University of Tehran Earth Observation and Geomatics Engineering 2588-4352 1 2 2017 12 01 The generalized F and G series for the satellite orbit propagation 71 81 64288 10.22059/eoge.2017.235787.1008 EN Mohammad Reza Seif Department of Civil Engineering, Imam Hossein University (IHU), Tehran, Iran Journal Article 2017 03 16 In this paper, an advanced version of the Lagrange method, F and G series, is proposed for the many applications in the celestial mechanics and space science such as initial orbit determination and satellite orbit propagation. In this development, the Lagrange coefficients were developed from a gravitational field of an inhomogeneous attractive body to all the perturbing accelerations acting on an orbiter. The efficiency of the method is tested for the satellite orbit propagation. This assessment is based on the comparison between the Lagrange solution and the analytical one for Keplerian motion and numerically integrated orbit for non-Keplerian motion. The discrepancy at centimeter and sub-centimeter accuracy shows the performance of the developed algorithm for MEO and LEO satellites orbit propagation. The results of computational time showed that the Lagrange method is as time-consuming as the multi-step methods where it is faster than the single-step methods. Besides the CPU-time, the stability test of the Lagrange method shows that it is as stable as the single-step and is more stable than the multi-step methods at the equivalent orders. Therefore, the Lagrange method offers the advantages of the single- and multi-step methods.

https://eoge.ut.ac.ir/article_64288_0e2af4ed9fcd13e83809cdd05c309cf8.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 1 2 2017 12 01 A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation 82 99 64289 10.22059/eoge.2017.220342.1006 EN Parham Pahlavani School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran Hosein Askarian Omran School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran Behnaz Bigdeli Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran Journal Article 2017 02 04 In this paper, a new combination of Artificial Neural Network (ANN), Markov Chain (MC), and Multi Objective Land Allocation (MOLA) was proposed and evaluated to simulate multiple land use changes using GIS-based techniques and multi temporal remote sensing data. The main objective of this paper is to predict land use changes for Tehran, the biggest and capital city of Iran. In this regard, by integration of ANN, MC, and MOLA, we found the pixels that have the highest tendency to change their states from one land use category to others. An ANN model was applied to create Transition Potential Maps (TPMs), and an MC model was used to calculate the quantity of the changes. Finally, a MOLA model was employed for spatial allocation of new changes. In order to analyze the effects of proximity, three types of neighborhood filters were combined with MOLA. The proposed method achieved 92.62%, 95.49%, and 92.74% of kappa index of agreement (KIA), overall accuracy (OA), and kappa of location (Klocation), respectively. This method was applied for Tehran to predict the situation in year 2020. The trend of the changes shows that the urban growth is moving toward southwest of the city, where the areas with poor infrastructure are situated.

https://eoge.ut.ac.ir/article_64289_1f447d1da4afb649b18be68de046c583.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 1 2 2017 12 01 Impact of Iranian permanent GPS network precipitable water estimates on numerical weather prediction 100 111 64290 10.22059/eoge.2017.243645.1013 EN Ali Sam-Khaniani Civil Engineering Department, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran Majid Azadi Atmospheric Science and Meteorological Research Center, Tehran, Iran Zeinab Zakeri I.R. Iran Meteorological Organization, P.O. Box 13185-461, Tehran, Iran Journal Article 2017 03 05 The aim of this study is to assess the impact of continuous and precise ground-based GPS water vapor estimates as a by-product of Iranian Permanent GPS Network (IPGN) geodetic data processing, together with conventional surface and upper air meteorological data on the short range prediction of rainfall and surface moisture fields, including 2 m relative humidity and Precipitable Water Vapor (PWV) over north of Iran. The Weather Research and Forecasting (WRF) model and its Four-Dimentional Variational Data Assimilation (4DVAR) system is used to determine the impact of data assimilation on simulation of three heavy rainfall cases that occurred over the northern part of Iran. All three rainfall cases considered in this study are associated with a shallow and cold high pressure located over Russia that extends towards the southern Caspian Sea. The results of numerical experiments showed that the assimilation of ground-based GPS PWV data, on average, improves simulation of precipitation, PWV and near surface relative humidity, even though the skill declines after 24-h simulation. It is found that inclusion of GPS PWV improved the predicted accumulated precipitation in day-1 of the model simulations for February and November cases up to 7 percent while there was almost no positive impact in September case. Results suggest that incorporation of observations in initial conditions of the WRF gives generally a slight improvement in 2 m relative humidity forecasts when compared with the control experiment without assimilation. Assimilation of GPS PWV in February and September cases reduces, on average, 0.8 mm the Mean Absolute Error (MAE) of the PWV model during 12-h forecast period. Overall, best results in terms of MAEs were achieved when GPS water vapor estimations were used along with conventional surface and upper air radiosonde data.

https://eoge.ut.ac.ir/article_64290_d3a0937125c8ba40328602a156d7b62e.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 1 2 2017 12 01 A spatio-temporal feature extraction algorithm for crop mapping using satellite image time-series data 112 121 64291 10.22059/eoge.2017.246546.1017 EN Saeid Niazmardi Department of remote sensing engineering, Graduate University of advanced technology, Kerman, Iran Journal Article 2017 02 20 Crop type identification is a prerequisite for several agricultural analyses. Thus, various methods have been used to accurately identify different crop types. Classification of satellite image time-series (SITS) data is probably the most efficient one, among these methods. Recently, the SITS data with high spatial and temporal resolution have become widely available. This category of SITS data, in addition to information about the temporal phenology of crops, provides valuable information about the spatial patterns of the croplands. This information, if extracted properly, can increase the accuracy of crop classification. In this paper, we proposed a novel feature extraction algorithm in order to extract this information. The proposed feature extraction algorithm is a two-step algorithm. In the first step, an image segmentation method is used to partition the time-series data into several homogenous segments. The pixels of each segment share similar spatial and temporal characteristics. In the second step, the algorithm fits a polynomial function to the average value of pixels of each segment. Finally, the coefficients of the fitted polynomial function are considered as the spatial-temporal (spatio-temporal) features. The effectiveness of the proposed spatiotemporal features was evaluated based on their obtained crop classification accuracies. In this paper, the SITS data were constructed by extracting normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) from 10 RapidEye images of an agricultural area. Support vector machines (SVM) was considered as the classification algorithm. The obtained results of the experiments showed that the proposed spatio-temporal features by proving the classification accuracy of 87.93% and 75.96% respectively for NDVI and SAVI time-series can be very efficient features for crop mapping. These features also sharply improved the crops classification accuracy in comparison with other spatial and temporal features.

https://eoge.ut.ac.ir/article_64291_5e90b6b1706a8744c75e05946fdc53d7.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 1 2 2017 12 01 Monocular vision based obstacle detection 122 130 64292 10.22059/eoge.2017.244709.1015 EN Samira Badrloo Faculty of Surveying and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran Masoud Varshosaz Faculty of Surveying and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran Journal Article 2017 03 25 Detecting and preventing incidents with obstacles is a challenging problem. Most of the common obstacle detection techniques are currently sensor-based. Mobile robots like Small Unmanned Aerial Vehicles (UAVs) are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo and mono techniques. Mono methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection. A recent research in this field has focused on matching the ScaleInvariant Feature Transform (SIFT) points along with SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. However, this method is not able to distinguish between near and far obstacles nor the obstacles in a complex environment and, thus, is sensitive to wrong matched points. This paper aims to solve the aforementioned problems through using the distance-ratio of matched points. Then, every point is investigated for distinguishing between far and near obstacles. The results demonstrated the high efficiency of the proposed method in complex environments. The least achieved accuracy of the algorithm was 60.0%, and the overall accuracy was 79.0%.

https://eoge.ut.ac.ir/article_64292_b274b54098ec2221529174db33281e10.pdf

University of Tehran Earth Observation and Geomatics Engineering 2588-4352 1 2 2017 12 01 Morphological discrimination amongst geological rock surfaces of Zagros thrust belt via SAR backscattering modelling 131 141 64293 10.22059/eoge.2017.244456.1014 EN Ali Ghafouri School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran 0000-0002-0541-0303 Jalal Amini School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran Mojtaba Dehmollaian Center of Excellence on Applied Electromagnetic Systems, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran Mohammad Ali Kavoosi Dept. of Geology, Exploration Directorate of National Iranian Oil Company, Tehran, Iran Journal Article 2017 03 11 Nowadays, processing and interpretation of remote sensing satellite images is the only method of surface geological rock surfaces mapping. This doubtlessly requires time-consuming field observations for complementary morphological information, i.e. field measurements in geomorphology is unavoidable since the hyper-spectral images that are used for geological mapping do not discriminate the lithologies texture and cannot be used to determine the geological morphology. However, due to the impassable and fault cliffs, comprehensive field operations within a geological map is almost impossible. Microwave or radar remote sensing via Synthetic Aperture Radar (SAR) images is capable of obtaining the surface morphology and alteration zones discrimination based on lithologies texture. To fulfill this aim, the Integral Equation Model (IEM), which has been proposed by Fung et al. (1992) and has been developed and improved several times, seems to be the most outstanding method being adopted to model the SAR backscattering coefficient against the surface roughness. Nonetheless, it needs to be asserted that the Euclidean calculation of this parameter is not capable enough to measure the morphology of a feature. In this paper, using the power-law geometry capability, one can improve the alteration zones discrimination. To implement and evaluate the proposed method of geomorphological mapping, IEM 𝜎° results for a region on the Zagros fold-thrust belt, in western Iran, were compared with the satellite SAR backscattering data in the L-band (i.e. ALOS-PALSAR) and the X-band (i.e. TerraSAR). Besides, the efficiency of the SAR data processing versus the geological field observations provide an average of more than 20% improvement in terms of the power-law geometry in comparison with the Euclidean geometry. Although this improvement for moderate rough formations is less than 3% at high frequency (X-band), it is about 30% for rough formations at low frequency (L-band).

https://eoge.ut.ac.ir/article_64293_1122efa1ef9bdc8611beefcf971a5897.pdf