نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال هفتم شماره 2 (پیاپی 26، خرداد 1395)

Spatial distribution of copper in Hormuz island soils were evaluated using different geostatistical methods including Kriging, Inverse Distance Weighting and Radial Basis Function. For this purpose, 58 surface soil samples (0-30cm) were collected from the study area. The total concentration of Cu element was measured by using four acid digestion procedure and inductively coupled plasma instrument (ICP-OES). The methods performances were evaluated based on Jacknife cross validation using Root Mean Square Error (RMSE), Standard Root Mean Square Error (SRMSE) and Coefficient of Residual Mass (CRM). Based on RMSE, SRMSE and CRM indices, the Kriging interpolator had best performance among the other interpolator and the Thin Plate Spline was also found as the most unsuitable method for estimating Cu spatial distribution throughout the Hormoz island Soils. The result of zoning showed that amount of copper concentration was lower in south parts of the study lands.

Nowadays, the developing LiDAR technology has attracted a great deal of attention for generating Digital Terrain Models (DTMs) especially in forest areas, due to great penetration of laser pulses in vegetation. Generating DTM using LiDAR data includes two seteps: filtering and interpolation. Although there is a wide variety of filtering methods, in this study, slope based filter and hybrid filter have been applied in order to filter the cloud of raw lidar data points.
Then Genetic Algorithm ,for improving the interpolation methods like Inverse Distance Weighted (IDW) and polynomials, have been employed. The performance of these intelligent methods has been compared with typical interpolation methods such as Kriging and Radial Basis Functions, etc.
Results of the present study indicated that interpolating by hybrid filter, which used Genetic Algorithm ,for improving the interpolation Inverse Distance Weighted(IDW), RMSE of 0.135 m for the first area having a dense vegetation conopy and RMSE of 0.230 m for the second area having grassland vegetation, had the best performance of all the applied interpolation methods in this study.

Deformation monitoring is a very important issue in engineering structures and studying ground deformation. So creating microgeodesy networks in the area of study is required. Detecting unstable points of network is an important issue in microgeodesy networks. Among the classical methods of identifying unstable points of the network, the global congruency test and L1-norm minimization can be referred. The results in the research done in this regard show that when displacement point vector is small, the number of true detection of displaced points using common ways of deformation analysis is less than the number of points that really displaced. The reason can be the spreading nature of the least squares estimation. Now according to the recent research results, in the field of detecting unstable points of network, the idea of using subnetwork analysis is proposed to confront this limitation. In this case the deformation monitoring network is divided into some subnetworks including a subject point and the other source points. In fact according to the unstable points, there will be sub-networks. In this method in whole network first of all using classical methods, the stable and unstable points are investigated. Then by dividing the whole network to subnetworks, every network will be adjusted and its unstable points will be detected. So the relation between unstable points is cutoff and the spreading effect of the least squares is decreased. In this paper this method is evaluated in a simulated and a real network. The results show that using subnetwork analysis compared to global congruency test will improve the results in the correct detection of unstable points. On average this improvement is about 35% in all of the stimulated states. In return improving the results of subnetwork method to L1- norm minimization is about one percent that it cannot be acceptable. In the following the algorithms of detecting unstable points in common methods and the method of analyzing subnetwork were conducted on a real network and the results are consistent with the results of simulated network.

According to the corrections applied to the measurements of sea surface height from satellite altimetry there is still a non-tidal effects on these data. We can use the height difference observations of the two adjacent points sequentially in one pass of satellite altimetry (below one second intervals ground footprint which is 6 km long) to eliminate these effects. In this study, a differential approach is provided to develop the harmonic tidal analysis during the pass of satellite altimetry data from the Jason-1 and Topex for Persian Gulf region.
Tidal analysis of the adjacent point is used to compare their height differences observed for refined harmonic tidal analysis along the pass of satellite altimetry for computing the harmonic constants which show the algorithm can be reduced non tidal effects in altimetry observations and increase the accuracy of tidal component.

Landslide is a geophysical phenomenon that event led to the displacement along the steep slopes and it caused the huge human and financial losses. Raining, ice, and human irrational use of nature is one of the factors causing landslides. Height, slope, soil type, geological and hydrological parameters are the most important factors affecting landslides. Chalus basin with an area of 929.28 km is located along the Karaj-Chalus road. The basin due to overlook the busy road of Chalus, has particular importance. So evaluate the potential of landslides in the area of the basin is necessary. In this study, some factors such as slope, aspect, geology, vegetation, hydrology and soil drainage parameters are examined. After mapping a variety of factors, fuzzy intelligent algorithms and Artificial Neural Networks (ANN) were used for landslide zoning. By applying fuzzy method, it was found that 34.1 percent of the area has high and very high potential for accruing landslide, 35.5 percent of the area has medium potential and 30.3 percent of the area has low potential. Also by applying ANN method, it was shown that 34.4 percent of the area has high and very high potential for accruing landslide, 29.05 percent of the area has medium potential and 27.5 percent has low potential.

Nowadays, the problem of transportation and traffic has become a social challenge in many countries. With the advancement of science, a favorable environment for an intelligent and purposeful management is achieved in order to improve productivity and increase efficiency of network traffic. In the field of information technology, Geographic Information Systems are very important in optimizing the performance of transportation systems. In this context, the capabilities of network analysis in GISs such as shortest path computing could be very useful.
Different criterions have ever considered for shortest path analysis in GIS. Distance, travel time, path comfort, path beauty and etc., are some of these criterions. Travel time criteria has some random continues variations, because of its relation to traffic. Therefore, the travel time of each street must be calculated based on its geometric and traffic momentarily.
In this paper we have developed a model for this purpose that is based on wavelet transform and least square method. This model uses the statistical information of travel time in previous days for prediction of travel time in future. Finally for testing the developed model a case study have been explained in this paper.

Nowadays the generation of a three-dimensional (3D) model is mainly achieved using non-contact systems based on light waves, in particular using active or passive sensors. We can currently distinguish four alternative methods for object and scene modeling: (i) image-based rendering, which does not include the generation of a geometric 3D model but, it might be considered a good technique for the generation of virtual views; (ii) image-based modeling (e.g. photogrammetry), the widely used method for geometric surfaces of architectural objects, precise terrain and city modeling as well as Cultural Heritage documentation; (iii) range-based modeling (e.g. laser scanning), which is becoming a very common approach for the scientific community but also for non-expert users such as Cultural Heritage professionals; (iv) combination of image- and range-based modeling, as they both have advantages and disadvantages and their integration can allow the generation of complete and detailed 3D models efficiently and quickly. Moreover, 3D models of outdoors objects by means of terrestrial laser scanner (TLS) point clouds has been one of the most important and reliable technical methods. However, TLS point clouds have two main drawbacks. They dont have sufficient information about the object texture, and sometimes a complete perspective of the facades cannot be obtained due to irregular circumstances. Advances in both TLS hardware and photogrammetric solutions combined are creating increasingly more precise and rich 3D colored models. The combining of close-range photogrammetry and TLS data have been shown to be effective and accurate techniques for the 3-dimensional documentation of complex buildings and sites, which is considered, by many institutions and organizations. In this paper the 3D model generation of two historical building based on the combination of two different surveying techniques has been presented. The main goal of the work was to merge in a unique result data derived from close-range photogrammetry and from TLS in such a way that the final model could be seamlessly explored. Close range photogrammetry and Laser scan, complementary, and combine their data in most applications is increasingly common. Laser scanner while collecting 3D data, are able to produce images of texture mapping applications, but for some reason these images cannot be used. Texture images separately with good quality, at the suitable time and the appropriate conditions are provided using the principles of photogrammetry. This paper explores the projection matrix method is a comprehensive reconstruction of ancient buildings. This research is a case study on two series of real data from our ancient monuments, Abarkooh dome in Yazd province and stone bridge of Lorestan, which can be used to provide documentation verifying in national or international scale and three-dimensional modeling to be used for different purposes. In this study along with projection matrix method, other mathematical models were examined using multiple texture images for produce 3D models with true textures. The use of multiple texture images improved quality, geometry and accuracy of the results in texture mapping. The results indicated that the errors and distortions of RGB colors, is depends to the accuracy of the measurement in the space of the image and cloud points and the mathematical models. Also, check average of errors in the two transverse and longitudinal coordinates, indicating the projection matrix method was suitable in compared to other methods. In general, the projection matrix method is computationally more easily and geometrically more accurate than other models which is tested in this study. The values of errors in the projection matrix method are 0.15 and 0.33 (pixel) in the stone bridge of Lorestan and -4.24E-13 and 7.67E-14 in the Abarkooh dome in pixel units, respectively. In contrast, the errors of iterative direct linear transformation method are -2.38 and -5.79 E-10 for the first point clouds and 5.40E-5 and 3.64E-5 in pixel units for the second point clouds, respectively. Therefore, the main advantage of the projection matrix method is that it requires no initial values and it operated as matrix multiplication for producing three-dimensional models with realistic textures that is recommended as a stable method.

With the advent of the high spatial resolution satellites in recent years, feature extraction has attracted the attention of many researchers. Road has a great importance in infrastructure, transportation and management. The extraction of urban road network has been the subject of numerous studies. Because of its low contrast, complex geometric structure and far away from crowded city centers, mountain road extraction from satellite images has been less studied. In this study to extract mountain roads QuickBird satellite imagery was used. In order to increase the spatial resolution of multispectral images, panchromatic and multispectral bands were fused using novel Esri method. Rod extraction was performed using advanced object-oriented classification methods and the results were evaluated. The overall accuracy for Object-oriented rule-based and Example-based classification methods was 93% and 92% respectively. Considering the results, road's central axis was extracted from the region generated by the Object-oriented rule-based method. Final accuracy of central axis was 87% and RMSE 1.1 pixel comparing to the reference data. Due to the Topographic complexity of the area and spatial resolution of the images, the results showed that advanced Object-oriented classification method has a good capability to extract homogeneous terrestrial objects especially the roads.

Remote sensing data is an important source of information in many applications such as change detection of natural resources, regional, global and local scale, also monitoring of land use and land cover changes and environmental studies. The principle of using satellite images to change detection in multi-temporal satellite images is behavior of spectral change complications during the time. The order of spectral behavior of complications is spectral and texture characteristics of them. Many methods have been developed to change detection which have been made as object-base and pixel-base approaches. The present research focuses on review and study of these methods. In much research, the input data sets have been used for performance evaluation of change detection methods. And these algorithms were compared to each other in terms of speed, simplicity of implementation and change detection accuracy and In most cases The have been conclusions, which these results contradicted the results of other researchers. The accuracy of each method of change detection is a function of the consistency between the algorithms and input data. In fact, accuracy of these methods are not related to the inherent advantage of the algorithm and any algorithm or combination of them absolutely have not advantaged over other methods. In this study, tried that change detection methods are examined from different aspects and be expressed goals of each of them.

The Applications of Multi Quadric equations in investigating geometric correction and geo Referencing of satellite images is studied in this paper which is not used yet .the conventional low order mathematic model based on the Ehlers theory is replaced by a mathematical model to evaluate the improvement of the residual of multi quadric equations .different 2d and 3d mathematic functions are investigated in geometric correction ,then multi quadric equations are used to reduce the geometric correction errors as a post processing technique .the result show that in the cases where control and check points have regular or semi-regular distributions .the usage of rational function with 11 parameters together with Appling the corrections 82 cm sub pixel resolution may be obtained which shows the significant improvement over the case where multi quadric equations was not used and the sub-pixel resolution was 92 cm.

As the GIS increases its influence in practical solutions especially for urban management, interests in complex features among software companies increase too. Nevertheless ontological developments has not been started to consider urban object relations especially for topological ones. Although topological relations have numerous applications in different fields such as urban management, there are few available concepts in both theoretical and software issues.
Therefore, in this paper, firstly based on first order descriptive logic and traditional math definitions, complex objects are defined along with their real samples in urban infrastructure and facilities. Then a new solution is proposed to extract topology for complex features based on Point Set Topology of Egenhofer's 9-intersection matrix and mentioned logic.
On the foundation of method, current geometric status of complex objects intersection are translated to first ordered logical predicates. These predicates then are deducted, combined or broken to ensure completeness, cover and redundancy of result normal logical constraints. Logical constraints as primary filters are exerted to matrices to remove any unreal matrix and then are passed them to secondary filter that is graphical proof which examine the existence of any matrix against real world conditions.
Next, over the theoretical concepts, it is tried to propose solution to practically save the topological relations in the context of traditional databases. This intends it toward software deployment where other required tools have been set before.
To complete the proposed solution, a flexible approach is offered which is able to let the users themselves define their demanded topology rather that standard ones. They will be able to customize their requirements without caring to composition of logical topological constraints.
Finally deficiencies which are important to complete the framework such as practical algorithm to extract topological relations from the context of real data structure are discussed for further researches.

Human life throughout history and around the world has always been exposed to a variety of natural hazards. Some of these risks arise from geological processes such as the earthquake and some others that are more abundant and widespread, are caused by climate processes. Among them, the importance and extent of drought is significant. In this paper, Fourier and wavelet spectral analysis have been used to study the behavior of the recorded precipitation time series related to Iran's main catchments. Due to expected hydrological characteristics of each catchment and based on the coordinates of I.R.OF IRAN Meteorological Organization's synoptic stations, monthly averaged precipitation time series have been produced from 1960 to 2010 for each catchment and the extracted signals have been analyzed using Fourier and wavelet transforms, separately and together, and the results are compared with corresponding modeled Total Water Storage time series analysis outcomes. The results, introducing certain frequencies of each catchment, their common frequencies and time durations, are in good agreements with the similar previous research findings and the reported droughts in Iran.