نشریه علوم و فنون نقشه برداری
سال هشتم شماره 1 (پیاپی 29، تابستان 1397)

Hydrologic models are useful tools for simulating water resources and flux(contain Runoff and evaporation) variations. Determining hydrological model parameters is primary condition for good simulating of hydrological process. With the estimated values of this parameters via the calibration process, the model can well simulate the natural system. The success of the existing models to simulate reality does not depend on the complexity and number of parameters. The structure of the model, identify influential parameters and calibration method can have a significant impact on improving the model outputs.
Optimization methods are from the useful automatic calibration methods. Practical experiences of hydrological models calibration have shown that single objective optimization methods often not enough to measure all important structures of observational data. In this research four different evolutionary multi-objective optimization algorithms are used for calibration and estimating main 4 parameters of GR4J (in French, mod`ele du G´enie Rural `a 4 param`etres au pas de temps Journalier) hydrological model over Danube basin. GR4J is a simple rainfall-runoff model and belongs to the family of hydrological models that focus on the soil moisture compartment.
All the four used evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II) and the Strength Pareto Evolutionary Algorithm II (SPEA-II) applied in the mode of two objective function. Both objective functions of these algorithms are regarded base on Nash–Sutcliffe model efficiency coefficient which can be calculated as follow:where and are observed and simulated values, respectively, and stands for the temporal mean of observations (, runoff or GRACE dTWS anomalies).
First objective is base on daily observed and simulated runoff and the other is according to daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) and its corresponding simulated daily total water storage using GR4J model. For the study basin, GR4J-derived Total Water Storage (TWS) at time t was calculated as the sum of amount of four water comportments which extracted from different simulation steps of GR4J as:TWSt = St + Rt + Vt + Wt
where S and R are the amount of water stored in the production store and routing store at the end of each day respectively. V and W are the remaining water in unite hydrographs (UH1 and UH2) during the simulation process at the end of each day, respectively. Finally, basin averaged TWS anomalies (dTWS) are computed as a difference between daily TWS (TWSt) and the temporal mean of TWS () during the study period as:GR4J parameters are estimated by maximizing the objective functions through each algorithm process. Some quality measures including Number of Pareto Solution(NPS), Generation Distance(GD), Spacing(SP) and Maximum Spread(MS) are used to compare the calibration results. The results indicate that NPS of all methods at the last iteration are the same. NSGA-II has better SP and MS criteria rather than other techniques for calibrating GR4J using GRACE dTWS and in-situ runoff data. MPSO show better response in all criteria respect to PESA-II and SPEA-II method and its GD is also better than NSGA-II. The calculated Nash-Sutcliffe model efficiency coefficient in calibration and evaluation periods show that GR4J simulation results can be judged as satisfactory results.

Today, ubiquitous systems has been growing to become the new generation of Geospatial Information Systems (GIS). In Ubiquitous GIS, servicing capabilities to any user, at any time, in any location, using any device, and in any condition are provided. Advances in Information Technology (IT) industry have led to the advent of low-cost three dimensional data acquisition technologies (e.g. Microsoft Kinect). However, there are some deficiencies and limitations in utilizing these novel technologies in Ubiquitous GIS environments. Among them is the lack of information about the three dimensional topological relations between identified objects. Moreover, the current data models for the extraction of topological relations in ubiquitous environments are not sufficient. Therefore, the aim of this study is to extract the topological relations between objects sensed by Microsoft Kinect by designing a novel Ubiquitous GIS Data Model (UGDM). To obtain the extent of each object, the bounding box algorithm has been employed. The results of analyses on the acquired data from Kinect sensor show the success of the proposed data model in topological information .Ability to use spatial information at any time and places based on the use of IT and different infrastructures like sensor network (SN), Internet, and communication devices used in the system must be arranged by a ubiquitous data model to make different services at any time, any place, by any device and data for any user. Spatial relationships between objects, containing the fundamental information relating to the environment. Among these spatial relationships can be noted such as directional relationships, like: “left”,”right”,” above”, and “bottom” or distance based relations such as: “near” or “far”. The topology relationships used more in GIS desired location can be also noted such as: “inside”, “intersection”, “touch”, and “outside” and etc. Extraction of relationships between different features are done on the basis of data modeling techniques. Since the traditional data models don’t cover capabilities of the ubiquitous computing tools used in ubiquitous GIS to extract 3D relationships, we discuss the creation of facilities for the extraction of spatial relationships in ubiquitous GIS. A new generation of GIS is ubiquitous GIS, which in this generation service functionality to any user, at any given time and place. With the advancement of new technologies in capturing 3D point clouds of the environment, general changes are created in ubiquitous computing. The sensors used to obtain environmental information is provided in this generation are often inexpensive sensors. One of the important issues associated with this generation is the extraction of 3D topological relationships to interact with the environment better, which these sensors don’t have this ability to extract 3D topological relationships. To provide such a ubiquitous data model it is required to use software and hardware standards in order to exchange information in different parts of the ubiquitous model properly. The goal of this article is providing a ubiquitous data model based on the predefined standards to extract spatial relations. Spatial relations include directional, projection, distance and topological manners examined in this article. Because there is no unique method for extracting different spatial relations topological relations. Developing a spatial language as Ubi-OCL for defining different elements used in the data model provides an interoperable connection between the user and the data model. In this language the user can select the type of sensor and spatial relations to be extracted. For evaluating the presented data model, topological relations extracted will be presented in the article. As conclusion this article shows the different results with different sensors, the bounding box method is used for processing Kinect’s point cloud for extracting spatial relations according to provided data model.

One of the main problems related to unsupervised change detection methods based on difference image lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by manual trial-and-error procedures, which affect both accuracy and the reliability of the change detection process. To overcome such drawbacks, this study proposes two automatic techniques for analysis of the difference image based on Gaussian Mixture model (GMM). The first technique allows an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference image are independent of one another. The second one analyzes the difference image by considering the spatial-contextual information included in the neighborhood of each pixel. In particular, this study presents an approach based on Markov Random Fields (MRFs) that exploits inter-pixel class dependency contexts. In this study, we define the problem of the analysis of the difference image for unsupervised change detection in terms of the Bayes decision theory. The application of this theory requires the estimations of the a priori probabilities and of the conditional density functions for the classes associated with the unchanged and changed pixels in the difference image. To this end, we present an approach based on Expectation-Maximization (EM) algorithm that allows such estimations to be performed in an unsupervised way. Within this framework, two automatic techniques for the analysis of the difference image are presented that overcome the main problems inherent in classical techniques. One assumes that the gray-level values of the pixels in the difference image are independent of one another. Under this assumption, the Bayes rule for minimum error is applied in order to select the decision threshold that minimizes the overall error probability in the change detection process. The other technique considers the spatial-contextual information contained in the difference image in order to increase the accuracy of the final change detection map. In particular, an approach based on Markov Random Fields (namely GMM-MRF) is proposed that exploits the inter-pixel class dependence to model the prior probabilities of classes. In order to assess the effectiveness of both proposed techniques, we carried out experiments on two different data sets. One was a real multi-temporal data set composed of two multispectral images acquired by the Landsat 8 satellite from Aleppo city. The other was a synthetic data set generated to evaluate the robustness of the proposed techniques against different levels of noise. Three different experiments were carried out to test the validity of the proposed techniques. The first experiment allowed an evaluation of the accuracy and stability of the proposed approach based on the GMM algorithm and the EM algorithm for the estimation of the statistical terms of the a priori probabilities. To this end, the true values of the a priori probabilities were compared by with the estimates obtained by the GMM approach. The second experiment aimed at assessing the effectiveness of the technique for the analysis of the difference image under the assumption of the independent pixel values. The third experiment made it possible to assess the capability of the GMM-MRF that exploits the spatial-contextual information to improve the change detection accuracies provided by the classical thresholding approach. Analytical evaluations of the final maps show the superiority of the GMM-MRF technique on both the simulated data and the real Landsat 8 data set of Aleppo city in terms of the kappa coefficient and the overall accuracy measures. Experimental results obtained on the simulated data show an improvement of about 0.8 and 82% in terms of the kappa coefficient and the overall accuracy, respectively. Also, the results obtained on the real Landsat 8 data set show an improvement of 8% and 4% in terms of the kappa coefficient and the overall accuracy, respectively.

Radar interferometry is a common technique for measuring surface deformations of the Earth. Tropospheric and Ionospheric delays are Tow challenges in this respect. The ionospheric effect on the interferogram phase is negligible. However, the tropospheric effect on the radar signal phase can produce a maximum error of about 10 to 14 centimeters in terms of displacement. The error generated by the troposphere is a seasonal systematic error. One of the methods for atmospheric correction in radar interferometry is based on using numerical weather forecasting models. These models provide meteorological parameters in the form of a 3D grid. In this study, the Weather Research and Forecasting (WRF) model was used to reduce the effect of tropospheric error. The WRF model is capable of processing and generating three-dimensional networks of meteorological parameters with arbitrary spatial resolution. It is also very flexible in terms of time and it can be output at precisely seconds. This advantage is very suitable for interferogram tropospheric correction, and themporal interpolation of the weather parameters, which itself is a source of error, is left out of the correction steps. But the disadvantages of this weather model is the time consuming process to produce a high spatial resolution outcome. The northwest of Iran was considered as the study area and the GP ray tracing method was used for calculating tropospheric delays. A new method which is independent of the estimated rate of deformation is proposed for analyzing the reliability of the applied corrections. In the method, the reliability of the applied corrections is checked through the analysis of RMSE errors of phase measurements. Interferograms whole displacement phase is statistically zero or is not comparable to the phase of tropospheric delay are used. To this end, the radar interferometry measurements are firstly corrected for the phase of tropospheric delays. In other words, the more the RMSE is closer to zero, the corrections have been done more successfully. The reliability of the applied corrections is then examined through the analysis of the repeatability of phase measurements wherever the rate of deformation is negligible. Obtained results show the positive performance of the WRF model (used with different spatial resolutions). According to the obtained results in the Tabriz urban area, application of the computed corrections improves the RMSE error of the tropospheric phase by 43%, 17%, 14% and 47% when the spatial resolutions of the WRF model is 1km, 3km, 9km and 27km, respectively. This reduces to 17%, 14%, 15% and 21% near the Urmia Lake. It is worth noting that the improvement of RMSE in the WRF model with spatial resolution of 27 km is more than other WRF models with Higher spatial resolutions. This is due to the spatial shift in the WRF model. Because usually the spatial shift in the WRF model with higher resolution is more than the lower resolution, it is accompanied by a greater error in tropospheric correction on phase data.

Recently, Synthetic Aperture Radar (SAR) data are of high interest for different applications in remote sensing specially land cover classification. SAR imaging is independent of solar illumination and weather conditions; it is not affected by rain, fog, hail, smoke, or most importantly, clouds. It can even penetrate some of the Earth’s surface materials to return information about subsurface features. However, SAR images are difficult to interpret due to their special characteristics, i.e., the geometry and spectral range of SAR are different from optical imagery. In addition, the exhibition of the property of speckle caused SAR image is visually difficult to interpret. Consequently, optical data can be applied in fusion of SAR data to improve land cover classification. On the other hand, Light Detection and Ranging (LiDAR) data provide accurate height information for objects on the Earth, which makes LiDAR become more and more popular in terrain and land surveying. Regarding to the limitations and benefits of these three remote-sensing sensors, fusion of them improved land-cover classification. For this purpose, it is necessary to apply data fusion techniques. In recent years, significant attention has focused on multisensory data fusion for remote sensing applications and, more specifically, for land cover mapping. Data fusion techniques combine information from multiple sources, providing potential advantages over a single sensor in terms of classification accuracy. In most cases, data fusion provided higher accuracy than single sensors. Furthermore, fusion of sensors with inherent differences such as SAR, optical and LiDAR data need higher level of fusion strategies. Ability to fuse different types of data from different sensors, independence to errors in data registration step and accurate fusion methods could be mentioned as the benefits of decision-level fusion methods rather than other level fusion (pixel and feature level fusion methods).
This paper presents a method based on the simultaneously using of RADAR, multispectral and LiDAR data for classification of urban areas. First, different feature extraction strategies are utilized on all three data then a feature selection method based on Ant Colony Optimization (ACO) is applied to select optimized features. Maximum Likelihood (ML), Support Vector Machine (SVM) and K-nearest neighbor (KNN) are applied to classify optimized feature space as three classification methods. Finally a decision fusion method based on Weighted Majority Voting (WMV) is applied to provide final decision. A co-registered TerrraSAR-X, WorldView-2 and LiDAR data set form San Francisco of USA was available to examine the effectiveness of the proposed method. The results show that the proposed method based on the simultaneous use of three radar, optical and LiDAR data can increase the accuracy of some classes more and some other classes less. Also, the results of the data fusion can provide a different improvement compared to the results of the classification of each data individually. Generally, the results show that the use of multisensor imagery is worthwhile and the classification accuracy is significantly increased by such data sets. There are also several practical issues to be considered in future studies. Note that only a decision ensemble system was explored in this study. Additional research is needed in areas such as further study on more powerful feature spaces on each data, further processing of DSM of LiDAR, novel fusion strategies such as rule-based and object based classification strategies.

Land use maps are among the most important information required layers in the managements of the catchments. Furthermore, utilizing forecasting methods of land use changes is of great importance in management and decision making of watersheds. In this regard, the aim of this research is the integrated use of Markov and Cellular automata models in the Multiple Criteria Decision Making (MCDM) method to model the land use changes. Those investigations were evaluated in the Talar Watershed in the north of Iran. The reason for selecting this site was the rapid changes occurred in this watershed in the recent years. Moreover, availability and completeness of the required data of this site is the other reason. Then, two Landsat 5 Thematic Mapper (TM) images of 2000 and 2010 with spatial resolution of 30 m and 1 SENTINEL-2 Multispectral Instrument (MSI) image of 2016 with spatial resolution of 10 m were prepared. The last image was used to evaluate the predicted land use based on the defined scenario. Furthermore, other required information layers including roads, villages, settlements, and protected areas maps were gathered from department of environment of Iran. Moreover, Digital elevation model (DEM) of Shuttle Radar Topography Mission (SRTM) was the other utilized information layer. Initially, geometric and radiometric corrections were accomplished on Landsat 5 TM and SENTINEL-2 MSI images. The land use maps of 2000 and 2010 were produced using maximum likelihood classification of those corrected images. Moreover, required layers for producing transition potential layer were prepared based on the available information. At the next satep, a scenario for forecasting land use changes was utilized to produce land use maps of the selected dates. This scenario was based on the increasing bare lands or degraded forests, increasing farmlands, and increasing settlement areas. Then, transition potential maps were produced using implementation of fuzzy transforms, weighted linear combination, and multiple criteria decision making methods. In this step, Markov chains models were utilized to generate transition probability matrix which is an indicative of the number of changing pixels and their area in the considered time range of the study. Finally, land use maps of 2016 and 2030 were produced by cellular automata using a neighborhood filter of 5 pixels. Validation of forecasted land use map of 2016 was performed by the classified SENTINEL-2 MSI image of that year. The validations results represented kappa coefficient of 0.84 which is a good result. Hence, the results suggested a good performance of the implemented model for forecasting land use changes of the study area. Furthermore, land use map of 2030 revealed that the settlement and bare land classes will increase significantly in that year. On the other hand, dense forest and degraded forest classes will be decreased which the last one can be due to transition of this class to the bare land class. Therefore, integrated use of Markov and Cellular automata models along with MCDM method can be lead to forecast the land use changes with an acceptable performance. Moreover, the results proved the proper performance of the selected methods.

Indoors positioning requires methods that can accurately represent the user's position. Unfortunately, Global Positioning System (GPS) due to signal attenuation when there are not the direct lines of sight from a mobile phone to least three satellites, especially in dense urban areas and inside the building cannot be used effectively. Internal positioning is based on four methods time of arrival (TOA), time difference of arrival signal (DTOA), signal angle of arrival (AOA) and signal strength (RSS).
Time of arrival, sometimes called time of flight (ToF), is the travel time of a radio signal from a single transmitter to a remote single receiver. This method is used the absolute time of arrival at a certain base station rather than the measured time difference between departing from one and arriving at the other station. The distance can be directly calculated from the time of arrival as signals travel with a known velocity. Time of arrival data from two base stations will narrow a position to a position circle; data from a third base station is required to resolve the precise position to a single point. Many radiolocation systems, including GPS, use ToA. The time difference of arrival (TDOA) is based on trilateration and uses the time difference measured from two stations to define a hyperbolic curve as an estimation of the position of the mobile device. Using an extra station provides a new hyperbolic and the intersection of two hyperbolic curves give the position of the mobile device. The angle of arrival (AOA) method is based on triangulation and determines the position of a mobile device using intersection of directional lines between the mobile device and at least two base stations. The RSS (Received Signal Strength) sometimes referred as RSSI (Received signal strength indicator) is a measurement of the power present in a received radio signal. The nodes used by the accurate Wi-Fi location monitor and Bluetooth beacon Tracker are capable of measuring the RSS of nearby Wi-Fi and BLE devices.
One of the methods of determining the position based on the RSS method is the wireless network signal, which is now used extensively in a local area network. An internal positioning algorithm for a wireless network can be done in three ways:  proximity algorithm, triangulation algorithms and sense analysis algorithms. Due to the multi-path effects and the effects of the signal propagation inside the internal environments and the lack of direct vision between the transmitter and the receiver, sense analysis algorithm is used, which is usually based on fingerprint location using signal strength. Fingerprint refers to the way adaptation some of the characteristics of the signal that is dependent on the location. There are at least 3 positioning algorithms based on the fingerprint that use the sense analysis method probabilistic, nearest neighbor, neural networks. In this paper, the positioning system is implemented based on all three methods and their accuracy is compared with each other. In each of these methods, in order to improve the accuracy of location and time of calculation, the choice of suitable transmitters and the increase of reference points by different methods are compared and their accuracy is compared with the methods of determining position in the usual mode.

Suitable spatial data is one of the most important needs of managers and planners in the field of accident management. An example of such accident was the fire and collapse of PLASKO building, one of the commercial and shopping center in Tehran. Over the past few years, regard to development of technology such as social networks, cloud computing, the participation of non-professional public in collecting and sharing geographic information has been increased. These type of information calls volunteered geographic information (VGI) and could be a precious alternative for authoritative counterpart due to offering substantial characteristic like free use of data, local knowledge and up-to-date creation, although VGI suffers from a lack of quality awareness which has led to hesitate about usage of this type of data in this particular subject. Therefore, assessment of VGI quality is necessary in terms of fitness for use in accident management. In this article, we have chosen routing of relief group as one of the most important needs in the PLASKO case. Moreover we have selected fitness for use as the quality indicator not only it is dominant aspect of quality which depends on user's need and intended purpose but also, this quality indicator was discussed generally on the other scholars work. Therefore, we aim to propose a workflow which could support the decision whether VGI dataset could be adequate for accident management or they could meet the active group's expectations or not. In order to perform, geometric and descriptive information was identified at first. For example Edges and Nodes of road network are the required geometric information. Name, speed, type, one way-two way and so on are descriptive information of road network. From the descriptive information, we have assessed name of road network. Then quality indicators accordance to mentioned information should be determined. Completeness, positional accuracy, logical consistency applied as quality indicators for describing edges and nodes, attribute accuracy and attribute completeness used as quality indicators of roads' name. Next, we have estimated the indicators by using intrinsic or extrinsic ways. Besides, we have applied a grid-base approach to divide the region and find the tiles with high heterogeneous or worse quality. After all, In order to assess fitness for use, which is described by other indicators, a fuzzy expert system was used to aggregate entire indicators. In the implementation process, district 11 of Tehran was selected as the study region. OSM site was used as VGI data set. Also, the data generated by the municipality was used as the standard data set. Meanwhile, in this article, a grass-based module was adopted to match two datasets as a prerequisite step in extrinsic ways. The results of this paper indicates that 74.4% of the total area has a fitness for use (0.93- 0.9) and the rest of all are in the range of (0.5-0.91). On average, the whole region has 89% fitness for use. In the following, analysis of the shortest path as a criteria for evaluation was applied in two datasets. The result was shown 95% similarity of OSM data compared with standard data.

Forest has been introduced as one of the resolvable sources in environment. The forest regions considerably effect on metrological condition and CO2 content of the regions. Hence, Management and preservation of these sources is so critical and important for forestry organizations. Since, propounding tree species maps are essential and useful issues for managers and also because of vast area of the forest, remote sensing could be powerful tool for forest mapping in a large terrain. Optical and synthetic aperture radar (SAR) are two prevalent remote sensing imaging systems which have high capacities to provide different information, such as recognizing type of tree species, biomass and CO2 content estimation, tree chemical combinations and tree species classification which commonly could be utilized in forest regions management. In this paper, because of same spectral and structural behaviors of trees to each other and existence of various types of trees in forest, a new algorithm has been developed to classify tree species by using Hyper-spectral and POLSAR images. The algorithm consists of two main stages, first the co-registered image is segmented to certain groups of homogenous pixels and forest and non-forest segments are separated to each others. Since in SAR images every object of land surface has certain scattering mechanism and because of volume mechanism of forest regions, we utilize polarimetric signatures so that recognize volume scattering mechanism in image. Second, specific features of each segment are extracted from Hyper-spectral and POLSAR images. Reflectance in each band, continuum removals features in special spectral ranges and spectral indices related to chemical contents tree structures, stress and spectral ratios are some extractable features from Hyper-spectral image and on the other hand, POLSAR features include original features, decomposition methods, and SAR discriminators. Although both hyperspectral and SAR images provide large number of features, but some of them have correlation to each other and became as extra features which should be removed in trend of process. For this reason, non-parametric feature selection algorithm has been proposed to select effective features among all. To choose the optimum features, genetic algorithm is applied and then, trained SVM algorithm in feature space with optimal dimensions classifies image to certain tree species. To find out ability of each type of features in classification, the features are divided to groups with certain number of features and overall accuracy and kappa coefficient related to each group is calculated. The results demonstrated that combination of GA algorithm and all features has more accuracy than others algorithm which was about 82.78% in overall accuracy and 79/36 in kappa coefficient. Spectral indices related to chemical behaviors of trees and reflectance in SWIR regions have better performance than other mentioned optical features. Some effective polarimetric features, such as lambda, degree of polarization, Alpha/H of cloude-potier decomposition, span and Kd of krogager decomposition were some freatures with high ability in tree species classification. SAR features play major roles in three parts of the result; first, constructed Polarimetric signature of SAR images was useful for displaying volume mechanism and separation of forest and non-forest segments. Second, mentioned features of SAR images had high ability to distinct pine species of needle leaf species from broad leaf species. The last application of SAR images was in helping the algorithm to classify short leaf loblolly and loblolly from each others.

One of the major problems in the Iranian cities is distressed urban areas, which has been caused by population growth and the rapid development of urbanization in recent years; this has led cities to displacement of citizens and consequently displacement of important urban utilities from primary residential areas to new ones. Gradually, the initial housing cores have been in major problems and changes in terms of performance and fabrics which leads to distress and decline in life quality of the citizens. Distressed urban textures is one of the most important issues of urban spaces, which is cause Disorganization, imbalance, lack of proportion and disproportion witch is classified into two categories, relative distressed (fabric or performance) and complete distressed (fabric and performance). The subject of restructuring urban decay is one of important subjects in developing urban frameworks. According to studies, 20 percent of urban areas located in urban decays, and 30 percent of the urban population are living in urban decay Therefore, renovation, rehabilitation and repair of these textures along with new ones is one of the important concerns of urban managers. This research tries to improve gentrification in Iran by providing a spatial model, Therefore the Qazvin city has been studied as one of the most important and historic cities in Iran. The research method in this research is descriptive-analytical and all of the functional and physical standards affecting urban distressing are identified and defined based on library studies, documentation and field observation. Then, by considering the ANP and Topsis models and validating them, the most appropriate method for determining the significance of the criteria is introduced as well as the combination of multi-criteria decision making models with Geographic Information System (GIS) and fuzzy logic has been used to identify and grade distressed neighborhoods in Qazvin.
The results of the research indicate that the social and economic factors (functional) of residents in urban neighborhoods have a significant impact on the structure and erosion of the neighborhoods, so that the final distressed texture map of the model`s output shows us that In contrast to the distressed texture map in 1387 and 1389, by using three indicators of Microhardness, impenetrability and instability, about only 14 percent of Qazvin city is estimated distressed (504 hectares), by considering 22 functional and physical indicators In this study, the identified area of distressed texture was 1564 hectares in the form of 27 neighborhoods, in the other words 42.8 percent of ​​ Qazvin`s area (3647 hectares) which all these studies indicates low quality of life in about half of the city of Qazvin.

Developing the scenarios for planning urban expansion is vitally important for urban planners especially because of its effects on land-use and improvement of land-use politics. However, urban expansion as a continuous and contiguous phenomenon is itself under the effect of human decisions and so uncertainty. Thus, in this research by using fuzzy logic, we tried to model the vagueness of urban expansion. Moreover, to determine the weights of fuzzy rules independent from the idea of experts, genetic algorithms was used to find the best values.
In this research by taking three criteria, namely land-value, accessibility and attractiveness, which are three pillars of sustainable development, a fuzzy-cellular automata model was developed to simulate the physical urban development in Qazvin area, Iran. After that, by setting the model parameters between years 2005 and 2010, the best results for 2010 were achieved. This model attained to the accuracy of 94.31% based on Cappa Index and 55.28% based on Figure of merit which is certainly better than previous researches on this area of study. Therefor using the calibrated model, urban development of year 2015 was simulated. For that year Cappa Index calculated as 88.73% and Figure of merit calculated equal to 39.05%. These results show the high performance of this model in the area of urban land use development. Thus combining cellular automata, fuzzy logic and genetic algorithm seems to be successful in simulating geographic phenomena.

Automatic roads detection in urban areas is of greater importance and is a persistent research focus in the remote sensing community. The spectral and geometrical varieties of road pixels; their spectral similarity to other features such as buildings, parking lots, and sidewalks; and the occasional obstruction by vehicles and trees are obstacles to the precise identification of urban roads through satellite images. For road detection, panchromatic or multi-spectral images, especially in urban areas, will yield ambiguous results, due to the additional complexities. For example, in an aerial photo or a high-resolution satellite image, both roads and buildings will appear similar, because their construction materials are usually the same. As a result, they cannot be readily distinguished. This becomes worse when they are in shadow or covered by roofs or walls of tall buildings. Accordingly, neither automatic nor semi-automatic methods will be entirely reliable in these dense urban areas. Moreover, the outputs of methods that use 2D images are more ambiguous than those with 3D inputs. Lidar point data have the potential to distinguish 3D features from one another, to distinguish 3D from 2D, and to distinguish 2D features from one another. However, Lidar intensity data are affected by a high amount of noise, and therefore are unable to distinguish roads from features with similar return signal power. Consequently, the full potential of the Lidar data cannot be exploited from raw data. Combining these two kinds of complementary data sources seem to be reasonably promising for road extraction, 3D urban modeling, etc. The main idea behind the integration of Lidar and optical imagery is that the strengths of one data type can compensate for the weaknesses of others. For example, being short of spectral information, Lidar data have high classification confusion between human-made and natural objects, whereas multispectral data have increasing classification confusion between spectrally identical objects in complex urban landscapes. In the light of these findings, in this paper, highresolution QuickBird satellite imagery and Lidar data processed through nearest-neighbor classification based on optimal features have been used together to extract various types of urban roads. This work designed and implemented a ruleoriented strategy based on a masking approach. A supplementary strategy based on optimal design features was also used. Accordingly in the vegetation class, the accuracy was 93% and 93% for the producer and user accuracies respectively. In the case of the high road class, the accuracy 91 % and 84% and in the buildings class, the accuracy was 93% and 93% for the producer and user accuracies, respectively. In the low roads class, the accuracy 89% and 86% and in the open-space class, the accuracy was 80 % and 85% for the producer and user, respectively. Finally the overall precision of class identification is 90 % with a kappa coefficient of 0.87, which shows a satisfactory precision according to different conditions and considerable interclass noise. The final results demonstrate the high capability of object-oriented methods in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite imagery and Lidar data.

Hyperspectral Imagery as a modern technology of remote sensing is a valuable source for various applications in geo-sciences such as land cover mapping, mine exploration and environmental monitoring. However, because of hardware and technological issues, these data have inherent problems. Problems such as various noises, data redundancy, absorption and damaged bands, etc., which are caused by different factors. Since the improvement of hardware system in hyperspectral sensors is very expensive, image processing methods like noise reduction and feature extraction are less expensive and more important. The most recent and competent method is multi-hypothesis prediction. The multi-hypothesis prediction method acts based on forming a linear combination from neighbors of a pixel, relying on the belief that the target pixel is most likely to its neighbors. As its drawback, this method does not use the convenient way to select the bands with maximum likelihood. The aim of this research is to apply the multi-hypothesis prediction method and to select the spectral bands, based on linear regression. The experiments revealed applying bands grouping using linear regression method improved greatly the signal to noise ratio which are noticeable in classification results. This approach was used because of its good flexibility in determining the coefficient similarity of spectral bands. The approach consists of several steps: first, the proposed method was applied on the data to reduce the noise. Second, a reasonable number of features were extracted based on existing classes and using Feature Space Discriminant Analysis method. And finally, the extracted features were classified. The data sets used in this research are the tip data available for hyperspectral image processing experiments which were collected by Bask University (Spain). The data sets include the images of training sites of Indian Pines and Pavia University farms which were acquired by AVIRIS and ROSIS sensors respectively. These data were selected to have variety option in bands and spatial resolutions, so that they can show the effectiveness of the proposed method more effectively. The results of implemented method using the Support Vector Machine and K Nearest Neighbor classification on two sites showed the overall accuracy of 95.82, 99.43, 92.89 and 98.88 respectively. This means an improvement of 0.3 and 0.4 by SVM classifier and 8.22 and 2 by KNN classifier for two sites respectively. The results revealed the good performance of applied method. The advantage of the proposed method appears when the accuracy of the KNN and SVM Classification approaches to each other. The results showed that the method was able to improve, significantly, the KNN classification more than SVM classification. From this we can conclude that the proposed method, regardless of the type of data and classification method, can greatly increases the classification accuracy. This is because of the proper manner of the method in data processing, regardless of the classification approach. Since each classification method responds to a particular type of data, each classification method has its own specific results for a given type of data. To overcome this problem, the data were matched with each specific classification method.

Tropospheric delay is considered as a disturbing parameter in GNSS positioning. Therefore, it is necessary to eliminate or reduce its effects in order to increase the accuracy of position determination. However, nowadays for meteorological applications such as precipitation prediction, the amount of water vapor in troposphere is determined by obtaining the delay of GPS signal while crossing Earth’s atmosphere because tropospheric delay is a function of pressure, temperature and humidity.
Since the development of the meteorology with GPS methods in 1990s, global navigation satellite system is known as an effective way to study atmosphere, e.g. the estimation of zenith tropospheric delay. Tropospheric delay can be estimated in two ways, the double differencing technique and precise point positioning technique. In precise point positioning technique, one receiver is used to collect data. This method is used to directly derive zenith tropospheric delay by non-differenced observations.
Today, permanent GPS networks not only are used for geodetic and surveying applications but also are used to determine the amount of water vapor in troposphere. In fact, these networks are used to predict weather condition. To do this, along with GPS receivers, meteorological sensors should be installed and functioned. These sensors are used to measure surface pressure, humidity and temperature. The collected data are used in existed equations such as Saastamoinen equation for the case of prediction.
Knowing the exact amount of water vapor in the atmosphere is of great importance in predicting the weather. In this study, due to the benefits of the dual differential method, such as low costs, Precise Point Positioning (PPP) approach has been used. Also by using Precise Point Positioning (PPP) approach, Zenith Tropospheric Delay (ZTD) parameter estimated with the help of GPS receivers of SAMT system and TEHN station. Observations have been used for twenty consecutive days from 07/10/2015 to 07/29/2015. By using synoptic meteorological data, the share of the hydrostatic (dry) section from the non-hydrostatic (wet) section in Zenith Tropospheric Delay is separated. Then by using the transfer function, the delay caused by the non-hydrostatic section which is due to water vapor in the atmosphere, turned into water vapor that could rain. For processing, the three types of clock and final orbital data, rapid and ultra-rapid data produced by the International Global Navigation Satellite System (GNSS) Service (IGS) have been used. Observations was Processed in the software Bernese 5.0. The obtained results were compared with the results of precipitation weather stations. To validate the amount of water vapor calculated by the GPS, we used the corresponding values were observed by radiosonde. For this purpose, the TEHN GPS stations and Mehrabad Airport weather station was selected. The results showed that when clock and final orbital data were used, the standard deviation, RMS and correlation were 1/2564, 1/0962 and 0/9698 Respectively, And when clock and rapid orbital data were used, these parameters were 1/5650, 1/2235 and 0/9647, respectively. Finally, The values of standard deviation, RMS and correlations for ultra-rapid data were 2/6086, 1/5796 and 0/9352, respectively.

Multi-sensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. In this paper, a deep learning decision fusion approach is presented to perform multi-sensor urban remote sensing data classification that contains deep feature extraction, logistic regression classifier, decision-level classifier fusion and context-aware object-based post-processing steps. In this context, a deep architecture is designed to progressively learn invariant and abstract feature representations of the input data comprised of spectral, spatial or joint utilization of spectral-spatial features. In the last one, the raw spectral data are firstly used to take advantage of the available contiguous spectral bands for classification applications. The spatial feature descriptors are secondly made by a local window considering that a set of neighboring pixels for many cases belong to one class. A hybrid set of joint spectral-spatial information is finally made by stacking the above-mentioned feature descriptors. Then, a logistic regression classifier is applied to train high-level features at the top layer and to optimize the deep learning framework by enforcing gradient descent from the current setting of the parameters to minimize the training error on the labeling samples. In the next step, an enhanced classification map is estimated by integrating multiple classifier outcomes, followed by a context-aware object-based post-processing refining the obtained pixel-based land cover classification map. In this case, a multiresolution image segmentation method is employed to split the image data into multiple spatially non-overlapping regions and then, the final label of each segmented region is made by majority voting and considering relationships among spatial objects. A series of comparative experiments are conducted on the widely-used dataset of 2014 IEEE GRSS data fusion contest. It enables a challenging multi-resolution and multi-sensor image analysis and data fusion opportunity; the visual data contains a series of color images associated with different strips, and the thermal hyperspectral image was acquired by the 84 spectral bands Hyper-Cam airborne sensor over Thetford Mines in Québec, Canada, with 874×751 pixels (spatial resolution of 1-m) and comes with a seven class labeled ground truth map. In the above-described datasets, the training samples are randomly detached as one hundred of each ground truth label and the rest are used as the testing samples. A set of comparative experiments are carried out on the above-mentioned datasets to quantitatively investigate the significant advantages of the proposed classification frameworks over the conventional classifiers containing decision tree, discriminant analysis, naïve Bayes, k-nearest neighbor and support vector machine. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers. In this context, the proposed method provides 3.91, 6.65, 2.81 and 5.52 percent enhancements for visual, thermal, combination and context-aware object-based post-processing data respectively in terms of OA/Kappa metrics. On the other side, the joint use of imaging systems improves the performance of classification up to 7.57 and 22.22 percent with respect to the visible and thermal hyperspectral data, respectively.

The Earth’s land-covers are exposed to several types of environmental changes, issued by either human activities or natural disasters. On 15 April 2017, severe precipitations in the west and southwest regions of Iran caused flooding in the rivers of Ilam, Lorestan and Khuzestan provinces. The peak of these rainfall was in the Karoon Basin and the Dez Dam, causing an unprecedented flood in recent years with an intensity of eight thousand cubic meters per second. The occurrence of this flood has led to damages to these villages and agricultural plains. Due to the occurrence of natural disasters across the world, there is a strong need to develop an automated algorithm for fast and accurate extraction of changed landscapes within the affected areas. Such techniques can accelerate the process of strategic planning and primary services for people to move into shelters, damage assessment, as well as risk management during a crisis. Therefore, a variety of CD techniques has been previously developed, based on various requirements and conditions. However, the selection of the most suitable method for change detection is not easy in practice. To our best of knowledge, there is no existing CD approach that is both optimal and applicable in the cases of (a) using a variety of high spatial resolution optics and radar remote sensing images, (b) lack of labelled samples, (c) noisy multi-temporal images (d) non-linearly separable change and no-change classes. In addition, a low degree of automation is not optimal for real-time CD applications. Hence this paper aims to resolve the above-mentioned problems.
Recently, the Object-Based Image Analysis (OBIA) are applied considered for change detection, namely, Object-Based Change Detection (OBCD) techniques. The OBCD method uses image objects for analysing change information by comparing segmentation and classification results. Otherwise, it can extract change information by comprehensively analysing the objects of multi-temporal images using spectral, texture and structure information. Consequently, this method has normally high detection accuracy and robustness. However, since both pixel and object based methods partition the observation space linearly or rely on a linear combination of multi-temporal data, suffer from high false alarm detection rates. As a result, they can be inefficient for images corrupted by either noise or radiometric differences, which cannot be normalized. This is particularly true for high spatial resolution images, where the class distributions are strongly overlapped.
Within the last two decades, kernel-based methods have demonstrated and reliable results success in many remote sensing applications, in particular for classification and change detection problems. The main idea of kernel methods is based on the fact that the nonlinear decision function can be obtained by running a linear algorithm in a higher dimensional feature space.
In order to resolve these problems, an integrated object-level CD method based on an object-based classifier and Support Vector Data Description (SVDD) method is proposed. In order to using the information contents of radar and optical data simultaneously for enhancing the accuracy of change map, the kernel based fusion method was proposed. In addition, parameter determination of the proposed method is addressed automatically by using an inter-cluster distance based approach. In order to evaluate the efficiency of the proposed method and extract the flooded areas, optical and radar remote sensing images from before and after of Shoosh 2017’s flood, acquired by sentinel-1 and 2, were used. The accuracy analysis of results showed a great flexibility for change detection of by finding nonlinear object-level solutions to the problem. Furthermore, the comparative analysis of proposed object-level CD method in the case of using fused radar and optical images (overall accuracy (O.A.): 94.24, area under ROC (AUC): 0.98) respect to Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD) (O.A.: 85.10, AUC: 0.87), Principal Component Analysis (PCA) (O.A.: 77.89, AUC: 0.78) and Spectral Angle Mapper (SAM) CD methods (O.A.: 80.67, AUC: 0.82) showed that the accuracy of the change maps is relatively improved.
The proposed CD approach leads to an acceptable level of accuracy for both optical and radar imagery. The results confirmed the fundamental role and potential of using both optical and radar data for natural hazard damage detection applications. The microwave signals have high sensitivity to water content of wetland and flooded areas which increase the intensity of the backscatter signal. Consequently, radar sensors have high potential in detecting environmental changes during natural disasters with adverse weather conditions.