ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2015 | Volume:2 | Issue:4|Papers Count:6

One of the most important applications of geodesy in geodynamics is study of crustal deformations. In this paper, surface deformation analysis is investigated using 3-D model. 3-D isoparametric method and Iranian permanent GPS network (IPGN) were used to estimate strain tensor. In this method, strain parameters obtained from compares the relative distance between base point and its neighboring points. Since the strain tensor is calculated for each GPS station, considers the strain inhomogeouns and its compatibility with the reality. Due to the special characteristics of isoparametric method, using regularization techniques to solve this problem is inevitable. Tikhonov regularization is used for solving corresponding problem. Optimum value of regularization parameter is selected using minimum relative error in strain parameters, as well as, diagonal elements of resolution matrix is used for error analysis. For estimated velocity field and strain parameters in other geodetic points, in this research artificial neural network (ANN) with 3 layers is used. 4 GPS stations with convenient distribution were used for validating and testing. Minimum relative error obtained from this evaluation for velocity field in eastern component (VE) is 6.25% and northern component (VN) is 6.80%. Also root mean square error (RMSE) is computed ±1.85 (mm) and ±1.72 (mm) in VN and VE respectively. These results are agreement with focal mechanism of earthquakes in this region as well as Iran's geodynamic mechanisms.

This paper introduces a novel road extraction algorithm in two stages of road detection and road vectorization. In the road detection stage, road class image is obtained using fuzzy C-means clustering and some post processing operations. In the vectorization stage road key points on the road centerline is obtained by an innovative approach of dynamic road pixels clustering using particle swarm optimization. The proposed algorithm is able to automatically optimize number and position of road key points without considering the prior information about the initial number and position of cluster centers by designing a new cost function. The optimized road key points were connected using weighted graph theory. Different high resolution images of Ikonos in urban, non-urban, and mountainous areas were tested and several quality measures including RMSE, correctness, completeness, and quality were calculated. Extracting different road shapes with RMSE less than 1.3 and quality greater than 0.86 in different areas proves the efficiency of the algorithm in yielding complete road networks.

For the optimal use of the spatial data in the interoperable environment, the gradual process of GISs have begotten SDI technologies. The main goal of SDI is to facilitate data sharing and informing about spatial data and creating an interoperable environment for users in order to access data. It‘s necessary to provide an appropriate base for analysis, decision making and planning to create interoperability between the various spatial information systems. The most important problem in respect with spatial data exchange among different organizations is semantic interoperability in order to discover common semantics of data of other organizations. Using Ontology with the formal specification the relations between the phrases in SDI, leads to the promotion and enhancement of semantic data sharing and retrieving spatial data. Since the start of SDI, different abilities and services were designed for it and also those are developing from the first generation SDI to the second and to third generation in the lapse of time. and we can observe these developments and enhancing of the capabilities. Semantic web has been appeared in third generation of SDI to resolve the semantic heterogeneous. SDI based on ontology has the main role to facilitate decision making in enabling the spatial knowledge of the societies. In this research, in order to solve semantic heterogeneous, ontology is used in Catalog Service of SDI for the roads class of the classes of the phenomenons in SDI pattern of two diverse organization. And also, accuracy and efficiency tests of two designed ontology for two assumed organizations; Ministry of Road & Urbanism and National Cartographic Center of Iran, to find similar concepts are accomplished based on implementing ontologies in catalog service and matching ontologies algorithm and many found matched results are achieved.

Coregistration of optical and radar imageries is a major pre-processing step in many remote sensing applications including change detection and interferometric processing. Specially, the coregistration faced more difficulties in radar imageries due to the high noise level and intense spatial-temporal decorrelation. Hence, non-automatic coregistration methods are much more time consuming and inefficient. Because, the probable geometrical and temporal (signal-target interaction) differences between two acquisitions (i.e, master and slave) caused spectral shifts between corresponding pixels. In this case, using grid points as a tie in the coregistration process drastically reduces the reliability of the process while use of the corner points in the images could solve the problem. To resolve this problem, an automatic algorithm based on angular histogram was proposed for coregistration of Synthetic Aperture Radar (SAR) imageries that have taken in different height, time and situation. This method automatically extracted the corners from the slave and master images and used these points as nodal points. However, unlike conventional methods, there is no need to perform the image matching process. The coregistration RMSE value for three case studies related to the Radarsat-2 imageries of Batala area, India, Radarsat-2 imageries of Sendai region, Japan and TerraSAR-X imageries in Sendai region, were 0.29, 0.35 and 0.43, respectively. Comparison of the proposed coregistration method with cubic convolution (CCI) interpolation based coregistration method showed that the accuracy of the proposed method was improved to 5%, 5% and 9% for three datasets, respectively. The results indicated high efficiency and accuracy of the proposed algorithm. Moreover, the proposed algorithm illustrates the high performance in the case that the angle and scale between the master and slave images are relatively large.

The aim of the present study is to determine the suitable areas for establishing Disaster Management Support Bases in the region one of Tehran municipality. For this purpose, at first, 26 factors were classified in 5 groups. Then, Weighted Linear Combination (WLC) was used as Multi Criteria Evaluation modeling by elimination of constraints, standardization by Fuzzy membership functions in byte scale, factor weighting by the AHP (Analytical Hierarchy Process), and site selection by zonal land suitability (ZLS) in GIS environment. The results shows that the eastern part of the study area (Zones 6, 9, and 10) has the least percent of differentiation and area reduction in texture, transportation and compatibility constraints including residential area, road, health & treatment centers, commercial centers, vacant lands, faults, multi-storey buildings, gas lines, aqueducts, gas stations & diplomatic area which shows more accessible area in the eastern part of the area. Also, the eastern part of the study area has a very high suitability average in non-proximity (incompatibility) factors such as faults, multi-storey buildings, aqueducts, power lines, gas stations, metro lines, diplomatic area & earthquake intensity. The result of siting shows 17 zones for disaster management support bases that there are 14 zones in the eastern part of the region. Then, five zones were selected in the study area including zone A with area of 4900 square meter and suitability average of 166, zone C with area of 2700 square meter and suitability average of 164, zone E with area of 3200 square meter and suitability average of 165, zone L with area of 28100 square meter and suitability average of 146 & zone N with area of 2400 square meter and suitability average of 155. Therefore, it is suggested that decision rule and the methodology of current study can be used in other studies of siting of Disaster Management Support Bases.

Current study was conducted in order to finding the best models to estimating groundwater depth using Hyperion hyperspectral satellite imagery in the sugarcane fields located in the southwest of Iran. For this purpose ground water level was measured in 132 observation wells from the beginning of May 2010 till end of September 2010, twice per week, in the Hakim Farabi farming and industrial lands. Moreover, from the other collected information like daily weather information, age and variety of sugarcane, planting and harvesting date of plants, managerial operations such as date and amount of the fertilization, irrigation and drainage information in the Hakim Farabi farming and industrial lands were used. In a same time with measuring the ground data, a hyperspectral satellite image of Hyperion sensor was acquired on September 2, 2010. After applying necessary pre-processing on the image, the changes in the spectral reflectance of the sugarcane under different values of groundwater depths was studied. Afterwards, it was tried to obtain appropriate models for estimating ground water depth. For this purpose, capability of 21 vegetation indices, related to defferent regions of spectral reflectance of crops, was studied. Besides of these indices three new vegetation indices (SWSI-1, SWSI-2 and SWSI-3) were developed in this study. Results show that, variations of groundwater depths have a significant effect on spectral reflectance of sugarcane. Among the vegetation indices, indices related to water absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with groundwater depth. Obtained models from the two vegetation indices developed in this study (SWSI-1, SWSI-3) and NDWI yield the best results for estimating groundwater depth with R2 of 0.48, 0.48 and 0.47 and root mean square errors of 8.20, 8.25 and 7.98 cm respectively. Conclusions from this study indicate that using hyperspectral satellite imagery to monitoring water table in the sugarcane fields has an acceptable, fast and economical results.