فصلنامه سنجش از دور و GIS ایران
سال هفتم شماره 1 (پیاپی 25، بهار 1394)

Location of some facilities such as shopping centers has a crucial role in their success. Finding the location of a new shopping centers where there are existing ones belonging to competitors, asks to consider and evaluate confusing factors and objectives. In this research based on multiple objective decision making (MODM) approach, two objectives are considered to maximize the profit: maximizing demand response and maximizing accessibility to shopping centers. Demand objective is evaluate based on two criteria: population and competitive conditions. Accessibility is related to access to major roads, public transit stations, parking, parks and entertainment centers. There are many methods for solving multi-objective problems that have been generally divided into classic and evolutionary methods. Classical methods are faced with several shortcoming and recent researches are focused on utilizing the heuristicmethods. In the proposed model, first unsuitable locations are removed based on their land use and then Tabu Search, as a multi-objective evolutionary algorithm, is used as a competitive location model for finding the location of new shopping centers based on the trade-off between objectives. The proposed model is tested in a case study area in Karaj city and the result is compared with traditional overly analysis.

The result of epipolar rearrangement process is pseudo normal images; which conjugate points are located along their rows or columns. But, unlike normalized images resulted from epipolar resampling process, there is no guarantee that parallel parallax of conjugate points to be linearly proportional with z-coordinate of corresponding point in the object space. However, pseudo normal images can positively affects many photogrammetric activities such as image matching, automatic aerial triangulation, automatic digital elevation model and orthophoto generation, and stereo viewing. In present paper, a novel approach for epipolar rearrangement of linear pushbroom satellite imagery is proposed based on Multiple Projection Centers model, and rearrangement procedure is separately investigated for both Cross Track and Along Track imaging systems. The proposed method is developed based on refinement of trajectory and attitude parameters of the sensor. One of advantages of this method is the capability of the correction of off-nadir viewing of the sensor through the physical interpretation of its parameters. According to the results of the accuracy assessment of pseudo normalized images using the proposed method in independent check points, the mean of residual vertical parallaxes in stereo model is determined 0.94 pixels; that corroborates the feasibility, correctness, and applicability of the method.

With the increase in population and consequent increasing needs of society, land use planning is of particular importance. Land use planningdue to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. But use of these algorithms should be examined according to the type of issues. In the study, addition to introducing a model to optimize land use, effective solution for the application of multi- objective genetic algorithm on a variety of problems related to land use planning was presented. In order to land uses optimization in the study, the algorithm NSGA-II was use in the model. Output of the model might be introduced patterns for reduction of erosion to an acceptable level and enhancing the economic benefits. This will be skillfully carried out while the land use adaptation is in the highest level and land use changes are easy with high level of continuity.An innovative operator which producing the initial population and an innovative operator with an appropriate Crossover of land use planning issues were developed.The developed model in the study was implemented in Kerman-Rodbar watershed. Evaluation results show that the model is able to suggest patterns to land use planning that reduce erosion about 30 to 35%. While the economic benefits of the changes will be about 40 to 50 %. Furthermore all models have a high consistency and low difficulty to change. These operators have had a significant impact on problem solving.

Optimizing of the arrangement of the land uses is one of the main goals of urban land use planning. This issue involves a variety of spatial data and analyses. Moreover, existing different arrangements for diverse land uses causes in complex and wide search space. In view of these matters, the land use arrangement can be supposed as a spatial multi-objective optimization problem. In this research, Multi-Objective Particle Swarm Optimization algorithm along with GIS is applied in the seventh distinct of Tehran to find optimum arrangement of urban land uses. GIS is used to generate and analysis different scenarios of land use arrangements for the optimization algorithm. The proposed approach provides a variety of optimized solutions, giving the possibility of choosing the most desirable results to decision-makers. A new aspect of this research is using the land parcels as the spatial unit. In addition, making dynamic decision on the different types of land uses is one advantages of this method. The test of the method shows an acceptable level of implementation speed along with a high level of repeatability and stability of the algorithm.

Classification is one of the most widely used remote sensing analysis techniques. In the conventional remote sensing supervised classification, training information and classification result are represented in a one-pixel-one-class method. Fuzzy methods have been widely applied in image classification, which are believed to be more appropriate for handling uncertainty and mixed pixels in remote sensing. Also recent researches show that using neighborhood information with spectral information lead to higher accuracy in classification. Due to the dependence on initial classifier,the use of neighborhood information in the post processing of classification results is one of the reasons for its use in this research. Connectivity rules in fuzzy topological space are one of methods for using neighborhood information in post processing step. In case of using more than one classifier, it is possible to integrate the results. In this research two methods have been proposed for spatial integration results by using connectivity rules in fuzzy topological space. In first method, one of the two classifiers will be based and in second method, only pixels that are classified in the same manner in both and simultaneously not boundary pixel, will keep their own labels in final image. The results show that first method Provides better accuracy compared with second method and generally accuracy is improved when spatial integration results is used in compare with using only one classifier. The maximum overall accuracy and overall kappa values are obtained respectively 89.01 and 88.98 when maximum likelihood classifier is based in first method.

Obtaining and Production of accurate and realistic information from mineral resources is one of the intellectual concerns of managers. To produce this kind of data and information, there are various methods that can be traditional methods combined with the field data and remote sensing techniques. Measurement and generation data using of satellite data and remote sensing methods especially, in desert areas because existing unfavorable conditions, opened up a new horizon to the managers to overcome the problems of traditional conventional methods.The purpose of this paper evaluate remote sensing and GIS techniques is to map evaporate minerals in the eastern part of Semnan using ASTER data.We used field data and false color, PCA and Tasseled cap transformation, ratio and data fusion techniques to estimate amount of gypsum, salt, sodium and magnesium sulfate soils. Regression and correlation relationships between satellite and field data were determined. The results showed that the thermal bands 9, 10, 12 and PCA 9, 10 and 12 can be used for separating Gypsum, Halite as well as sulfate. Finally using maximum likelihood classification map was used to map Gypsum, halite and sulfate contents with accuracy of 73.33, 66.67, 66.67% also using Kappa coefficient were prepared respectively, 0.61, 0.53 and 0.55.

In this study for evaluation capability, OLI data of Landsat8 to estimate canopy density 2300 ha. in protected Manesht area in Zagros forests of Iran was selected. For ground truth data, 100 square plots (0.36 ha) were measured and systematic random sampling method was used. The dimensions of network inventory were 500m×400m. In each plot, crown cover was measured and then canopy percent in each plot was calculated. For classification and mapping, maximum likelihood and minimum distance to mean classifiers were used. The Transformed Divergence index was used to determine best combination of image bands. Result of this study showed that minimum distance to mean classifier had overall accuracy and kappa coefficient of 80% and 0.68 respectively on OLI image data. In addition, the maximum likelihood classifier had overall accuracy and kappa coefficient of 60% and 0.35 respectively. The result of this study showed that minimum distance to mean classifier was most suitable classifier for canopy classification of Zagros forests on the OLI image data.