نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 111 (پاییز 1398)

 Site selection for health centers and hospitals in proper locations and the allocation of population to them is an important issue in urban planning. The location and allocation of health and medical facilities including hospitals, have long been an important issue for urban planners that has become more complicated with the growth of population. Location and allocation of hospitals is basically planned to ensure the availability of proper and comprehensive health services as well as the reduction of the establishment costs. Improper planning of the health centers has created multiple problems for big cities in developing countries in recent years. In the present study, the Genetic Algorithm (GA), Hybrid Particle Swarm Optimization algorithm (HPSO), Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used for selecting proper sites of hospital and allocating the demanded locations to these centers in District 2 of Tehran. 

The main goal of this research is to compare and evaluate the performance of the Genetic Algorithm (GA) and Hybrid Particle Swarm Optimization algorithm (HPSO) for determining the optimal locations of hospital centers and allocating the population blocks to them. In order to limit the search space, the analyzing capabilities of the Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used to select the candidate sites satisfying the initial conditions and criteria. The locations of such candidate centers are the input of the optimization section. The accuracy of the entire process strongly depends on the selection of these candidate sites. Hence, in this paper, the Analytic Hierarchy Process (AHP) method has been used to select the candidate centers. Then, two optimization algorithms were applied in choosing six optimum sites from the candidate locations and allocating the population to them through minimizing the overall distances between the centers and their allocated blocks. In this study, to improve the Particle Swarm Optimization, a simple neighborhood search has been proposed for better exploitation of the elite particles. The main purpose of this neighborhood search is to increase the convergence rate of the algorithm without decreasing the random search. Since the neighborhood search has a specific definition proportional to each issue, and the issues of location and allocation are spatial issues as well, therefore, the geographic principle of appropriate distribution of the centers in space has been used to define the neighborhood search (the distance between the centers should not be less than a certain amount). In an elite particle, two centers with the lowest distance are selected and one of them is replaced by a new and randomly selected center. If such a change provides a better objective function, the newly created solution in the elite particle is replaced. To calibrate the algorithms parameters, a simulated data set has been used. Having proper values for those parameters, the algorithms were tested on the real data of the study area. 

Given the results of algorithms on real data, the performances of both algorithms are highly dependent on the initial population and the allowed number of iterations. In general, lower numbers of iterations and more populations brings better results than the higher iterations and lower populations. The results show that the Hybrid Particle Swarm Optimization (HPSO) has better performance than the Genetic Algorithm (GA). The convergence rate of the Hybrid Particle Swarm Optimization (HPSO) algorithm is faster than the genetic algorithm (GA), which can be attributed to the particle’s motion toward the best personal and global experiences. Furthermore, the proposed neighborhood search has caused the HPSO algorithm to converge earlier. To evaluate the repeatability of the algorithms, they were performed 40 times for both simulated and real data. Both algorithms have displayed high levels of repeatability, but the Hybrid Particle Swarm Optimization (HPSO) algorithm is more stable. However, the use of Genetic Algorithm (GA) on simulated data has shown more stability than its use on real data. For both the simulated data and real data, the Hybrid Particle Swarm Optimization (HPSO) algorithm performs faster than the Genetic Algorithm (GA).  

Simplicity and repeatability of the algorithm are among the important factors which are very significant from the user’s point of view. In this research, the HPSO algorithm has not only been repeatable and simple, but has performed faster than the GA. Therefore, considering these criteria, regarding the special case of this research, the HPSO seems to be more promising than the GA.

Maritime territories and quasi-territories are, in fact, continuation of territories underwater,formedbased on rules and principles governing political systems,international law and international relationsin maritime environment.Place making, territoriality, delimitation and demarcation of territories in the seas are performed based on geographical factors (particularly physical geography). As one of many different physical geographic factors, tideplays a decisive role in maritime delimitation and territoriality. It is considered as the basis upon which boundaries of different maritime territories and quasi-territories in different countriesare demarcated and delimited and formalnautical maps are drawn. Each country of the worldapplies a different basisfor determining low water lineand thisresults in many issues and challenges in maritime territoriality. Meanwhile, sea level has risen due toclimate changesand is expected to increase in the future. Thiswill also affect the above mentioned phenomenon, and may cause serious challenges for demarcation of the existing boundaries. Thus, the present study employs a descriptive-analytical method toinvestigate the role and significance of tides in maritime delimitation andanalyze the impact of sea levelrise on delimitationand maritime territoriality process. 

The present study is an applied research following a descriptive-analytical method. Related data was collected through library and internet-based methods and the research follows a qualitative method of analysis. Moreover, GIS and mathematical map calculator known as the “Raster Calculator” were used to draw the maps required for therise of sea levels. Based on the existing scenarios and their average values,the present study considers a two-meter rise for the sea level rise by the end of the 21st century. 

Tideis the most significant factor based on which baselines are drawn. Setting low water line as a fixed basisused for delimitation of maritime territories and quasi-territories is only possible ifcoastline is stabilized at one level or in other words at a definite plate during tide. This function is carried out by the tidal datum. Datum is the reference level based on which all depths andcorresponding elevations are plotted. Therefore, tides play a significant role in determining the Law of the Sea. On flat coastlines, baseline is determined based on the low water line. On dented and jagged beaches, the base points are also determined according to the same phenomenon. Moreover, some features such as the low–tide elevations and islands are also identified based on the datum used. Selection of low water line (as opposed to high water line) leads outer limits of the territorial sea and consequently other areas toward the sea. This will expandmaritime area under sovereignty and jurisdiction of different countries. The country which uses a lower datum will expand its marine sovereignty and jurisdiction. In Persian sources, terms such as the lowest tide line have been used mistakenly as a translation for the term “Low Water line” stipulated in Article 5 of the 1982 Convention. This is while lowest tide is only one type of low water lines, and though this concept plays the most important role in maritime territoriality, no clear reference has been mentioned for datum in the 1982 Convention. The convention stipulates thatdatum used in the official nautical charts published by different states is the tidal datum based on which normal baseline must be defined. Although the International Hydrographic Organization has proposed the lowest astronomical tide as the basis for determining the datum, there is a major difference between states in this regard and they use a variety of tidal base lines. However, coastal countries usually prefer to use the lowest datum. It seems that sea level rise, as the most important phenomenon resulting from climate change, has significant impacts on tides and boundaries delimitated based on tides. This is because low water line may retreat due to sea level rise, and as a result base points upon which baselines are drawn, along with marine territories and quasi-territories might also move closer to the land. However, states which have based their datum on higher averages oflow water will encounter fewer challenges caused by the retreating baseline compared to those that have selected the lowest low water line. However, evaluating a two-meter rise in sea level by the end of the 21st century shows that in different coastal regions of the world,the impact of sea level rise on low water line is not balanced and similar. Thus, given the spatial-geographical variations, only flat regions of the world will encounter submersion of coastlines and retreating low-water line.

Results indicate that sea level rise has dramatically changed tides and will challenge Political Geography of the Sea. However, a two-meter sea levelrise will severely affect tides in areas facing coastal retreat, and since normal baselines are drawn based on this geographic factor, a retreat in those baseline should also be expected. In case ofstraight baselines, if base points immerse due to two-meter rise in sea level, these lines will also retreat. Yet, this largely dependson the datum considered for drawing the baselines. On the other hand, the approach used bydifferent states, especially in areas in which maritime boundaries have been delimited, shows that low-water line drawn onformal maps is more referable than the actual low-water line and, therefore, maps can play a stabilizing role as the most important geographic instrumentin the future.

 Frost causes a lot of damage to the agricultural sector every year.From the meteorological point of view, when the temperature drops below a certain value, frost occurs. This threshold may vary from one crop to the other. Not much research has been done to predict frost using remote sensing technology. Most of the models used to predict frost have been provided by climatologists, geographers and meteorologists based on data collected at meteorological stations.The measurements at meteorological stations are at a point and the number of these stations are limited. Therefore, depending on the surface coverage and texture around the station, the air temperature would only be valid in certain and limited distance from the stations. On the other hand, satellite images have relatively acceptable spatial resolution specially for using in the environmental studies.This indicates the necessity of using remote sensing data in many occasions including frost prediction.This work tried to predict areas at risk of frost using the NEAT method in the state of Georgia, USA. For this purpose, the MODIS satellite data and the data collected in meteorological stations of AEMN network are used.  

The State of Georgia, in the southern part of the United States between latitude of 30o31’ to 35o north, and longitude of 81o to 85o53’ west with an area of 154077 square kilometers, was chosen for this case study.The reason for choosing this region was merely because of accessibility and availability of surface collected data mostly in cultivating and agricultural zones. In this study, data collected in 10 AEMN stations from 2005 to 2015 were used for modeling and evaluation. Also, data collected in 68 stations of AEMN were used for evaluation of model for two different periods. The satellite images used in this study is collected by Moderate Resolution Imaging Spectroradiometer (MODIS) on board of Terra and Aqua platforms. The MODIS products used in this study consist of LST (MOD11 and MYD11), lifted index (MOD07 and MYD07), total precipitable water (MOD05 and MYD05), and normalized differential vegetation index (MOD13). Also, in this study, to estimate air temperature in each 1 by 1 km grid box, the method developed by Mobashari et al. (2018) was used. The method offered an accuracy of 2.33 °C and a correlation coefficient of 0.94. Khesali and Mobasheri, 2019 presented Near-surface Estimated Air Temperature (NEAT) model in which extrapolation coefficients for air temperature to the next hours are calculated. To increase the accuracy of the NEAT model, it was recalculated using AEMN data at Aqua and Tera passing times. The methodology in this study consists of the following steps. •  Selection of study area and collecting temperature data from AEMN meteorological stations, •  Reproducing NEAT model coefficients  usinga set of AEMN data, • Evaluating NEAT equation using another set of AEMN data, •  Receiving and preparation of MODIS products and calculation of air temperature at the passing time of Terra and Aqua, • Applying NEAT to the MODIS images, • Producing Frost map using temperatures estimated by NEAT • Evaluation of frost prediction accuracy   Results and Discussion In order to implement the model, Two periods were selected: 3–9 December 2006 and 3–11 April 2007 in which severe crop damage across the southeastern United States has happened (Prabha and Hoogenboom, 2008). First, the NEAT model coefficients are calculated using the AEMN network data, and evaluated for air temperature extrapolation to the next hours.  Then, the air temperature was extracted using MODIS products for Aqua and Terra night time sensors. Finally, the NEAT model was applied to the air temperature extracted from satellite images, and the nighttime temperature was predicted from approximately 22:30 pm to 7:30 am of next day at 15 minute intervals. Then in the extracted images the air temperature was classified into two degreeintervals. Areas with temperatures below zero degrees Celsius are considered frost zones. Data from 68 AEMN network stations were used for evaluation. Statistical parameters like RMSE and variations of User Accuracy and Overall Accuracy were analyzed over the night. The RMSE value for all data, which is 13,840, is estimated to be 2.5 degrees. This parameter has an increasing trend from the satellite passing time to 6 hours and varies from 0.1 to 2.5 degrees Celsius. The results show the effectiveness of the proposed model in frost prediction.

In this study, AEMN meteorological data and MODIS satellite images were used for frost prediction. The study area is located in the Georgia state in the southeast of the US. Using the Neat model, air temperature is extrapolated during night in 15 minute intervals. Air temperature maps for two periods of time are produced. The results and accuracy assessment parameters show the ability of the proposed model in air temperature prediction and its effectivenessin frost prediction

 Due to the location of Iran in dry regions of the Middle East, and also because of the rapid increase in its urban population and water consumption, every day the issue of water scarcity becomes more severe in Iran. In recent years, Iran has faced serious water scarcity and excessive consumption of water resources. Therefore, patterns of urban water consumption, different geographic, spatial, demographic, social, and economic parameters, and the relation between these parameter and water consumption are considered to be among important issues affecting management of water resources. The present study seeks to investigate and analyze the spatial pattern of domestic water consumption in Babol County, and also to identify parameters affecting the pattern of water use. This is achieved by extracting association rules from some spatial and socio-economic parameters and based on the water use level in this County. The study also aims to determine regions with high/low level of water use, investigate spatial distribution of water consumption and finally, identify and categorize parameters affecting domestic water consumption at neighborhood level in this County using Decision Tree model. 

Domestic water consumption data, census data, spatial and socio-economic parameters such as distance from main roads, distance from Babolrood, total area of garden and green space in each building, building site and standing property (total area of house yard), population density, total number of houses vs. apartments, number of housing units, average number of people per household, percentage of young/old people per household were extracted from the Statistical Center of Iran for the time period of 2011 to 2016. Then, these data were used to analyze urban water consumption in Babol County.

Apriori algorithm - a data mining algorithm used to extract association rules- has been used to discover and extract relationships between different spatial socio-economic parameters and domestic water consumption patterns. Moreover, a decision tree has been developed which takes advantage of these parameters to predict domestic water use.   Results and Discussion  Results indicated that number of houses, number of household members, green space in each house, total area of house yard and distance from main roads are directly related with the household water consumption. On the other hand, population density, percentage of youth population, number of residential units and distance from Babolrood River are inversely related to domestic water consumption. Among all parameters considered in the present study, total area of house yard, distance from Babolrood River, number of residential units and number of household members exhibited a stronger relationship with water consumption. Thus, they were located on higher branches of the final decision tree. Additionally, results of global Moran’s I index indicated that there exists a spatial autocorrelation among household water consumption data. Moreover, this index indicated the clustered nature of residential water consumption distribution in Babol County. Also, spatial distribution of domestic water consumption in this County demonstrated that western and coastal areas with minimum distance from Babolrood River have the highest level of domestic water consumption. Therefore, it can be concluded that with an increase in distance from Babolrood River, domestic water consumption decreases. Only terraced and semi-detached houses exist in these neighborhoods. Thus compared to other neighborhoods, they have a lower population density, larger green space and larger yard. 

  The present study applies Apriori algorithm to extract association rules and discover the relationship between spatial and socio-economic parameters and domestic water consumption. Results indicated that spatial and socio-economic parameters affect the spatial distribution of domestic water consumption in Babol County. Developing a decision tree, parameters associated with domestic water consumption were categorized and amount of water consumption was predicted. Extracted rules predicted domestic water consumption of test data with an accuracy of 75%. In this study, global Moran’s I index indicated the existence of a spatial autocorrelation among water consumption data. It also proves the clustered nature of domestic water consumption distribution in the study area. Additionally, spatial distribution of domestic water consumption in Babol County indicated that western and coastal neighborhoods have the highest level of domestic water consumption, while southern neighborhoods of Babol County have the lowest level of domestic water consumption. Model developed in the present study provides an opportunity for analyzing and predicting the level of water consumption. This will make planning for the reduction of water consumption and management of water resources possible. We suggest that future works evaluate the effect of other spatial and socio-economic parameters such as water cost and educational status of household members in a longer period (more than 5 years) to improve the accuracy of the model.

 Road signs not only provide drivers with the necessary information and guidance, but also inform them of related rules and probable risks along roads. Safety of roads, and thus minimum delay and discomfort for drivers depends on traffic order. This order is only achieved if road signs can accurately guide drivers. Design of road signs have been evaluated in different fields of traffic engineering and urban design. Based on these evaluations, parameters like proper distance (distance in which a sign is legible for those driving in different speeds), and proper height (the height in which light reflection from the surface of the sign is minimized) have been introduced. Lack of a generalized method for designing and positioning of road signs, along with inadequate attention to their proper installation can cause a serious risk for drivers. Systematic positioning of road signs on highways and urban pathways with an especial attention to different criteria of sights has a significant impact on drivers’ ability to find the best route on time, and thus minimizes probable confusion and heavy traffic. Visibility in three-dimensional space refers to three-dimensional characteristic of different barriers along the roads. In most analytical studies, extruded objects and a perspective of the three-dimensional model are simulated. In this approach, three-dimensional analysis is usually performed based on an analysis in two-dimensional space. As an instance, the concept of spatial openness index (SOI) was introduced in 3D space. This concept refers to the volume of space observable for an observer. SOI is measured by defining a cone in the observers’ position based on which simulation is performed. In this way, the volume of observable space will be reduced in the presence of obstacles. 3D visibility analysis is closely related to human perception. When human eyes observe a scene, distant objects appear smaller than closer ones. Thus, if this difference in distance is considered, the final simulation will be closer to reality. Distance index shows the space width scale by calculating the distance between the observer and the target. In this method, a decrease in distance results in a more comprehensive perception, while increased distance decreases observers’ ability to perceive the environment. Based on the distance to target and observer’s view angle, three-dimensional projection simulates observers’ view and illustrates 3D obstacles on a 2D plane. The present study seeks to provide an approach based on spatial analysis in 3D space to evaluate the visibility of road signs. 

Indices like height and direction of road signs, perceivable distance and horizontal angle between signs and the observer (driver), and finally perceivable area of the signs effect the visibility of signs. In the proposed method, total area of each sign perceivable for drivers driving in different situations is calculated using projective geometry. In order to evaluate visibility of road signs for vehicles (driver) in different positions, spatial indices such as overlap area (area resulted from the reflection of barriers on the sign face), distance between the center of road signs and the center of overlap area, and a combination of overlap area and distance are presented. Then, different simulation scenarios are designed for the vehicle’s motion on a simulated roadway and the performance of each indicator are evaluated. Index of combination (combination of overlap area and distance) was selected as final visibility measure. With an increase in distance from the center of the sign, the overlap area decreases and visibility increases. In order to determine visibility, visual status of the vehicle (driver) is evaluated based on four categories: poor, good, medium and excellent. 

In order to simulate drivers’ vision, model spatial objects along the route and find optimal position for road signs, an appropriate analytical model is required. Results indicate that the proposed method can be used as an appropriate tool for optimal positioning of road signs along a route.

Monitoring and assessment of the biosphere are two essential tasks at any scale. Based on this, forests play an important role in controlling the climate and the global carbon cycle. For this reason, biomass and consequently forest height are known as the key information for forest monitoring. In the recent decade, several studies have shown that the Synthetic Aperture RADAR (SAR) imaging systems in Compact Polarimetry (CP) mode can overcome the disadvantages of Full Polarimetric (FP) SAR imaging systems and provide a good performance in various remote sensing applications such as monitoring and managing the important natural resources like forests. In this regard, a novel technique named Polarimetric Interferometry SAR (PolInSAR) has been further considered as a powerful tool for forest height estimation. 

In this research, the performance of the Compact PolInSAR (C-PolInSAR) data in Dual Circular Polarization (DCP) mode has been investigated in order to retrieve the forest height. For this reason, the common methods which are used for forest height estimation including Digital Elevation Model (DEM) differential method, coherence amplitude inversion, and phase & coherence inversion methods were applied and implemented on these data. In all of the aforementioned methods, LL+RR and LR polarizations were considered as the selected channels for estimating the volumetric and ground coherences, respectively. Then, the estimated coherences were considered as the input parameters for all of the mentioned methods. 

To evaluate the performance and the efficiency of C-PolInSAR data in DCP mode, the results obtained from these data were compared with those obtained from Full PolInSAR (F-PolInSAR) data. The results obtained in this study in two datasets simulated from PolSARProSim software in both L and P bands showed that the C-PolInSAR data in DCP mode yielded a similar result compared to the F-PolInSAR data for forest height estimation (when the HH+VV polarization is adopted as the ground backscattering), because, in this case the LL+RR and the LR polarizations are equal to the HV and the HH+VV polarizations, respectively, particularly, the C-PolInSAR data in DCP mode yielded 0.78 m and 0.55 m improvements for forest height estimation in L and P bands, respectively. In addition, all of the employed methods provided better and closer results compared to the real forest height (i.e. 18 m) in L band compared to P band, because the electromagnetic (EM) waves have a more penetration into the canopy in L band compared to P band. Thus, the attenuation of these waves is low and consequently the height estimation is more accurate. Without considering the used bands, the DEM method provided the lowest precision compared to other methods, because the HV (or LL+RR) phase center can lie anywhere between half the tree height and top of the canopy. The exact location of this phase depends on two vegetation parameters which are the wave mean attenuation and the vertical canopy structure variations. In this case, the trees have very thin canopies, and consequently, the attenuation is small, but the phase center is high due to the structure. In other words, when the canopy extends over the entire forest height, then the phase center can be at half the true height for low density (low attenuation), through to the top of the canopy for dense vegetation (high attenuation). This ambiguity is inherent in single baseline methods, and in order to overcome this, model-based correction methods need to be employed. It was also observed that the coherence amplitude method is among the weak algorithms due to ignoring the phase and its sensitivity to the attenuation and structural variations but it can be used as a backup solution when other approaches fail. Finally, the phase and the coherence inversion method had better results than two aforementioned methods for the forest height estimation. In this method, selecting the factor ‘’ is very important and it should be selected in a way to be strong towards the attenuation changes. In this study,  0.4 was adopted to maintain the height error variations.

As the final result, the C-PolInSAR data can be an efficient strategy due to its performance, when the full polarimetric imaging systems are either limited or not available. Moreover, utilizing these data in long wavelengths (e.g. P band) is more appropriate due to the effect of the Faraday rotation on the transmitted polarization.

Obtaining reliable environmental values in vast geographic areas is usually costly and difficult; therefore, the ability to predict unknown values or in other words, the use of better interpolation methods is very important. Interpolation methods utilize a set of different mathematical and statistical models to predict the unknown values. The similarity of the unknown points to the nearest known points or the principle of the nearest neighbor is the basis of interpolation methods, and how this principle is used depends on the selected model. In a general classification, interpolation methods are divided into two large classes. The first method is deterministic, in which interpolation is carried out based on determining the level of sampled points and also based on the similarities such as Inverse Distance Weighting (IDW) method or Radial Basis Function (RBFs). In the second method, interpolation is probabilistic – geostatistical, that is done based on the statistical properties of the sampled points. On the other hand, due to the growing increase in the problems of urbanization and urban heat islands, current cities need to have a detailed planning for future developments and preserving the quality of urban environment. Also, the geometry of urban valleys, which is defined by changing the height, length and distance of buildings, has a significant impact on the energy exchange and thus, the temperature of urban areas. But, this temperature, in turn, depends on a number of geographical - geometric factors (such as SVF) and meteorological variables. The Sky View Factor (SVF), as one of the usual indicators of describing urban geometry that refers to the amount of sky observable from a point on the Earth, has become one of the most important predictors of UHI due to its applicability in the urban climate, its contribution to the spatial data, and the existence of available techniques. In the climatic studies, the SVF is also considered as an important geometric parameter due to its correlation with the local temperature performance and its potential importance in the urban design process.Although urban Climatologists know this indicator well, it is not that much known among the urban designers and planners. This issue has not progressed much in Iran and there are no reliable sources about it. Despite the fact that different methods and models have been introduced for interpolation of Point data, no specific method has been proposed for estimating this index. Hence, this study has empirically compared the interpolation models with an emphasis on the Empirical Bayesian Kriging (EBK). This comparison is important since EBK has automated the most difficult aspects of the construction of a kriging model. This is while in other Kriging methods, the parameters are adjusted manually to obtain accurate results. EBK automatically simulates and calculates these parameters through a setup process. In classical kriging, it is also assumed that the estimated semivariogram is a true semivariogram of the observed data. This means that the data are generated from Gaussian distribution with the correlation structure defined by the estimated semivariogram. This is a very strong assumption, and it rarely holds true in practice. Accordingly, measures should be taken to make the statistical model more realistic. 

 The present study is an applied research in terms of its objective and it is quantitative in terms of the data analysis method. The study area is district 6 of Shiraz Municipality (496 hectares). Due to the multiplicity of interpolation methods and techniques as well as kernel functions and model fit functions, about 138 interpolation scenarios arewereimplemented. Also, four indices of Root-Mean-Square (RMS), Mean Standardized (MS), Root-Mean-Square Standardized (RMSS) and Average Standard Error (ASE) have been used for evaluating the models. The input data (sample) contains 6157 points, measured at intervals of 30 m distances in the study area. These points are werecreated based on the SVF calculation software method and using the GIS base model in ArcGIS10.6.  

Out of 138 scenarios, seven scenarios with the lowest RMS values arewereseparately examined in detail taking into account three other indicators. Another variable called “Neighborhood type” iswas added to the surveys in two standard and smooth modes. The results show that simple kriging and EBK have better results than the other models. Also, among the simple Kriging fitted models, the RQ model shows better results than other fitting models.  

Based on the RMS index, EBK is one of the best reliable automatic interpolation models (ranked second) for estimating the SVF. In general, based on RMS, MS, RMSS, it is the best automatic interpolation model for estimating SVF.

 Estimation of the Moho depth and thickness of the crust using the gravity anomaly datais one of the basic researches in the geophysics and geology sciences.

Based on many geophysical studies, the three-dimensional thickness determination of the density variationinterface using the gravity anomaly is a common method. One practical instanceis the modeling of the crustal discontinuity like Mohorovicic discontinuity using the gravity anomalies. To analyze the anomalies associated with these crustal discontinuities, many techniques are used. Among the common methods generallyused for estimating the Moho depth and studying thecrustal structure arethe analysis of surface and body wavesof the earthquakesrecorded at theseismological stations, the analysis of post-seismic waves, the gravity data inversion method and thermal analysis. In these cases, the inversion of the filtered gravity anomalies for determining the interface geometry of the density variations is one of the main goals. Different researches have proposed different methods for calculating the interface geometry of the density variationsbased on thegravity anomaly. Many of them approximate an irregular body with several cubic prismelements withconstant density. The overall gravity field of the bodyis calculated based on the sum of the gravity field effects of the prisms. Some methods such as Oldenburg (1974) have been developed based on the rewrite of Parker's forward method (Parker, 1973). Based on the Parker’s method,the Fourier transform of the gravity anomaly is consideredas an outcomeof thesum of theFourier transforms of the createddepth powersrelated tothe gravity anomaly. Oldenburg shows that theParker's formula can be rewritten to determine the geometry of the density interface from thegravity anomaly data. In this method, the Parker’s formula inversion is used to calculate the gravity anomaly created by an uneven layer of materials based on the Fourier series. Oldenburg rewrote this formula to calculate the interface depth of the density with undulating geometry using thegravity anomaly based on an iterative method (Parker-Oldenburg method). Therefore, the topography ofthe densityinterface is estimatedbased onan iterative inversion method, which is repeated until an acceptable solution is obtained. According to the method (Oldenburg, 1974), the process is convergedin casethe depth of the interface is greater than zero and is not removed from the topography. Moreover, the range of theinterface variations should be less than the average depth of the interface. When a specific number of iterations is performed or the difference between two successful approximations is less than a specific value, the iterative procedure ends. In general, this gravity anomaly modeled by the inversion method should be very similar to the input gravity anomaly in the first stage. This paper investigates the Moho depth behavior using gravity anomaly data based on the Parker-Oldenburg method. The formula rewritten by Oldenburg through integration with the Parker’smethod called the Parker-Oldenburg method is used here to obtain the results by the iterative inversion method oftheFourier transform of the gravity anomaly. Since this method is based on the Fast Fourier Transform(FFT), it has a very high speed which can be used to compute models with a very high number of points without spending too much time on computation. Good results can also be achieved by using a high-quality gravity field.  

In this study, the gravity anomalies derived from EGM08, EGM96 geopotential models and one of the GOCE-based global geopotential models (obtained only from the global satellite gravimetry data of GOCE), as well as those derived from terrestrial gravity data provided by the National Cartography Center (NCC) have been usedin Khorasan region. A  cell grid has been createdto generate the gravity field and estimate the Moho depth. Investigation of the results obtained from theMoho depth calculation in this region shows that the Moho depth model obtained from NCC data is very different from other models due to the limited number of observation points to reach the gravity field interpolation model. The difference of theMoho depth derived from the EGM08 model and the onederived from theEGM96 and GOCE models, gave 1.66 and 1.07 km for the RMS values, respectively. This accuracy improvement can be attributed to the quality and resolution of the geopotential models. Furthermore, comparing the results of the GOCE model with the EGM96 model, the RMS value is 0.85 km which is due to the close proximity of the two models’ qualities.  

In this paper, the Moho depth model has beenobtained based on the Parker-Oldenburg method using the gravity anomaly data forKhorasan region. In this method,the Fourier transform ofthe gravity anomalies accelerates themodeling for a large number of points. On the other hand, the high-quality of the models for the production of anomaly, results in the production of thehighly precise geometry of the density interface to a certain extent.

 There is a huge potential in the usage of renewable energy sources because these natural resources are inexpensive and harmless to the environment. Solar, wind, and geothermal energies are among the renewable energies. Solar photovoltaic (PV) technology is one of the fastest growing renewable energy technologies across the world. Solar energy is a practical and suitable technology, especially in arid areas with high solar energy potential. The first step in using renewable energy in Iran was in 1994, and since then, much attention has been paid to this type of energy in the society and the government. In Iran, 850 million tons of greenhouse gases are produced annually. Consequently, renewable energy sources such as solar energy can have a significant impact on reducing the greenhouse gas emissions. The integration of GIS and MCDA helps the decision maker to perform decision analysis functions such as ranking the options to select a suitable location so that the GIS is used as a powerful and integrated tool for storing, manipulating and analyzing the solar energy criteria. The use of the MCDA method can facilitate the evaluation and selection of the most appropriate location (s), taking into account the key criteria in the decision-making process. In this study, the optimal areas for the construction of the solar power plants have been identified in five highly optimistic, optimistic, moderate, pessimistic, and highly pessimistic levels using the spatial criteria and the OWA model. One of the most prominent features of this research in relation to the other articles is the inclusion of the concept of risk into the solar power plant site selection process to determine the optimum areas for the construction of solar power plants using the OWA model. 

The primary data used in this study include the Digital Elevation Model (DEM) derived from the Aster satellite data for the extraction of solar radiation and the region slope, the extraction of the mean land surface temperature for 2017 using the Terra Sensor MOD11A1, the preparation of the average map of the vegetation for 2017 using MODRA13A2 Terra sensor, the 1.250000 fault map prepared by the geological organization, the statistics and data of the rainfall prepared by the Meteorological Organization of Chahar mahal-o-Bakhtiari province, shapefile of road network prepared by the Organization of Roads and Urban Development,  the climaticshapefile of  the country prepared by the Iran Meteorological Organization, the shapefile of urban areas generated by the National Cartographic Center (NCC).The proposed methodology works by employing AHP to obtain the appropriate weights for each criterion, and utilizing OWA to extract suitable locations to varying degrees of risk. Sensitivity analysis for the criteria weights were conducted by virtue of the OAT method. 

The northern sectors of Razavi Khorasan province are endowed with cold temperatures and cold mountainous climate, which has had a substantial contribution to the increased cloudy and rainy days as well as the relatively extensive vegetation cover in this area. In this light, with respect to all ‘ORness’s, the target areas fall within the ‘very unsuitable’ and ‘unsuitable’ classes for construction of solar power plants. Moreover, the high slope factor in these areas has contributed to high levels of surface radiation, albeit, as the slope criterion is considered a constraint, the target areas are, in fact, not suitable for the construction of solar power plants. Moving southwards, the suitability of the regions, in terms of construction of solar power plants, tends to shift in the positive direction (very suitable class), which is most likely the result of the low rainfall and vegetation cover in conjunction with high surface temperatures in these areas, as opposed to their counterparts in the north. Areas falling within the very suitable class for construction of solar power plants in Razavi Khorasan can be realized by dint of calculating the percentage of area attributed to each class at ORness = 0.5 per city. The findings show that cities located towards the south and southwest of the province contribute to the highest area in the suitable class, while counties in the northern regions have the lowest share of area in the very suitable class. The highest sensitivity in locating suitable areas in Razavi Khorasan province were observed among the factors of slope, road, and urban criteria. Alterations in the weights assigned to these criteria would entail a significantly strong impact on the extent of the very suitable class. This highlights the significance of accurately determining the weights for these three criteria in Razavi Khorasan Province. Based on the findings, the rate of change in weight assigned to the of fault criteria ranges from 0 to 0.2, which in turn causes substantial change in the area of regions in the very suitable class extent. However, setting the criteria weight at between 0.2 and 1 appears to have no significant effects in the area of this class. 

The results of this research indicate that the northern parts of Razavi Khorasan province are highly unsuitable and unsuitable for all of ‘ORness’ values, while a significant extent of ​​the highly suitable class for the construction of solar power plants is comprised of sectors of the southern regions. Areas within the very suitable class corresponding to an ORness=1 comprise 5% of the class, whereas those with an ORness=0 have a 74% share. The three cities of Ferdows, Bardaskan, and Gonabad, had the highest share of the area attributed to the very suitable class (0.8-1), as maintained by a per city analysis of the area for each class. However, the cities of Dergas, Quchan, Mashhad, and Kalat had no share of the areas within ​​the very suitable class. This most probably stems from the high geographic latitudes of said regions, which has engendered unsuitable climatic conditions in these areas. Finally, results from sensitivity analysis of the criteria showed that increases in the weights assigned to the factors of slope, road, and urban criteria, would cause a further increase in the area of the very suitable class. Stated differently, the selection of suitable locations for the establishment of solar power plants is highly sensitive to these criteria. Changes in the weight of the surface temperature criterion had no considerable effect on the area of the very suitable class. Moreover, shifts in the weight allotted to solar radiation and precipitation in the province, ranging from 0 and 0.6, brought about substantial changes in the area of ​​the very suitable class. Whereas, shifts within the 0.6–1 range had no significant effects on the area of the very suitable class.

 change monitoring is generally used to evaluate natural processes such as the long-term effects of climate change, which is affected by the interaction of the climatic system’s constructive components such as the biosphere, lithosphere, or factors that control the climate changes outside the climatic system, over a long period of time, as well as the short-term processes that include vegetation sequence and geomorphological processes. Change monitoring is also used to evaluate the effects derived from human activities such as deforestation, agriculture and urban development. Remote sensing is a very useful technology, which can be used to obtain information layers from the soil and vegetation. 

Land Cover Product was used to process the MODIS1  Satellite data which is one of the most frequently used products designed relating to MODIS Satellite, and is used annually. This Sensor with 250-500 meter and also 1-kilometer spatial resolution has 36 spectral bands in the range of visible, reflectional infrared and thermal infrared wavelengths, which can well be used for various applications of the surface, the Earth surface, atmosphere and the oceans. MOD12Q1, which is one of the MODIS products, was used to investigate and analyze the profile of the vegetation changes in Mazandaran province using the NDVI and EVI indicators from 2005 to 2017. The related images have been prepared annually with 500-meter resolution and sine coordinate system in the form of a combination of Terra and Aqua data. Given the standards provided by NASA, the changes were investigated using the “decision tree” classification method, and the map for the prediction of its changes was calculated using the Markov Chain Model. The ArcGis software was then used to analyze these changes in order to determine which use of land with what percentage of changes has been allocated to which area.  

In 2005, land-uses associated with dense vegetation dominated an area of 398.77 m2. These land-uses include wasteland, dense vegetation and scattered vegetation. The estimation of the changes occurring in the aforementioned land-uses showed that the maximum changes relating to the low density vegetation with an average of 55.62% are in the northwestern and the eastern parts, and the minimum changes relating to the in dense vegetation with an average of 77.21% are in the central parts of the region, respectively. Furthermore, the observations of the images of the year 2005 show that the use of dense vegetation which has turned into low density vegetation in the image of the year 2017, has had the minimum changes. Finally, considering the prediction of the observed changes, it can be concluded that these changes were more related to the altitude range of 1400 m to 2260 m with the slope coefficients of 15% to 99%. The prediction carried out using the Markov Chain also suggests that the low-density land cover, which was over 864/80 km2 in 2017, will turn into barren lands in proportion to the changes occurringin 2022.  

A major part of the vegetation changes in the area is due tothelack of job opportunities, extra labor attraction and the economic poverty of the inhabitants.In addition,the pressure on the meadow fields hasreached its highest limit by ranchers,which has resulted inthe reduction of grasslands. Eventually, it could be stated that the evaluationmethods and modelsof the vegetation changes have their own featuresand no method on its own is usable andappropriate for all cases, hence,the identification of an appropriate method for evaluating thevegetation changesneeds to be examined quantitatively and qualitativelyin order to provide the best result.

Digital elevation model (DEM) is the raster representation of the ground surface so that the information of each cell on the image has a value equal to the altitude from the sea level corresponding to the same spot on the ground. DEM is an appropriate tool for the generation of topographic maps and contour lines, access to the information of surface roughness, three dimensional vision, etc. (Jacobsen, 2004). The accuracy of the digital elevation model is effective on the accuracy of the information from which it is obtained. This is why researchers are always looking for a way to increase the accuracy of digital elevation models. Among the information resources that are used to generate this model are ground mapping, aerial photography, satellite images, radar data, and Lidar. Some of these data generate the digital elevation model with little accuracy due to the insufficiency of the elevation information. The aim of this paper is to investigate the accuracy of DEMs derived from ASTER satellite images and SRTM data with 30 and 90-meter pixel dimensions and the digital elevation model derived from the topographic 1:25000-scale maps with Differential Global Positioning System (DGPS) in different landforms including plains, hills and mountains.  

The study area is a part of the project of dam and water transfer system from the Azad dam to the plain of Ghorve-Dehgolan (with the goal of transferring water from the catchments of Sirvan River into the country) in the province of Kurdistan and the city of Sanandaj. In this study, the Real-Time kinematic method (RTK) was used to locate the points. In this method, assuming that the coordinates of the reference station are known and comparing it with the location obtained from the GPS receiver, a correction value is obtained that is applied to the coordinates obtained for the Rover Station, which is known as the relative or differential method. In this method, the corrections are calculated asreal-time during the observations and are considered in the determination of the Rover location.The Leica GS10 GNSS receivers were used in this study. First, two reference stations were determined using the Fast Static method and then, the Real-Time kinematic (RTK) method was used. In order to investigate the extent of the data compliance and relation, the Pearson linear correlation analysis was used and the accuracy assessment of the extracted digital elevation models was carried out using the RMSE, mean error and standard deviation. 

The statistical parameters such as root mean square error (RMSE), bias (µ) and standard deviation () were used to assess the accuracy of each one of the investigated digital models. By comparing different sources that create DEMs, it can be seen that the minimum error is first related to the digital elevation model extracted from the contour lines of the 1:25000-scale map (27/6 = RMSE) and then to the ASTER digital elevation model with the pixel size of 30 meters (RMSE=7.43). The 30-meter pixel size DEM has always led to better results than the 90- meter pixel size DEM. Based on the mean error standard, the minimum bias is related to ASTER30 m (bias of 2 m) and then to the 1: 25,000 DEM (2.17). The maximum bias was related to 30-and 90-meter models extracted from the SRTM data. The results of standard deviation error were in compliance with the RMSE results, which confirmed the superiority of 1:25000-scale map and ASTER30 m DEMs. The results showed that the determination coefficient of relationship between the ground data and digital elevation models is between 97 and 99. The maximum compliance is related to the digital elevation model extracted from the 1:25000-scale topographic data and the ASTER30 m DEM, while the minimum compliance is related to the SRTM90 m data. In general, the compliance of the digital elevation models with the ground data decreased as the field's conditions became more difficult, i.e. from plain to mountain.

The results of DEMs accuracy assessment showed that the minimum error was primarily related to 1:25000 contour lines DEM (RMSE=6.27) and then, to the ASTER30 m DEM (RMSE=7.43). The pixel size of 30 meters has always been better than the pixels size of 90 meters. Based on the mean error standard, the minimum bias is related to the ASTER 30 m (bias of 2 m) and then, to the 1: 25,000 DEM (2.17). The maximum bias was related to 30-and 90-meter models extracted from the SRTM data. The results of the standard deviation error were consistent with the RMSE results, which confirmed the superiority of the digital elevation models extracted from the topographic 1:25000-scale maps and the ASTER30 m DEM.

 Nowadays,satellite imagery is used as a suitable toolforproduction of land use maps. It is also considered to be an important resource used for urban and rural land use planning. Due to the general coverage of different phenomena and natural resources, satellite imageriesplay a major role in spatial and temporal analysis. Using these images in various fields can show us their capabilities and limitations. The important point is to consider increasing advances in their spectral and spatial capabilities. Systematicexploitation of natural resources requires patterns and models of the region, so that related regulations are observedand sustainable utilization is also considered.Obviously,exact, accurate, fast and economic estimate of these changes is impossible without modern technologiesused for regional and environmental studies.Land use change modelingis an indispensable tool for environmental analysis, planning and management. Eastern parts of Tehran metropolis are among regions facing unstructuredand unscheduled constructions in Iran. Urban development and population growth have led to rapid changes in spatial patterns and have severely affected land use and natural resources.  

In order to investigate land use changes, the present study takes advantage of satellite imageries, remote sensing techniques and spatial information systems.The trend of land use changeswas separately extracted from satellite imageries received in1986, 2002, and 2018.After visual interpretation and error correction,four categories were selected (residential and non-residential construction, vegetation, mountain and grassland) based on which changes were investigated. After data collection (including imageries received from Landsat satellite and TM, ETM and OLI sensors) classification and detection commenced.Then, suitable band was selected for classification, spectral reflectance curves of each land use class were evaluated and bands correlation histograms were compared.since changing bandsgives a comprehensive understanding of the classes, their relations and resolution, two-band diagram of pixels’ distribution in two different bands was used.Properties of the texture were extracted using GLCM matrix and principal component analysis was performed. Support Vector Machine was selected as an optimal classification method. Feature vectors and the training rangeweregiven to this algorithm as its input.Markov chain works well in predicting probability of change, and especiallyland use changes. Cellular automaton is also a powerful method used for detecting changes in spatial component. Thus,Markov chain and automated cells model were both used in order to predict changes in quantity and space, and land use map was predicted and simulated for 2050.Results indicate that Markov models provide useful information which can be beneficial for future land use planning.  

Calculations indicate thatdue to creeping discrete growth and in some areas continuous growth, most changes in Damavand (in Tehran)have happened in the category of residential construction (9.06%) and road (1%).This increasing trend has reduced two classes of mountain/grassland and vegetation cover by 9.07% and 0.1%, respectively. After field operations and sampling with dual-frequency GPS receivers, data was introduced to software and classification was performed using support vector machines with an average overall accuracy of 96.62% and a mean kappa coefficient of 85.33%. Change detection studiesindicate that in time period of 1986 to 2002,most changes have occurred in residential and non-residential construction category. In fact, ​​residential and non-residential construction has reached from 3.1% in 1986 to 6.1% in 2002 year, while mountain and grassland category has faced 2.96% decrease. Also, vegetation cover has decreased by 0.76%.Likewise, we also saw a 6.15% increase in residential and non-residential construction, a 6.11% decrease in mountain and grassland and a 0.22% decrease in vegetation cover of the study area in the time period of 2002 to 2018.Road category had an 81% increase in the first time period and an 18% increase in the second time period. Overall, residential/non-residential construction and roads have increased, while mountains/grassland and vegetation cover have decreasedin the time period of 1986 to 2018. Due to population overflow in recent decades, and unplanned construction, land uses like vegetation cover and grassland have changed into residential construction, and especially industrial land use in the area under study (Jajrood, Kamard, KhorramDasht, Shamsabad, Mehrabad, Pardis and Siasang).  

While investigating spatial evolution and agricultural land use changes, it is important to distinguish betweenrapidly changing phenomenon, and slowly changing one.Results of the present study indicate that compared to other land uses,vegetation cove has changed more severely. Therefore, without necessary policies and actions to prevent this process,pressure on naturalresources, land use changes, and consequently destruction of valuable resourceswill result in harmful environmental impacts. This will also change the economic performance of the villages, and have many negative spatial, socio-economic consequences.

There has been an increase in the occurrence of dust storms in the Middle East in recent years. The World Meteorological Organization has introduced dust storms as the result of atmospheric turbulence, which injects a large amount of dust into the atmosphere and makes the horizontal visibility less than 1000 meters. Iran is involved in dust storms due to its geographical location and weather conditions. Long-term evaluation of statistical data, identifying the origin and routing dust storms can be effective in identifying the time and location of this event. 

In this research, the temporal distribution of Khuzestan dust storms from 2000 to 2015 was investigated at five synoptic stations including Ahvaz, Abadan, Aghajari, Safi Abad and Mahshahr. Given the World Meteorological Organization’s codes on the dust storm incidents, and in order to minimize human error, the information related to the event was extracted using the Linux operating system. Furthermore, Mann-Kendall test and Pearson and Spearman correlation coefficients were used to evaluate the trend of the temporal changes of dust storms and the rate of the correlation of the effective factors with the frequency of dust storm occurrence, respectively. Regression models were used to determine the rate of the effectiveness and the prioritization of the factors affecting storms. The entire statistical analyses were performed using the SPSS 20 software.  

According to the results obtained, out of 1507 recorded dust storms, the Ahvaz station with 509 (34%) and the Aghajari station with 156 (10%) recorded events, have had the highest and the lowest number of recorded dust storms, respectively. The temporal variations trend of dust events at the study stations was not significant at the 1 and 5% levels. However, the frequency of dusty days in the Ahvaz and Abadan stations was positively correlated with the frequency of days with the region’s prevailing wind speed and direction at the 99% confidence level (p<0.01). There was also no significant correlation between soil texture and type. The results of linear regression model showed that there is a positive relationship between the frequency of dusty days with the frequency of days with the region’s prevailing wind direction at all stations at the 99 and 95% levels. Based on the standardized regression coefficient, at most stations, the occurrence frequency of the prevailing wind at the study stations has the highest impact on the occurrence frequency of dust storms.  

About 65 percent of dust events have occurred in two cities of Ahvaz and Abadan, located in the center and southwest part of the Khuzestan province. This could be due to the further proximity of these two stations to the local and regional dust sources. Another reason could be the flow of atmospheric circulations in different regions of the province. In this regard, the northwest-southeast winds which carry dust, hit Ahvaz and Abadan more frequently. The highest number of dust storms were recorded during summer and spring. A downward trend of dust events has been observed at all studied stations since 2008. Nevertheless, the problems caused by this event have become more apparent and have affected the lives of people. For this reason, the general view is that the number of dust storm events has increased. The high concentration and the higher persistence of dust storm events could be the reason of such an idea as well. These possible causes could be addressed in future studies in analysis and control of dust storm events.

With the advent of remote sensing technology, huge volume of remotely sensed data is now availablein different areas. As the fastest and the most cost-efficient method, satellite data is available for both researchers and responsible authorities seeking to produce land use (LU) maps. Compared to traditional methods, object based image analysis (OBIA) techniques use more comprehensive datasets,including geometric information (shape and placement of phenomena), digital elevation models, andvarious spectralindicesfor LU classification.Therefore, different OBIA methods have been widely used forclassification of satellite imageriesin different regions. Despite large amount of researches performed in this area, little attention has been paid to the systematic comparison ofdifferent object-based methods. Therefore, examining different techniques used for object-based processing of satellite imageries in diffrent situations can be considered as an appropriate research field for researchers.  The present studyexamines some powerful OBIA classification techniques such as threshold, nearest neighbor algorithm and fuzzy object based classification to determine the most suitable OBIA algorithm for classification of Ikonos satellite images.  

An Ikonos satellite imagery was used in this studywhich included red, green, blue and near-infrared bandswith spatial resolution of 4 m and a1 m resolutionpanchromatic band.Object based classification can be implemented in three general phases: segmentation, classification, and accuracy assessment.The present study has appliedmulti-resolution segmentation method in the segmentation phase. Three techniques ofthreshold, nearest neighbor algorithm and fuzzy based OBIA were also used for classification. 

The present study takes advantage of various features to extract land use classesfrom Ikonos satellite imageswith high level of accuracy.Textual information (Grey Level Co-occurrenceMatrix), mean of the imagery’s spectral bands, geometry (shape, density and asymmetry), and normalized difference vegetation index (NDVI)were among these features.Compared to threshold method,nearest neighbor algorithm withoverall accuracy of 92% and kappacoefficient of 0.9hada higher level of accuracy.Also, FOS algorithm was used to optimize the nearest neighbor technique. This algorithm optimizes intervals between the training samples using secondary information provided by the user.The eighteenth dimension, which contains the mean of spectral bands3 and 4, vegetation index, brightness, length to width ratio, indices of shape, compactness, asymmetry, texture information (homogeneityand contrast), were determined by FOS algorithmas the best dimension for extracting each LU classes. Finally,featuresproposed by FOS algorithm were used for image classification in nearest neighbor method.This optimizing process is considered to be one of the main reasons for superior performance ofnearest neighbor technique compared to threshold method.  

In this research, three OBIA methods including threshold technique, nearest neighbor algorithm and fuzzy based OBIA algorithm were compared based on their capability in producing land use map from Ikonos satellite image. Identical ground control pointsof the study areawere used to classify and compare the results of these three OBIA classification methods.Finally, the best classification algorithmwas determinedbased on thevalues of accuracy assessment metrics including overall accuracy and kappa coefficient. Results indicate thatwith overall accuracy of 97%, and kappa coefficient of 0.95, fuzzy based OBIA classification algorithm has thehighest accuracy as compared to nearest neighbor algorithm and threshold method. Generally, the accuracy of fuzzy based OBIA classification method largely depends on the selection of appropriateclassification parameters and suitablealgorithm to obtain membership degrees.Investigating membership degree of effective parameters in the classification and using parameters with maximum degree of membership are considered to be two main reasons for achieving this high accuracy. Results of the present study indicate that fuzzy based OBIA techniqueis the best algorithm for classification ofIKONOS satellite images in the study area, andareas with similar conditions. This findingcanguide researchers and organizations producingLU map from IKONOS satellite imagery. Finally, investigating different techniques using satellite imageries (imageries with different spatial resolution, and received from areas with different land uses) is considered to be an appropriate area of study for OBIA researches.

 One of the important features of desert areas (arid and semi-arid) is dust phenomena that occurs in most days of the year. Dust phenomena occur especially in tropical areas. In some parts of the world, including Africa, Australia and the Middle East, the annual sediment volume carried by the flow of the wind is greater than the sediment volume carried by the rivers. Today, the dust phenomena are among the most important environmental hazards which have put human and environmental health at serious risk. Based on the country’s comprehensive water plan, the size of the real deserts of Iran has increased to 4.7 million hectares or 35.5 percent of the country’s land area.  

The study area was the southwest of Iran including Khuzestan and the Persian Gulf regions. In recent years, these regions have strongly been affected by the dust with internal source and especially with external sources such as dust sources in Iraq, Syria, and Saudi Arabia. In this research, we employed the library method and also determined the days of the dust storm using the weather data of the province. We used satellite data, MODIS sensor data and several algorithms based on the image processing to detect dust. In order to evaluate the different methods of dust detection, it is necessary to compare the results of the algorithms with another independent source. This source can be a natural color images, aerosol sensor products, MODIS dust indicators or other sensors products. In this research, we first introduced the HDF file of MOD021k MODIS images into the ENVI5.2 software to visualize the dust. After preprocessing the satellite images, we employed different methods such as creating False Color images, BTD and NDDI algorithms, and the neural network method to detect dust on satellite imagery. In this regard, we stored the required bands for the NDDI and BTD algorithms as a single band in the ENVI software, and entered it into MATLAB software to apply the detection algorithms. Due to the importance of remote sensing and satellite images and also the efficiency of the artificial neural networks method we decided to classify the images of the MODIS sensor by using the methods of the Artificial Neural Network and dust detection indexes. In general, the bands 20, 23, 31 and 32 of MODIS sensor and the infrared thermal bands were used more to detect dust storms. The Brightness Temperature Difference between these bands can detect dust storms from other phenomena. In this study, a Feed Forward Neural Network (FFNN) was used to detect dust storm in Khuzestan and the north of the Persian Gulf, using 20 data sets for the day and 11 data sets for the night. To categorize different pixels in the neural network based on BTD values, BTD of the bands 20-31, BTD of the bands 23-31, BTD of the bands 31-32 and bands 1, 3 and 4 were used. MODIS bands 1, 3 and 4 were used to create realistic color images to for the better detection of the Earth’s surface phenomena. These three bands were used only for MODIS’s daily images.  

The results show that the emissivity of sand in band 31 (0.96) is slightly lower than the band 32 (0.98), while the soil emissivity for these two bands was (0.97) and water emissivity (0.99). Also, the emissivity value of band 31 for the cloud was (0.98) and for band 32 was (0.95). There was a difference between the emissivity value of bands 23 and 31 for soil, sand, and water, which can be used to distinguish dust from other surfaces. The brightness temperature of dust storm (K298/4) and cloud (K276) in the band 23 (4.6 µm) was higher than the brightness temperature  of dust storm (K287) and cloud (K271) in the band 31 (11.02 micrometers), while the brightness temperature of water (K285), ground (K310) and vegetation (K295) in the band 23 was lower than that in band 31 for the same items (Water (286K), ground (310K) and vegetation (296K). For these reasons, the difference in brightness temperature between bands 23 and 31 is useful for detecting dust from the ground, vegetation, cloud and water. In the artificial neural network, the correlation coefficient of the training, evaluation, test and total data was equal to  R = 0.996, R = 0.99505, R = 0.99559 and R = 0.9958, respectively. These results show the good capability of the neural network in detecting dust. The data was divided into two classes of dust (0.9) and no dust (0.1). In fact, various inputs entered the network and were divided into two classes of dust and no dust. The results showed that the error started from a large amount and gradually decreased. Epoch is referred to as every step of the data correction. In other words, when an input passes through the network and generates an overall error, the weight factors are corrected with the help of that error, a process which is called the number of repetitions or the Epoch. Thus, as itis shown in the figure, the training ends after 151 repetitions. Given the results of the neural network output images, it is observed that dust is well distinguished in both the aquatic and terrestrial ecosystems and a better differentiation will be done with higher dust concentration. The ACC parameter indicates that the neural network method has had a good accuracy and performance. Results show that neural network is a more appropriate method than the BTD index in dust detection, and the neural network does not need to determine the threshold for examining each image.  

The results of the NDDI index show that this parameter alone, is not able to distinguish dust pixels existing in the atmosphere from the pixels of sand and other than dust, and has poor accuracy in images with cloud or water. It seems that this low efficiency is related to the features of the earth’s surface such as land use, land cover, topographical differences, as well as chemical properties of dust minerals in the region. According to the results of this study, the results of applying the BTD index have suitable performance for the detection of dust. In the present research, the artificial neural network shows a fairly good accuracy and performance for the daytime images with an accuracy of 60%.

 Knowing type and percentage of each land use and land cover are considered to be a fundamental need for understanding and managing an area. Given the ever-increasing changes in land use, managers and experts need to be aware of past changes and developments. This is because, policy making and solving existing problems require detecting changes and determining the trend of changes over time. Satellite data is one of the quickest and least expensive methods available based on which researchers can produce different land use map. In this regard, Landsat Satellite imageries are one of the most important data sources used to study different types of land use and land cover changes, such as deforestation, agricultural expansion and urban growth. Extracting information from satellite imagery through classification is one of the most widely used methods. One of the most important applications of remote sensing data is for investigating and discovering changes in phenomena with a spatial-temporal nature (i.e. phenomena whose position and status changes over time). In fact, change detection is the process of identifying and determining the type and extent of land cover or land use in a given period of time based on remote sensing images. The present study seeks to monitor land use changes in Shahr-e Kord during the period of 1985 to 2017, and to prepare land use maps of the area using Landsat satellite imageries. 

In the present study, satellite imageries received from TM, ETM+, and OLI sensors of Landsat satellites in 1985, 2000, 2015, and 2017 were extracted from the United States Geological Survey (www.usgs.gov) and analyzed using different remote sensing software and geographical information systems like ENVI 4.7 and ArcGIS 10.4. In order to produce land use changes map, error correction was first performed. Then, images were processed using supervised classification method and maximum likelihood algorithm, which based on previous studies have a higher accuracy compared to other algorithms. In order to classify land use/land covers, a training sample was produced for  each land use based on field observations, topographic maps (1:25000) produced by Iran National Cartographic Center, Google Earth imageries, and visual study of the imageries. Then, classification results were corrected using auxiliary data, visual interpretation, experiential knowledge, and GIS techniques. Prior familiarity with the region, visual study of imageries, previous experience and field operations revealed that following land uses exist in the region and are detachable on the images as well: a) urban, b) agricultural, c) industrial, d) meadow, e) airport, and c) other land uses (including pasture, rocky areas and areas without any specific land cover). Confusion or error matrix –including overall accuracy, producer’s accuracy, user accuracy and kappa coefficient- was also used to evaluate the accuracy of the classification. Also, urban land use changes were monitored using image differentiation functions.

After production of land use maps based on imageries received in 1985, 2000, 2015, and 2017, area of the six land cover classes was obtained. Results indicate that during these four periods (1985 to 2000), urban, industrial, agricultural and airport land uses have increased to 13, 111.7, 5.2 and 3.4 km2 (1.26, 10.16, 0.51 and 0.4 % increase) respectively, while meadows and other land uses have faced a decreasing trend. In other words, it can be concluded that most changes during this 15-year period occurred in meadows and other land uses. Since development of the airport have resulted in destruction of a large part of meadows, this land use have faced more severe changes. Land use changes from 1985 to 2017 indicate that 7.8 km2 of agricultural lands were transformed into urban land use, 1.4 km2 to industrial land use, 1.08 km2 to airport and 7.7 km2 to other land uses. Also, 20.5 km2 of other land uses were transformed into urban land use, 203.1 km2 to agricultural land use, 0.03 km2 to dried meadows, 0.17 km2 to airport and 14.5 km2 to industrial land use. 2.8 km2 of meadows were also transformed into agricultural land use, 0.05 km2 to industrial land use and 2.04 km2 to airport. During this period, urban and industrial land uses have remained unchanged. 

Generally, results indicate that urban, industrial and agricultural land uses have developed over time, and these land uses have always had a positive increasing trend. While meadows and other land uses have had a decreasing and negative trend. This is due to the construction of Shahr-e Kord Airport, uncontrolled exploitations, digging wells and drought phenomena, which have led to a decrease in the level of water in aquifers and destruction of natural ecosystem in this region. In this way, previous meadows have turned into the source of intense dust generation in the city, which is a sign of desertification and ecosystem destruction. Due to drought and water scarcity in recent years, new deep wells have been dug with the aim of supplying water. This have occurred despite the critical condition of the meadows, and thus, have resulted in repeated protests by farmers and livestock farmers. Dramatic decrease in other land uses, including pastures, can also be attributed to recent droughts in Iran and intense dust generation. Increased population, increased human pressure on natural resources and also development of agricultural lands are among other causes of the present situation. Based on existing maps and satellite imageries, Shahr-e Kord is developing towards North and North West. In some areas, this development has occurred in pastures. Therefore, due to very high population density in the region which is still increasing, and also ongoing migration of villagers to the city, supplying appropriate accommodation and occupation for this population requires finding new suitable locations for urban and industrial development of the city. This development process should happen with correct management and according to the goals of sustainable development.