2024-03-29T17:01:28+03:30 http://jgit.kntu.ac.ir/browse.php?mag_id=40&slc_lang=fa&sid=1
40-791 2024-03-29 10.1002
Engineering Journal of Geospatial Information Technology jgit 2008-9635 10.61186/jgit 2023 10 3 Investigation of the effects of geomagnetic storms on ionospheric irregularities using the combination of ground-based GNSS and SWARM satellites data Alireza Atabati Atabati@email.kntu.ac.ir Iraj Jazireeyan jazirian@kntu.ac.ir M. Mahdi Alizadeh alizadeh@kntu.ac.ir Amirhossein Pourmina pourmina@emial.kntu.ac.ir Ahad Malekzadeh malekzadeh@kntu.ac.ir Geomagnetic storms are one of the main causes of ionospheric perturbations in different sizes, which  depending on their intensity, they can  disturb the radio signals passing through this medium. On September 6-12, 2017, the sudden storm commencement (SSC) was the most massive geomagnetic storm of the year due to the X9 solar flare caused by a coronal mass ejection (CME). IMF-Bz and Dst values increased when the first SSC occurred at 23:43 on September 6. The second SSC has a more vigorous intensity at 23:00 on September 7 that caused a dramatic increas the other geophysical parameters such as Kp and AE. During the second SSC, Kp index reached 8, and AE reached 2500 nT. In this research, the ionospheric irregularities over OLO3 station (-2.75E,35.87N,1483.00H) located at Arusha in Tanzania were analyzed using ground-based GNSS data and in situ measurements SWARM satellites. This procedure was applied to VTEC, signal to noise ratio (S4), and Rate of TEC Index (ROTI) values obtained from ground-based GNSS (GB-GNSS) and SWARM A & C in order to identify ionospheric perturbations during the geomagnetic storm. Furthermore, Langmuir plasma probes of SWARM satellites were implemented to recognize the rate of electron density (RODI). The results show that GB-GNSS and Swarm satellite geophysical ionospheric parameters increased during September 6-12, that  indicate the effect of the geomagnetic storm on the increase of ionospheric perturbations. This work shows the potential of using spaceā€based in situ measurement to detect ionospheric irregularities caused by the geomagnetic storm for areas such as oceans and deserts, where ionospheric observations are hardly possible. Ionospheric Irregularity Geomagnetic Storm Total Electron Content (TEC) Rate of Tec (ROTI) Ionospheric Scintillation SWARM 2023 2 01 1 27 http://jgit.kntu.ac.ir/article-1-791-en.pdf 10.52547/jgit.10.3.1
40-572 2024-03-29 10.1002
Engineering Journal of Geospatial Information Technology jgit 2008-9635 10.61186/jgit 2023 10 3 Three-stage inversion improvement for forest height estimation using dual-PolInSAR data Tayebe Managhebi tb.managhebi@gmail.com Yasser Maghsoudi ymaghsoudi@kntu.ac.ir Mohammad Javad Valadan Zoej valadanzouj@kntu.ac.ir This paper addresses an algorithm for forest height estimation using single frequency and single baseline dual polarization radar interferometry data. The proposed method is based on a physical two layer volume over ground model and is represented by using polarimetric synthetic aperture radar interferometry (PolInSAR) technique. The presented algorithm provides the opportunity to take advantages of the dual polarimetric data, i.e, better spatial resolution and wider swath width, in comparison with the full polarimetric data, in forest height estimation application. In this research, a polarimetric optimization method is utilized to choose the optimum volume polarization basis in order to improve the results of the three-stage inversion algorithm. For the performance analysis of the proposed approach, the L-band ESAR data of the European Space Agency from BioSAR 2007 campaign (ESA) which is acquired over the Remningstorp test site in southern Sweden, is employed. The experimental result shows the dual PolInSAR HH/HV data capability in the forest height estimation without decreasing the accuracy of the result compared with the full polarimetric data.  The suggested method leads to the average root mean square error (RMSE) of 4.39 m and the determination of coefficient of 0.66 in the forest height estimation in 15 predetermined stands in comparison with the  LiDAR reference heights. Dual polarimetry Forest Height Polarimetric optimization Polarimetric synthetic aperture radar interferometry Three-stage inversion algorithm 2023 2 01 29 47 http://jgit.kntu.ac.ir/article-1-572-en.pdf 10.52547/jgit.10.3.29
40-839 2024-03-29 10.1002
Engineering Journal of Geospatial Information Technology jgit 2008-9635 10.61186/jgit 2023 10 3 An efficient method using the fusion of deep convolutional neural network features for cloud detection using Landsat-8 OLI spectral bands Arastou Zarei arastou.zarei@ut.ac.ir Reza Shah-Hosseini rshahosseini@ut.ac.ir Morteza Seyyed-Mousavi mortezamousavi@ut.ac.ir Cloud segmentation is a critical pre-processing step for any multi-spectral satellite image application. In particular, disaster-related applications e.g., flood monitoring or rapid damage mapping, which are highly time and data-critical, require methods that produce accurate cloud masks in a short time while being able to adapt to large variations in the target domain (induced by atmospheric conditions, different sensors, and scene properties). This research presented a deep convolutional neural network for cloud detection in the Landsat-8 dataset at the pixel level. Two key components of the proposed network are convolutional layers in the decoder branch and two convolution kernels in various scales. The near-infrared band in this study was added to the network inputs, including red, green, and blue bands, in order to improve the network performance. In the proposed network architecture, the encoder-decoder branches which are symmetrical with the density of feature maps resulting from the multiplicity of filters and the designing of multi-dimension filters, provided a local and general context for the accurate identification of the cloud and its margins which are used to extract the spatial features in high-level scales. However, multi-scale feature maps will be sampled and integrated to accuracy o-generate high Finally, the proposed method uses 3500 patches of Landsat-8 satellite images with various cloud challenges by using several kernels in sizes 3 x 3 and 5 x 5 with an F1-score of 96.6 and a Jaccard index (JI) of 93.5, provides a higher accuracy than the other methods. In general, the suggested method outperformed the alternatives in the same, uncorrected data set in terms of accuracy, particularly in regions with bright surfaces. Due to the effectiveness of the proposed framework, it has a lot of potential for practical application with different types of satellite images. Remote Sensing Landsat-8 Convolution Neural Network Cloud Detection 2023 2 01 49 70 http://jgit.kntu.ac.ir/article-1-839-en.pdf 10.52547/jgit.10.3.49
40-875 2024-03-29 10.1002
Engineering Journal of Geospatial Information Technology jgit 2008-9635 10.61186/jgit 2023 10 3 Analyzing the performance of different machine learning methods in determining the transportation mode using trajectory data Morteza Tayebi mortezatayebi Parham Pahlavani pahlavani@ut.ac.ir With the widespread advent of the smart phones equipping with Global Positioning System (GPS), a huge volume of users’ trajectory data was generated. To facilitate the urban management and present appropriate services to users, studying these data was raised as a widespread research field and has been developing since then. In this research, the transportation mode of users’ trajectories was identified based on their raw GPS data. These data are often associated with errors, it was attempted to minimize them by applying a comprehensive pre-processing procedure in this research. Accordingly, various features were extracted to identify the transportation modes including walk, bike, train, bus, and driving. In this regard, four classification methods including decision tree, multilayer perceptron neural network, Naïve Bayes, and support vector machine were used to build a predictive model. In order to improve the performance of the implementation methods, the percentage of the points of each trajectory on the distance of one standard deviation from the total speed average of transportation modes has been used as a new feature. The above-mentioned four models were implemented with different regularization parameters and their values were set to the optimal values by applying a comprehensive grid search. Then, Kappa and the overall accuracy indices were employed to evaluate different methods. The results of this study show that the multilayer perceptron neural network with overall accuracy of 0.88 has the best results compared to the other models.   Trajectory data Determining the transportation mode Classification Machine learning 2023 2 01 71 94 http://jgit.kntu.ac.ir/article-1-875-en.pdf 10.52547/jgit.10.3.71
40-889 2024-03-29 10.1002
Engineering Journal of Geospatial Information Technology jgit 2008-9635 10.61186/jgit 2023 10 3 Spatio-temporal analysis of the covid-19 impacts on the using Chicago urban shared bicycles by tensor-based approach Mostafa Golmohammadi mstafagolmohammadi@gmail.com Hossein Etemadfrad etemadfard@um.ac.ir Hamed Kharaghani ha.kharaghani@gmail.com Cycling is a phenomenon in urban transportation that has the ability to allocate a specific location at any moment of time. Accordingly, spatial analysis of bicycle trips can be accompanied by temporal analysis. Commonly, the use of GIS environment is recommended to display the extent of the phenomenon's spatial changes. However, in order to apply and display changes over time, it requires the production of other layers of information. In this study, it is possible to simultaneously display the spatial and temporal information by tensor concept and tool. Using this tool, the abilities of displaying and analyzing a high volume of spatial information with the power of temporal resolution are created due to the simultaneous representation of the information layers. This study tries to investigate the spatial and temporal analyses at the same time by using the concept of tensor in order to understand how users request Chicago's shared bikes at different times of the day.  In fact, this research analyzes the spatial and temporal distribution of the shared bike trips simultaneously. In addition, the number of bicycle trips over time, along with the amount of reduction and increase in trips from different neighborhoods, are studied separately from a spatial point of view. The results of this study show that the April 2020 coronavirus outbreak in Chicago resulted in a 68.4% decrease in the use of shared bicycles in the city compared to the same period of time in 2019. A more detailed survey showed that, the city experienced a 64.3% decrease for the holidays of this month and 72.6% on normal and non-holiday days. Spatial attitude also shows that in April 2020, the central neighborhoods experienced the greatest reduction in the use of shared bicycles. In addition, after the spread of the disease, requests to use bicycles from the same origins and destinations have increased. Coronavirus Tensor Bike Sharing Spatio-Temporal Analysis GIS 2023 2 01 95 119 http://jgit.kntu.ac.ir/article-1-889-en.pdf 10.52547/jgit.10.3.95
40-891 2024-03-29 10.1002
Engineering Journal of Geospatial Information Technology jgit 2008-9635 10.61186/jgit 2023 10 3 Evaluation of remote sensing-based drought monitoring indexes using support vector regression and random forest models (Case study: Marivan city) Jamal Seyedi Ghaldareh jamalseyedi76@gmail.om Salman Ahmadi s.ahmadi@uok.a.ir Mehdi Gholamnia Mehdi_Gholamnia@ut.ac.ir Drought is a natural and climatic phenomenon that occurs in large areas around the world every year, and its occurrence is caused by the shortage of rainfall and increased evaporation and transpiration at high temperatures. The purpose of this research is evaluating the remote sensing data in drought monitoring for Marivan city and analyzing the spatial-temporal distribution of the drought conditions and identifying its severity. In this study, we used different drought indicators produced from MADIS and TRMM satellite data, which were extracted from Google Earth Engine platform to analyze the drought conditions in Marivan city from February to November for the years 2001 to 2017 .In this research, remote sensing indices such as normalized difference index of vegetation, index of vegetation conditions, index of temperature conditions, index of improved vegetation, index of evaporation and transpiration and index of rainfall status were selected as independent variables. Furthermore. the standard rainfall index obtained from meteorological data has been calculated as a dependent variable to evaluate drought conditions. Random forest methods and support vector regression were used to compare the remote sensing data and the ground data and to check the correlation between them and the importance of the remote sensing indicators for drought monitoring. The result of the modeling was obtained using the support vector regression algorithm with the values of the explanatory coefficient of 0.88 and the mean square error of 0.313.The results of the random forest model with the values of the coefficient of explanation of 0.909 and the mean square error of 0.259 indicated the high efficiency of this model. Then, the correlation between  the remote sensing indices and  the meteorological index was investigated. And PCI, ET, EVI, NDVI indices had the most correlation among the other variables.Therefore, the remote sensing indicators can be used to predict the drought situation in the research area.   Drought Remote sensing images Random forest Support vector regression 2023 2 01 121 141 http://jgit.kntu.ac.ir/article-1-891-en.pdf 10.52547/jgit.10.3.121