2017
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The generalized F and G series for the satellite orbit propagation
https://eoge.ut.ac.ir/article_64288.html
10.22059/eoge.2017.235787.1008
1
In this paper, an advanced version of the Lagrange method, F and G series, is proposed for the manyapplications in the celestial mechanics and space science such as initial orbit determination and satelliteorbit propagation. In this development, the Lagrange coefficients were developed from a gravitationalfield of an inhomogeneous attractive body to all the perturbing accelerations acting on an orbiter. Theefficiency of the method is tested for the satellite orbit propagation. This assessment is based on thecomparison between the Lagrange solution and the analytical one for Keplerian motion and numericallyintegrated orbit for nonKeplerian motion. The discrepancy at centimeter and subcentimeter accuracyshows the performance of the developed algorithm for MEO and LEO satellites orbit propagation. Theresults of computational time showed that the Lagrange method is as timeconsuming as the multistepmethods where it is faster than the singlestep methods. Besides the CPUtime, the stability test of theLagrange method shows that it is as stable as the singlestep and is more stable than the multistepmethods at the equivalent orders. Therefore, the Lagrange method offers the advantages of the single andmultistep methods.
0

71
81


Mohammad Reza
Seif
Department of Civil Engineering, Imam Hossein University (IHU), Tehran, Iran
Iran
m.r.seif@ut.ac.ir
Lagrange coefficients
Satellite orbit propagation
F and G functions
LEO satellite
MEO satellites
[Bem, J., & SzczodrowskaKozar, B. (1995). High order F and G power series for orbit determination. Astronomy and Astrophysics Supplement Series, 110, 411.##Beutler, G. (2004). Methods of celestial mechanics volume ii: application to planetary system, geodynamics and satellite geodesy [M]: Berlin, Heidelberg: SpringerVerlag.##Butcher, J. (1987). The Numerical Analysis of Ordinary Differential Equations: Runge–Kutta and General Linear Methods: John Wiley & Sons, Chichester, New York.##ChaprontTouzé, M., & Chapront, J. (1983). The lunar ephemeris ELP 2000. Astronomy and Astrophysics, 124, 5062.##Curtis, H. D. (2005). Orbital mechanics for engineering students (Third edition. ed.).##Dahlquist, G., & Björk, Å. (1974). Translated by N. Anderson. 1974. Numerical Methods: Prentice Hall, New Jersey.##Dormand, J., & Prince, P. (1978). New RungeKutta algorithms for numerical simulation in dynamical astronomy. Celestial Mechanics, 18(3), 223232.##Escobal, P. R. (1965). Methods of orbit determination. New York: Wiley, 1965, 1.##Feng, Y. (2001). An alternative orbit integration algorithm for gpsbased precise leo autonomous navigation. GPS Solutions, 5(2), 111.##Goodyear, W. H. (1965). Completely general closedform solution for coordinates and partial derivative of the twobody problem. The Astronomical Journal, 70, 189.##Hairer, E., & Wanner, G. (1991). Solving ordinary differential equations, vol. II: Springer Verlag, Berlin.##Lemoine, F. G., Kenyon, S. C., Factor, J. K., Trimmer, R. G., Pavlis, N. K., Chinn, D. S., Torrence, M. H. (1998). The Development of the Joint NASA GSFC and the National Imagery and Mapping Agency(NIMA) Geopotential Model EGM 96. NASA(19980218814).##Lin, L., & Xin, W. (2003). A method of orbit computation taking into account the earth's oblateness. Chinese Astronomy and Astrophysics, 27(3), 335339. doi:##http://dx.doi.org/10.1016/S02751062(03)900567##Taylor, C. J., & Kriegman, D. J. (1994). Minimization on the Lie group SO (3) and related manifolds. Yale University.##McCarthy, D. D., & Petit, G. (2003). IERS conventions. Paper presented at the IAU Joint Discussion.##Montenbruck, O. (1989). Practical ephemeris calculations (A. H. Armstrong, Trans.): Springerverlag Heidelberg.##Montenbruck, O. (1992). Numerical integration of orbital motion using Taylor series. Spaceflight mechanics 1992, 12171231.##Montenbruck, O., & Gill, E. (2000). Satellite orbits: Springer.##Pellegrini, E., Russell, R., & Vittaldev, V. (2014). F and G Taylor series solutions to the Stark and Kepler problems with Sundman transformations. Celestial Mechanics and Dynamical Astronomy, 118(4), 355378. doi: 10.1007/s1056901495387##Picone, J., Hedin, A., Drob, D. P., & Aikin, A. (2002). NRLMSISE‐00 empirical model of the atmosphere: Statistical comparisons and scientific issues. Journal of Geophysical Research: Space Physics (1978–2012), 107(A12), SIA 1511SIA 1516.##Schaub, H., & Junkins, J. L. (2003). Analytical mechanics of space systems: Aiaa.##Sconzo, P., LeSchack, A., & Tobey, R. (1965). Symbolic computation of F and G series by computer. The Astronomical Journal, 70, 269.##Seeber, G. (2003). Satellite geodesy: foundations, methods, and applications: Walter de Gruyter.##Shampine, L. F. (2005). Error estimation and control for ODEs. Journal of Scientific Computing, 25(1), 316.##Shampine, L. F., & Reichelt, M. W. (1997). The matlab ode suite. SIAM journal on scientific computing, 18(1), 122.##Sharifi, M. A., & Seif, M. R. (2011). Dynamic orbit propagation in a gravitational field of an inhomogeneous attractive body using the Lagrange coefficients. Advances in Space Research, 48(5), 904913. doi: http://dx.doi.org/10.1016/j.asr.2011.04.021##Sharifi, M. A., Sneeuw, N., Seif, M. R., & Farzaneh, S. (2013). A semianalytical formulation of the Earth's flattening on the satellite formation flying observables using the Lagrange coefficients. Paper presented at the HotineMarussi Symposium, Rome, Italy.##Steffensen, J. F. (1956). On the restricted problem of three bodies. Mat.fys. medd.; Bd 30.##Tapley, B., Schutz, B., & Born, G. H. (2004). Statistical orbit determination: Academic Press.##Taylor, C. J., & Kriegman, D. J. (1994). Minimization on the Lie group SO and related manifolds. Yale University.##]
1

A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation
https://eoge.ut.ac.ir/article_64289.html
10.22059/eoge.2017.220342.1006
1
In this paper, a new combination of Artificial Neural Network (ANN), Markov Chain (MC), and MultiObjective Land Allocation (MOLA) was proposed and evaluated to simulate multiple land use changesusing GISbased techniques and multi temporal remote sensing data. The main objective of this paper is topredict land use changes for Tehran, the biggest and capital city of Iran. In this regard, by integration ofANN, MC, and MOLA, we found the pixels that have the highest tendency to change their states from oneland use category to others. An ANN model was applied to create Transition Potential Maps (TPMs), andan MC model was used to calculate the quantity of the changes. Finally, a MOLA model was employed forspatial allocation of new changes. In order to analyze the effects of proximity, three types of neighborhoodfilters were combined with MOLA. The proposed method achieved 92.62%, 95.49%, and 92.74% of kappaindex of agreement (KIA), overall accuracy (OA), and kappa of location (Klocation), respectively. Thismethod was applied for Tehran to predict the situation in year 2020. The trend of the changes shows thatthe urban growth is moving toward southwest of the city, where the areas with poor infrastructure aresituated.
0

82
99


Parham
Pahlavani
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
pahlavani@ut.ac.ir


Hosein
Askarian Omran
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
hosein.askarian@ut.ac.ir


Behnaz
Bigdeli
Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
bigdeli@ut.ac.ir
Multiple land use changes
Artificial Neural Network
Markov Chain
Multi objective land allocation
Neighborhood filter
[Achmad, A., Hasyim, S., Dahlan, B., & Aulia, D. N. (2015). Modeling of urban growth in tsunamiprone city using logistic regression: analysis of Banda Aceh, Indonesia. Applied Geography, 62, 237246.##Ahmed, B., & Ahmed, R. (2012). Modeling urban land cover growth dynamics using multi‑temporal satellite images: A case study of dhaka, bangladesh. ISPRS International Journal of GeoInformation, 1(1), 331.##Alsharif, A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian journal of geosciences, 7(10), 42914301.##Alsharif, A. A., & Pradhan, B. (2014). Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149163.##Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). 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(2011). Predictive ability of logistic regression, autologistic regression and neural network models in empirical landuse change modeling–a case study. International Journal of Geographical Information Science, 25(1), 6587.##Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agentbased landuse models: a review of applications. Landscape Ecology, 22(10), 14471459.##Millington, J. D., Perry, G. L., & RomeroCalcerrada, R. (2007). Regression techniques for examining land use/cover change: a case study of a Mediterranean landscape. Ecosystems, 10(4), 562578.##Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and urban planning, 99(2), 141153.##Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chainscellular automata urban growth model. Applied Geography, 40, 140149.##MolownyHoras, R., Basnou, C., & Pino, J. (2015). A multivariate fractional regression approach to modeling land use and cover dynamics in a Mediterranean landscape. Computers, environment and urban systems, 54, 4755.##Munshi, T., Zuidgeest, M., Brussel, M., & van Maarseveen, M. (2014). Logistic regression and cellular automatabased modelling of retail, commercial and residential development in the city of Ahmedabad, India. Cities, 39, 6886.##Nouri, J., Gharagozlou, A., Arjmandi, R., Faryadi, S., & Adl, M. (2014). Predicting urban land use changes using a CA–Markov model. Arabian Journal for Science and Engineering, 39(7), 55655573.##Paola, J. D., & Schowengerdt, R. A. (1995). A detailed comparison of backpropagation neural network and maximumlikelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and remote sensing, 33(4), 981996.##Pijanowski, B. C., Brown, D. G., Shellito, B. A., & Manik, G. A. (2002). Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, environment and urban systems, 26(6), 553575.##Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling & Software, 51, 250268.##Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric engineering and remote sensing, 66(8), 10111016.##Pontius, R. G., & Schneider, L. C. (2001). Landcover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1), 239248.##Porta, J., Parapar, J., Doallo, R., Rivera, F. F., Santé, I., & Crecente, R. (2013). High performance genetic algorithm for land use planning. 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Amar.org.ir. ##ShafizadehMoghadam, H., Hagenauer, J., Farajzadeh, M., & Helbich, M. (2015). Performance analysis of radial basis function networks and multilayer perceptron networks in modeling urban change: a case study. International Journal of Geographical Information Science, 29(4), 606623.##Sun, P., Xu, Y., Yu, Z., Liu, Q., Xie, B., & Liu, J. (2016). Scenario simulation and landscape pattern dynamic changes of land use in the Poverty Belt around Beijing and Tianjin: A case study of Zhangjiakou city, Hebei Province. Journal of Geographical Sciences, 26(3), 272296.##Tan, R., Liu, Y., Zhou, K., Jiao, L., & Tang, W. (2015). A gametheory based agentcellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China. Computers, environment and urban systems, 49, 1529.##Tayyebi, A., Delavar, M. R., Yazdanpanah, M. J., Pijanowski, B. C., Saeedi, S., & Tayyebi, A. H. (2010). A spatial logistic regression model for simulating land use patterns: a case study of the Shiraz Metropolitan area of Iran Advances in earth observation of global change (pp. 2742): Springer.##Tayyebi, A., Perry, P. C., & Tayyebi, A. H. (2014). Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS. International Journal of Geographical Information Science, 28(4), 639659.##Tayyebi, A. H., Tayyebi, A., & Khanna, N. (2014). Assessing uncertainty dimensions in landuse change models: using swap and multiplicative error models for injecting attribute and positional errors in spatial data. International Journal of Remote Sensing, 35(1), 149170.##Wang, S., Zheng, X., & Zang, X. (2012). Accuracy assessments of land use change simulation based on Markovcellular automata model. Procedia Environmental Sciences, 13, 12381245.##Wang, Y., & Li, S. (2011). 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1

Impact of Iranian permanent GPS network precipitable water estimates on numerical weather prediction
https://eoge.ut.ac.ir/article_64290.html
10.22059/eoge.2017.243645.1013
1
The aim of this study is to assess the impact of continuous and precise groundbased GPS water vaporestimates as a byproduct of Iranian Permanent GPS Network (IPGN) geodetic data processing, togetherwith conventional surface and upper air meteorological data on the short range prediction of rainfall andsurface moisture fields, including 2 m relative humidity and Precipitable Water Vapor (PWV) over northof Iran. The Weather Research and Forecasting (WRF) model and its FourDimentional Variational DataAssimilation (4DVAR) system is used to determine the impact of data assimilation on simulation of threeheavy rainfall cases that occurred over the northern part of Iran. All three rainfall cases considered in thisstudy are associated with a shallow and cold high pressure located over Russia that extends towards thesouthern Caspian Sea. The results of numerical experiments showed that the assimilation of groundbasedGPS PWV data, on average, improves simulation of precipitation, PWV and near surface relativehumidity, even though the skill declines after 24h simulation. It is found that inclusion of GPS PWVimproved the predicted accumulated precipitation in day1 of the model simulations for February andNovember cases up to 7 percent while there was almost no positive impact in September case. Resultssuggest that incorporation of observations in initial conditions of the WRF gives generally a slightimprovement in 2 m relative humidity forecasts when compared with the control experiment withoutassimilation. Assimilation of GPS PWV in February and September cases reduces, on average, 0.8 mm theMean Absolute Error (MAE) of the PWV model during 12h forecast period. Overall, best results in termsof MAEs were achieved when GPS water vapor estimations were used along with conventional surfaceand upper air radiosonde data.
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100
111


Ali
SamKhaniani
Civil Engineering Department, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran
Iran
ali.sam@ut.ac.ir


Majid
Azadi
Atmospheric Science and Meteorological Research Center, Tehran, Iran
Iran
azadi68@hotmail.com


Zeinab
Zakeri
I.R. Iran Meteorological Organization, P.O. Box 13185461, Tehran, Iran
Iran
z_zaaker@yahoo.com
4DVAR assimilation
WRF
GPS PWV
Surface observations
precipitation
[Barker, D. M., Huang, W., Guo, Y. R., & Xiao, Q. (2004). A three dimensional variational (3DVAR) system with## MM5: implementation and initial results. Mon. Weather Rev.132:897–914.##Barker, D., Huang, XY., Liu, Z., Auligne, T., Zhang, X., Rugg, S., Ajjaji, R., Bourgeois, A., Bray, J., Chen, Y., Demirtas, M., Guo, YR., Henderson, T., Huang, W., Lin, HC., Michalakes, J., Rizvi, S., & Zhang, X. (2012). The weather research and forecasting model's community variational/ensemble data assimilation system: WRFDA. Bulletin of the American Meteorological Society 93: 831843.##Bauer, H.S., Wulfmeyer, V., Schwitalla, T., Zus, F., & Grzeschik, M. (2010). Operational assimilation of GPS slant path delay measurements into the MM5 4DVAR system.Tellus A. 2010 International Meteorological Institute in Stockholm DOI: 10.1111/j.16000870.2010.00489.x,##Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A., & Ware R.H. (1992). GPS meteorology: remote sensing of the atmospheric water vapor using the global positioning system. J. Geophys. Res., 97(D14):15787–15801.##Bevis, M., Chiswell, S., Herring, T.A., Anthes, R.A., Rocken, C., & Ware, R.H. (1994). GPS meteorology: mapping zenith wet delays onto precipitable water. J. Appl. Meteor.,33:379–386.##Boniface, K., Ducrocq, V., Jaubert, G., Yan, X., Brousseau, P., Masson, F., Champollion, C., Ch´ery, J., & Doerflinger, E. (2009). Impact of highresolution data assimilation of GPS zenith delay on Mediterranean heavy rainfall forecasting. Annales Geophysicae 27: 2739–2753.##Chen, F., & Dudhia, J. (2001). Coupling an advanced land surfacehydrology model with the Penn StateNCAR MM5 Modeling System. II: preliminary model validation. Mon. Weather Rev.129:587–604.##Choy, S., Wang, C., Zhang, K., & Kuleshov, Y. (2013). GPS sensing of precipitable water vapour during the March 2010 Melbourne storm. Adv. Space Res. 52 (2013) 1688–1699.##Dai, A., Wang, J., Ware, R.H., & Van Hove, T. (2002). Diurnal variation in water vapor over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res. 107, 4090, doi:10.1029/2001JD000642.##Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E.E., & Elgered, G. (1985). Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio Science, Vol. 20, Issue 6, pages 1593–1607.##Deeter, M.N. (2007). A new satellite retrieval method for precipitable water vapor over land and ocean. Geophys Res Lett 34:L02815. doi:10.1029/2006GL028019.##Dietrich, S.V.R., Johnsen, K.P., Miao, J., & Heygster, G. (2004). Comparison of tropospheric water vapour over Antarctica derived from AMSUB data, groundbased GPS data and the NCEP/NCAR reanalysis. J Meteorol Soc Jpn 82:259–267.##Divakarla, M.G., Barnet, C.D., Goldberg, M.D., McMillin, L.M., Maddy, E., Wolf, W., Zhou, L., & Liu, X. (2006). Validation of atmospheric infrared sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts. J Geophys Res 111:D09S15. doi:10.1029/2005JD006116.##Dong, J., & Xue, M. (2012). Assimilation of radial velocity and reflectivity data from coastal WSR88D radars using ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike (2008). Q. J. R. Meteorol. Soc., DOI: 10.1002/qj.1970.##Duan, J.P., Bevis, M., & Fang, P. (1996). GPS meteorology: Direct estimation of the absolute value of precipitable water. J. Appl. Meteor., 35, 830–838.##Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a##mesoscale twodimensional model. J. Atmos. Sci. 46:3077–3107.##Emardson, T.R., Simons, M., & Webb, F.H. (2003). Neutral atmospheric delay in interferometric synthetic aperture radar applications: statistical description and mitigation. J. Geophys. Res. 108(B5): 2231. doi:10.1029/2002JB001781.##Falvey, M., & Beavan, J. (2002). The impact of GPS precipitable water assimilation on mesoscale model retrievals of orographic rainfall during SALPEX’96. Mon. Weather Rev.130:2874–2888.##Fischer, C., Montmerle, T., Berre, L., Auger, L. & Stefanescu, S. E. (2006). An overview of the variational assimilation in the ALADIN/ FRANCE NWP system. Q. J. R. Meteorol. Soc., 131, 3477–3492.##Foster, J., Bevis, M., Chen, Y.L., Businger, S., & Zhang, Y. (2003). The Ka‘storm (November 2000): imaging precipitable water using GPS. J Geophys Res Atmos (1984–2012) 108(D18):4585. doi:10.1029/2003JD003413.##Gauthier, P., Tanguay, M., Laroche, S., & Pellerin, S. (2007). Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada. Mon. Wea. Rev., 135, 2339– 2364.##Govindankutty, M., & Chandrasekar, A. (2011). 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(2005).A preoperational variational data assimilation system for a nonhydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results. Q. J. R. Meteorol. Soc., 131, 3465–3475.##Hong, S.Y., Dudhia, J., & Chen, S.H. (2004). A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Weather Rev. 132, 103–120.##Hong, S.Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev., 134, 2318–2341.##Huang, W., Henderson, T., Bray, J., Chen, Y., Ma, Z., Dudhia, J., Guo, Y., Zhang, X., Won, D.J., Lin, H.C., & Kuo, Y.H. (2008). Fourdimensional variational data assimilation for WRF: formulation and preliminary results. Submitted to Mon. Weather Rev.##Ide, K., Courtier, P., Ghil, M., & Lorenc, A.C. (1997). Unified notation for data assimilation: operational, sequential and variational. J. Meteorol. Soc. Japan 75:181–189.##Iwabuchi, T., Guo, Y.R., Rocken, C., Van Hove, T., & Kuo, Y.H. (2005). Impact of Groundbased GPS retrievals on moisture field and rainfall forecast in WRF/3DVAR. WRF/MM5 Users’ Workshop June.##Jadidi, A., Walpersdorf, A., Nankali, H., Aghamohamadi, A., Tavakoli, F., & Djamur, Y. (2006). Sterategy of Iranaian Permanent GPS Network, Geomatics 85 Conference (NCC) Iran.##Kain, J.S. (2004). The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181.##Kalnay, E. (2003). Atmospheric modeling, data assimilation and predictability. Published Cambridge University Press, Cambridge, p 364.##Kuo, Y.H., Guo, Y.R., & Westwater, E.R. (1993). Assimilation of precipitable water measurements into a mesoscale numerical model. Mon. Weather Rev., 121, 12151238, DOI: 10.1175/15200493.##Kuo, Y.H., Zou, X., & Guo, Y.R. (1996). Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part I: Moisture retrieval and sensitivity experiments. Mon. Weather Rev., 124, 122147, DOI: 10.1175.##Leiming, M., Wuyun, Q., Geng, F., & Zhou, G. (2012). Numerical weather prediction in Yangtze River Delta region with assimilation of AWS and GPS/PWV data. Robotics and Applications (ISRA), 2012 IEEE Symposium on . DOI: 10.1109/ISRA.2012.6219297.##Li, G., & Deng, J. (2013). Atmospheric water monitoring by using ground based GPS during heavy rains produced by TPV and SWV. Adv Meteorol 2013, Article ID 793957. doi:10.1155/2013/793957.##Li, Z., Muller, J.P., & Cross, P. (2003). Comparison of precipitable water vapor derived from radiosonde, GPS, and moderateresolution imaging spectroradiometer measurements. J Geophys Res 108(D20):4651. doi:10.1029/2003JD003372.##Liou, Y.A., & Huang, C.Y. (2000). GPS observations of PW during the passage of a typhoon. Earth Planets Space 52, 709–712.##Lipton, A.E., Modica, G.D., Heckman, S.T., & Jackson A.J. (1995). Satellitemodel coupled analysis of convective potential in Florida with VAS water vapor and surface temperature data. Mon. Weather Rev.123:3292–3304.##Mazany, A., Businger, S., Gutman, S.I., & Roeder, W. (2002). A lightning prediction index that utilizes GPS integrated precipitable water vapor. Weather Forecast 17(5):1034–1047.##Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., & Clough, S.A. (1997). Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlatedk model for the longwave. J. Geophys. Res. 102(D14):16663–16682.##Monin, A.S., & Obukhov, A.M. (1954). Basic laws of turbulent mixing in the surface layer of the atmosphere. Contributions of the Geophysical Institute of the Slovak Academy of Sciences, vol. 24, no. 151, pp. 163–187.##Nilsson, T., & Elgered, G. (2008). Longterm trends in the atmospheric water vapor content estimated from groundbased GPS data, J. Geophys. Res., 113, D19101, doi:10. 1029/2008JD010110.##Ohtani, R., & Naito, I. (2000). Comparison of GPSderived precipitable water vapors with radiosonde observations in Japan. J Geophys Res. 105:26917–26929.##Ortiz de Galisteo, J. P., Toledano, C., Cachorro, V., & Torres, B. (2010). Improvement in PWV estimation from GPS due to the absolute calibration of antenna phase center variations, GPS Solut., 14, 389–395, doi:10.1007/s102910100163y.##Pottiaux, E., & Warnant, R. (2002). First comparisons of precipitable water vapor estimation using GPS and water vapor radiometers at the Royal Observatory of Belgium. GPS Solut 6(12):1117.##Pramualsakdikul, S., Haas, R., Elgered, G., & Scherneck, H.G. (2007). Sensing of diurnal and semidiurnal variability in the watervapour content in the tropics using GPS measurements. Meteorol Appl 14:403412. Doi:10.1002/met.39.##Prasad, A.K., & Singh, R.P. (2009). Validation of MODIS Terra, AIRS, NCEP/DOE AMIPII Reanalysis2, and AERONET Sun photometer derived integrated precipitable water vapor using groundbased GPS receivers over India, J.. 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Res., 103(D22), 28,701–28,710.##Van Baelen, J., Aubagnac, J.P., & Dabas, A. (2005). Comparison of Near–Real time estimates of integrated water vapor derived with GPS, radiosondes, and microwave radiometer. J. Atmos. Oceanic Technol. 22, 201–210.##Wang, Y., Liu, Y., Liu, L., Guo, Z., Ge, X., & Xu, H. (2009). Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland. Adv. Space Res. 43 (2009) 82–88.##Wu, P., Hamada, J., Mori, S., Tauhid, Y.I., Yamanaka, M.D., & Kimura, F. (2003). Diurnal variation of precipitable water over a mountainous area of Sumatra Island. J. Appl. Meteorol., 42, pp. 1107–1115 http://dx.doi.org/10.1175/15200450(2003)042<1107:DVOPWO>2.0.CO;2.##Yunck, T.P., Liu, C.H., & Ware, R. (2002). A history of GPS sounding. Terr Atmos Ocean Sci 11:1–20.##Zhang, M., Ni, Y., & Zhan, F. (2007). Variational assimilation of GPS precipitable water vapor and hourly rainfall observations for a meso βscale heavy precipitation event during the 2002 MeiYu season. Adv. Atmos. Sci. 24, 509–526.##]
1

A spatiotemporal feature extraction algorithm for crop mapping using satellite image timeseries data
https://eoge.ut.ac.ir/article_64291.html
10.22059/eoge.2017.246546.1017
1
Crop type identification is a prerequisite for several agricultural analyses. Thus, various methods have beenused to accurately identify different crop types. Classification of satellite image timeseries (SITS) data isprobably the most efficient one, among these methods. Recently, the SITS data with high spatial andtemporal resolution have become widely available. This category of SITS data, in addition to informationabout the temporal phenology of crops, provides valuable information about the spatial patterns of thecroplands. This information, if extracted properly, can increase the accuracy of crop classification. In thispaper, we proposed a novel feature extraction algorithm in order to extract this information. The proposedfeature extraction algorithm is a twostep algorithm. In the first step, an image segmentation method is usedto partition the timeseries data into several homogenous segments. The pixels of each segment share similarspatial and temporal characteristics. In the second step, the algorithm fits a polynomial function to theaverage value of pixels of each segment. Finally, the coefficients of the fitted polynomial function areconsidered as the spatialtemporal (spatiotemporal) 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 soiladjusted 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 spatiotemporal features by proving the classification accuracy of 87.93% and 75.96% respectively for NDVI and SAVI timeseries can be very efficient features for crop mapping. These features also sharplyimproved the crops classification accuracy in comparison with other spatial and temporal features.
0

112
121


Saeid
Niazmardi
Department of remote sensing engineering, Graduate University of advanced technology, Kerman, Iran
Iran
s.niazmardi@gmail.com
Crop mapping
Feature extraction
Satellite image timeseries
Spatiotemporal features
Timeseries classification
[Ali, M., Montzka, C., Stadler, A., Menz, G., Thonfeld, F., & Vereecken, H. (2015). Estimation and validation of RapidEyebased timeseries of leaf area index for winter wheat in the Rur catchment (Germany). Remote sensing, 7(3), 28082831.##Bach, H., Friese, M., Spannraft, K., Migdall, S., Dotzler, S., Hank, T., . . . Mauser, W. (2012). Integrative use of multitemporal RapidEye and TerrasarX data for agricultural monitoring. Paper presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany##Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multiresolution, objectoriented fuzzy analysis of remote sensing data for GISready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3), 239258.##CampsValls, G., & Bruzzone, L. (2005). Kernelbased methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 13511362.##Daya Sagar, B. S., & Serra, J. (2010). Spatial information retrieval, analysis, reasoning and modelling. International Journal of Remote Sensing, 31(22), 57475750. doi: 10.1080/01431161.2010.512315##Definiens, A. (2009). Definiens eCognition developer 8 user guide. Definens AG, Munchen, Germany.##DeFries, R., Hansen, M., & Townshend, J. (1995). Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote sensing of environment, 54(3), 209222.##Geerken, R., Zaitchik, B., & Evans, J. (2005). Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. International Journal of Remote Sensing, 26(24), 55355554.##Gerstmann, H., Möller, M., & Gläßer, C. (2016). Optimization of spectral indices and longterm separability analysis for classification of cereal crops using multispectral RapidEye imagery. International Journal of Applied Earth Observation and Geoinformation, 52, 115125.##Hill, M. J., & Donald, G. E. (2003). Estimating spatiotemporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series. Remote sensing of environment, 84(3), 367384.##Huete, A. R. (1988). A soiladjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295309. doi: http://dx.doi.org/10.1016/00344257(88)90106X##Jackson, R. D. (1986). Remote sensing of biotic and abiotic plant stress. Annual review of phytopathology, 24(1), 265287.##Jamali, S., Seaquist, J., Eklundh, L., & Ardö, J. (2014). Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote sensing of environment, 141, 7989.##Jonsson, P., & Eklundh, L. (2004). TIMESAT  a program for analyzing timeseries of satellite sensor data. Computers & Geosciences, 30, 833845.##Julea, A., Méger, N., Rigotti, C., Trouvé, E., Jolivet, R., & Bolon, P. (2012). Efficient Spatiotemporal Mining of Satellite Image Time Series for Agricultural Monitoring. Trans. MLDM, 5(1), 2344.##Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235248.##Li, Q., Cao, X., Jia, K., Zhang, M., & Dong, Q. (2014). Crop type identification by integration of highspatial resolution multispectral data with features extracted from coarseresolution timeseries vegetation index data. International Journal of Remote Sensing, 35(16), 60766088.##Löw, F., Conrad, C., & Michel, U. (2015). Decision fusion and nonparametric classifiers for land use mapping using multitemporal RapidEye data. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 191204.##McNairn, H., Jackson, T. J., Wiseman, G., Bélair, S., Berg, A., Bullock, P., . . . Hosseini, M. (2015). The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): PreLaunch Calibration and Validation of the SMAP Soil Moisture Algorithms. IEEE Trans. Geosci. Remote Sens, 53(5).##Meroni, M., Verstraete, M. M., Rembold, F., Urbano, F., & Kayitakire, F. (2014). A phenologybased method to derive biomass production anomalies for food security monitoring in the Horn of Africa. International Journal of Remote Sensing, 35(7), 24722492.##Mirik, M., Ansley, R., Michels Jr, G., & Elliott, N. (2012). Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.). Precision Agriculture, 13(4), 501516.##Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247259.##Niazmardi, S., Homayouni, S., Safari, A., Shang, J., & McNairn, H. (2018). Multiple kernel representation and classification of multivariate satelliteimage timeseries for crop mapping. International Journal of Remote Sensing, 39(1), 149168.##Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., . . . Liu, J. (2015). Mapping crop phenology using NDVI timeseries derived from HJ1 A/B data. International Journal of Applied Earth Observation and Geoinformation, 34(Supplement C), 188197. doi: https://doi.org/10.1016/j.jag.2014.08.011##Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1), 2633.##Rouse Jr, J. W., Haas, R., Schell, J., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309.##Simonneaux, V., Duchemin, B., Helson, D., Er‐Raki, S., Olioso, A., & Chehbouni, A. G. (2008). The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco. International Journal of Remote Sensing, 29(1), 95116. doi: 10.1080/01431160701250390##Spruce, J. P., Sader, S., Ryan, R. E., Smoot, J., Kuper, P., Ross, K., . . . McKellip, R. (2011). Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote sensing of environment, 115(2), 427437.##Verhegghen, A., Bontemps, S., & Defourny, P. (2014). A global NDVI and EVI reference data set for landsurface phenology using 13 years of daily SPOTVEGETATION observations. International Journal of Remote Sensing, 35(7), 24402471.##Wagenseil, H., & Samimi, C. (2006). Assessing spatio‐temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia. International Journal of Remote Sensing, 27(16), 34553471.##Zhou, F., Zhang, A., & TownleySmith, L. (2013). A data mining approach for evaluation of optimal timeseries of MODIS data for land cover mapping at a regional level. ISPRS Journal of Photogrammetry and Remote Sensing, 84, 114129.##]
1

Monocular vision based obstacle detection
https://eoge.ut.ac.ir/article_64292.html
10.22059/eoge.2017.244709.1015
1
Detecting and preventing incidents with obstacles is a challenging problem. Most of the common obstacledetection techniques are currently sensorbased. Mobile robots like Small Unmanned Aerial Vehicles(UAVs) are not able to carry obstacle detection sensors such as radar; therefore, visionbased methods areconsidered, which can be divided into stereo and mono techniques. Mono methods are classified into twogroups: Foregroundbackground separation, and braininspired methods. Braininspired methods arehighly efficient in obstacle detection. A recent research in this field has focused on matching the ScaleInvariant Feature Transform (SIFT) points along with SIFT sizeratio factor and arearatio of convex hullsin two consecutive frames to detect obstacles. However, this method is not able to distinguish betweennear and far obstacles nor the obstacles in a complex environment and, thus, is sensitive to wrong matchedpoints. This paper aims to solve the aforementioned problems through using the distanceratio of matchedpoints. Then, every point is investigated for distinguishing between far and near obstacles. The resultsdemonstrated the high efficiency of the proposed method in complex environments. The least achievedaccuracy of the algorithm was 60.0%, and the overall accuracy was 79.0%.
0

122
130


Samira
Badrloo
Faculty of Surveying and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran
Iran
badrloo.samiraa@gmail.com


Masoud
Varshosaz
Faculty of Surveying and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran
Iran
varshosazm@kntu.ac.ir
Obstacle detection
Visionbased
Monobased
Braininspired
Distanceratio
[AlKaff, A., García, F., Martín, D., De La Escalera, A., & Armingol, J. M. (2017). Obstacle Detection and Avoidance System Based on Monocoular camera and Size Expansion Algorithm for UAVs. Sensors, 17(5),122.##Ariyur, K., Lommel, P., & Enns, D. (2005). Reactive inflight obstacle avoidance via radar feedback. in American Control Conference, Proceedings of 2005, 4, 2978–2982.##Heidarsson, H.K., & Sukhatme, G.S. (2011). Obstacle detection and avoidance of an autonomous surface vehicle using a profiling sonar. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation, 731–736.##Huh, S., Sungwook, C., Yeondeuk, J., & David, S. (2015). VisionBased Senseand Avoid Framework for Unmanned Aerial Vehicles. IEEE Transactions On Aerospace And Electronic Systems, 51(4), 34273439.##Labayrade, R., Aubert, D., & Tarel, J.P. (2002). Realtime obstacle detection in stereo vision on nonflat road geometry through “vdisparity” representation. In: Proceedings of the 2002 IEEE Intelligent Vehicles Symposium, 2, 646–651.##Mashaly, A., Yunhong, W., & Qingjie, L. (2016). Efficient sky segmentation approach for small UAV autonomous obstacles avoidance in cluttered environment. 2016 IEEE International Geoscience and Remote Sensing Symposium.##Menezes, P., Dias, J., Ara´ujo, H., & de Almeida, A. (2005). Low cost sensor based obstacle detection and description. Lecture Notes in Control and Information Sciences, 223, 231–237.##Mori, T., & Scherer, S. (2013). First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. IEEE International Conference on Robotics and Automation (ICRA), Germany, 17501757.## Park, J., & Youdan, K. (2015). Collision avoidance for Quadrotor using stereo vision. IEEE Transactions on Aerospace and Electronic Systems, 51(4), 32263241.##Shang, E., An, X., Li, J., & He, H. (2014). A novel setup method of 3d LIDAR for negative obstacle detection in field environment. in 2014 IEEE17th International Conference on Intelligent Transportation Systems (ITSC), 1436–1441.##Shim, D., Chung, H., & Sastry, S. (2006).Conflictfree navigation in unknown urban environments. IEEE Robotics Automation Magazine, 13(3), 27–33.##Zeng, Y., Feifei, Z., Guiaiang, W., Lingyu, Z., & Bo, X. (2016). BrainInspired Obstacle Detection Based on the Biological Visual Pathway. Brain Informatics and Health, 9919, 355364.##]
1

Morphological discrimination amongst geological rock surfaces of Zagros thrust belt via SAR backscattering modelling
https://eoge.ut.ac.ir/article_64293.html
10.22059/eoge.2017.244456.1014
1
Nowadays, processing and interpretation of remote sensing satellite images is the only method of surfacegeological rock surfaces mapping. This doubtlessly requires timeconsuming field observations forcomplementary morphological information, i.e. field measurements in geomorphology is unavoidable sincethe hyperspectral images that are used for geological mapping do not discriminate the lithologies textureand 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 remotesensing via Synthetic Aperture Radar (SAR) images is capable of obtaining the surface morphology andalteration 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 againstthe surface roughness. Nonetheless, it needs to be asserted that the Euclidean calculation of this parameteris not capable enough to measure the morphology of a feature. In this paper, using the powerlaw geometrycapability, one can improve the alteration zones discrimination. To implement and evaluate the proposedmethod of geomorphological mapping, IEM 𝜎° results for a region on the Zagros foldthrust belt, in westernIran, were compared with the satellite SAR backscattering data in the Lband (i.e. ALOSPALSAR) and theXband (i.e. TerraSAR). Besides, the efficiency of the SAR data processing versus the geological fieldobservations provide an average of more than 20% improvement in terms of the powerlaw geometry incomparison with the Euclidean geometry. Although this improvement for moderate rough formations is lessthan 3% at high frequency (Xband), it is about 30% for rough formations at low frequency (Lband).
0

131
141


Ali
Ghafouri
School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran
Iran
ali.ghafouri@ut.ac.ir


Jalal
Amini
School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran
Iran
jamini@ut.ac.ir


Mojtaba
Dehmollaian
Center of Excellence on Applied Electromagnetic Systems, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
Iran
mdehmollaian@ut.ac.ir


Mohammad Ali
Kavoosi
Dept. of Geology, Exploration Directorate of National Iranian Oil Company, Tehran, Iran
Iran
kavoosi@niocexp.ir
Geology mapping
Synthetic Aperture Radar
Integral equation model
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