Farzad Moradi; Ali Reza Azmoudeh Ardalan; Parham Pahlavani
Abstract
Introduction Recently, National Cartographic Center, the Organizationfor Registrationof Deeds and Properties, and alsoon a limited scale some municipalities have developed systems to provide real-time differential positioning services. Although these systems have proved to be efficient for quick mapping ...
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Introduction Recently, National Cartographic Center, the Organizationfor Registrationof Deeds and Properties, and alsoon a limited scale some municipalities have developed systems to provide real-time differential positioning services. Although these systems have proved to be efficient for quick mapping purposes in this country, they do not provide accurate differential positioning in coastal and offshore areas and thus cannot meet the needs of navigation and exploration and extraction of marine resources in oil fields. However, Iran has long maritime boundary in its south and north, and maritime economy is considered to be a priorityin its development planning. Since site selection for permanent positioning stationsis considered to be the main step of creating a real-time differential positioning system, finding the most suitable location for permanent positioning stations in the south of the country was selected as the purpose of the present study. To reach this aim, pairwise comparison matrix of the required information layers was first constructed using Delphi methodbased on the opinion of 5 experts, and in the next step, computer coding was performedin MATLAB using Fuzzy Analytic Hierarchy Process to compute the weight of each layer and sublayer.Then, layers were classified in GIS environment based on the weights obtained from the analysis of pairwise comparison matrices for each sublayer. Finally, layers were integrated usingweighted index overlay analysis methodto select optimal sites for permanent stations based on the weights obtained for each layer. Details of the calculations and the results are presented in the article. Materials and Methods High efficiency of analytichierarchyprocess and spatial information systems in management and analysis of spatial data have led to the creation of a highly efficient environment in which various stages of different analysis such as site selection for permanent GNSS stations can be performed. One of the advantages of this procedure is that the analysis can beupdated in the shortest possible time and the result can be depicted visuallyat any stage of decision makingwith a simple changing of the values (weights) of each input data based on the expert opinion. Thisgreatly impacts experts' understanding of changes in the studyarea. Accordingly,fuzzy analytichierarchyprocess method is used within the GIS environment in the present study. Results and Discussion The present study addresses the issue of site selection for permanent GNSS stations. In the first step,pairwise comparison matrix was created for the criteria and sub-criteria and filled in by 5 experts. Then, layers were classified in GIS environment based on the weights obtained for each sub-layers of pairwise comparison matricesand the codes written in MATLAB. Finally, suitable locations for permanent GNSS stations were obtainedby integrating the layers usingweighted index overlay. Conclusion The present study has provided the results of optimal site selection for GNSS permanent stations. These selected sites meet the needsofprecise positioning in the coastal areas of the country and can be used in navigation and exploration and extraction of marine resources and oil fields. Afterthe selection of southern coasts as the study area, 7 criteria (proximity to urban areas and facilities, slope, distance from faults, distance from access roads, soil type, distance from rivers and distance from railways) were selected based on the expert opinion. A pairwise comparison matrix was createdfor these criteria and sub-criteria and 5 expert experts were consulted in this regard. Expert opinions were analyzed using codes written in MATLAB software andFuzzy Analytic Hierarchy Process method and thus, the weight of each criterion and sub-criterion was obtained. These weights were then integrated using the geometric mean method and the final weight of each layer and sublayer was determined. Using Arc map software, these weights were applied to different layers and sublayers, and finally, optimal locations for permanent GNSS stations were divided into 5 classesof very good, good, medium, bad, and very bad stations. Good and very good classes can be considered as optimal places forcontinuously operating reference stations.
Parham Pahlavani; Mahdi Hasanlou
Abstract
Abstract
Nowadays, the combination of data and images obtained from different remote sensing sources is considered as an optimal solution for extracting more information, since these data, with their own wide vision, digital format, their periodically preparation, and high temporal resolution provide ...
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Abstract
Nowadays, the combination of data and images obtained from different remote sensing sources is considered as an optimal solution for extracting more information, since these data, with their own wide vision, digital format, their periodically preparation, and high temporal resolution provide researchers with a variety of information about the land surface. In this regard, the passive optical sensors are widely used in mapping horizontal structures. Given that, radar data can often be collected 24-hours a day and Independent of atmospheric conditions, and also some ground structures and artificial targets have a specific response in the radar frequency, they complete the capabilities of optical images. LiDAR airborne data can also provide sample measurements from vertical structures with very high accuracy. As a result, the simultaneous use of optical, radar and LiDAR data can provide more information in a variety of applications. In this research, by simultaneously applying these three categories of data, we tried to identify the urban specific features in an optimal way. In this regard, by utilizing and producing various descriptors (57 descriptors), and using the feature extraction methods (including PCA and ICA) and estimating the intrinsic dimensions of the data (including SML and NWHFC), an optimal space for the supervised classification was created. After classifying (K-NN method) using the obtained results, descriptors (information layers) produced to identify specific urban features including buildings, roads and vegetation were obtained and grouped according to the classification accuracy. The numerical results indicate the high efficiency of the proposed procedure as well as the applied methods of estimating intrinsic dimension and extracting the features.
