نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 119 (پاییز 1400)

Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, identification of thermal islands, monitoring of urban vegetation, and assessment of air pollution are just a few examples of areas related to urban management that remote sensing technology is the basis for indirect measurement of the related quantities. Maps of urban structures such as building blocks are commonly used in crisis management, urban design, and urban development studies.
 Materials

In this study, the production of urban building block maps using Sentinel 1 and 2 satellite images has been conducted. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index ( NDBI ) for three consecutive months, the slope feature derived from the 30-meter Shuttle Radar Topographic Mission (SRTM)Digital Elevation Model of the study area, along with two Vertical – Vertical (VV) and Vertical - Horizontal ( VH ) polarization in both ascending and descending orbits, form the set of input features.

The proposed method of this paper relies on the use of a generalizable trained classifier. Initially, the classifier is trained in 2015 using training samples obtained from a new rigorous refining process using different remote sensing and spatial products. This rigorous refining process uses a reference urban map of 2015. In the first step, the corresponding areas related to the ways and roads are removed using the OpenStreetMap data layer. Areas suspected of vegetation with NDVI greater than 0.2 are then discarded. Also, due to the high backscattering of buildings in Synthetic Aperture Radar images, areas with a value less than the average backscattering coefficient of the remaining areas are eliminated. Finally, the residual map is refined using the Mahalanabis distance and the Otsu automatic thresholding method. The trained classifier is then used to generate a map of building blocks at similar time intervals for the three target years (2018, 2019, and 2020). Due to the diversity of texture and density of building blocks in the metropolis of Tehran, the proposed method has been evaluated in this area. Due to the concentration of political, welfare, and social facilities, Tehran has experienced more unplanned and irregular expansion and urbanization than other cities in Iran, which has lead to changes in buildings and constructions. Also, due to the availability of free satellite images and various online processing operations, the Google Earth Engine platform has been used in this study. The performance of three different classifiers including Random Forest (RF), Minimum Mahalanabis Distance (MD), and Support Vector Machines (SVM) are examined in this process. In order to evaluate the proposed method, reference samples obtained from visual interpretation of high-resolution satellite images (Google Earth) in all three target years have been used.

The performance of the aforementioned classifiers has been investigated using 3 different criteria: overall accuracy, user accuracy, and F-score of building blocks. The RF method with an overall accuracy of over 93% in all three target years has shown the best performance. The SVM method ranks second with an accuracy of about 91% every three years. However, the MD method with an overall accuracy below 85% in all three target years has not performed well.

The results show better performance of the RF method in all three target years with an overall accuracy of over 93%. It should be noted that the MD classifier with higher user accuracy than other methods, has shown better performance in detecting the class of building blocks. However, the RF method is the best classifier in terms of the user accuracy of the background class. The effect of using two VV and VH polarization and also the slope derived from the SRTM Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a more than 10% reduction in overall classification accuracy.

Based on calculated overall accuracies which are above 80% in the majority of investigated cases, two different results can be concluded. First, the trained classifier has shown good temporal generalization and has achieved acceptable accuracy in the target years. Second, due to the different collection processes of training and evaluation data, the proposed rigorous refining method for the preparation of training data has shown good performance. The effect of using two VV and VH polarization and also the slope derived from the SRTM  Digital Elevation Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a tangible decreasein overall classification accuracy.

Nowadays, UAV photogrammetry has become one of the most effective methods of collecting spatial data according to the factors time, cost, quality and variety of outputs among terrestrial and aerial mapping technologies. Because the quality of a UAV photogrammetry products depends on the network design parameters setting according to the existing conditions and limitations, therefore, awareness of the behavior and impact of network design parameters on the quality of 3D reconstruction to achieve optimal quality of outputs is a very important issue. However, due to the time-consuming and the high cost of doing this study with huge real data, comprehensive research has not yet been conducted to measure the behavior of the effective parameters in network design and 3D reconstruction. There are various parameters include camera field of view, positioning error and imaging tilt in flight navigation, flight altitude and designed ground pixel dimensions, amount of sidelap and overlap images, image observation noise due to image quality, aerial triangulation error, in the process of preparing the map from aerial images, which is known as the most important parameters of UAV photogrammetric network design. In this paper, the simulation method is used to investigate the effect and behavior of the above parameters on the quality of three-dimensional reconstruction. 

In the proposed method in MATLAB software environment, from a point with known 3D coordinates, using the collinearity equations and the value set for the network design parameters and their standard deviation according to the reality and experience of the expert, the imaging is done in a simulated manner. Then, by applying random and systematic errors on the visual observations and aerial triangulation parameters, the collinearity equations of the photographic observations form the desired point and using the least squares method of error in solving nonlinear equations, three-dimensional reconstruction, and quality are performed, then it has been evaluated by the Monte Carlo method. To achieve the results with high reliability, the quality of three-dimensional reconstruction is evaluated in five modes, respectively, ideal, excellent, good, average and bad, according to the expert opinion in setting the values of each parameter.

The results of this study show, most effective parameters in the quality of three-dimensional reconstruction in ideal conditions are camera instability, error of exterior orientation parameters and image quality, respectively, which gradually give way to parameters of flight altitude, imaging coverage and camera field of view in bad conditions. The results of the flight navigation error show, increased imaging platform instability has no significant effect on the average accuracy of 3D reconstruction, however, the accuracy changes in different places increase up to 20% due to the heterogeneity of the coverage and the visibility of different parts of the earth in the video network. The results also show that with increasing geometric instability of the non-metric camera, the accuracy of 3D reconstruction decreases linearly, in this regard, the imaging in bad conditions and the quality of the camera, the slower the reduction speed. It has also been shown that with increasing image observation error, which depends on image quality, the accuracy of 3D reconstruction decreases linearly. The results of the study of aerial triangulation parameters show that the three-dimensional reconstruction error increases linearly with increasing tie point matching error. In addition, as the focal length increases in the fixed flight altitude mode, the horizontal accuracy increases in proportion to the inverse magnification, and as the focal length decreases, the altitude accuracy decreases linearly, in the fixed ground sampling distance (GSD) mode, the horizontal error of 3D reconstruction is slowly reduced to 20%, while the height error increases with increasing height and decreasing the geometric resistance of the network by a factor of half magnification. The results also show that unlike traditional photogrammetry here, with increasing flight altitude, the horizontal and altitude errors of the 3D reconstruction increase linearly. The results of the study of the parameters of sidelap and overlap images show that the sidelap and overlap images can change the surface error up to 10 times and the height error and complete three-dimensional reconstruction up to 5 times. 

This study, while introducing the effective parameters in three-dimensional reconstruction by UAV photogrammetric method, has investigated the behavior and effect of these parameters on the quality of three-dimensional reconstruction in the simulation environment. This means how the quality of the reconstruction changes with minor changes to each of the parameters from half to twice the standard mode. Therefore, the closer this simulation is to reality, the more practical the results will be. Naturally, this complicates the simulation and increases the computational volume. Although this simulation is not entirely consistent with the actual situation, it can provide a kind of behavioral measurement of the parameters that serves as a complementary research to routine try and error investigations.

The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems. In generally, global waters are classified into two types of water (the first case and the second case). In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However Case-2 waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter and Total suspended matter. Coastal wetlands are considered as the Case-2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities.  Phytoplankton are floating or scattered single-celled algae that travel primarily through water waves. Chlorophyll-a considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale. The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll A based on blue and green bands (OC2) in Landsat-8 and Sentinel-2 data, evaluating the results using Field data and finally the time series mapping of chlorophyll-a concentration.

In this study, Landsat 8, Sentinel 2 satellite time series data and field data collected from the study area were used. First, the satellite images used in ENVI 5.3.1 software were converted to Surface Reflectance and Top of Atmosphere Reflectance. Then, MATLAB 2018a software was used for image processing and coding. to estimate the chlorophyll-A concentration, the bio-optical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results two statistical parameters R2 and RMSE were used.

Based on the analysis of field data, the concentration of chlorophyll-A in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere Reflectance Sentinel 2 and Landsat 8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat 8 and Sentinel 2 satellites systems. The OC2 algorithm had amounts R2 (0.91 and 0.64) and RMSE (0.13 and 0.33) in Landsat 8 and Sentinel 2 Surface Reflectance data, respectively, while Landsat 8 and Sentinel 2 Top of Atmosphere Reflectance data had amounts R2 (0.12 and 0.53) and RMSE (0.45 and 0.51), respectively. The time series of chlorophyll-A concentration estimated using surface reflectance data (Landsat 8) corresponds to the natural conditions of the region, However, the time series of chlorophyll-a concentrations using the surface reflectance data (Sentinel 2) during the seasons estimated the chlorophyll concentration to be uniformly and downward. The reason for this poor performance in the Sentinel 2 is the lack of sufficient field data for calibration.

In this study, we tried to evaluate and compare the reflectance algorithms (Landsat 8 and Sentinel 2) in the OC2 algorithm. Preliminary results indicate that the type of satellite data used (surface reflectance and Top Atmosphere reflectance) is of great importance for entering the OC2 bio-optical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to high-atmosphere reflectance data. In general, the results showed that 10 field data is enough to calibrate with Landsat 8 data, but for Sentinel 2 data, more than 10 numbers field data must be calibrated to obtain a good result.

   As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal and spatial scales, and thus a complete description of LST require measurements involving spatial and temporal frequencies. Hence, climatological, meteorological, and hydrogeological studies require having access to wide scale information about spatial changes of air temperature. Since the LST product of SLSTR uses linear split-window algorithm, the present study has used nonlinear split-window algorithm to estimate LST in Sentinel-3 images. Linearity of the radiation transfer equation in linear algorithm and some approximations used in split-window algorithms (such as transfer approximation as a linear function of vapor value) result in considerable errors because of which nonlinear algorithm is used in the present study. Using linear split-window algorithm to estimate LST in tropical climates also leads to a high level of error. The present study seeks to estimate LST using a nonlinear split-window algorithm and data retrieved from Sentinel-3 in different seasons of 2018 and 2019. The results are also evaluated using temperature product of MODIS and SLSTR.

   A time series of sentinel-3 images retrieved from 2018 to 2019 was used as research data. Data were collected by Sentinel-3 SLSTR sensors operated by the European Space Agency (ESA). Obviously, images shall be radio-metrically corrected before calculating physical land surface parameters such as temperature, emissivity, reflectance and radiance, albedo, and etc. To reach this goal, it is necessary to omit or minimize the effect of atmosphere, epipolar geometry of sensor, sunlight, topography, and surface characteristics while estimating surface parameters in these images. The current study seeks to estimate LST applying a nonlinear split-window algorithm on Sentinel-3 data collected during different seasons of 2018 and 2019 and to evaluate the results using temperature product of MODIS, SLSTR, and in-situ data. Pearson Correlation Coefficient and Root Mean Square Error (RMSE) were also used as relative and quantitative criteria to evaluate the accuracy of the proposed method and determine the deference between temperature calculated by the proposed method and temperature product of MODIS and SLSTR sensor. Hence, four frames of LST product collected by MODIS, and SLSTR in April, June, and October, 2018 and January, 2019 were used to evaluate the proposed method.

   The proposed method was also indirectly evaluated using temperature products of MODIS and SLSTR sensor. Applying parameters of mean and root mean square error, the evaluation has shown that the results obtained from the proposed method in the one-year reference period were more similar to the results obtained from MODIS sensor. Comparing nonlinear Split-Window algorithm and MODIS products, RMSE ranged from 1.21 to 2.46 and the highest and lowest accuracy belonged to winter and summer, respectively. Comparing this algorithm with the SLSTR product, RMSE ranged from 0.76 to 2.24 and the highest and lowest accuracy belonged to winter and summer, respectively. Proper performance of the algorithm in winter is due to the relative balance of atmospheric water vapour in this season. Comparing nonlinear modelling of atmospheric water vapour in the non-linear algorithm of a Split-window and the linear algorithm in SLSTR and MODIS products, the small difference between temperature calculated by the algorithm and the products can be justified. However, due to temperature fluctuations in summer, results obtained by the proposed method were not reliable enough compared to both temperature products. Generally, results obtained from the proposed method showed a higher correlation with the temperature product of SLSTR sensor, which is due to the similar spectral bands used in calculating the surface temperature. Relative comparison of the Split-Window and the MODIS product’s nonlinear algorithm showed a coefficient of determination ranging from 0.76 to 0.96, while comparing this algorithm with the SLSTR product showed a determination coefficient of 0.80 to 0.98. Comparing temperature obtained from the nonlinear Split-Window algorithm with SLSTR and MODIS temperature products, the proposed algorithm was relatively stable no matter which season was taken into account.

   The present study seeks to estimate Land Surface Temperature using a nonlinear Split-Window algorithm and Sentinel-3 data collected in different seasons. Values obtained from the algorithm were validated using in-situ dataset retrieved from the meteorological station. They were also evaluated using temperature product of MODIS and SLSTR. To increase the accuracy level, temperature product of MODIS and SLSTR were also evaluated and compared with the in-situ dataset and provided good results. Generally, there is a significant difference between temperature values estimated by the NSW algorithm for different seasons especially summer. However, a similar trend was observed in temperature changes reported by SLSTR and MODIS, and the proposed algorithm in different seasons of the study area. Although, the nonlinear Split-Window algorithm showed a higher accuracy in spring and winter, overall results indicated that the proposed method was relatively stable no matter which season was taken into account. It can be concluded that LST estimation with nonlinear Split-window method and Sentinel-3 satellite data has an acceptable level of accuracy and thus, can be used in large scale environmental crises such as climate changes.

The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned in various instructions (Habib et al, 1999; Shiea 2018; Saeedinia 2004) but also makes it possible to analyze with comparing the existing conditions of the city with the standards. There is some research to examine the current situation of the city. Most of these studies have used statistical methods (Hosseinzadeh et al. 1399; Omidipour et al, 2017; Mohammadnejad et al. 2012). A few of them have utilized data mining methods, but none of these studies examine existing patterns between one type of land use with other land uses. In addition, the method used in this research is a new method that tries to use the capabilities of association rules and decision trees in exploring co-located patterns by combining these methods.Therefore, considering the importance and necessity of addressing this issue, the purpose of this research is to explore the current situation of urban land use by using data mining methods to discover the current patterns in the location of land uses in the vicinity and at different distances. Finally, providing rules derived from these models may help planners and managers to understand the current status of land use appropriately and improve urban land-use plans by utilizing them in combination with standards and rules based on expert knowledge.

Spatial association rules:Association rules discover the laws of interdependence between the data of a large database. In other words, patterns that are frequently repeated in the data set are identified and used to explain the rules of dependence (Han & et al, 2011: 54; Li 2015). The rules of the association in which one of the propositions in the premise or sequence contains a spatial relation are called spatial association rules (Geissen & et al, 2007: 277-287, Mennis & et al, 2005: 5-17).  Decision Tree: The decision tree is one of the most powerful and common techniques for classification and prediction. Among the algorithms used to construct the decision tree, the most important is the C5 algorithm which is the developed ID3 algorithm.

A n*l transaction matrix is generated. Where n is the number of available features and l represents the number of types of land use studied, which is 19 in this article. The elements of this matrix can be zero or one. To fill the transaction matrix, we first consider the distance and apply buffer analysis for all the features in the land use layer. Then, for each feature, we intersect the buffer layer of that feature with the land-use layer and extract all the features that appeared at the intersection. Arc GIS software was used to perform spatial analysis.Then, to extract the current rules of land use in the urban environment, the a priori algorithm is selected as one of the association rules algorithms, and the C5 algorithm is selected as one of the decision tree algorithms.In this research, the user data of neighborhood 4, district 5 of Tehran Municipality, including 1065 property plots, were used.

In this step, the proposed model for deriving the rules of land use dependence based on the current situation of land use in the study area is implemented step by step and the results are presented. According to existing standards, three distances are considered to extract spatial rules with an apriori algorithm. After extracting the rules, they are compared with the values of approved standards in urban land use planning. Vicinity and compatibility are examples of indicators in common standards for locating and determining land use for the land. Using the extracted rules, the indicators are examined. Due to the lack of extraction of some rules by association rules, for example, not extracted rules related to therapeutic land uses within 300 meters from residential land uses, we use the decision tree algorithm to extract related rules in more detail. The graphs obtain from the decision tree shows which land uses are effective for predicting and categorizing specific land uses, based on the current status of the land uses located in the case study area.

The purpose of this paper is to data mining the current status of urban land uses to extract the rules of neighborhood and proximity of different land uses. Using the proposed model in this article, it is possible to extract the existing rules of land uses in detail and as well as to evaluate its compliance with conventional standards and criteria in urban land use planning.

Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models.üArea resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground.
ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible.
The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1- Forward 2- Vertical 3- Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan).

The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images.

In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0.47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i.e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7.46, 8.77, 3.66 and 6.8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1.9 meters and in the transverse direction of 2.3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images.

The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i.e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.

In recent years, the development of the country in the space industry and the ability of building, launching and infusion of satellites in the lower orbit has put the limited number of countries with such technology. In order to complete the entire cycle of the space industry, the satellite navigation and control, which has been neglected since the beginning of the movement of space science in the country, has to be considered specially. The attitude determination in one sentence is the application of a variety of techniques for estimating the attitude of spacecrafts. In dynamic astronomy, the attitude determination is the the process of controlling the orientation of an aerospace vehicle with respect to an inertial frame of reference or another entity such as the celestial sphere, certain fields, and nearby objects, etc. A spacecraft attitude determination and control system typically uses a variety of sensors and actuators. Because attitude is described by three or more attitude variables, the di®erence between the desired and measured states is slightly more complicated than for a thermostat, or even for the position of the satellite in space. Furthermore, the mathematical analysis of attitude determination is complicated by the fact that attitude determination is necessarily either underdetermined or overdetermined.

Attitude determination typically requires finding three independent quantities, such as any minimal parameterization of the attitude matrix. The mathematics behind attitude determination can be broadly characterized into approaches that use stochastic analysis and approaches that do not. This paper considers a computationally efficient algorithm to optimally estimate the spacecraft attitude from vector observations taken at a single time, which is known as single-point or single-frame attitude determination method. There have been a number of attitude determination algorithms that compute optimal attitude of a spacecraft from various observation sources (known as the Wahba’s problem), and each of the methods has advantages and limitations in terms of accuracy and computational speed. The most popular are: the very important ˆq-Method, the most popular TRIAD and QUEST, SVD, FOAM, and ESOQ-1, the fastest ESOQ-2, and many others approaches introducing new insights or different characteristics, for instance, the EAA, Euler-2, Euler-ˆq, and OLAE.

Since star detection algorithms can provide more than two stars, the star detector field of view often consists of two or more stars that are passed through the identification algorithms will be detected, those star vectors that have measurement errors can be compensated by using more than two stars. Methods such as the QUEST algorithm usually optimize an error function to the minimum optimal. In fact, the QUEST algorithm estimates the optimum specific eigenvalue and vector for the problem described in the Q_method method without the need for complex numerical calculations. The fact that the QUEST algorithm retains all the computational advantages of a fast definitive algorithm while maintaining the desired result efficiency underscores why it is typically used.

Simulation results showed that the traid and quest algorithms with shuster method attitude determination algorithm can be an efficient alternative over the eight tested algorithm in terms of computational efficiency for singularity-free attitude representation.

Due to the kind of its usage in a relatively long period, the analysis of temperature levels of modern cities is among the most important subjects that can be considered in the field of geography and environment, and its results can be used in promoting the science and solving the problems of today’s societies. The effect of temperature on climate, especially in metropolises, is one of the crucial indexes of this procedure. The rise in the land surface temperature, which is an indicator of the heat intensity, is among the important elements for identifying the changes in weather. The emergence of heat in cities is one of the most known forms of such changes. Urban heat islands are indicated by a temperature inversion and annoying temperatures throughout winters and summers. The temperature of some cities or urban areas has remarkably grown compared to the suburbs or rural areas around them. This phenomenon, called urban heat island, has caused numerous problems. The term “heat island” was proposed by Havard for the first time almost a century ago in 1833 (Sook, 2004: 10). Afterward, numerous studies were carried out in the large and industrialized cities of the world, whose results demonstrated that civilization has exerted significant changes in the meteorological parameters and properties of the ground surface, and consequently, remarkable variations in local climate (Mousavi Baygi et al., 2012: 36).

The present research is aimed at identifying the places with high heat, which have created the known thermal patterns in the city of Arak in Iran. Assessment of the spatial-temporal variations of the urban heat islands can be used as a critical component in the management strategies of natural resources and environmental changes, whose results can be useful for environmental, regional, and urban planners.

The studied area, Arak, is the capital of the Markazi Province in Iran, with an area of 304.8 km2 at 1755 m above mean sea level. The city has temperate weather tending to cold and semi-arid. According to enactment in 2011, Arak has five municipal districts.
The research method was analytical-statistical, and an effort was made to evaluate the relationship between land surface temperature and land cover of the city. In order to evaluate the development of hot places in Arak and determine its urban thermal patterns and heat islands in the long term, the data of the satellite images of the Landsat scanners 4, 5, 7, and 8, including the data of the TM scanners of Landsat’s 4 and 5, Landsat 7 (+ETM), and Landsat 8 (OLI/TIRS), during the period 1985-2017 were used. These images include two sets of reflective spectral and thermal bands. The thermal bands were used to identify the surface temperature and thermal islands, and the reflective bands were employed to apply the indexes of image processing. The data of the TM, +ETM, and OLI/TIRS scanners were provided in the bands 6, 8, and 11, respectively. The data of the thermal band 6 of Landsats 5 and 7 with wavelengths of 10.40-12.5 micrometers and the band 10 of Landsat 8 with wavelengths of 10.60-11.19 micrometers were used to calculate the surface temperature distribution patterns of Arak. The bands 3 and 4 of Landsats 5 and 7, along with bands 4 and 5 of Landsat 8, were also utilized to calculate the NDVI index (NASA, 2014). In the global imaging system, the images of the area of Arak exist in the 165th And 36th row.
Generally, the following steps were taken to analyze the urban heat islands of Arak:
Calculation of LST and spectral radiance
Conversion of the calculated radiation to Kelvin temperature
Calculation of the temperature levels of five districts of Arak
Calculation of the density percentage of the fourth level of temperature (hot points of the city)
The minimum, maximum, and average temperatures of Arak
Calculation of normalized difference vegetation index (NDVI)
Calculation of the urban thermal field variance index (UTFVI)

Evaluation of the land surface temperature changes and patterns
The analysis of the vegetation variations demonstrated that depending on different uses of urban lands, vegetation is in accordance with the temperature level. Generally, the low temperature in the southwest of the city, which was observed in the land surface temperature maps, is caused by the gardens of the cities of Senejan and Karahroud. The eastern and southeastern parts of district 1, which has industrial uses, streets with heavy traffic, and accumulation of uses, and the north of the city, i.e., the north of district 3 with the accumulation of residential uses and heavy traffics, have higher temperatures. Generally, during the studied period on vegetation, all areas having this use, except for the northwestern part, had considerable changes. Most of the vegetation in the studied period was concentrated in districts 4 and 5, which included the gardens of Senejan and Karahroud. However, other parts of the city, including the northwest and, to some extent, the city center and district 1, whose vegetation includes several parks and green spaces, show decreasing changes in temperature. Based on the results obtained from evaluating the urban thermal field variance index (UTFV) of Arak, using 20 land surface temperature (LST) maps and normalized difference vegetation index (NDVI), obtained from Landsat satellite, (TM), (ETM+), (OLI/TRS), the very hot temperature level of Arak was widely observed mostly in the north, northeast, east, and southeast of district 1, north and northwest of district 3, west and southwest of district 2, and district 5.

The evaluation of the LST maps to identify the hot points and urban thermal patterns revealed that most of the hot points are located in the areas with idle lands in the suburbs. These lands are mostly observed in the recently developed areas of the suburbs, including districts 1 and 3. Inside the city, most of the hot places conform to the formation of thermal patterns close to industrial towns, streets with heavy traffic and high pollution, and residential areas with dense and urban decay. The largest area of the third temperature level is observed in district 1 due to the presence of industrial towns, dense residential towns, cultural and governmental organizations, heavy traffics in the streets, the northern and southern belts in the district, and idle lands in the north and east of the district. The presence of the industrial towns and factories in the city of Arak, especially in district 1, is one of the effective factors in increasing the heat and creating thermal patterns. The thermal patterns in district 1 had the highest intensity in 1988/09/08 and 2017/01/08 during the studied period.

   To achieve sustainable security in Countries where security regards as a main concern, must implement land use planning programs in order of priority from the border to the interior. In land use studies, geography as a context plays a main role in the realization of codified plans and programs. All three vertices of the golden triangle of land management mean; Man, activity and space are influenced by the natural and human geographical features of the study area. Border zone planning is a type of planning that integrates border development with security and defense, based on the geographical characteristics of border areas, by establishing a link between development indicators and security plans, introduces strategies for sustainable development of border areas, that are bound to each other. Security considerations are among those categories that have received less attention in macro-planning. In addition, awareness of the current situation is essential for any kind of careful planning for the development and progress of regions, especially in less developed provinces (Alipour et al., 2016: 159). The most important issues and problems in the formulation and implementation of planning in the country, include; Lack of attention to geographical-security considerations in locating the bases of law enforcement, border and military units, vital and sensitive centers and facilities, commercial, economic and communication uses. These issues have made political borders vulnerable to threats, military and terrorist attacks, border vulnerability to armed and opposition groups, border insecurity, dissatisfaction and conflict among border residents, poverty and underdevelopment, etc. in the country’s border areas.

   The method of this research is descriptive-analytical and data analysis has been done with a quantitative and qualitative (mixed) analysis approach. To analyze the data, the method of contextual and basic theory (foundation data) has been used. In terms of method, this research is descriptive and survey based on field work, using open questionnaire, closed questionnaire and using SPSS and MAXQDA analytical software and Arc GIS. In MAXQDA software, it was proved that border management indicators are effective in security management, implementation and execution of security plans along the country’s political borders. After classifying the extracted indices, to examine the factor status of each of the indices under the relevant components through factor analysis in SPSS software, factors were classified into three categories. In order to analyze the status of application of selected indicators in the northwestern borders of the country, a questionnaire was designed and referred to the expert community was statistically analyzed.

   Based on factor analysis; Thirteen border operational plans based on indicators Border planning was evaluated in the form of three factors that after reviewing the indicators: first factor; designing of ambush and anti-ambush operations in the border area is based on the shape of the land, the location of natural features in relation to the passages, the location of the escape routes and the connection points, the second factor; In the border monitoring and control planning, determining the location of telecommunication and communication systems in the region based on the situation of the repression points with enough view of the surrounding areas, the third factor; Determining the optimal routes for border patrols is based on the geographical realities prevailing in the border strip like land slope, distance to zero border, snowfall, flooding, etc., these three main plans were selected among the border operational plans influenced by border planning indicators in the northwestern borders of the country.

   By analyzing the status of application of border management indicators in the implementation of plans in the border areas of the studied provinces, which was based on the Likert questionnaire and referring to the expert community, the status of the provinces was determined based on calculations and statistical analysis. Then, by summarizing the mean of the indicators based on which three provinces were examined, the status of the provinces was compared and ranked. Based on the results of statistical analysis, the first place is Ardabil province with an average of 3.92, the second place is East Azerbaijan province with an overall average of 3.64 and the third place is West Azerbaijan province with an overall average of 3.61.

Floods are one of the natural hazards that cause a lot of financial and human losses every year. Flood zoning plans contain basic and important information in the study of development projects in the world, and before any investment or implementation of development plans, its review is on the agenda of relevant organizations. The Taleghan River has faced numerous floods over the years. However, no comprehensive studies have been conducted in this regard regarding the damage caused by the flood of Taleghan River and its zoning. The town of Taleghan, which is the main population settlement in the region, passes through and the construction of residential and commercial buildings along the river is expanding. Taleghan has identified the areas most affected by flood risk and using ARC GIS software to determine these areas in the form of a zoning map. Preparation of flood risk zoning map using FuzzyVIKOR method and by determining the weight through critic for 7 effective criteria in evaluating flood zones including: altitude, slope, slope directions, land use, geology, distance from waterway and average rainfall, Done. The results of this study, which has been prepared in five categories, show that 83% of the total area of the basin includes safe or low-risk areas. However, 17% of its lands have moderate and high flood risk, which includes areas around the main waterway and sub-waterways with residential and agricultural uses in the basin. Therefore, the need to respect the Taleghan River in low and medium slope lowlands, in the development of rural urban uses in the region, in order to reduce floods, should be implemented.

The present research is descriptive-analytical in terms of method and applied research in terms of purpose. Many factors must be considered in flood zoning, each of which has a different degree of importance. In this study, based on previous experiences, the factors that had the greatest impact on flood occurrence in the Taleghan watershed were selected in the VIKOR Fuuzy model. The data used in this study include sea level elevation, slope, slope directions, average rainfall, distance from waterway lines, land use and formation, which were used to determine areas vulnerable to floods.Some part of the required data including Digital Elevation Model (DEM), land use map of the region and map of geological formations be collected in raw form with a shape file format in the scale of 1: 250,000 from the rangeland and watershed management department of the Faculty of Agriculture and Natural Resources, University of Tehran. Elevation, slope and geographical aspect maps were extracted from DEM 10 m. The layer of waterways, including permanent canals and rivers, was provided by the National Forests, Rangelands and Watershed Management Organization.The map contains same rain line is received from the Meteorological Organization. the raster map of the average precipitation of the basin it was prepared based on the information of the precipitation rain lines and the statistics of rainfall data related to 5 stations of Dizan, Ciancranchal, Gotehdeh, Jostan, Glird, Armut and Zidasht, using the Interpolation technique. The criteria were normalized after preparing the maps (GIS READY) and applying the required edits such as defining the unit coordinate system for the maps, eliminating the errors that occurred during digitization and reducing the descriptive data by adding a new column to the related descriptive information table. All the maps were converted from Vector format to Raster, after the normalization step, and then the layers were weighed through the Critic method. Using the VIKOR model and the weights that obtained by the Critic method, which were calculated in Excel software, the value of the Vikor index (Q) was obtained for each of the options (pixels). Finally, the ultimate map of flood risk zoning in Taleghan watershed was obtained by assigning the values of Vikor index (Q) obtained from the previous step to each of the relevant points (options), by ARC GIS software.

The results of flood zoning map show that 83% of the total area of the basin includes safe or low risk areas. However, 17% of this area has a moderate and high flood risk, which mostly includes urban, rural settlements, orchards and agricultural lands, which shows the importance of paying attention to proper management in these areas. According to the obtained results, it can be said the distance from the waterway in Taleghan watershed has had a significant effect on the amount of flooding, so that by moving away from the main waterway and sub-waterways of the basin, the risk of floods and flooding is reduced. Becomes. The results of the terming flood risk zoning, show that 27 villages and settlements out of 68 villages in the region are in high-risk areas, including the villages of Eskan, Gotehdeh, Narian, Prachan, Mehran, Joostan, Nisa Olya , Hasanjoon, Jazan, and Mochan are at the highest risk.

Multi-criteria decision analysis methods in GIS have been proven to be a robust approach to generating risk maps with acceptable accuracy. Judgment of the acceptable feature of the model can be made using external information from real ground data. In this study, relatively high compliance with the final zoning map was obtained by examining the history of floods in the study area.

The snow cover is one of the quickest changing phenomena on earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and also, biogeochemical as well as human activities. Precise estimate of snow cover is regarded as one of the fundamental operations in precipitation. Thus, monitoring the snow-covered surfaces is of specific importance from the perspective of climatic, ecologic and hydrologic studies. Researchers believe that remote sensing data can lead to assess the snow-covered areas better than traditional topography methods. Therefore, nowadays, in efficient management of water resources, applying remote sensing data aims to achieve exact information on snow-covered areas operationally. Satellites are suitable tools to measure the mentioned areas since high snow reflection creates a good contrast with other natural surfaces except clouds. This research is conducted to compare the performance of Cornell functions of support vector and object-oriented Fuzzy operators in estimating the desired areas in Almabolaq Mountain, Asadabad.

The data used in this research are the bands with 10 m spatial segregation of 2B Sentinel satellite including bands 2, 3, 4 and 8 on 6th March 2020. To classify Cornell functions of support vector machine and compute their accuracy, ENVI software was implemented. The eCognation software was used to partition and categorize those with the same object-oriented Fuzzy operators. Separating similar spectral sets and classifying those with the same spectral behaviour are regarded as satellite information classification. In other words, categorizing the photo pixels, and allocating one pixel to one class or phenomenon are the mentioned classification. Support vector machine is one of the most common classifiers in learning machine, which divides data using an optimum separation super plate. One of the important advantages of support vector machine is the ability to deal with high dimensional data using almost less training samples for remote sensing applications. Objective analysis is an advanced technique of image processing which is used to assess the digital images and typical conflicts of basic pixel classification based on different methods. Traditionally, pixel-based analysis is done by available data of each pixel whereas object-based analysis considers a set of similar pixels called objects or image objects. It regards adjacent pixels with the same information value as one distinct unit called piece or segment. In fact, pieces are the areas produced by one or few homogeneous criteria in one or few dimensions of a specific space so that the pieces have extra spectral information in each band, mean, maximum and minimum amounts, variance, etc. as compared to single pixels. Combined object-oriented and Fuzzy methods provide the classification of image pieces with a specific membership degree. In this process, image pieces with different membership degrees are classified in more than one class and according to the membership degree, image piece classification is done leading to the increased final precision.

In the research, after preparing satellite images in SNAP software using Sen2Cor, radiometric correction was conducted on the images. To prepare the classification map of Cornell functions of support vector machine, TIFF satellite images were called by ENVI software. Using the shape file of the case study, the area cutting operation was done. Afterwards, two classes of snow and non-snow regions were created to pick up the training points and based on imagery processing, training points were specified for each class. To classify support vector machine algorithm, linear, polynomial, radial and sigmoid Cornell functions were applied and classification maps were separately produced. To draw the classification map of object-oriented Fuzzy operators, satellite images pre-processed in previous stages were called by eCognation software and then they were defined as a project. Afterwards, two mentioned classes were defined to do the classification process and for each class, the desired Fuzzy operator was determined. For suitable classification, it was done in various scales and weight coefficients of shape and compactness. Scale 75, shape 6.0 and compactness 8.0 presented suitable classification. After selecting the training samples, parameters of lighting, mean and standard deviations were chosen as distinct features of classes for object-oriented classification. Using the nearest adjacent neighbor algorithm, object-oriented classification was done for each of the Fuzzy operators. After drawing the snow-covered areas through Cornell functions of support vector machine and object-oriented fuzzy operators, the accuracy of classification was computed.

The results indicate that AND algorithm showing the logic share and minimum return value out of Fuzzy values is of the highest accuracy (98%) and to classify digital images, the object-oriented processing methods of satellite imagery enable more precision due to the data related to texture, shape, position, content and geometrical features as compared to Cornell functions of support vector machine.

Determining landscape changes and the impact of urban development requires analyzing land surface changes and identifying appropriate algorithms. And it cannot be ignored that traditional methods for examining land use change and land cover, such as land surveying, are generally time-consuming and costly and require special skills. In this regard, the advent of remote sensing techniques, GIS has enabled researchers, planners and city managers to have a comprehensive view of land and land use change over time at a lower cost. However, these tools alone cannot describe the main trends and patterns of the city and urban development; Therefore, a combination of land use metrics and development index was proposed, which, along with remote sensing and GIS, lead to more desirable and accurate results. As a result of the present study, with the aim of analyzing the structural changes of the landscape and urban development patterns of Mashhad city using multi-time satellite images during the years 2000, 2010 and 2020 has been done. Also, in this regard, the main research questions are as follows: 1- Which direction will the growth and development of Mashhad city from 2000 to the horizon of 2040? 2- What kind of growth has followed the expansion of Mashhad from 2000 to 2040?

The present study is descriptive-analytical in nature. Information was prepared and adjusted through Landsat TM satellite images of 2000 and 2010, OLI sensor for 2020. Before performing the operations related to image processing, radiometric and atmospheric corrections were used using ENVI5.3 software and the FLAASH method was used for atmospheric correction. The images were then categorized using the maximum probability algorithm. In this method, educational samples were used to classify the pixels. Markov chain model in TERSET software was used for prediction on horizons 2030 and 2040. Then the generated maps were entered into FRAHSTATS4.2 software to measure the metrics of the landscape. Also, the Urban Growth Type Outlook Development Index (LEI) was evaluated using GIS software.

According to the land use map prepared for a period of 20 years, land related to the city in this period for the city of Mashhad due to population growth and demand for land as a result of urbanization growth in recent decades has the most area changes. So that the area of these lands has increased from 7% in 2000 to 12% in 2020 and this shows a 5% growth in the land area of this land use during this period. Agriculture and gardens from 2000 to 2020 has had an increasing trend 1. Therefore, the area of this user has increased from 11% in 2000 to 17% in 2010 and this shows a 6% growth in the area of this user. But from 2010 to 2020, the area of agricultural use and gardens has been drastically reduced. As a result, the area of this user in 2010 is equal to 17% and for 2020 is equal to 8%, which indicates a 9% decrease in the area of this user. Desert land use has been declining over the period, with a 4% reduction in area. The use of rangelands has not changed much during this period. The analysis of metrics on the surface of the land for the horizon of 2030 Mashhad showed that the area of this city will not change. The number of spots will decrease, indicating that the shape of the city will become more cohesive over time. The index of the largest spot and the density of the margin will have a decreasing trend, and this indicates that the city will become more cohesive on the horizon of 2030. Landscape shape index will have a decreasing trend. Also, the analysis of metrics on the surface of the land for the horizon of 2040 Mashhad showed that the area of this city will not change. The number of spots will decrease, indicating that the shape of the city will become more cohesive over time. The index of the largest spot and the density of the margin will have a decreasing trend, and this indicates that the city will become more cohesive on the horizon of 2030. Landscape shape index will have a decreasing trend.

In examining the first question based on the growth and development of the city of Mashhad from 2000 to 2040, which direction will it be? According to the maps classified in a period of 20 years and the projected maps for the horizons of 2030 and 2040 for the city of Mashhad, it was determined that the most change is related to the city limits, so that in this period, the constructions and physical growth of the city have been in the northwest direction, and on the other hand, because the constructions are usually done on lands related to gardens and agriculture. In this part of the city, we are witnessing a decrease in agricultural lands and gardens, followed by an increase in urban areas. According to the map of 2020, agricultural lands and gardens in the southeast side still remain and one of the reasons could be the lack of development of the city in this direction. Also, in reviewing the second research question, what kind of growth has followed the expansion of Mashhad from 2000 to 2040? Findings showed that according to the urban development index and based on the numerical value given to the buffer, it was found that the development of Mashhad in the period between 2000 to 2040 is of the type of development from the edge of the city (edge-expansion).