alavipanah

Diurnal air temperature modeling is a beneficial experimental and mathematical approach which can be used in many fields related to Geosciences. The modeling and spatio-temporal analysis of air Diurnal Temperature Cycle (DTC) was conducted using data obtained from 105 synoptic stations in Iran during the years 2013-2014 for the first time; the key variable for controlling the cosine term in DTC modeling known as β was analyzed and considered both as monthly and annual parameter. The effect of environmental variables of humidity, pressure, diurnal air temperature range, and wind speed were analyzed on β. The results showed that there is no significant difference between considering β as monthly (dynamic) or annual (constant) parameter through the year. The RMSE of approach with dynamic β was 2.1 °C and with constant 2.2 °C at 95% percent of whole data in all stations. The analysis of environmental variables showed that humidity had an indirect effect on β. Low pressure areas showed higher β values but high pressure areas showed higher variability in β and lower mean values. In areas with high air diurnal temperature range, lower β values with less standard deviation were observed. High wind areas showed positive effect on β values.

Land surface temperature plays an important role in the physics of surface atmosphere interactions. It is at the same time a driver and a signature of the energy and mass exchanges over land. Land surface temperature is highly variable in both space and time mainly as a result of the heterogeneity of the meteorological forcing, land cover, soil water availability, surface radiative properties and topography. Therefore, satellite-derived land surface temperature is widely used in a variety of applications including evapotranspiration monitoring, climate change studies, soil moisture estimation, vegetation monitoring, urban climate studies and forest fire detection. The normalization of the surface temperature relative to environmental parameters is essential in scientific studies and management decisions of urban and non-urban areas. For the first time, a normalization method for topography-induced variations of instantaneous solar radiation and air temperature has been applied to satellite land surface temperature. While land surface temperature data are widely used over relatively flat areas, this new approach offers the opportunity for new applications over mountainous areas. As a significant perspective, such a normalization method could potentially be used in conjunction with land surface temperature-based evapotranspiration methods over agricultural and complex terrain, soil moisture disaggregation methods and forest fire prediction models, among others. In practice, when applyingthe normalized land surface temperature as into to energy balance models, the energy balance would be driven by the mean (instead of the spatially-variable) downward radiation within the study area as it is commonly done over flat areas. The aim of the current study is to utilize the physical model based on the soil and vegetation energy balance equations for normalizing the land surface temperature relative to environmental parameters. For this purpose, Landsat 7 satellite bands, AST08 Surface kinetic temperature, MODIS water vapor product, ASTER digital elevation model and meteorological and climatic data sets were used. In the current work, topographic factors, the radiation which reached the surface, albedo, environmental lapse rate and vegetation, were considered as environmental parameters. For calculating the surface temperature, the single channel algorithm was used. Moreover, for calculating the downward radiation to the surface, the albedo of the surface, lapse rate and vegetation; respectively, direct and diffuss radiation of the solar and neighboring surfaces, a combination of Landsat 8 reflective bands, the digital elevation model, and NDVI index, were used. Finally, by creating the energy balance equations for dry bare soil cover, wet bare soil, fully stressed vegetation and unstressed vegetation, the temperature of various coverages was extracted by exploiting Newton's method and by optimizing parameters of the model in both global and local optimizations and combining resultant temperatures; modeled and normalized surface temperature was obtained. The environmental parameters normalization model is calibrated in two main steps using Landsat land surface temperature observations. The first step minimizes the mean difference between observed and modeled land surface temperature. The second step adjusts environmental lapse rate, surface soil dryness index and vegetation water stress index by minimizing the RMSE between Landsat land surface temperature and model-derived land surface temperature. For evaluating the accuracy of the proposed model results, coefficient correlation indexes and RMSE were used between the modeled and observed surface temperature values as well as the variance of normalized surface temperature values. The results of this study indicate that in global optimization, the values ​​of the correlation coefficient, RMSE and variance for AST08 data were 0.89, 2.6 and 6.44, respectively for Landsat 7 data 0.93, 2.08 and 1.1 and in local optimization mode, the values ​​of these criteria for AST08 data were 0.962, 1.61 and 0.71 respectively and for the data of Landsat 7, 0.977, 1.2 and 0.13. The results of the study showed that, in both global and local optimization methods, the performance of Landsat 7 for normalizing the land surface temperature is higher than ASTER. Also, the use of local optimization method for global optimization to estimate the optimal values ​​of the missing parameters increased the accuracy of normalization results. The investigation of results of the relation between the surface temperature and considered environmental parameters in this study before and after the normalization indicate that the effect of environmental parameters on the surface temperature noticeably reduced after the normalization. Results of the current study imply the high efficiency of the proposed model for normalizing the surface temperature relative to environmental parameters. Generally, the results of the research were an indicator of the efficiency of the proposed model for normalizing the surface temperature relative to environmental parameters.

Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. The analysis of urban growth using spatial and attribute data of the past and present, is regarded as one of the basic requirements of urban geographical studies, future planning as well as the establishment of political policies for urban development. Various methods have been used to investigate the physical growth of cities up to now. The remote sensing method and geographical information system are the most updated, precise and economical methods used to investigate the physical growth of cities. Since physical expansion occurs in temporal and spatial scale, the built-up land use can be extracted by using remote sensing multi-temporal data. Then, by comparing these data in different time periods by using statistical and spatial analysis of geographical information system, amount, and the ratio of changes were evaluated and its trend was modeled to be used in the urban planning. In this study six temporal satellite images of 44 years interval (1972, 1984, 1992, 2000, 2008 and 2016) have been classified to determine the urban extent and growth of Tehran in 8 different geographical directions within a circular region. So as to analyze data, Pearson’s chi-square statistics, Shannon’s entropy model and degree of goodness index were utilized. Pearson’s chi-square statistical model determines the amount of urban growth difference in various time periods which can be used along with Shannon’s entropy model to determine changes and scattering in the expansion of urban boundaries. The degree of goodness index can be used to investigate the urban growth quality. In the last studies, these models have been used to analyze spatial phenomena of the city such as changes trend in the city structure and shape in order to spatial expansion and land use changes. In this study, the used method of analyzing spatial data completely differs from the literature. In this model, a prediction model of CA-Markov is used to anticipate the urban growth in the year 2024 and also statistical parameters such as Shannon’s entropy and Pearson’s chi-square are used to analyze the way, the amount and the degree of goodness of the urban growth in the past and future. In this way, it was found that the city of Tehran has a high degree-of-freedom, high sprawl, and a negative goodness in urban growth. The total sprawl equals to 4.71 which is dramatically higher than half of value. As a result, it can be generally inferred that the city experienced a high sprawl value during 1972-2016 and this trend would continue to next years. The results indicate that in various time periods the city does not experience a good urban growth. The investigation of the degree of goodness index in various sectors also follows the same trend as various time periods. Nonetheless, different sectors and time periods can be compared to each other. Sectors of North and NorthEast compared to the other sectors, have the best and the worst urban growth, respectively. This study establishes the foundation for an in-depth recognition of urban expansion in Tehran and optimization of future urban planning.

Combination of GIS and Multicriteria Decision Analysis (MCDA) in the Web setting has recently gained much attention in participatory (collaborative) spatial decision making. GIS-based MCDA tools offer a wide spectrum of visual and computational decision support capabilities that can be used by the experts and lay participants on the Web for selection, prioritization, and integration of spatial decision options. These tools provide appropriate analytical tools and platforms for direct involvement of the public in the spatial planning process. The overall aim of GIS-based MCDA techniques is to contribute to the decision making process by selecting the best alternative from the number of feasible alternatives according to multiple criteria. It involves the use of geographical data, decision maker preferences, and an aggregation function (decision rule) that combines spatial data and the decision maker’s preference to evaluate decision alternatives. The main rationale behind integrating GIS and MCDA is that these two separate areas of research can complement each other. While GIS is normally known as a powerful and integrated tool with exclusive abilities for storing, manipulating, analyzing and visualizing geographically referenced information for decision-making, MCDA provides a rich collection of procedures and algorithms for structuring decision problems, designing, evaluating and prioritizing alternatives. Studies on the asynchronous spatial MCDA suggest that the space-time distributed environment of the Internet not only provides the flexibility to work in different places and times for the convenience of the participants, but also offers equal participation opportunity. In other words, the Web-based GIS-MCDA provides an access to the relevant GIS-MCDA data and tools anyplace (any location that has the Internet access), anytime (24 hours a day, seven days a week), and through any PCs or handheld devices (e.g., PDA, smart phones) and networks (wired or wireless technologies).
However, the currently used GIS-MCDA tools do not provide the participants with a choice of their own criteria, alternatives, and preferences. To address this problem, this study represents a user-oriented GIS-MCDA system where decision participants can (i) define their own alternatives and determine individual decision maps and (ii) integrate their individual decision maps using a group decision rule to reach a compromise solution. The problem of public toilet site selection in district #1 of Mashhad, Iran, was selected as the case study to demonstrate the proposed approach. The proposed system was used as a potential decision support tool for collaborative site selection of the public toilets. Every decision maker could define his/her toilet alternatives by drawing a set of polygons, evaluate them based on AHP-based weighted linear combination and eventually develop an individual decision map. The group decision rule, fuzzy majority approach, was employed to create a group decision map based own individual solution maps. In other words, the individual solutions (individual rankings of public toilet facilities) are aggregated using the group decision rule to obtain a group solution (group rankings of public toilet facilities). Such tool enhances the level of community participation in spatial planning. The study’s implications can advance public participatory planning and allow for more informed and democratic land-use decisions.

Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Worldview satellites were used. Pieces with different land applications were cropped from these images to serve as test images. The assessment of the metrics tested on these images resulted in the categorization of metrics into three groups as per their capability for displaying spectral and spatial distortions. The first group included methods that functioned on the basis of noise for overall assessment of images with respect to their noise; these methods included ERGAS, MSE, PSNR, WSNR, and SNR indices. The second group were those aligned with Spectral Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.

As urban areas develop، changes occur in the landscape. Buildings، roads، and other infrastructure replace open land and vegetation. This development leads to the formation of urban heat islands، whereby urban regions experience warmer temperatures than their rural surroundings. The temperature changes decrease the quality of urban life، especially in matters relating to thermal comfort. Where as most urban surfaces formed from rooftops، streets and sidewalks، considering these surfaces، can be effective in reducing the urban heat island effect. The aim of this study was to evaluate the role of reflective roofs in reducing the urban heat island effect. In this study، using multitemporal images of Landsat TM، the impact of reflective roof (tar paper with Coating Aluminum foil) on changes in reflection، absorption and surface temperatures in Kermanshah city، between 1987 and 2011 were examined. To obtain the land surface temperature used the Qin ET all’s single-band algorithm. Results Statistical analysis showed that the reflectance in the period under study had an average 8% increase and this increase reflects increased a cooler temperature class’s area that distributed over of residential buildings. This is while with the development of the city and declining vegetation within the city and surrounding Gharehsou River، there has been a reduction the very cool temperatures area and Very warm temperatures have increased floor area. The spatial variation of the reflect and surface temperature during the study period showed that in areas that reflect the increased surface temperature was associated with a decrease and The use of reflective roof to reduce heat island effect can be effective.

Using renewable energy, particularly solar radiation is considered today as one of the most important energy sources. Therefore the study of solar energy is very important. Although the sun is a great source of energy, but the energy output at ground level does not act alike, so that the amount of solar radiation in different parts of the world is changing and it is high in low latitudes. Thus, identifying appropriate locations for implementation of solar energy is necessary. The best and most accurate way to measure this parameter is using devices that measure radiation in stations, but because of natural, economic conditions and other conditions we cannot use them On the other hand, these devices have a limited radius of point measurements. So, in recent years using satellite images these problems have been resolved partially. Therefore, in this study using satellite images of MODIS sensor and narrow band to broadband albedo conversion method, the instantaneous short-wave solar radiation in the central Iranian province of Qom, Isfahan, Tehran and Semnan was estimated. To analyze the results of the coefficient of determination (R2) root mean square error (RMSe) and the mean average error (MAE) was used. Consequently the rate of RMSE was about 42 watts per square meter and MAE rate was about 40.75 watts per square meter indicating the high accuracy of the method used to estimate radiation.

Gandom Beryan or Burned Sand region is located in the Lut Desert, NW of Kerman. This region has always been under consideration due to its high temperature. In this study, by utilizing thermal Remote Sensing studies, ENVI software, and thermal images ETM + (imaging time 10 am on Wednesday 7 October 2011) the surface temperature of Gandom Beryan was measured. Non-uniform distribution of basaltic rocks, erosion, and fracturing of rocks in Gandom Beryan has caused the calculated surface temperature in the southern part of the Gandom Beryan, which has been covered with dark basaltic rocks, shows reflections intensity more than the northern part of the study area. On the other hand, the photolineament index factor shows that the zones with greater fracture intensity than the other side have less temperature. The existence of mountains in all directions of the Lut desert)north, south, east, and west) have limited the penetration of sea moisture especially from the Indian Ocean, and this problem causes the increasing of drought, sunshine, and decreasing of cloud in this region. In general, desert location, including latitude and low altitude are factors that cause the high temperature of Basaltic messa besides the special surface absorption of basalt. Respect to daily and monthly corrections, the final temperature is calculated as the estimated maximum temperature, which is at least 76° C for Basaltic messa in Gandom Beryan and based on the surveys, this region can be considered as one of the Earth thermal poles.

In order to assess the satellite data for soil investigation, ASTER digital data 20 June 2006, field study and phisiochemical properties of soil, were analyzed. All landcover classes including soils are classified based on morphological and physico-chemical characteristics. Images were geocorrected and photomorphic units were selected based upon visual interpretation and sampling in study area. The images was classified using maximum likelihood algorithm; with eight approaches. The classified image was compared with the ground truth map. The lowest classification accuracy was achieved by optimum index factor (OIF) approaches and hence application of OIF for discrimination of soils was not effective way. The results showed that best index is not only efficient and other information such as DEM (digital elevation model) with the spectral combination increase the accuracy of classification and Kappa coefficient. Salinity Indexes (SI) and Normalized Soil Index (NDSI) and Brightness Index (BI) were useful for discrimination of the soils in the study area. Typic Haplocambids showed the maximum reflection due to bright color. In addition, the minimum value was related to the Typic Torriorthents class, because of dark gravels. The result showed ASTER data can differentiate Typic Haplocambids from Typic Torriorthents, Typic Haplosalids and Typic Haplogypsids in arid lands.

Soil Salinity has been a large problem in arid and semi arid regions. Preparation of such maps is useful for Natural resource managers. Old methods of preparing such maps require a lot of time and cost. Multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. This investigation is conducted to provide saline soil maps with sensor LISS-III of IRS-P6 satellite data, in Najmabad of Savojbolagh. Satellite images belonging to 25 June 2006. For enhancement of images, salt Indices, Digital Elevation Model (DEM), False Color Composite imageries (FCC) and Principal Component Analysis (PCA), were used. Supervised classification method includes Box classifier, Minimum Distance, Minimum Mahalanobis Distance and Maximum Likelihood classifier, DEM, PCA1, PCA4 and Saline Indices (SI) were used. After classification, the class map salinity S0, S1, S2, S3 S4, were prepared. The results shows highest overall accuracy and kappa coefficient for the maximum Likelihood classifier estimate, respectively 99% and 97% and the lowest overall accuracy and kappa coefficient for PCA1 estimate, respectively 1% and 0% were obtained. Using Digital Elevation Model (DEM) also due to the difference in height position to the separation of saline lands is usefully. Most spectral interference relatedto non-saline soils and low saline soil. From among indices INT2 and PVI greatest ability to segregate is salty soils(especially classes S0 and S1).

With the rapid development of spatial data production, collection, and processing techniques large amounts of data, which cover the same geographical extent, are available. These huge amounts of data motivate the researchers in geospatial analysis domain to create multiscale database in order to update the datasets and perform the multi-scale analysis. In the core of any spatial multiscale database is the data matching process. In this paper, a new algorithm is proposed for data matching which is implemented and evaluated in two major steps. Firstly, after pre-processing, the search and identification of candidate objects in the two datasets are carried out based on the topological features. Secondly, the best pair of corresponding objects is selected using the least squares method and comparing the geometric features of linear objects between the candidate object selected in the previous step. The algorithm is evaluated on the roads maps of scales1:25000 and1:50000 produced by national cartography center of Iran (NCC).The results show that the algorithm is successful and 94.2% of the objects were correctly identified as the corresponding pairs.

The hydrocarbon seepages theory puts forward a cause and effect relationship between the oil and gas reservoirs and the specific surface anomalies which are basically related to hydrocarbon leakages as well as their related alterations. Hence, the spectral reflectance of the hydrocarbons and their linked mineral alterations produce credible pieces of evidence for oil and gas exploration. Hyperion images of EO-1satellite was used in this study for identifying the oil seepages and their relevant alterations. After collecting the required data, the images underwent the needed preprocessing. In order to recognize the oil seepages, these corrected data along with field-sampled spectrometric ones were employed. Then, VISA and SCM combined model was applied to indirectly identify the hydrocarbon seepages. Moreover, two hydrocarbon indexes were developed for direct recognition of the hydrocarbon seeps using Hyperion images. The findings indicate that the two mentioned techniques are efficacious for the purpose of the study at hand.

The first and most important step in any floodwater-spreading project is the identification of the most appropriate areas for water infiltration into the aquifers. For this purpose، conjunctive use of Geographical Information System (GIS) and Multi-Criteria Decision Making (MCDM) may lead to more accurate results. In this study، the study area is Garbaygan Watershed located in the Fars province، Iran. Then، parameters affecting the site selection of floodwater spreading projects were defined and appropriately classified. To enhance the accuracy of the study output، Gray Clustering Analysis (GCA) and Analytical Hierarchy Process (AHP) approaches were considered. In the GCA، the data have grey membership functions and in contrast to fuzzy systems، they have defined limits. Grey membership functions aim to enhance the quality and weight of data by valuation of information layers. The GCA and AHP approaches were applied using this data set and the final maps of the watershed based on each model were prepared in different suitability classes. The results of both models showed that quaternary formations including Qscg، Qgsc، Qb، Mm-1، Qc2 and PLQb have the suitable condition for floodwater spreading. Also، upper parts of alluvial fans، pedimonts and sparse rangelands have acceptable conditions for such projects based on geomorphologic and land use characteristics. Meanwhile، the results showed the GCA outperformed in site selection compared to the AHP approach.

As the pattern of urban development, especially in the developing countries, shows that the density of activities and volume of settlement in the cities are increasing. These increases reflect the growing requirements and demands of citizens for public services.The absences of such facilities result in dissatisfaction of citizens. According to Muslims belief, cemeteries are one of the important places. Replete capacity of cemeteries and lack of studies for finding suitable new cemetery location in big cities in the next decade cause a serious crisis. Thus, the problem of finding a suitable location for a cemetery in city of Isfahan, one of the big cities in Iran, is addressed in this paper. In the first phaseof this study, the effective criteria are identified, and required spatial data are collected. Then, the fuzzy factor map of each layer is generated and multiplied to the weight of corresponding criterion. Afterwards, the maps are overplayed using fuzzy operators. According to evaluation of results, the selected potential locations for cemetery satisfy all economic, social, and environmental requirements.

Land cover is one of basic data layers in geographic information system for physical planning and environmental monitoring. Digital image classification is generally performed to produce land cover maps from remote sensing data, particularly for large areas. In the present study the multispectral image from IRS LISS-III image along with ancillary data such as vegetation indices, principal component analysis and digital elevation layers, have been used to perform image classification using maximum likelihood classifier and decision tree method. The selected study area that is located in north-east Iran represents a wide range of physiographical and environmental phenomena. In this study, based on Land Cover Classification System (LCCS), seven land cover classes were defined. Comparison of the results using statistical techniques showed that while supervised classification (i.e. MLC) produces an overall accuracy of about 72%; the decision tree method, which improves the classification accuracy, can increase the results by about percent to 79%. The results illustrated that ancillary data, especially vegetation indices and DEM, are able to improve significantly classification accuracy in arid and semi arid regions, and also the mountainous or hilly areas.

This research designed, constructed and evaluated a map-based variable rate application system for liquid nitrogen fertilizer. Solenoid valves were used to change nozzle flow using pulse-wide modulation technology. A turbine flow sensor and GPS module were used to measure solenoid valve flow and online system coordination, respectively. The graphical user interface software was designed by Visual Basic 6. This program received online coordination from a GPS module, received fertilizer map data, compared online and map coordinates, specified application order, received sensor output and compared it withoptimum data and then applied proportional closed-loop control. To evaluate the system, accuracy and time-delay experiments were carried out with 3 replications. The results showed that the average difference between map data and system flow for open and close-loop controllers were 10.0 and 3.6%, respectively. The maximum time delay was 0.54 s. Results also showed that, at 1% probability, no significant difference was observed between the system flow and nitrogen fertilizer map data.

Unsuitable exploitation of land is the most important problem of land resources utilization in the country. Unsuitable pattern of land use together with severe land use changes cause different environmental crisis including: environmental pollution and deterioration, expanding desertification, soil erosion, natural resources depletion, reduction of biological diversity and reduction in potential of land capability. These problems significantly influence the production of resources needed for recent and future generation and prevent sustainable development of the country. In other words, it can be concluded that the reduction of excess resources, is the result of unreasonable using of land by human. Land use planning is a science that determines the type of land use through studying the ecological character of the land as well as its socio-economic structure. Assessment of ecological capability, as the core of environmental studies provides an appropriate context for environmental planning. To this purpose, the agricultural and rangeland ecological capability assessment was performed using the PROMETHEE II and Fuzzy-AHP (Fuzzy Analytic Hierarchy Processes) methods in combination with GIS. The Fuzzy-AHP method was used to quantify the subjective judgments of the evaluators as the weights of the criteria whereas the PROMETHEE method was employed to prioritize the alternatives based on the weights obtained from the Fuzzy-AHP. The proposed PROMETHEE method can be useful for environmentalists in ecological capability assessment and other site selection issues.

Snowmelt runoff prediction is one of the important challenges in watershed management. The present research was carried out for snowmelt runoff prediction using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the Taleghan watershed located in Alborz province. For this research, 38 MODIS instrument images have been obtained from Iranian space agency for years of 2003, 2004, 2005 and 2006. Snow cover area (SCA) was extracted from all images. Then, snow water equivalent volume was computed using SCA and snow depth and density for mentioned years. Daily rainfall, temperature, snow water equivalent variables were used as inputs and daily discharge as output to multilayer feedforward perceptrons using backpropagation algorithm and compared with artificial neural fuzzy interference system (ANFIS). The results reveal that for the Galink hydrometry station, it was found that ANN with RMSE=0.133 m3/s and R2=0.71 in the validation stage are superior in snowmelt runoff forecasting than the ANFIS with RMSE=0.84 m3/s and R2=0.52, respectively. For this station, the ANN models without daily snow water equivalent as input are superior than the ANN models with daily snow water equivalent as input. An increase in the number of inputs from one previous time period to three consecative previous time period proved to be an excellent alternative to perform high quality daily snowmelt runoff prediction. In the other part of study, these comparisons were performed for three other gauging stations, it was found that the ANN in the validation stage is superior in snomelt runoff forecasting than the ANFIS. The ANN models with daily snow water equivalent as input are superior than the ANN models without daily snow water equivalent as input and also an increase in the number of inputs from one previous time period to three consecative previous time period proved to be an excellent alternative to perform high quality daily snowmelt runoff prediction for two stations.

Nowadays, because of the competition between banks and financial and credit institutions for attracting more resources, dominating the factors affecting the mobilization of financial resource has become very important. One of the effective elements in the mobilization of money resource in modern banking is attending the desirability of banks and financial and credit institution installations. The settlement place of banks and financial and credit institutions is an important motivation in attracting customers and marketers must consider this very carefully. In this research, the authors present a model for finding new settlement places for bank and financial and credit institution branches. To achieve this purpose, effective criteria and economic factors were recognized using library study, and then were completed by experts’ opinions. Due to the interaction of the criteria in the actual world, DEMATEL technique was used to recognize this relation, and Fuzzy ANP was used after completing the paired comparisons questionnaires by expert’s viewpoints. Then, based on radius of influence, these weights were used for spatial modeling. Also, the Saw method was used for zoning the area. This process was performed in zone 6 of Tehran to recognize the new settlement places of Tat bank new branches, regarding 7 other existing branches in the zone. Based on field observations, results from model were evaluated and Tau - Kendall b coefficient was employed to determine the homogeneity between the results derived from the model and the observed results. The coefficient was calculated 0.855 that indicates a strong relation between the two variables.

In this research the data of LISS-IIIsensor used for mapping saline soils on the around of Tashk and Bakhtegan lake, Fars Province. After the corrections of the imagery, the training and control pixels selected and the soil samples (depth of 5 cm) prepaired. Then pixels of the imagery classified in different approaches by maximum likelihood algorithm. Analysis of spectral graphs of classes indicated the most reflectance of saline soils was in visible spectral. The highest of correlation coefficient between salinity values and digital numbers of bands (0.45) observed in band 2. Investigation of feature space graphs indicated, almost single clusters formed in all of the classes except in low and moderate saline soils. The white salt crusts lied in the highest portion of soil line and black salt crusts in lower parts. The roughness and moisture were the main factors in reflectance decreasing of salt crusts in studied region. In non-saline soils the roughness produced by large clods in ploughed areas was the effective factor of reflectance decreasing. Presence of plant cover and residual of the straw caused error in identifying of saline soils. Results of classification of different approaches indicated the highest accuracy of procedure, user, overall and kappa index was observed in integration of all the bands. In all of the approaches, soils with low and moderate salinity had low accuracy. The produced map of LISS-III sensor indicated salinity values above of 16 dS/m there were on the around soils of lake and decreased one with distance, in center of the plain and increased in agriculture lands again.

In recent years, acquiring information of remote sensing data, especially satellite data has excessively increased and several methods are presented in order to improve the quality of remote sensing studies in earth sciences. It is possible to manage many projects and provide different types of thematic maps in a short period of time, and a low cost by utilizing satellite data and GIS method. Recent researches show that utilizing satellite data in studying natural phenomena can effectively help to reduce the time and cost at the same time maximize the precision. But, many users of these data face confusion at choosing suitable image for their subject and lack a special criterion for that end, Or else they merely take one or two criteria in to account and lack a comprehensive view in choosing the best image. Therefore, defining and analyzing criteria for correct and precise selection of satellite data, in accordance with case-study, is crucial. So, in this article, we investigate the image selection criteria, especially their role in minimizing time, cost and extracting useful data. On the basis of the results, prior to doing of the project, users of these data need to study selection criteria properly. After that, on the basis of these criteria and phenomena under study they should set out to choose sensor type, date of image acquisition, image type, and methods of information extraction. Therefore in research, different practical aspects of satellite images as well as criteria for selecting suitable images are investigated and subsequently information and suitable solutions are provided for users.

The purpose of this study is to track and analyze the environmental effects of droughts by remote sensing indices in Kashan desert and dry zone. Temporal changes of droughts was evaluated using normal Z index in annual and seaonal (spring) scales. Spectral and thermal data from data series of NOAA-AVHRR satellite between 1998 and 2004 were used to determine the drought indices. Vegetative and thermal drought indices were calculated using NDVI, VCI and TCI values derived from NOAA-AVHRR data. Results from applying mentioned indices showed that this area had low vegetation index values as NDVI index was generally less than 0.2. According to NDVI and VCI maps, years of 2000 and 2001 were characterized as drought condition contrary to 2002 and 2004 as rainy years. However, land surface temperature (LST), TCI and VHI indices showed different temperature conditions specifically in the months of April and May. Using validation of results derived from remote sensing indices, test of significance between them and climatic indices was calculated. According to the calculations, climatic condition of the study area was more compliant with the results of vegetation indices. Also thermal condition of the environment was more accurately indicated by thermal indices. According to the results, applying remote sensing data in environmental studies of arid and desert regions like Kashan is recommended.

The geometric mean particle diameter (dg) and lime are two of the most important properties from the viewpoint of soil management. Nowadays remote sensing technology which has emerged walking with science development throughout the world, has made soil study faster, more facile and more cost-efficient. An investigation of soil dg and lime was performed in Pol-e-Dokhtar area by use of four sets of spectral data of IRS P6, LISS III obtained from the Organizations of Geography of Armed Forces and Aerospace of Iran, in September 7th 2007. Subsequently, Principle Component Analysis, Normalized Difference Vegetation Index, Soil Line Euclidean Distance and Unsupervised Classification was carried out for satellite data sets following image preprocessing operations. Through stratified randomized sampling method and according to the false color composite and photomorphic units of the main image, 95 samples were selected and eventually collected from 0-5cm depth of soil surface, likewise 43 samples from 5-20cm. Afterwards, dg and lime contents were determined for each sampled point in soil laboratory. By means of multivariate regression operations there were eventually shown pronounced relationships (P< 0.01) between soil dg and lime with green (R2adj= 0.78) and NIR (R2adj= 0.77) bands in the first sampling depth. In addition, this was true for the second sampling depth with green (R2adj= 0.57), NIR (R2adj= 0.55) and red (R2adj= 0.59) bands with lower coefficients of determination. Consequently it has been substantiated with evidence that dg and lime contents are able to impress soil spectral reflectance. So it is possible to find out about these parameters using satellite and ancillary data.

Introduction Remote sensing provides fast and economical information to study morphotectonic and structural geology. Morphological and geomorphological analyses of topographic features have useful in structural and tectonic investigations. Some features and models of topography, for example; peaks, strike- ridge geomorphology, passes, plans, channels, pits, three-dimensional views with imagery drape, cross-sections profile geometry, slope and aspect calculations, shaded relief, curvature maps, rose diagrams of lineations (faults, ridges,…) and structural trend can be extracted from SRTM through systematic digital tectonic geomorphology and digital terrain analysis. These procedures can be fundamental tools in tectonic analyses base on using remotely sensing data. Altogether, bases of this research are structural geology, geomorphology and digital terrain analysis that all extracted from SRTM. There are close communication between fault type and landforms, therefore, we investigated topographic fractures along Dehshir fault and calculated models of topography around it for morphotectonic study. Materials and methods We used SRTM to extracted topography features around Dehshir fault. We utilized satellite data include; Landsat, Aster data and geology maps with conjunction fieldwork observations. Digital topography investigation methods for systematic study of tectonic geomorphology features by DEMs along Dehshir fault are constructed respectively; (1) Geometric investigation of features linked to lineations. (2) Feature detection and parameter extraction from DEMs for tectonic geomorphology. In this section, we analyzed; elevation data, numerical differential geometry, digital drainage network and digital geomorphometry. Then, digital image processing of terrain data and spatial analysis of lineaments are done. This paper describes the morphotectonic of Dehshir area using SRTM. Therefore, to access to the goal of study, analysis of digital drainage network analysis; digital geomorphometry; digital image processing; lineament extraction and analysis; spatial and statistical analysis and digital elevation model-specific digital methods, such as shaded relief models, digital cross-sections, 3D surface modeling and topographic features are extracted from SRTM. Structural information from other sources, such as geological and topography maps, remotely sensed images and field observations were analyzed with geographic information system techniques. Results and discussion SRTM analysis of the study area showed a distribution of elevation. Cross-sections across perpendicular of Dehshir fault in NE–SW direction also illustrated fault scarps. Shaded relief images have obviously shown lineaments (e.g.; drainages, channels, fault trace, ridges …). Contour map extracted from SRTM elongated along Dehshir fault and showed fault scarps. Rose diagram of lineaments illustrated morphotectonic pattern NW-SE lanforms of the study area. In classified aspect maps are predominantly NE and SW hillside. Lineaments in the shaded relief, aspect, slope and curvature models showed much information for explanation of morphotectonic. The results provide an acceptable regional morphotectonic view of the study area. The geomorphology of the study area is dominated by a pattern of sub-parallel mountain ranges and intermountain plains (lowlands, mainly grabens). The study area landforms are strongly influenced by active faults movements (particularly strike-slip faults). The piedmont areas are covered with low slope surfaces (predominantly Holocene alluvium), originating from the mountain channels and spread for kilometers towards the lowlands. The bottom of the medial plains, known as "Kavir" (Persian term for playa), is covered with playa-type mud flats, evaporate (salt) lakes (e.g. Abarkooh, Marvast) and, only locally, by mobile sand dunes (e.g. along Dehshir fault at east of Abarkooh playa). Conclusion The results show that digital terrain analysis methods applied on SRTM in the proposed way in this study could extract morphotectonic features from SRTM along Dehshir fault and they contributed to the tectonic interpretation of the study area. According to the evidences extracted from SRTM along Dehshir fault, for example; fault traces, deflected and beheaded drainages, pattern of network drainages, erosion surfaces of uplifted and back erosion of drainages because of the location (situated in quaternary landforms), they are neotectonic evidences for activity of Dehshir fault during quaternary.