نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال هفتم شماره 4 (پیاپی 28، آذر 1395)

Maps and images play animportant role in social and geographical communication. They are considered as a means to display large amounts of data in parallel and in a format understandable to humans. Many of these statistical data can be used in a geographical context by GIS technology. One of the ways to display statistical data in the geographical context is to use Cartograms. Cartograms, or value-by-area diagrams, are thematic maps, where geographic regions are changed in order to display a statistical information by their areas.There are several types of cartograms and algorithm for making them. In this study cartograms and their variants were introduced. Also, the methods of building these maps and number of common algorithms in this field are expressed. Finally, a comparison between the type of cartograms and methods of making them have been presented.

Agriculture as a supplier of food needs of the country is one of the most important economic sectors. The development of the agricultural sector is one of the most important ways of economic development. To achieve this objective, scientific planning is essential for optimum use of available resources and potentials. The purpose of this research is to achieve optimal crop pattern in the plain of Asadabad in Hamadan With effective criteria analysis in the areas of soil and climate. For this purpose, preliminary data were taken from the Ministry of Agriculture, Ministry of Energy and Meteorological Agency. Therefore criteria such as PH, EC, depth and… of soil area and the average temperature of crop growth period of climate area was considered. Then these data layers were created in GIS software. For modeling, to each of the layers using the AHP model, was assigned an appropriate weight. Finally, using the results from a combination of layers optimal areas for crops was introduced And the regions Were classified into four groups: non-susceptible to susceptible.

Traffic condition is one of the most important issues in urban transportation system of large cities, because it affects other important areas directly and indirectly. For example, water and air pollution as an environmental issue and physical or mental health of citizens as a health issue. Suggesting best routes based on different criteria such as traffic condition is one of the ways to reduce urban traffic, implicitly. In GIS, best routes can be suggested to users based on different algorithms that have been developed so far, to move in urban road network, and consider different costs, travel time, traffic, distance and other factors. Aim of this paper is reviewing route finding algorithms in GIS. For this purpose, algorithms are investigated in two categories, deterministic and heuristic algorithms. Advanced route finding problems which are closer to the real world problems are also described. Finally, a comparison between algorithms are done based on different parameters so that researchers can find the best suited algorithm based on their requirement.

In recent years we are faced with the growing trend of illegal buildings (IBs) construction in urban areas as the urban population and expansion have been significantly increased. Field check, as the currently used method of IB detection in Iran is time, cost and man power consuming and it may be ended in some collusion between the municipality inspectors and constructors. In this research, a semi-automatic method of IB detection based on an integrated remote sensing and GIS change detection method, using two-temporal satellite images of west of Tehran, the urban maps at the scale of 1:2000 and an up to date municipality property database containing building information, is proposed. In the pre-processing step, firstly the radiometric and geometric corrections have been applied to the images. Then, the changed pixels were detected using a conflation of fuzzy XOR operator and intuitionistic fuzzy implication methods. Afterward, the change percentage of each building was calculated using some spatial analyses and was compared with the standard construction threshold of municipality study area. Therefore, the buildings which were under construction were detected. In the next step, the IBs were detected as a suspected point undertaking a query to the municipal database. Finally, the existence of authenticity of IBs was checked undertaking a field visit from the suspected point. The results of this research verified that from the 338 buildings detected in the image, 16 buildings were detected as under construction and 2 buildings detected as illegal buildings, respectively. Furthermore, the overall accuracies of 98%, 86% and 100% were obtained for the fuzzy-based image classification, detection of under construction buildings and IBs detection, respectively.

This article compares the physical and data mining techniques in cloud detection. Presence of Clouds in optical satellite images, requires radiometric pre-processing in remote sensing applications. Usually it is possible to identify clouds in satellite images using supervised classification. In this article Landsat 8 satellite images of two areas in Alborz Mountains, using artificial neural network, support vector machine (SVM) and decision tree were classified and performance of these three methods in terms of overall accuracy, cloud and snow producer accuracy and the kappa coefficient were compared. In the first area, neural network and SVM showed higher overall accuracy and cloud producer accuracy than decision tree and in the second area, decision tree showed better performance for cloud identification. This indicates that in the first area the thresholds used in the decision tree, do not detect clouds well. While in the second area indices listed before, show higher numbers. The results show that decision tree can achieve comparable to or higher accuracy in comparison to data mining techniques (such as neural networks and SVM), however thresholds used in this method may not be suitable for all areas. In contrast, data mining methods, especially SVM can identify different cloud types in two classes’ case, increase in the number of classes can reduce cloud detection accuracy in these methods.

A watershed forms an area that catches and drains all the runoff made from precipitation and snow melting through the streams. Understanding the physical aspects of a watershed is of prime importance for morphological and geomorphological assessments such as studying erosion, sediments, floods, water resources development, and go forth. Meanwhile, geomorphological parameters play a critical role due to affecting infiltration process. The current study is aimed at assessing some of morphometric indices which have been recently used in quantitative geomorphology. Hence, five indices were assessed namely vector ruggedness measure (VRM), the area ratio, topographic position index (TPI), plan curvature, and terrain ruggedness index (TRI) in the Chehel-Chai watershed located in the Golestan province. First, the digital elevation model were made and afterward corresponding indices’ maps were prepared using related equations and the extensions in ArcGIS. As a result, the VRM values were ranged from 0 to 0.478. Moreover, the TPI values were ranged between -23.31 and 77.81 distinguishing hollows and bumps, respectively. Also, the TRI index values, as the height difference between a pixel and the neighboring eight other ones, were ranged from 0 to 170.68 and the plan curvature values of the study area were bounded between -3.16 and 3.96. The watershed area ratio, as a representation of the real terrain surface proportional to the flat (2D) surface, were bounded between 1 and 2.55.

The different methods used to estimate flooding in watersheds that the major difference between them is maximum flood discharge. The Curve number of each watershed shows its hydrological behavior and water regime during precipitations. This study was carried out on Hasanso watershed, Khorasan province and its main object was produce the curve number map for the study area. Using GIS, the average weighted curve number of the watershed was 80 in humidity level of grade II and major part of the watershed area had curve number of 80. Results revealed that, the maximum number of curves during mean humidity reaches up to 91 and the minimum number to 60. Accordingly, high number of weighted average curve number could be an evident of low infiltration rate of watershed and increasing probability of flood occurrence owing to loess soil. In order to reduce water resources loss, it is necessary to implement basic practices at watershed in line with increasing of water infiltration rate and establishing water harvesting systems including pitting and contour furrow, combined with vegetation increasing projects such as seeding and shrub planting.