نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال نهم شماره 1 (پیاپی 33، بهمن 1396)

Volunteered geographic information (VGI) provides an alternative way to produce spatial data by non-professional users. Unlike the traditional process, any user can produce enormously spatial information and make it available for others. Although VGI is considered beneficial for the main reasons such as being free, accessible and up-to-date, quality issue is its obstacle in this context and has been investigated in several scientific studies. Using VGI in various applications, the quality plays the important role. Therefore; Introducing appropriate assessment tools and methods to improve the quality of data will be an effective way of accelerating the usability of data. One of the possible methods to evaluate VGI quality is determining indicators particularly without usage of reference data. In this paper, we have struggled to overview on the quality of geographic data, then discuss on the quality of VGI from the perspective of the indicators and their evaluating methods specially fitness for use. Moreover, fitness for use will significantly be described in this paper.

The era of big data is approaching with the rapid growth in development of complex information and communication technologies like internet and 3rd and 4th generation of Mobile Networks (3G/4G). These days more than ever, real-time and concurrent access to heterogeneous data from different sources and with different formats is made possible. Meanwhile, with recent advances in sensor technology and in order to monitor, explore and visualize complex spatial systems, a large amount of data in different Spatio-temporal scales is being generated every day. For example, according to the estimation by United Nations Initiative on Global Geospatial Information Management (UN-GGIM), 2.5 quintillion bytes of data are generated every day which large portion of the data is location-aware. This unprecedented trend of spatial data generation provides new opportunities for information and knowledge extraction, industrial development and business decision making. Although the big data brings new chances for scientific, business and engineering fields, it presents some challenges. To be more specific, storage, management, process and analysis of the spatial big data on traditional spatial information platforms is difficult and expensive. Such challenges affect modeling, analysis, simulation and concurrent access to spatial data. The need of real-time analysis in some applications like dynamic routing, fleet management or emergency management is also influenced by such limitations. In order to face spatial big data challenges, cloud computing technology to support spatial information applications appears to be very promising. Emergence of cloud computing technology provides an effective, scalable and affordable solution to big data challenges in spatial information application. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software. In this paper, we discussed fundamental theories and key technologies of cloud computing in storage, process and analysis of spatial big data. We have surveyed storage and management of big data using distributed file systems and NoSQL databases and made a comparison between different types of this databases. We also discussed recent trends and methods in parallel processing and big data analysis. MapReduce as a prominent parallel programming method and Hadoop as the most popular implementation of MapReduce are surveyed. We reviewed and made a comparison of spatial tools which is developed on cloud platforms. One of the most important challenges in spatial cloud computing that geospatial scientists is facing is spatial indexing and query processing. Due to distributed systems limits, developing dedicated spatial indexing and query processing techniques is needed. So we focused on novel spatial indexing methods and query processing technologies. As the case study, this paper surveys usage of cloud computing technologies in transportation, traffic and Intelligent Transportation Systems (ITS) and remote sensing and earth observations. The aim of this paper is reviewing and introducing fundamental theories, new technologies and recent trends of spatial big data to researchers of geospatial sciences.

Nowadays, much attention has been paid on using geostatistical methods for estimation of spatial parameters using the variables of these parameters. The quality of groundwater for multiple usage and its costly and time consuming to measure physical and chemical parameters, itis essential to explore methods for estimation of these parameters based on those with can be easily measured. The main purpose of this paper is investigate and offer solutions to improve quality of drinking water sources using GIS in Saveh city. Therefore, qualitive parameters, turbidity, pH, conductivity, TDS, total hardness, sodium, nitrate, sulfate, chloride, phosphate and alkalinity of water and wastewater libratory personnel, according to the discretion of the Central province was investigated. Information about the study of low rainfall and rainy seasons of 1393 and a total of 528 samples were collected and tested for 24 wells and then normalizing the data, geostatistical methods IDW, Ordinary Kriging and Disjunctive Kriging Were examined. The results of these three methods to estimate the quality parameters studied using statistical criteria RMSE shows that the accuracy was evaluated. The results of calculation showed RMSE for normally distributed data, the IDW method has higher accuracy for data with normal distribution, Disjunctive Kriging method is more accurate. to improve the situation there. Zoning maps of underground water quality components of the methods mention in the GIS environment.

Given the diversity of urban problems and the sensitivity of each of the specified spectral range of the electromagnetic spectrum, various imaging sensors exhibit different behaviors in relation to some of the toll record. The color distortion and respond to abnormal sensor spectral range identified as one of the most important challenges the correct diagnosis in Remote sensing, effects, and advocacy. Data integration in order to take advantage of spectral data recorded by the various sensors in better detection vegetation is also significant benefits. In this study, the results of two Landsat 8 and WorldView-2 images merge in order to better identify areas of vegetation in the urban area, using statistical transfer PCA is used. Among the benefits that this integration as well as integration and coherence spectral data cited two sensors. Using Landsat 8-band SWIR image, increase transparency and thus improve detection and associated vegetation. In this paper, after the merger of two images taken from an urban area in Tehran at the level of spectral characteristics, process knowledge base to identify and classify areas of vegetation was carried out which resulted in the mining areas of vegetation with an accuracy of 81.3% was achieved.

Producing digital elevation models (DTMs) for forested areas is a challenging task. Nowadays, because of the capability of LiDAR pulses to penetrate through the vegetation canopy, airborne laser scanner technology has attracted enormous interest for DEM extraction especially in forested terrain. Producing Digital Terrain Models from airborne LiDAR data consist of two main steps of filtering and interpolation. Several automatic filtering algorithms for laser scanning data have been proposed. The capability of these algorithms to separate ground and non-ground points is not the same. The aim of this study is to combine two algorithms to achieve higher accuracy. In order to separate ground from non-ground points at first, noise was removed from LiDAR data, then the third moment (skewness) was calculated and finally slope-based algorithm was applied. The skew amount of LiDAR data was equal to 0.48; so the points with greater height were recognized as object points and removed from point clouds. Some of the non-ground points were recognized by mistake as the ground points which were removed by applying slope-based algorithm. Slope-based algorithm with five threshold slopes 18, 20, 22, 24 and 26 degrees was applied on point clouds and after calculating type I, type II and total errors the best threshold slope of 22 degrees was found and implemented. Finally, a digital terrain model was produced by B-spline interpolation method. The results showed 21 cm vertical accuracy for produced digital elevation model which comparing to previous works was acceptable. User's and producer's accuracy for ground points was 93.38 and 94.5 and for non-ground points was 94.5 and 95.2, respectively. This accuracy indicates that the ground and non-ground points were separated very carefully.

Classification is one of the prominent issue in processing of Hyperspectral images because of their variety applications such as urban planning, mining and military. Hyperspectral imaging due to high spectral resolution can provide much more information about the surveyed area. Ability of classifying objects in the scene can raise by using their spectral properties. However, the classification of Hyperspectral image is a very challenging issue because of their huge number of band. The large number of spectral bands in Hyperspectral data not only increases the computational burden but also can reduce classification accuracy. Moreover, there are usually high correlation between bands. Dimensionality reduction has become a crucial step for successfully classify Hyperspectral image and getting better results. Feature extraction by using the intrinsic dimensionality of the data is one useful approach to decrease the dimensionality. In this paper, Principle component analysis method using intrinsic dimension used as a pre-processing step to improve classification result. By using intrinsic dimension Estimation, can recognize proper bands number for processing and by PCA can extract optimal bands to perform classifications SVM, ANN and C4.5. The results reveal the implemented method can improve the overall accuracy of classified imagery nearly 5-10 %. ANN method with accuracy about 0.95 have highest accuracy among all methods.

Rate of change and the destruction of resources of previous years and the feasibility and predict changes of future could be an important step in planning and optimum use of resources and control of non-normative changes for the future. In the last few years due to vegetation changes in the country's western regions fine dusts entry has created many problems for residents in western and central. For this reason, this work focuses on the westernmost region of the country. We apply ETM Landsat satellite images of 2002 and 2012 to predict vegetation changes. Vegetation changes zoning map of the area of study was prepared after geometric correction and radiometric images through combining comparison of images classifications of every two years Using matrix multiplication applications method and appropriate vegetation indices. Our results show among vegetation indices, the SAVI index has the highest correlation coefficient between the quantitative data of canopy cover and its numerical values based on which this index was used to investigating the region vegetation coverage. The vegetation coverage can be categorized in three classes of poor, medium, and fine. In the period of 2002-2012, area of the poor and fine vegetation coverage classes has increased whereas that of the medium class has decreased. The greatest reduction has been occurred in the medium class where the area of coverage decreased from 304 km2 to 173 km2 and the reason for this is the conversion of vegetation areas to desserts. Areas with poor vegetation are the source of fine dusts to the country that require attentions to prevent their extension.