نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 109 (بهار 1398)

Cutaneous Leishmaniasis (CL) is a vector-borne disease, endemic of the Middle East. The spread of CL is highly associated with the socio-ecological interactions of vectors, hosts and environmental conditions. CL is the most frequent vector-borne disease in Iran and especially in the north-eastern province, Golestan, which has long been known as one of the most important endemic areas for CL dispersion. Therefore, Golestan province was selected as the study area of this research. The main objectives of the study are to analyze annual spatial distribution of CL, investigate the relations between environmental/climate factors and incidence rate of CL and also provide a model to predict CL distribution at rural district level in Golestan province. Data: CL incidences, census data, environmental and climate factors have been used in this study to provide a model and produce a map to predict the CL distribution. The CL incidences are continuously recorded by the Center for Disease Control and Prevention (CDC) of Golestan province. The population and census data for 2013-2015 period were obtained from Iranian Statistical Center. Environmental and climate data such as vegetation, average humidity, average temperature, precipitation, number of rainy days, number of freezing days, maximum wind speed and evaporation rate were used as parameters affecting the model. The statistical and geo-statistical analyses were used to investigate the relation between environmental/climate factors and CL incidence rate, and to investigate the existence of spatial autocorrelation between CL cases, respectively. Additionally, Multilayer perceptron (MLP) neural network was used to model the relation between the distribution of CL incidences with environmental/climate factors, and also to generate the risk maps of CL. MLP is a type of neural network which consists of multiple layers of neurons or processing elements connected in a feed forward fashion. It encompasses three types of layers: input, hidden, and output. It has a unidirectional flow of information. Generally, information flow starts from input layer, goes through hidden layer, and then to output layer, which provides the response of the network to the input stimuli. In this type of network, there are generally three distinct types of neurons in layers. The input layer contains some neurons as the input variables. The hidden neurons, which are contained in one or more hidden layers, process and encode information within the network. The hidden layer receives, processes, and passes the input data to the output layer. Number of hidden layers and number of neurons within each layer affect the accuracy and functionality of the network. The output layer contains target output vector. In this study, effective parameters along with CL incidence rate of 2013-2014 were fed to the MLP as training data. The trained MLP was used afterward to generate the risk map of 2015 and test accuracy of the model. In order to determine the optimal parameters of the MLP, the grid-search and cross-validation techniques were used on 25% of the training dataset in the training phase. The performance of MLP was investigated using the root mean square error (RMSE), mean absolute percentage error (MAPE) and area under curve (AUC) of receiver operating characteristic (ROC) measures. Sensitivity analysis was also used to determine most effective variables regarding predictive mapping of CL distribution.  Results of global Moran’s I index indicated that there is spatial autocorrelation among CL cases, and also distribution of CL cases in Golestan province in each 3 years is clustered. Moreover, statistical analyses showed that majority of the incidences belonged to rural districts of Gonbad-Kavos and Maraveh-Tappeh. Based on the results of statistical analyses (including Pearson correlation and Spearman rank correlation), positive correlations were observed between the CL incidence rate and average temperature, maximum wind speed and evaporation. In addition, negative correlation was found between the CL incidence rate and average humidity, precipitation, number of rainy days, number of freezing days and vegetation. According to the results of evaluation criteria including RMSE, MAPE and AUC, the trained MLP model was able to generate risk maps of CL in 2013-2015 for each rural district with acceptable accuracy. Additionally, results of sensitivity analysis indicate that vegetation and average humidity are the most influencing variables in the incidence of CL and in predictive mapping of CL distribution in Golestan province.
Conclusion and Future works:
In this study, the global Moran’s I index indicated the presence of spatial autocorrelation among CL cases, and clustered distribution of disease in the study area. The statistical analyses showed that environmental and climate factors greatly affect the spatial distribution of CL. The MLP method, used to generate CL distribution risk maps, was able to generate the study area risk maps with acceptable accuracy. Results highlight the potential high risk areas requiring special plans and resources for monitoring and control of the disease. As a future work, we suggest that the effects of other environmental and socio-economic parameters should be evaluated to improve the accuracy of the model. It is also recommended that other methods such as regression and other neural network techniques be used to generate CL risk maps.

Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal infrastructure of coastal countries. Providing proper hydrographic services ensures safe and efficient sailing. Thus, development of hydrographic services on the national level can improve safety of mariners, and protect people’s lives and belongings on the sea, while providing some facilities for the protection of marine environment. The advancement of space technologies in recent years has increased the speed of spatial information production and facilitated sea monitoring. Different methods are used for bathymetry. Lyzanga et al (1978) used a linear combination of the logarithm of corrected radiance ratio. This method is based on the simplification of Beer's physical model in which a linear equation of five unknowns is obtained for two bands. In 2006, Lyzanga et al. presented an improved version of their model. Using Tow-Bands Reflection Ratio, Stampf et al (2003) not only reduced the number of unknown variables in Lyzenga method, but also decreased the sensitivity of depth determination to different substrates. In this method, the difference between absorption properties of green and blue bands is used. TCarta is a global supplier of geospatial products. The company generated Satellite Derived Bathymetry (SDB) dataset by accurately extracting water depth from multispectral imageries received from the European Space Agency’s Sentinel-2 Satellite. The resulting bathymetric data had a point spacing of 10 meters, while measuring up to a depth of 15 meters. Data covered a 30-square kilometer area around Preparis Island on the Bay of Bengal.
The present article used images received from Sentinel-2 in 7 different periods for depth determination, and 1: 25,000 ADMIRALTY Nautical Charts for accuracy evaluation. Following the assessment of water transparency in received images, the 12/15/2018 image was used for depth determination. Case study area contains around 130 km along the Port of Salalah, Oman. In order to implement the model, it is necessary to separate land from water in images using NDVI, NDWI, MNDWI and AWEI indices. The NDVI index has been used in this project. NDVI is primarily used to estimate vegetation cover, but since this index exhibits a negative value in areas covered with water, this property is used to provide a mask for separating land from water. In this step, 68 control points and 68 check points were selected from the existing ADMIRALTY map. The DN values of the corresponding pixels of the selected points were extracted from four 10-meter bands of Sentinel-2 images. The control and checkpoints and the DN value of their corresponding pixels were extracted in 4 separate files, then these 4 files were logged into the Bathymetry software and the parameters of LMR and Stumpf methods were calculated. The root mean square error (RMSE) and correlation coefficient (CC) were used to assess geometric accuracy. In order to extract necessary parameters for each model, RMSE= 2.15 m and CC= 92.5% were calculated at depth distances of 0 to 20m. Results indicates higher accuracy and stronger correlation of LMR findings. Therefore, this method was used for depth determination between 0 to 20 meters. The 5 parameters extracted from the Bathymetry software and the corresponding pixel values of the four bands with 10-meter resolution extracted from the Sentinel-2 image (received from the on 12-15-2018) were used as input. Linear Regression Model was applied to transform 4 bands of Sentinel-2 image into depth. The output of the model (depth) was presented as the Substrate DEM of the coasts of Port of Salaleh, Oman. Hence, it can be concluded that Remote Sensing technologies can be used for depth determination and sea monitoring at critical times (during wars or other periods of insecurity) for an acceptable time period. It also provides an appropriate context for bathymetry of inaccessible coastlines and monitoring of strategic widespread water zones. In this way, the depth of sea bed in shallow areas is extracted using spectral analysis of satellite data and different models.

Neighborhoods that have once lied at the heart of cities and enjoyed cultural richness, now face a problem called "worn-out and inefficient urban texture". New needs emerged and old urban textures faced physical-spatial defects. On the one hand, physical decay, social and economic life in problematic and ineffective textures have exacerbated the deterioration of urban life quality and degraded urban ecosystems in old urban textures of Iran. On the other hand, with rapid scientific and technological advancement in the contemporary world, urban planning system has also undergone transformations and the effects of such changes can be challenging for the city progress. Today urban planning, previously based on traditional system, seems to lack the power and the ability to respond to and solve problems of worn-out urban textures. Therefore, by identifying the opportunities and threats of the future of these textures, ‘futures studies’ approach will help us to better utilize future opportunities and prevent the negative consequences of old urban textures as much as possible. Thus, the most important issue in studies focusing on the future of this part of urban context is the consideration of other factors, including human factors (residents, owners, beneficiaries, and stakeholders) and their contribution to the long-term planning process. The general purpose of futures research is to create awareness about the external environment in order to understand the gaps, trends, and developed technologies. In this way, we can improve the environment as much as possible.   The present study sought to identify the key variables in reducing worn-out urban texture and to examine the relationships between these variables and their influence on each other in the time horizon of 2037. Therefore, using environmental scanning technique (reviewing articles and other published resources, interviewing experts and monitoring conferences) and examining related literature, the initial and existing variables (160 variables) were extracted from the worn-out texture of the 9th district (1st area). The dominant process in this research is applied in terms of purpose, and descriptive-analytical in terms of research method. The nature of data is qualitative. Data were collected from library sources using documentary and survey methods. Since, it is very difficult and even impossible in some cases to manually calculate the cross-impact matrix, complex calculations and data analysis were performed based on the cross-impact analysis method, using Micmac analysis software.
Discussion and Results: Organizing two consultative workshops with experts and managers of worn-out texture, information and variables were integrated, resulting in 61 variables classified into six sub-categories of economic, social, cultural, managerial, legal and legislative, physical, and political. Thus, in order to extract the main factors affecting the reduction of worn-out textures of the area in the horizon of 2037, we entered each variables using Micmac software. Cross-impact analysis matrix (61*61) was created and the degrees of mutual influencing and impressionability were evaluated and ranked by experts and practitioners. In order to arrive at a reliable coefficient of the data validity, the number of iterations was increased up to 5 times to reach one hundred percent desirability and optimization of the matrix. By analyzing data using the software, research results indicate that key strategic and most influential variables for reducing worn-out texture in the 1st area of 9th district are "changes of the government " (the comingup of the new government every four years), "lack of specific laws regarding worn-out texture and their organization", "balancing and realizing social justice", "weakness of attitudes and knowledge in urban managers" and "establishment of organized and continuous local organizations to encourage residents to participate".   Based on the research findings, it can be concluded that only two key and strategic variables (i.e. "changes of the government in every four years" and "good urban governance”) in the 1st area of the 9th district of Tehran played a determinant and influential role. These were considered to be the key players in the system. Therefore, adopting a comprehensive and far-reaching approach to the future of worn-out textures seems to be vital and necessary.

Increasing development of urban areas, the need for various information from the urban environment, and also technological advancements have increased the importance of automatic and semi-automatic classification and identification of this type of land cover. The diversity of remote sensing data have created a wide scope for urban feature detection. Moreover, by launching satellite sensors with a spatial resolving power of less than 1 meter, a dramatic revolution has occurred in the tendency of remote sensing researchers toward classification of urban features. The existence of various features and different applications of spatial information in urban areas have made it possible to integrate various data sources with the aim of identifying different urban features. The present study seeks to integrate optimal properties extracted from optical and LiDAR data in order to identify urban features in the study area. In this regard, colored features, normal difference vegetative index (NDVI), first-order statistical texture in three windows of 5×5, 7×7 and 9×9, second-order statistical texture in three windows of 7×7, 11×11 and 15×15 extracted from the multispectral optical data were calculated along with features of normalized difference index (NDI), slope, slope direction, profile curve, surface curve, roughness, variance, laplacian, smoothness and normalized digital surface model (nDSM) extracted from the LiDAR data. Since increased amount of information has made the process of identifying features in the region time-consuming, the present study applies intelligent genetic algorithm to select optimal features from the calculated features. A total number of 361 features were produced from this data, including 9 colored features, a vegetation index, 144 first-order statistical texture, and 192 second-order statistical texture from multispectral optical data and 14 features from LiDAR data. Then, 17 features including seven features of the LiDAR data and 10 features of the multispectral optical data were determined using genetic algorithm as the optimal features for more appropriate identification of urban features. Finally, support vector machine (SVM) classification method was used to identify the desired features. Results indicate that compared to LiDAR data, multispectral optical data have a better performance in classifying vegetation features, while LiDAR data have been more suitable for the classification of road and building features. In other words, multispectral optical data work appropriately in identifying features with different radiometric information, while classification of features with similar radiometric information, such as roads and buildings is problematic. Thus, LiDAR elevation data help in identification of these features. Additionally, using optimal features along with the primary bands have increased the accuracy of urban features classification. Using optimal features and initial data, the accuracy of support vector machine algorithm classifier in the study area is calculated to be 88.734, which shows 25.438% improvement compared to the initial multispectral optical data classification, and 18. 236% improvement compared to the initial LiDAR data classification.

Presently, population growth, urban development, the importance of agriculture in economic development, the need for supplying water demands of this sector and improving public health have multiplied water consumption as compared to the past. For appropriate and optimal use of water resources, it is necessary to know the amount of available water in the area, its temporal and spatial changes, and the exact planning for maintenance and utilization of the available water. Accordingly, studying and measuring changes in snow levels, as an important source of water in mountainous areas, is very important. Snow cover is one of the important parameters involved in the amount of snowmelt. Due to the difficulty of monitoring and measuring snow cover level in mountainous basins, satellite images are used as alternatives to monitoring and ground operations in the preparation process of snow cover map. In this regard, the use of satellite imagery and remote sensing, due to low cost, up-to-date and extensive coverage, is a major breakthrough which can be used to identify snowy areas and evaluate changes in that method. Detailed analysis of snow-related issues requires a set of snow measurements and observations. OLI and TIRS sensors with various advantages like appropriate number of bands, referable spatial resolution, and sequential time series are considered to be an appropriate tool for this purpose. The main objective of this research is to estimate snow coverage of Sahand Mountain using satellite images received from OLI and TIRS sensors and by object-oriented classification method.   The present study use images received from OLI and TIRS sensors, and Landsat 8 satellite on 08/02/2017 (Pass and Row no. 34-168), as well as Digital Elevation Model based on data received from Aster Sensor and Terra satellite with a resolution of 28.5 meters to produce snow coverage map. Geo TIFF satellite data were originally requested from the American Geological organization and received from USGS site. Envi 5.3, eCognition 9.1, and ArcMap 10.4.1 software were used for processing and preparation of images, as well as classification and extraction of the final maps. In order to classify and extract snow cover surface with high precision, NDSI, NDVI, LST, and Brightness algorithms were used along with fuzzy algorithms.   Classification of satellite digital images is one of the most important methods for extracting applied information, which is currently performed by two general methods of pixel-based processing and object-based processing. The former method is based on the classification of numerical values of images, and the latter use not only numerical values, but also content, texture, and background information in the image classification process. Recent researches have processed image pixels and have only applied NDSI algorithm to estimate snow cover level. Therefore, pixels recognized as snow in such researches may contain snow cover and other land uses, which reduces the precision of snow cover extraction and makes the process of extracting all snow covers difficult. Extraction of snow cover using MODIS images is one of such researches. Due to low spatial resolution of these satellite imageries, extraction of mountainous valleys snow cover, as well as the separation of snow cover from the cloud cover is done with very low accuracy. Therefore, due to higher accuracy of object-oriented classification as compared to pixel-based classification, object-based techniques were used to classify and estimate snow cover. In object-oriented method, pixels are classified based on shape, texture and gray tone of the image. Thereby, pixels change into image objects and resolves the pixel blend problem. Therefore, by assigning each object to a specific land use, classification accuracy increases. Also, using complementary algorithms such as Brightness and NDVI along with the NDSI algorithm will improve the accuracy of findings as compared to other recent research. Therefore, using Landsat 8 satellite images and the new method of image classification, the present study extract snow cover from different domains of the study area. The snow cover in valleys was also extracted with appropriate and acceptable accuracy using different algorithms.  Using LST algorithm in object-oriented processing method, detecting and separating snow cover from cloud cover was made possible. In this way, a satisfactory result was obtained from the snow cover. Finally, snow cover for Sahand Mountain Range was calculated to be 1882.88 km2. The results can be used as an alternative to snow measurement stations.   Based on research findings, using Landsat 8 satellite imagery and object-oriented processing methods for image classification have the necessary efficiency in extracting snow cover in mountainous regions. Given the precise estimation of the snow surface and the low cost of using this type of satellite imagery, it is possible to use this type of images and check the snow cover with great confidence. While ground observations are expensive due to impassibility of mountainous regions, and also they are not sufficiently precise.

The study of the areas with geothermal energy potential is of particular importance in realizing the goals ofsustainable development. Areas with geothermal potential areof great importance in terms of application as renewable energy resources, tourist attraction, greenhouse construction, etc.Generally, in geothermal exploration projects, studies are initially carried out with regard to the existing indicators, and the outcome of the primary location is used for more detailed studies. The identification of the areas with geothermal potential, which is the first phase of geothermal energy exploration, is complex and difficult.
Determining areas with geothermal energypotential as a basis for clean and environment friendly natural energyexploration studies, is important for achieving sustainable development. The purpose of this paper is to identify the areas with geothermal potential with regard to the characteristics of the northwest regions of Iran and the application of Geospatial Information Systems and Multi-criteria analysis methods, which have many advantages in the field of exploring the regions with geothermal potential.
In this study, the spatial Multi-criteria analysis package of ILWIS software and also the decision-making method based on the Ordered Weighted Average (OWA) in TerrSet(IDRISI) software have been used
Different scenarios of decision-making were implemented in the case study area and, the results were compared with the location of hot water springs in the region. The results indicate that the location of the determined sites is close to the hot water springs, which confirms the results of the proposed model of the paper.
Materials & Methods: The study of geothermal energy with the help of the spatial information system has drawn the attention in recent years. The purpose of this paper is to study areas with geothermal potential in the northwestern regions of Iran. These regions have different effects on the Earthand the researchers of this field use these effects to find new methods for measuring geothermal resources (Yousefy, 2006). Nowadays, GIS-based MCDM techniques are effectively used in these types of studies. Therefore, it has been tried to use some of these techniques in this research. In addition to the novelty of the topic of the geothermal studies in Iran, the issue of modeling different decision-making scenarios has been taken into consideration fromthe pessimistic view (with low risk) to the optimistic one (with high risk). Therefore, in this research,areas with geothermal potential have been identified and compared, with the help of study with the help of spatial data and Multi-criteria decision-making methods. In this study, decision-making criteria are evaluated and selected usinglibrary studies from previous researches. Also, based on the weighting methods and the integration of criteria, 8 scenarios were produced and their results were compared with each other. Meanwhile, the weight of the criteria was calculated using questionnaires and the analytic hierarchy process (AHP) method. The Ordered Weighted Average (OWA) method was applied to create various scenarios. Figure-1 shows the stages of this research.
Results & Discussion: The two software (ILWIS and TerrSet), provide powerful tool for standardizing, weighting and integratingthe standard maps associated with the decision-making process. In the implementation stage, the maps are standardizedafter the preparation of thestandard maps in the acceptable format of each software. In this study, fuzzy and AHP methods were used for standardization and weighting,respectively. Finally, the input factors are integrated according to different scenarios. The results are shown in Fig-8. In order to evaluate the results, the geothermal map produced based on the model proposed in this article has been compared with the location of hot water springs. The results of most scenarios show that, hot water springs are generally located in two classes with high suitability which confirms the results of the research. In Fig-9, hot springs are located in the classes with high suitability, as it was expected. This means that the results of this research are acceptable. Adaptation and compatibility of the geothermal map and the existing situation provide the possibility of using the results of the case study area in the exploration studies of other regions. In this research, multi-criteria decision-making based on the use of GIS tool was used as a feasibility study in the first phase of geothermal exploration. The layers were processed and using theAHP-OWA integration methods in the 8 scenarios, they were integrated and the obtained results were investigated and compared. In most scenarios, hot water springs are in suitable or very suitable classes. This reflects the acceptable results obtained from the proposed modeling of this research.

 One of the major problems that is noticeable in planning and management of most cities in our country today, is the management of increasing solid waste. The last phase in the management of waste is the final disposal, which has always engaged man. There are different ways for urban waste disposal, but their burial is more important and there has not been a perfect alternative to that so far. Choosing a suitable landfill site for waste is the most important step in waste management. It is a complicated matter that requires a vast evaluation process and the environmental, economic, social & tectonic standards should be taken into consideration. With regard to the fact that most of effective factors in determining the suitability of lands for special purposes such as waste burial, are not of the same significance, they must be weighed for more accurate evaluation. The analysis of the layers and numerous factors which are effective in the site location process will only be possible in the framework of multi-criteria decision-making (MCDM) systems and the use of GIS technology. Due to the importance of the subject matter, the present research has sought to locate the landfill site in the city of Ghaleh Ganj in order to take a step towards environmental protection and achieving sustainable development in this city. To achieve this goal, Geographic Information System (GIS) and multi-criteria decision-making systems have been used.   The present research is an applied one in terms of the goal, and its approach dominating the research space is exploratory with regard to the nature of the subject. To develop the theoretical framework of the research and review the previous researches and extract the indices being used as well, the library method (documentary) was used. Field information was provided through questionnaire and field observations. Analytic Hierarchy Process (AHP) model was used for weighting the indices and its calculations were carried out in Excel software. The Fuzzy method was used for location and zonation that was implemented in GIS environment, which, according to many researchers, is the most accurate and the best method of site location.
The study area of this research is the city of Ghaleh Ganj in the province of Kerman located at 440 km from the capital of the province with a population of 12663 in 3034 families according to the general census of population and housing in the year 1390 (2011).    AHP model was used to weight the landfill site location indices in the city of Ghaleh Ganj. In this regard, a questionnaire was edited based on Analytic Hierarchy Process (AHP) model and pair-wise comparison matrix, which was completed by 10 experts. The experts ranked the indices from 1 to 9, whose weights were calculated after entering the weights of indexes in Excel software. Then, landfill site location in the city of Ghaleh Ganj was carried out using GIS-Fuzzy model. The stages are as the following:Preparing the shape files of the layers.
Forming the spatial matrix of the layers (Rasterising the layers).
Fuzzification of the layers by special methods of each layer.
Multiplying fuzzy maps by the weights obtained from AHP model.
Designing fuzzy inference network and integrating the layers to extract the final map.
15 layers were used to locate the landfills of the city of Ghaleh Ganj which are:Distance from agricultural lands and gardens, land cover, distance from flood zones, distance from floodways, distance from drinking water wells, wind direction, distance from the city of Ghaleh Ganj, distance from rural centers and residential areas, distance from communication lines, distance from industrial park, distance from facilities, distance from the fault, type of soil, slope and finally, the material of bedrock.
After preparing the shape file related to each layer, the layers were grouped into 3 groups, each of which had its own method in rasterisation and fuzzification of the maps. The layers of the first and second groups were programmed using Python programming language and were modeled in GIS environment, and the layers of the third group were fuzzified by the “Raster Calculator” tool. In the next step, the fuzzy maps were multiplied by the weights obtained from the AHP model. Finally, in order to identify the appropriate zones for burying waste materials in the city of Galeh Ganj, the weighted fuzzy maps were combined with each other using “Fuzzy AND” operator, which is a subset of the Fuzzy Overlay tool operators, and the final map was obtained. The obtained map was reclassified and the lands of the city were classified into three classes of very suitable, relatively suitable and unsuitable in terms of waste burial capability. Finally, the land capability map for waste burial in Ghaleh Ganj was presented.   Based on the results of the research, the lands of the city were classified into three groups of very suitable, relatively suitable and unsuitable in terms of waste burial capability. Lands with very suitable capability are 1451.5 Hectares and lands with relatively suitable capability are2425.2 Hectares. The results also showed that the current location of the landfill in the city of Ghaleh Ganj is in the lands with unsuitable capability and it is imperative that the municipality abandon the current location as soon as possible and select a site from the lands with very suitable capability for waste disposal of the city, and if this is not possible, the lands with relatively suitable capability can be used.  Finally, the results showed that the application of the AHP-FUZZY method in the GIS environment for locating the uses, including landfill sites, has a high efficiency and the method used in this research can be generalized to different cities of the country.

In recent decades, rapid climate changes in the Middle East have led to the rapid growth of drought phenomenon. Given the recent observations and surveys in the countries of the region, the rate of evaporation of surface water has increased. In addition, water consumption has increased dramatically in recent decades due to various causes, including industrial and agricultural development and population growth. Apart from the natural causes, the lack of proper management and planning of water resources is one of the main reasons for drought occurrence in the country. It’s obvious that every program needs reliable and updated information to help planners make decisions. Therefore, information onthe volume changes and the amount of water is of great importance in the management of the limited water resources. This information is usually obtained using conventional methods of remote sensing, surveying and photogrammetry which require significant amount of time and money. On the other hand, obtaining information from people is very beneficial due to the high speed, low cost of preparation and high volume of shared information. Volunteered Geographic Information provides an environment for the acquisition of spatial information from ordinary and expert people.Recently, many researcheshave been carried outon geography, natural resources and geosciencesin relation to the voluntary applications of spatial information in crisis management, and it has been proved that these data are suitable for managing long-term and short-term crises. Regarding the water management in particular, researches have been carried out on collecting water pollution information through sharing the location and the type of water pollution. The issue of drought in water resources has not been taken into consideration in this category of investigations.Another category of the researches is about the type of architecture used to obtain popular information from the volunteered spatial information, while the quality of this information has been ignored. In another category of the researches, users identify and report the rate of changes in water resources using remote sensing and aerial images but, this method is indirect and does not share information instantaneously and directly. The aim of this paper is to design and develop a Volunteered Geographic Information system for receiving and updating drought information on natural water resources. To do so, users send the information on water volume and its changes as well as the location, to the drought information spatial database. The case study of this paper is the water resources of the rivers and lakes of the TashkBakhteganMaharloubasin. TashkBakhteganMaharlou is a basin region in the province of Fars in Iran,and the reason for choosing this basin as a case study isthe abundance and diversity of water resources in the area. A Web-based mobile application has been developed to receive the popular information of water resources. Users can share information based on the level of access. This level of access is determined based on the level of users’ expertise and occupation relative to water sciences. In order to implement this system, a client - server architecture was employed, in which SQL Server was used as the updating and managing system of the spatial database, ArcGIS Server as a spatial server, WCF Service to receive thematic information, JSON as the format of exchanging data and Android as the client’s development language. 
Discussion and results: In order to evaluate this system,the spatial accuracy, descriptive accuracy and the integrity of the Volunteered Geographic Information were measured. After the evaluation, a spatial accuracy of 12.5 meters, a descriptive accuracy of 67%, and the integrity of 75% were obtained. Moreover, 80% of the Volunteered Geographic Information hadan area of more than 1 Hectares,representing the interest of the users in sharing lakes with wider areas. Meanwhile, 65% of the Volunteered Geographic Information has a density of 27 lines per Hectare, indicating that people are willing to share rivers with denser branches. Since the datawas collected by smartphones’ GPS,this amount of spatial accuracy was predictable. Descriptive accuracy obtained is relatively low due tothe ambiguity in the naming of received data. The high integrityindicatesthe capability of the system in updating drought spatial database over a short period of time. Therefore, it seems that the Volunteered Geographic Informationon drought is generally acceptable for completing the water resources database and for the management and making decisions on the planning of conserving water resources in a short time with low cost. 

Drought is a serious danger with very extensive impacts on the soil, economy, and the threat to the livelihood and health of local communities. This disaster as an unpleasant climatic phenomenon that directly affects the communities through restrictions on access to water resources, causes high economic, social and environmental costs. Meteorological drought indicators are calculated directly from meteorological events such as precipitation, and in the absence of these data, drought monitoring will not be useful. Due to the fact that meteorological drought indicators are only valid for a single location and do not have the required spatial resolution and are also dependent on weather station information, and these stations are often distributed distantly, the reliability of these indicators has been questioned. Given the characteristics of satellite data such as spatial and temporal resolution, extensive coverage of studied areas, and direct investigation of vegetation status by satellite indices, many studies have been carried out for drought modeling using this technology and these indicators. Over the past four decades, far-reaching drought monitoring tools have been widely developed and drought monitoring models are widely proposed, which are generally based on vegetation indices, surface temperature, humidity and reflection in the visible and infrared regions. These include leaf water content index, vegetation cover index and temperature - drought - vegetation index. Therefore, remote sensing techniques can be a useful tool in drought monitoring. The purpose of this study is to monitor drought and vegetation health in the city of Kermanshah using LANDSAT satellite imagery. For this purpose, first, by examining the rain-gauging and synoptic data of existing stations and using the standard precipitation index model, the driest year and one wet year were selected as the sample. In this study, two years of 2015 and 2016 were selected as the dry and wet years and then, the vegetation cover of the region was compared with the Landsat images. To use these images, it is first necessary to make sure that there is no geometric error. For this purpose, the road vector layer was used, which was revealed that the images have geometric errors. Images with less than a half-pixel error were corrected geometrically using 21 and 24 auxiliary points. The adaptation of the vector layers with the roads existing in the image indicated the accuracy of the correction. At the next stage, the driest year and one wet year were selected as samples by examining the rain-gauging and synoptic data of the existing stations and by using the standardized model of rainfall index. At the next stage, the Temperature Condition Indices and Vegetation Health Index (VHI) were compared in two wet and drought periods were studied in order to determine the differences of these indices during a dry year and a year with high precipitation. For this purpose, each of the aforementioned indices was built using the LANDSAT-8 imagery, and the stages of building these indices were subsequently presented.  The required pre-processing and processing as well as the geometric and radiometric corrections were first performed on the satellite images. Then, temperature condition indices, vegetation status index and vegetation health index were prepared for drought monitoring. Considering that, the meteorological drought indices are only valid for a single location and lack the required spatial resolution and are also dependent on the information of the meteorological stations, and these stations are often distributed far apart from each other, the reliability of these indexes has been questioned. Satellite data characteristics like high spatial and temporal resolution, extensive coverage of the study areas, and direct survey of the vegetation status by the satellite indexes have led to a large number of studies on drought modeling using this technology, and the confirmation of the use of these indices. The aim of this study is to determine the moisture, heat and health of the vegetation using the LANDST images. Thus, the results of the study in the next stage indicated that the LANDSAT images and the built indices have the required capabilities to monitor drought. The results of this research can be a proper option for decision-makers to effectively supervise, examine and resolve the drought conditions and double the necessity of profile definition. Supplementary studies are suggested for spatial drought monitoring by satellite imagery through ground measurements of the quantitative changes in the coverage and temperature of the earth’s surface. There are limitations in the use of NDVI and satellite thermal bands. These include weather and cloud conditions that should be considered. Using maps obtained from the drought monitoring and evaluating indices can help improve drought management programs and play a significant role in reducing the effects of drought. Using vegetation health status index, it was determined that the vegetation status has had a lot of changes during drought compared to the wet period, hydrological drought has had a major share in the destruction of vegetation and drying of the lakes and, consequently, the abandonment of agricultural lands and the lack of access to alternative water resources, as well as the lack of groundwater resources or the lack of alternative surface water resources have intensified, and it seems that, this part of Iran will face numerous problems if the drought continues in the coming years and the appropriate methods are not used to deal with it. Also, given that the water resources of the region are going to decrease in the coming decades, the necessity of using comprehensive water management methods in all sectors, including the reserve, transfer and distribution sectors seems very essential and inevitable. Finally, it is expected that the trend of destruction of vegetation decreases in the future by applying proper management practices, sustainable water distribution, regional negotiations, methodical agriculture as well as the establishment of optimal hydrological conditions.

Nowadays, rapid urbanization; mismatch between modern streets and the demands of population; population attracting land uses along streets; and vicinity of incompatible land uses have resulted in traffic congestion in cities. Traffic is one of the major problems in most large cities, and even medium and small cities. It is also one of the social problems of modern societies and cities. Although, extensive studies have been carried out on the network structure and land use separately, their interaction has been disregarded. Like other modern cities, the city of Kashan faces this problem. The central texture of Kashan attracts a large population throughout the day, and especially during rush hours. This is on the one hand, due to the presence of historical elements, such as Kashan historical bazaar, historical buildings and schools, and on the other hand, because of population attracting land uses like commercial, educational, and therapeutic land use. Therefore, it is necessary to consider this problem, and the spatial redistribution of population attracting land uses.
Materials & Methods : The present study applies a descriptive-analytic methodology. The necessary information was collected using library research, documentary method, and expert interview. Then, the data was entered in GIS software. GIS software and network analysis model were used for data analysis.  Results & Discussion: In this study, the role of educational and therapeutic land use in traffic congestion in central areas of Kashan was investigated. To carry out network analysis, the network map of Kashan streets and their operating speed were required. The street network was depicted in GIS software. Then, the maximum operating speed of the main streets of Kashan was determined based on the master plan. Table 1 presents operating speed in five main axes of Kashan based on the master plan.  These include main and crowded streets of Kashan. The operating speed of other streets was collected through expert interviews. After designing the network and determining operating speed of streets, (educational and therapeutic) land uses with the most significant impact on the traffic congestion of Kashan were identified by interviewing ten experts, with the aim of determining service areas. For each sample land use, a test was performed to determine service areas in the network analysis phase. To conduct this test, the standard service radius of educational and sanitation land uses in Iran was used. In network analysis, the test was separately conducted for each primary school (minimum operating radius of 4 minutes/ maximum operating radius of 5 minutes), middle school (minimum access radius of 6 minutes/ maximum access radius of 7 minutes), high school (minimum access radius of 8 minutes/maximum access radius of 10 minutes), and therapeutic land uses (minimum access radius of 7 minutes/ maximum access radius of 8 minutes). Based on the analysis of service provision range in Kashan downtown, we conclude that compared to other areas in the city, primary schools (in their minimum access radius) face 2.25% increase in traffic congestion; middle schools in their minimum radius of access face 4.67%, increase and in their maximum radius of access face 1.83% increase; high schools in their minimum radius of access face 3.25% increase, and in their maximum radius of access face 7.95% increase, and therapeutic land use in their minimum radius face 7.46% increase, and in their maximum radius face 6.16 % increase in traffic congestion. However, primary schools in other areas of the city face 0.24% higher traffic congestion in maximum access radius as compared to downtown. Thus, downtown attracts 13.13% more unnecessary urban commutes and traffic in its minimum radius of access. This reaches 20.68% in the maximum radius of access, which is due to a larger overlap between educational and therapeutic land u

Nowadays, detailed land cover and land use information is considered to be an important research topic in geosciences, environmental changes and natural resources.  In this regard, monitoring agricultural land-use provides essential information for land use planners and decision makers. Multiple methods are used for monitoring agricultural land use from remotely sensed images. Object-oriented techniques used for processing satellite images makes high accuracy recognition of various land use patterns possible. Compared to traditional pixel-based approaches, these techniques reach a higher accuracy in extracting land-use information from satellite imageries using geometric information and features of different phenomena. In fact, object oriented methods relay on processing unit or image objects made by the integration of homogenized pixels in the segmentation process. Once segments are formed, various indices and spatial information like texture, pattern, form, content, and etc. are applied on processing units. In this way, identifying appropriate land use classes based on geometric features becomes possible. As compared to traditional pixel-based methods, these methods are more flexible and thus can apply a combination of spectral and spatial information. The main goal of the present article is to develop land-use maps and to evaluate agricultural activities in eastern basin of Urmia Lake using object-oriented processing techniques.
Study area and martial : The study area was chosen based on a mixture of agricultural land use, human settlements, and salt marshes in Urmia lake eastern margins. The main goal of the present study was to produce accurate maps of agricultural systems in Urmia lake eastern margins. Thus, various agricultural land uses were extracted from satellite imageries with an emphasis on orchard land use classes. Training data were collected through field operation using GPS. Moreover, 1:25000 scale topographic maps were used for geometric correction and rectifying of the satellite images.
Methods and techniques:  Various agricultural and orchard land use were extracted from Aster satellite imageries received in 2016. In pre-processing stage, geometric correction including geo-referencing, orthorectification and atmospheric corrections were performed on imageries. In processing stage, detection functions were applied and images were then classified according to the research goals based on pixel-based and object-oriented algorithms. In this regard, maximum likelihood, parallelepiped, and minimum distance algorithms were used to classify images. Segmentation process was performed based on homogeneity, shape and compactness parameters. Accordingly, the geometric and spectral algorithms were used for modeling each class in object-oriented environment and object-oriented classification was applied based on nearest neighbor algorithm. Using object oriented and pixel based processing techniques, four land use maps were extracted. In order to evaluate and compare final results, overall accuracy and Kappa coefficients were extracted for each algorithm. Results indicate that among pixel-based classification algorithms, maximum likelihood algorithm with overall accuracy of 87.67 percent and kappa coefficient of 0.86 is more accurate than other methods. However, with a Kappa coefficient of about 0.93 and overall accuracy of 94.20 percent, this algorithm has a lower accuracy level as compared to object-oriented methods.
Discussion and conclusion: Results indicate that compared to pixel based techniques, object oriented processing techniques possesses a higher potentiality for extracting agricultural land use. The main advantage of object oriented methods is that they employ a combination of spatial information, spectral information and integrate them with GIS and remote sensing datasets. Moreover, using texture and shape algorithms in object based classification leads to improved accuracy of land use maps. Besides, it is possible to improve the accuracy of results using effective techniques in object oriented classification. According to research findings, object oriented techniques provide an effective method for classification of satellite imageries and extraction of land use maps. It is possible to use these techniques in landscape planning, natural resources, regional land-use and land-cover changes, sustainability of land cover, and etc.

Drought is a critical climate condition affecting many places on Earth. Drought severity is often measured using a combination of different variables including rainfall, temperature, humidity, wind, soil moisture, and steam flow. During the last decades, Iran has suffered from drought conditions and it may suffer more in future. The frequent occurrence of drought in Iran is mainly due to lack of sufficient precipitation and improper water management system. Drought is often categorized into three types: meteorological, agricultural, and hydrological. There are various methods for measuring and quantifying drought severity. The most commonly used ones are Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI). Remotely sensed data can also be used for monitoring drought condition. The most widely used ones are Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Condition Index (VCI), Temperature Vegetation Index (TVX) and NDVI deviation Index (DEV). Neural Network (NN) and Autoregressive Integrated Moving Average (ARIMA) are two of the most widely applied methods for modeling and monitoring drought severity indices.
In this paper, monthly time series data (2000 to 2014) of three remotely sensed indices (i.e., NDVI, VCI, and TVX) and one meteorological index (i.e., SPI) were applied for modeling drought severity. In addition, the NN and ARIMA were developed for modeling these indices.
Materials & Methods: Data used in this paper were the time series of NDVI, VCI, TVX, and SPI. The study area in this paper was Arak, center of Markazi province. It has cold and wet winters with warm and dry summers. ARIMA and NN were employed for modeling indices.
ARIMA model is generally derived from three basic time series models: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA). These basic models are used with static time series, i.e., they have constant mean and covariance in relation to time.
Usually, NN method has three layers. The first layer or the input layer introduces data to network. Input data is processed in the second layer or the hidden layer. Finally, the output layer produces the results of the input data. In this paper, single hidden layer feed forward network, which is the most widely utilized NN form, was employed for modeling indices.
Results & Discussion: After implementing NN and ARIMA models on the time series data, the performance of the models was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The RMSE obtained by NN and used for modeling NDVI, VCI, TVX, and SPI indices of Arak were 0.1944, 0.2191, 0.1295, and 0.2990, respectively. In addition, RMSE obtained from ARMIA, and used for modeling these indices were 0.0770, 37.2318, 0.2658, and 1.3370. In another experiment, the correlation between remotely sensed indices and SPI was studied. Among the remotely sensed indices, TVX shows the most powerful correlation with SPI. In the present study, drought condition in the central region of Markazi province was studied during the 2000 to 2014 period. We used the time series of remotely sensed data (such as LST and NDVI) and meteorological data (such as SPI). Then TVX, VCI, and DEV indices were extracted from NDVI and LST data. NN and ARIMA were applied for modeling time series data. Based on the findings, it is concluded that NN is more successful and efficient than ARIMA for this study area. In addition, TVX, which is built based on NDVI and LST, had the most powerful correlation with SPI. This issue implies that both vegetation index and temperature index had an important role in modeling and monitoring drought condition.

The sun is the primary source of energy and life for Earth, and without solar radiation, there will be no atmospheric and climate processes on the Earth. Animal, human and plant life on the Earth depend on the energy received from the sun. Shortwave solar radiation is very important, due to its role in biological processes, especially photosynthesis and human life. Outgoing Long Radiation (OLR), which is the result of heat reflection from the Earth’s surface, plays a vital role in the thermal balance of the Earth with regard to the presence of greenhouse gases. Part of the OLR goes out through atmospheric windows, but a large part of it is returned to the Earth by greenhouse gases, and plays an important role in the Earth’s thermal balance, especially during nights and in winters. Estimating Outgoing Long Radiation (OLR) is very difficult and remote sensing can be used to evaluate OLR on a planetary and regional scale. The purpose of this study is to examine long-term average of outgoing longwave radiation (OLR) over Iran using data received from the Iranian National Center for Oceanography and atmospheric science. Solar radiation is one of the most important parameters affecting the Earth atmosphere thermal balance (Isoman and Mayer, 2002). It also forms the basis for most of climate studies, because the process of evapotranspiration depends on the amount of available energy for evaporation (Alan et al, 1998). Since 99.8 percent of the energy at the Earth’s surface comes from the sun, the effect of solar radiation on evapotranspiration has been of great interest to researchers working in the field of agricultural science, especially irrigation sciences (De Souza et al, 2005). Some studies have used OLR trend to explore feedback and climate processes (Chu and Wang, 1997; Suuskind et al, 2012). Chuudi and Harrison studied El Niño’s impact on seasonal rainfall, temperature and atmospheric cycles’ anomalies in the U.S. using OLR. In another study, they have also estimated global seasonal rainfall anomalies related to El Niño and La niña using OLR (Chiody and Harrison, 2013, 2015). Knowing the amount of solar radiation in different locations is important for many practical issues such as estimating evapotranspiration, architectural design, agricultural products growth models, and etc. (Moradi, 2005; Alizadeh and Khalili, 2009; Mousavi Baygi et al, 2010). Considering the importance of climate change effects on the fluctuations of short wave and long wave radiations from the Earth surface and its relation with regional climate, research on this issue seems necessary. Since this issue has been underestimated in our country, and most researchers have only tried to find different coefficients and equations for estimating received solar radiation based on other meteorological parameters, making previous sporadic studies and researches on outgoing longwave radiation changes over Iran and other parts of the world applicable seems to be necessary.
Materials & Methods: In this study, HIRS satellite data were used to analyze long-term average of OLR on planetary and regional scale. NOAA satellites were launched by the National Oceanic and Atmospheric Administration of the United States. The latest satellite in these series (version 19) was launched in February 2009. This polar-orbiting satellite circles the Earth from the North Pole to the South Pole 14 times a day. This allows NOAA-19 to observe the whole Earth twice every day (NOAA website). Since the purpose of the present study is to examine long-term average of outgoing longwave radiation over Iran based on data received from NOAA, daily OLR averages were retrieved from the CDR database with 1 arc degree resolution on a global scale for the period 1/1/1979 - 12/29/2016. Then, Iran long-term average of OLR and also its global average were calculated based on nearly 1 billion cells. The Gi* analysis method was also used to study the spatial distribution of outgoing long wave radiation over Iran. Since data received from outside Iranian territory were also included, we used “In polygon” function in MATLAB software to extract data specific to geographic borders of Iran.
Results & Discussion: After calculating long-term average, results indicated that maximum OLR occurs between 30˚ north and south latitude, especially over the Middle East and North Africa, which is due to the radiation angle and ground cover. Results also showed that long-term average of the OLR was 222 W/m2. However, the mentioned areas have a reflection of more than 280 W/m2. Maximum OLR (289W/m2) occurs over Rub’ al-Khali desert and minimum OLR occurs over Antarctic glaciers (126 W/m2). These two points are one of the warmest and coldest areas on the Earth, respectively. They also have different ground cover. Therefore, it is natural to have a 173 W/m2 difference between the highest and lowest outgoing long-wave radiation over the Earth. Regional scale findings indicated that long-term average of OLR over Iran is 265 W/m2, which is 43 W/m2 (19 percent) higher than the global average. Results also showed that maximum OLR occurs to the west of Poshti region in Konnak city, Sistan and Baluchestan province (289 W/m2), and minimum OLR occurs over Ararat mountains in north-west Iran (approximately 235 W/m2). This 50 W/m2 difference is due to different latitude and altitude of these locations, which shows the significant role of temperature in the amount of outgoing long-wave radiation.
Conclusion: Findings indicated that average global OLR is 222W/m2 and maximum reflection over the Earth surface occurs between 20˚ north and south latitude. This is because the average reflection between these latitudes reaches 270 W/m2, which can be attributed to the proximity of Tropic of Cancer and Tropic of Capricorn. Findings also showed that average long-wave radiation over Iran (264 W/m2) is %19 higher than the global long-term average. Although, maximum global OLR occurs in Rub’ al-Khali desert in Saudi Arabia (299W/m2), Iran is also considered to have a high level of OLR due to its geographic location and limited ground cover. With a reflection of more than 280 W/m2,vast regions in southern Iran are considered to have excessive energy and thus play an important role in environmental warming. Spatial analysis of hot and cold spots concentration patterns (above 90% level of confidence) showed that nearly 40 percent of Iran is considered to be hot spots, 17 percent neutral and 43 percent cold spots, the pattern of which is affected by difference in latitude and ground cover.

The notion of climate change indicates a significant change in climate and environmental conditions over a long period of time (from a few decades to centuries). These changes can occur in mean radiation, temperature, precipitation, atmospheric patterns, wind, and other climate parameters. Increased global average temperature and occurrence of climate extremes such as floods, storms, hails, tropical storms, heat waves, sea level rise and melting of polar ice caps are the most important effect of climate change. The present study sought to analyze the effect of climate change and global warming on temperature trends in Iran atmospheric levels. One advantage of the present study is that it investigates temperature changes at sea surface and other atmospheric levels, whereas many recent researches just emphasize on sea level. The present study used data received from the European Center for Medium-range Weather Forecast (ECMWF) for a period of 60 years, from 1951 to 2010, with a network resolution of 1 × 1° Latitude and Longitude for sea level data (Slp) and 850, 700 and 500 hPa levels. After converting extracted data using statistical extension of Net-cdf for excel 2007, the temperature trend for sea levels of 850, 700 and 500 hPa were calculated. The correlation between temperature and its anomalies was measured using elevation levels of 850, 700 and 500 hPa and the temperature anomaly maps and synoptic pattern were developed on a regional scale, and finally their relationship with temperature trends were analyzed and interpreted. Iran had an average temperature of 18.06 °C during the 60 year period (1951 to 2010). 1999, with an average temperature of 20 C°, was the hottest year during this time. From 1993 onwards (except for 1997 and 2007), the average temperature was more than the 60-year average (18.06 C°). By comparing 30-year periods (from 1951 to 1980 and from 1981 to 2010) with each other, we observed that sea level temperature increase in the second 30-year period was more than the first period temperature increase at other atmospheric levels. This increase is most possibly due to the effects of global warming. Temperature increase in the first and second periods were 0.24 and 0.63 °C, respectively. Because of closeness to sea level and under the influence of surface conditions, 850 hPa level shows maximum temperature increase compared to other atmospheric levels (after sea level). Also due to the impact of sea level during the first and second periods, this factor is highly correlated with the sea level atmospheric condition. Despite the fact that correlation values of 850, 700 and 500 hPa levels were significant in both first and second periods at 1% level, they have increased in second period at all atmospheric levels.  In other words, there is a clear increasing trend in the second period and few decreasing changes are observed.
Regarding the patterns observed at sea level in the second period, two low-pressure closed cell trough which had been observed in the first period in India and Pakistan, merged in the second period. At 850 hPa, the subtropical high pressure located over Atlantic in the first period moved to East Africa in the second period and created a closed high pressure subtropical cell over Libya with an elevation of 1500 hPa. Compared to the first period, this high pressure cell has a higher altitude. At 700 hPa level, STHP ridge extended significantly in the second period, and in this period, central regions of Iran exhibit wide ranges of air sinking with a deep layer of warm air. Over the 60 year-period, temperature of atmospheric levels in Iran have exhibited an increasing trend, which from 1993 onwards had a much steeper slope of increase. Compared to the first period (with almost normal periods of increasing and decreasing, and a slightly fluctuating rhythm), the second thirty-year period is expected to exhibit a constant and continuous increase. Additionally, warmer SLP at sea level and 850 hPa level, the northward expansion of the Hadley cell, and finally more intense subsidence of STHP toward lower atmospheric levels (above sea level and 850 hPa) exacerbate the effects of global warming on Iran atmosphere.

About one quarter of world’s deserts are covered with quick sands, whereby, sand fields are the most common landforms. The movements of the sand fields are considered as a threat to the roads, natural resources, urban areas, agriculture and infrastructure.Factors such aspoverty of vegetation, increasing of drought due to global warming have led to the dynamic of sand fields with different speeds in manydirections that threat the transportation, health, economic and human activities. Thus, the spatial-temporal monitoring of sand fields dynamic behavior and identifying their directions of development are of great importancein the management of dry regions and conservation of natural resources. Therefore, the aim of this research is the Multi-temporal monitoring of sand field dynamic behavior in the west of Damghanplaya from 1972 to 2016, in the form of three 15-year period through data and remote sensing methods. Damghanplaya Basin with an area of 18070.918 km2issituated between Toroud-ChahShirin Horst and the Alborz Mountains with an elevation of 2319 and 3884 meters respectively. Its general slope is towards the center of Damghan desert with an elevation of 1028m. Damghan playa is a tectonic-sedimentary hole, which is presently influenced by different geomorphic and climatic morphogenetic processes. Because of the vegetation and precipitation shortage,the wind morphogenetic systems dominate other processes. Thus, several types of wind erosion landforms can be observed in this region. The study area is the western erg of Damghanplaya with an area of 71.155 Km2 which is situated in Damghan Basin in the north of Iran’s great central desert. The region is located between latitudes 35° 51´ to 35° 58´ N and longitudes 54° 13´ to 54° 25´ E. This is an applied research and its methodology is a combination of remote sensing analyses. In this regard, topographic maps with a scale of 1: 25,000, geological maps with a scale of 1: 100,000 and Google Earth’s satellite images were used first to determine the position of the study area. Then, spatial database was completed through receiving Landsat satellite images during the period 1972 to 2016. Sinceseveral series of remote sensing satellite images belonging to multiple time periods are needed for monitoring the dynamic behavior of the sand field, four series of Landsat satellite images, MSS, TM , ETM+  and OLI  sensors related to  three 15 year periods of 1972,1987, 2002 and 2016 respectively, were used in this research. The aforementioned images were obtained from the Landsat satellite archive on the American geological organization website (http://earthexplorer.usgs.gov/). Then,color combinations, IHS transformation, and supervised classification of Maximum Likelihood methods were used to enhance the spatial area of the sand field, and the method of images difference and the calculation of the changing classes level  were used to examine the type and trend of the changes..
Findings and Results: The results show that the maximum and minimum area of the sand filed are observed in 2002 and 2016 with an area of 92.2641 and 49.2803 km2 respectively. The results of change detection show that there are three types of changes including increasing, decreasing, and no-changes. As it can be observed,the maximum area of the classes of change belongs to the no change class that the periods of 1972 to 1987 and 2002 to 2016 with the amounts of 58.3506 and 48.2841 km2 respectively,have the highest and lowest areas, while, the minimum area of ​​the classes of change belongs to the class of incremental changes that the periods of 1987 to 2002 and 2002 to 2016 have the highest and lowest areas with the amounts of 38.2833 and 1.0359 km2 respectively. The maximum and minimum areas of decreasing class of changes belong to the periods of 2002 to 2016 and 1987 to 2002 with the amounts of 43.9829 and 14.2693 km2 respectively. In this regard, the no-change and increasing change classes with the standard deviation of 5.0445 and 19.4699 respectively, have the minimum and maximum range of changes during the entire period of 44 years.
The results obtained fromstudying thetemporal trend of changes indicate the existence of a decreasing trend in the no-change and increasing change classes, and also the existence of an increasing trend in the class of decreasing changes.Descending trend of no-change class is uniform and continuous. In contrast, the trend ofincreasing and decreasing classes of changehas a periodic jump in the second time period (1987-2002), but their overall trend is almost uniform.
Discussion and Conclusion: Western erg of Damghanplaya has decreased by approximately 6.7225 km2 in 1987 compared to 1972. Most of this reduction has occurred in the southwestern and eastern parts of the sand field. The southwestern contraction of the erg is in accordance with the pediment and the sand harvesting area, the causes ofwhich are the sand transfer by local winds blowing from the southwest to the northeast, as well as the formation of the desert pavementfacies. In contrast, the eastern contraction of the erg is due to the increase in moisture content from the Haj Aligholiplaya and the increase in humidity caused by agricultural lands adjacent to the erg. In the second period, the trend was completely reversed and the sand field was expanded in 2002 by approximately 17.3659 and 24.0885 km2in 2002 compared to the years 1972 and 1987 respectively. This period is considered to be the most risky periods in terms of environmental hazards. In this period, major spatial expansion of the erg has taken place to the east and especially to the northeast. This expansion can be due to the increased drought severity and the continuation of dry periods and the release ofthe agricultural lands in some cases. In the third period, the situation has improved and the dynamic of sand has reduced, so that the extent of sand field has decreased in 2016 by 25.6178, 18.8952 and 42.9837 km2compared to the years 1972, 1987 and 2002 respectively, which represents the negative balance in the erg. In other words, the amount of the sand entering the erg is far less than that of the sand going out. In terms of location, the contraction of this period on the margins of the ergextends continuously and almost uniformly, but the largest contraction isobserved in the eastern, northeastern and southwestern parts. This decrease is due to the implementation of desert greening plans in the form of quick sands stabilization projects by planting Haloxylon. This indicates the positive and successfulfunction and role of desert greening projects. Also, due to the favorable natural and climatic conditions, the species of Haloxylon has been able to regenerate naturally in the area under cultivation. This has had a positive impact on the stabilization of  quick sands and the reduction of erg changes.

Iran is a rugged landscape with varied morphologies. Kermanshah Province is a mountainous region which lies between the Iranian plateau and Mesopotamia at Zagros Mountain Range. This area has geopolitical military value and importance. Past experiences have shown that large military units are not suitable for operations in this area.  This is a problematic area for opposing forces, because natural geographical conditions make coordination difficult. During the Imposed War, the area has been continuously attacked by the enemy, due to its prevailing geographic conditions. After the war, the presence of US forces and more recently ISIS terrorists in Iraq have increased the sensitivity of the region. Now, despite the presence of a Shi'a government in Iraq, the region is continuously provoked and made insecure by transborder forces. Considering the policy of our enemies (America, terrorists, etc.) in the region, the vicinity of Iranian city of Qasar-e Shirin to the city of Khanqin in Iraq has made it possible to use the region as a source for offensive operations against Iran or conducting any operation in the region. Regarding military threats from the west, this axis is the best place to advance and dominate the western regions. It is also used by armed insurgents seeking to threaten and perform ruinous operations in the region. In this regard, it is necessary to examine and analyze topographical phenomena and defensive capabilities of this axis from a natural and human geographic perspective. Qasr-e-Shirin axis in Kermanshah has several capabilities for territorial defense of the province. One of the most important capabilities is the use of geographic topographic factors affecting widespread military regular and irregular operations. These factors result in channeling and delaying of operations and show the effects of topography on the implementation of military movements, disrupting the order of operations and loss of commanders’ concentration. Few researches have considered this important issue and geographical capabilities of this axis, as a means for defending the western region, have not been investigated scientifically and systematically.
Materials & Methods: The research method is descriptive-analytic. Thus, the following process was predicted to achieve the objectives of the plan.
-Secondary research
-Investigating natural factors in the studied area considering 1: 50000 and 1: 250000topographic maps, data and indexes are recorded on digital model of the region.
- Digital capturing and 3D reconstruction of the region by Surfer Software.
- Field observations, photographing and filming geographical phenomena in the region, adapting to documentary data and using the comments of military experts and senior commanders in the region and province.
-Analyzing the morphology and studying topographic phenomena and defense capabilities of this axis from a geographic perspective.
Results & Discussion: The axis extends from Khosravi to the entrance of Kermanshah city with a length of 188 km.This road was previously called Iran's security corridor. Now, it is called Karbala Highway and has a significant and strategic role in the relationship between the two countries, Iran and Iraq, and other countries with similar religious interests. The new and old route connecting Tehran to Karbala or Baghdad cross this region. The geographic location of the region and the direction of these elevations are parallel to the border. The ground slope gradually decreases from east to west, and the elevations continue up to the Mesopotamia.
In the eight years of Iranian Sacred Defense, Qasr-e-Shirin, Diyala, Khanqinin, Dehliz-e patag were considered to be operational axes. Therefore, due to being mountainous; ethnic, racial, religious convergence and divergence of indigenous inhabitants with the Islamic Republic of Iran and Iraq, this axis was studied and analyzed with the aim of protecting territorial zones of the Islamic Republic of Iran and creating a defensive plan for the region.
Perhaps, it can be claimed that the most appropriate axis for advancing toward the central parts of Iran is the western region of the country (Qasr-e Shirin axis, Eslamabad-e Gharb, Kermanshah). Therefore, it is necessary to study the defensive features and identify its strengths and weaknesses. Also, recognizing natural landscapes, identifying passages, routes, and important defensible bridges makes it possible to use natural and topographical features and prevent the enemy from advancing toward the center of the country. Kermanshah Province and the city of Qasr-e-Shirin are bordered by Iraq. They are also located near the centers of Takfiri, ISIS, and terrorist crisis, which can cause insecurity on local, regional and national levels. In order to ensure security, some aspects need to be investigated, which to some extent rely on natural and human factors and the presentation of a territorial defense plan in the region. Regarding the geographic location of the region, natural factors are a positive point. By taking control of sensitive areas, it is possible to control and stop military movements. Examining geographic factors, we can mention the following issues:(A) Elevations in the Kermanshah province: going from east to west, the ground elevation decreases, and Kermanshah highlands overshadow the Mesopotamian plain, which provides the possibility of performing any offensive and defensive operation for Iran.
(B) Studying communication pathways in border regions of Kermanshah province, it can be concluded that communication pathways have an important role in achieving military, political, economic goals. Routes connecting Qasr-e-Shirin to Sar-e Pol Zahab, Eslamabad-e Gharb and Kermanshah have strategic military, political, and economic value. During Iran-Iraq war, these road was used for military and political purposes, but today they are used for economic purposes.
C) By examining the geometric features of the border between Kermanshah province and Iraq, it can be concluded that the convex form of Qasar-e Shirin border is of military importance for Iran and thus, Iraq first occupied this area during Iran-Iraq war. Finally, this axis passes through mountainous narrow areas, and thus it is the best place to delay, stop, and channel enemy's ground movements.