes in the tropopause pressure levels do not follow the latitude; on the Zagros and Kerman heights, the tropopause height is at its lowest, while the highest tropopause elevation is in these months at higher latitudes than in other months.The temperature of the upper and lower levels of tropopause also showed that the temperature of the lower levels of the tropopause in all seasons was below the temperature of the upper levels of the tropopause and the temperature of the two levels changed with the changes in the levels of tropopause pressure in different months.The study of low and high levels of tropopause showed that during the cold season, the temperature of the two levels around the tropopause, following the tropopause pressure levels, follows the latitude, and with increasing latitude, temperature increases in the two levels around the tropopause.In two studied seasons, the lowest temperature of the two levels of the tropopause is consistent with the highest level of the tropopause, but the highest two-level temperature is only consistent with the lowest tropopause pressure level during the warm season months, and in other months, this observation coordination failed.Investigating the thermal difference between two levels of tropopause showed that the temperature difference between the two levels of the tropopause in the warm season is more significant than that of the cold season, while in the cold season, the temperature difference in most regions of the latitude is obeyed. Slowly, the difference in temperature decreases with increasing latitude.

Examination of the characteristics of the tropopause and its related factors for summer and winter showed that in each season due to local conditions and changes in large-scale factors, the height of the tropopause changes, and therefore the tropopause in each season has completely different characteristics from the other season.Examination of the characteristics of the tropopause and its related factors for summer and winter showed that in each season due to local conditions and changes in large-scale factors, the height of the tropopause changes, and therefore the tropopause in each season has completely different characteristics from the other season.

Ecological agricultural zoning is a tool for proper assessment of land resources, better planning and management of cultivation in order to achieve sustainable agriculture.  Due to the importance of Khuzestan province in the country's agriculture and the strategic nature of wheat production, in this study, the zoning of wheat production potential in the DashtBagheh region of Khuzestan was done. Modern GIS technology is widely used in such studies to prepare land suitability. Separated agro-climatic zones can provide the ground for optimizing and expanding the growth of agricultural products (Balgaku, 2016). Cultivation of land can be attributed to the potential of the region in terms of food distribution and the availability of climatic factors. In a study using GIS and RS, Beijing region of China was divided into four regions in terms of winter wheat cultivation based on the weight of variables: appropriate, relatively appropriate, inappropriate and very appropriate (Wang et al., 2011).In another study evaluating arable lands such as wheat, barley and sunflower in Spain, environmental factors, topography and soil including altitude, slope, soil texture, temperature, rainfall, day length and the impact of each on this The plants were studied and then combined with the above data by weighing each layer in the GIS environment and finally mapped the susceptible areas (Khan et al., 2010). Due to the importance of the subject, the aim of this study is to use fuzzy methods and multi-criteria decision models (Analytic Hierarchy Process (AHP)) in order to identify suitable areas for wheat cultivation in Bagheh plain of Shousha city in Khuzestan province. It is worth mentioning that in this study, the most important parameters affecting wheat cultivation before entering the model were selected using statistical methods, which distinguishes it from previous studies.

Climatic characteristics included average, minimum and maximum temperatures as well as annual rainfall. Also, environmental factors including topographic characteristics (slope) and soil characteristics (chemical and physical) were considered. Soil characteristics were determined from the data of 96 soil profiles obtained from semi-detailed studies in the region. Zoning of different soil characteristics and climatic variables was done the inverse distance weighting (IDW) method.  Then, using membership functions, a fuzzy map of each of the effective parameters in determining the areas prone to wheat cultivation was prepared.  Then, using the Analytic Hierarchy Process (AHP) model, the weight of each layer was determined and finally, in the GIS environment, a land suitability map was prepared for wheat cultivation. In this study, linear membership functions have been used. This function has four parameters that determine the shape of the function. Trapezoidal, triangular, S-shaped or L-shaped membership functions can be defined by selecting appropriate values for different states (Carter and Grime, 1994). Weighing to the layers was done to prepare the final map of land suitability. The weight parameter is an important parameter for relating the factors used in land suitability. Because each of the characteristics has a different effect on wheat cultivation, weighting was done using AHP method.the AHP is a method that makes it easy to weigh parameters. AHP relies On a pairwise comparison of each of the parameters. Each of the factors is in the range of 1 to 9 according to the importance of determining the suitable areas for wheat cultivation, according to Table 2.

To prepare an interpolation map for each input data was used IDW method. The accuracy of the IDW method in mapping each of the variables showed that the climatic parameters have higher accuracy than the soil variables. Based on the evaluation statistics, the highest and lowest accuracy in climatic variables were obtained for the mean temperature (R2 = 0.99) and maximum temperature (R2 = 0.96), respectively.  However, the highest interpolation accuracy in the studied soil properties was related to the percentage of exchangeable sodium (R2 = 0.81) and the lowest accuracy was observed in the interpolation and zoning of soil clay.  The results of the AHP method showed that the greatest importance in preparing the land suitability map is related to rainfall with the highest weight and the least importance is related to the slope with the lowest weight. The results showed that the western part of the study area is suitable for wheat cultivation based on soil, climate and topographic characteristics of the area.  It occupies about 46% of the total area of the study area (4220 hectares) and parts of the south and north of the study area have the most unsuitable conditions for wheat cultivation.

In this study, suitable areas for wheat cultivation were studied using the fuzzy AHP method in the GIS environment. For this purpose, the zoning map of each parameter was first determined using the IDW model method. Then, using membership functions, a fuzzy map of each of the effective parameters in determining the areas prone to wheat cultivation was prepared. Then, using the AHP model, the weight of each layer was determined and finally, in the GIS environment, a land suitability map was prepared for wheat cultivation. According to the results, it is clear that this method has high accuracy in determining areas prone to wheat cultivation.