bakhtiar feizizadeh

 the process of water scarcity in Iraq has intensified due to natural and political factors, especially in its central and southern parts. In this regard, the current research has evaluated the trend of land use and land cover changes in Babol province in the center of Iraq and analyzed the changes and developments in the surface water resources of this region. The current research is based on the supervised classification of satellite images of Babol Province, Iraq, and in this regard, ETM+ sensor images of Landsat 7 satellite in 2003, OLI-TIRS sensor images of Landsat 8 satellite in 2013 and Landsat 9 satellite in 2023 were used and the maximum likelihood technique was performed on them. According to the results, during the first ten-year period (2003-2013), the surface water area of the region has decreased from 729.44 square kilometers to 174.14 square kilometers, which means a 76.13% decrease in the area of this group of lands. In the second ten-year period (2013-2023), the extent of surface water has increased from 174.14 square kilometers to 825.61 square kilometers, which indicates a growth of 374.1 percent. The results of the research have determined that factors such as the development of agricultural lands, the growth of urban and rural constructions, and climatic factors have emerged as the main factors of land use change in Babol province, and the cause and origin of land use changes in Babol province is not only water shortage.

Considering its key effect on the welfare and health of society, water plays a crucial role in the growth of communities and is one of the most important natural resources. In recent years, the problem of scarcity of water resources has been exacerbated by the growing population worldwide and increased competition for access to water resources, which leads to increased costs of water use. Therefore, the use of this vital resource requires proper management. The present study was entitled Predicting and Modeling the Urban Water Consumption Pattern in Tabriz to Investigate and Analyze the Urban Water Consumption Pattern Using Geographic Information System (GIS).

Codling moth, Cydia pomonella L. (Lepidoptera: Tortricidae) is the key insect pest of apple orchards in Iran. This study was conducted in the main apple-growing regions of East Azarbaijan Province to generate potential habitat suitability maps of C. pomonella using MaxEnt modeling and to determine the importance of vegetation index in improving the accuracy of these models. Field surveys for collecting the occurrence data of codling moth were conducted during three growing seasons, 2017 - 2019. The activity of codling moth adult males was monitored using delta-shaped traps baited with female sex pheromone. Fifteen environmental variables were considered as potential predictors for estimating codling moth distribution. These variables were categorized into topographic, climatic, and remote sensing variables. A MaxEnt modeling algorithm was used to predict the distribution of codling moth. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). By using the topographic, climatic, and topographic+climatic variables, the AUC values were 0.840, 0.951, and 0.938, respectively. The model including normalized difference vegetation index (NDVI) had the highest AUC value (0.99), which strongly supports model predictive power and indicates the importance of vegetation index in codling moth distribution modeling. NDVI was the most contributed variable in the model followed by precipitation of September, slope, minimum temperature of May, and mean temperature of April. The distribution map obtained in MaxEnt provides an important tool for identifying potential risk zones of codling moth. This map can assist managers in forecasting and planning control measures and therefore, effective management of current infestations of codling moth.

Public art has followed a new trend in urban spaces in recent years, so that cities invest highly in the design and implementing of public works of art and improving the visual quality of urban spaces, since it is effective in creating good urban spaces and making the city attractive and memorable. It also encourages people to communicate with works of art and each other. Although public art as an art integrated with the public space of cities is not a new concept, many changes have been created in public art and it caused the creation of various types of public art in most cases from a purely static art to a dynamic and interactive art. This article aims to investigate the effect of public art in urban spaces on promoting the perceived vitality of young people. Despite the importance and role of public art in the quality of urban public spaces, studies conducted in the area of public art have not paid much attention to the effect of different types of public arts in public spaces on vitality of people, especially young people. Thus, this study was conducted with the aim of answering the question of “which type of public art has a greater impact on promoting the perceived vitality of young people in urban spaces?”

The present study is a qualitative-quantitative (mixed) study conducted as a survey. In the present study, visual approach and image reading were used to collect information.  Visual research is a term that includes methods based on data collection and analysis of them through designing, painting, photography, film, or video images.  In this article, public arts and interactive arts were divided into different types based on theoretical foundations and research literature and  an image was selected from each type of art and through an online questionnaire was submitted to the participants and they were asked to express their level of vitality from watching that work of art in urban spaces on a 5-point Likert scales. This questionnaire was designed and sent to the target population through cyberspace applications.  Since the target population of the present article was young people, the sample was selected among the students of Tabriz University and the students of Nabi Akram Institute of Higher Education in Tabriz in the second semester of 2020-2021. To determine the sample size, Cochran's formula with an error level of 5% was used. According to the number of students in these two universities (27000 people), the sample size of 379 was selected. After sending the questionnaire to nearly 380 people, 144 people completed the questionnaire. In this research, both art and architecture students and non-art and architecture students in both bachelor and master levels were surveyed.

The research results revealed that among different types of public arts in urban spaces, sound and digital arts such as musical fountain, and the integrated art embedded in the elements available in space such as wall relief in the body of the building and the art appropriate to the site, such as plants formed in parks, regardless of the three criteria of gender, age, education and field of study, are accepted by all young people and increase vitality, but abstract static art is less welcomed by young people. Among the interactive arts, dynamic-passive interactive art and static interactive art obtained the highest score in terms of creating a sense of vitality among young people, and in contrast, dynamic-dynamic interactive art obtained the lowest score. Art related to public and local culture caused the highest vitality among male young people and young non-art and architecture master students, and digital art caused the highest vitality among females and bachelor art and architecture students. In addition, among non-art and architecture bachelor students, two-dimensional applied art obtained a relatively high score.

 The results show that creating static works of art is not effective in designing cities and using public art to create a sense of vitality in urban space and young people show higher tendency to dynamic work of arts. Also, interactive works of art are more acceptable than non-interactive types, and urban planners should pay attention to this issue in embedding works of art in urban spaces and take right decisions and actions to increase presence, interaction, and vitality among young people in urban spaces. Also, the use of modern technologies and creativity in the artistic design of cities is one of the requirements for adapting the urban space to spirits of young people.

Landslide susceptibility analysis is beneficial information for a wide range of applications. We aimed to explore and compare three machine learning (ML) techniques, namely the random forests (RF), support vector machine (SVM) and multiple layer neural networks (MLP) for landslide susceptibility assessment in the Ahar county of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 266 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. Three landslide models (SVM, RF, and MLP) were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were employed to validate and contrast the predictive capability of the obtained three models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the MLP model had the utmost rate of prediction capability (85.22 %), after which, the SVM model (78.26 %) and the RF model (75.22 %) demonstrated the second and third rates. Besides, the study revealed that benefiting the optimal machine with the proper selection of the techniques could facilitate landslide susceptibility modeling.

In this study, 5 main DEM derivatives of 12.5 m ALOS satellite (slope layer, slope direction layer, curvature layer, cumulative flow layer and altitude layer) as well as Sentinel-2 satellite images and NDVI vegetation index were used as auxiliary layers. Segmentation in this area was performed using segmentation multi resolation method. In this segmentation, the height layer was given a value of 3, the curvature layer was given a value of 2, and the other layers were given a value of 1. Then, using Layer Values ​​and Geometry algorithms and assign class commands, landforms located in the western and southwestern slopes of Zagros (Aligudarz city area) have been classified. The results showed that the use of Layer Values ​​and Geometry algorithms and assign class commands have a good ability to isolate and classify landforms, so that 8 types of landforms (slopes, ridges, water areas, precipices, peaks, ridges, lowlands and lowlands) Kappa coefficient was 0.87 and overall accuracy was 91.71%. The ridge landforms form the largest part of the region and are the dominant landforms of the region and have a good distribution in different parts, but the peak landforms with the minimum area have formed only a limited part of the study area.

Nowadays, subsidence is a terrestrial hazard caused by human and natural activity around the world and needs careful consideration in many countries (e.g. USA, UK, Australia, China, Egypt, France, Germany, India, Iran, Italy, Japan, Mexico, Poland, Saudi Arabia, Sweden and the Netherlands). It should be noted that subsidence events have been observed in more than 150 countries and regions around the world. Today, subsidence-affected areas are a major concern from different perspectives on socio-economic, environmental and conservation issues. The present research is applied in terms of purpose and descriptive-analytical research method. In this research, documentary sources and library resources and satellite images have been used. In the present study, by combining GIS and multi-criteria decision making systems, the potential for the risk of subsidence risk in the city of Tabriz was zoned. After reviewing the research background and research, eleven factors affecting the occurrence of subsidence risk: rainfall, slope, evaporation, geology, land use, runoff height, distance from the fault, drainage network density, distance from the river, vegetation index and depth Groundwater was identified and weighed using the ANP process. After standardizing the layers in ArcGIS, all indicators were combined with the Weighted Overlay function. According to the results of the total settlements located within the study area, 20% of settlements have high subsidence potential, 36% have high risk potential, 18% have medium risk potential, 14% have low risk potential and 12% have potential range. Subsidence is very low.

Today, about 60% of the world's energy resources are oil and gas. Due to the different methods of transporting crude oil and its products, the largest share of the transfer of these resources is through the transmission pipeline lines. The present study aims to model the GIS model of fire-based urban gas distribution networks and the seismic effects of Tabriz in intensifying fire. For this purpose, multi-criteria decision-making methods (MCDA) with geographic information systems (GIS) were used. Also, to determine the importance of the relationship between criteria and sub-criteria and their relative importance coefficient, the FANP model was used. And 20 sub-criteria were studied to study the vulnerability to gas network fires. To determine the effect of seismicity of Tabriz city on fire in urban gas distribution networks, the seismic hazard zoning map of Tabriz city was compared with the output map of the present study and it was determined that the most vulnerability in both seismic hazard map and fire zoning map The gas network is in the northern and northwestern part of the area, which is a worn and marginal part of the city. Residential use with 70.63 hectares with the most damage from the fire of urban gas distribution networks due to earthquake intensification is in the first place. Considering the high risk of fire in urban gas networks, in the region, especially the worn-out and marginal structures, it is necessary to organize these structures and carry out protective operations of gas transmission lines in the mentioned issues. Also, according to the results of the research, the complexity and length of gas transmission lines in the suburban fabric of the city are high, so it is recommended to use polyethylene pipes in these areas, which have a high resistance to steel pipes.

The main goal of present research is the study of correlation of social security with Urban Designing indexes in old and modern Urmia texture. In this city, various Urban Designing and presence of different ethics make the importance of security more dominant and in this regard, the quality of buildings, population, the number of floors, and also the domain in which the crimes have recorded by police were considered as criteria. In this study, the data of crimes offered by the police and also the data of city were used. All the pre-processing data and standardization of criteria is done by GIS in order to do the Spatial analysis. At the end, by using MCDM and SUPER DCISION software weight of any criteria was determined and by utilizing weights in standard GIS maps, and finally blending weighted fuzzy maps with AND operator, the security map of the city was provided. The result of the study was compared with crime scattering map which based on the results by developing modern texture social security has increased and police region 13, with an old texture in the central part of the city has the least security.

The wind erosion occurs when wind speed exceeds the soil erosion threshold and plants or their residues,surface roughness, or other obstacles do not protect the soil surface. Also wind erodibility is oneof the most important determining parameters of wind erosion under certain climatic conditions. The main objective of this research was mapping of soil erodibility through empirical relationship between satellite imagery and physicochemical properties and estimation of soil erosion using a comprehensive assessment model, on the east shore of the Urmia Lake. For this research work soil sampling carried out in 153 points of three elevation classes(1271-1273, 1273-1275 and 1275-1278meters)and4supervised classification methods such as, support vector machine(SVM), Maximum likehood classification (MLC), Minimum distance and Artificial neural network(ANN)were used in classifying and mapping of soil erodibility. Soil samples physicochemical properties measured and 26 samples of them randomly were selected for wind erodibility measurement in wind tunnel laboratory. Wind tunnel experiments at a distance of 20 cm from the tunnel floor, revealed wind erodibility of 2.92 grm-2.min-1/ms-1. Also stepwise regression (SPSSv.25) results showed that among the physicochemical properties of soils, erodible fraction were the most important soil properties, which used in estimating erodibility and have a positive correlation with soil erodibility. The mean weight of aggregate diameter had negative correlation with soil erodibility and no relationship was found between soil chemical properties and erodibility. Of the4supervised classification methods, the artificial neural network has a higher capability in classifying and mapping erodibility and finally the results showed that the overall classification accuracy is57.1%.

In recent years, due to the widespread use of remote sensing drones, the qualitative and quantitative monitoring of agricultural farms using this technology has also increased significantly. In this regard, many vegetation indices were introduced to study the plants specifications. It is understood that each method has different strength and capabilities which should be taken into account of consideration when pressing the drone images. In this research, the efficiency of four high frequently vegetation indices were evaluated using the drone spectral data for monitoring the corn field. Field experiments were carried out in the research farm of Urmia University in 2018. The research methodology was developed by evaluating the effect of different levels of irrigation and fertilization on the crop biomass and four spectral indices such as NDVI, GNDVI, SAVI and NDRE. The experimental design was considered in the form of complete randomized blocks with three levels of irrigation and fertilization application, including 100, 80 and 60% of irrigation water requirements and fertilizer requirements within the four evolution step. The imaging operation was designed and performed using an ebee+ fixed wing drone equipped with the Sequoia remote sensing sensor. After performing the required photogrammetric and preprocessing operations by Pix4Dmapper software, the images were used to calculate vegetation index layers. Finally, the effect of different irrigation and fertilization application levels on crop biomass and vegetation indices were evaluated using statistical analysis of variance in the SPSS software. The results indicated that the crop biomass was significantly affected by different levels of water and fertilizer usage, and no significant effect observed on NDVI and SAVI indices in response to water and fertilizer levels. In contrast, The SAVI index was significant to irrigation levels and the NDRE index was significant to irrigation and fertilizer levels.

Water erosion is one of the most important causes of soil destruction, and it is considered a serious environmental hazard all over the world. Recently, remote sensing is customarily used in conservation and erosion projects that most of them use air photography which, despite the many benefits, bot have limitations. The present study was aimed to detect and zoning soil erosion levels using high resolution of Sentinel-2 satellite image, its integration with aerial photographs, base maps, and implementation of various classification methods, including pixel-based and object-oriented techniques. After the staff operations, atmospheric and geometric corrections, pre-processing, and processing done on images of Sentinel-2 for detecting the area of erosion in the Lighvan watershed. In order to evaluate the correctness and accuracy of each method in this study, the criterions of user and producer accuracy, accuracy and kappa coefficient were compared. Based on the results, the supervised tuning method with user accuracy equal 77.78 and 33.33, has the highest and lowest accuracy for classification using spectral angle map algorithm and Mahalanubis distance, respectively. The maximum of overall accuracy and kappa coefficients, 72 and 62 percent, respectively, indicate the medium accuracy of the produced maps with pixel based algorithms. The results of object-oriented processing show that based on user and producer accuracy, object-oriented methods have increased accuracy (12 %) compared to pixel-based methods. Classification results with object-oriented algorithms and based on overall accuracy, 88 and 84 percent, respectively, for the brightness and the combination of brightness and slope, and the kappa coefficient for these two algorithms was 0.86 and 0.79, respectively. This result represents an acceptable increase in classification accuracy in the use of object-oriented algorithms compared to pixel base algorithms.

Wind erosion is one of the important aspects of land degradation in arid and semi-arid regions and is a serious challenge to sustainable production and land management. In this study, a comprehensive evaluation model is developed which proves to be effective with an overall precision of 66.12% to estimate soil erosion on the eastern shore of Urmia Lake. Six critical factors including soil erodibility, aridity index, soil crustal indices, vegetation fraction, wind field intensity and snow cover days are employed to model the wind erosion. 153 soil samples randomly were collected from 0-10 cm depth points from three elevation layers (1271-1273, 1273-1275 and 1275-1278 meter above sea level) and transfer to laboratory for physical and chemical analyzing. The AHP method is applied to determine the weight of each factor in a multi-criteria system, during the years 2005–2017 and poor vegetation cover with low annual variations was identified as the most important factor affecting the wind erosion model. Results show that high, low and moderate erosion classes have 0.76, 0.64 and 0.57 overall accuracy, respectively. The reason of low accuracy of low and medium erosion classes was the lower number of field observation points of these classes. The results shows that 45.56% of the study area classified as severe wind erosion class (WEI <0.53), while 23.97% has erosion Moderate (0.48> WEI> 0.53) and 30.47% have been labeled as low erosion (0.48> WEI). Consequently, the accuracy of the overall assessment of soil erosion model developed in this study is acceptable and could be applied in the eastern part of Lake Urmia. Results shows the descending trend in erosion intensity from the middle parts to the north and south parts of the study area.

Nowadays the tourism industry has turned as critical section of the economy. In order to extend this industry, developing the required infrastructure can be considered as very important issue. The main objective of this research is to analysis the urban infrastructure and tourism hospitality in Tabriz city. For this to happen, variety of data (e.g. population, social, economic, urban infrastructure, tourism hospitality and etc) where taken into account. The research methodology was established based on Geographical Information Systems (GIS) and its spatial analysis such as multi criteria decision analysis MCDA. The relevant criteria were ranked using analytical hierarchy process. Accordingly, the sensitivity and uncertainty analysis using Mont Carlo simulation and global sensitivity analysis were applied to identify and remove uncertain weights. Results were achieved using GIS aggregation functions for mapping the tourism hospitality radiances assessment in Tabriz city. Results indicated that the central section of the city (e.g. Bazar of Tabriz) has already sufficiently developed from the tourism hospitality perspective. While, the suburb area, where tourism destinations are distributed (e.g. Magbartolashara) still required further attention from the decision makers and authorities. Results of this research are great of important for identifying the critical issues and challenges for tourism industry in Tabriz city. Results are also represented a new approach for GIS based tourism hospitality assessment by means of proposing a sufficient methodology with critical steps such as sensitivity and uncertainty analysis. The proposed methodology can be applied for future GIS based tourism mapping.

Soil salinity is one of the major environmental problems affecting vast areas in many countries and this problem reduces the ability to produce and fertilize the soil for cost-effective production. Identification and monitoring of saline areas is necessary for controlling land degradation behavior and its sustainable management, especially in semi-arid areas. Expansion of the process of soil salinization is one of the important environmental challenges of the eastern margin of Urmia Lake. The research results of the researcher indicate that less work has been done in this salinity field by means of object-oriented methods. As well as among the various types of classifiers in the eCognition software environment, comparisons between salient classification algorithms are less significant. Therefore, it is necessary to compare the images classification algorithms and algorithms that have a good accuracy in extracting image complications are identified.

The study area is part of the eastern catchment of Urmia Lake which includes the plains of Tabriz, Shabestar, Oskuo, Azarshahr, Ajbashir and Bonab, Malekan and part of Maragheh. And it is located in the range of 37 degrees, 9 minutes to 38 degrees, 11 minutes north latitude and 45 degrees, 41 minutes to 46 degrees and 17 minutes east longitude in the northwest of Iran. The study area is 3847 square kilometers. For this purpose, two types of data including satellite images and GPS data obtained from field operation were employed. First, the stages of image preprocessing, including radiometric correction, to calculate the spectral indices, subset, and image bands stack in the ENVI 5.1 software were performed. After performing the preprocessing stages, the images and GIS data include (Topographic data from DEM 30m of area, soil classification, vegetation index (NDVI) and other information layers) were imported to the eCognition software for object based image processing and to classify knowledge based classification based on different algorithms. In this study, the effectiveness of the object based fuzzy technique and the nearest neighbor approach in the extraction of saline areas in the eastern of Urmia Lake were also investigated as part of methodology.

In order to achieve high accuracy results, using the ESP algorithm, the segmentation scale was optimized. By predicting the appropriate scale for creating image units using the ESP algorithm, the scale 170 with coefficients of shape 0.2 and compression 0.5 was selected as the appropriate scale for extraction of image symptoms. Accordingly, to classify the nearest neighbor classification, at the first, the intervals between educational samples were optimized using FSO algorithm. And for the image of the study, the eighteenth dimension with a separation distance 2.52 was identified as the best dimension for the separation of the desired classes. The results indicate that both methods with small difference have relatively similar results. However, the nearest neighbor approach has been estimated non-saline lands more than the object based fuzzy method. Based on our statement it could be due to the presence of mixed pixels

The results showed that the fuzzy object-oriented method, due to the use of membership degree functions, has overall accuracy of 0.94 and a kappa coefficient of 0.91, and it is superior to the nearest neighboring algorithm in extraction of eastern lake margins. It was also found that the brightness index is the most effective indicator in identifying and distinguishing salty lands from non-saline areas.

In recent years, the study of climate changes as well as their effects, has become a constant topic in the scientific fields of many countries. One of the main features of these changes is the increase in air temperature over the last 5 decades compared to the last 500 years. Statistics show an increase of one degree centigrade in air temperature over the last 5 decades. The land surface temperature means the radiant temperature of the earth's crust and the amount of pure energy that is balanced on the earth's surface under climatic conditions and depends on the reached the amount of energy, surface emissivity, humidity and atmospheric airflow. Land surface temperature is considered as one of the key variables in climate and environmental studies of the Earth’s surface. It is also one of the basic parameters in the physical features of  the earth's surface at all scales from local to global. Currently, the most important sources of climatic data are meteorological stations, and these stations provide climatic statistics for certain points, while the temperature may alter at different intervals stations and decrease or increase compared to the desired station. Therefore, it is necessary to have a technology that can eliminate the shortcomings of meteorological stations in calculating the temperature at sampling intervals and in impassable places where it is not possible to build a meteorological station. In recent years, new sciences such as remote sensing have provided new ways to monitor the environment and acquire, evaluate, and analyze environmental data, and can provide a wide range of parameters relating to the environment. This technology is considered as an important and increasing source of information for studying climate change that has a direct impact on global warming. Over the past two decades, 18 algorithms have been developed to calculate the land surface temperature. These algorithms fall into four categories: emissivity-dependent models, two-factor models, complex models, and radio-based models. The results of the comparisons between different algorithms shows that different algorithms perform differently in different situations with different geographical climates. Therefore, the present study aims to compare the types of LST calculation algorithms for MODIS sensor images and determine the best algorithm for East Azarbaijan province.

 Convert digital numbers (DN) to spectral radiation. The following equation was used to convert the numerical values to spectral radiation for thermal bands of MODIS sensor images. Planck's equation was used to convert spectural radation to spectral reflection when the radiant power of thermak data of MODIS sensor is considered to be a maximum of one. In order to estimate the surface emissivity, the Normalized Difference Vegetation Index (NDVI) thresholding method is used. The radiant power is divided into three categories to determine the soil characteristics in each pixel and to calculate the emissivity rate and emissivity difference; 0.2>NDVI, it is considered as dry soil and its radiant power is considered to be equal to 0.978. 0.5 NDVI, it is related to pixels with higher vegetation density and its radiant power is considered 0.985. 0.5>NDVI<0.2, it is based on a combination of pixels relating to vegetation and soil and the radiant power for them can be calculated. The vegetation ratio, that its value can be calculated. The value of each scientific finding depends on its accuracy. To compare the obtained results from the algorithms used to calculate the land surface temperature with the recorded temperature in meteorological station.

The results of the present study show that among the 18 algorithms for the land surface temperature estimation for MODIS sensor images, the Sobrino algorithm with RMSE value of 1.79 has the highest accuracy, Cole Casillas and Prata algorithm with RMSE value of 2.85 is in the second position, and also the Salisbury and Sobrino algorithms with RMSE values of 2.39 have the third place for LST calculation among the other algorithms. The Qin algorithm with a RMSE value of 5.28 has the lowest accuracy for LST estimation.

A review of the data obtained from comparing split-window algorithms shows the overall compliance of the calculated temperatures with the topographic conditions of the region, so that almost the lowest temperature values in all algorithms are related to the parts having more height (mountainous) and green cover of the region and also, temperature values have risen in low-lying areas lacking dense vegetation.

One of the most important risks that ever has threaten human society is earthquake. we studied these ground changes after occurring earthquake in Bam by using optic and radar images. In this research we estimated amount of movement occurred in preceding area by using these Envisat satellite Remote Sensing images before and after earthquake in Bam. Also by using Indian IRS pancromatic image with 5 meter local accuracy and doing fusion techniques like HPF with Aster images and aerial photos of Bam in 2004 with Fuzzy object-based algorithm technique automatically studied building determination in this area. According to the effects of geological formations, in terms of finding appropriate places for building and construction as well as evaluate the effects of regional formations in the study area, which earthquake was occurred, 1:100,000 geological maps are used. In step analysis of research , the results from every step adapted with each other so that amount of movement through earthquake had changed between -0.19 to 0.32 and results from automatic determination of ruined buildings by And & Or operators had accuracy about 93% and 98% respectively. Correlation between ground movement and damage of buildings was evaluated by adapting these levels with each other. This correlation demonstrates linear correlation between results. Awareness of correlation between construction and building reactivity to the earthquake movements can improve urban managing and planning and decrease damages to the minimum.

Due to the location of Iran in a dry and semi-arid region, it is always affected by sloping instability and erosion as the most severe type of erosion, gully erosion. This erosion pattern occurred in different parts of Iran and continuously over many years, and during erosion and the transfer of high sediment volume, it destroyed roads, facilities, pastures, slopes, etc. This will require the identification of high risk areas and the development of sensitivity maps. In recent years, the processing of satellite imagery as an advanced method has been widely used by researchers to increase the accuracy and save time and money. The object-oriented analysis of images is one of the most important methods for extracting information from satellite imagery, which is based on spectral, form and spatial characteristics and using expert knowledge to identify complications.

In this research, the Lighwan watershed basin was studied as one of the most important sub-basins of Aji Chay in East Azarbaijan Province and the satellite images of Sentinel-2 (2016) with spatial resolution of 10, 20 and 60 meters for the processing and identification of gully Was used. The images were processed using the eCognition software and applied with different types of algorithms to design a semi-automatic model based on object-oriented analysis. Finally, in order to evaluate the accuracy of the model, the identified Gully were mapped out and calculated using ArcGIS software to match the ground reality map and the formation of the error matrix, manufacturer accuracy, user accuracy and kappa coefficient for each of the algorithms.

The results showed that the Density and Compactness algorithms had the highest and lowest accuracy of the manufacturer (manufacturer accuracy was 88 and 78 respectively). While based on Kappa coefficient, the asymmetry algorithm has the highest accuracy compared to other methods (kappa = 0.91). Then, the shape index and density algorithms with kappa coefficient equal to 0.89 and 0.85 provided acceptable accuracy for the classification and identification of the gully.

The use of object-oriented methods due to the increased accuracy of classifying and identifying surface effects and phenomena can be used as a suitable solution for future soil studies and natural phenomena. In the present study, semi-automatic semi-automatic model for ditch identification was presented using spectral and geometric properties of Sentinel-2 satellite images and object-oriented processing in eCognition software environment. Key words: Object Oriented Algorithms, Object-Oriented Processing, Segmentation, Classification

Snow cover in mountainous basins is known as an important and reliable fresh water resource. Due to the physical conditions of the mountainous environments, it is believed that snow can measurement is still challenge based on traditional methods and techniques. In order to deal with this issue, remote sensing satellite images can be considered as efficient method for identifying snow covers and computing their depth and volume. Within the current research, the Landsat 8 satellite images were employed to detect snow cover surface for the Yamchi basin using NDSI indices and object based image analysis methods. The ground control points were applied to compute the accuracy of results while the overall accuracy was computed to be about 90%. In order to measure the snow cover depth and compute water discharge, the Sentinel A1 and D-InSAR satellite imagery were employed. In context of detection snow cover using Landsat 8 images, the satellite images for September indicated no snow cover for the study which accordingly employed as basic data for comparing against winter season satellite images and accordingly computing the snow cover and depth using the interferometry approaches. The results were verified by ground data.The correlation coefficients were computed to be about 85%. With confidence in the accuracy of the maps, the snow volume was also obtained through surface and snow depth maps. Accordingly, the correlation between snow depth of ground control points the obtained snows depth from differential radar interference were employed to compute water discharge of the study area.

Gully erosion is an important challenge in natural resource management and sustainable development that often has severe environmental, economic, and social consequences. Thus, the objective of the present study is to assess the capability of maximum entropy (ME) model for spatial prediction of gully erosion susceptibility at Kashkan-Poldokhtar Watershed, between Lorestan and Ilam provinces, Iran. At first, a gully erosion inventory map was produced using GPS in field surveys. The gully conditioning factors including lithology, soil texture, land use, drainage density, distance to streams, topographic wetness index, altitude, slope percent, slope aspect, plan curvature, and distance from road were selected, and their maps were prepared in geographical information system (GIS). A total of 65 gully locations were divided into two groups (1) training of the model (45 gully occurrences), and (2) validation of the model (20 gully occurrences). The prediction of gully susceptibility and variables importance analysis were carried out based on maximum entropy model using MAXENT software. Finally, the validation of the prediction results was conducted based on the receiver operating characteristic (ROC) curve method, and the area under the curve (AUC) was calculated using MedCalc software. Results indicated that highest gully erosion susceptibility is located on the center parts of the study area. According to validation results, the resulting map of areas susceptible to gully erosion obtained by ME model has a prediction accuracy of 90.7%. In addition, the results demonstrated soil texture, drainage density, lithology, and distance to streams are most important factors and their variable importance index (VII) 23, 18, 15.2, and 15.1 were obtained, respectively. However, altitude, distance from road, slope aspect, land use, topographic wetness index, and plan curvature have a less influence on gully erosion occurrence. Therefore, it was established in current study that the ME is promising of make accurate prediction in gully erosion susceptibility.

Urban street network modeling is known as one of the most important applications of GIS in the field of crisis management. The main objective of this research is to development of an urban smart network based on open street map (OSM) data, which can be used for crisis management. For this goal, after the collecting OSM data, the errors and miscalculations of this data were removed and data certainty was discovered to checking the quality of data. The roads network database of Tabriz municipal were developed for GIS based network analysis. In the following step, location of the Abresan subway station was compared with the optimal access road of assistance centers. For this purpose, we generated a spatial database, which including the fire station location, emergency medical centers, hospital, also information of municipal passage networks data. In addition the expressive data which associated with the route networks, legal speed of movement, direction of movement, the degree of crossover, the intersection in form of network analysis were also taken in account. The results indicated that the OSM data represent suitable functionality and high accuracy for developing urban smart networks, also according to the results the Abresan subway station is located in suitable and accessible area to assistance centers. Finally, the network distance of the nearest fire station and emergency medicine from the Abresan subway station were 2253 and 988 meters, respectively. The nearest distance of hospital from Abresan station is measured to be 1308 meters. The time accessibility for the passage of these paths for fire centers, emergency medical and hospital were 3, 2, and 1 minutes, respectively. According to results, we conclude that the results of this study are in total agreement with the national and worldwide standards

Segmentation is one of the basic method of the information extraction within the object-based image analysis (OBIA) approach. This process separates initial and main objects which are basis for OBIA. According to this, generating appropriate segments plays an important role for performing high accurate object-based classification. Within this research, we aimed to employ multi spectral and spatial satellite images including: IRS, Quick Bird and Spot5, for the purpose of image fusion and optimizing the scale of segmentation. For this to happen, Multi-resolution segmentation approach was performed based on various satellite images with different spatial resolution. As that, spatial information of Quick Bird and panchromatic band of IRS and Spot5 images, alongside spectral resolution of Spot5 (red band, especially) and Quick Bird, have a significant impact in increasing the contrast of image and improve the quality of segmentation, subsequently. The results of this research, indicate the importance of applying spatial information for optimizing the scale of segmentation. In addition, results confirmed that object based image fusion techniques can be employed for integrating different spatial resolution of satellite images. It also turned out that integrating lower spatial resolution with high spatial resolution is an efficient procedure for improving segmentation quality. The results of research, are great of importance for identifying different segmentation approach of object-based classification. The achieved results are also important for executive departments such as Natural resource, agriculture, etc. in light of presentation appropriate approach for rapid extraction of information from satellite image.

Developing of urban neighborhoods in line with the growth and expansion of the city and population growth has undergone many changes in time pass. Such change is more visible in cities environment with historical background. Thus, it can be seen well the difference between the traditional neighborhoods that no plan have been formed and after the formation, planning was done for them with new neighborhoods which were created and formed by plan.
Remote sensing is known as very effective technology for monitoring urban environments. There are several approaches for processing remote sensing satellite imagery such as pixel based and object based approaches. An Object Based Image Analysis (OBIA) is considered as one of the well-established techniques for processing satellite images when applied to monitoring cities environment. In despite of pixel based approach, the OBIA make use spectral information together with spatial characteristics of ground objects. Such specific ability allows to model ground objects effectively.
OBIA has gained prominence in the field of remote sensing over the last decade. It is credited to have the potential to overcome weaknesses associated with per-pixel analysis such as, for instance, disregarding geometric and contextual information. When it is used within the “geo-domain” or at scales which are related to earth “geo- centric” applications, it is in scientific literature often referred to as geographic object-based image analysis (GEOBIA). OBIA is a knowledge-driven approach in which a range of diagnostic features for a particular object can be integrated on the basis of expert knowledge. This approach aims to represent the content of a complex scene in a manner that best describes the imaged reality, by mimicking human perception. By incorporating spectral information (e.g., color) and spatial characteristics (e.g., size, shape), together with textural data and contextual information (e.g., association with neighboring objects), OBIA approaches the way that humans visually interpret the information on aerial photos and satellite images. OBIA techniques can be used in the review and observation of the difference and adaptive compare between the traditional and modern quarters pattern of the urban environments. In this regard, OBIA is known as effective and powerful image analysis processing method which leads to obtain high accuracy form satellite images. OBIA make use spatial and spectral information together by means of integration, segmentation and class modeling. Current research makes use of OBIA’s capabilities for modeling urban characteristics. The aim of this study is to compare textural- patterns of distressed and modern areas in Esfahan city by applying an object based approach. To achieve this goal, two types of urban neighborhoods namely Nokhajo and Mardavij were selected from distressed and modern areas respectively. The Quick Bird satellite images were acquired for year 2015. In order to perform object based approach, the object based image processing started off by applying multi resolution segmentation based on spatial and spectral patterns of each area. We used shape index, compactness for segmentation under specific scale parameters. The segmentation process was performed several times to obtain more accurate scale parameter. In order to extract the urban texture patterns, the rule based classification was performed by applying OBIA based algorithms and considering physical and spectral characteristics of urban objects. For this to happen, variety of OBIA techniques including: geometrical information, texture, compression ratio, irregular shapes and etc were employed to derive spatial patterns of each part. The outcome of these OBIA based algorithms were used to identify spatial patterns of distressed and modern zones. In doing so, after identifying the appropriate algorithms, fuzzy classification with nearest neighbor algorithm was applied for class modeling process. In terms of fuzzy rule based classification, the process was performed by employing fuzzy membership function as well as fuzzy operators. Membership functions allow you to define the relationship between feature values and the degree of membership to a class using fuzzy logic. By comparing the membership degree achieved from membership function, the “AND” operator was selected to be effective operator for object based fuzzy classification. Accordingly fuzzy rule based classification was performed by employing “AND” operator and applying textural, shape, geometric, statistical, spatial and spectral indices. In order to assess the accuracy of results, the accuracy assessment process was performed based on data which were gathered in field operation. The error matrix and kappa coefficient were derived by comparing the ground truth dataset and results of classifications. Results of this research indicated that OBIA is indeed effective method for modeling urban structure and classifying their based on characteristic of each item. According the results integration of spectral and spatial patterns leads to model urban structure effectively. Our research results also confirmed that textural algorithms leads to detect urban component effectively. Well organized road network system together with distribution of green space and normal density in building were identified as most important indicators in modern part of Esfahan. While, very high density in building, less of green space area with narrow road network systems were observed in distressed section of the study area. According to this statement OBIA represent very effective and powerful methodology for modeling urban structure by means of integration spectral and spatial characteristics. Results of this research are great of important for identifying and classifying urban textures patterns. The archived results can be used in rapid identification of texture patterns in urban environments and is a useful to a variety of urban planning studies. The proposed approach in this research will guide researchers/students to employ effective algorithms in OBIA which leads to obtain more accurate results. Results are also great of important for regional governmental departments such a Municipality of Esfahan for updating land use/cover maps which are base of any decision and planning.

Digital Elevation Models (DEMs) is one of the main geographical data models which form the basis of the different spatial analysis. DEM is known as fundamental data in for many modeling tasks. Nowadays, the results validation of GIS spatial analyzes, has become a major challenge in the world of GIS .The quality of a DEMis dependent upon a number of interrelated factors, including the methods of data acquisition, the nature of the input data, and the methods employed in generating the DEMs.Analysis of uncertainty in different fields, due to data quality and related issues such as error, uncertainty models, errors propagation, errors elimination and uncertainties in the data, are felt more than any other times. Of all these factors, data acquisition is the most critical one. Previous studies on DEM data acquisition have focused either on examination of generation method(s), or on case studies of accuracy testing. These studies are not adequate, however, for the purpose of understanding uncertainty (an indicator used to approximate the discrepancy between geographic data and the geographic reality that these data intend to represent) associated with DEM data and the propagation of this uncertainty through GIS based analyses. The development of strategies for identifying, quantifying, tracking, reducing, visualizing, and reporting uncertainty in DEM data are called for by the GIS community.
In order to apply uncertainty analysis on DEMs this study aimed to evaluate the error rate and uncertainty of elevation data obtained from SRTM and ASTER satellites. The objectives of this study are: (1) to understand the sources and reasons for uncertainty in DEMs produced by cartographic digitizing; (2) to develop methods for quantifying the uncertainty of DEMs using distributional measures and (3) to measure the uncertainty associated with DEMs and minimize the chances of error by manse of optimizing models. Quantifying uncertainty in DEMs requires comparison of the original elevations (e.g. elevations read from topographic maps) with the elevations in a DEM surface. Such a comparison results in height differences (or residuals) at the tested points to analysis the pattern of deviation between two sets of elevation data, conventional ways are to yield statistical expressions of the accuracy, such as the root mean square error, standard deviation, and mean. In fact, all statistical measures that are effective for describing a frequency distribution, including central tendency and dispersion measures, may be used, as long as various assumptions for specific methods are satisfied. Our research methodology includes several steps. The first step causing the statistical indices ME, STD and RMSE, the error rate of DTMs ​​ for obtaining the chances of error in ach model. It has to be mentioned that the main attraction of the RMSE lies in its easy computation and straightforward concept. However, this index is essentially a single global measure of deviations, thus incapable of accounting for spatial variation of errors over the interpolated surface. In order to obtain more accurate results, then uncertainty of data errors was also simulated by Monte Carlo method and error propagation pattern was extracted by interpolation of results.
The results of this step show that, the DEM derived from pair stereo ASTER despite having better spatial resolution, included more errors and practically lacking the details of DTM 30 meters. Finally, removing the error propagation pattern from DEMs, the secondary DEM was produced. By recalculating indicators describing the error and comparing these values with the initial values, the results indicate that, both DEMs show more accuracy after eliminating the error propagation pattern. TPI Index was used to determine the location of basin topography and the basin is divided into 6 classes and error rate in each class was calculated before and after the simulation. The results showed that, the error rates in all classes before and after the simulation in both DEMs were reduced. In terms of uncertainty analysis methods for DEMs, results of our research indicated that the RMSE methods alone is not sufficient for quantifying DEM uncertainty, because this measure rarely addresses the issue of distributional accuracy. To fully understand and quantify the DEM uncertainty, spatial accuracy measures, such accuracy surfaces, indices for spatial autocorrelation, and variograms, should be used results also indicated that Monet Carlo simulation is indeed sufficient methods for simulation error in DEMs. Results of this research are great of important for uncertainty analysis in domain of Geosciences and can be used for improving the accuracy of modeling in variety of applications.

Object based image analysis (OBIA) techniques are known as new methods in the domain of remote sensing image processing methods which are having significant potentialities for a variety of applications in pedology sciences. In this regard OBIA known as approach which integrate spectral and spatial pattern of satellite images for obtaining more accurate results. This approach has developed against of pixel based methods which are facing serious challenges due to the similarity index in spectral properties. The main objective of this study is to analyze soil salinity and apply an integrated approach of Fuzzy-object based for monitoring changes in soil characterize in the eastern area of Uremia Lake which has been under an environmental impact of lake drought. For this goal, Landsat 7 satellite images (ETM) of 2000 and Landsat 8 (OLI) satellite image, for 2015 was prepared. In doing so, first pre-processing steps on satellite images were established. Accordingly, soil salinity trends of agricultural croplands in eastern area were evaluated using fuzzy object based image analysis approach. For this goal, we employed the object based features including: NDVI, spectral indices, brightness and NDSI. The results of this research indicated that, significant progress in increasing salinity areas while the soil salinity rate measured up about 21.54 % (833/18 Km2). According to the results, the salty barren lands with positive slope 19.7 % represent a positive growth rate while it has increased from 15.3 % to 35.05 %. Results indicated the critical environmental situation for the agriculture croplands located in the Eastern area of Uremia Lake which requires the attention of decision makers and authorizations in the East Azerbaijan province.