جستجوی مقالات مرتبط با کلیدواژه « Satellite image » در نشریات گروه « جغرافیا »

Aims :

This research aimed to introduce an optimal method for water body extraction from multispectral satellite images.

Methodology : 

The applied research method is practical and based on the case study. In order to the extraction of water from Landsat 8 satellite images, the modified normalized difference water index along with three thresholding methods including the average method, Otsu’s algorithm, and zero-limit thresholding were applied. For evaluation of the results, the Sentinel 2 image was classified using the support vector machine method then the class of water was used as the reference to calculate the
confusion matrix and correlation coefficients.

Findings:

 The results showed that applying the zero-limit thresholding on modified normalized difference water index for water extraction from Landsat 8 satellite images is not suitable as it cannot identify shallow areas near the coastline. In contrast, Otsu’s thresholding method was able to accurately identify water and had good spatial matching with the reference data.

Conclusion :

Modified normalized difference water index method can successfully identify water areas on multispectral images but choosing the right threshold to separate water from land has a great impact on output accuracy. Most of the past research, use zero-limit thresholding, while the low efficiency of this thresholding method was proven in the current research. Otsu’s thresholding method, in addition to increasing the accuracy, does not require user intervention and uses image statistical information, so it is ideal for the automation of the process of water body extraction from satellite images

The Hedijan coastal plain around the Zohreh River is a hub for essential agriculture, fisheries, trade, and the military. Any change in the regime and territory of the Zohreh River will have significant economic, social, and security effects in the region. Due to the privation of the region and preserve the available resources and capital, it is necessary to carefully study the hydraulic parameters affecting the morphological changes and the complex pattern of the meandering of the Zohre River. In this study, considering the special conditions of this coastal plain, it has been tried to study geometric features such as curvature coefficient, curvature radius, central angle, and river arc length in three time periods by studying and examining satellite images in a GIS environment.

In this study, to study the morphological changes and meandering pattern of the Zohre River in the coastal plain of Hindijan, Google Earth satellite images from 2000 and 2014 were used. Based on these two images, the old route of the Zohreh River can be identified in the study area. In the next step, to investigate the meandering and morphological changes of the river, the route drawn from Google Earth entered the GIS environment. Using the ArcMap 10.2 software, to examine essential parameters in river morphological changes, circles with a suitable radius tangent to the existing curves in the river path were drawn. In the old route, 35 meanders were detected, and in the 2000 and 2014 routes, 21 meanders were identified and examined.

 Results :

In order to study the morphology and meandering pattern of the Zohre River in the study area, various parameters such as bending coefficient, central angle, arc length, half-wavelength, and radius of curvature were calculated. According to Wolfert (2001), four types of rivers, including direct, sinusoidal, meander, and more meander, have been introduced in terms of the degree of curvature of rivers. According to this criterion, the Zohre River can be placed in a meandering river in the study area. Also, the number of meander from previous years, 2000 and 2014, was 35, 21, and 21. The maximum radius of the meander of this river was 450.1, 661.8, and 632.3 m. The lengths of the arch were 1197, 1118.2, and 1154.6 m, respectively. There have been alternating positive and negative changes in the values of the central angles in all three study periods. The average central angle from the old route to 2000 has been declining, and from 2000 to 2014, it has been increasing. In the old route, based on the size of the central angles, 57.14% of the torsions are in the overdeveloped area, 14.30% of the torsions are in the developed inverted 11.42% are in the undeveloped area. 17.14% was the size of a cowchr('39')s horn, and no percentage was found for quasi-twisting curves. In 2000, based on the size of central angles, 42.86% of turns were over-developed, 14.30% of the turns were over-developed, and 23.80% of the turns were twisted. The average amount of radius in the old path increased from 450.1 to 66.81, and from 2000 to 2014, it decreased to 632.3. In total, this parameter has increased over three time periods. Finally, the obtained values for this parameter indicate that the river is meandering and the morphological changes in these three periods.

The results showed that significant changes have taken place and are taking place over the years under study. During the three times of periods, four significant shortcuts and other significant morphological changes occurred. It is predicted that in the place of the 18th meander at the location of the existing bottleneck, there will be a shortcut in the future, which will eliminate the existing meander and create new ones.Also, according to the Wolfert classification criterion based on the bending coefficient (1.5-2) and the Corneiss classification based on the central angle (158-296), the Zohreh River is located in the Hindijan Plain is in the category of meandering and the meander is overdeveloped.

Statement of the problem:

 Due to the over-concentration of population and fund, metropolises have been portended to the surrounding areas for their growth and expansion and merging the surrounding villages and lands cause various changes including land-use conversion. These changes have created environmental hazards and caused spatial instability, so it is not possible to be prepared for the issues and challenges ahead regardless of the mentioned problems, especially about land use conversion around metropolises.

This study, analyzing the spatial congestion of rural areas, aimed to identify lands which changed their use in Roudbar, Qasran, Shemiranat by satellite images during 1986, 2001 and 2016.

It is an applied research based on descriptive- analytical method. The data was gathered by library and observation method and remote sensing method was used to analyze land use conversion during 30-year period in rural areas in Roudbar Qasran.Satellite image analysis was performed via ENVI 5.3 and ARC MAP 10.2 softwares.

The findings show the decrease of the area of mountainous and wasteland lands (13.06 %) versus the increase of the area of gardens (2.12 %), pasture (7.55 %) and constructed lands (3.39 %). Also, these findings based on the Markov Chain -CA showed that, the area of green lands (gardens and meadows), wastelands and mountainous will be decreased but constructed ones will be increased until 2031 if the current trend is continued. Results present the changes in land cover of this case study created land-use incompatibility with the environment caused by urban demand, urban sprawling, weakness of lawful control, and achieving greater profit. These kinds of changes eventuated in spatial congestion in addition to the economic and social impacts in Roudbar Qasran.

Innovation:

 In this study, spatial congestion of created land-use conversion is noticed whilst land-use conversion is considered and specified. Besides, it has been attempted to outline the future status of the region by predicting land cover and use conversion.

Different image fusion methodsprimarily seek to improve spectral and spatial content of the final result. However, the final fused image often suffers from some spectral distortions. Moreover, some image fusion methods are too slow. Image fusion using IHS transformation is known as a fast image fusion method. Unfortunately, the resulting image fused with IHS also suffers from some spectral distortions and therefore several versions of this method have been developed. Defining weights of each band for generation of the intensity component is one of the main problems discussed in the literature. Spectral response curves are used as one of the major sources for defining relative weight of each spectral band. Scientific reports indicate that spectral response curves can improve the quality of the final fused image. Weights of each individual band is often calculated based on the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands. But, information like the non-overlapping areas of the curves are also considered to play a role in the calculation of the weights. The present comparative studyinvestigatesthe potential of using this information.  

A multi-spectral Geoeye-1 satellite image with 2 meter spatial resolution, four spectral bands and the corresponding panchromatic band with a spatial resolution of0.5 meter were used to test the idea. Seven variants of the FastIHS fusion method have been developed based on different approaches of intensity component estimation using the information obtained from spectral response curves. The test methods have been compared with the original FastIHS image fusion method. The only difference of these methods was in the way they calculate the weights of each band. The seven tested methods included: 1) ratio of the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands and multispectral response curves, 2) the ratio of the area of the multispectral band’s response curves and the area of the panchromatic band’s response curve, 3) the inverse of the distance between the central wavelength of the panchromatic and multispectral response curves, 4) the ratio of the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands and the area of the panchromatic response curve, 5) the ratio of the non-overlapping area of the panchromatic and multi-spectral response curves and the area of the multispectral response curves, 6) ratio of the overlapping area of both panchromatic and multi-spectral response curves and the area of the panchromatic response curve minus the area of the multispectral response curves, 7) the ratio of the panchromatic and multispectral response curves’non-overlapping area and the area of the multispectral response curves multiplied by the ratio of the area of the multispectral response curve and the area of the overlapping regions of the panchromatic and multispectral response curves.

In order to evaluate the fused images, four criteria were used, including ERGAS, RMSE, Correlation Coefficient, and edge correlation with panchromatic band. In order to calculate edge correlation Coefficient, a Sobel filter was applied on the panchromatic and fused bands. Then, the correlation coefficient between the individual filtered spectral bands and the filtered panchromatic bands was calculated. All eight methods were ranked based on the four evaluation criteria. Because of the inconsistencies in the ranking results, the four criteria have been merged and a new ranking method was obtained based on the final results. Based on this final ranking, the fifth method is in the first rank and the second method is in the eighth rank.  Therefore, the sorted list of the methods based on the final ranking is: IHS5, IHS3, IHS6, IHS1, IHS4, IHS7, FastIHS, and IHS2. As the ranking shows, almost all tested methods have a higher level of accuracy as compared to the base method (FastIHS).  

The results indicates that using the information obtained from the spectral response curves can improve the final results of the FastIHS image fusion. This information can improvethe fusion speed and reduce spectral distortions of the final fused image. Unfortunately, the spectral feature of the data is preserved and the total number of detected edges is decreased. Spectral response curves are directly tied with the physics of the imaging, therefore using their information can produce some natural fused images with better visualization and enhanced spatial contents.

 Nowadays, most land use changes occur in urban areas, due to the growing population in cities and villages and the desire to live in urban areas. Urban rapid development in recent decades has led to large changes in the cities around and has had many environmental impacts. In this research, we evaluated land use changes and urban development simulation using satellite imagery and with neural network model and Markov chain auto-cells in Rasht city. For this purpose, Landsat satellite imageries were used from 2000, 2008 and 2017. After preprocessing the image and selecting the best band combination, the images were classified using the neural network method. Then the classified images were entered into the land changes model and predicted modeling output maps using the CA-MARCOVE method for 2027. The results obtained between 2000 and 2017 indicate that the area changes in urban lands, rice fields and forests were 9041.88, 7841.33 and 55.78 hectares, respectively, which were positive in Rasht city and negative in rice fields and forest and the projection map for 2027 with the CA-MARCOVE method also indicated a significant increase in urban use of 14105.04 hectares in the coming years. The results of this study indicate that the current trend of land use changes will lead to adverse environmental impacts and, consequently, irreversible socio-economic damage. Therefore, it is essential for the region planning and management unit to adopt a comprehensive approach to conduct future environmental problems and to curb the horizontal development of settlements in the area.

 The importance of land use as a key component in natural resource management, environmental change and a dynamic and affecting biological condition requires accurate quantitative and qualitative information to be provided and varied in the short term. (Triantakonstantis & Stathakis, 2015: 194; Akbari and Rezaei, 1397: 94). In the meantime, remote sensing data provide valuable multi-temporal data on the processes and patterns of land cover change and land use, and help to develop an understanding of the impact of human activities on natural resources. (Esfahanzadeh, 2016: 34). Urban development is a global phenomenon and one of the most important phenomena that has a great impact on both nature and human environment due to its many ecological and socio-economic aspects. The city of Rasht, like other urban areas, has undergone numerous changes in agricultural and horticultural uses and residential uses over many years. In this study, satellite imagery is used to evaluate land use changes and simulate urban development in the period 2000 to 2017, so that the results of the research can be of great help in micro planning. And provided the experts with a great deal to prevent environmental degradation.

In this study, using satellite imagery of land use changes and simulation of growth and development of Rasht city using neural network model and Markov chain automated cells. Landsat 2000, 2008 and 2017 images were used for this purpose. After image preprocessing and selecting the best band composition, the images were classified by neural network method. Selected classes include 7 classes, forest, man-made areas, paddy fields, sand, sea, ponds and vacant lots. The digital layers used to classify and apply Markov auto cells include: GPS capture points for image classification and accuracy assessment, proximity to main roads, river avoidance, distance from surrounding villages, slope And height. Then, the classified images were entered into the land change modeler and the model outputs were predicted by CA-MARCOVE for 2027.

 The results show that out of the total area of ​​man-made area increased, 3612 hectares were converted to paddy fields and 1 hectare to water use, 2138 hectares were made to man-made areas, 1646 hectares to the sea and 24 hectares to the Bayer area. In the present study, Markov chains and automated cell fusion methods were used to predict land use changes in Rasht. To do this using IDRISI software, three series of land use maps were prepared for the years 2000 to 2017. Finally, based on the factors involved in urban land use changes in the study area, the inputs of the automated cell model were selected as Table (1). The prediction is a function of the model inputs. Table 1: Input variables in the automated cell simulation model Row Variables affecting land use 1 near the main ways 2 distance from the river 3 elevation 4 slopes 5 distance from surrounding villages Source: Authors' Studies, 2018        Then, by calculating the Kramer coefficient in the model, one can obtain an estimate of the correlation of each variable with the existing land uses and hence its ability to predict land use changes. By repeating 10,000 times of trial and error in the multilayer neural network, calibration and conversion potential maps were generated in the images from 2000 to 2008 and 2008 to 2017. Following the acceptable accuracy of the model for prediction, using the CA-Markov model, the 2027 User Prediction Map was prepared for the study area shown in Figure (1).   Figure 1: Land use forecasting map of Rasht city using CA-Markov for 2027, Source: Research Findings, 2018.     Table 2. Land use area of 2027 using CA-Markov User class 2027 forecast area (ha) Jungle 0/13 Sands 0/9639 Water 974/26 rice field 4797/82 Man-made areas 14105/04 Wasteland 1599/03 Sea 6/39          As can be seen from Table 2, the area of most land uses, except for man-made areas and the sea, declined as forest land use from 1031/95, sandy land from 15/42, water from 22/333, paddy fields from 88/12/85 and wasteland from / 66. 3629 hectares decreased in 2000 to 0.13, 0.96, 97.26, 47.72 and 15.03 ha in 2027, respectively. In contrast, the land use area of the man-made areas increased sharply to 14105.04 hectares, while sea use increased by 6.39 hectares.

The use of Landsat satellite imagery is useful in terms of availability, duplicate coverage and lower cost of source data, as well as determining the extent of land cover changes and land use prediction using the models used in Research can be a good alternative to costly methods of discovering change in the shortest time possible. Other objectives of this study were to use satellite imagery and LCM tools to detect changes occurring in the region during the study years 2000–2008 and 2008–2017. Therefore, multi-layer neural network method was used to detect the changes. Examination of changes from 2000 to 2008 showed an increase in urban class area, with the city area increasing from 6793.91 hectares in 2000 to 8940.41 hectares in 2008. The highest increase in urban area was observed from 2008 to 2017 after image classification. During the study periods, paddy, forest and wilderness land use has been steadily declining, and vegetation use has had a protective role as urban land use. In this study, the prediction of physical growth in the city of Rasht in the coming years (2027) was investigated. This is how the 2017 forecast map was first derived using the CA-MARKOV model. Comparison of the results of the prediction map with that of the image classification showed high accuracy. The 2027 forecast map also shows a significant increase in urban land use by 14105.04 hectares in the coming years. Considering the results, it is possible to study changes in vegetation cover and to prevent its unnecessary changes and transformations. Because vegetation plays an important role in reducing environmental issues in urban areas. In contrast, the disappearance of vegetation causes severe environmental crises in relation to the rapid growth of urbanization and the formation of the thermal island of the city. As a result, vegetation is considered as an indicator of environmental sustainability in urban communities. Therefore, proper vegetation management is considered as an integral part of any sustainable urban development. Since degradation of vegetation and rising surface temperature can have adverse effects on the environment, identifying environmental sensitivities (crises) caused by this factor is essential as it can play an important role in urban development management.

A dust storm is a common environmental hazards in the world, especially in arid and semiarid regions of the world. The incidence of dust has been higher in recent decades in Iran, especially in Khuzestan Province, which has become an environmental crisis in recent years. The occurrence of dust in Khuzestan Province is due to natural conditions such as successive droughts and loss of moisture sources as well as human factors such as drying of the water zones and the reduction of river water discharge due to the dam. Various methods have been developed to identify dust sources, most notably the use of remote sensing techniques. Accordingly, for the detection of dust storms, a number of dust indices have been developed based on MODIS images, the most effective of which is the use of the BADI. Therefore, in this study, three consecutive dusts in winter and spring of 2018 in Khuzestan Province were studied through the BADI.

The study area includes the lands west of Khuzestan Province between north latitudes of 32°8'.62" to 30°57'.26" and east longitudes of 47°43'.21" to 48°32'.29". In terms of political divisions, this area has two cities, shush city in the north, and the Dasht Azadegan is in the south, which has a common border with Iraq. Karkheh River is the most important river in the area which is flowing through Khuzestan plain to Hoor Al-Azim Wetland.

In this study, the BADI index was used for desert dust   detection. First, to use this index, Modis 1B satellite images were obtained from the NOAA Archive (https:// ladsweb. modaps. eosdis.nasa.gov/search/).
Subsequently, geometric corrections were made to them and using calibrated images, the brightness temperatures were calculated in bands 20, 31 and 32 of Modis images. Also, Meteosat 8 satellite images were used to specify the dust source regions. In addition, Sentinel 3 satellite images were overlaid with Google Earth satellite images to determine the exact locations of dust particles.

According to BADI index extraction, in the dust of 20/1/2018 it was observed that the main source of dust is in the western part of Shush City and the dry bed of Huralazim wetland in Khuzestan Province. These conditions cause severe dusting due to local topography (flat area and non-vegetation lands). In addition, conditions for dust formation have been provided due to development plans and the elimination of local morphology as well as the loss of soil moisture (land drainage) in the south and west of Khuzestan Province. In the next dust storm that occurred about a month later in the winter (20/2/2018), the results show that erosion in the Hoor Al-Azim Wetland and the desert areas in the north of Susangerd can be considered as the main source of dust particles. Therefore, the amount of soil erosion after dust based on field operations and the measurement of fixed rod height indicates approximately one centimeter of height difference between the two successive dusts. In the third dust on 23/4/2018, a very large dust with high mass, several density cores were seen which would begin in the desert regions of Saudi Arabia and extend to Kuwait and Southwestern Iran and also Eastern Iraq.

Meteosat 8 satellite images were used at different times of the day to identify the high dust areas. The spatial area of dust particle loading was determined from the beginning to the time of the dust mass expansion. In the dust on 20/1/2018, first the inner sources of the Dust were activated due to the cyclonic motion of the air mass. In the dust of 19/2/2018, it can be stated that the soil erosion of the Hoor Al-Azim bed and the surrounding areas such as Gofer and arid lands of southern Bostan are the main cause of this phenomenon. In the next dust on 23/4/2018 the main sources of dust are the deserts of Saudi Arabia (Kuwait and Saudi Arabia) and the deserts of Northern Najaf and Southern Karbala in Iraq. Then, Sentinel 3 satellite images were used to determine more precisely the dust loading points in the study areas. Therefore, the results illustrated had a significant role in increasing the volume and concentration of dust in winter the eastern areas of Hoor Al-Azim (Gofer and Southwest Bostan) and the western deserts of Shush. Also, based on the detection of dust dumping sites, the results indicated that three areas in the west of Riyadh and east of Medina (Al-Dhakari, Al-Raqqa and Al-Bujadiyah) play an important role in the formation of the dust mass.

The purpose of this study was to monitor the dust and extract dust sources of West Khuzestan using the BADI index. Accordingly, in the three dusts investigated in winter and spring of the 2018, different areas within and outside the country supply dust particles in the west of Khuzestan Province. However, it is important to note that internal sources of dust due to proximity to cities and villages in Western Khuzestan are capable of creating dust storms with a radius of less than 1000 m, which makes them more vulnerable.

One of the most important applications of satellite images is the use of it to determine and estimate the surface temperature of the earth. To investigate this, the Landsat 8 series was used to calculate and estimate the air temperature in the city of Isfahan and surrounding area. The surface of the earth's surface temperature (LST) was calculated using the Subrino window algorithm. The results showed that the use of this method on satellite images of Isfahan region, which covers a variety of urban areas, green spaces, and major industrial areas, shows the ability of this model and the proportion of these images for this purpose. The comparison of a temperature measured on the image and the temperature calculated in two of Ozone-metric and Airport stations in Isfahan shows a range of changes of about 4. 5 degrees Celsius. However, the calculated temperature in four days of the year is more than the measured temperature at a depth of 5 cm in the soil at the site of the two Ozone-metric and Isfahan Airport stations and this value is at a maximum of 4. 25 degrees Celsius above the station's temperature. The correlation relations between the LST surface temperature map and the normalized vegetation difference index (NDVI) at 9100 pixels of the area code with a correlation coefficient of 0. 445 are confirmed at a confidence level of 99% Which confirmed the validity of the method used to estimate the surface temperature.

Given the population growth and increasing urbanization, the occurrence of natural disasters like earthquake can cause heavy losses and damages and interrupt the development of cities and countries. Among these disasters, the earthquakeisof great importance due to its unpredictability and high frequency in relation to other events, as well as its location on the earthquake belt. According to the last year's estimate, Iran has been one of the 6 countries with high mortality rates in earthquakes. Therefore, finding a way to minimize the losses can be critical. Crisis managers need quick information from the affected area after the earthquake to minimize the fatalities and financiallosses. The destruction map is one of the information that helps crisis managers. These maps show the destructed buildings or roads with their degree of destruction. With these maps, the destructed buildings and roads can be found quickly. Many methods are used to prepare the destruction maps, such as aerial/satellite images, LiDAR data, etc. These information can be used to determine the destructed buildings automatically or by visual interpretation. Visual interpretation for determining the degree of destruction requires operator. Although this method has high accuracy, it is less considered because it is time consuming and needs specialists to interpret the data. Therefore, researchers have focused on automated processing techniques for the production of the destruction maps. Various automatic change detection techniques are used to evaluate the destruction resulting from earthquakeby comparing satellite images in two pre and post-earthquake periods based on satellite and aerial images. LiDARdata is one of the most important sources of information to determine destructed buildings with high accuracy and speed. LiDAR data provides the possibility of 3-D demonstration of the destructed region. This information is a great help in preparing the destruction map automatically. The recent expansion of the LIDAR technology is due to the high spatial power of these data. As a result, many researchers have focused on developing an automatic destruction map using Lidardata.Although considering the textural information from the Lidar data, like homogeneity in the destructed region can be effective in distinguishing between the destroyedand undestroyed buildings.
In this paper, a new algorithm is proposed to prepare the destruction map after the earthquake by integratingthe post-event high resolution satellite images and post-event LiDAR data. In the proposed method, different textural descriptors of the LiDAR image and data are extractedafter the necessary preprocessing on the satellite image andLiDAR data after the earthquake. In the next step, using the layer of buildings extracted from the map,the areas of the buildings are extracted from the satellite image and LiDAR data, as well as the satellite image descriptors and LiDARdata.Then, the textural descriptorsextracted from the satellite image and LiDAR data are combinedtogether. After that, the points inside this area are categorized into two classes of "debris" and "intact" by the method of support vector machine. Finally, based on the area of the debris class of each building, destroyed and undestroyed buildings were identified by taking a threshold limit into consideration. This algorithm is executed on each building from the destruction part to produce the final destruction map In order to evaluate the proposed method,the data set was selected from the city of Port-au-Prince, the capital of Haiti, after the 2010 earthquake. According to the USGS reports, 97,294 buildings were damaged and 188,383 were destroyed in Port-au-Prince and most of the southern parts of Haiti. Furthermore, reports show that 222,570 people were killed, 300,000 were injured, and 1.3 million people were displaced. The sample data set include post-event WorldView II satellite images as well as post-event LiDAR data. The WorldView II satellite took images on January, 16 2010, and the LiDAR date was also obtained from this topography website. Obtaining LiDAR data is from January, 21 2010 to January, 27 2010. The vector map of the selected test area was generated in ArcGIS environment. By evaluating the proposed method and using the existing data, the overall accuracy of 97% and the Kappa coefficient of 92% were obtained which proved the reliability of this technique. In this paper, a new method for the generation of damage map based on the integration of high resolution satellite images and LiDAR data was proposed. The results show the ability of this method in generating destruction maps based on the satellite images with high resolution and LiDAR data. In comparing similar studies, the results are satisfactory. The selection of the appropriate descriptors, correct training data, the elimination of non-building areas from the sample data, the integration of satellite images and LiDARdate can be known as the reason behind obtaining these results.

Urban heat island refers to an area where the temperature is higher than of the surrounding environment. It is referred to as an island since the temperature of a point or an area of the earth's surface (the city) is more than of the surrounding environment, which mostly has, a homogenous and uniform temperature. Therefore, this island, which can be separated and distinguished from the surrounding environment of the city, is named the city of heat island. Urban heat island can be due to growing population and its concentration in the urban centers, and consequently the increase in air pollution. Changes in land use can be regarded as one of the underlying causes of the occurrence of urban heat island. For example, using satellite images to study the heat island in the city of Mashhad, by Mousavi Bayeghi et al. (2012) showed that along with the land use change from the garden level to the city one, there has been a rise in the temperature. Frequent pollution in the city of Isfahan in recent years has led to the development of some hypotheses assuming some relationship between urban heat island and pollution. In this regard, the growing population density and the vertical expansion of the city, as well as urban development, have resulted in the development of the idea that the study of urban heat island in relation to air pollution can be very important as it can help cope with and decrease the degree of urban heat island. With this objective in mind, this study is designed to address this important concern.
The growing intensity of the thermal island due to air pollution in addition to the undesirable effects on human health can seriously damage the valuable historical works in the popular tourist city of Isfahan. It is hoped that the results of this research could help reduce the effects of the thermal island, leading to the development a better living environment in the city of Isfahan accordingly. Methods based on Satellite images can be regarded as one of the best ones for studying the thermal island; in fact, such methods have been used in many studies inside and outside the country. In this research, the thermal island in the city of Isfahan has also been analyzed using satellite imagery. In order to study urban heat island in the city of Isfahan, a series of landsat 8 satellite images with good resolution were used. This satellite conducts imaging through 11 spectral bands. The bands 1 to 9, which are known as OLI, carry out imaging in color and NIR spectra. The bands 10 and 11 are well-known as TIRS thermal bands. They do imaging within long infrared waves. In this study, the bands 4, representing red, and 5, representing the near-infrared, and the thermal bands 10 and 11 were used to study the urban island in the city of Isfahan. The data related to AQI air quality index was used to check days with air pollution. Due to the restrictions over the passage of satellite within a period of 16 days over the city of Isfahan, the limited number of days with extremely high pollution in the city, and the non-conformity between polluted days and the time of satellite passage, images of three days representing the very unhealthy (the red condition), unhealthy (the orange condition), and healthy (yellow condition) air were thus used. In this study, Subrino Split-Window algorithm was used as the technique to calculate the Earth's surface temperature. This method is based on the calculation of brightness temperature of the bands 10 and 11 as well as the vegetation index of the bands 4 and 5. For this purpose, Arc_GIS10.3 software was used to compute the Earth's surface temperature and the air temperature of the city. ENVI software was also used for the atmospheric correction. Isfahan, due to climatic conditions and a flat topography, has a relatively quiet atmosphere throughout the year. Although Mount Soffeh is located in the south-west of the city, this mountain can only help reduce the speed of westward winds over the city as it is a single peak with a low height. In addition, the vertical expansion of buildings in all parts of the city causes the wind speed to be slow due to the increased friction. High buildings with greater height than the other points, especially in the western part of Isfahan, have led to the considerable reduction of wind speed, which is an important parameter. On the other hand, population growth and its concentration in the central part of the city have increased the intensity of urban heat island. To address the possible relationship between air pollution and urban heat island, the date of January 22nd, 2014 was chosen for investigation. On this day, the temperature calculated from satellite images of the city was within the range of 3 and 17 degrees Celsius. In general, the temperature fluctuated between 5 and 9 ° C in most parts of the city, and the 7° C isotherm bypassed most parts of the city.
The correlation coefficient between vegetation index (NDVI) and land surface temperature, LST, was equal to the 0/37064, which was significant at the confidence level of 99%. Given that the urban heat island is based on urban atmospheric temperature, not the temperature of emitting or reflecting surfaces, some empirical relationships were used to convert surface temperature to air temperature. Accordingly, it was found that the air temperature in the city fluctuated between +5.5 and -1.8 ° C on January 22nd, 2014.
The comparison of the city thermal island map with the map of pollution distribution confirmed the increase in the city temperature as a result of urban air pollution.
That is, the highest concentration of pollution in the East and North East of related to the areas with the higher air temperature. The observed maximum temperature shown by the color red in the map was 13.8 degrees and this was 10.6 degrees in the suburbs. Temperature difference between the city heat-island and suburban areas on this days reached to 3.2 degrees Celsius.
The highest rates of pollution were observed in the stations located in the Eastern Isfahan, including the Ahmadabad SQ and Kharazi Highway. Correlation analysis between the earth's surface temperature map and the map of the distribution of air quality index in the city of Isfahan showed a correlation coefficient of 0.242 at 99% confidence level on January 22nd, 2014. On this day, air pollution AQI index was between 117 to 169, and the temperature ranged from 8.5 to 13.7 degrees Celsius. The city of Isfahan, due to stable climatic conditions at most times of the year and the spread of pollution, is one of the most polluted cities in Iran. The increased intensity of thermal island is one consequence of this phenomenon. In this study, Landsat 8 satellite images with a high resolution were used to estimate the surface temperature of the Earth. The temperature calculated by the model was compared with the soil temperature at the meteorological stations. A slight difference between the temperature in soil at the depth of 5 centimeters and the LST obtained through satellite images, which ranged from a maximum of 6 to a minimum of 1.8, showed that the Subrino's Split Window method could be suitable for estimating the surface temperature of the earth. Finally, the urban heat island in the city of Isfahan was obtained based on the statistical relationships between the land surface temperature (LST) and the air temperature. The analysis of topographic profile related to thermal island temperature showed the promotion of the thermal island in the city as compared with the surrounding area. That is, the temperature inside the city was 7.3 degrees and 9.5 degrees Celsius warmer than the surrounding area respectively, which could show the city's urban heat island clearly. The difference between the temperature of the city's thermal island and the city's surrounding temperature was higher at the time of the maximum pollution. The comparison of the temperature maps of Isfahan with the pollution distribution maps revealed the maximum correlation between temperature and the pollutant areas. The correlation coefficient between the pollution distribution map and the urban temperature map was positive and significant. In more polluted days, the linear relationship between the pollution increase and urban air temperature increase was stronger. As a result of the increased air pollution, the intensity of the thermal island of the city was aggravated. The study of air pollution distribution in the city of Isfahan showed that the eastern regions had more contamination. This was influenced by the urban topography. This is because the southern and western parts of Isfahan are at a higher elevation due to the presence of Mount Soffeh, and the pollution due to mass weight tends to subside in the lower northern and eastern regions of Isfahan. On the other hand, pollution is more concentrated in the eastern regions of the city due to the slow prevailing western winds. The comparison of the thermal map of the thermal Island in the city of Isfahan to the pollution distribution map confirmed the effect of pollution on the increase of urban air temperature. Therefore, the most pollution was concentrated in the eastern and northeastern Isfahan, which were the areas with the higher air temperature. The observed maximum temperature reached to 13.8 degrees, whereas it was 10.6 degrees in the suburbs of Isfahan.

River bank erosion is one of the major sources of sediment for many rivers around the world. The aim of this study was to identify erodible riches in Bashar river because the role of this river's in development of Kohgiluyeh and Boyerahmad province. In this study, using Satellite imagery, aerial photos and field survey from 1975 to 2015, the riverbank migration patterns for period of 40 years were investigated. After creating database of recorded data Bashar river, bank lines in different years digitized. Then, by comparing successive changes in bank line position, patterns of erosion and accretion was determined. Due to dischargr and Geological factores effectes on river pattern, these factors were evaluated. The Result of for identify erodible riches in Bashar river using GPS and field surveys, verified. The area of bank erosion and accretion by comparing sequential changes in banklines position determined. For short-term analysis, the migration rate from one any image to the next image is estimated. For long-term analysis, the migration rates are based on the difference between the 1975 image as the reference, and subsequent images. The results indicate that the erosion– accretion patterns in the sub aerial and subaqueous areas of the beshar river have changed significantly since 1994. For the short-term analysis, the highest erosion and accretion amounts are 4.7 and 7 ha from 1975-1984 and 1984-1990, respectively. the lowest erosion and accretion amounts are 2.6 and 2.3 ha from 2008-2011, respectively. The highest rate of bank erosion in different reaches equal to 9.6 and 4.6 ha/y in reachs of fifth and second bserverd. Also rate of bank accretion in fifth and second reachs equal to 9.6 and 4.6 ha/y observerd. the lowest erosion and accretion rate in reach of seven equaled 11.7 and 6.4 ha/y accounted, respectively. The correlation coefficient the between erosion and discharge and annual discharge averagelly for the short-term analysis observed 0.54 and 0.44, respectively.

The aim of this research is exploring the impact of changes in runoff and sediment of the upstream catchment on the changes in the area and amount of canopy in Gabric-Jask mangrove. Hence, hydro-climatological parameters including amount of precipitation, discharge, and sediment in Gabric catchment during the statistical period of 1993 to 2010 were selected and the annual standardized index related to each parameter respectively including SRI, SPI, and SSI were calculated. Also, satellite images of Landsat of TM and ETM sensors were used in order to explore the changes in the canopy by the use of NDVI and the area was prepared and its calculations were done. Cross correlation function and moving average of 3, 5, and 7 years were used to explore the relation between hydro-climatologic factors and the area and canopy of mangrove. Based on the results related to the canopy changes there was two increasing periods and one decreasing period and the area changes had an increasing trend during the study statistical period. Results of exploring the relation between incoming precipitation, discharge, and sediment to the mangrove forests show that NDVI changes and the area of mangrove forests have a significant relationship with three years moving average of SPI, SRI, and SSI indices with the correlation coefficients of 0.6, 0.8, and 0.56, respectively. Finally, results showed an increase in the rate of NDVI index with an amount of 0.33 and an increase in the area of Gabric-Jask mangrove forests with an amount of 0.22 ha/year.

A descriptor is computed on a local region around a feature point and is used to characterize and compare the features. Various descriptors have been proposed in the literature which have different properties and performance in different image data. Evaluation of the local feature descriptor is important to identify the strengths and weaknesses of each algorithm in different applications. In this paper a performance evaluation of the state of the art in local descriptors is performed on a set of satellite images under varying imaging conditions. Ten descriptors are included, which are spin image (SI), shape context (SC), SIFT, PIIFD, SURF, DAISY, LSS, LBP, LIOP and BRISK. 80 satellite image pairs in three groups including simulated images, multi-temporal, and multi sensor images are used as data set and descriptors are evaluated using four evaluation criteria including Recall, Precision, positional accuracy and speed. The evaluation results indicate that there does not exist one descriptor which outperforms the other descriptor for all scene types and all types of transformations, but in average DAISY and SIFT show the best performance.

Lake Urmia in the northwestern corner of Iran is one of the largest permanent hyper saline lakes in the world and the largest lake in the Middle East. It extends as much as 140 km from north to south and is as wide as 85 km east to west during high water periods. Qualitative and quantitative degradation of water resources is one of the major challenges in the way of sustainable development، Baghvand et al.، 2010; Nasrabadi et al.، 2010a; Nasrabadi et al.، 2010b; Rowshan et al.، 2007; Serbaji et al.، 2012; Guinder et al.، 2012; Li et al.، 2012; Siddiqui، 2011; Pei et al.، 2011; Ying et al.، 2011،. Features and phenomena in the earth’s surface have been changed over time; the lakes as one of these features and due to having a closed environment are not considered as an exception. Due to climatic changes such as reduced rainfall، increased temperature and also uncontrolled use of surface water resources in watershed areas، distinguished changes are exposed، Sadatipour et al.، 2012; Sandeep et al.، 2012; Madani et al.، 2012; Mirbegheri et al.، 2012; Arias et al.، 2012،. Monitoring such changes should be considered as an important issue in the national and regional development and natural resource management. Currently، monitoring the coastal areas and extraction of water at different intervals is regarded as an infrastructural research interest due to the significance of coastal zone management and dynamic nature e of such sensitive ecological environments، Reveshty and Maruyama، 2010; Nasrabadi et al.، 2009; Pamer et al.، 2011; Kavian et al.، 2011; Karbassi et al.، 2011; Mortazavi and Sharifian، 2011; Lei et al.، 2011; Hudak، 2011،. Urmia lake is the twentieth largest lake of the world and at the same time one of the most unique and invaluable global water ecosystems، Karbassi، et al.، 2010; Ahmadi et al.، 2011; Ahmadzadeh Kokya et al.، 2011; Farzin et al.، 2012، . The lake’s surface area has been estimated to have been as large as 5620 km2 but since 1989 it has generally been declining and was estimated from satellite data to be only 2032 km2 in August of 2011، Land sat data،. The decline is generally blamed on a combination of drought، increased water diversion for irrigated agriculture within the lake’s watershed and mismanagement. The main sight of this study are to Assessment and analyze of Urmia water levels fluctuation and increasing salt area، to model the lake surface fluctuation regime by linking the observed data to satellite data in the north west of Iran. Lake Urmia is an endorheic or terminal lake meaning that water leaves the lake only by evaporation. As is generally the case، this leads to a saltwater body and in the case of Lake Urmia، salinity is quite high. The lake is bounded between 37°5´ -38°16´ latitudes and 45°01´ - 46°longitudes at 1275 m above sea level. Its surface area ranges from4750 to 6100 km2 and the average and greatest depths account for 6 and 16 m، respectively. In order to study the fluctuations of Urmia Lake surface area، multi temporal Land sat Images، ETM، TM sensors، over a 23-year period، 1989 to 2011، were used. In the present study، the coastlines extraction for each year was done in two major steps using the ERADS software & ARC GIS. Firstly، geometric and radiometric corrections as well as different filters on the selected images were applied to make the spectral difference of objects more clear. Secondly، supervised classification method has been used to extract coastlines. To use the supervised classification، the preparation of the training area from the lake surface has been carried out. The reflection values in these areas have been generalized to the entire lake surface using the software and thus، the border between the lake and surrounding areas has been set precisely. Finally، through the algorithm for conversion of two vectors، the coastline limits have been drawn for different years. To carry out the analysis of images the ARC GIS software has been used. Satellite altimeter data measured the lake’s level in 1989 to be at its highest level of any time in the past 30 years. This is in agreement with Hassanzadeh and others، 2011، who state a measured water level of roughly 1 278 m above sea level for the same time. Both measures show a steady decline from that year forward with the most recent satellite altimeter data indicating a drop of approximately 7/40 meters between 1989 and 2011. Because Lake Urmia is a terminal lake with no significant water outflow the only way water leaves the lake is by evaporation. Therefore، if the lake declines it is either by increased evaporation or a decrease in water coming into the system. The Zarrineh Rood River is the largest of the thirteen main rivers discharging into Lake Urmia which are the source of the majority of the Lake’s water budget. Additional input comes from rainfall directly over the lake، floodwater from the immediate watershed and a very small fraction from groundwater flow. The main objective of this study was to assess lake water levels fluctuation using satellite images and geographic information system. To achieve this goal along with field observations and the satellite images of Land sat Multi-spectral images of the years، 2011-1989، sensors، TM، ETM +،. were used and processed. Based on the results achieved during the 23-year-old when the final models، an indication average height Lake water level to the level of 7/40 meter and the decrease in 3588 square kilometers of surface area of Lake water and land turned into the arid soil and sediment salts salt in the last few decades. Most changes due to water loss height، especially in the South East and the east coast of the lake is visible. The rise of water level fluctuations during the study period was significantly accelerated change and backward along the coastal lines 1 to 8/5 km coastline is.

Study the land use and land cover changes and exploring spatial pattern trends is essential for better understanding of landscape dynamics during the time as well as land-use planning and reaching conservation goals. Determining the Land use and land cover changes in the region can help managers to perform appropriate management. Nowadays، mine is one of the current interferences in protected areas. Mines change natural landscape during processes of discovery، extraction and production. All activities of the mines are limited with permissible operation boundaries by Ministry of Industries and Mines but always consistent and accurate monitoring is necessary to limit mining activities in permissible boundaries. Such monitoring has a great importance when the under operation mines are located in the areas which are under the preservation of Environment Organization. In this research، the monitoring objective was the permissible boundaries in the area and land use map was used as a basis for comparison. Satellite images of IRS-P6 LISS-III acquired in 2006 were used to generate land use map. After geometric and topographic correction، land use map was produced using hybrid classification method. The overall accuracy of generated map was 87%. According to the documents of Ministry of Industries and Mines and National Geosciences Database of Iran، the permissible boundaries were drawn on topographic map at a scale of 1:50000 and were digitized in polygon type layer in GIS. The result of conformity of permissible boundaries and land use map shows that mining activities in some areas were extended to out of permissible boundaries and it is required to be followed up more seriously by managing organizations specially by Department of Environment and put the new utilizations in a proper path in permissible boundaries.

Edge detection is a fundamental concept of image processing in remote sensing which aims at detecting points of the image at which the brightness changes sharply. Sharp changes in the image properties usually represent important events and changes in environmental factors. This kind of detection method can be useful in different image processing applications such as satellite image segmentation. Many classical mathematical algorithms for the edge detection, such as Gradient operators, Laplacian and Laplacian of Gaussian operators,are based on the derivative of the original image pixels. In the remote-sensing imagery, because of the high rate of changes, these edge detection operators perform weakly in correct diagnosis of the feature boundaries and in keeping their consistency. In order to solve these problems, this research presents a knowledge-based system to detect the feature edges based on the properties of remote-sensing images. In this method, the threshold is firstly determined piecewisely in different regions of the image using a knowledge-based system, and then the area boundaries are extracted using the Shannon entropy, which safeguards the continuity of the edges. The advantage of this approach is simultaneous evaluation of the image features in both partial and general cases. In addition, the system is flexible in terms of the type of user needs and the intended details and applications. Eventually, to evaluate the method, the results are compared with Sobel, Perwitts and LOG standard algorithms. It is observed that the proposed technique can efficientlydetect the edges for different digital images.

Over time, patterns of land Use changes and consequently the land use are essentially altered. Thus for efficient using of capabilities of natural resources, finding detailed information about potential of land uses is necessary. Nowadays, remote sensing technology as a valuable approach to identify natural resources, particularly in the process of preparing land use plans in different regions of the world are documented scientifically. On this basis, for studying the land use changes of Tabriz county during 2010, 2007, 2001, 1989; TM 1989, ETM 2001&+2010, IRS 2007 images were processed and classified by an object-oriented technique. Then, bypre-classification comparing method, all changes were displayed during time period. The results show that the rate of changes over periods of 1989-2001, 2001-2007, 2007-2010, 1989-2010 are about 60.80, 66.30, 56.80, 56.40 percent respectively, so that vegetation cover and the water surfaces had the maximum changes. Meanwhile the most using conversion had happened on built-up lands

The use of new methods of Remote Sensing (RS) and Geographical Information System (GIS) in zoning the flooding potential and its variations in a particular geographical area (Tang-e-Bostanak Catchment Area) constitutes the main subjects of this study. In this regard، the production، combination، and composition of effective information layers such as K index and run off in identifying these zones have been noted، highlighting the capacity of GIS. Theaim is the identification and percent of flood zoning and their variations within an 18-year period by RS and land use variation maps. In this study، we have observed excess rainfall under the curve number (CN). According to the results، the above mentioned area isexperiencing a 15% increase in flooding during the 18-year period.

Delta areas are dynamic features with changes occurring at many spatial and temporal scales due to catastrophic events. Change detection and providing fluvial map of changes are the main requirements for environmental and zonal planners in many studies. Geomorphic changes such as temporal and periodic changes in riverbeds are common events in all deltaic areas. The Mehran river delta is located in the west of Hormozgan State. Changes in various geomorphic features، shuch as riverbed migration، during a 21-year period، were detected and identified using landsat TM and ETM satellite data and topographic maps. Simple band subtraction and linear directional mean were used to identify changes in the case study. The results of this study show that the mehran river channel has migrated several times over the last 21 years so that several meanders and ox-bow lakes remain as a result of migration. Moreover a little change in coast line toward Persian Gulf has been identified as a result of migration and deposition. The Changes of vegetation cover area in this zone which has mangrove vegetation cover have been noticeable so that these covers have incremental trend according to undergone action in artificial forests. The results of this study may be used in an integrated coastal zone information system as it has been proposed for the Mehran river delta.

The increasing development of urban community under the influence of irregulargrowth population and immigrations, leading to unplanned constructions and itsuncontrolled expansion brought about many changes in the spatial structures of urban,This overall changes in its turn require more than ever informed direction, substantialorganization and suitable spatial design. On the other hand, the fundament social,cultural and economical changes are seeking an extensive transformation in the spatialstructure of environmental complexes. Certainly, such changes can not advancewithout effective planning and guidance of urban developers.Urban and regional planners advocating formulating and regulating plans andadopting policies need continuous acquisition of information. Urban planning anddesigning from perception to designing and implementing urban plans need correct,precise , rapid and up-to-date information of local, urban and region.So far, cartographical elements (such as graph, histogram, map, photo, aerialphoto, satellite image and geographical information system) acted as a basic tool forsecuring correct, precise and up to date information leading to better understanding ofurban and regional potentialities, trends of urban planning and designing and finallyproviding an accepted image for urban .Over the recent years, cartographic knowledge, which is influenced byelectronic, satellite and automation has been on the threshold of a fundamental changeand in addition to its determining role of simulation, it has transformed into a uniquetechnique in modeling (designing for future) for the purpose of designing and planningand testing of processes and analyzing the results prior to the implementation.The aim of this article is conducting a research to explain the influence ofautomation in changing the cartography and transforming it into an effective andsalient technique in urban and regional planning and presenting a suitable model.