ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2018 | Volume:6 | Issue:3 |Papers Count:10

Nowadays, after a relatively short time from the emergence of location-based social networks, these social media have become an inseparable element of social life, especially in big cities. The increased popularity of social networks, the development of location-based technologies including Global Positioning System (GPS) as well as the pervasive use of smart-phones that besides being equipped with GPS can connect to the worldwide web easily have led to the generation of update, low-cost and valuable spatial data. Beside various applications, these data can be used to resolve urban challenges. As an example, in this paper, the behaviors of the location-based social networks’ users were assessed to classify the land use in urban environments. In this regard, initially, we attempted to recognize regions in urban environments by clustering the users’ spatial data, then the variation patterns of users’ social behaviors through the day and night were extracted and used as an index to land use determination and a type of social land use was designated to each region. Evaluating the obtained results and comparing them with satellite images show the high potential of location-based social networks to recognize urban environments and resolve existing challenges.

With development of mobile devices equipped with a global positioning system, such as smartphones, large amounts of spatial information are generated. These data, which are often stored and modeled as a sequence of spatial locations over time, are called trajectory. The large amount of trajectory data has increased the cost of transferring, storing and processing such data. To overcome these problems, a number of compression algorithms have been proposed for reducing the size of trajectory data. In this paper, seven algorithms including uniform sampling, Douglas Poker, TD-TR, Opening Window, OPW-TR, TD-SB and SQUISH-E algorithms are being discussed and the advantages and disadvantages of these algorithms are investigated as well. The SQUISH-E algorithm can create a balance between the compression rate and the Synchronized Euclidean Distance error, but has a high compression rate than other compression algorithms. To solve mentioned problem, this paper proposed a method for changing the priority window of the SQUISH-E algorithm, which improves the compression rate of this algorithm. In order to evaluate the performance of the proposed method, all algorithms are implemented on six trajectories of varying complexity and compared with each other in terms of criteria such as compression rate, run-time, and concurrency Euclidean distance errors. The results of implementation of the proposed method indicate the improvement of the proposed algorithm at the compression rate, computation time, and Synchronized Euclidean Distance error. In compare to SQUISH-E algorithm, the computation time and compression rate of proposed algorithm is decreased about 130 millisecond and 0. 015, respectively.

Today getting access to the monitoring systems with high accuracy in order to produce healthy agricultural products has been raised though the following effective factors are of high importance as well; prevent pest risk and reduce the coast raised from unmanned aerial systems according to low cost, low weight, possibility of taking images in cloudy weather under the cover of clouds, flexibility in time and high spatial resolution imaging which are usable in obtaining remote sensing data and considerable interest. In this regard, the use of these systems shows high potential in obtaining required data in monitoring products and provided control. In this paper, multi-spectral sensor mounted on an unmanned airborne platform in the health status of the regional citrus trees infected with the decline disease in the Fars province investigated. Using data by this system, classified images of studied area based on vegetation index and distinctions between healthy and infected trees, use of SVM are provided. Collected ground data shows the feasibility of these methods in tree’ s health status diagnosis. About 90 percent overall accuracy was achieved in the trees classification.

Soil moisture is a key variable in the hydrologic process, which is affected by the exchange of water and energy on the Earth's surface. Precise estimation of spatial and temporal variations of soil moisture is crucial for environmental studies. The Polarimetric SAR (PolSAR) images are a convenient tool for this purpose. These images also guarantee both broad coverage and suitable spatial resolution. In this study, a linear analytical model has been suggested for estimating soil moisture. This model uses data gathered by the AIRSAR sensor in 2003 in C, L, and P bands. For this purpose, with incorporation of a genetic algorithm (GA), sequential forward selection (SFS), and sequential backward selection (SBS), we examine and select appropriate features best fitted for soil moisture modeling. Also in this estimation, soil moisture measurements were compared to in-situ data. The results showed that the proposed method (linear analysis model) had a good efficiency by using GA feature selection compare to both SFS and SBS feature selection. Regarding statistical parameters for proposed method, R2 model is higher than %80 and RMSE is less than 0. 027 for P, L, and C bands, which in comparison with other algorithms, the R2 model estimates soil moisture more accurately. Also, the best bands to estimate soil moisture model using proposed model and incorporated PolSAR features is the C band.

The Digital terrain model is an important geospatial product used as the basis of many practical projects related to geospatial information. Nowadays, a dense point cloud can be generated using the LiDAR data. Actually, the acquired point cloud of the LiDAR, presents a digital surface model that contains ground and non-ground objects. The purpose of this paper is to present a new approach of extracting the digital terrain model from the digital surface model. In the first step, noises were removed by preprocessing; then the irregular point cloud was converted to raster data. In the next step, the proposed gradual geodesic dilation and labeling approaches scan were applied in order to detect and eliminate the non-ground objects. The basis of gradual geodesic dilation approach was to increase the structural element size in each step, investigate the height heterogeneity and remove the non-ground objects, gradually. Also, utilizing the innovative scan labeling approach which operated based on slope differential helped to remove the non-ground objects completely. Finally, the non-ground objects were removed and the lost regions were retrieved and the digital terrain model was generated by interpolation. For analyzing the proposed approach, the reference data of the ISPRS was employed. The analyzing results in the five test areas indicated 4. 61%, 6. 97% and 3. 17% for Type I, Type II and total errors, respectively. These results clarify the good performance of the proposed approach for detecting the non-ground objects.

Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub pixel composition of spectral mixtures. The mathematical solution of the mixing problem is to resolve a set of linear equations using least squares approaches. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcome of conventional SMA approaches applying a linear mixture model using a set of fixed endmembers. Also, if endmembers are highly correlated, the matrix will become non-orthogonal, the inversion will be unstable and the inverse or estimated fractions will become highly sensitive to random errors (e. g., noise). In this paper, we present a new band selection method that comprises a band prioritization and a band de-correlation. The band prioritization will prioritizes all bands according to the reduced spectral variability of endmembers which will be used for unmixing. Bands are then selected on the basis of their associated priorities. Since the band prioritization does not consider as spectral correlation, a band de-correlation using the angles between bands are being applied to de-correlate prioritized bands. It is shown that the proposed band selection method effectively eliminates a great number of insignificant bands. Surprisingly, the experimental results on real and synthetic data sets show that with a proper band selection less than 0. 2 of the total number of bands can achieve comparable performance using all bands.

Shadow detection is an important preprocessing step in many applications of remote sensing, particularly in high resolution images. Shadow represents information about the shape, relative position and direction of the object and in urban environments occupy a significant portion of the image. Shadow can have positive and negative effects in objects interpretation. Shadows can be regarded as a type of useful information in building position recognition and height estimation. Researchers have presented model-based methods, property-based methods and based on index to shadow detection. In this research, we use local feature to identify the shadow of buildings in order to detect building position recognition. After point local feature extraction, we estimate their orientation. Orientation histogram calculate and due to the perpendicular edges of different buildings can extract main orientations. Edge map definition, which emphasizes edges only in the main orientations and dilate in the main orientations. This improved edge map is fused with initial shadow features. Edges are used to detect shadow of building and remove shadows that are not for the building. The proposed method was performed on four high-resolution image. Ground reference data obtained manually and Recall, precision and F-score, in best situation, respectively 90. 1, 88. 86 and 89. 48 indicate the efficiency and proper performance of the proposed method.

The atmospheric Aerosol Optical Depth (AOD) is one of the major uncertainties in retrieving Downward Surface Shortwave Radiation (DSSR(. In the current study, Yang’ s model, which determines the effect of each component of atmosphere separately, was used to estimate DSSR under cloud-free conditions. Recent studies have used MODIS AOD data to calculate DSSR, while in this article the retrieved AOD from the Simplified Aerosol Retrieval Algorithm (SARA) was used to estimate DSSR over the Chitgar site in Tehran, Iran. For comparison purpose, MODIS AOD products were also used to estimate DSSR over the studied region. Estimated DSSR based on SARA AOD, MODIS Level 2 (L2) AOD, and MODIS Level 3 (L3) AOD were evaluated with ground-based measurements of DSSR at the Chitgar site during summer of 2013. Results show R2 = 0. 88, RMSE = 25. 09 W/m2, and Bias = 2. 05 W/m2 for SARA-based DSSR, and R2 = 0. 61, RMSE = 41. 75 W/m2, and Bias = 15. 93 W/m2 for MODIS-L2 based DSSR, and R2 = 0. 82, RMSE = 60. 73 W/m2, and Bias = 53. 77 W/m2 for MODIS L3-based DSSR. The results showed higher accuracy of estimated DSSR based on the SARA AOD than the estimated DSSR based on MODIS products. As RMSE of SARA-based DSSR is 1. 6 and 2 times MODIS-L2 based DSSR and MODIS-L3 based DSSR, respectively. In the proposed method, Bias reached close to zero. These results also showed that our modeling scheme of combining Yang’ s model with the SARA algorithm can be used from the regional to global scale under the assumption of future access to spatially distributed AERONET sites.

Increasing population in urban areas and limitations of urban vacant lands and infrastructures have caused vertical development of apartments and infrastructures. The organizations related to land administration in the international system, recognize 3D cadastre as a solution to respond to this issue. Although 2D cadastre is almost unable to meet the expected responsibilities and manage the recognized restrictions, it is currently used in the majority of the associated organizations in registration and property presentations. Limitations in analyze and presentation of easement rights are considered as examples of such restrictions. This study has been accomplished by recording spatial and descriptive information of the apartments in a geodatabase, and also by applying visualization and analyzing techniques in GIS, 3D modeling and visualization of the apartments, which all are mainly used in cadastre. In addition, modeling and visualization of easement rights in apartments have been performed by tools development.

Earth’ s upper atmosphere, called the ionosphere, is a highly variable region with complex physical characteristics in which the density of free electrons are large enough to have considerable effects on signals’ propagation travelling through this dispersive medium. As GPS signals travel through the ionosphere, they may experience rapid amplitude fluctuations or unexpected phase changes. This is referred to as ionospheric scintillation. Ionospheric scintillation which caused by small scale irregularities in the electron density, is one of the dominant propagation disturbances in radio frequency signals. These irregularities severely affect the accuracy and reliability of GPS measurements. Therefore, it is necessary to investigate ionospheric scintillation and its effects on GPS observations. Hence, the focus of this paper is to detect ionospheric scintillations over Iran’ s region and to investigate these effects on GPS observations in more details. The results will show the occurrance of this phenomenon and its effects on GPS observations.