نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 127 (پاییز 1402)

A digital orthophoto is a reliable, accurate, and low-cost map for acquiring knowledge, including geolocation, distance, area, and changes in imagery features. It is now considered one of the most widely used and sophisticated digital photogrammetry products. Orthophoto map creation is substantially faster than traditional topographic map production because of the development of powerful algorithms for processing aerial, drone, ground, and satellite imagery. To begin, orthophoto is a result of photogrammetry processing that employs the Digital Terrain Model (DTM), which is commonly observed in classic aerial photogrammetry. In orthophotos, you will frequently notice an effect in which the terrain representation is very accurate, but there is a tilt in the buildings and other tall structures, which is caused by the use of DTM, which only maps the natural shape of the earth, excluding vegetation and all man-made objects and structures. A true orthophoto map provides a vertical view of the earth's surface, eliminating building tilting and providing access to practically any location on the ground. Traditionally, measuring digital surface models has been highly complex and costly. It is generally accomplished through the use of LiDAR or ground measurements. The end result of drone photogrammetry is known as an orthomosaic. In actuality, an orthomosaic is comparable to a true orthophoto (since it is formed using a digital surface model), but it is often not based on a metric camera with accurate focal length and internal dimensions, as they are expensive and not readily accessible for UAVs. Furthermore, orthomosaics may be generated using both nadir and oblique images. Drone-based orthomosaics are created based on the digital surface model rather than as a separate survey like traditional aerial photogrammetry. The DSM is produced by drone photogrammetry based on the 3D point cloud, which is the initial output of data processing.

The huge success of online services like Google Earth, Google Maps, Bing Maps, and so on increased demand for orthophotos, resulting in the development of new algorithms and sensors. It is commonly understood that orthophoto quality is determined by image resolution, camera calibration, orientation accuracy, and DTM accuracy. Because digital cameras produce high-resolution imagery, one of the most important consequences in orthophoto generation is the spatial resolution of the DTM: standing objects, such as buildings, plants, and so on, exhibit radial displacement in the final orthophoto. In practical applications, orthophotos are utilized as small and medium scale maps; updated earth surface maps; three-dimensional urban scene reconstruction; village surveying; land planning; precision agriculture; desertification monitoring; land use surveying; and other sectors. True orthophotos are orthophotos that have been improved to minimize tilt inaccuracy and projection discrepancies. The true orthophoto is exceedingly stringent with the original image; the heading overlap and side overlap are at least 80% and 60% overlap, respectively. Due to the reduction of displacements produced by camera tilt and height difference, the use of orthophoto as a spatial data format with high geometric accuracy has found growing applications in recent years. With the growing relevance of geographic information systems, particularly in metropolitan areas, the use of orthophoto in conjunction with spatial data has grown. Because orthophoto contains correct spatial and textural information about complications, it is feasible to produce virtual reality by integrating it with 3D models, where it is able to properly quantify the height and plane location of complications during 3D viewing. In this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images.

The result demonstrated the total error of orthomosaic generation from Google Earth imagery and UAV data to be 0.124 and 0.059 m/pixel, respectively. Moreover, the visual findings reveal that the edges of low-height barriers in the orthophoto generated from Google Earth images are superior to those in the orthophoto generated from drone imagery, but the edges of high-height obstacles, particularly those with noticeable shadows, are of poor quality. The findings of statistical parameters in quantitative surveys using randomly selected points in non-building regions revealed that the errors in the orthophoto derived from Google Earth data are 1.10 meters and 1.34 meters in terms of mean error and root mean square error (RMSE), respectively. In addition, the orthophoto generated from UAV data and Google Earth showed a 95% correlation and a 91% determination coefficient. In contrast, in building regions, the average height error and average square root error in the orthophoto generated from Google Earth data compared to UAV data were around 9 meters and 5 meters, respectively. Statistical metrics in these locations also revealed a low correlation of 80% and a determination coefficient of 65%.

In this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images. As a result, as the height of the obstacles and the presence of lengthy shadows increase, so does the inaccuracy of the height component of the orthophoto derived from Google Earth imagery. Therefore, it is advised that orthophotos for special applications, flat regions, and hills be created using Google Earth images. Additionally, Google Earth data offers the following advantages: free of charge; the utilization of historical imagery to generate orthophotos; and nearly four times less processing time and volume.

With the expansion of the urban limits, some of the lands that were used for gardening years ago have been located within the urban limits. The difference between the value of garden land use and urban land use, such as residential and commercial, encourages gardeners to change their land use. Urban managers try to prevent this change of use by enforcing strict rules.Assessing the success of such plans requires examining land-use change in the urban over a long periodof time. The main purpose of this study is to detect abandoned urban gardens using Landsat satellite imagery. The second goal is to determine the extent of changes in urban gardens in the study area over the past 30 years. In this study, based on Landsat satellite images in 2018 and 1988 for the northern slope of Alvand Mountain in Hamadan province and the city of Hamadan, the normalized differential index of vegetation (NDVI) along with land surface temperature (LST) in 9 time periods per year was extracted. The results indicated a 4/75 ° C increase in LSTfor the region over 30 years. Also, the inverse relationship of LST with NDVI is confirmed. Based on the separation of urban gardens, a comparison was made between 2018 and 1988, which showed a decrease of 175 hectares of urban gardens in the study area, which is equivalent to a 49% reduction in urban gardens. In the main part of the research, based on the behavioral evaluation of urban gardens, in these two characteristics, a differentiation index for active and abandoned gardens is presented. Examination of the results based on ground truth data including 25 active gardens and 25 abandoned gardens suggested that the proposed method had an overall accuracy of 82% and a Kappa coefficient of 0/64.

The study area includes a part of the northern slope of Alvand Mountain, which is limited to the southern part of Hamedan and has a latitude of 34 degrees and 45 minutes to 34 degrees and 48 minutes north and a longitude of 48 degrees and 27 minutes to 48 degrees and 31 minutes east. Ground truth data including 25 active gardens and 25 abandoned gardens were collected as field visits using a Garmin GPSMAP 62s handheld navigator so that coordinates were collected by attending the location of abandoned and active gardens. The satellite data used in this study concern the time series data of Landsat 8 satellite OLI and TIRS sensors for 2018 and Landsat 5 satellite TM sensor for 1988.To achieve the first objective and separate active and abandoned gardens in 2018, the land surface temperature (LST) and the normalized difference vegetation index (NDVI) are calculated and the behavior pattern of these two components is examined during the year for active and abandoned gardens in nine periods according to the proposed method, a final index for separating active and abandoned gardens is presented based on the NDVI behavior pattern throughout the year. The time series of NDVI for each year is evaluated in 9 periods and garden maps are extracted in 1988 and 2018 to achieve the second objective and prepare the maps of 30-year changes in active gardens in the study area. The rate of change of area and the percentage of changes in the class of gardens are obtained by comparing the maps.

Since this study is conducted mainly to identify abandoned gardens in urban space, two criteria for assessing user accuracy and errors of commission in the abandoned garden class are very important. In other words, in this problem, the number of gardens that are properly divided into the abandoned garden class is important, and the proposed method provides an accuracy of 86%. The most important issue is the number of abandoned gardens that the proposed method has mistakenly labeled as active gardens, which is 14% in this method. Both accuracies provided are evaluated as acceptable. The overall accuracy of the proposed method is estimated at 82%, which is acceptable, indicating the efficiency of the proposed method.

One of the problems facing human societies today is the reduction of forests and gardens. Given the important role that trees play in improving the quality of human life, protecting them is one of the inherent duties of rulers. Various factors cause the destruction of trees, one of which is the development of urban areas in the vicinity of forests and gardens. Traditional methods of conserving natural resources and monitoring their changes have failed in practice. For example, in the study area, 49% of the tree-covered areas have declined over the past 30 years. However, the ban on construction in the area has always been emphasized by city managers in the years under study, and the inefficiency of the methods used has been proven by the statistics provided. New methods of monitoring changes based on satellite image processing can be alternatives to traditional methods due to their high accuracy and speed and significant cost reduction. The proposed index is recommended to be evaluated to separate active and abandoned gardens in other areas facing this problem using images with higher spatial resolution. In different cases of threshold limit, the overall accuracy of the proposed method is examined based on the ground truth data of the evaluator. At best, the separation of active and abandoned gardens is associated with an overall accuracy of 82%.

Unfortunately, seismic data recorded globally during the last fifty years does not include every type of wave propagation conditions in the environment, types of construction, the rupture process on the fault, and the geometrical relationship between the construction and the fault. This is especially seen in near-field regions. Before the 1999 Chi-chi earthquake in Taiwan and the 1999 Izmit earthquake in Turkey, there were only about 20 records of earthquakes with a magnitude greater than 7 at a distance of less than 20 kilometers from the fault.The Turkish earthquake added 5 records and the Thai earthquake added 65 records to this collection, but only two fault rupture scenarios were added to our knowledge, while thousands of other possible scenarios may occur. Thus, seismologists and earthquake engineers have tried to estimate parameters related to strong near-field motions of the earth with an acceptable confidence using various experimental and theoretical simulation methods.           

In earthquake engineering and seismology, earthquake phenomenon and the resulting movements are generally investigated and analyzed using dynamic and kinematic methods. Seismological models and problems are thus divided into two categories: Kinematic models which are based on slip distribution and do not take the state of stress on the fault into account. Dynamic models deal with the physics of fault rupture and its causes. Simulation methods are also divided into three main categories: deterministic (low frequencies), stochastic (high frequencies) and hybrid (broad band) methods.Generally speaking, simulating strong ground motion plays an important role in the estimation of related parameters especially in regions lacking such data. Accelerographs are used to simulate strong ground motions. The present study has introduced, investigated and validated two methods decisive simulation models (Discrete-Wave Number and Finite Fault) and Finite Fault models. It also explains how the simulated recording are produced for near-field (less than 20 km to a seismogenic fault) and far-field events, presents attenuation relationships for the Zagros seismotectonics region, and predicts parameters of strong ground motions.

Due to the special geological conditions and the existence of many active faults in Iran, our country is considered to be located in an earthquake-prone region. Zagros region is considered to be the most earthquake-prone region of Iran. Finite fault modeling combines various aspects of plate source with the ground motion model based on point source. Since previously mentioned limitations are not naturally present in finite fault modelling, the method takes geometry of the fault and the directivity effect into account. Time delay method and the sum of accelerations recorded in maps of a two-dimensional network are used for simulation in finite fault model. The fault plate is divided into various elements and a minor event is simulated for each one. The overall seismic acceleration equals the sum of the effects of these minor events. The strong ground motions in each micro-fault are calculated using the random point source method and then summed up at the desired point with an appropriate time delay to obtain the ground motion of the entire fault.Previous geological and seismic studies of each seismic region are used to determine the key parameters of the simulation input. To produce a comprehensive database, a significant number of stations are taken into account around the fault based on different hypothesesand artificial accelerograms are produced in accordance with the seismological parameters of the region. A suitable function is then selected and an attenuation relationship is fitted. The simulation results and the resulting attenuation relationship are then compared with valid global attenuation relationships and their consistency (compliance percentage) is investigated.

The present study has produced a wide range of simulated records (about 20 thousand records) for Zagros seismotectonics region. Thus, the resulting relationships will hopefully have sufficient accuracy and efficiency to be used in structure designing and urban development. It worth noting that the regression correlation coefficient (R-Square) was above 0.95 in all fits.These attenuation relationships can provide a new perspective on site selection, and help us in understanding the dynamic behavior of structures, and the development of various infrastructure. They also help urban managers to predict and reduce earthquake damages.

Various Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro planning and information generation. Methods using air temperature recorded in weather stations to estimate the relative heat in urban areas are considered to be both time-consuming and costly. On the other hands, data with relatively high spatial resolution are capable of measuring ground surface parameters more efficiently and accurately. Thus, remote sensing technology is now considered to be a solution used to improve previously mentioned methods. Remotely sensed data are now widely used to find the quantitative relationship between patterns of vegetation cover and the elements of climate. Predicting the conditions of vegetation cover is considered to be essential for planners seeking an efficient plan for its exploitation and protection.

The present study seeks to investigate the effects of climatic factors on the vegetation trend observed in Frame forest in Mazandaran province using Sentinel 2 images and to determine the most suitable index for this area. Climatic Data collected from the nearest weather station in Farim City have been used to model climate factors (temperature and precipitation). Changes in the height above mean sea level were also considered. Following the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and applied as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. 70% of the resulting data were used for training and the rest were used for testing or evaluating the model. Mean square error, correlation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. Models with the highest correlation and the lowest standard error, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for the studied variables.

Results & Discussion:

 A correlation coefficient of 0.43 and 0.56 was observed between temperature and precipitation and vegetation indices. AIC and BIC values equaled (565 and 3209) and (739 and 3383) respectively. Differential Vegetation Index (DVI) has proved to be the most effective parameter in relation to both temperature and precipitation factors in the region. Results indicated that differential vegetation index, green normalized difference vegetation index (GNDVI) and green difference vegetation index (GDVI) have a positive correlation with temperature, while there is a negative correlation between temperature and normalized vegetation index. Precipitation is considered to be one of the most important factors affecting vegetation. Results indicate that differential vegetation index, green difference vegetation index, green normalized difference vegetation index, non-linear vegetation index and normalized difference vegetation index have the highest impact on precipitation. In forest ecosystems, changes in climatic factors may affect trees differently. 

Collecting information about the state of vegetation cover in forests is considered to be very important. Thus, the present study has endeavored to investigate the relationship between indices of vegetation cover and climatic variables. To reach this aim, satellite data are used as a suitable and efficient tool for investigating forest ecosystems with a relatively low cost. This provides the possibility of continuously monitoring land surface. Results indicated that climatic factors affect vegetation indices in the study area. Vegetation cover protects and stabilizes the environment and thus, many researchers have tried to investigate the growth and spatial patterns of vegetation cover in different regions. It is also suggested to study the effects of climatic factors on the vegetation cover of the study areas in different geographical directions. In addition, using other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and higher resolution images can increase the accuracy of the study.

City is a living, dynamic being evolving over time in the context of physical and anthropogenic components and complex relationships between them. It is the reflection of the role and attitude of man-kind influenced by social, economic, political, cultural and geographical factors and conditions. Increased population and density in urban areas have far-reaching consequences, such as increased consumption of natural resources, land-use changes, climate change, and disruptions in the exchange of material and energy. Consequently, cities face many issues and problems, the most important of which are issues related to urban design. These include poor ventilation, high heat load, air pollution caused by the physical characteristics of cities, and insufficient attention to the capabilities, natural characteristics and climate of the region and the city.

The present study seeks to prepare an urban climate analysis map to study and analyze spatial and climatic information collected from Tehran. Urban Climate Map (UCMap) is an information-based and analytical tool that combines factors of urban climate with urban planning factors and some environmental conditions to provide an image of urban climate issues in a two-dimensional environment. Urban climate map consists of an urban climate analysis maps (UCAnMap) and an urban climate recommendation map (UCReMap). Urban climate analysis maps apply various spatial information layers of heat load maps such as building volume, urban topography and green space along with layers of land cover, natural landscape, and proximity to open spaces in dynamic capacity maps. The proposed model is generally based on the evaluation and analysis of variables affecting climatic conditions. Based on six layers of building volume, land cover, topography, proximity to open spaces, green space, and natural landscape, maps were prepared in Arc/GIS10.4.1 environment for Tehran urban area. To eliminate the unit and reach comparability and overlap, the layers were standardized and used to prepare maps of ambient heat load and dynamic capacity.

Three layers of building volume, topography, and green space were weighted and combined to create a heat load map. The other three layers of land cover, natural landscape, and proximity to open spaces were also combined to create a dynamic capacity map. Afterwards, these two maps were combined to create an UCAnMap. The resulting map was close to the on the ground realities. For example, building volume has a negative effect and increases heat load in urban areas. On the other hands, green space reduces heat load and has a positive effect. The central and southwestern parts of the city have a high heat load and core areas of the urban heat island have been calculated and obtained in these areas. The resulting map was classified into 8 categories to create urban climate analysis map of Tehran.

Results indicated that 59% of the urban area in Tehran, mostly located in the northern part of the city, has a good cooling and ventilation condition while 19% of the study area, mainly in the central, southern, and southwestern parts, faces heat stress and lacks an appropriate air ventilation condition. 22% of the study area, scattered all over the city but mostly located in the northern, western and eastern parts, faces an intermediate condition. According to the calculated heat load map, the central, southern, and western parts (in region 21) of the study area face a high and unfavorable ambient heat load. And many parts of the 4th, 1st, 2nd, 5th, and 22nd urban districts are characterized with low ambient heat load and favorable climatic conditions.

Remote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts in geospatial science is one of the dimensions of this development. The land cover product is one of these products that is used more than other remote sensing products. When presenting these products, their qualitative and quantitative characteristics, including their global accuracy, are also published. Expressing the accuracy of these products globally makes it necessary and necessary to re-evaluate their accuracy regionally for the users of these products in different regions of the world.

In this research, the accuracy of the European Space Agency's Copernicus Global Land Service (CGLS), GlobeLand30 and Esri's land cover product were evaluated for regional use in the north of Iran - Mazandaran province. After calculating the area of the classes for each of the land cover products, Pearson's correlation coefficient was used to calculate the correlation between them. For quantitative evaluation, the error matrix was used as one of the most common ways to evaluate the accuracy of land cover products. This method is based on the comparison of classified data and ground reality data. Also, the categorized random sampling method was used to select 1329 evaluation samples in Mazandaran province. For visual evaluation, three areas with dimensions of 6 x 6 km were selected.

The regional accuracy evaluation of the studied products shows opposite results compared to the global accuracy of these products. Based on the global accuracy reported for the studied products, the highest accuracy is calculated for the Esri product at 86%, followed by GlobeLand30 and CGLS at 83-85 and 80%. Meanwhile, based on the regional accuracy obtained from the results of this research, the highest regional accuracy for the CGLS product has been calculated at 84% and then for GlobeLand30 and Esri products at 81 and 75%. In evaluating the regional accuracy of the classes, all three studied products (CGLS, GlobeLand30 and Esri) have acceptable accuracy (above 90%) in the classes of snow and ice (100, 100 and 100%), forest (90, 95 and 98 percent), water (96, 94 and 90 percent) and impervious surface (94, 91 and 90 percent). For the agricultural class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers.In the 3 classes of shrubland, Impervious surface and wetland, the accuracy results are less than other classes for all three land cover products and in the amount of (29, 0 and 13 percent), (65, 66 and 42 percent) and (67, 38 and 0 percent).

Conclusion:

By evaluating and comparing the regional accuracy of three CGLS products, GlobeLand30 and Esri, this research answered the question of whether the accuracy stated in global land cover products can be trusted for regional studies and planning. The results show that the regional accuracy of CGLS, GlobeLand30, and Esri are 84, 81, and 75 percent, respectively, compared to their global accuracy (80, 83, 85, and 86 percent). These results show the difference obtained for the Esri product more than the two products CGLS and GlobeLand30. Meanwhile, the remote sensing data used for the Esri product (Sentinel-2 data) and its pixel size (10 meters) are of higher quality and quantity than the other two products. In fact, these results show that only paying attention to the type of data used and the global accuracy is not enough to use products in regional scales and requires evaluations before using them.In addition, by evaluating the classes of each product and comparing them, the need for this evaluation before using these products seems necessary. The results showed that in the evaluation of the regional accuracy of the classes, all three studied products had an accuracy of over 90% in the classes of snow and ice, forest, water areas and human construction. For the agricultural land class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers. In the 3 classes of shrubland, herbaceous cover and wetland, the results show lower accuracy than other classes for all three land cover products. Significant results were also obtained in the visual evaluation, and it seems necessary to pay attention to this evaluation before the applications where it is important to pay attention to a particular class.

The planetary boundary layer (PBL) as the lowest part of the troposphere is the most dynamic part of the atmosphere that is directly affected by the interactions of the atmosphere and the surface of the Earth (Stell, 2012 and Gert, 1992). These atmospheric surface interactions occur in short periods of time and play an important role in the development of the boundary layer. The height of this layer is also influenced by atmospheric conditions, topography characteristics, and type of land cover, and is an important parameter for many meteorological phenomena that have various applications such as monitoring air quality, cloud formation and evolution, surface fluids, and atmospheric hydrological cycles (Garrett 1994). Since the height of the boundary layer indicates the depth of turbulent vertical mixing, it is very effective in increasing or decreasing the concentration of pollutants near the surface and is considered as an essential parameter in air quality monitoring (Su and Khan, 2018). In addition, the height of this layer is a key factor in numerical weather forecasts. Since the height of the base of clouds is usually close to the height of the boundary layer, this layer determines the extent of cloud development and causes the transition from shallow convection to deep in the clouds.           

Materials:

The data used in this study included re-analysis data on the monthly time scale of the planetary boundary layer height for the entire Iranian region with a resolution of 0.25×0.25 which was obtained from the ERA5 version of ECMWF site during the period 1959-2021. In order to analyze the relationship between different climatic variables (mean temperature, mean relative humidity and air pressure), the meteorological data of 187 synoptic weather stations during the statistical period 2000-2022 has been used. Methods In this study, in order to prepare the data using programming capabilities in MATLAB software, maps with an average of 62 years old have been prepared and then using ARC GIS software to map the monthly average height of the boundary layer in Iran. In the next step, spatial statistics index of Getis-Ord Gi* was used to analyze the spatial changes in the height of the boundary layer in different months. In order to analyze the effective variables in elevation changes in the boundary layer temperature, relative humidity, soil moisture, etc. Multivariate standard regression method was used.

The annual average elevation map of the boundary layer also shows that the maximum height of this layer in Iran is 1600 m which is located in the south of Iran in Kerman province and south of Sistan and Baluchestan province and in general, the southern half of Iran with the exception of a narrow strip of southern coasts is higher than the northern half. The lowest elevation between 520 and 1000 meters is mainly located in the northern half, the eastern part and a narrow strip of southern coast. The average height of the entire boundary layer of Iran during the year is 1131 meters. The height of the boundary layer in different months of the year has significant changes in Iran and in terms of spatial changes it follows severe cluster patterns. Analysis of hot and cold spots showed that the spatial distribution of the height of the boundary layer has completely homogeneous spatial patterns so that the northern half of the country, especially the northwest and northeastern regions of the country, have a high significance as cold spots in most months of the year.

The results of this study showed that the elevation of the boundary layer in Iran during the year has a lot of spatial and temporal changes due to geographical diversity and climatic characteristics in different regions of the country. The existence of diverse topography, expansion in latitude, large differences in relative moisture content and soil moisture content are among the factors that have caused significant changes in the height of the boundary layer at different times and places. The results of multivariate regression analysis showed that the height of this layer is mainly affected by six parameters in particular, temperature and relative humidity.

Passive defense refers to a set of non-armed actions and activities which reduces the vulnerability of buildings, manpower, facilities, equipment, funds and vital arteries of the country against destructive and hostile operations of the enemy as well as natural disasters such as earthquakes and floods. The Including passive defense policies in most countries of world and especially Iran is Building public shelters to protect and maintain of Citizens' lives, also minimizing possible damages in the human domain. But what is important in Proportion with the proposed policy, location and choice of place is suitable for the construction of urban shelters. That People can take refuge to these shelters during enemy attacks or during natural crises. For this end, the aim of doing this study is locating potential areas of urban shelters based on passive defense principles in Ilam city.             

Present research In terms of the purpose, is of the type applied research and in terms of the nature and method of investigation, is of the type descriptive-analytical. Also, in terms of the data collection method, is included in the category of documentary and field research. The statistical population studied is including experts, professors and experts working in academic centers and higher education institutions. Sample size for research based on pairwise comparisons it's limited According to Saati (2002) and are selected Minimum 5 and maximum 15 people for this type of studies. Therefore, in this research, 15 university professors and experts were selected by available sampling method. To weight to information layers used from Saati's 9-option spectrum (superiority of one criterion over another) in the form of a questionnaire and a plan of language expressions. In this Research selected 10 location index of urban shelters (Distance from densely populated places, Distance from the centers of population, Distance from the canal, river and surface water, slope of the land, Distance from vulnerable areas and worn tissue, Distance from main roads for access and movement, Distance from historical and cultural monuments, Distance from industrial centers and hazardous products, The distance from the target centers of enemy and Distance from centers with support functions in times of crisis) in the form of four general criteria (Demographic, functional, physical and natural-environmental). In the next step was determined Coefficients of importance of indicators and criteria using the network analysis process technique (ANP), Eventually has been identified the most preferred places In proportion to the purpose through overlapping layers of information and applying the obtained coefficients. Data analysis has been done in a descriptive-analytical way and Using Analytical Network Process (ANP) and also by using Excel, Super Decisions and GIS software.

The research results show that: Among the general criteria studied, two demographic and functional criteria in order with weights 0.427 and 0.305 and among the studied indicators, two indicators Proximity to densely populated places and Establishing at a suitable distance from the enemy's targets in order with weights 0.303 and 0.236 have been highest coefficients of importance. In the following Results of combined analysis GIS- ANP showed that: All four urban areas of Ilam (including Haniwan, Ostandari, Markazi, Banborz, Sabzi Abad, Nowruz Abad, Janbazan and Razmandegan districts) is prone to shelter construction, But is in priority Respectively Region 2 (Banborz and Sabzi Abad districts), Region 1 (Haniwan and Ostandari) and Region 3 (Nowruz Abad).

Examining the first question based on Current status of urban shelters in Ilam city show that, most urban shelters located in the average status from the aspect of spatial distribution. The result of second question based on identification most important indicators affecting on location of urban shelters show that, two demographic and natural-environmental criteria identified as the most important and least important effective criterion in Location of urban shelter respectively with weights of 0.427 and 0.056. Eventually the results of third question based on identification best places to build urban shelters in Ilam city show that, most suitable place to build urban shelter situated in Haniwan, Ostandari and central districts of region 1, Banborz and Sabzi Abad districts of region 2, Nowruz Abad district of region 3 and Janbazan and Razmandegan districts of region 4.

Assessments of water quality have recently developed and now include surface and groundwater pollution issues. Permanent changes occurring in the quality of groundwater, especially those affecting drinking water and salinization of water sources, are considered to be a serious threat to rural development. Unfortunately, many people lack enough knowledge about the importance of groundwater and the harmful effects of environmental pollution on these valuable resources. The present study has investigated the quality of potable groundwater in the rural parts of central Ardabil County using multi-criteria decision-making models and geostatistical analysis in GIS environment. Parameters such as EC, PH, SO4-, Cl-, Na and TH (in terms of CaCo3) have been used to create an overall picture of the quality of potable groundwater resources in Ardabil County based on which related zoning map was developed in geographic information system. The kriging interpolation method was also used to obtain the spatial distribution of the parameters and the simple additive weighting method was used to weigh and rank the layers. According to the final map of water quality, approximately 36% of the central Ardabil County (around 88 square kilometers, mainly in the southern part of the study area) has access to optimal quality of drinking water. On the other hand, low quality of drinking water is observed in the northern and northeastern parts which cover 46% of the study area (112 square kilometers). Moreover, a direct relationship is observed between the population density and the density of deep and semi-deep wells and the decrease in the quality of water.            

The present study has applied library research and field study methods. Rstudio and Arc GIS 10 software were also used to perform related analyses. Study Area Case study area includes 243 square kilometers of the central Ardabil County consisting of three cities and nineteen villages as illustrated in Figure 1. The following methods were used in this research:1- Direct rating 2- Kriging interpolation 3- Standardization method 4- Simple weighing method 5- Fuzzification of the final data The following parameters have also been used to assess the quality of drinking water:1- Electrical conductivity (EC) 2- Chlorine level (Cl-) 3-The amount of sulfate (SO4+) 4- The amount of nitrate (Na) 5 Total water hardness(TH) 6- Water acidity level (PH)

Groundwater chemical quality is primarily assessed using parameters such as changes in the amount of dissolved salts, and limitations on various uses of water especially water used for drinking. Table 1 shows different types of conventional kriging methods selected through the method test for the parameters. These can be obtained using the mutual evaluation method and RMS error. Factors affecting the quality of drinking water are then ranked and weighted according to the expert opinions. The final quality map is thus prepared. Layers are then standardized in accordance with data description and the results are presented in Table 1. It also exhibits maximum permissible and desirable level of non-toxic chemicals in drinking water in accordance with the Iranian Standards and Industrial Research Institute (ISIRI) and the World Health Organization (WHO) standard, along with the maximum permissible level of mineral substances in drinking water. Semivariograms used for kriging interpolation are also presented. Table 2 shows the RMS and RMSE errors as well as the average standard error of the water quality parameters in the study area. The interpolated primary layers are presented in Figure 3. The final map prepared for the quality of potable water in the study area indicates that the quality of groundwater in the northern part and a little section of the central part (46% or 112 square kilometers of the study area) is unfavorable. This includes 8 villages of the County. 6 villages have access to drinking water with semi-optimal quality and 5 villages are located in the optimal area of water quality. Accordingly, the quality of potable groundwater decreases drastically as we move towards the northern and northeastern parts of the study area, and the lowest quality of groundwater is observed in the most northerly part of the study area (covering 46% of the study area). Figure 4 shows the density of deep and semi-deep wells, the amount of annual harvest from rivers in the central part of Ardabil (in thousand cubic meters per year), the population density and industrial areas in this region. A direct relationship is therefore observed between population density, the density of existing wells, the level of water extraction from rivers and the sharp drop in the quality of groundwater. According to the reports prepared by Ardabil Regional Water Company, around 32 million cubic meters of water is annually needed to meet the drinking requirements of urban and rural uses, which can seriously damage the quality of underground water in the area.

According to the final map of groundwater quality, only 36% of the study area (88 square kilometers) has access to drinking water with favorable quality which can be a great concern for planners and managers. Finally, it is suggested to use geostatistical methods and geographic information system as a useful tool to assess the quality of underground water. These methods can gradually replace the old methods and thus prepare more accurate statistics, increase the efficiency of water-related projects, and reduce their cost.

Urbanization is a developing phenomenon, and the analysis of the appropriate location and the geographical distribution of urban green space plays a significant role in the development and future of the city. Although in the past, green spaces were primarily manifested in the beautification and appearance of urban areas, nowadays, for several reasons, it is considered as a breathing space of the cities. The growth of industry and the increase in population in cities have led to speculative constructions that do not pay enough attention to health issues, provision of sufficient light and healthy air, and leisure spaces in buildings. Moreover, the necessity of creating new urban land use to meet the ever-increasing needs of urban dwellers has gradually reduced the share of urban green space, which is the consequence of limiting the access of urban dwellers to nature. But for some reason, at the beginning of the 20th century, the urban man showed a renewed attention to nature and green spaces, which manifests itself in creating functional gardens instead of recreational gardens that respond to the new needs of citizens. The present study aims to Rank the influencing factors to locate urban green spaces in Masjed Soleyman city.              

The present applied study employed an analytical-descriptive method. Reliable internal and external sources related to the subject were reviewed, and in some cases, field studies and referrals to related organizations were conducted for data collection. In this research, the DEMATEL-ANP-integrated approach was employed, and the criteria weights were calculated. Then, the layer of each weight was entered into the Arc GIS software.

As the research findings show, 14 criteria are involved in the optimal location of urban green spaces in Masjid Suleiman, distance to commercial centers, distance to waste and empty lands, distance to administrative centers, distance to medical centers, distance to educational centers, distance to existing green spaces, distance to industrial centers, distance to urban facilities and equipment, distance to military centers, distance to religious centers, distance to communication paths, and density.

The results of this study showed the priority of the mentioned 14 indicators in order from low to high: proximity to residential centers (0.09263, rank 1), proximity to educational centers (0.07428, rank 2), proximity to cultural centers (0.07268, rank 3), population density (0.07154 and rank 4), proximity to communication ways (0.07092, rank 5), proximity to religious centers (0.06979, rank 6), proximity to existing green spaces (0.06967, rank 7), proximity to medical centers (0.06934, rank 8), proximity to commercial centers (0.06923, rank 9), proximity to urban facilities and equipment (0.06902, rank 10), proximity to military centers (0.06874, rank 11), proximity to administrative centers (0.06761, rank 12), proximity to industrial centers (0.06729, rank 13) and proximity to empty and barren land (0.06726, rank 14).