GEOGRAPHICAL DATA
Year:2020 | Volume:28 | Issue:112|Papers Count:16

Introduction Development of reliable models for estimation and prediction of changes inTotal Electron Content (TEC) of the ionosphere is still considered to be a real challenge for geodesists and geophysicists. This ispartly due to the nonlinear behavior of the physical and geophysical parameters affecting the TEC variations, as well as the difficulty in accurate measurement of some of these parameters. Due to its specific nature, as well as its physical and geophysical properties, quantity of TEC hasspatio-temporal variations, which can be attributable to daily, and seasonal variations, various anomalies, or periods of solar activity. Total Electron Content is the quantity which can be used to study ionospheric activities, as well as the spatio-temporal variations in electron density of this layer. In fact, TEC is the total number of free electrons in the path between the satellite and the receiver in a one square meter column. The measurement unit of TEC is TECU, which is equivalent to 1016electrons/m2. Due to inappropriate spatial distribution of GPS receivers and their limited number, as well as observationaldiscontinuity in the time domain, TEC values and electron density obtained from theGPS measurements will be spatiallyand temporallyconstrained. In order to calculate TEC value in areas lacking observation or appropriatestation distribution, TEC value obtained from GPS measurements must be interpolated or extrapolated in a suitable manner. Materials and Methods By combining wavelet localization features with standard neural networks, Wavelet Neural Networks (WNN) have emerged as a new mathematical method for modeling and predicting the behavior of different phenomena. In WNNs, the output parameter is usually calculated by the following equation: (1) wherex is the inputobservations vector, is a the multi-variablewavelet whichcan be calculated by the tensor productof m (basic function of single variable wavelets), ë is the number of neurons in the hiddenlayer, and ù shows the network weight. Unlike the Backpropagation (BP) algorithm, PSO is a global search algorithm that can optimize the initial weights and introduce the appropriate structure for the network. Equations used in this algorithm are as follows: (2) (3) In which, shows the initial weight, represents the particle’ s velocity i in repetition t, c1 and c2, indicate the particle acceleration coefficients, is the current position of particle i in repetition t and gbest represents the best particle position. The present study took advantage of a smoothing algorithm to determine STEC observations. Observed STEC values are as follows: (4) To obtain TEC value along the zenith, the following mapping function can be used: (5) Which we will have: (6) Elev. in relation (6) is the satellite’ s elevation angle. Results and Discussion Observations of 37 Iranian GeodynamicNetworkson 2012. 08. 11 (DAY 224) were used to evaluate the efficiency of WNN and PSO training algorithm in modeling and predictingspatio-temporal variations of TEC in Iran. Of the 37 stations, 5 were used as test stations, 2 were used to evaluate the wavelet neural network, and the rest were used to train the network. Four different combinations of input observations are examined in this paper. Number of input observations selected from the Iranian Permanent Geodynamic Network(IPGN) to train the WNN using PSO algorithm was25, 20, 15 and 10, respectively. Table 1 shows the characteristics of different combinations evaluated in this paper. Table 1. Characteristics of the observations used in the different combinationsevaluated To evaluate the accuracy of the results obtained from IRI and WNN model, all results were compared with TEC observations obtained from GPS. Table 2 shows the correlation coefficient for different scenarios. Table 2. correlation coefficient for different scenarios According to Table (2), the first scenario in WNN method with GPS hasthe highest correlation coefficient. Even when the number of observations in the databasedecreases in the third scenario, theWNN method still has a higher correlation coefficient compared to the IRI2012 model. In the fourth scenario, the correlation coefficient for WNN method is reduced to some degree. The average relative and absolute error values at the 5 test stations were calculated for the four different scenarios and presented in Table3. Table 3. Comparison of mean relative error and absolute error values at 5 test stations for four different scenarios. Statistical analysis of relative and absolute error showssuperiority of WNN method in TEC modeling as compared to the IRI2012. Conclusion To model total electron content of the ionosphere, 4 combinations of observations were evaluated. 25, 20, 15 and 10 stations were used to train the wavelet neural network. 300, 240, 180, and 120 observations (latitude and longitude, observation time)were considered in the database, respectively. Results of the analysis indicated that with a decrease in the number of observations in the database, the absolute and relative error increase, while correlation coefficient decreases. This decrease was not evident before 180 observations, but relative and absolute errorreached up to twice their values with 120 observations. It should be noted that even with 120 observations (10 stations for training), results of the wavelet neural network model are more accurate than the results of the IRI2012 model.

Introduction Due to mainly mountanous topography, specific geographical conditions, various geological formations, neo tectonical activities, and seismicity, Iran is potantially prone to landslides. Gilan and Roudbar region in the southern part of this province are among areas potentially susceptibleof landslides, rock falls, and other events associated with seismichillsides. Landslide results in severe erosions and sometimes leads to financial losses or loss of lives. Therefore, it is important to investigate the causes behind this phenomenon and determinezones prone to land sliding. Materials and methods In this study, we tried to usea sample of trenches and boundaries offaultslocated inRoudbar sliding slopes in order to characterize the sliding soils. Following this field investigatation, soil samples were obtained from 10 landslide zones. Then, factors affecting the sliding slopes were identified and a digital map was produced for each factor. Nine data layers including direction and degree of slopes, geology, landuse, precipitation, relative changes in elevation, distance from roads, rivers and faults were used in GIS environment to prepare the weighted maps. Afterwards, LNSF statistical method was used for data analysis in GIS environment and the study area was divided into 5 zones with very low (1), low (2), moderate (3), high (4), and very high (5) sliding susceptibility. Following the integration and analysis of layers using LNSF model, 26 zonation mapswere calculated, and the best map was selected using success rate curves. Then, the zone with highest potentiality for landslide occurrence was selected for further studies from the five zones mentioned before. Hydrometry, Atterberg limits and direct shear tests were performedin the Soil Mechanics Laboratory of Gilan University with the aim of identifying physical and mechanical properties of soil samples. Results and discussion Results indicate that with LNSF method, it is possible tozone a vast area (12814. 2 hectaresin this research) based on landslide potentiality and then focus on the most critical area (very high landslide potentiality) toinvestigate factors and conditions resulting in the occurrence of landslides or prevention strategies. Success rate charts helps us to determine the most optimal landslide zoning map (i. e. a map inwhich the highest percentage of landslide pixels occur in the “ very high potentiality” zone). Following the selection of final zonebased on success rate graphs, from the 26 zoning maps, it was concluded that the landslide zone with very high potentiality encompasses 282. 6825 hectares or 2. 2% of the total area under study. At the weighting stage, the highest weight was allocated to the seventh category of the land use layer, which at the final zoning stage covers nearly the whole area with very high potantiality of landslides. Therefore, there is a direct relation between the allocated weight in the subject categories and the percentage of its occupancy level in the final zoning. Zoning the results of granulation experiments by Thiessen Polygon, it was concluded that CL type soil coversnearly half of the area with very high landslide potentiality. Determining the static reliability coefficient of the area with very high landslide potentiality, we found that in case soil reaches saturation, unstability of hillsidesin a large part of the study areacanbe expected. Conclusion Dispersion of landslides in Iran is mainly concentrated in Southern Gilan Province. Based on the investigation of the situations in the study area, geology, landuse, distance from highway are identified as the most affective factors in theoccurance of landslides. Following the weighting stage with LNSF method, rated layers were prepared in GIS enviornment, final overlapping was performed, and landslide zoning map of the study area was produced. Based on the landslide risk zonning map, the study area was divided into 5 subsections: 2. 21% of the study area had very high sensetivity, 26. 43% high sensetivity, 42. 28% avarage sensetivity, 25. 25% low sensetivity, and 3. 83% very low sensetivity. Considering the zonning map produced, it properly overlaps with identified landslides in the area, and help governmental policy makings. It specifically helps Organization of Roads in construction of new roads.

Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires have turned into a serious natural risk nowadays. Wildfires are a natural disaster threatening forests and ecosystem, from local to global level. Evaluating the risk of wildfires is an important factor in fire management. This can be performed at different spatial and temporal scales: global and local; short term, and long-term. At global scales, it can contribute to the establishment of general guidelines for fire management at continental level, while at local scales, it is more suitable for resources focusing on preventing specific fires in small regions. Long-term estimation addresses general, more permanent planning of firefighting resources, which is related to the more structural factors affectingwildfires or their spread, such as topography or terrain characteristics, vegetation structure, human activities or weather patterns. Materials & Methods Wildfire risk has become a major concern in recent years, particularly in areas where human settlements are in close proximity to forests. Wildfire origin canbe determined largely by environmental factors. However, fire related data is either unavailable, or mostly incomplete. Thus, reaching an overall annual estimate of wildfires is difficult. Some common methods are used toestimate the risk ofwildfires, including qualitative methods, quantitative methods based on specialized knowledge (multi-criteria evaluation techniques), regression techniques (linear regression and logical regression), and artificial neural networks. Wildfire initiation and spread depend on several important factors, including precipitation, presence of ignition elements, factors like topography, temperature, thunder, spreading of fuel, relative humidity, wind speed, and etc. The present study integrates data produced by remote sensing with data received from geographic information system. It also takes advantage of LDCM satellite imagery, and digital elevation model, along with natural/human factors such as wind speed and direction, vegetation, land surface temperature, slope, proximity to roads and residential areas. The present study seeks to quantify environmental and human elements effective in occurrence and spread of wildfires in the protected jungles of Arasbaran. To this end, a risk zone map was produced for the area, along with a map for areas with 50% risk. In the present study, the final map of risk zone was produced using the Fire Risk Index (FRI) and spatial statistics method. Results & Discussion In the present study, factors such as land cover type, slope, distance from residential area, distance from the road, and elevation were taken into account. During the process, different indices were assigned to each class of these factorsbased on their sensitivity to fire or their flammability. Land cover was one of the most important factors affecting the occurrence of wildfires. Slope was another important factor with a significant influence on the spread of fire. This natural factor affects fire spread and fire intensity. Proximity of human settlements to jungles is another important factor which sometimes threatsjungles. Therefore, forests in proximity of human settlements face a higher risk of wildfires. Elevation is another important topographical factorclosely related to wind behaviour, with a significant role in fire spreading. In Arasbaran forest, northern, eastern, and north-easternareas are more elevatedand thus, more prone to wildfires. In this study, a combination of environmental and human factors was applied to produce fire hazard maps along with a map for areas with 50% risk of wildfire. Conclusion Occurrence and spread of wildfires depends on many factors, some of which are more important and play a more significant role in these fires. A risk zone map was produced for wildfiresusing an integrated method consisting ofremote sensing and GIS methods. Risk zone was divided into 5 areas, i. e. very low, low, average, high, very high. Results indicate that the methodology presented based on a combination of RS and GIS techniquesin this study, is a reliable approach and tool for the prevention and mitigation of forest fires. They are also useful for all active institutes working in crisis management and emergency services, while helping jungle protectingorganizations to prevent fires or manage them. In addition, quantitative results indicate that vegetation index with a correlation of 58. 36%, and slope with a correlation of 38. 38 are the most affective factors, and other parameters are in the next ranks. Moreover, land cover, land surface temperature, direction, and slope with 29. 20%, 29. 11%, 21. 93% and 19. 75% normalized correlation coefficient respectively, have the highest correlation with the map of fire risk zone. In addition, results of evaluating 50% risk zone map indicate that around 17% of the study area have a high fire risk and more than 50% of the area is located in a high fire risk zone. In addition to environmental elements, results indicate that proximity to the road was the most affective factor in the occurrence of fire. Quantitative results showed that roads and residential areas were at least 32% and at most 68% correlated with fire risk in the study area.

Introduction Nowadays, studying urban expansion is very importantin developing countries. Rapid growth of cities has devastating environmental impacts, and irreparable economic and social consequences. Moreover, studying urban expansion is of great importance for managers and planners of a society. Land surface temperature (LST) is one of the important parameters in urban-regional planning. Urban heat, which is usually referred to as urban heat island, can affect human health, theecosystem, surrounding air, air pollution, urban planning, and energy management. The phenomenon of urban heat island (UHI) is closely related toland-use changes in urban areas, especially when natural surfaces turn intoimpermeable urban surfaces, and increases heat flux and reduces latent heat. Materials & Methods In this study, a collection of Landsat-5 multi-temporal satellite images received in 1986, 1989, 1993, 1998, 2001, 2008, and Landsat 8 multi-temporal satellite images received in 2013, 2015 and 2017, was used along with night images of the MODIS sensor recieved in 2001, 2008, 2013, 2015, 2017 (on the same day Landsat-5 and Landsat-8 satellite images were received). In order to classify land cover and calculate land surface temperature usingLandsat 5, Landsat 8 and MODIS sensorsatellite images, initial pre-processing (radiometric and geometric corrections)was performed. In order to classifyland cover in the study area, training areas were selected using Google Earth andthen, land cover classification was carried outusing Neural Network Algorithm. Since, classifying urban areas wasthe priority ofthe present study, Normalized Difference Built-up Index (NDBI) was also used. Ultimately, pixelidentified by classification algorithm and NDBI index was allocated tourban areas. A simple relationship suggested by the United States Geological Survey (USGS) was used to estimate land surface temperature from Landsat-5 imageries. Split-window algorithm was also used to estimate land surface temperature from Landsat-8 and MODIS imageries. Since, Landsat-8 and MODIS imageries were collectedwith only afew hours (or less than that)time difference, and their thermal bands’ spectral rangeswere close to each other, Landsat-8 thermal bands’ emissivity coefficient with a higher spatial resolution (30 m) was used to calculate land surface temperature from MODIS images. Results & Discussion Classifying land cover in Shahr-e Kordusing Landsat-5 and Landsat-8 imageries received in 1986, 1989, 1993, 1998, 2001, 2008, 2013, 2015, and 2017 indicated that in this31-year time period, residential areas were approximately duplicatedand reached from 1004 hectares to 2112 hectares. Analysis of land surface temperature maps using Landsat 5, and Landsat 8 imageries indicated that urban areas and areas with dense vegetation had lower surface temperatures compared to areas with thin vegetation cover. Therefore, land surface temperature of urban areas is lower than the surrounding areas. However, land surface temperature obtained from MODIS imageries indicated that land surface temperature of urban areas is higher at nights. Therefore, urban heat islands in this city occur at nights. Results indicated that with increasingexpansion of urban areas, urban heat islands also intensifyat nights. Conclusion Although, Shahr-ekordis a less developed urban area as compared to other Iranian metropolises, expansion of its constructed areas can stillhave negative effects on the environment and climate of the region. The present study investigates urban growth, and itsinfluence on land surface temperature and occurrence of urban heat island. Thermal maps produced in the present study indicated that daytime air temperature of this city was relatively lower than other regions. But this is not the case at nights: compared to other areas, residential areas have a higher temperature at nights. This indicates the existence of a heat island in the city, and possibly have adverse and devastating effects on humidity, reduces precipitation, changes local winds and the climate. Results also indicate that urban expansion have directlyaffected urban heat islands. Thus, urban heat islandshave intensified and expanded during this time period. Therefore, it is concluded that there is a direct relationship between land surface temperature and land use type.

Introduction Underground infrastructure such as electricity, gas, telecommunications, water and sewage are managed by different organizations. Since most projects in these organizations require drilling, and imprecise excavations will endanger infrastructure and result in extensive financial and physical losses, drilling projects require having accurate information about the infrastructure status. However, reaching accurate position of facilities such as pipes and cables is difficult due to their being concealed underground. Nowadays, ubiquitous computing and new developments in Geospatial Information Systems (GIS) can be an appropriate solution to such problems. This new generation of GIS is called the Ubiquitous Geospatial Information System (UBGIS). New technologies such as Augmented Reality (AR) can visualize this infrastructure on platforms like smart phones or tablets. Such technologies show spatial and descriptive attributes of these utilities more interactively, and thus can be applied as a modern solution for this problem. One of the major features of AR is identifying and locating real-world objects with respect to the person’ s head or a camera. To have an accurate Augmented Reality, the position and orientation (pose) of the camera should be estimated with high accuracy. Therefore, exterior orientation parameters of the camera are required for AR and tracking. Different methods are used to calculate these exterior orientation parameters. One of the most common methods applies different sensors, such as Global Positioning System (GPS) and Inertial Measuring Unit (IMU), embedded in smart phones or tablets to calculate these parameters. These sensors include accelerometers, gyroscopes, magnetic sensors and compasses. Althoughsimple and fast, this method is not suitable for accurate cases, because sensors of mobile phones or tabletscannot provide such high accuracy. Vision-based (sometimes called image-based) method is another way of estimating exterior orientation parameters. In this method, fixed or dynamic images are used to determine the position and orientation of camera. The method is more complex and slower, but more accurate than the first one. Materials and Methods Regarding previously mentioned issues, the present article aims to visualize underground infrastructure using both sensor-based and vision-based approaches of Augmented Reality. Since the sensors embedded in a mobile phone or tablet do not provide such an accuracy (an accuracy of a few centimeters considering diameter of pipes and width of streets and pavements), a novel vision-based approach is proposed. In this method, image-based techniques and special kinds of targets, known as coded targets, are used to estimate camera’ s position and orientation along with space resection method. In photogrammetry, space resection involves determining the spatial position and orientation of an image based on thesize of ground control points appearing on the image. Since space resection is a nonlinear problem, existing methods involve linearization of the collinearity condition and the use of an iterative process to determine the final solution using the least squares method. The process also requires determination of the initial approximate values of the unknown parameters, some of which must be estimated using another least squares solution. In order to obtain suitable initial values for space resection procedure, data received from GPS, accelerometers, and magnetic sensors are used and a low-pass filter is applied to reduce noise and increase precision. Then, due to improved camera pose parameters, the resulting virtual model is overlaid at its correct real worldplanimetriclocation. The planimetric coordinates are shown graphically on the ground and the Z coordinate (depth) is presented as a descriptive parameter. Results and Discussion Both proposed methods were implemented and tested in an Android Operating System. Camera pose parameters were estimated and the virtual modelwas overlaid at its correct real world planimetric location and shown on camera. Then, the results were compared and evaluatedusingthe well-known photogrammetry software, Agisoft, with the aim of modelling and precise measuring based on basic photogrammetry and machine vision. For sensor-based method, mean accuracy of the position parameters equals 4. 2908± 3. 951 meters and mean accuracy of orientation parameters equals 6. 1796± 1. 478 degrees, whilein vision-based method, these decreases to 0. 1227± 0. 325 meters and 2. 2017± 0. 536 degrees, respectively. Thus, results indicate that the proposed methodimprove accuracy and efficiency of AR technologies. Conclusion Augmented Reality is a technology that can be used to visualize underground facilities. Although, processing in sensor-based methods is sufficiently fast and simple, they lack the precision required for this purpose. Despite the fact that noise elimination and sensor integration using Kalman filter improves accuracy to some degree, it still does not reach the required accuracy. The present article sought to improve the accuracy of augmented reality in underground infrastructureusing targets. Results indicated that the machine vision and vision-based methods improve the accuracy. In drillings, third dimension (accuracy of height measurements) is as crucial as other parameters, thusit is suggested that future researches consider this not as a descriptive parameter, but as a three dimensional parameter to reach 3dimensional visualization.

Introduction Topographic maps are source mapson whichdetails of geographical features and ground-based phenomena are displayed. To exhibit these phenomenaand visualize their geometric shapes, contour lines are used. In archaeology, topographic maps are used to exhibit not only relief, but also layout of ancient structures and remains, buildings, archaeological excavation trenches, layout of squares used for geophysical data collection and, in general, location and layout ofanyinformation necessary for different interdisciplinary studies, including the layout of electrical catheterization profiles, geological sectionslayout, and botanical pollen sampling locations. Topographic maps are also usedto systematically study ancient sites and hills and prepare a dispersion and distribution map of sherdsremaining from ancient potteries. However, the application of topographic surveys in archaeology is muchmore extensive than only preparation of topographic maps. With an engineering and technical perspective, surveyors only collect visible geographic features, whereas the smallest features on an ancient site can be a possible sign of architectural structure. Thus in general, all reliefs or ditches are important in an ancient site. Materials & Methods Pasargadae World Heritage Site, in which the first scientific research beganin 1928, contains monumentsstill hidden from all archaeologists’ sights. Since then, many maps have been preparedfor this site by Herzfeld, Sami, Stronach, Pasargadae Parse Research Foundation, etc. all of which were confined to the layout of existing buildings and hills. The first topographic surveys in an ancient site were carried out at the Pasargadae World Heritage Site by ajoint Iranian-French board in 2015. In the first season, surveys were performedusing Theodolite and Total Station Camera, but because of the limitations of these types of cameras, Differential Global Positioning Systems (DGPS) with 2 and 3 frequency bands andreal time correction possibility were usedin the second and third seasons of the surveys. Inductive research method was used in this research, and data was collectedbased on library research and field observations. First, a brief history of Pasargadae Tol-e Takht and the archaeological activities carried out on this site is provided. Then, discussing GPS and topographic maps, related advanced equipment is introduced. Afterwards, topographic surveys in this historical site are discussed. Results & Discussion As mentioned before, anarchaeologically-minded surveyor looks at hills or ancient sitesfrom a regional perspective. In addition to ancient hills, surveyors collect information about all natural and artificial geographical features in the environment, including agricultural lands, water resources, geological structures, region’ s erosive dynamics such as gullies, and dispersion of all rubbles, megaliths and slate rock. For example, the presence of several stones in close proximity is usually not very important to surveyors, but can be a sign of an architectural structure or a grave structure for an archaeologist. In topographic surveys, we simultaneously carry out mapping and archaeological activities; we are archaeologists carrying out a systematic archaeological survey on the site, and producing map of the area. An archaeologically-minded surveyor, moves around a site, pays attention to the remains of walls and architectural structures, remains of graves and ancient fireplaces, outcrop of large slate rock in ancient hills, remains of canals built in the site at the present or in the past, areas in which there is an aggregation of pottery and stone pieces, and in general, the smallest slopes and features, and collect these points using GPS. In fact, techniques of topographic survey help archaeologist toproduce accurate maps and discover hidden elements that are notusually noticed in mapping. Conclusion Results indicate that with the help of the topographic survey team and advanced technologies, some structures were identified in the Pasargadae World Heritage Site that had been hidden from archeologists to this day, despitethe long history of the presence of reputable and world renowned archaeologists in this site. In fact, one of the main questions of archaeologists was related to empty spaces among the structures in Pasargadae site. Topographic surveys of the area have led to the discovery of new walls and architectural structures on the southern and northern slopes of Tol-e Takht. In addition, the discovery of some regular buildings behind Tol-e Takht, the new rampart and several graves around Tol-e Takht and scattered walls on the hill opposite Solomon’ s Prison and inside the site, are also results of topographic surveys at Pasargadae. Also, with the help of these surveys, two areas used forhuman settlementwere identified at Pasargadae site.

Introduction Recently, different volunteered Geographic Information (VGI)databases and websites have been launched for a variety of purposes and different groups of users. Various groups and portals collect and share these data. Thus, there is a huge potential for the participation of millions of people who can act like remote sensors and share their data with other members of the group without any cost. Therefore, diffrent users with different skill levelscan provide spatial data through personalized measurements. Various research perspectives have shown that sometimes Volunteered Geographic Information can compete with business data. The present research seeks to solve the problems in searching and finding properties, and describe indoor space using visual components in web-basedplatforms. The impact of spatial information on satisfaction of residentsortheir problems has made this research especially important. Most of related studiessought to provide models for estimationof prices, and the impact of environmental factors on the price of real estates. They also have endeavored tocreate websites for residential real estatesearch with an emphasis on descriptive information. The present research seeks to describe indoor space of residential real estate using spatial tools. In this regard, criteria like height, dimensions, topological relationships, shape, color, geographic location, and directional relationships are considered. Description of residential properties’ indoor space requires information in both spatial and descriptive dimensions. Due to the especial potential of Geospatial Information System in the simultaneous visualization of spatial and descriptive information, spatial analysis was used in the present study. Clearly, any research is performed based on a set of presuppositions. Particularly when we seek to theoretically investigate a process like modeling or design an information system, the work scope will be very wide and serious challenges will occur without proper assumptions. The present study assumes equal spatial perception, verbal expression and visualizationabilityin all people. It is also assumed that all estate visitors havecell phones equipped with cameras and Global Positioning System and their response to qualitative relationships is better than that of quantitative relationships. Moreover, real estateis used as a synonym for apartmentin this research. Materials & Methods Considering the critical role of the ordinary users and the fact that survey processes are usually expensive and time consuming, volunteered spatial information environments are the most appropriate way of gathering people’ s spatial perception. Not only these environments are rather easy to use, but also they simultaneously receive up-to-date information from the participant and provide them with appropriate services according to their status. After modeling and designing, the proposed systemwas implemented in Visual Studio 2012 platform using ASP. NET framework andC#language. Server Structured Query Language (SQL) Database 2012 was usedto save spatial information. Tehran District 14 (longitude: 51. 46207, latitude: 35. 66905) was chosen as the study area and data collected from several residential properties was recorded in our database. Results & Discussion Results indicate more than 65 percent conformity between the mental image generated using the proposed method and the reality. Users’ satisfaction with the proposed model was compared with their satisfaction with three popular Iranian sites, and a foreign site regarding. The impact of tools applied in these websites was also investigated. Results indicate 78. 78% satisfaction with the proposed system, which is the highest level of satisfaction as compared to other studied websites. Moreover, compared to other toolsinvestigated in the present study, virtual tours and thenmaps are more in visualization. Sincespatial perceptions depends on various parameters such aspersonal interests, spatial dimensions, gender, age, education, culture, and fields of study, different groups were investigated in the present study. Conclusion Using information collected inVolunteered Geographic Informationenvironments, ordinary people can share information and use each other’ s experiences and opinions. This improves their knowledge level and results in a better understanding of the advantages and disadvantages of different real estates. Due to increased knowledge level, people will not select undesirable properties. This will create a competitive market and increase designers and engineers’ attention to indoor space, which will consequently increase ordinary users’ welfare.

Introduction Landslide is one of the most important types of natural disasters,which endangers lives and financial security of many people and destroys environment and natural resources.With the present population growth and expansion of urban areas towardsteep areas and hillsides,landslide-related losses can be catastrophic.For an instance,landslides in Badakhshan Province in Afghanistan killed around 2,700 people in 2014,and a landslide in China (Shan’xiprovince)resulted in the disappearance of 64 people in 2015.Therefore,assessingthe possibility of landslides occurrence seems to becrucial.Providing zoning maps is one of the measures which makes identification of areas prone to future landslides possible.Inferences drawn from these maps can be used for land use planning,prevention of unauthorized construction activities,infrastructure development,refurbishment and restoration. Materials & Methods The present research selectsEast Rudbar-e Alamut (a district of Qazvin province),which is affected by landslides and instability of hillsides,as the study area.It takes advantage of Shannon entropy and information value models to develop landslide susceptibility map of the study areain GIS environment.Shannon entropy theory has been used in extensive researcheswith the aim of prioritizinginfluential factors in the probable occurrence of natural disasters such as landslide.Information value (IV) model is one of the statistical models drawn from information theory with a widespread application in the modeling of geological hazards and disaster risk assessment.Information value model aims to find a combination of significant factors anddeterminetheir impacton theoccurrence oflandslide in an area.To implement this model,relevant data and its related criteria maps were prepared.In this regard,the location of previous landslide events in the study area was determined based on the information received from Forests,Range and Watershed Management Organization.49 landslides were identified in this way.Then,data was randomly divided into 2 categories:training data and validation data.Thus,70% of data (35 landslides) were used to produce the models and the remaining 30% (14 landslides) were used for validation purposes.In addition to previous landslides,a collection of topographic,environmental and climatic characteristics of the study area including seven criteria of lithology,slope,distance from faults,land use,precipitation,slope-direction and elevation were selected as the most effective independent variablesto produce criteria maps with 30-meter spatial resolution.Basic information used to map these seven influential factors was obtained from Forests,Range and Watershed Management Organization,as well as the SRTM Digital Elevation Model (DEM),and used after some modifications.Considering the capability of ArcGIS in spatial data analysis,thissoftwarewas used to produce information layers and implement the models. Results & Discussion Prioritizing influential factors using Shannon entropy model introducesthree factors (i.e.land use,elevation and precipitation)as the most significant factorsin the occurrence of landslides in the study area.Factors of slope angle,distance from faults (almost equal to slope angle),lithology and slope-direction were in the next influential factors.Also,results of information value model indicate that looking from lithology perspective,the category of marl,calcareous sandstone,sandy limestone and minor conglomerate has an information value of 1 and thus,the highest probability of landslide occurrence.Category of basaltic volcanic rocks,along with category of well bedded green tuff and tuffaceous shale have the lowest probability of landslide occurrence with information values of-2.03 and-1.70,respectively.Only two categories of theslope angle criterionhave a positive-index.The highest information value (0.93) in this category occurs in the class of 5-12 degrees,followed by the class of 12-20 degrees.The lowest information value occurs in slopes of more than 30 degrees.Based on this observation,it can be clearly concluded that the slope angles of 5 to 20 degrees are most prone to landslides.Distance to faults criterion indicate that the category of500 to 1000-meter distance to faultshave the highest information value (1.67).Regarding land use criterion,three land uses of garden,agriculture and garden-agriculture have the highest information values of 2.16 and 1.59 and 1.11,respectively.Regarding precipitation,average annual rainfall of less than 400 millimeters have the highest information value (1.50).Regardingslope-direction criterion,most landslides occur in southwest,south and eastdirections.Northeast,west,and northwest directions have the lowest probability of landslide occurrence,respectively.In terms of elevation,the information value is reduced as the height increases,and the maximum information value is related to the elevations of less than 1200 meters.After assigning a weight to each criterion and related classes,the landslide risk zone map was generated based on Shannon entropy and information valuemodels.The resulting zoning map produced based on natural breaks methods dividesthe area into five classeswith very high,high,moderate,low and very low risk.Resultsof Shannon entropy modelindicate that out of 14 landslides considered as the validation data,3,7,2,1,1 landslideshave occurred in very high,high,moderate,low and very low risk zones,respectively.Resultsof the information value modelindicatethat 8,4,0,1,1 landslideshave occurred in very high,high,moderate,low and very low risk zones,respectively. Conclusion Evaluation of results using experimental probability index indicates that with 86% experimental probability,both models of Shannon entropy and information value are effective inidentification of landslide hazard in the East Rudbar-e Alamut region.Also,considering the number of landslides in very high and high risk zones,Shannon entropy and information value modelshave an experimental probability index of 72% and 86%,respectively,which prove higher efficiency of information value model.In Shannon entropy model,total area of very high,high and moderate risk zones covers 34% and 56% of the study area,respectively.In information value model,total area of very high and high risk zones covers 20% and 29% of the study area,respectively.Based on the landslide risk zone map,high and very high risk zones are mainly located in the west of the study area.

Introduction As one of the largest ports in southern Iran, MahshahrPort faces the risk of seawater intrusion from Mahshahr estuary. Mahshahr and Mousa estuary form an 86 kilometerwaterway. Midday mixed tide is dominant throughout this waterway. Moving towards the vertex of the estuary, this dominance of the midday tidebecomes much more evident. Considering an average depth of 38. 4 m, midday tidal wave of M2 with an approximate wavelength of 860 km can be reinforced in this waterway. Grounding of boats and otherammunitions in the basin of Mahshahr port (betweenMajidiyeh garrison and the Persian Gulf) was one of the most important issues during the Imposed War. Mahshahrestuary is the only possible way to access the Persian Gulf fromImam Khomeini port and Majidiyehnaval garrison. From a military perspective, Mahshahrestuary has a special feature that can be considered as a strategic factor and used in various types of active and passive defense. In spite of the shallowness of its internal parts, it is considered to be a safe haven for small vesselsin times of crisis and air attacks. Sometimes, it is critical to use these estuaries and their navigable waterways as the main route for transportation of military weapons, medical aid, and rescue services. Crisis managers and urban planners need an appropriate plan based on risk map of the study areato reduce the risk of tidal process. However, zoning tidal process and mapping its risk are not enough by their own: a useful system needs toidentify sensitive and vulnerable pointsto increase the efficiency of tide risk zoning. Materials & Methods Seeking to reduce the risk of tidal process, the present study determinesmaximum level of development in areas covered during high tides usingHecgeoras software. In order to achieve the goals, hydrographic data of the substrate was used along with information and statistics received from high accuracy hydrometric stations. This zonesurrounds an area of around 944 hectares. Field visits indicate thatduring tidal process, soil conditions in the study area hampers sending emergency aids using land routes. Considering the conditions and structures inMahshahr estuary and the tide zone in the study area, investigating geology of the study area seems necessary. Results & Discussion Considering the soil conditions in the study area, the highest level of soil flooding was observed during high tide. This increasessoil adhesion, affects water intrusion, and hampers sending aids. Critical areas in danger of water intrusion from the estuary (such as Mahshahr gas reservoirs and Majidieh military garrison) were identified in the present study. Moreover, results were investigated and land visits were performed to present accurate and reliable results. Land visits were carried out during 2016-2017 winter and 2017 summer in order to achieve better and more accurate results based on the climatic changes. Investigating the recorded level of water in the study area shows up to 3-metervariation in the water level of Mahshahr estuary. Necessary decisions regardingthe use of different types of vessels, and passive/active defenses can be made in accordance with water level fluctuations and specified times for each water level. During local visits performed for quantitative analysis of results, soil type and its role in passive defense were also discussed. Conclusion Field studies and observations in the study area indicated that 1) dominant soil type of the region is a highly adhesive type with a high clay content; 2) during tide time, soil flooding results in increased adhesion of soil and practically hampers movements of ammunition; 3) In summer and winter, water level changes repeatedly and thus, necessary measures shall be considered for military programs in accordance with the time in which water fluctuations occur. Although the depth of water in the study area is significant, it should be noted that Mahshahr estuary finally reaches an urban area, and thus can be used for military actions. In fact, mapping tide zone is a very effective method for reducing the risk of tidal process in coastal areas.

Introduction The process of identifyingthe differences in the status of an object or phenomenon by observing it at different times is called the change detection. In remote sensing change detection, the quantity of a phenomenon is examined from multi-temporal images, and is usually done with the help of multispectral sensors. In remote sensing change detection studies, the type and manner of performing atmospheric correction is one of the most important questions of researchers. In most cases, due to lack of sufficient information or experience and knowledge, absolute radiometric correction is not possible and researchers need to use relative radiometric correction and image-based methods. One of the best image-basedmethods is the radiometric normalization using pseudo-invariant features (PIFs). However, the proper way to select these homogeneous regions remains an important challenge. Therefore, in this research, a very simple method is proposed based on the definition of pseudo-invariant features that automatically identifies these areas and uses a regression process for automatic normalization. Materials and Methods The proposed method in the research is based on the radiometric normalization using pseudo-invariant features. Therefore, it was necessary to identify these areas at first, however, the aim was the automatic extraction of PIFs. According to the definition of pseudo-invariant features, a few basic conditions are needed to define a PIF, therefore, here we have tried to simplify these conditions in order to fall into an automated process: 1-Removing water bodies: The study area has a major part of the Persian Gulf coast and water body, which is affected by the tidal wave and under flood conditions; it is affected by the suspended particles of the rivers. Hence, the first step was to remove the water bodies from the images. To mask water from the images, one of the conditions was the pixel value in the NIR band should be less thanthe pixel value in the blue or green band; and another condition was the pixel value in the NIR band should be less the average minus 1 standard deviation of the entire image. 2-Removing the areas with vegetation: Generally, in regions with vegetation, the reflectance of the NIR band is higher than RED, therefore, a simple criterion for masking the vegetation is the use of this condition. However, given that the images used in this research are raw and unprocessed, a statistically average was used in this condition. First, the water mask was applied to the images and then, the average of difference of the NIR band and the RED band in the remaining area was obtained. Finally, those areas were selected as vegetation in the whole image, in whichthe difference between these two bands was higher than the calculated average. 3-Flatness criterion: The flatness of the area is the simplest criterion for identifying the pseudo-invariant features (PIFs) and is accessible by a digital elevation model with only a slope threshold however, due to the flatness of the study area, this criterion was ignored in this study4-Identifying areas with little or no change over time: In this study, in order to evaluate the effect of radiometric correction in the remote sensing change detection, image algebra change detectionmethodwas used. In this method, spectral image enhancement is done by the use of commonly used spectral vegetation indices. Among the spectral vegetation indices based on the unsupervised classification function, and the measures of the dispersion about the mean of a distribution such as the coefficient of variation, the NDVI index showed a better performance. Accordingly, the NDVI index, which proved to be effective in similar studies, was used further in the analysis. In this index, the NIR band and RED bands are used. Therefore, to identify the unchanged areas, unchanged regions in the NIR and RED bands used in this spectral index were identified and combined. For this purpose, water and vegetation masks were first applied to the multispectral image. Then, the OLI image was stretched to 8 bit to match the ETM + image. In the next step, the difference between the two NIR bands for these two sensors was obtained and the mean value and the standard deviation were calculated. Finally, in order to have the least error, an area was taken into consideration as unchanged area, in which the following relation was present: . The same analysis was done on the red band (). These two criteria were combined together to obtain the unchanged areas by the AND Boolean logic method. Each one of this four conditions is easy to manually apply to the data with the least processing experience, but in this study, these conditions were automatically generated by the Spatial Model Editor of ERDAS IMAGINE. Radiometric normalization was performed by identifying the pseudo-invariant features (PIFs). In order to validate the accuracy of the proposed method, absolute radiometric correction using ATCOR, FLAASH and ATMOSC methods, and relative radiometric correction using both empirical line calibration method and dark object subtraction method and automatic radiometric correction using QAC and AAIC methods were applied on the data. The output of all atmospheric correction methods and the proposed method was applied in image algebra change detection in the form of a difference and with a threshold of twice the standard deviation from the mean to be checked by 219 points. Results and Discussion The results of validation along with the qualitative studies derived from the histogram comparison proved the proper functioning of the proposed method (Kappa greater than 0. 8), and investigating with the help of cross tables indicated that the performance of the proposed method is very similar to that of the empirical line calibration method (More than 76%). Conclusions It should be noted that, some unique features in the present research proposal, including simplicity, automation, negligible systematic error, the possibility of using in a biomarker for degradation warning system, the independence on the type of sensor used, differentiate it from other radiometric correction methods, hence, our suggestion to the researchers interested in the remote sensing change detection of the natural ecosystems is to use the findings of this research.

Introduction Conflict represents a dispute or war between two or more actors on a local, national, regional or global scale. Geographical factors and values play a fundamental role in conflicts. Actually, conflicts occur due to a combination of geographical, political, and power-related parameters, which can be explained within the framework of geopolitics. Africa has long been involved in a variety of conflicts most of which relate to the 0-15-degreeNorthernlatitude of the continent. From 29 countries in the region, 15 are involved in 11 boundary and territorial conflicts. The present study seeks to find an answer for the question that what the geopolitical roots of boundary and territorial conflicts in the 0 – 15-degree north belt of Africa are? Materials & Methods The current study is considered to be a ‘ Basic theoretical Research’ in terms of goals, and a “ descriptive” study in terms of nature and method. In terms of attitude, it is classified as a “ descriptive-analytic” research. Data collection is performed by documentary method using library resources. Qualitative method of data analysis is applied. Results & Discussion In 0 – 15-degree north belt of Africa, the following variables are discussedas rootsof boundary and territorial conflicts: “ Territorial conflicts among different ethnical groups and tribes in border areas of different countries” are considered to be among the effective causes of conflicts in 5 countries and 4 cases including Somalia and Kenya; Kenya and Ethiopia; Kenya, South Sudan and Uganda; and the northern half of Kenya bordering Ethiopia, South Sudan and Uganda. “ Territoriality and territorial expansionism of statesand their endeavor to conquer space and control its resources” are considered to be causes of conflicts in 12 countries and 6 casesincluding Ethiopia and Eritrea; Sudan and South Sudan; Cameroon and Nigeria; Ghana and Cô te d’ Ivoire; Equatorial Guinea with Gabon; and Uganda and area around the Albert lakein theDemocratic Republic of Congo. “ Dispute over a strategic border area between countries” has been an effective variablein 8 countries and 4 casesincluding between Ethiopia and Eritrea over Assab port; Ethiopia with Egypt and Sudan over Nile; Cameroon and Nigerian over the Bakassi Peninsula; and Eritrean and Yemen over Hanish-Zukar archipelago. “ Actions of colonial powers which result in determining and mapping territorial boundaries of countries” are considered to be among roots of conflicts in 18 countries and 10 casesincluding conflicts between Ethiopia and Eritrea; Ethiopia with Egypt and Sudan over Nile; Eritrea and Djibouti; Sudan and South Sudan; Cameroon and Nigerian over the Bakassi Peninsula; Ghana and Cô te d’ Ivoire; Equatorial Guinea and Gabon; ethnic and tribal conflicts in boundary regions of Somalia and Kenya; Eritrean and Yemen over Hanish-Zukar archipelago; and Uganda and area around the Albert lake in the Democratic Republic of Congo. “ Settling of a minority group from the neighboring country in a disputed border region” has been among the causes of conflicts in 4 countries and 2 cases including conflicts between Cameroon and Nigerian, and Uganda and the Democratic Republic of Congo. “ Historical mentality and Nationalism” have been among effective variables in conflicts of 6 countries and 3 cases including conflicts between Ethiopia and Eritrea; Sudan and South Sudan; and Uganda and the Democratic Republic of Congo. “ Weak performance in dividing and exact demarking ofboundary” has been among influential causes of conflicts in 6 countries and 3 cases including conflicts between Ethiopia and Eritrea; Sudan and South Sudan; and Uganda and the Democratic Republic of Congo. Climate condition and climate change” has been among the causes of conflicts in 5 countries and 2 cases including conflict of Ethiopia with Egypt and Sudan over Nile; and ethnic and tribal conflicts in boundary regions of Somalia and Kenya. “ Remote areas and Marginalization” have been among the causes of conflicts in 5 countries and 2 cases including ethnic and tribal conflicts in border regions of Somalia and Kenya; and the northern half of Kenya bordering Ethiopia, South Sudan and Uganda. Conclusion Geopolitical roots of boundary and territorial conflicts in the 0 – 15-degree north belt of Africafrom the end of the Cold War in 1991 to 2014 have been ranked based on their influence as follows: 1. Actions of colonial powers which results in determining and mapping territorial boundaries of countries; 2. Territoriality and territorial expansionism of statesand their endeavor to conquer space and control its resources; 3. Dispute over a strategic border area between countries; 4. Territorial conflicts among different ethnical groups and tribes in boundary regions of different countries; 5. Historical mentality and Nationalism; 6. Weak performance in dividing and exact demarking of boundary; 7. Climate condition and climate change; 8. Remote areas and Marginalization; 9. Settling of a minority group from the neighboring country in a disputed border regionThese roots are geopolitical in nature and the role of geopolitics, along with a combination of mutual relationshipsamong politics, geography and power is observedin each one of them. Due to the inclusiveness of the present study in introducing geopolitical causes of boundary and territorial conflicts, which have the potential of creating this type of conflicts in other regions of the world, the results of the present research are generalizable. Therefore, the results are generalizable to boundary and territorial conflicts in other parts of the worldwithin the framework of “ the geopolitical theory of boundary and territorial conflicts” .

Introduction Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful launch of Landsat 8 with two thermal bands has provided a good opportunity for retrieving land surface temperature usingthermal remote sensing technology. Many studies had been performedwith the aim of retrieving land surface temperature, but available evidencesshow a significant calibration uncertainty inThermal Infrared Sensor (TIRS) of Landsat 8 band 11 and thus development of new studies based on onethermal band seems to be necessary. However, calibration documents issued by the United States Geological Survey (USGS) indicated uncertainty ofdata received from Band 11 Thermal Infrared Sensor (TIRS) of Landsat 8 and suggested using Band 10 data as a single spectral band for LST estimation. Materials & Methods In this study, mono-window algorithm with its three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature)has been developedunderan automated algorithmin MATLABand was used for Landsat 8 data. Thermal band 10 was used to estimate brightness temperature. Bands 4 and 5 were also used to calculate the NDVI. Retrieval of LST from Landsat 8 TIRS data is performed based on the premise that brightness temperature (Ti)can be computed for any pixel of Band 10 using the mono-window algorithm. Since the observed thermal radiance for Band 10 of Landsat 8 TIRS is stored and transferredasa digital number (DNs) with 16 digits between 0 and 65, 535, it is possible toconvertthe DN value into thermal radiance and then convert radiance into brightness temperature. Ground emissivity is calculatedusing land cover patterns received from other bands of Landsat 8, and the other two parameters are estimated based on the local meteorologicaldata. Usually, obtaining an accurate estimate of ground emissivity is very difficult, and the atmospheric water vapor content is considered to be a sensitive parameter in traditional LST retrieval methods. Results & Discussion The algorithm has been successfully applied to Tabriz city in north west of Iran with the aim of analyzing spatial distribution of LST. After running the algorithm on the satellite images of the study area on July 18, 2016, a lower land surface temperature was observed in green spaces with 1. 2° C accuracy as compared to urban areas and wastelands. The lowest temperature in the study area was 20° C and the highest temperature was 53° C and mean temperature was 38. 78° C. Results indicate that the algorithm candiscover natural urban heat islands accurately. Moreover, spatial distribution of LST in the region is quite well matched with the land covers. Successful application of the algorithm proves the efficiency of improved mono-window algorithm as a method used for retrieving LST from Landsat 8 data. Conclusion Compared to common methods, the proposed algorithm estimates land surface temperature with minimum requirement for user intervention, least possible time and an acceptable accuracy. Itgives researches an opportunity to easily compute LST and apply it in other studies, and thus it is a significant tool.

Introduction Among all Earth’ s ecosystems, arid and semi-arid regions (about 30% of the Earth’ s land) have experienced significant degradation over the past century due to the intensive land use practices and the increasing effects of droughts and climate changes (Maynard et al., 2016). Remote sensing is capable of detecting several groups of disturbances and changes, and has been widely used as a toolto identify long-term changes. Recent technological advancements in the methodology of mapping and monitoring land cover changesprovide new opportunities for the utilization of satellite imageries with high temporal frequency. Image fusion technique has been applied in different fields of environmental science, such asmapping crop growth, studying daily pollution of water resources, studying patterns of short-time ecological changes, determining regions with short-term erosion risk, etc. Image fusion algorithms include color combinations in three bands ofRGBimages, statistical and multi-scale methods. The present study seeks toevaluate the efficiency of image fusion algorithms and select the best algorithm for mapping vegetation in SouthKhorasan Province. Materials and Methods Following the pre-processing ofLandsat 8 and MODIS images, six image fusion algorithms, including NNDiffuse, HPF, Brovey, Gram-Schmidt, PC and CN, were studied and evaluated usingdifferent statistical criteria. Three statistical indices, including Root MeanSquare Error(RMSE), Mean Absolute Error(MAE) and Mean Error (MEB)were usedto evaluate the aforementioned algorithms. Then, the best image fusion algorithm was used to merge two different images received from Landsat8 (30m) and MODIS (250m). Finally, two vegetation indices, including NDVI and HVCI, were usedto map vegetation in SouthKhorasan Province. Results and Discussion Results indicate that all six algorithms used in the present research can improvespatial resolution of the merged images. Compared to other 5 algorithms, NNDiffusecan merge thered and NIR bands of Landsat 8 and MODISwith a relatively higher accuracy. Therefore, NDVI extracted from this algorithm has the lowest RMSE and MAE compared to the original Landsat 8images. NDVI obtained from thefusion algorithms used in systematic-random transects of three land uses (including agricultural, urban and pastures) indicate that the index obtained from NNDiffuse algorithm has a better conformitywith the NDVI obtained from the original Landsat 8image. Then, redand NIR bands of Landsat8 and MODIS were combined forsimultaneous mapping of NDVI and HVCI in the case study area. Overall, a great part of SouthKhorasan Province has a vegetation cover of less than 10% and 40-50%, vegetation cover is only limited to small parts of the study area (agricultural land use and gardens). Conclusions Generally, accessing simultaneous satellite images with high spatial resolutions, such as the Landsat series, is considered to be a challenge in vast area. The present study took advantage of different algorithms for image fusion and vegetation mapping in South Khorasan Province. Image fusion techniques, such as integration of Landsat and MODIS images, can be very useful for mapping purposes. Evaluation of 6image fusion techniques indicated thatNNDiffuse algorithm is the most suitable method for mapping vegetation in the study area.

Introduction The land subsidence is the descending or collapse of the land under the influence of natural and human factors. The land subsidence is one of the issues that are being exacerbated by human factors, including excessive exploitation of groundwater. Subsidence can affect many constructions and facilities, causing problems for the industry, the environment, etc. This phenomenon is one of the most important environmental hazards that have been less considered than other natural phenomena due to the low human losses. The Qorveh plain is considered as one of the plains which have been introduced as a forbidden plain in the province of Kurdistan in recent years due to the over-exploitation of groundwater. Considering the amount of groundwater level drop and its direct impact on the subsidence level of the region, the present study evaluates the subsidence rate of the Qorveh plain during the period of 2017. 12. 19 to 2016. 01. 11. In this research, in order to evaluate the status of the groundwater drop, the statistical data from the Regional Water Organization of Kurdistan province has been used, and the Sentinel-1 images and the SBAS method were used (due to the unique capabilities of this method in terms of dimension, cost, time and accuracy compared to other remote sensing techniques) to estimate the subsidence rate of the region. Material and Methods In this research, first, the status of the groundwater of the Qorveh plain and the drop rate of its level has been investigated. Then, the subsidence rate of the area and its relation with the groundwater drop has been investigated. Radar interferometry and SBAS were used to evaluate the subsidence of the study area. Radar interferometry method is one of the most powerful tools for monitoring the subsidence phenomenon. By comparing the phases of two radar images taken from a region at two different times, this method can determine the land surface changes at that time interval. The phase taken from a feature on the land surface is proportional to its distance to the radar sensor. Therefore, making any changes in this distance affects the measured phase. In this research, the Sentinel-1 images (2017. 12. 19 and 2016. 01. 11) have been used to perform the radar interferometry. Discussion and results The hydrograph of the alluvial aquifer of the Qorveh plain has been provided for the water years of 1966-1676 to 2010-2011. During the 24 yeas, the groundwater level fluctuations in this plain are-13. 29 meters, with an annual average of-0. 55 meters. The least rate of dropping in the wells is in the wells located south of the Qorveh plain, and the rate increases toward the eastern and northeastern parts. In this research, the subsidence rate of the Qorveh Plain was estimated from 2017. 12. 19 to 2016. 01. 11 using the SBAS method. The final map indicates that during this period, the study area subsided between +61 and 216 cm, with the lowest subsidence occurring in the southern areas of the Qorveh plain, which corresponding to the sedimentary heights and slopes of Badr and Parishan and the rate has increased toward the east and west of the Qorveh plain. Conclusion The results of this study indicate that Qorveh Plain has witnessed a sharp drop in groundwater level over the recent years. Considering that the southern parts of the Qorveh plain corresponds to the heights and slopes of Badr and Parishan, and the rate of exploiting groundwater in these parts is lower, the rate of subsidence is less. The plain has also subsided further towards theeastern, western and northern parts and the outlet of the Shoor River, due to the growing increase of exploitation. The results indicate that the rate of subsidence is consistent with the rate of groundwater drop so that in the southern part which corresponds to the Badr and Parishan slopes, the rate was less than 10 millimeters during the period of 2017. 12. 19 to 2016. 01. 11. The results of the SBAS method indicate that the study area had subsidence of 216 mm during the 2 years and also a 61 mm uplift. Based on the final result, the highest rate of subsidence was related to the eastern and western parts of Qorveh plain and on the outskirts of the city of Dezaj and the villages of Ghasem-Abad, Shokuh-Abad, Avangan, Ganji, and others. A series of the aforementioned factors suggests that the Qorveh plain subsides about 20 centimeters per year. This is due to the over-exploitation of the groundwater. Unlike some areas where the displacement (subsidence and uplift) is due to the tectonic conditions, the results of this study have shown that in the Qorveh plain, the subsidence has a direct relationship to the drop of the groundwater. Therefore, it is necessary to monitor the use of groundwater, especially in the agricultural sector, and the rate of the exploitation should be proportional to the amount of recharge because in addition to the water shortage problems, the continuous use of the groundwater can lead to the irreversible risks of subsidence.

Introduction After United States of America, China and Turkey, Iran has the highest karst percentage, and karst formations cover more than 11%of our country. The volume of water stored in these areas can supply the water demand of many cities and villages. Characteristics of karst aquifers’ feeding area determine the type of feed, flow andvulnerability of the aquifer tocontamination. Therefore, identification of feeding areas in karst aquifers plays a key role in understanding their hydrodynamic and hydrochemical characteristics, along with management and optimal scientific exploitation of them. Given the critical impact of karst water resources on human life and limited number of researcheson karst, any fundamental, applied, and developmental research performed with the aim of modelingkarst landforms and investigating the potential of karst water resources in these areas seems necessary. In order to assess andevaluatethe potential of karst water resources from a qualitativeand quantitative perspective, understand pollution, and vulnerability and also assessrisks facing aquifers, the present study models feeding areas of Dalahoowasaquifer using KARSTLOP model. Methodology The present applied-developmental study is based on library research, field observation, and evaluation methods and seeks to prepare the map of karst water resourcesfeeding Dalahookarst aquifer. Fuzzy logic and gamma operator model were used to produce a zoning map for surface karst development. And finally, a map was produced for the feeding areas of Dalahoowaskarst aquifer using KARSTLOP model. Result Using Natural Breaks method, the zoning map of Dalahoo’ ssurface karst development divides the study area are into four classes: areas without karst formations (0-0. 224), karst formations with low development (0. 224-0. 558), karst formations with moderate development (0. 588-0. 777) and developed karstformations(0. 777-0. 982). The final map of Dalahoo’ sfeeding areas indicates that Bistoon karst aquifer has anannual charge rate of 37 to 81 percent. Discussion and conclusion Systematic study of karst aquifer’ s water tables is very important, especially for drinking and agricultural purposes. The final mapof feeding areas, as well as the layers obtained from KARSTLOP method can be used as inputs for modeling groundwater. They may also be used to address practical issues of karst in relation to water management, including water supply, spatial distribution of watersheds, transboundary management of water, and initial assessment of groundwater vulnerability. Results obtained from zoning of feeding areas are consistent with the results obtained from zoning of surface karst development. High feeding values as well as spatial distribution of the aquifer’ s feeding zones indicate that the aquifer has a high potential to store groundwater resources. This potentialityshould be properly managed to makeharvestingand protecting groundwaterpossible.

Introduction With the development of urbanization, a large part of agricultural areas and forests have been replaced by residential areas, industrial centers, and other infrastructures. This is due to human life style and his endeavor to reach sustainable urbanization. A series of changes in the reflection of light from different material’ s surface, heat storage and heat transfer, have changednatural and artificial landscape orsignificantly affected local climate. Therefore, public concerns about urban sprawl, increasing urban population and quality of urban environmental have motivated planners to seek better perspectives for development of urban areas. Increasing temperature of urban areas is considered to be one of the most important environmental problem in cities. This increasing temperature results in creation of Urban Heat Islands (UHI) in some parts of urban areas, which are significantly warmer than surrounding urban environment. Therefore, a new and successful method of urban planning should be introduced with respect to spatial distribution of land surface temperature (LST) to achieve better urbanization and reduce environmental impacts on cities. Materials & Methods The present study takes advantage of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) thematic maps to investigate therelationship between air pollution, and two indexes of NDBI and NDVI with land surface temperature (LST) and Urban Heat Islands (UHI) in urban areas. Satellite imageries of Arak (an industrial city in Iran) has been chosen for the case study. Urban and natural areas and impermeable surfaces such as roads, buildings and other constructions are rapidly developing in this city. In the first step of research methodology, necessary pre-processing programs such as radiometric corrections were performed on the satellite imageries. Then satellite imageries were transformed toatmospheric images to produce NDBI and NDVI indexes. Finally, land surface temperature maps wereproduced using the method of Landsat Project Science Institute in Arc GIS 10. 3. To classify satellite images, seven land use classes were identified as poor pastures, averagepastures, rich pastures, bare lands, Lake’ s Shore, agricultural lands and residential lands. Then, training images classification method was used to collect samples from the study area and classification was performed using maximum likelihood method for monitoring. In order to analyze LST parameter using NDBI and NDVI indexes, air quality data, and statistical methods like Kolmogorov-Smirnov test, paired t test and Pearson correlation test were used. The results of Kolmogorov-Smirnov test indicated that data used in this study was normally distributed. The results of t test, temperature recorded by synoptic stations in Arak and remotely sensed data indicated that the accuracy of the test is more than 5%. Thus, the difference between residential land use and other urban land uses was not statistically significant. Moreover, results indicate that there is a more than 99 percent correlation between temperature recorded by the synoptic stations in Arak and data collected from satellite imageries. Results of correlation with remotely sensed data indicatedthatthere is a significant correlation between99 percent of results and less than 5 micron particles. Results & Discussion Correlation between air pollution data andremotely sensed data (LST) indicated that LST and less than 5. 2 micronparticlesare significantly correlated with 99% accuracy. Urban heat island usually occurs in metropolitan area and its surroundings. Due to climate changes, urban heat islands are constantly developing. This results in increased energy consumption for air conditioning systems. Thus, reducing the effects of urban heat islands has become an important global issue. The present study has successfully explained the effects of urban heat islands and their environmental problems on normal life. Detailed program of related measures and policies should reduce the intensityof urban heat island. Final development of the cities should be based on land surface temperatures in surrounding areas in a way that cities can reach a lower surface temperature as compared to the temperature before urban development. Conclusion Following strategies are suggested for a more comprehensive consideration of urban green spaces in urban planning and future development of cities: Paying attention to architecturalcriteria and urban land use, and alsopaying attention to soil and water management parametersbased on the principles of green architecture, paying attention to standards of anthropogenic temperature rise caused by human activities, and the problem of urban heat islands. Moreover, it is crucially important to prepare the necessary situation for the community to reach a good physical and mental health.