نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال هفتم شماره 3 (پیاپی 27، شهریور 1395)

Investigate the phenomenon of land use change and land development in terms of object-oriented has led to the increasing popularity of the use of agent-based models (ABM) in land use planning studies. Agents can be considered as self-directed objects which sense their environment and react to it using certain functions and adjust their activities to reach their goals. Agent-based models considered as a method to determine the global outcome of a system which is derived from the behavior of a group of local factors. These models in a bottom-up approach and using local components in a heterogeneous environment are trying to retrieve the global patterns. Land use change and land development is a dynamic and complex process and agent-based models have inherent ability to modeling complex systems and provide an ideal framework for modeling of these process. In this paper¡ a comprehensive survey has been conducted on the articles published in the field of agent-based modeling of land use change and land development during the last few years. This paper aims to classify research issues to provide a better understanding of the prospects ahead for further studies on this type of modeling.

Forests have covered 31 percent of the land surface and have an important role for mankind and nature. Hundreds of fires occur annually in Iran and thousands of hectares of trees and plants make their prey. Due to the country's arid belt of the Earth and subtropical high pressure zone, climatic conditions for the occurrence of forest and ranges fires are provided. Fire has harmful effects such as destruction of the cycles of nature and the environment, global warming, loss of vegetation, soil, dry soil and leads. As a result, predict and prevent the spread of fire in these areas is more important, that in order to detect fires in these areas and study the results. This study aimed to investigate various methods of fire detection sensors in remote sensing point of view. As you know, due to the expansion in the field of remote sensing satellite, a variety of remote sensing satellites and sensors that detect possible fires provides for us. In this study, the identification of fire by sensors such as MODIS, AVHRR, HJ-IRS and Landsat has been done. The sensors in this research study cannot be cut to an absolute certainty that one sensor is suitable for fire detection studies, but depending on the method and algorithm to detect fires on various sensor data is used and due to the limitations of the region and achieved mixed results. In general, we can use all of sensors with thermal and reflective bands to detect fire.

Spatial Multi-Criteria Analysis is a reliable and useful tool in modeling of flood risk in GIS domain. In the current research, flood modeling and vulnerability of historical sites has been studied and zoning maps for flood risk has been produced in the study area. The study area was Izeh plain and the KulFarah valley in which six ancient monements exist. This area is prone to seasonal flood risk. In this research, required data are prepared after determination of affecting parameters. Then, using ANP-OWA analysis and based on seven different scenarios with various risk levels and weights, seven flood risk zoning maps were produced. To perform this, OWA operator was applied to the criteria layers resulted from ANP analysis and the flood risk zoning maps were created. The main principle in OWA is the ability in the implementation of a wide range of combinatoric operators by means of selecting an appropriate set of ordinal weights. These ordinal weights are called OWA. The comparison of the resulted maps of seven different scenarios shows that KulFarah valley is under 'high risk' zone in five of the aforementioned scenarios. At the next phase, micro-zoning for flood risk was performed in KulFarah valley and the ancient reliefs were evaluated regarding the flood risk level. Micro-zoning was created under three scenarios and by overlaying the resulted zoning maps, it was concluded that reliefs No. 2, No. 3 and No. 6 are in high flood risk zone even with changing the decision making scenario.

Common primary method of information extraction from remote sensing data based pixel classification which is designed for images with medium spatial resolution. With the development of remote sensing data and images with high spatial resolution, created has problems in the use of pixel basis. Object - based method reduces spectral changes in elementary and creating homogeneous segment, to consider the possibility of spectral characteristics and to have delivered geometrically. The first stage of this method is that segmentation. The purpose of segmentation creation of separate phenomena and homogeneous regions that are remote sensing images. Segmentation before classification as an important influence on the results of this classification for quality of the parts of this process in any way shall be evaluated. The specific method there are for measuring the quality and quantity of the segmented. Segmentation results are of particular importance in the evaluation of the quality parameters. Important factor in the use of segmentation techniques segmentation quality, so in the first place must have an overview about the software segment. To do so, the 4-band image with high spatial resolution as Ultracam - D is used. The quantity segment of the segmentation by value evaluation index AFI was placed examined. segmentation by visual observation of workflow software (spring5¡ ecognition8.7¡ envi5¡ saga2.1) And quantitative evaluation results showed that software spring5 and ecognition 8.7 Due to the use of different parameters such scale in the segmentation phenomena have been better isolated. For best results tested in different places and on different images with high spatial resolution and with different segmentation index. There are many different methods for segmentation of the performance of each segment depends on the quality and scale is desired.

To extract the geometric parameters of individual trees, photogrammetry and field methods used in the past. The field method is to manually extract the geometric parameters of individual trees. Also, Aerial photos cannot directly provide information three-dimensional structure of the forest. That's why LIDAR technology is currently widely used. The first step is to extract the geometric parameters of the tree, is separation the points trees of LIDAR data by using Skewness Balancing and Slope Filter algorithms. The second step, With the obtained points trees, Create a linear approximation of the actual shape of trees using triangulation Delauney that it is known alpha form and in general a three-dimensional convex hull is produced from trees, Then doing the separation incorporated trees using Local Maxima and Minima algorithms. Finally, By using 2D closed circle algorithm, Can be calculated geometric parameters tree such as height, diameter of vegetation, trunk diameter, height trunk, tree vegetation height.

The emergence of Location-Based Social Networks has made significant revolution in spatial information technology. These Networks are one of the best enriched sources for supplying various location based services by mobile devices due to spatial data acquisition and updating from all around the globe. One of the most important and most applied location-based services are way finding service. Way finding is one of the common problems in present life for conducting Mobile Agent to its destination without feeling lost. Using landmarks is the most effective approach for way finding in an unfamiliar environment. In this research, landmark dataset and local users are extracted from geotagged photos with text and data mining with creating Location-Based Social Networks. In addition, the graph is designed for optimal route determination that its nodes are landmarks and crowded road segmentation which, frequently traveled by local users, and its edges are determined by ordering those road segmentations. According to this graph, two steps of routing are used to obtain the optimal path. To implementation, the used data are gathered from 22 vehicles trajectory in Tehran city for 3 months. Finally, a recommendation system has been implemented for recommending place of interest as personal landmarks to facilitate user orientation in searching region. To evaluate performance of the two-step routing, we experimentally compared the waste of time in proposed routing with Dijkstera shortest path for different lengths and users with different levels of regional knowledge.

Three-dimensional modelling of environment and buildings especially in urban areas are used in many fields such as facility management, urban management, disaster management, navigation, traffic management, and tourism. Collecting spatial data by professional and commercial providers often need to spend high cost and time, which is the major reason that Volunteered Geographic Information (VGI) has become more attractive to professionals in the geo-information industry in last few years. One of the most popular VGI projects is OSM. In the early beginnings, OSM included only two-dimensional (2D) data, but now more and more users also share 3D-compliant data. Many projects have been created to demonstrate 3D model, which is extracted from OSM database. The aim of this research is to introduce some of these projects based on OSM project, review each case, and determine the unique characteristics of each one. Moreover, based on the criteria defined for displaying three-dimensional, projects have been compared with each other and the advantages and disadvantages of each project are discussed. The results of this research can be used in development of three-dimensional modeling and future research to create new three-dimensional model.

Nowadays with production of low-cost GPS receivers and equipping various tool with them (from smartphones to in-car navigation systems), large amounts of spatial movement data are produced. These data, that is in the form of a series of spatial and temporal points as (x, y, t), is called trajectory. Among the numerous analyzes performed on these data, semantic enrichment of trajectories is a relatively new research topic, that has been extremely a subject of interest among researchers in recent years. Semantic enrichment of trajectories that is an effective method for management of movement data and performing semantic analyzes, including multi-step process. In this process, raw movement data collected by positioning system such as GPS, are processed and semantic trajectories are produced. Episodes identification in trajectory is an important step that aims to identify meaningful part in the trajectory. The proposed methods in episode identification mostly are based on applications. Therefore, by understanding the capabilities and limitations of each method, depending on the type of data and its application, semantic enrichment process can be done more efficiently in various applications.
This paper reviews the studies in the field of semantic enrichment has focused on the identification of episodes of trajectory. With the focus on stop-move model as the first conceptual model presented in the field of semantic enrichment, various methods of identifying episodes of trajectory is divided into two groups. The first group of methods is based on defined criteria in the stop-move model to identifying episodes of trajectory. The second category of methods uses other than stop-move model in process. The methods in which uses stop-model, divided into two categories: The first that uses merely raw data to identification and the second are based on combination raw data with application-based and geographical environment information. The method that uses raw data such as location or derived information from trajectory such as speed and acceleration, mostly are suitable when there is not enough information in the path of the moving object. On the other hand, when geographical and applications-based information are used, identification of the homogeneous parts in trajectories process is more efficient. In the methods that uses other than stop and move criteria, identification homogeneous segments, in spatial trajectories are based on various applications. One of the most important criteria is to identify homogenous sub-trajectories based on different mode of transportation. This approach that is used in processes of network constraint trajectories, the purpose of enrichment, is semantic analysis of the user's path use in applications such as traffic management and urban transport. The other criterion in identifying homogeneous parts in trajectories is target of moving object in path. In this approach, identification of the heterogeneous trajectory can be defined as hierarchical, from common state to a special purpose. This approach is the most common applications aimed at enriching the user's path trajectories in semantic application such as urban planning. Various methods have been introduced in this paper based on the proposed categorization and the performances of them in different application domains have been evaluated.

This paper describes the use of Iranian tide gauges to instantaneously measure sea level in same geocentric reference frame as altimetry data for TOPEX/Poseidon (T/P), Jason-1 and Jason-2 to provide ongoing absolute calibration of altimeter bias near the Persian Gulf in Iran. The method makes use of Iranian tide gauges that use GPS receivers to measure sea surface heights synchronously and in the same geocentric reference frames as the altimetry heights recorded. Three Iranian tide gauges at Bushehr, Bandar Abbas and Chahbahar ports and Geophysical Data Records (GDR) for TOPEX/Poseidon (T/P), Jason-1and Jason-2 were used to estimate a bias for each altimeter. Biases obtained for TOPEX/Poseidon (T/P) were -6 mm and for Jason-1 were 121 mm and for Jason 2 is 198 mm in Bushehr. Datum connections between the tide gauge and altimetric sea surface heights were calculated by means, geoid differences using the Earth Gravitational Model 1996 (EGM96).

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. Unfortunately, some of the building constructors commit some building infractions which may have irreparable consequences for urban structures. Lack of awareness of some of the constructors from the building construction laws and regulations, the high costs of getting building license from the municipality, the willingness of some of the constructors to construct more than allowable building density, are some of the main reasons of building infractions. Providing some new and appropriate solutions of detecting and monitoring building infractions is essential to prevent the continuation of IBs construction. 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 addition, using this unplanned and irregular method of illegal building monitoring may be ended in untimely detection of illegal buildings. Acquiring multi-temporal satellite images and using image processing techniques for semi-automatic change detection is one of the optimum methods which can be used to detect the suspected IBs.
In this research, a semi-automatic method of IB detection based on the pixel wise fuzzy XOR operator change detection, using bi-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 undertaken to the images. Then, the changed pixels are detected using a fuzzy XOR classification method. High accuracy and speed of the mentioned fuzzy method are the main reasons of taking advantage of this method. The change percentage of each building is obtained by calculating the ratio of the changed pixels to the total pixels of each building. The change percentage of each building is compared with the standard change threshold of the study area (allowable building density (60%)) and consequently the buildings which are under construction are detected. In the next step, the IBs are detected as a suspected point undertaking a query to the municipal database. Finally, the existence of authenticity of IBs is checked undertaking a field visit from the suspected point.
The results verified that from the 343 buildings appeared in the image, 21 buildings were detected as under construction and 4 buildings detected as illegal buildings, respectively. Furthermore, the overall accuracies of 92%, 83% and 100% were obtained for the fuzzy-based classification, detection of under construction buildings and IBs detection, respectively.

Due to the water shortage and drought that have occurred in recent years in our country, the planning for the optimal management of water use in different applications is necessary. The agricultural sector is the largest consumer of water and yet most of the water loss is also related to it. As it is said, about 60% of drinking water is wasted in agriculture.
Since irrigation is primarily a matter of spatial case, and is related to the location information such as the location and position of land, location of transmission canals and etc., so capabilities of spatial information systems (GIS) could be used for better and more successful planning.
In this paper, using optimization capabilities in GIS, program of irrigation of reservoir dams have been set, so that the farmer’s water need to be covered and the amount of waste water to be at least. For this purpose, the path of the water motion in the canals is minimized and the irrigation of farms that are under one canal be done simultaneously. If not enough water in the reservoir, Method of determining the optimum cropping pattern using the AHP method are provided. The method presented in this paper has been implemented and evaluated in a case study in Golestan dam irrigation network.

Urban environment is inherently heterogeneous and vary in location and time continuously. Therefore, monitoring urban environment requires measuring environmental factors at high temporal and spatial scales. Moreover, using spatial web sensors for monitoring environmental phenomena are just essential. In recent years, along with the development of related technologies in production of sensors, which push them to cheaper, smaller and more efficient ones, sensor networks have grown considerably. The wide variety of sensors in terms of communication protocols, data formats and data have been provided by various organizations, the need of interoperable infrastructure to share their data in a standard manner in observation systems is inevitable. So, creating an open spatial data infrastructure to access real-time and archived sensors observations by various organizations is serious for optimal city management.
In order to overcome interoperability obstacles in sharing data of sensor networks, OGC SWE Working Group have been defined open standard protocols and data models for data and sensing devices. The purpose of SWE is providing users the ability of discovery and query from sensor networks, observations and observation methods on the Internet. This process involves identifying sensors capabilities and their measuring quality and access to sensor parameters. These capabilities provides the process and georeferencing observations, retrieve real time and archived observations automatically for software components. From SWE, SOS, SensorML and O&M were utilized. Proposed architecture was built up of three layers including data layer, service layer and application layer. Data layer includes sensors that collect pollutant concentration from physical world. These data will be transferred to and stored in database. Service layer relates the application layer to data layer will be used to publish spatial and non-spatial data on the Internet. Two Server is considered in this layer: SOS server, to publish collected data by the monitoring network from providers’ side to the users’ side at the application layer and the server of spatial data for publishing spatial data over the web. This layer which is considered as the main driver of designed architecture, applies open standards to communicate among other sectors. Application layer includes a web application that runs in the browser. This program involves components that provide for users access to near real-time and archived data of monitoring sensors network of urban pollution. Moreover, include capability of spatiotemporal query from sensors observations and showing the air quality index on the map component for each station.
Due to the benefits of free and open source softwares (FOSS) including simple development and low cost to use them and also the growing tendency of enterprises in applying this kind of softwares, utilized software components in implementation of the mentioned architecture have been selected from this applications. In fact, the main purpose of this study is to provide an open approach on air quality observation system.
Therefore, in this study, a method was presented for establishing a spatial data infrastructure based on sensor web to monitor air quality. In this method, air quality monitoring takes place utilizing service-oriented architecture, open spatial standards and open source software components. The proposed method, was implemented on the air quality monitoring network in Tehran and the results were presented.