نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 118 (تابستان 1400)

Due to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). 

ASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. 

Findings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. 

Results indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.

As global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. 

Data collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. 

Appropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October.  

Selecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.

The complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response received from SAR radar. Investigating these characteristics facilitate target recognition. Synthetic Aperture Radar sensors are widely used in both airborne and space-borne systems. Space-borne systems equipped with Synthetic Aperture Radar sensors are side-looking and because of their nature as a radar, many parameters such as vision geometry will affect their ability (or disability) in seeing the target and change the resulting images. Therefore, it is very important to study the effect of this parameter to identify the target and interpret these images. The visibility geometry includes incidence angle, look angle, and the direction of the imaging. 

The present study investigates visibility geometry in revision images and ascending and descending scenes. To reach this aim, a single scene captured by Sentinel-1 from a residential area is examined in different images with different directions, incidence angles, and imaging time. Results indicate that incidence angle changed slightly (4 degrees) and thus, left a negligible effect on the image. Moreover, there was a 5-day time interval between the captured images and therefore, this factor had the least effect on Synthetic Aperture Radar images. Unlike optical images, the direction of imaging had the greatest effect on SAR images. For an instance, a single ramp behaves differently in two images captured from different directions. Therefore, direction of imaging and its effects on seeing (or not seeing) the target are analyzed in ascending and descending images.

The effect of vision geometry on radar images has been rarely investigated in similar studies, and the present paper has taken a step forward in this regard. Fallahpour et al., (2016) have simulated the effect of incidence angle, which is a parameter of visibility geometry and the shape of the targets in SAR images. Shapes such as cones, cylinders, and cubes were used in this simulation representing real buildings, niches, tree trunks, etc. which are very common in SAR images. Moreover, behavioral pattern of the aforementioned geometric shapes were simulated at different landing angles (30, 40, 45, 50, and 60 degrees) from the viewpoint of SAR imaging systems to reach a more comprehensive result. Then, various studies investigating the effects of incidence angle and direction on radar images have been reviewed. Some of these studies have dealt with the effect of these parameters on the classification of radar images. Dumitru et al. have examined the effects of resolution, pixel spacing, patch size, path direction, and incidence angle on the classification of TerraSAR-X images. To reach this aim, they have selected an optimal TerraSAR-X product and then specified the number of classes. They have finally investigated the effects of incidence angle and path direction on the classification results. Results indicated that images captured in ascending direction were 80% better than the descending images. Moreover, images captured from an incidence angle near the upper wing showed better results.

The present study has investigated the effect of usually neglected parameter of visibility geometry on SAR images. Images were captured by Sentinel-1 in both ascending and descending directions. Following speckle noise reduction and geometric correction, incidence angle and its effects on the detected changes were investigated. The slight 4-degree changes of this parameter have not caused the resulting changes. Moreover, there was a 5 day time interval between these two images and thus, time could not be an effective parameter too. Results indicate that detected changes in the residential area were due to a change in the direction of imaging. Changes of this parameter can result in seeing (or not seeing) the target, and therefore, it is very important to investigate the effects of this parameter and correct it.

The global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an important part of recent scientific and political debates. Covering only a small area of land, cities are responsible for many global environmental problems such as carbon emissions, energy and resource consumption, biodiversity degradation, and ecosystem degradation. They also convert natural forests to human settlements, farms, roads, gardens, and other human-made land uses, leaving many direct and indirect effects on natural conditions and ecological functions of upstream and downstream in forests (such as changes in quantity and quality of water, changes in water flow in rivers, changes in climatic condition and habitat quality). These structural and functional changes undermine environmental services provided by ecological infrastructure and threaten the environmental security of cities and their sustainable development. Therefore, urban managers and experts have always sought a suitable way for urban planning to regulate the structure of cities, support the stability of ecosystem and its performance, and maintain the ecological security of cities. 

Lavasanat is a district in Shemiranat County in Tehran province of Iran, which is located in the northeast of Tehran. MethodsThe present study analyzes temporal-spatial changes of land use / land cover and then, uses InVEST 3.7.0 model to evaluate temporal-spatial changes of land uses. Results & DiscussionChanges occurring in the reference period were depicted in maps prepared for various land cover / land use classes. Validation of image classification shows a total accuracy of 95.72%, 96.26% and 95.32% and a Kappa coefficient of 0.948, 0.943 and 0.936 for classifications in 2000, 2010 and 2020, respectively, which is acceptable and indicates the compatibility of classified land uses and reality. Classification of images using maximum likelihood algorithm showed the presence of five classes of residential areas (urban area, villages, industries and roads), barren lands, pastures, water bodies and green space in the region.Land use maps and information derived from satellite images indicate that residential areas have experienced a growing trend due to increasing population, demand for land and consequent growth of urbanism, while green space had a decreasing trend during the reference period. Development of residential areas and reduction in green space are quite evident between 2010 and 2020. According to the present trend of land use change, there will be a sharp decline in green space in the coming years. Pastures experienced a decreasing trend from 2000 to 2010. However, it faces an increasing trend from 2010 to 2020 since more green areas were converted into pastures. Barren lands experienced a decreasing trend from 2000 to 2020. 

The present paper offers the results of modeling water production services in Lavasanat Basin in different decades. Results indicate that the water production in the entire Lavasanat basin equals 2641734.816 cubic meters in 2000, 3318950.915 cubic meters in 2010 and 7737201.215 cubic meters in 2020. Of these volumes, 1677926.367 cubic meters in 2000, 2287145.055 cubic meters in 2010, and 4908786.651 cubic meters in 2020 belonged to residential areas. This class contained an area of 4820578.505 square meters in 2000, 6885513.787 square meters in 2010 and 10407948.705 square meters in 2020 in the whole basin.The results obtained from InVEST scenario building model and water production model showed that the increasing trend of human-made land uses in the study area has a significant impact on increasing water production and, consequently, increases runoff. In fact, water production has experienced a growth rate of 1.25 or 125% from 2000 to 2010, and a growth rate of 2.33 or 233% from 2010 to 2020. Thus in 20 years, water production has increased by 2.92 (292%). The volume of water production in residential areas has increased by 1.36 times (136 %) from 2000 to 2010, 2.14 times (214 %) from 2010 to 2020 and 2.92 times (292%) in 20 years. Also, the total area covered by residential land use has grown 1.42 times from 2000 to 2010 (142 %), and 1.51 times (151%) from 2010 to 2020.  Therefore, an increase of 2.15 or 215% was observed in residential areas over this 20 year period.

Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.

Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely. 

The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.

Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.

Nowadays, tourism is widely accepted as a fundamental basis of development. As a sector of economy, tourism is considered to be one of the most important activities of contemporary human beings, which not only makes dramatic changes to the landscape, and political, economic, and cultural condition, but also transforms lifestyle of many individuals. The contemporary world considers tourism as one of the most important sectors of the tertiary industry which affects job creation and income generation, results in significant economic growth, and consequently provides the prerequisites for sustainable development of different societies. Iran is among the top 10 countries of the world in terms of tourist attractions, possessing many sites with potential attractions. Thus, tourism can be considered as an effective tool in dealing with economic problems of the country. As the basis of sustainable development, tourism can solve some problems of the country and thus, development of its infrastructure results in optimal allocation of available resources. The present study seeks to investigate the overall condition of tourism infrastructure in Markazi province. Previous studies in France, Austria, Switzerland, the United Kingdom, Ireland, Thailand and Japan indicate that their tourism sector has developed rapidly and now aids other sectors of the economy. Therefore, a comprehensive analysis of necessary infrastructure for the development of this industry can result in a more dynamic rural economy. 

This applied study has a descriptive-comparative design and its study area includes all counties of Markazi Province. Library method and questionnaires were used for data collection. Statistical data and information were collected from the General Directorate of Cultural Heritage, Tourism and Handicrafts of Markazi Province and the statistical yearbook (2015) of this province. In accordance with Delfi method and targeted sampling, related indices were sent to 17 rural development experts and specialists via Email.  It should be noted that some of these experts had previous experience in tourism. Finally, 10 completed questionnaires were received. PROMETHEE multifunctional decision-making model was also used to determine the overall condition of counties in Markazi Province in relation to tourism infrastructure.Since the present study seeks to classify counties in Markazi province, the first function of this technique has been used. An appropriate weight is first assigned to each of the 20 indices of tourism infrastructure using Delphi method. Then, these weights are evaluated and measured along with the value of each component and option in Visual PROMETHEE software. Structural equation modeling was used to investigate relations between variables more comprehensively. SPSS 26 and Smart PLS 3 software were also used to analyze the data. 

Findings indicate that Arak with a value of 0.7739 has ranked first among the counties. Several factors can be the reason: First, as the capital of the province, Arak possesses better facilities, larger population, etc. Second, as the main access road connecting neighboring provinces, Arak has developed more than other counties. With a value of 0.4673, Saveh has the second rank. Saveh also contains the access road connecting some of neighboring provinces and is located near Tehran. Thus, a strong industrial town has developed in this county attracting many workers with different ethnicities seeking employment and income. Due to these factors, relatively good facilities have developed in Saveh. With a value of 0.3536, Shazand has ranked third. Due to its proximity to Arak (the capital of the province), this county has attracted large industries such as petrochemical industry along with suitable facilities and infrastructure. Khomein (0.3166), Delijan (0.0168), Mahalat (-0.1023), Tafresh (-0.1634), Khandab (-0.3002), Zarandieh (-0.3266), Farahan (-0.3320), Ashtian (-0.3514) and Komijan (-0.3523) are next in rank.Analyzing the relationships between variables indicates that service-related components (0.279) and transportation-related components (0.096) have the most powerful direct influence on the level of development and other variables are next in rank. 

Findings of the present study and previous studies indicate that centrality and population can be considered as influential factors resulting in easier access to desirable and appropriate facilities in different countries of the world. However, such a difference is not observed between different regions in developed countries due to their integrated development. Developing countries such as Iran lack such an integrated development environment and thus, the condition in provincial capitals is much more different from other counties. As indicated in the present study, the level of development in Arak was much higher than other counties of Markazi province. Therefore, an appropriate plan is required for other counties to achieve sustainable development, and especially sustainable tourism development.

Surface winds move and transport soil particles on the ground and thus, affect the intensity of erosion to a great degree (Tage Din et al, 1986: 118). Various studies have found a decreasing trend for surface wind speed in different parts of the world in recent years. This decrease has been more widely reported in mid-latitudes (McVicar et al, 2008). Continuous drought in consecutive years is one of the factors that can reduce soil moisture and stop the growth of vegetation cover. (Hereher at el, 2009). Iran is located in the arid belt of the world and two thirds of its total area is located in these arid regions (Maghsoudi, 2006). Previous studies have shown that 17 provinces of the country are affected by wind erosion, among which Kerman faces a more severe conditions. Iran has more than 20 relatively large ergs and several small ergs covering an area of approximately 36,000 square kilometers (Mahmoudi, 1991). The present study investigates different characteristics of winds and its effects on morphology and displacement of sand dunes using Sentinel-2 optical and Sentinel_1 radar images.

Due to the lack of any synoptic station in the Lut Desert, related data including wind direction and speed were collected from 6 neighboring stations (Bam, Dehsalm, Zabol, Shahdad, Nusratabad and Nehbandan). Then, a wind rose and a sand rose graph were prepared for each station using WR Plot and Sand Rose Graph software. Resultant force vector acting in the displacement of sands and formation of sand dunes was determined. Following an examination of wind characteristics in the study area using Sentinel-2 optical images collected in the 2016 - 2019 reference period, changes of sand dunes and direction of their movements were also analyzed. In order to investigate vertical displacement in the region, radar interference method and SBAS time series have been used. This method only uses pairs of images in which vertical component of the baseline is less than its critical value, and also have a minimum baseline time. 45 Sentinel_1 radar images were used in the present study to measure radar interference. 

Recorded data in Dehsalm, Nehbandan, and Nosrat Abad stations indicate that winds blowing in these stations affect the Lut Desert. The prevailing wind recorded in Dehsalm station blows in northwest to southeast direction of the Lut Erg, while in Nehbandan station, the prevailing wind blows in north to south direction of this Erg. The prevailing wind in Nosrat Abad station blows in southeast to northwest direction of this erg. Sand rose graphs show that DPt in Dehsalam station equals 422.6 and in Nehbandan station equals 484.2. Since both DPts are more than 400, wind in this region has a high energy level and is potentially capable of sand displacement. Changes of sand dunes and direction of their movements were analyzed using Sentinel-2 and Sentinel-1 images in 2016-2019 reference period. 

Hourly wind speed and direction data in Nehbandan, Dehsalam, and Nosratabad stations were investigated in the present study to evaluate their impact on geomorphological changes in the Lut Erg and its sand dunes. Results indicate that the prevailing wind in these stations blows in north, northwest and southeast direction towards the Lut Erg, respectively. Investigating wind speed changes in Nehbandan station shows that during the last 34 years, average monthly wind speed in this station has decreased from 3.7 meters per second in 1986 to about 2.2 meters per second in 2020, which means a 1.5 meters per second decrease has occurred during this period. Apart from wind speed and direction data, Sentinel-2 optical images were also used to monitor changes in sand dunes of the Lut Erg. Results indicate that during the 2017 - 2018 reference period, most changes have occurred in the sand dunes of the northwest and northeast regions and the margins of this erg, while in the 2018 - 2019 reference period, most changes have occurred in the northwest and southeast regions of the Lut Erg. Analysis of satellite images indicates that the direction of wind force vectors is consistent with the direction of sand transport vector. In other words, sand dune changes in the Lut Erg have occurred under the influence of winds blowing in northwest and southeast directions, which is consistent with the direction of the sand transport vector in plots prepared for the three stations (Nehbandan, Dehsalam, and Nusrataba). In order to validate the results of wind direction and speed analysis and remote sensing of optical images, vertical displacement of the erg surface was measured in 4-year periods using Sentinel_1 radar images and SBAS time series. In general, southern parts of the Lut Erg and especially sand dunes in these parts have experienced an increase in elevation, while the northern parts of Erg have experienced a decrease in elevation. This can be due to erosion and deposition of sediments in the southern regions of the Lut Erg, which is consistent with the sand rose and wind rose graphs prepared for the region .

Selecting a suitable place for a new retail store is a very important decision since new shops cost a lot and new retailers puts themselves at financial risk. Physical location of stores affects the consumer's perception of their first purchase and their subsequent loyalty to the store. Therefore, spatial analysis is very important for retail stores. Site selection for retail stores has always been difficult and the current competitive market has made decision making even more difficult since stores face increased competition and consumers have many options to satisfy their needs. They generally choose a suitable store in their vicinity which provides high quality, cheap, and diverse products. Therefore, markets and especially retailers shall follow an accurate and valid location strategy for new stores. Retail stores have various marketing and customer service strategies. Marketing strategies require a lot of information about different aspects such as customers, shops, competitors, and products. Many marketing strategies only provide information about consumer behavior or customer satisfaction. However, spatial aspects are more important and in fact determine future success of a store. Several methods are used for spatial analysis in retail sector. The present study use a multiplicative interaction model to forecast sales of confectionaries. This can help retailers develop strategies and find an optimal location for their new stores. 

The present study has developed a location-based marketing model for online confectioneries in Tehran which can improve site selection strategies of new confectioneries. This marketing model is based on the multiplicative competitive interaction model (MCI) of the retail location theory. To do so, characteristics attracting customers to confectioneries are determined and related data are collected from the Snappfood online platform through web crawling. ArcMap software is then used to analyze and process the collected data. After data normalization, MCI model is implemented using Python programming language. The model is then calibrated using 80% of the collected data and the ordinary least squares (OLS) method. The model is then evaluated using root mean square error (RMSE) method and the remaining data. 

Mean errors obtained for districts number 1 to 22 of Tehran municipality show high accuracy of the model. Snappfood site lacked any information about districts number 9 and 18 and thus these districts were not considered in the calculations. Depending on the available data, other districts showed different levels of accuracy. Results indicate that district number 22 had the lowest level of accuracy and district 17 had the highest level of accuracy.  In general, this model predicts customer behavior with an error rate of 17.03%. Results of the present study show the probability of purchasing from each confectionery which can be used to map market potential for a new store. This map determines the best place with maximum sale and helps in site selection for new stores based on specific features of the store, competitors and the environment. 

MCI model predicts sales. From a geomarketing perspective, this model shows that distance between customers and the store and accessibility affect location strategies in new stores. Variables such as pricing and customer satisfaction (scoring) are used to improve the goodness-of- fit of the model. This precise method identifies some key factors to success in a retail strategy. It predicts the probability of purchasing in each district, the number of customers in each store, and distribution of customers in each district. Experts and new retailers can use the results to design various location and sales strategies. Using this model, new retailers in confectionary market can accurately predict their sales before even opening the store and thus protect themselves against possible financial losses. Moreover, this model predicts total sales of different stores and help retailers compare their market shares with those of their competitors. They also can enter features of a new store into the model and find several potential sales strategies. In other words, the model helps determine sales of existing and new shops. In this way, retailers can find an optimum location for their new confectioneries based on the principles of geomarketing.

Advances in computer vision and the development of remote sensing instruments have made indirect measurement of tree features possible. Individual tree crown delineation is an important step towards information collection and mapping trees in an urban area. This information is then used to help planners design strategies for optimization of urban ecosystem services and adapt to climate changes. Common methods of Individual tree crown delineation (ITCD) were based on very high-resolution satellite or Light Detection and Ranging (LiDAR) data. However, satellite data are usually covered by clouds and thus, cannot be appropriate for the measurement of individual trees. Aerial Laser scanning is also relatively expensive. Remote sensing with unmanned aerial vehicle (UAV) captures low altitude imagery and thus, is potentially capable of mapping complex urban vegetation. Automatic delineation of trees with UAV data makes collection of detailed information from trees in large geographic and urban regions possible. Therefore, a multirotor UAV equipped with a high-resolution RGB camera was used in the present study to obtain aerial images and delineate individual trees.

The present study has compared the performance of Inverse watershed segmentation (IWS) and region growing (RG) algorithms using point clouds derived from Structure from Motion (SfM) algorithm and UAV imagery captured with the aim of tree delineation in Fateh urban forest located in Karaj. Region growing (RG) is used to separate regions and distinguish objects in an image. It starts at the initial seed points and determines whether the neighboring pixels should be added to the growing region. If the neighboring pixels are sufficiently similar to the seed pixel, they are labeled as belonging to the seed pixel. To implement the algorithm, the window size and the growing threshold were set for all resolutions. In order to obtain the most appropriate size for the search window, we examined different window sizes with a growing threshold of 0.5 for each resolution. Individual trees delineation was performed for each CHM resolution in the three different sites using "itcSegment" package of R software. Watershed segmentation algorithm is also similar to RG algorithm. The only difference is that it sets the growing seeds at the local minima. In other words, the local maxima in this algorithm change into local minima and vice versa. Inverse Watershed Segmentation (IWS) method was implemented in ArcGIS 10.3 because of its capability in delineation of distinct tree entities. In the summer of 2018, three sites with different structures including a mixed uneven-aged dense stand (site 1), a mixed uneven-aged sparse stand (site 2), and a homogeneous even-aged dense stand (site 3) were surveyed and photographed, and a 3D point cloud was extracted from the images. Then, the performance of algorithms was tested using a series of different canopy height models (CHM) with spatial resolutions of 25, 50, 75, 100, and 120 cm. To generate these models, digital surface model (DSM) was subtracted from digital terrain model (DTM). Results of individual tree delineation were validated using data collected in field observation of the aforementioned sites.

Results indicated that both RG and IWS algorithms yielded their best performance in the dense homogeneous structure. Moreover, the number of segments resulting from CHMs with low resolution was often much more than the actual number of trees. This was due to the occurrence of several peaks within an individual tree crown especially in low resolution images. With an F-score of 0.88, homogeneous even-aged dense stand (site 3) showed the highest overall accuracy in RG algorithm with a pixel size of 75 cm. Generally, results indicated that RG is an appropriate approach for individual tree delineation due to its flexibility in delineation of varying crown sizes. Furthermore, this method does not assume a circular shape for tree crowns and thus, is capable of detecting and segmenting irregular crowns. Generally, delineation of trees in urban forests using CHMs obtained from UAV-captured aerial imagery was highly accurate in homogeneous sites, while such models lacked efficiency in heterogeneous sites.

Human activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential for taking measures and making decisions based on the goals of sustainable water and soil resources management. Over the past decade, many researchers around the world have been attracted to remote sensing and especially satellite remote sensing and used this technology to detect such changes over time. The present study has used Landsat (monitoring the area of water body), TRMM (monitoring rainfall), MODIS (monitoring vegetation and evapotranspiration), Grace (monitoring groundwater) satellite images available in Google Earth Engine to study last two decades changes (from 2000 to 2019) in Maharloo wetland, Goshnegan catchment and their surroundings. 

Maharloo wetland is located in Fars province and Goshnegan catchment (426 square kilometers). The present study has used Landsat 7 and 8 images to extract the area of water body, TRMM images to obtain precipitation values, MODIS products to calculate NDVI and evapotranspiration, and data received from Grace to extract changes in groundwater level. These satellite images were available in Google Earth Engine. Mann-Kendall test was also used to assess the overall trend of the aforementioned factors. 

The automated water extraction index was used in the present study to identify and estimate the area covered by ​​water bodies in the study area. The largest area belonged to 2006 (216.76 square kilometers) and the smallest belonged to 2018 (66 square kilometers). In 2000 (the beginning of the reference period), an area of ​​216.52 square kilometers was covered by this wetland which is close to what was observed in 2006. In 2018, this has reduced to 66 square kilometers. Thus, there is about 150.72 square kilometers (69.54 percent) difference between these two years. In 2009, the total area has reduced to 66.67 square kilometers. A numerical comparison between 2000 and 2019 also indicates a reduction of 91.17 square kilometers (42% decrease) in the total area covered by this wetland. Also, a 53.72 square kilometers (29.60%) difference was observed between the average area covered by the water body in the first and second ten years. Since calculated p-value value (< 0.00001) is less than the alpha level (0.05), so a significant trend was observed in the average annual data of the area covered by this wetland. Kendall's tau also indicated declining trend of the collected data. Groundwater level was calculated using data received from Grace Satellite to investigate the role of groundwater level in reducing the area covered by the ​​water body. Results indicated that since 2008, groundwater level ​​have always showed a negative value (a decreasing trend). For an instance, a groundwater level of -10.86 cm in 2019 indicates a decrease in the water level in the study area. As the calculated p-value (< 0.0001) is less than the alpha level (0.05), so a significant decreasing trend was observed in the groundwater level. Results of Mann-Kendall test (-0.6) also indicated that changes in water bodies, vegetation, rainfall and groundwater level had a decreasing, increasing, increasing and decreasing trend, respectively. No significant trend was observed in evapotranspiration. It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. 

Wetlands provide many ecological services including water treatment, natural hazard prevention, soil and water protection, and coastline management (Amani et al., 2019). Therefore, understanding the importance of wetlands and their management need to be seriously considered by relevant organizations in different countries of the world, and Iran is no exception. Satellite data and remote sensing methods and techniques are considered to be one of the most important and cost-effective methods of monitoring wetlands. The present study used satellite data collected by Landsat, MODIS, Grace, and TRMM to monitor water bodies, vegetation, groundwater level, and rainfall in Goshnegan catchment in which Maharloo wetland is located. The results of Mann-Kendall test showed a decreasing annual trend for changes in the average area of ​​this wetland. This decreasing trend is considered to be a serious threat to human settlements around the wetland which can intensify over time. It will also affect the thermal islands of Shiraz and Sarvestan in near future. Obviously, management of agricultural and forest land uses with the aim of stopping their increasing trend can improve water balance in catchment areas. A 132.2 ha (approximately 36.16%) difference was observed between the average vegetation cover in this catchment area over the first and second ten years (233.4 vs. 365.6 ha). It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. Due to the proximity of this wetland to the city of Shiraz and its importance as an ecological and tourist attraction, it is suggested that related authorities (Department of Environment and Water Organization) demarcate lake bed and riparian zone with the help of remote sensing researchers to improve the management of this wetland and prevent it from drying up. Also, it is suggested that the Organization of Agriculture Jihad review and improve water consumption methods and cultivation patterns in the areas surrounding this wetland.

Ultraviolet radiation (UVA) is a dangerous part of solar radiation that makes a small percentage of total solar radiation energy (5 to 7 percent). Despite the beneficial role of solar energy in the production of vitamin D3, it can cause irreparable damage to human cells. The majority of previous studies on ultraviolet radiation in Iran have focused on in-vitro impacts of UV radiation on human health and plant physiology in a limited study area. The present study estimates daily cumulative UVA radiation in central regions of Iran and compare it with total column ozone (TCO), cloud optical depth (COD) and aerosol optical depth (AOD) in different seasons.

The present study estimates daily cumulative UVA radiation (320-400 nm) over a 13-year reference period (2005-2017) in a large area in Central Plateau of Iran with arid and semi-arid climate using TUV5 multilayer radiative transfer model (Madronich, 1993). 22 synoptic stations in 9 provinces were investigated in this study. Daily cumulative UVA radiation under three different sky conditions (clear-sky, overcast and real-sky) was also compared with geographical distribution of total column ozone (TCO), cloud optical depth (COD), aerosol optical depth (AOD) and surface albedo (SALB). Required data were extracted from satellite images (downloaded from http://disc.gsfc.nasa.gov) and Iran Meteorological Organization data center.

In general, maximum daily UVA radiation was recorded in the southern half of the study area. During warm seasons of the year, the eastern part of the study area (Kerman and Khorasan-e-Jonubi Provinces) and during the cold seasons of the year, central and southwestern part of the study area (Yazd and Fars Provinces) experience maximum daily UVA radiation. Maximum cloudiness in spring has occurred in northeastern and western parts of the study area and a lower level of cloudiness has always been recorded in its southern parts. Thus, the highest level of UVA radiation has been recorded in southeastern parts of the study area and especially in Birjand station (1071.12 kj/m2 per day). As expected, maximum UVA radiations in all sky conditions and all stations were recorded in summer. The lowest level of cloudiness was also recorded in this season. During autumn and in overcast condition, the highest concentration of UVA was recorded in southeastern parts of the study area and Birjand station (725.85 kj/m2 per day). This is consistent with cloud optical depth and total column ozone, and so, the lowest amount of ozone in this season was recorded in Birjand station (276.57 Dobson). The highest values of atmospheric aerosol with an average of 0.59 optical depth were recorded in winter in the eastern parts of the study area. Thus unlike other seasons, maximum UVA radiation in overcast condition moves toward central stations in winter. Comparison of daily cumulative radiation maps in overcast condition shows that there is a good agreement between daily cumulative radiation and cloud optical depth (COD) and aerosol optical depth (AOD). This indicates that in overcast condition, total column ozone (TCO) have a weaker impact on UVA radiation as compared to other sky conditions. However, UVA radiation is consistent with total column ozone in clear-sky conditions.

Geographical distribution of UVA radiation indicates that maximum daily radiation in warm seasons has often occurred in the eastern parts of the study area. However, maximum concentration of UVA radiation moves towards southwestern parts of the region in cold seasons. Therefore, residents of the eastern and southwestern regions face a higher risk due to daily cumulative UVA radiation. Findings indicate high biological risk of solar UVA wavelengths in clear-sky condition within the study area. Overcast conditions can reduce daily UVA radiation up to 52% in winter and 21% in summer as compared to clear sky conditions. In real-sky conditions, daily UVA radiation decreases up to 19% in summers and up to 32% in winters as compared to clear-sky conditions. As a result of lower solar zenith angle, the impact of cloudiness on surface UVA radiation in summer is relatively less than cold seasons.

Recently, Disaster Mitigation and Management Organization of Iran has focused on construction of disaster management and support bases. To reach such an aim, it is necessary to investigate different areas and select an appropriate geographical location for this type of land use. The selected location must be safe in critical conditions and make the base as efficient as possible. Accurate site selection is a necessary step towards prevention, preparedness and dealing with various disasters. Selecting an appropriate location for these bases is extremely important in their relief operations. Thus, the bases must be properly accessible while having a strong structure unthreatened by any risk. Disaster management measures are based on information analysis and take advantage of geographical information tools for spatial analysis. Geographic Information System (GIS) can be used in all stages of disaster management. Geographic information system is a very useful tool for implementation of logical models, decision making about land use allocation, selection of the most suitable site, evaluation of suitable options and finally reaching an integrated choice. Geographical location of Baneh in vicinity of Iran-Iraq border, and related natural hazards (geological, climatic, and etc.) and unnatural threats (political-security) have made passive defense and especially site selection for disaster management and support bases a crucial issue in this border area. Therefore, the present study seeks to select the best possible site for disaster management and support bases in Baneh. 

Data have been collected through library research and questionnaires distributed among experts. Research criteria have also been classified into natural and unnatural (man-made) categories which include altitude, slope, slope direction, vegetation, lithology, distance from fault line, distance from river, climatic factor (precipitation), link roads, distance from city, distance from village, gas stations, health centers, relief agencies, military and police stations, open spaces and distance from the international Iran-Iraq border. Analytic Hierarchy Process (AHP) was used to analyze the questionnaires and Expert Choice and Arc GIS software were used for data processing. The importance coefficient of the criteria were analyzed in Arc GIS software using AHP FUZZY model and the final results were presented in the form of a map.

Results indicate that unnatural (man-made) criteria received a higher weight as compared to natural criteria. Among these criteria, vicinity to health centers received the highest weight (0.151), while vegetation and slope direction received the lowest weight (0.016). ​​Baneh generally lacks a proper situation for the construction of disaster management and support bases except for the cities in the middle of the county.

Due to the occurrence of various natural disasters and unnatural (human) threats in the border areas of the country including the county of Baneh which have affected the security of the region by their serious damages and high death tolls, it is necessary to focus on passive defense in site selection for disaster management and support bases in these areas. Results indicated that quick and easy access from bases to health centers and relief agencies, link roads, police stations and proximity to cities have the highest priority in site selection for disaster management and support bases. Other desirable passive defense criteria include distance from Iran-Iraq border and distance from fault lines. The final map shows unacceptable situation of the county in terms of disaster management bases. Except for cities located in the middle of this county such as Baneh, Buin, Armardeh and Kanisour, other cities lack a proper situation for construction of these bases. Such a situation results in higher vulnerability of these areas in probable disasters. Therefore, it is necessary to adopt short-term and practical programs along with important and effective criteria of passive defense to find the most optimal sites for disaster management and support bases in Baneh County.

Proper distribution of urban green space is one of the most important issues in urban planning and especially in management of urban green space. In other words, the physical expansion of cities destroys surrounding natural environments and arable lands. It also results in fundamental changes in ecological structure and functionof urban landscape, along with gradual changesin spatial structure and patterns of this landscape (Wang et al., 2008). Since ecosystem processes depends on its structure, landscape metrics have been accepted as a very useful tool for expressing the structure of urban green space and its human-causedchanges (Hessburg et al., 2013).There has always been discussion onacceptable per capita green space or changes in green space over time and place. Iranian cities are no exception in this regard, thougheven a city enjoying a high ratio of green space per capita may still lack enough green space per capita in some districts. This suggests the necessity of investigating various measures and avoiding studies limited to per capita green space and urban forestry. (Botequilha and Ahren, 2002). If as an ecological structure,green space is proportional to populationcomposition and distribution, ecological performance and land use type of an urban area, it can have important ecological functions. Since most studies on urban green space have primarily focused onfinding a proper location, calculating appropriate per capita green space and introducing suitable species for green space, investigatingthe spatial distribution of urban green spaceseems to be of great importance. Therefore, the present study seeks to investigate the spatial pattern and distribution of public green space in Khomein using a landscape approach.

Study area The study area, Khomein, is bounded by agricultural lands and gardens in its northeast, west, and partly in its south. Only the main area of urban texture is located on barren lands (Abbasi et al., 1986). The study area includes four districts of Khomeinin which the pattern of green space distribution isinvestigated. Methods Sentinel-2 images were used in the present study. Satellite images were processed and then, their geographical effects were extracted inthe first step of classification. Different indices were defined for each patch of the image and using supervised method, images were classified into four classes of agricultural lands, barren lands, urban parks and residential areas in accordance with the training data. Visual method was used to improve classification results. In this method, classification results are matched with the imagesand possible errors are rectified. Google Earth was used to evaluate the accuracy of results obtained from classification of satellite images. In the next step,the base map of the present study was produced and then, the layer containing urban parkswas integrated with the layers prepared for four districts of Khomein. It should be noted that the present study focuses on urban parks prepared by the municipality for public use and does not include other urban green spaceareas such as the green belt or private gardens, etc. To study the spatial distribution of green space, measures of land cover were calculated and analyzed in each of the four districts. Geographic Information System (GIS) and Landscape Measurement Analysis Program (FRAGSTATS) were among the tools used to calculate and measure landusein the present study. Landscape metrics used in the present study included: Landscape Shape Index (LSI) which measures the area of ​​the largest patch in a class divided by the total area of ​​that landscape (multiplied by 100 to convert to percent) Euclidean Nearest Neighbor distance (ENN) which is the average distance between patches in a class. Meter is used as the standard unit of measurement for this index. Perimeter /Area Ratio (PARA) which is the ratio of the perimeter of ​​the patch (m) to its area (m2). This measure lacks a specific unit and for PARA> 0 it is without a limit. Number of Patches (NP) equals the number of patchesof the corresponding patch type (class). Shape Index:sum of patches’ perimeter divided by the square root of the area of ​​the patch (ha) for each class (class surface) or the entire patch (land surface). This index iscalculated for circle standard (polygon), or square standard (grid) and divided by the number of patches. Largest Patch Index (LPI) which measures the area of ​​the largest patch in a class divided by the total area of ​​the landscape (multiplied by 100 to convert to percent) Mean Patch Size (MPS) which measures the average size of a patchin the landscape.  

District 3 ranked highest and district 1 ranked lowest in ENN indexindicating that ​​urban green space patches in this district were closer together, while green space patches in the third district were limited and far apart from each other. Regarding LPI index, the second district ranked thehighest and the third district ranked the lowest indicating that the largest urban parks in this districtwere much smaller than other districts. Other district had a relatively acceptable statusin this respect. In MPS index, district 2 with 697 patches ranked highest and district 1 with ​​564 patches ranked lowest indicating that average green space patches in district 1 were smaller. This was also confirmed by maps prepared based on other metrics.Regarding the LSI index, district 1 ranked highest and district 2 ranked lowest, while districts 3 and 4 had a similar status in this measure. The first district had the highest number of patches (NP), while the third district had the lowest NP. The highestPARA ratio was observed in District 1, and the lowestin District 4, while districts 3 and 2 ranked near the middle. In Landscape shape index which increases with the heterogeneity of patches,district 1 (with 13.12) ranked highest and District 3(6.64) ranked lowestwhiledistricts 2 and 4 ranked near the middle.This indicates the heterogeneous shape of green space patches in district 1, while showing that patches of green space in district 3 are very simple and homogeneous.Finally it should be noted that calculating landscape metrics for the four districts ofKhomein indicated a very low per capita green space in this city and also absence of a proper and equitable spatial distribution.  

Calculatinglandscape metrics in the four districts of Khomeinindicated thatcompared to other districts, district 1, located in the southern part of the city, has a more desirable status in indices such as PARA, LSI, NP, and ENN. At the same time, district 3, located in the southeastern part of the city, has the least appropriate status regarding these metrics indicating the necessity of a comprehensive analysis of green space areas in this district in near future. Urban managers and planners need to focus on this district and its green space, and if possible find appropriate sites for future green space areas in this district.Although the status of districts 2 and 4, located in the west and north of the cityrespectively, were not very desirable, theyranked higher than districts 3in NP, LPI, and MPS. Using GIS in combination with satellite imagery, and land use metrics provided an innovative way to study the gradual spatial changes in urban green space. Results of landscape metrics analysis indicated an unbalanced distribution of land use in the four urban districts in this study.

From ancient times, stone has always been a symbol of stability and strength, and ancient human beings took refuge and chose to settle in mountains and mountainsides (Santos et al, 2018: 2). However, rocks on the ground or near its surface decay and decompose gradually due to factors such as weathering (Memarian, 2000: 2). Climatic geomorphology is a scientific field in which shape and distribution of landforms are analyzed according to climate type. Specific weathering processes affected by the climate are in place in different morphological zones (Jafaria Aqdam et al., 2012: 1). The present study seeks to investigate the lithology of southwestern mountainsides of West Azerbaijan province using Lewis Peltier model.

Peltier weathering and morphogenetic models were used in the present study. Topographic, geologic, isothermal and isohyetal maps were produced using Inverse Distance Weighted (IDW) method in GIS environment. Temperature and precipitation ​​were analyzed using different graphs and tables to determine drought and humidity conditions. 

Results indicated that the northern mountainside is wider and thus, its ​​precipitation 407-477 mm and temperature of 15-17 ° C have the greatest impact on the region. Data collected from four synoptic stations in the province with a common 30-year reference period (1986 to 2018) were used to investigate weathering and morphological condition of rocks in the study area. Table (1) shows the location of these stations. Climatic data such as average annual temperature and precipitation were reviewed and corrected in ArcGis environment. Then, ArcGis was used to create a basic database to store data and prepare relevant maps. Weathering regimes are determined based on the Peltier chart (1950). In this diagram model, two variables -average temperature and annual rainfall- are used and weathering regimes are divided into seven classes each of which represents a type of weathering condition. The model of morphogenetic regimes is more similar to a climatic or vegetation classification than a weathering model. In this model, two variables of average temperature and annual precipitation are used and morphogenetic regions are divided into nine different classes. Areas having a low temperature are mainly classified as glacial areas and areas having a high temperatures and low rainfall are classified as arid and semi-arid areas. Areas having a high precipitation and temperature classified as temperate and cold areas. To apply Peltier model to the study area, the specifications of synoptic stations were first presented separately in a table. Then, zoning was performed based on the square value of temperature and precipitation using IDW method and then, the percentage of area covered by each ​​temperature and precipitation class was determined. Precipitation class of 407-477 mm covers 32.67 percent of the area. Moreover, temperature changes in the region indicated that 15-17 ° C temperature range has covered the largest part of the study area with a percentage of 39.41. The values of temperature and precipitation along with the results of Peltier model indicated that a very low level of weathering is present in the study area. Farahmand et al. (2015; 10) have shown that temperature and precipitation parameters in this region depend on elevation. To determine the morphological condition of the region, it was divided based on its climatic conditions. To determine the accuracy of weathering results, a map of geographical directions in the region was produced. Vegetation and soil in western and northwestern parts of West Azerbaijan province have a pretty good condition. These were divided into three different classes and weighed based on the weighing parameter. Result was presented as a map and a table in which mechanical weathering with a lower-intensity had a weight of 1 and chemical weathering with a higher-intensity had a weight of 3. The classification results are consistent with Hanafi et al. (2002; 72) who introduced mechanical weathering as a factor leading to rock disintegration in northwestern Iran due to climatic conditions. They are also consistent with Maghsoudi et al. who used climatic parameters of temperature, precipitation, and weathering intensity to determine weights for the Peltier model. In mountainous areas of the country such as Zagros, Alborz and northwestern Iran, low temperature and frost may lead to a low level of mechanical weathering (Maghsoudi et al., 2010; 36).

Suitable sites for waste disposal must leave the least environmental effects while being executable in various aspects. Combination of AHP and GIS is a popular approach used for selecting suitable waste disposal sites, since AHP classifies and prioritizes selected sites based on different types of information layers and GIS provides an effective way for data management and display. Various studies have been recently conducted to select suitable sites for waste disposal using GIS and AHP. Rahimi et al. selected a sustainable site for urban solid waste disposal in Mahallat, Iran, using AHP and GIS. Fourteen environmental, economic and social parameters affecting sustainability of landfills were examined in this study and a site was selected in vicinity of this city as the most suitable landfill for solid waste disposal. Improper disposal of waste produced in oil-based drilling of oil and gas wells not only increases costs, but also cause the aforementioned problems. Thus to prevent these problems, it is necessary to select appropriate landfills for this kind of waste.Although, Iranian Offshore Oil Company (IOOC) is responsible for most of oil and gas extraction from Iranian fields in the Persian Gulf, no specific solution has been provided for selection of suitable locations for drilling waste produced in these areas. The present study seeks to select suitable sites for disposal of drilling waste produced in east of the Persian Gulf Iranian oil and gas fields in Lavan Island using AHP method and GIS software. 

Case study Oil and gas fields in Qeshm, Kish, Siri and Lavan operational areas are located in the eastern part of the Persian Gulf. Lavan is one of the islands of Hormozgan province in the Persian Gulf. It is about 2.5 kilometers long and 4.8 kilometers wide.  Oil Based Drilling Fluid used in Iranian Oil and Gas Fields in the Persian GulfDue to the type and depth of formation (layers of shale and deep reservoirs), oil based drilling fluids are generally used in Iranian oil and gas fields in the eastern Persian Gulf (Qeshm, Kish, Siri and Lavan). The main component of oil based drilling fluid is petroleum hydrocarbons, especially those with high flashpoint. Usually diesel fuel is used as the main component, which may be added up to 90% to the fluid used in drilling operations. Drilling waste produced in Iranian oil and gas fields in the Persian GulfBased on the latest statistical information provided by IOOC, annually  drilling waste is generated by this company in the Persian Gulf, which has declined in recent years due to a reduction in excavation activities. The volume fractions of humidity and oil in the drilling waste are 65% and 30%, which according to the standards of Iranian DOE and HSE unit of IOOC must be reduced to 15% and 1%, respectively after the recycling process. Analytic hierarchy processThe analytic hierarchy process (AHP) is a logical framework that divides complex decisions into hierarchical structures and thus, simplifies their understanding and analysis. This process can be used when decision-making faces some alternatives. GIS Site selection in land-related sciences is an operation through which an expert presents needs, objectives, and information related to the current situation to find the best choice among available alternatives for the concerned land use. The main objective of site selection is to ensure that considering all limitations and available facilities, human activities in the selected site is consistent with the surrounding environment. Nowadays, GIS is used to reach a more scientific and realistic site selection. GIS is a coherent system of hardware, software, data, which allows the storage, analysis, transfer, and recovery of input data and makes it possible to publish the output data as maps, tables, and models of geographical zones. MethodologyThe present study is applied in terms of its objectives and descriptive-analytical in terms of its methodology. The criteria and sub-criteria (layers) involved in site selection for drilling waste disposal in Lavan Island were chosen based on the specifications of the region, recommendations of experts, and related literature. Base data were collected from various sources such as IOOC, Iranian department of environment, and geological survey and mineral exploration of Iran. Accordingly, 15 information layers (sub-criterion) affecting waste disposal site selection in Lavan Island were introduced and classified into three indices. These information layers include industrial building, slope, elevation, gas lines, oil lines, oil storages, roads, population centres, industrial regions, land use, airport, fault line, vegetation, river, and geology which have been classified as technical-economic, social-cultural, and environmental indices (criteria). Figure 1 depicts the hierarchical tree of site selection for disposal of drilling waste produced in eastern Persian Gulf Iranian oil and gas fields in Lavan Island.  Figure 1. The hierarchical tree of site selection for oil based drilling waste in Lavan Island.

Properties of each layer (layer values) were weighted in GIS environment based on al-saati method and experts’ opinions. Classification, weighting and normalization of effective layers used for selecting appropriate sites for oil drilling waste disposal in Lavan Island were performed and results were used to prepare a weighed map for each layer. These maps were combined in the final step to obtain the proposed map for waste disposal site. Figures 2 shows the weights assigned to information layers prepared for waste disposal in lavan Island.   Figure 2. Weights assigned to information layers prepared for oil based waste disposal in lavan Island  After the internal weighing of each layer, the AHP model was used to prepare the final map for the optimal site. Weighing each of these 15 layers is one of the most important stages of this model in which significance of each layer is expressed compared to other layers. The ultimate normalized weight of each layer was calculated by an AHP matrix with an inconsistency rate lower than 1.0.  Chart 1 shows the ultimate normalized weight of each layer which will be used in overlapping operations to find appropriate sites for oil based drilling waste disposal in Lavan Island.  Chart 1. Importance of weights assigned to layers in the selection of oil based waste disposal site in Lavan    IslandResults indicate that distance from population centers, distance from roads, distance from rivers and distance from airport are the most important parameters used to select appropriate sites for oil based waste disposal in Lavan Island. Results confirm the sensitivity of environmental and socio-cultural criteria for oil based drilling waste disposal in Lavan Island.Then, information layers were integrated using weighted overlay method in AHP to obtain the final map of the appropriate region for waste disposal. In this stage, the layers were overlapped based on their level of effectiveness in GIS environment and the final site selection map was prepared for waste disposal in Lavan Island (see Figure 3). The appropriate sites for waste disposal were classified into 5 classes (from “very good” to “very poor”) and depicted in this map.   Figure 3. Classification of selected sites for oil based waste disposal in Lavan Island Spatial analysis of final maps shows that some regions in the center of Lavan Island (sites number 1, 2, 3, 4 and 5)are appropriate for drilling waste disposal due to their distance from population centers, roads, rivers, and the airport. These barren lands are the farthest sites from urban centers, roads, rivers, and the airport. Therefore, construction of waste disposal sites in these regions of Lavan Island is suggested in the final map to decision-makers. Figure 4 shows the prioritized waste disposal sites in Lavan Island.   Figure 4. Prioritization of oil based waste disposal sites in Lavan Island.

The present study was performed due to the lack of similar studies on waste disposal site selection in this region. GIS and AHP were used to select suitable sites for the disposal of drilling waste in Lavan Island. This drilling waste is produced in the Iranian oil and gas fields in the eastern parts of the Persian Gulf. Effective factors were weighted in different layers of GIS environment and weighted maps were prepared. Priorities were selected using the AHP, and site selection for drilling waste disposal was performed in GIS. Distance from rivers was recognized as the top priority parameter in environmental criteria due to the importance of environmental standards and avoiding surface water pollution. Moreover, distance from population centers, roads, and the airport were selected as top priorities in social-cultural sub-criteria due to the importance of the Island residents’ health and beauty of the landscape. Information layers were thus produced and combined using weighted overlay method in AHP to reach the final maps of suitable locations for oil based waste disposal in GIS. In accordance with effective criteria in the waste disposal site selection, suggested sites were classified into five classes ranging from “very good” to “very poor”. Accordingly, some sites located in the central part of Lavan Island were selected as appropriate sites for the disposal of drilling waste due to their distance from urban and population centers, roads, rivers, the airport, and so forth

Air pollution is a major problemin large industrial cities and affects the life of urban citizens.Due to population growth,significant increase in the number of motor vehicles as well as the concentration and accumulation of industries, Tehran is in the grip of an air pollution crisis. Previous studies have indicated that once every three days, Tehran faces increased levels of pollutants and air pollution.Ozone is produced through photochemical reactions between hydrocarbons in carexhaust and nitrogen oxides in the atmosphere. Producedthrough reactions between atmospheric pollutants,this pollutant is not primarily released into the environment by a specific sourceand thus, it is called a secondary pollutant.Concentration of ground-level ozone has doubled over the last century.Exposure to this pollutant is very harmful for human health, especially those who exercise outdoors because it severely damages their lungs.Therefore, increased concentration of pollutants has become a major challenge for the management of metropolises such as Tehran. Having information about the spatial distribution of pollutants allows urban managers to take appropriate measures and reduce pollution related risksfor areas and people in danger.Due to excessive concentration of industries and factories inside the geographical boundaries of Tehran, along with its specific geographical condition, topography and climate, Tehran has become one of the seven most polluted cities of the world.The present study seeks to model the spatial and temporal changes of ozone and nitrogen oxidesin Tehran metropolis. 

In this cross-sectional descriptive study, spatial analysis of pollutants (ozone(O3) and nitrogen oxides)is performed based on data measured by Tehran air quality monitoring stations for the 2008, 2009, and 2018reference periods. For 2008 reference period, data were collected on a monthly basisfrom the website ofTehranair quality control company,while for 2008 and 2018, data were collected annually. Arc GIS 10.5 released by ESRI was usedfor spatial analysis, and Microsoft Excel 2013 was usedto drawdiagrams and perform other analysis.Inverse distance weighting (IDW) model was used for spatial analysis of ozone and nitrogen oxidesin Tehran metropolitan area inthe three reference periods. Finally, the reference periods were compared and the most polluted one was zoned using the IDW model. In the second method, Google Earth Engine was used to model the spatial distribution of ozone and nitrogen oxides. In this method, Sentinel-5p NRTI O3: Near Real Time Ozone product was used to model ozone and nitrogen oxideson an annual basis (11/01/2018 and 28/03/2020).This is the date in which sentinel has started monitoring ozone and nitrogen pollutants. As the most important product available for measuring the average rate of change,column of ozone and nitrogen oxides’ changes in the atmosphere (O3_Column_number_density) was used in this study. Annual average concentration of ozone and nitrogen pollutants in Tehran was compared with the Sentinel-5 product in Google Earth Engine. 

2018, average annual concentration of ozone and nitrogen oxides in studied stations equaled 12.7 ppb. The accuracy of modeling was also calculated using the coefficient of determination(R2) or coefficient of detection (CD). The average annual concentration of ozone and nitrogen oxides in 2008 was also measured for all air quality control stations to determine their correlation.All independent variables used in this model had an acceptable level of significance (P.> 0.001).In other words, all parameters improved the performance of the model in estimating the concentration of ozone and nitrogen oxidespollutants. The model was developed and R2 rate for 2008 monthly average equaled 0.9188%.The coefficient of determination (R2)for ozone and nitrogen oxides’ concentration in 2009 equaled 0.9134%, but the annual average of 2018showed a much lower R2which equaled 0.476%.It should be noted that not all stations have been evaluated in this study, because the concentrations of ozone and nitrogen oxidesin some air quality monitoring stations equaled zero. Thus, only stations showing a greater than zero value have been used in this study. 

As previously mentioned, various models have been proposed for modeling the concentration of ozone and nitrogen oxides, each showing a different result. In the present study, the inverse distance weighting (IDW) model was used for three reference periods (2008, 2009 and 2018), and the concentrations of ozone and nitrogen oxides in the atmosphere were also modeled using the variables related to air quality monitoring stations.Ozone concentration modeled by inverse distance weighting method was compared with the average annual change of ozone concentration derived from Sentinel-5 product in Google Earth Engine. Results obtained from the concentration of ozone and nitrogen oxides in the three reference periods were investigated using thecoefficient of detection.The resulting coefficient of determination for ozone concentration in 2008 and 2009 equaled 0.9188% and 0.9134%, respectively. The lowest coefficient ofdetermination for ozone and nitrogen oxidesconcentration was obtained for 2018 which equaled 0.476%. Regarding the spatial distribution of ozone and nitrogen oxides in 2008, the highest concentrations were observed inMasoudiyeh, Punak, Rose Park and Aqdasiyeh stations, and the highest concentration of nitrogen oxides was observed in District4, Crisis Management Headquarterand Sadr Expressway(District 3). In 2009,the station in Rose Park (District 22) showed the highest concentration of ozone and nitrogen oxides.In 2018, IDW modelling and spatial distribution of ozone and nitrogen oxidesshowed a different result. In this reference period, the station in district 4 received the highest annual concentration of ozone and nitrogen oxides, and north eastern areas ofTehran was regarded as the most polluted areas based on the concentration of these pollutants. But stations in16th, 19th and 20th districts and Masoudieh station (15th district) had the lowest annual concentration of ozone and nitrogen oxides. In general, it can be said that spatial modeling with Sentinel-5 product has been able to model the concentration of ozone and nitrogen oxides inall stationsof Tehran on a pixel by pixel basis.