نشریه علوم و فنون نقشه برداری
سال هشتم شماره 4 (پیاپی 32، بهار 1398)

Synthetic Aperture Radar (SAR) is the only way to evaluate deformation of the Earth’s surface from space on the order of centimeters and millimeters due to its coherent nature and short wavelengths. Hence, by this means the long term risk monitoring and security are performed as precisely as possible. Traditional SAR imaging delivers a projection of the 3-D object to the two dimensional (2-D) azimuth-range (x-r) plane. Due to the side looking geometry of SAR sensors problems like foreshortening, layover and shadow should be dealt with. For overcoming the problem of layover in high urban environment different SARtomography methods are developed. The aim of SARtomography methods are not only overcoming layover problem but unambiguous 3-D and 4-D (space-time) dynamic map of the city can be achieved. The assumption, made by the classical interferometric techniques (i.e.: PSI), the present of a single scatterer per pixel, neglects the fact of occurrence of mulitiple scatterers. For instance PSI initial assumption is the presence of single sactterer in each azimuth-cell range. However, this assumption is not plausible in a high rise urban environment where suffers from the presence of multiple scatter. Furthermore, by the advent of Very High Resolution sensors like Cosmos-Skymed (1,2,3) constellation SARtomography is revolutionized. Needless to say that due to the very high resolution of the images (up to 1m resolution), precise shape and deformation of each individual building can be obtained. Nonetheless, it has to be noted that the increasingly impact of layover on new generation of VHR sensors. To this end several practical SARtomography methods such as first order model and Non Linear least Square (NLS) are introduced. This paper has presented the capacity of the new class of VHR spaceborne SAR systems, like COSMO-Skymed, for TomoSAR processing in high urban environment. However, particular problems related to the side looking geometry of SAR has proved to be more obvious comparing to the previous generation of high resolution sensors (10 m resolution): Layover is one of them. The main aim of this paper is at comparing SARtomography methods and their advantage and disadvantage to the older version of SAR methods like PSI for monitoring high urban environment. The project has been implemented on Very High Resolution (VHR) Cosmo-skymed Stripmap mode (up to 3m azimuth-range resolution) images from Astana-Kazakhstan. Like PSI, TomoSAR benefits greatly from the high resolution of Cosmo-skymed data, as the density of coherent pixels and the signal-to-clutter ratio increase significantly with resolution. The results reveal that the number of permanent scatterers found by NLS and first order model are far more than PS method which indicate the superiority of these methods in overcoming the layover problem in high environment urban areas comparing to the PSI; Besides, the mean deformation velocity, height and coherence of every scatterer were obtained by SARtomographic methods are compared with PSI. As it comes to tomographic processing methods different factors must be taken into account such as the accuracy in estimating the heights, computational cost and the model order selection. Results indicate that the first order method has low computational cost but it suffers severely from side-lobes on the other hand the computational cost of NLSM is very high but it is an accurate method as long as the correct model order is selected. SARTomography proved to be an efficient method for detecting multiple scattrers and layover removal in high urban environment.

The Unmanned Aerial Vehicles (UAV) positioning in the outdoor environment is usually done by the Global Positioning System (GPS). Due to the low power of the GPS signal at the earth surface, its performance disrupted in the contaminated environments with the jamming attacks. The UAV positioning and its accuracy using GPS will be degraded in the jamming attacks. A positioning error about tens of kilometers off the true location was reported in the research articles, due to jamming. Assessing the performance accuracy of UAV’s GPS receiver during the jamming attacks has great importance in maintaining the UAV’s flight safety, especially in the military applications. Currently, the developed and presented methods in jamming detection and mitigation relies on the analysis of the GPS signals. On the other side, many study works are being performed for effective and successful jamming attacks for security, military and intelligence purposes. To detect the occurrence of errors in the GPS positioning, therefore, it is necessary to research and introduce some new independent techniques of the GPS signal processing methods. The presented method in this paper to detect the occurrence of the jamming attacks and to assess the UAV's GPS performance is an independent method from the GPS signals processing. This method is an image-based approach which uses the UAV images taken from its flight path. As the camera is a passive sensor, its performance will not be affected by the external signals and electronic warfare such as jamming attacks. The UAV's flight trajectory is extracted using visual odometry (VO) from the UAV images, and then this trajectory is compared with the UAV's trajectory derived from the corresponding GPS data. In this method, there is no need for the initialization of the coordinate system parameters of VO as it is a relative method in agent positioning. Also, to reduce the drift error in VO only a few images within a selection window is used in the computation of the relative and absolute trajectories of UAV. For reduction of drift-error of visual odometry within this selection window, sliding widow bundle adjustment technique is used here. This process repeated for each UAV position from the begin to the end of the UAV trajectory. For each selection window, two trajectory descriptors correspond to the UAV trajectory from VO and its replica from GPS data are computed.  Then these trajectories compared using their trajectory descriptors. Two trajectory descriptors were defined here to compare these trajectories: The Normalized Distance of Consecutive Points (NDCP) trajectory descriptor and the Consecutive Directions Angle (CDA) trajectory descriptor. These trajectory descriptors are the functions of the UAV’s positions in its flight path within each selection window. The comparison of these trajectory descriptors is done using the cosine similarity index (CSI). CSI takes into account the normalized angle between two codimension normalized vectors to compare them. If there is a problem in the UAV's GPS performance, the cosine similarity index will differ from its ideal number one or 100%. The results show that the proposed method, using these trajectory descriptors, can detect the occurrence of jamming attacks and to assess the accuracy of the UAV's GPS performance during its flight.

The national height system of Iran, orthomertic height, is referred to geoid as the vertical datum. Consequently, the geoid has many important applications in engineering. The slow, laborious and expensive orthomertic heights can be obtained in sufficient accuracy level from geodetic height (derived by GNSS observations) and a precise geoidal height. Over the past two decades, the gravity division of national cartographic center (NCC) has developed multi-purpose physical geodesy to refinement of geoid models. In this study, the efficiency of gravity data of this network to determine of one-centimeter gravimetric geoid are investigated. In order to demonstrate geoid accuracy achievable, the simulated of high frequencies of gravity data using EGM2008 at the actual position od stations was used. Error of geoid are estimated using the Stokes-Helmert method in a closed cycle. The test area is located in the northwest of Iran, where the multipurpose network has the highest density (about 10 km). Numerical results show that accuracy achievable of geoid is about 25cm using irregular distributed multi-purpose network gravity data. Gridded data also improves geoid accuracy by up to 40%.

Close-range photogrammetry has been used in many applications in recent decades in various fields such as industry, cultural heritage, medicine and civil engineering. As an important tool for displacement measurement and deformation monitoring, close-range photogrammetry has generally been employed in industrial plants, quality control and accidents. Although close-range photogrammetric application in displacement measurement of large-scale structures was not introduced as much as its other application, but successful utilizations in this field prove it to be a potentially effective procedure in this area. In order to get familiar with these applications, this paper reviews the research conducted on the use of close-range photogrammetry for measuring displacement in large-scale structures. There are several unique advantages of having close-range photogrammetry employed in this field including the unnecessary of being in direct contact to the structure during the observation, the rapid acquisition of observations, and the immediate access to the results by automating the procedures. Moreover, the instant recording of observations in moving features and possibility of creating an archive from these observations to be used in future processing if required are more examples of these beneficial characteristics. In addition, the high flexibility and adaptability of this method to the project conditions made it possible to achieve high accuracies while, each photogrammetric target practically acts as an instrumentation sensor at a lower cost. Accordingly, based on the reviewed literature, on average, this method was able to reduce up to 60% of the costs and time compared to the other conventional methods as an effective and efficient tool.

Synthetic Aperture Radar (SAR) images are inherently affected by a multiplicative noise-like phenomenon called speckle, which is indeed the nature of all coherent systems. Speckle decreases the performance of almost all the information extraction methods such as classification, segmentation, and change detection, therefore speckle must be suppressed. Despeckling can be applied by the multilooking method when the image is formed or by spatial filters after the image formation. However, multilooking decreases the spatial resolution. Moreover, the performance of spatial filters depends on the size and the orientation of used window’s kernel. To overcome these limitations, Multi-Resolution Analysis (MRA), e.g., Wavelet Transform (WT), can be used. In this article, based on the intra-scale and inter-scale dependencies of wavelet coefficients and by employing the Maximum a Posteriori (MAP) estimator, a method for denoising the wavelet coefficients was proposed. Distributions of noise and noise-free wavelet coefficients in wavelet domain were considered as bivariate Gaussian and bivariate circular symmetric Laplace PDFs, respectively. For comparison analysis, Lee and Frost filters were used, also several classical thresholding methods such as VisuShrink, SureShrink, and BayesShrink were employed. Peak Signal-to-Noise Ratio (PSNR) and edge-preserving index beta were used to evaluate the simulated SAR data. Also, Equivalent Number of Looks (ENL) was employed for real SAR data. Experimental results showed that the proposed despeckling method performed more efficiently to suppress the speckle and preserve the edges than others. For instance, the PSNR and beta values that computed for 16 looks simulated SAR data were equal to 30.42 and 0.734, respectively. Also, the ENL values for region 1 of Noerdlinger and San Francisco images corresponded to 71.12 and 34.57, respectively.

The last few decades witnessed high urban growth rates in many countries. Urban growth can be mapped and measured by using remote sensing data and techniques along with several statistical measures. The purpose of this research is to detect the urban change that is used for urban planning. Change detection using remote sensing images can be classified into three algebra-based, transformation-based and classification-based. By using any of these methods and applying them to SAR and optical data has advantages and disadvantages. Fusion of these methods and datasets can give us this opportunity to overcome their disadvantages and complement each other. For this purpose, here, a decision level fusion technique based on the majority voting algorithm is proposed for integrating the change maps extracted by different methods. After extracting features for optical and polarimetric data, object-based and pixel-based classification methods applied to optic images and also Wishart and SVM classification methods applied on SAR data. Change maps extracted from applying different CD methods such as post-classification, image differencing and principal component analysis. In order to evaluate the efficiency of the proposed method, various optical and radar remote sensing images from before and after of urban growth, acquired by QuickBird and UAVSAR, were utilized. In order to clarify the importance of using both optical and polarimetric images, the majority voting fusion algorithm on the change maps extracted by optical and polarimetric images was also applied separately. The results show that by fusing optical and polarimetric data at the decision level, it is possible to obtain a better accuracy because these two types of data, due to differences, can detect changes in a different way, thus covering each other's deficiencies. Polarimetric images better detect changes in altitude changes, and optical images better detect changes resulting from spectral changes. In order to perform a comparative evaluation, the accuracy of the change map obtained using optical images (total accuracy: 80.86% and kappa: 0.67), polarimetric images (overall accuracy: 75.43% and kappa: 0.5), simultaneous applying both datasets (overall accuracy: 88.48% and kappa: 0.79), as well as using the change maps of both data sets with the highest accuracy (overall accuracy: 88.81% and kappa: 0.79) have been obtained. In the end, due to the noise characterization of the post-classification method, the obtained change map improves with an overall accuracy of 90.11% and a kappa of 0.82.

In remote sensing, image-based change detection techniques, analyze two images acquired over the same area at different times t1 and t2 to identify the changes occurred on the Earth's surface. Change detection approaches are mainly categorized as supervised and unsupervised. Generating the change index is a key step for change detection in multi-temporal remote sensing images. Unsupervised change detection is generally based on the analysis of the magnitude of change index.
In multispectral remote sensing images, in addition to the magnitude of change index, the direction of change index could be calculated with similarity measures such as spectral angle mapper (SAM). Literature reveals that the magnitude of change index has been widely used in change detection of multispectral images, whereas the use of the direction of change index is always ignored. The magnitude and direction of change indices have different and limited capability for detecting different types of land cover change. These indices contain complementary information about the changed phenomenon. Combining the magnitude and direction of change indices would increase the performance of change detection in multi-temporal multispectral images. 
In this paper, a new fused change index based on the weighted linear combination of magnitude and direction of change indices has been proposed. In the proposed method, the weighting parameter of each index is determined automatically based on the ability of that index for unsupervised change detection. The proposed method uses the Xie-Beni (XB) index as unsupervised change detection validity measure for determining the optimal combination weights. XB is a ratio-type index, which measures the within-cluster compactness against the between-cluster separateness. The more separate the clusters, the smaller the XB index. Hence, the combination weight of each index should be inversely related to XB index. After calculating the fused change index, a thresholding method should be applied to generate the binary change map. In this paper, Otsu's thresholding method has been used because of its simplicity, efficiency, and low computational cost.
The performance of the proposed approach has been evaluated on two bi-temporal and multispectral data sets having different properties (different types of land cover/land use changes). The first data set is made up of a couple of acquired multispectral images on the Urmia Lake (Iran) by the ETM+ sensor (mounted on the Landsat-7 satellite) and TM sensor (mounted on the Landsat-5 satellite) in August 1999 and September 2010 respectively. The second case study was conducted based on a couple of Landsat TM 4, 5 multispectral images acquired on the city of Maraghe (Iran) in June 1989 and June 1998 respectively. These data sets are characterized by a spatial resolution of 30 m×30 m and 6 spectral bands ranged from blue light to shortwave infrared (the 6th band of these images, which is in thermal infrared ranged, is not utilized due to low spatial resolution).
Experimental results show that direction and magnitude of change indices have different and restricted abilities to detect multiple changes due to their different properties. For this reason, direction and magnitude of change indices can only detect three of the four possible change categories, properly. The fusion of magnitude and direction of change indices in the proposed index makes it possible to more accurately detect all of the four change categories as compared with the individual indices alone. The total error (TE) of obtained binary change map (BCM) by proposed index in the first dataset is 10.17% which demonstrates 21.78% and 6.11% improvements in overall accuracy compared with the magnitude and direction of change indices respectively. Similarly, in the second case study, the fused change index (proposed approach) had a significantly lower total error (12.89%) than the magnitude of change index (18.20%) and the direction of change index (29.49%).

Global positioning system (GPS) measurements provide accurate and continuous 3-dimensional position, velocity and time data anywhere on or above the surface of the earth, anytime, and in all weather conditions. However, the predominant ranging error source for GPS signals is an ionospheric error. The ionosphere is the region of the atmosphere from about 60 km to more than 1500 km above the earth surface. The ionospheric delay is the main error source for GPS. The delay can vary from a few meters to tens of meters depending on the solar cycle, hour of day, season, geographic location and satellite elevation angle. Knowledge of the ionospheric electron density is essential for a wide range of applications, e.g., radio and telecommunications, satellite tracking, and earth observation from space. In order to understand the nature of those causes and to analyze ionospheric structure, it is necessary to monitor the variations on the electron density of the ionosphere both spatially and dynamically. Because of the dispersion of the ionospheric layer and its destructive effect on passing waves, modeling and predicting the behavior of this layer of the atmosphere is one of the most useful topics in geodesy and space studies. The parameter used to study the physical properties of the ionosphere is called the Total Electron Content (TEC).
For modeling the TEC, many methods have been proposed that require large computational operations and sometimes lack sufficient precision for ionospheric modeling. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is used to predict TEC variations for the next day. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered a universal estimator.
In order to do this, observations of Tehran's permanent GPS station were used for three different months (May, April and December) for years (2015 and 2011) to train the Anfis network, and predictions is made for days (30, 3, and 6) in the months of May , December and April. These observations have been selected to include high, medium, and low solar activity. The genetic algorithm has been designed to determine the optimal time lag for training the Anfis network. Also, to evaluate the results of the adaptive neuro-fuzzy inference system, the TEC values obtained from this system has been compared with artificial neural network (ANN) values with the Levenberg-Marquardt training algorithm, TEC derived from the GPS, and finally with the international reference ionosphere (IRI2016) TEC. The maximum RMSE for the difference between the predicted TEC and the observed TEC is 4.6 TECU for the Anfis, 5.06 TECU for the ANN and 5.8 TECU for the IRI 2016. Also, the minimum RMSE is computed 2.1 TECU for the Anfis, 2.6 TECU for the ANN and 4.3 TECU for the IRI 2016. The results demonstrate the high capability of the ANFIS network in the ionospheric time series modeling.
For modeling the TEC, many methods have been proposed that require large computational operations and sometimes lack sufficient precision for ionospheric modeling. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is used to predict TEC variations for the next day. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered a universal estimator.
In order to do this, observations of Tehran's permanent GPS station ( ) were used for three different months (May, April and December) for years (2015 and 2011) to train the Anfis network, and predictions is made for days (30, 3, and 6) in the months of May , December and April. These observations have been selected to include high, medium, and low solar activity. The genetic algorithm has been designed to determine the optimal time lag for training the Anfis network. Also, to evaluate the results of the adaptive neuro-fuzzy inference system, the TEC values obtained from this system has been compared with artificial neural network (ANN) values with the Levenberg-Marquardt training algorithm, TEC derived from the GPS, and finally with the international reference ionosphere (IRI2016) TEC. The maximum RMSE for the difference between the predicted TEC and the observed TEC is 4.6 TECU for the Anfis, 5.06 TECU for the ANN and 5.8 TECU for the IRI 2016. Also, the minimum RMSE is computed 2.1 TECU for the Anfis, 2.6 TECU for the ANN and 4.3 TECU for the IRI 2016. The results demonstrate the high capability of the ANFIS network in the ionospheric time series modeling.

Asthma is an untreatable but manageable disease that has increased dramatically during the last fifty years due to contamination of the surrounding environment with triggers such as air pollution and has become one of the common chronic diseases throughout the world. Researchers consider allergies and environmental stimuli to be one of the essential components of asthma management programs. Predicting the impact of these factors, identifying potential risk areas for asthma exacerbation and informing patients about preventing exposure to them in all places and times are an appropriate and effective approach in managing the disease in the long-run. For this reason, specialists consider that the design, development, and promotion of asthma management programs are vital to controlling asthmatic exacerbations. Today, with the development of information technology tools and influence web geography information system in health area, the design of managing programs for controlling asthma has undergone fundamental changes by removing spatial and temporal constraints. The tool is available at any location and time and provides appropriate information about the living environment of patients and useful information about the role of air pollution factors in allergic asthma exacerbations.
Study area: The study area is Tehran city in the article. We collected required data from patients with asthmatic exacerbations referring to one of the pulmonary hospitals (1702 asthmatic patients) in the city. The air pollutants data also were prepared from pollutant stations in Tehran. In two databases were merged in a unique geographical database.
Materials & In this study, we have developed an asthma management system based on air pollution using tree-based classification. Our research process includes four main steps. Firstly, required data is collected and prepared in a geographical database as spatial data. Then with the help of tree-based classification algorithm, predictive model of asthmatic exacerbations is made in Rstudio and its performance is assessed. Then with help of if-then rules of the model, it was prepared a toolbox in ArcGIS to reproduced maps of the prediction daily, because the conditions for air pollution factors change on a daily basis, and it is necessary for these maps to be updated continuously. In the final step, it was published these maps to the users in the Web, which will be realized with the help of ArcGIS Server tools. Patients can view these maps through ArcGIS Online (via the browsers of desktop and mobile devices).
Results & In this study, we have developed a tool based on decision tree algorithm integrated spatial data with accuracy 82% and the web-based geographic information system to provide daily maps of the occurrence of asthma exacerbations based on air pollution to alert patients about potentially dangerous areas and places which asthma exacerbates. This tool, which is available on browsers of both of web and smartphones platforms, improves patient adherence to asthma management programs and significantly raises their awareness about of an environmental-related disease. With the help of these maps, they will have a powerful self-management tool that can decide whether or not they will be present in these areas. Our asthma management system provides the useful and valuable map for users with asthma. Patients could use this tool in every place and every time without any limitation.  Our system improves adherence ‘s patients to asthma management plans Compared with traditional and paper-based tools. Patients inform about surrounding environment continually with the practical system to avoid places that are hazardous to their health. Therefore, we provide a suitable environment for users with help of various tools Arc GIS that is available for all users without extra cost. These maps are easy to understand and users can easily use it with a low level of literacy.

In recent years, the tremendous and increasing growth of spatial trajectory data and the necessity of processing and extraction of useful information and meaningful patterns have led to the fact that many researchers have been attracted to the field of spatio-temporal trajectory clustering. The process and analysis of these trajectories have resulted in the extraction of useful information which is able to respond to different challenges in real-world applications such as traffic management and control, smart transportation, surveillance, security and biological studies. Clustering is one of the most important methods for trajectory pattern extraction, their volume reduction, discovering outliers in trajectories, indexing and their simple visualization. So far, different similarity functions and clustering algorithms have been proposed for trajectory clustering. The diversity of clustering algorithms and their unique results highlights the need for paying attention to their weaknesses and strengths. Some clustering algorithms are only effective on low volume datasets. There are also some algorithms which are only able to extract clusters with convex shape, whereas some of them extract clusters of any shapes. On the other hand, several clustering functions require the determination of the initial value, such as the number of clusters by the users while some others do not need initial inputs. In addition, outlier detection is not possible in all clustering algorithms. In this study, spatial trajectories clustering algorithms that are extended from point clustering algorithms is divided into four general categories: partitioning-based clustering, hierarchical clustering, optimization-based clustering and density-based clustering. Then, the most commonly used algorithms in each category are implemented and evaluated. The evaluation process is performed on two sets of data (cross and i5) with dissimilar complexity. The effect of noise and outliers is one of the most critical parameters engaged in the performance quality of clustering functions which is considered in this study. The Silhouette index and computational time are used as two parameters for comparison and evaluation. According to obtained results, it is crucial to consider the data, its features, and also the utilized distance function in order to decide on the proper clustering method.  However, generally, the best results regarding the clustering quality are obtained from optimization-based clustering. With the integration of genetic algorithm into the K-means, all results in two cases of using both two datasets and using two different distance functions are improved. Using the genetic algorithm in K-means leads to finding the optimum location of cluster centers and dealing with the local minimum problem. It is important to note that high computational time is one of the weaknesses of optimization-based clustering. After the optimization-based clustering, regarding the clustering quality, partitioning-based, hierarchical and density-based clustering have achieved the second, third and fourth ranks respectively. With regard to the computational time, the best results are obtained from the density-based, hierarchical, partitioning-based and optimization-based clustering consecutively. Some methods such as K-means (a sub-category of partitioning-based clustering) are severely sensitive to outliers while spectral sub-category of partitioning-based clustering has a high resistance against them. Moreover, the density-based and optimization-based clustering methods have the highest tolerance against noise.

Precise positioning in Real Time Kinematic (RTK) applications depends on the accurate resolution of the phase ambiguities. In RTK positioning, ambiguity parameters are highly correlated, especially when the positioning rate is high. Consequently, application of de-correlation techniques for the accurate resolution of ambiguities is inevitable. Phase ambiguity as positioning observations by the Global Positioning System (GPS) is referred to the number of the complete cycles of the signal emitted by a satellite, just before its reception by a receiver. Fast and accurate estimation of the phase ambiguities still is a challenge in real time positioning by GPS. Various methods have been developed for the ambiguity resolution. Initialization time, reliability and accuracy of the resolved ambiguities are the key sectors in each resolution technique. 
Methods of ambiguity resolution usually start with the float solution of the ambiguity parameters and end up with their integer values. The method of least-squares is usually used for computing the float solution for ambiguity parameters. To search and fix the corresponding integer values, conditional least-squares is normally used. Search-based methods, as the most commonly used techniques, are usually executed in three successive steps. At first, standard least-squares is used for estimating a float solution for ambiguity parameters and their associated variance-covariance (V-C) information. In this step, the integer nature of the ambiguity parameters is ignored. Next, the method of Weighted Integer Least-Squares (WILS) is used for resolving the integer values of the ambiguity parameters. Real-valued unknowns are then estimated using the integer estimates of the phase ambiguities. The previous step is the most important part of the problem. De-correlation of the VC matrix of the ambiguities' float solution was firstly suggested by Teunissen in order to increase the reliability and speed up the resolution process.
This paper proposes a new method for de-correlating the V-C matrix of ambiguity parameters. A regularization algorithm has been used to achieve the lowest correlation between floating ambiguities. The regularization parameter has been selected in such a way so that the traces (sum of diagonal elements of V-C matrix) of V-C matrix of floating ambiguities are minimized. In order to investigate the de-correlating and efficiency of the proposed method, two criteria of the condition number and also the trace of V-C matrix of floating ambiguities have been used. After de-correlation of V-C matrix of floating ambiguities and space transformation, the sequential conditional least squares is used to search for the integer ambiguities. This method calculates the phase ambiguity with considering the correlation between them. Also, a-posteriori variances of unit weight for the float and fixed solutions can be used to check the consistency of a resolved ambiguity with the measurements. When some of the ambiguities are not correctly resolved, a-posteriori estimate of the variance of unit weight may not statistically conform to the estimate of this parameter in the float solution. Therefore, the comparison of the a-posteriori estimates of this parameter for the float and fixed solutions provides a measure to analyze the consistency of the resolved ambiguities with measurements. All results from the proposed method of this paper have been compared with the results of the famous Lambda method.

Dust is one of the important climatic phenomena that has occurred in arid and semi-arid regions of the world. In recent years, one of the most important environmental issues in the Middle East and Iran is the occurrence of dust phenomena that affects a variety of factors, including human health, plants and other living organisms, economic conditions, and etc. One of the effective factors in soil stabilization and reducing the amount of dust particles in the air is vegetation and especially agricultural products, which play a significant role in the environmental cycle, human life and the alive creatures. However, plants will suffer from tension and disease through the influence of the dust occurrence. Hence, the main objective of this research is study the effect of dust on the plant in Ahwaz city One of the methods, which reduce costs and prevent direct tests on the plants and thus reduce the needed time, is to use remote sensing techniques. Therefore, two types of data including spectroscopy in different measurement conditions and satellite images (Landsat8) were used to obtain required data in this research. The ASD Field spek 3 spectrometer with a range of 350 to 2500 nm and a resolution of 1 nm was used in this study. For this purpose, 20 leaves of Spindle tree (Shamshad) plant were prepared for spectrophotometry. The leaves of this plant were examined in a dark room and based on specific standards and in each of the conditions, five replicates of the spectrophotometry were performed. Then, noise of spectrophotometric data was removed by wavelet transform. In the next step, the bands that were affected by the dust were identified by using the obtained spectra. Afterward, different vegetation indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Divergence Vegetation Index (DVI) and Ratio Vegetation Index (RVI) were extracted and analyzed in proportion to the image bands. In the next step, nonparametric spectrum analyzes (e.g. periododogram, Welches and multispepers) were used to analyze the effect of dust on signal strength. The analysis was performed individually on measured data in the laboratory environment and outdoors. The CR method illustrated that the best wavelengths for detecting vegetation free of dust and dust cover is from about 450 to 750 nm and 1500 to 2500 nm. Therefore, the vegetation behavior at the time before and after the occurrence of dust in the study area at 450 to 750 nm wavelengths was investigated using Landsat satellite imagery. Based on this, the results of the studied vegetation indices using the red and infrared band obtained after the occurrence of dust showed that the dependence of the indices NDVI, DVI, RVI before the occurrence of dust is 0.302, -0.47, and 0.35 respectively. Also, the dependence of the EVI index (which has three bands) obtained for the time before the occurrence of dust and the actual values ​​of EVI before the occurrence of dust was equal to 0.69, which is a relatively good correlation between the EVI values and measures the values ​​of this index before the occurrence of dust event. Also, spectrophotometry results demonstrated that the signal strength decreases with increasing dust. This result can be deduced for both field and laboratory spectra. According to the accuracy obtained for the vegetation indices, it was determined that using the images can not detect the effect of dust on the plant, properly. While comparison of the results of triodegraph, welch and multispeed methods showed that only periodogram signal processing is not suitable for detecting the effect of dust on the plant signal, and the other two methods (multifrequency and volcano) are superior to identify the effect of dust on the plant spectrum. The accuracy obtained for the periododogram in the laboratory environment and in the external environment were respectively 83% and 69%, the accuracy obtained from the Welch method in the laboratory environment was 60% and in the natural external environment was 97%, and the accuracy obtained from the multi-tipper method was 87% in the laboratory environment and 88% in the natural environment. According to the results, it can be observed that the dust on the plant can be determined better by using Welches and Multitipper methods.

Measurements of the dual frequency Global Positioning System (GPS) receivers can be used to calculate the electron density and the total electron content (TEC) of the ionosphere layer of the Earth atmosphere. TEC is a key parameter for investigating the ongoing spatial and temporal physical process of the ionosphere. For accurate estimation of TEC from GPS measurements, GPS satellites and GPS receivers’ instrumental frequency-dependent biases should be removed from the measurements properly. Instrumental biases are thought to be due to the delays caused by the analog hardware of the satellite and receiver.  Thus Differential Code Bias (DCB) of GPS satellites and receivers are one of the most important error sources and in fact, the unknowns in the TEC calculation and consequently the positioning using GPS code observations. International GNSS Services (IGS) estimates and publishes the DCBs of GPS satellites and its network GPS receivers as an ionosphere single layer model byproduct. Although DCBs of all GPS satellites are provided by IGS, but DCBs of GPS receivers are not provided for all IGS network GPS receivers every days, Furthermore the DCBs of regional and local GPS networks receivers are not provided by IGS. Estimating the DCBs of GPS receivers in regional and local networks independent of single-layer ionosphere modeling will be practical. Therefore the aim this paper is to use Global Ionospheric Maps (GIM) and GPS satellite DCBs which are published by IGS to estimate Iranian Permanent GPS Network (IPGN) receivers DCBs independent of single-layer ionosphere modeling. Code geometry-free linear combination of GPS observations, also known as the ionospheric observable are used in receivers DCBs estimating. Code geometry-free observations are absolute value but they are noisier than more precise but relative carrier phase observations. In most researches the “carrier to code leveling process” algorithm is used to smooth code geometry-free observations which benefit from both ambiguity independent code observations as well as the high precision of carrier phase observations. But in this paper the moving average filter, which is a kind of low pass filters was used for smoothing and reducing the noise of code geometry-free observations. The algorithm of this method is easier and has less computational load with the same accuracy in smoothing result. Spatial and temporal interpolations are used to derive the VTEC values form IGS GIM at each epoch. The interpolated VTEC is mapped into STEC using a mapping function and having GPS satellite DCBs from IGS, the GPS receivers DCBs will be computed at each epoch. Daily averages of derived GPS receivers DCBs are computed to compare it with the IGS published one. First, the proposed method was implemented on some IGS network stations in different latitudes and in two days with quiet and disturbed solar activity and the values derived for receivers DCB were compared with the values published by Center for Orbit Determination in Europe (CODE) as a member of IGS ionosphere working group. The maximum difference between estimated DCB and DCB from CODE is 0.644 ns and the root mean square of these differences (RMSE) is equal to 0.257 ns which show the high efficiency of the proposed method for GPS receivers DCBs estimation. Then IPGN GPS receivers DCBs are computed using proposed method.

Todays, urban distressed textures are considered as a vital concern of managers, particularly in large cities. These areas, which also appear in official parts of cities, are vulnerable because of prospective problems. Lack of proper accessibility, services, facilities, and infrastructures, as well as social, economic, environmental and spatial problems, are key problems of these areas. Although extensive modernization and refinement have always been emphasized by urban managers, there is always a top-down approach apply for the renovation of these areas. Because of inhabitant’s poverty, social and constitutional problems, there is no chance for the renovation of these areas without the contribution of the public and private sector. Failure to pay enough attention to the key role of the people and the participation of citizens has promptly led to the failure of many modernization plans and programs. Considering the active interaction of citizens and the private sector in the process of building and upgrading these areas will increase the success of such projects. Using spatial decision support tools can represent an appropriate way for effective communication between stakeholders (residents, private and public sector). The purpose of the present research is to develop a citizen-centric spatial decision-making system to carefully foster and facilitate the process of constructive participation in urban distressed texture. The proposed system provides a combination of citizen-centered GIS capabilities and multi-criteria decision analysis in an open-source framework (open-source software and technologies) to facilitate citizen-centered modernization of urban distressed textures.

According to the cities expansion, updating urban maps for urban planning is important and its effectiveness is depend on the information extraction / change detection accuracy. Information extraction methods are divided into two groups, including Pixel-Based (PB) and Object-Based (OB). OB analysis has overcome the limitations of PB analysis (producing salt-pepper results and features with holes). In the information extraction by SVM classification in complex urban areas, using various features was suggested to improve accuracy result. Also, in SVM, it is necessary to determine the values of the model parameters. In most of the previous OB research, the two important steps were determined by trial and error or based on an expert knowledge. The necessity of selecting independent features and determining the optimal values of SVM parameters, with the aim of minimizing the maximum user interaction, have resulted in proposing a novel method with a relatively high automation level based on SVM simultaneously optimization with meta-heuristic algorithms for large scale updating maps in  high spatial resolution and elevation data. Semi-automatic selection of train/test samples also has increased the automation level of the updating process. Therefore, according to the effect of information extraction on the updating results, the proposed method is trying to improve this step results. The results of the proposed method had no salt-pepper results in comparison with PB analysis. Also, the time processing of the proposed method in optimization and classification steps had been decreased. Finally, the results of change detection map obtained from the proposed method led to 9% and 5% improvement in comparison with other methods in changed class.

Epidemic diseases are a public health concern that has many economic costs and health problems. The geographical distribution of these diseases is a spatial and temporal process. By understanding the process and identifying the factors that affect it, we can take an effective step in preventing and treating these diseases. Brucellosis is one of the most important zoonotic diseases in Iran. To better understand the epidemiology of this disease in northern Iran, the main objectives of this study are to review the annual and monthly trends, to identify spatial and space-time clusters, and to determine the impact of ecological variables. The study was conducted on 6895 patients from April 2009 to March 2017. Data of disease incidence with environmental data including average temperature, maximum temperature, minimum temperature, number of freezing days, humidity, precipitation, evaporation, wind speed, vegetation, elevation, slope and aspect in Golestan, Mazandaran and Gilan provinces as Monthly collected. Global Moran's I was used to investigate spatial autocorrelation. Spatial scan, local moran’s I and space-time scan were used to identify clusters. Spearman correlation was used to study the effect of ecological parameters. The results showed that brucellosis was clustered in northern Iran. The incidence was maximum in the summer (34%) and at least in winter (16%). The months of May, June, July and August were the most susceptible months with a total of 3396 patients. In Local Moran's I, the cities of Minudasht, Kurdkuy, Bandar Ghaz and Galughah were identified as high-high, the cities of Lahijan, Astaneh Ashrafieh and Rasht as low-low and the shaft town was identified as high-low region. In the spatial scan, 10 classes were discovered, most of which were located in Golestan and Mazandaran, and only one was in Gilan. The first and most dangerous classes were in Golestan province. In a space-time scan, 4 classes were identified. The results obtained from the Moran index and spatial scan and space-time scan were confirmed by each other. Spearman showed a positive correlation between incidence of disease with evaporation, aspect, maximum temperature, height and mean temperature, and negative correlation with precipitation, moisture and vegetation. This study shows that in the north of Iran, we see an increase in the incidence of this disease from west to east. Brucellosis is higher in spring and summer in mountainous regions with warm and dry weather. This information can be used for the control strategy used by decision-makers in the field of health.