نشریه علوم و فنون نقشه برداری
سال هفتم شماره 4 (پیاپی 28، بهار 1397)

Spatial Data Infrastructure (SDI) has key role in management of many human activities by building a suitable information infrastructure for collaboration and cooperation among different data producer organizations and private sectors. SDI traditionally follows top-down approach that that only use data of organizations and private sectors, so does not consider user generated contents. In other side, web 2.0 platforms and GPS equipped devices provide user friendly tools for collaboration people in producing and sharing spatial data recently. This approach has enabled people even with no knowledge in spatial science to produce and share spatial data. These data that called volunteered geographic Information (VGI) are valuable data that could be integrated with other resources of SDI to complete and update SDI resources. VGI describes any type of content that has a geographic element which has been voluntarily collected. In this regard, user-centric SDI approach (the third generation of the SDI) that concentrate on user needs and preferences, while in the past SDI initiatives had concentrated mainly on technological issues such as data harmonization, standardized metadata models, standardized web services for data discovery, visualization and download, supports opportunities of VGI as viable means of updating and enriching SDI. However, some components of SDI should develop to consider valuable VGI resources. In this paper, Volunteered spatial data infrastructure (VSDI) model is proposed. In this model, VGI is used as other resources of spatial information in SDI. In addition, a system is developed based on VSDI concepts in road transportation projects. Road transportation projects and specially route selection project need to transform huge spatial data. In the route planning for road transportation, best route selection process is carried out with consideration of environmental, social and economic effects and as well as supplying technical transportation criteria to access sustainability. This process gets into trouble without suitable programming for public participation in route transportation planning and coordination between different related organizations and private sectors to the project. In addition, lack of sufficient information, lack of updated information or lack of same way to gathering required information, usually is one of the biggest obstacles to obtain the results based on real world. This VSDI based system could give decision makers update information of region to get better decisions by providing collocation environment. Based on the concepts of VSDI model, the system is designed by using web 2.0 technologies that enabled user to produce and share spatial data as well as to express opinions. To select best route in this system, fuzzy group AHP and VIKOR decision making methods are used with environmental, socio-economic, tourist and technical route characteristic’s criteria. The results of the research show that people like to collaborate in transportation planning and to share spatial data. So the use of VGI as another resource of SDI helps to make better and more effective decisions and to increase people satisfaction. The achieved results of implementation indicate that the system has good performance and selected route in the system corresponds to determined route by consult engineering company.

Although close range photogrammetry has not been a common approach in the Civil Engineering field especially the displacement monitoring of large scale constructions, many project successfully used this approach and as a result this method have a high potential in this application. Nowadays, as a result of improving the computer vision and photogrammetry techniques implemented in many software and high resolution images captured by the off-the-shelf digital cameras, the use of progressive photogrammetric techniques instead of traditional displacement methods become more resendable. This paper aims to explain a novel method for monitoring of the large scale constructions based on visual inspection. This method can specify the displacement of the constructions based on photogrammetric and computer vision methods using drones. The evaluation of the proposed system is done on long wall in two epochs with a short time interval. Having known the zero displacement for this construction, the difference between the coordinates of some sample points obtained in first epoch and the second epoch, 1.89 mm, shows the accuracy of the proposed system in detecting displacement. Moreover, the accuracy of this photogrammetric method was investigated by developing a tool which manually provides a known displacement between two points in the second epoch. The displacements of these points were estimated using the proposed method and compared with the known displacement. The accuracy for this method, less than 2 mm, can confirm the capability of this method for such applications.

With the increase in world population, industrialization and improvement in the standard of living, there has been a continuous increase in consumption of energy. In the recent years, a new resource of energy, gas-hydrates, is drawing worldwide attention. Detection and identification of suitable areas of shallow geothermal energy, using remote sensing data is one of the new methods in many applications. In areas of anomalously high heat flow, geothermal systems transfer heat to the Earth’s surface often forming surface expression such as hot spring, heated ground, and associated mineral deposits. Geothermal systems are increasingly important as sources of renewable energy, or as natural wonders of protected status attracting tourists, and their study is relevant to monitoring deeper magmatic processes. Thermal infrared (TIR) remote sensing provides a unique tool for mapping the surface expressions of geothermal activity as applied to the exploration for new geothermal power resources and long term monitoring studies. Airborne and space borne TIR data supports long-term monitoring of geothermal systems by providing a rapid and repeatable method of inventorying surface geothermal features. In addition, methods for relating the temperatures of surface geothermal phenomena to estimates of near-surface heat loss provide important inputs to the monitoring of geothermal activity and as applied to geothermal resource assessment and modeling. A geothermal resource can be simply defined as a reservoir inside the Earth from which heat can be extracted economically (cost wise less expensive than or comparable with other conventional sources of energy such as hydroelectric power or fossil fuels) and utilized for generating electric power or any other suitable industrial, agricultural or domestic application in the near future. Geothermal resources vary widely from one location to another, depending on the temperature and depth of the resource, the rock chemistry and the abundance of groundwater. Utilization of geothermal resources can broadly be classified into electric power generation and non-electric use. The type of the geothermal resource determines the method of its utilization. This research is based on applications of remote sensing as a decision support system that focused on the exploration of geothermal energy and environmental management. The aim of this study is to identify suitable areas for Shadow geothermal energy detection by integrating of land surface temperature (LST) anomalies and the energy flows of surface energy balance algorithms for land (SEBAL) algorithm using data LDCM data, , has been evaluated and analyzed in the North West of Iran. To this end, and because of at least the effect of solar radiation, two examined the scenes of LDCM data was used for dates October 13, 2016. Then, using two single-band algorithms (Radiative Transfer Equation (RTE) and SCJM&S) to calculate the LST and the LST anomaly maps of were identified. In addition, using the SEBAL Algorithm was calculating the amount of net radiation received by the Earth's surface (Rn), the amount of heat flow between the different layers of soil (G) and the amount of radiation absorbed by the solar surface (Rsolar). By assessing and combining this information layers with the LST anomaly maps the shadow geothermal prone areas were identified and determined. The results showed that the areas between the cities of Marand and Tasuj as well as between Gator and Khoy cities prone shadow geothermal areas, the existence of large natural spa in the region, the possibility of geothermal resources increases and this is confirmed . Also, similar results were obtained in areas south of the city of Urmia and west of Oshnavieh. These obtained areas have the maximum distance that the location of energy consumption (in Urmia, Khoy, Marand, Tasuj, Sharafkhaneh and Oshnavieh) equal to 30 km, which is economically justified and it can provide a large part of the clean energy used in industry and cities and brings a healthy environment.

Many real-world spatial decisions are multi-criteria by nature. A multi-criteria spatial decision analysis is a process in which one or more spatial alternatives are evaluated and selected based on a set of different criteria, by one or a group of individuals. Weighting the criteria is an important step of spatial multi-criteria decision-making. It represents the priority of that criterion relative to other criteria in decision making process. According to the range sensitivity principle, the weight of a criterion is the function of the range of changes in the values ​​of that criterion (the difference between the minimum and maximum values ​​for the criterion), in addition to its relative importance of the criterion. However, decision makers often ignore the range values when weighing the criteria in decision making processes.
The main objective of this paper is to examine the research question “Do decision makers consider criteria ranges during the weighting process in a multi-criteria spatial decision-making process? Understanding how decision makers acquire and integrate decision-related information (i.e., criteria range values) helps to use an appropriate level of decision information in a multicriteria spatial decision analysis. In order to achieve the objectives of this research, the problem of locating public parking facilities in the district # 22 of Tehran was selected as the case study. The decision information including criteria values and ranges were presented to decision makers using decision table and map. The decision table represents the decision information in an alternative × attribute matrix. It consists of a set of values associated with each alternative-attribute pair. The rows of the matrix represent alternatives, the columns represent attributes, and the cells contain the measured values of the attributes associated with the alternatives. In addition to the alternative-attribute values, the table includes the range values of the attributes in the last row. The simultaneous map-table information aids facilitates understanding of the decision problem by enabling the decision makers to explore the basic relationships between the non-spatial attribute values of decision alternatives (criterion outcomes) and the spatial patterns of alternatives (decision space).
The results show that decision makers in individual decision-making (without access to group decision) examined 55.5%, 26.8%, 25.5% and 14.5% of criteria ranges in four levels of decision-making information, respectively. When it comes to the group decision-making mode (with access to group decision), they looked at 21.8%, 6.6%, 8.8%, and 7.9% of criteria ranges in the four levels. Overall, the results of ANOVA test show that this number decreases with increasing amount of decision making information. Therefore, it can be concluded that decision makers mainly consider the relative importance of the criteria, and in most cases ignore the ranges of changes in the criteria values, when faced with higher levels of decision-making information. The results of this study has implications for investigating behavioral theories in the spatial decision making context and practical implications for the development of multicriteria decision analysis. Explicitly, the findings provide a new perception on the use of decision support aids, and significant signs for designers to develop a suitable user-centered Web-based participatory decision analyses.

Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors is effective at land cover classification. All these data have different characteristics, e.g., different spatial and spectral resolutions, different angle of view, and different abilities and disabilities. For many applications, the information provided by individual sensors is incomplete, inconsistent, or imprecise. Fusion of information from different sensors can produce a better understanding of the observed site, which is not possible with single sensor. Particularly, Light Detection And Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. On the other hand, hyperspectral imaging is a relatively new technique in remote sensing that acquires hundreds of images corresponding to different spectral channels. The rich spectral information of HS data increases the capability to distinguish different physical materials, leading to the potential of a more accurate image classification. As hyperspectral and LIDAR data provide complementary information (spectral reflectance, and vertical structure, respectively), a promising and challenging approach is to fuse these data in the information extraction procedure.
This paper presents a multiple fuzzy classifier system (Multiple Classifier System or MCS) for fusions of hyperspectral and LiDAR data based on Decision Template (DT). After feature extraction on each data, the classification was performed by fuzzy K-Nearest Neighbor (KNN) on hyperspectral and LiDAR data separately. In a multiple fuzzy decision system, a set of decisions is first produced and then combined by a specific fusion method. The output of the fuzzy classifiers that provide the class belongingness of an input pattern to different classes is arranged in a matrix form defined as decision profile (DP) matrix. Then, a fuzzy decision fusion method (Decision Tempate) is utilized to fuse the results of fuzzy KNNs on hyperspectral and LiDAR data. In order to assess the fuzzy MCS proposed method, a crisp MCS based on (Support Vector Machine) SVM as crisp classifier and Naive Bayes (NB) as crisp classifier fusion method is applied on hyperspectral and LiDAR data. The experiments were executed on a hyperspectral image and a LiDAR derived Digital Surface Model (DSM); both with spatial resolution of 2.5 m. The dataset have captured over the University of Houston campus and the neighbouring urban area by the NSF-funded Centre for Airborne Laser Mapping (NCALM). Also hyperspectral image has 144 spectral bands in 380 nm to 1050 nm region. Training and testing samples were selected from different areas of the images. They are spatially disjointed.
Fuzzy MCS on hyperspectral and LiDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data. Overall accuracies of fuzzy classifiers on LiDAR and hyperspectral data are %75 and %88 respectively. Fusion of these two fuzzy classifiers produced %96 as overall accuracy. Second scenario for joint use of hyperspectral and LiDAR data is fusion of these two data through a crisp decision fusion system. The results show that fuzzy classifier provided higher accuracies than crisp classification based on SVM for both data. In the presence of mixed coverage pixels in remote sensing data, crisp classifiers may produce errors while fuzzy classifiers are not affected by such errors and in principle can produce a classification that is more accurate than any crisp classifier. Also, fusion of ensemble of fuzzy classifiers based on Decision Template method produced more accuracy than fusion of crisp SVMs based on Bayesian Theory.

The task of identifying corresponding objects between different geospatial datasets is known as matching problem which has variety of applications, e.g., conflation, spatial data enrichment, updating, change detection, and quality assessment. Matching problems in vector data models can be divided into three categories of point, linear, and polygon problems according to the type of geometry are being used in matching process. Furthermore, the similarity measures utilized in order to calculate the degree of similarity between two objects can be classified into three groups of semantic, geometric, and spatial relation measures. Since there are few studies of polygon matching problems compared to linear objects and geometric measures are easily accessible, among these various kinds of matching problems, the purpose of this study is to propose an approach to identify corresponding polygon objects based on geometric properties. The proposed approach contains four stages of preprocessing, spatial similarity calculation, extraction of corresponding relations, and results analysis. The preprocessing stage consists of creating uniformity among data formats and coordinated systems, and also removing topological errors. In the similarity calculation stage, a probability based matching algorithm is presented in which the four similarity measures of distance, overlapped area, orientation, and shape are being used. Then, the six kinds of corresponding relations including 1:0, 0:1, 1:1, 1:N, N:1, and N:M relations are obtained as the result of similarity calculation stage. At the end, the results are analyzed through evaluation of the algorithm. Besides that, the impacts of each similarity measure, solitary and in combination with other measures, have been studied in the final precision of algorithm as the evaluation process. The implementation is carried out on the district 6 of Tehran city as the case study area by using two different datasets at the scale of 1:2000 and 1:25000. The evaluation of proposed method has been achieved according to three criterions of Precision, Recall, and F1-score. Also the manual matching of two datasets is needed to evaluate the proposed algorithm. The results show that the proposed algorithm by using all four similarity measures has reached the F1-score precision criteria of 99% which is quite high over the case study area. Furthermore, the influence of each similarity measure has been studies both solitary and in combination with other measures which shows that the precision is not necessarily increased by the increase in the number of similarity measures. As an illustration, the exclusive usage of overlapped area measure has far higher precision in compared with the utilization of two measures of distance and orientation. Consequently, in order to decrease the cost and time of processing, it is better to use the least number of similarity measures which positively affect the algorithm precision. Also the precision of proposed method was compared to one of the latest work of polygon matching problems using geometric measures. The results demonstrate that the precision of polygon matching problem has been improved compared to the previous work.

Nowadays, tourists are planning trips by their own using the available services on the web. Travelling individually does not have the pleasure of group tours, so some tourists decide to find similar friends on their destination to share the joy of tours and trips. Also, they may not be familiar with the routes available in the city. In this paper, a mobile application is designed, developed, implemented and evaluated to recommend similar tourists to each other and generate optimal route based for each individual. This system is integrating mobile GIS, recommender systems and artificial intelligence tour planning algorithms into a single application. Being on a mobile platform is necessary, especially for navigation and tour planning systems. User may have to carry his cellphone in order to follow the suggested tour. The recommender system is a demographic filtering recommender to find the similar tourists. It considers age, nationality, education and interest to find the proper matches. After suggesting similar tourists to each other, one of the versions of Ant Colony Optimization namely Ant System is used to plan the optimal tours for each individual. This problem can be defined as a new version of Multiple Travelling Salesman Problem with mutual nodes to visit simultaneously. Different tourists decide to share a tour that stay in different places. This system should find a proper sequence of sightsees to minimize the length of the tour and also it should maximize the number of mutual streets in a tour to let tourists travel together during the tour. The objective function is to minimize the sum of the distances each tourist traversed and to maximize the number of mutual streets with a proper POI sequence. Maximizing number of mutual streets was considered because tourists want to travel as a group in a shared trip. Implementation of the system took place in Shiraz, Iran. Shiraz is the fifth-most-populous city of Iran and the capital of Fars Province with more than 4000 years of history that made it one of the key tourism sites in Iran. Numbers of questionnaires were distributed among different people to evaluate the people recommendation system. In addition, tour planning algorithm was evaluated using a well-known evolutionary algorithm (i.e. Genetic Algorithm). Mean, standard deviation and the convergence graphs of the two mentioned algorithms were compared. Results indicated accurate performance of the recommender system and high accuracy and precision of the route planning algorithm. Recommender System had a mean difference of 0.2 with the questionnaire results, which indicated its good functionality. Ant System reached the minimum value of the objective function (34.70) with a better standard deviation (0.61) compared to Genetic Algorithm. However, Genetic Algorithm performed better in mean value of the tests (34.32) which is a measure of the precision. Convergence graph of the Genetic Algorithm showed a fast convergence with lower objective values in the beginning. Ant System convergence graph showed a smooth convergence toward the optimal solution with an initial population with higher objective values. This indicated the proper functionality of the Ant System and the possibility of improving the results with generating better initial population.

In recent decades, we have witnessed a rapid and increasing growth in urban population and urbanization. One of the most notable effects of rapid urbanization in countries such as Iran is the emergence of a phenomenon known as land scarcity, which in turn leads to a struggle for land ownership in urban areas. The most noticeable political measure taken by urban masses in many cities is the illegal seizure of land in areas outside city limits. Such measures have contributed to the unplanned urban development and have further hindered future urban land-use planning efforts. Rapid growth of technology and increased complexity of daily activities have encouraged people to seek awareness and knowledge about the latest technologies in an attempt to adapt themselves to the world around them. At the same time, limitations have prompted individuals to use natural resources responsibly, take actions to prevent environmental damage, and put in an increasingly great deal of effort to improve workforce health and conditions. Today, the use of agent-based technologies in solving problems associated with the approaches and methods applied in urban land-use planning efforts has considerably increased. In the present study, Tehran’s urban growth was investigated within a five-year period. The model’s agents included parcels of land, physical limitations, suitability, and protected areas. In each time step, these agents were able to change their status from undeveloped to developed. This study was aimed at developing a dynamic agent-based model to model urban growth as the major factor influencing land-use changes. An agent’s decision to change its status depended on the specified eligibility criteria and the existing limitations and constrains. In addition, each agent’s relevant features were also assessed. Finally, an overview of the agent-based modeling (ABM) approach and its integration into a geographic information system (GIS) were presented.
The findings showed that if the land suitability were estimated 50 and 100, the number of developed parcels of land would be 4192 and 4424, respectively. This results indicated that the behaviors of Tehran residents followed the initial estimated land suitability (100).

Land use changes is an ecological processes in global and local status and it will be major problem in the twenty one century and even scientists believe more impact of land use changes than climate change. One of the methods used in planning to control land use changes, is its modelling. This research aims to predict land use changes carried out and with emphasis on physical development in Sari city using logistic regression and Markov chain. To analyze land use changes in the central area of the Sari city, TM sensor (Landsat 5) for 1987, 2001 and 2011 were used. For this purpose, the images taken from the USGS web, the necessary pre-processing (including radiometric and atmospheric correction) was performed in ENVI 5.1 software. Then, using supervised classification method and maximum likelihood algorithm land use maps were extracted in the study area. At this stage to predict of Land use, maps prepared imported to IDRISI software. Transition potential modeling was conducted using logistic regression in thee IDRISI software. The 6 variables were used (including two variable as a dynamic and four variables as a static variable) and 3 sub-models calibrated over time (1987- 2001, 2001- 2011 and 1987-2011). In order to prediction of land use in 2011 year, the calibration period of 1987-2011 using Markov chain model. And hard prediction was used. For accuracy assessment of LCM the KIA parameters was used. Finally from the 1987-2011 period in order to predict changes in land use, land use maps in 2025 and 2039 were used. Results showed that during 1987-2011, important changes occurred, including increasing of (4.49%) in residential area and decreasing in agricultural area by 13.4%. Also the results of transition potential modeling using logistic showed high accuracy in all scenarios (0.72 to 0.92). Kappa coefficient in the land use modeling for 2011 with calibration periods 1987-2011 and reference map in 2011 was higher than in other scenarios. The modeling results for the years 2025 and 2039 showed that physical development Sari in West, South, North and the East directions 8.02, 6.47, 6.37 and 4.41 % are respectively.

Recognizing the urban environments and understanding the citizens’ motion behavior is an important research field in the area of spatial data analysis. The location-based social networks record and gather update, rich, and enormous data that users share them honestly through their spatial, temporal and semantic behavior. Undoubtedly the physical structure of an urban area as well as its land use impress the spatial behavior of its citizens and this impression propagates to the data of location-based social networks. Because of that, nowadays, researchers use the users’ data in location-based social networks in order to recognize urban environments. In this research, we attempted to cluster urban environments based on social land uses by using the location-based social networks’ spatial and semantic data. In this regard, in the first step, the spatial data of users are clustered by employing a clustering algorithm that is based on a competitive neural network (SOM). To cluster the spatial data of users, we first should calculate the optimal number of clusters. In this regard, Elbow chart was used as DB index. Then, the urban environment is partitioned into several regions by drawing the Voronoi diagram on the cluster centers and the data which users have been recorded in each region are identified. The number of data available in each region was computed for semantic categories separately, then the vector of each region was normalized. Similarly, these operations were repeated for all data in whole urban environment and the. The initial idea is usage of the abundance of each category of semantic data; however, this criterion cannot determine the land use of a region properly; because it is possible that users share more information about, for example, creation places than residential ones. Finally after extracting the percentage of the different groups of semantic data and by considering the weight of each group, a semantic dimension that is the representative of the region’s social land use was assigned to each region by taking advantage of a clustering algorithm based on the semantic dimension of users’ data. To evaluate the proposed method, the number of data in each category was calculated for every 15 minutes of a day to verify the validity of data that users share about their activities in the foursquare social network. To more accurate study, the working days and weekend days were studied separately; i.e. for each category, we formed a vector with 192 members. The chart of temporal variations of data numbers during a day (24 hours) was plotted for clusters identified from proposed method too. Then, the correlation among these charts was used as the evaluation index of the proposed method. This research and the performed evaluation show that the big data of social networks are not only low cost and updated but also shared by citizens honestly and have suitable validity. Also, the urban regions with common or similar social land uses have spatial continuity. The results of the research show the high potential of the location-based social networks to recognize urban environments.

Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, urban heat islands, fire detection and earthquake thermal precursors. There is a shortage of daily high spatial land surface temperature data for using in high spatial and temporal resolution environmental process monitoring. Due to the technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result one sensor doesn’t provide both high spatial resolution and high temporal resolution. The 16-day revisit cycle of ASTER leads to a disadvantage in studying the global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors’ ability to quantify biophysical processes in heterogeneous landscapes. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. Since ASTER and MODIS sensors are onboard a platform (Terra or Aqua), therefore, this study has used an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm to blend ASTER and MODIS LST product. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from ASTER can be blended for applications that require high resolution in both time and space. The MODIS daily 1-km LST product and the 16-day repeat cycle ASTER 90-m LST product are used to produce a synthetic “daily” LST product at ASTER spatial resolution. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. The results show that the accuracy of ESTARFM algorithm is better than the accuracy of the STARFM algorithm in the studied area—with an average difference of 1.77 Kelvin from the real observation data. The STARFM algorithm couldn’t preserve the spatial details in the predicted virtual image as well as two other algorithms. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations with a high correlation coefficient (r) of 0.87 between synthetic imageries and the actual observations.

3D building reconstruction is a mathematic model and representation of 3D surfaces for building details in urban areas. There are many methods for 3D modeling such as Image Based Rendering (IBR), Image Based Modeling (IBM) and Range Based Modeling (RBM). These methods use generated 3D point cloud from different techniques sources such aerial laser scanners and photogrammetry multi view imageries. In this paper, 3D model generation methods based on triangulation algorithms such as Poisson, ball-pivoting and volumetric triangulation using Marching Cubes (MC) are evaluated using a raw dense point cloud. Also two mesh simplification methods called clustering decimation and quadric edge collapse are used to improve the quality of triangulated models with decrease the surface and vertex numbers. A geometric metric called Hausdorff distance is used for comparison of each model with a reference. The results show that the accuracy of generated 3D model based on volumetric triangulation method using Marching Cubes (MC) is better than other methods. Also, quadric edge collapse method can simplified 3D models better than clustering decimation method.

Nowadays, SAR imaging is a well-developed remote sensing technique for providing high spatial resolution images of the Earth’s surface which provides a vast amount of information for environmental monitoring. Fully polarimetric (FP) SAR systems alternately transmit two orthogonal polarizations and receive the response of the scatters to each of them by two antennas with orthogonal polarizations. Transmitting two interleaved electromagnetic waves requires doubling the pulse repetition frequency which implies immediately that the image swath must be only half of the width of a single-polarized or dual-polarized SAR. In order to achieve a better swath width, and coincidentally reduce average power requirements and simplify transmitting hardware, compact polarimetric (CP) systems have been proposed with the promise of being able to maintain many capabilities of fully polarimetric systems (Souyris et al., 2005). One of the most important CP configurations is dual circular polarimetric (DCP) mode.In order to extract the physical scattering mechanism (PSM) of targets using polarimetric data many classification methods have been presented. One of the most common such methods is H-α decomposition (Cloude and Pottier, 1998) that is proposed for FP data. Its principle relies on the analysis of eigenvalues and eigenvectors of the coherency matrix. The space of scattering entropy (H) and mean alpha angle (α) namely H-α plane is used to classify the polarimetric image into 8 canonical PSMs.In recent years two approaches have been proposed in order to find dual H-α classification zones for DCP data. (Guo et al., 2012) proposed an H-α classification space by mapping the points of each PSM from the original FP data into the space of H-α for CP data and subsequently (Zhang et al., 2014) proposed an H-α space on the basis of the distribution centers and densities of different PSMs. Experimental results showed that the classification accuracy of each PSM is improved compared with the results of Guo’s H-α space, however Zhang’s method is not well accurate and there are still overlaps between different PSMs. The results of Zhang’s method for H- α boundaries is highly dependent on the choice of data. For example, in one data it might exist a special class of plants that are dominant in the image and in another one another class might be dominant. So, the maximum distribution densities of these two images are different from each other. Furthermore, the specifications of different sensors are different. For example, the base noise of each sensor is different and entropy is dependent on this parameter. So, for each specific sensor its own optimum boundaries should be found.
According to the fact that fully polarimetric data contains maximum polarimetric information, the efforts of the researchers in this field is to achieve the nearest information from CP data to FP data. Therefore, in this research we have found the H-α boundaries of DCP data which maximize the total class agreement of classification results of the DCP and FP data for RADARSAT-2 sensor. Two images over San Francisco and Vancouver acquired by Radarsat-2 at C-band in quad polarization mode, with the image size being 1151×1776 and 1766×1558 respectively have been used for this study. In order to evaluate the ability of the proposed H-α zones in comparison with Zhang’s zones, Each experimental image is classified into eight PSMs. Confusion matrices have been achieved and the resultant mean agreements have been calculated. It has been shown that the proposed boundaries have increased the mean agreements of the results by 3%.
In order to extract the physical scattering mechanism (PSM) of targets using polarimetric data many classification methods have been presented. One of the most common such methods is Cloude–Pottier H-α decomposition that is proposed for FP data. Its principle relies on the analysis of eigenvalues and eigenvectors of the coherency matrix. Entropy and α-angle are two important parameters for the interpretation of fully polarimetric data which are extracted from this method. They indicate the randomness of the polarisation of the back scattered waves and the scattering mechanisms of the targets respectively. For fully polarimetric data an H-α classification space has been presented. This H-α classification space is devided by H and α borders and cllassifies 8 feasible PSM regions without the need for training data.
In recent years two approaches have been proposed in order to find dual H-α classification zones for DCP data. In 2012, Guo proposed an H-α classification space by mapping the points of each PSM from the original FP data into the space of H-α for DCP data and extract approximate borders. Subsequently, in 2014 Zhang proposed an H-α space on the basis of the distribution centers and densities of different PSMs. Experimental results showed that the classification accuracy of each PSM is improved compared with the results of Guo’s H-α space, however Zhang’s method is not well accurate and there are still overlaps between different PSMs. Both Zhang’s and Guo’s methods are not based on an optimization method. Therefore, they do not present optimum H-α borders for classification of DCP data. Furthermore, each sensor has its own specifications. One of which is the system noise floor which affects entropy borders for classification. Thus, it is important to find optimum H-α boundaries for each sensor separately.
In this paper we have proposed a novel approach for finding optimum H/α classification borders for DCP data. The optimum borders have been found in such a way to maximize the agreement of the H-α classification results of DCP data with the H-α classification results of FP data. ‘Mean class agreement’ is introduced and the borders which maximize this parameter have been found. The results of classification using the proposed borders have been compared with the rival method and the superiority of the proposed method has been revealed.

Vegetation is considered as an important measuring indicator in rangelands, which play a significant role in ecological processes. In this regard and due to the lack of models in estimating green vegetation canopy, this research aimed to evaluate temporal changes of canopy cover of vegetation in Marjan rangelands of Broujen. As the completion of the past investigated studies in other areas, it is tried to present the growth stages and time effect on destroyed green canopy cover through a model which has an appropriate estimation power throughout the growing season and for a time such that the education sample do not exist. To this end, the canopy of green vegetation was measured in 19 sampling points over a length of 10 km transects. In each of the points, there are about five quadrats as one quadrat in center and four quadrats at the four directions around the central quadrat (a total of 95 quadrats in a period). Sampling points were placed with distance of 400-1000 meter apart from each other. Measurements were repeated during 4 periods of field operations (in total 380 quadrats). Thus regarding to the index type (Atmospheric Resistant Vegetation Index and the moderating effect of soil line) 12 vegetation indices due to the use of multi-temporal images and the effect of soil reflectance were calculated in this arid region. In the next stage, the model of estimating green plant canopy was prepared using the regression equations between obtained canopy cover from five sampling periods and derived vegetation indices of Landsat 8 image data. Thus this model was also generated to prepare the vegetation cover map to other 4 periods which have not education data. Finally canopy cover map of vegetation was prepared at the 8 periods during the growth season (April to mid-September). Using image differencing technique, the changes of the vegetation coverage of area was estimated. Validation of cover estimating model represents no significant difference between estimated cover and land cover data. It is concluded that achieving to the growth model which can be used for all seasons are possible by using Landsat 8 images. The results showed that the indicators of ARVI, SARVI and EVI (with coefficients of determination about 8.0) have the best correlation relationship with canopy cover. So not only there is a significant relationship between vegetation indices and vegetation in arid areas, also it is possible to prepare only a unique data model for estimating all different time of growing seasons. According to the results of this research, the vegetation is in its peak on May among the eight growth times in the Mrjan rangelands and the canopy cover has heterogeneous distribution. In terms of coverage changes during the growing season, most of area is categorized in small variations class. In the period from April to mid-May we are witnessed for increased class in the region, but after this period, reduction in area were the dominant phenomena, as well as amplification of increased class area from 3 to 19 September is related to the emergence of new autumn species in the highlands region.

Most of the times identifying the terrains in some points of some images which are influenced by the others is difficult. So, some algorithms must be developed in such points. the auto selection color constancy algorithms have been indicated as a highly applicable algorithm to improve identifying of dark non metric laboratorial images. This paper is aimed to investigate and assess capability of this algorithms to reconstruct of images of remote sensing. By using a fuzzy logic, these algorithms help to choose an appropriate color selection algorithm of Gray-Edge, Gray-World or White-Patch. These algorithms are considered because of precision movement of light in addition to significantly illustration of color in images. Also, the study area has been divided into 50 equal sections in order to assessing the presented method and then the application of GE, GW and WP has been assessed in each section by two experts. Since the study area has the different shadow positions and objects, the existent information of sections is not integrated and the assessing of the result would be reliable. It is shown in most of the times the presented method has better results in clarifying of shadow terrains and could better clarify the edge of the terrains.

During recent years various studies have been conducted concerning the trend of urban structure changes, and the decay process in the district of urban neighborhoods. In the present study, the Multi-Criteria Decision Making (MCDM) model and some other statistical models have been used in order to provide a susceptibility map of decayed area. In addition, different criteria have been implemented to identify and analyze the decayed area.
According to a review of background literature, the present study takes into account four criteria: i.e. ecological and environmental, economic, social and structural based on specified sub-criteria for the purpose of providing and analyzing the susceptibility map of decayed area. The quality and quantity of the collected information are of pivotal importance in the studies of urban decayed area. The wider the range of parameters related to research the collected spatial and attribute data cover, and the more accurate these data are, the more precise and high-quality the results obtained from the analysis will be naturally. By doing so, the study will provide a better representation of existing real conditions.
The aim of the present study is to assess the susceptibility of decayed area in the city of Bandar Abbas, located in southern Iran. In order to achieve this purpose, five methods (i.e. AHP, Frequency Ratio, Statistical Index (Wi), Weighting Factor (Wf), and Logistic Regression) were applied together with their combination with the Geographic Information System (GIS) techniques.
During the past years, lack of special attention to urban district, particularly old area has resulted in their degradation and inefficiency. Today, identification and determination of their susceptibility is very essential and important for the urban planning of Bandar Abbas for the present and future. On the other hand, the decay of urban district has led to economic recession, creation of social problems, threats to the security of citizens, and its impacts on/transferring to other adjacent district. In order to mitigate the impacts of this phenomenon, scientific assessment of urban areas exposed to the process of decayed area, or those affected by decay is vitally important.
To achieve this, mapping was performed on areas prone to decayed areas; and the effective parameters on the occurrence of decay in the urban district were analyzed using the six mentioned different methods. In order to confirm the results obtained from these six methods and their concordance with the susceptibility map of decayed area, field observations and data collection was performed at 1300 points of the city with dilapidated and abandoned buildings. Afterward, the methods with the most accurate results were determined and selected. Validation shows that the Wf method presents results with the highest degree of precision, compared to the other five methods (i.e. AHP, Frequency Ratio, Statistical Index (Wi), and Logistic Regression).