University of TehranEarth Observation and Geomatics Engineering2588-43522120180601On the use of two L1 norm minimization methods in geodetic networks186694510.22059/eoge.2018.256034.1021ENAlirezaAmiri-SimkooeiDepartment of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran0000-0002-2952-0160Journal Article20171105<span>L1 norm adjustment is a powerful technique to detect gross errors in geodetic observations. This paper<br /><span>investigates the results of two formulations that provide the L1 norm adjustment of a linear functional model.<br /><span>The usual method for implementation of the L1 norm adjustment leads to solving a linear programming (LP)<br /><span>problem. The formulation of the L1 norm minimization is presented based on the LP problem for a rank<br /><span>deficient linear(ized) system of equations. Then, an alternative technique is explained based on the least<br /><span>squares residuals. The results are tested on both linear and non-linear functional models, which confirm the<br /><span>efficiency of both formulations. The results also indicate that the L1 norm minimization, compared to the<br /><span>weighted least squares method, is a robust technique for the detection of blunders in geodetic observations.<br /><span>Finally, this contribution presents a data snooping procedure to the residuals obtained by the L1 norm<br /><span>minimization method.</span></span></span></span></span></span></span></span></span><br /></span>University of TehranEarth Observation and Geomatics Engineering2588-43522120180601Automatic change detection in remotely sensed hyperspectral imagery (Case study: wetlands and waterbodies)9256694610.22059/eoge.2018.238510.1010ENMahdiHasanlouSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran0000-0002-7254-4475Seyd TeymoorSeydiSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran0000-0002-3678-4877Journal Article20170724<span>Wetlands are one of the important types of ecosystems that play a fundamental role in the environment and<br /><span>provide significant benefits due to the resources that they contain. Therefore, it is necessary to monitor the<br /><span>changes in these ecosystems. <span>The alterations in Earth’s ecosystems caused by the natural activities, such as<br /><span>drought, as well as human activities and population growth has been affecting the wetlands and waterbodies<br /><span>area. Therefore, for achieving a better detection of these changes over time, it is important to generate<br /><span>descriptive location maps based on the characteristics of wetlands. Hyperspectral images have shown<br /><span>potential use in many applications due to their high spectral resolution, and consequently, their high<br /><span>informative value. This study presents a hybrid procedure for automatic detection of changes in wetlands<br /><span>using a new approach which can provide more details about the changes with high accuracy. The hybrid<br /><span>proposed method is based on incorporating chronochrome, Z-score analysis, Otsu algorithm, simplex via<br /><span>split augmented lagrangian (SISAL), Harsanyi<span>–<span>Farrand<span>–<span>Chang (HFC), Pearson correlation coefficient<br /><span>(PCC), and support vector machine (SVM) to detect changes using hyperspectral imagery. The proposed<br /><span>method in the first step, produce a training data for tuning SVM and kernel parameters. The second step,<br /><span>predicted change areas based on a chronochrome algorithm and binary change map obtained using SVM<br /><span>classifier. The third step, the amplitude of changes is created by Z-Score analysis and binary change mask.<br /><span>Finally, the multiple change map is produced based on the estimation of number and extraction of<br /><span>endmembers and similarity measure. The proposed method evaluated and compared the performances with<br /><span>other common hyperspectral change detection methods using three real-world datasets of multi-temporal<br /><span>hyperspectral imagery. The empirical results reveal the superiority of the proposed hybrid method in<br /><span>extracting the change map with an overall accuracy of nearly 96% and a kappa coefficient of 0.89 while<br /><span>other hyperspectral change detection methods have the overall accuracy lower than 93% and kappa<br /><span>coefficient 0.80. In addition, this hybrid method can provide ‘multiple changes’ as well as the magnitude of<br /><span>extracted changes.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span></span></span></span></span></span>University of TehranEarth Observation and Geomatics Engineering2588-43522120180601Numerical determination of the geodesic curves: the solution of a two-point boundary value problem26356694710.22059/eoge.2018.257564.1023ENMohammad RezaSeifDepartment of Surveying Engineering, Arak University of Technology, Arak, IranEmadGhalenoeiDepartment of Geomatics Engineering, University of Calgary, Calgary, Alberta, CanadaJournal Article20171020<span>In this paper, we suggest a simple iterative method to find the geodesic path on a surface parameterized by<br /><span>orthogonal curvilinear system between two given points based on solving Boundary Value Problem. In this<br /><span>supposed method, an iterative algorithm is used for finding the sufficient initial values as the destination<br /><span>point agree with the boundary conditions. Geodesic determination between two given points is formulated<br /><span>for a general surface, and specially tested for reference ellipsoid which has many applications in<br /><span>geosciences and geodesy. Accuracy of the method is independent on the distant between two points on the<br /><span>surface. Moreover, it can be used in aviation and sailings for finding the shortest path between start and<br /><span>destination points.</span></span></span></span></span></span></span><br /></span>University of TehranEarth Observation and Geomatics Engineering2588-43522120180601Extraction of ground points from LiDAR data based on slope and progressive window thresholding (SPWT)36446694910.22059/eoge.2018.240284.1012ENPejmanRashidiSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran,HeidarRastiveisSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran0000-0002-2767-3462Journal Article20170822<span>Filtering of airborne LiDAR point clouds has broad applications, such as Digital Terrain Model (DTM)<br /><span>generation and three-dimensional urban modeling. Although several methods have been developed to<br /><span>separate the point clouds into ground and non-ground points, there are some challenges to identify the<br /><span>complex objects such as bridge and eccentric roofs. In this study, a new algorithm based on the Slope and<br /><span>Progressive Window Thresholding (SPWT) is proposed for ground filtering of LiDAR data. This algorithm<br /><span>is based on both multi-scale and slope methods that have strong effects on filtering the LiDAR data. The<br /><span>proposed algorithm utilizes the slope between adjacent points and the elevation information of points in a<br /><span>local window to detect non-ground objects. Therefore, not only it benefits from vertical information in each<br /><span>local window to detect the non-ground points, but it also uses the neighbor information in directional<br /><span>scanning, and it prevents the errors introduced by the sensitivity to direction. According to the physical<br /><span>characteristics of the ground surface and the size of objects, the best threshold values are considered. In<br /><span>order to evaluate the performance of the SPWT method, both low and high resolution datasets were applied<br /><span>that their average overall accuracy were reported to be 94.21% and 93.08%, respectively. These results<br /><span>proved that, irrespective of data resolution, the SPWT method could effectively remove the non-ground<br /><span>points from airborne LiDAR data.</span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span>University of TehranEarth Observation and Geomatics Engineering2588-43522120180601Assessing the impact of cold and warm ENSO on drought over Iran45556695010.22059/eoge.2018.257714.1022ENZahirNikraftarDepartment of Geomatics Engineering, Shahid Rajaee Teacher Training University, Tehran, IranAliSam-KhanianiBabol Noshirvani University of Technology, Civil Engineering Department, P.O.Box 484, Shariati Ave, Babol,Mazandaran 47148-71167, IranJournal Article20171201<span>The impacts of El Niño Southern Oscillation (ENSO) on climate change and in the global scale are well<br /><span>known, and have attracted the attention of researchers since the twentieth century. The study of ENSO<br /><span>impact on climate using precipitation and near surface temperature data from re-analysis products makes<br /><span>global and long-term analyses of this phenomenon possible. The common method to analyze the ENSO<br /><span>impact is to quantify the probability of extreme drought occurrences when the surface temperatures of<br /><span>central-east equatorial Pacific sea are abnormal. Although the results are always uncertain due to the<br /><span>complexity of atmospheric teleconnections, application of the recently available gridded datasets helps one<br /><span>to conduct more precise modeling and predictions. Spatiotemporal patterns of ENSO impact from 1980 to<br /><span>the end of 2013 for four ENSO indices (e.g. Nino 3.4, MEI, ONI, SOI) over Iran was investigated in this<br /><span>study. Spatial maps of the Pearson correlation coefficients and a composite analysis were obtained between<br /><span>the GPCC precipitation and temperature dataset with ENSO states. In addition, the frequency maps of<br /><span>extreme drought conditions during ENSO states were acquired. The results show that the western (along the<br /><span>range of Zagros Mountain) and northern (along the Alborz Mountain and the coastlines of the Caspian Sea<br /><span>to Khorasan Province) regions are more affected by ENSO events. The Pearson correlation coefficient for<br /><span>all four ENSO indices over the mentioned regions was determined to be about 0.70 for precipitation datasets<br /><span>and -0.70 for temperature datasets. The frequency analysis of extreme drought based and CZI (Chinese Z<br /><span>Index) and ENSO phases shows that the western and northeast parts of Iran are more affected by centraleast equatorial Pacific teleconnections. Composite analysis for all four ENSO indices shows the precipitation<br /><span>(over the rainy months)/temperature (over the summer months) anomalies, for the El Niño states about +25<br /><span>(mm)/ -0.5˚ (C) and for the La Niña states about -25 (mm)/+0.6˚ to 1˚ (C).</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span>University of TehranEarth Observation and Geomatics Engineering2588-43522120180601A comparison of four methods for extracting Land Surface Emissivity and Temperature in the Thermal Infrared Hyperspectral Data56636695110.22059/eoge.2018.239666.1011ENFaezeSoleimani Vosta KolaeiSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranMehdiAkhoondzadehSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20170812<span>Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are two important physical properties<br /><span>of Earth's surface. LST retrieval plays a valuable role in environmental studies. Therefore, in order to<br /><span>estimate LST accurately, it is necessary to obtain LSEs. The HyTES (Hyperspectral Thermal Emission<br /><span>Spectrometer) instrument has 256 spectral bands covering the thermal infrared (TIR) spectral range. Due to<br /><span>a large number of narrow bandwidths of HyTES, this sensor can produce the accurate LST and LSEs. The<br /><span>main goal of this paper is to evaluate the accuracy of LSE and LST retrieval methods from HyTES data and<br /><span>to improve the accuracy of the Normalization (NOR) method. For this purpose, four different methods have<br /><span>been considered to retrieve LSE: (i) the Reference Channel method (REF), (ii) the Emissivity Normalization<br /><span>method, (iii) the Alpha emissivity method (AlPHA) and (iv) a method to improve the NOR algorithm. The<br /><span>first three methods have been widely used with thermal multispectral data in other researches. These<br /><span>methods were used with HyTES hyperspectral data in this paper; the fourth is a new method that improved<br /><span>the accuracy of the NOR method. The results of quality assessment show that the emissivity RMSEs of the<br /><span>REF, NOR, ALPHA methods and the new proposed method are 0.021, 0.815, 0.034 and 0.0201,<br /><span>respectively. Also, LST RMSEs of the REF and NOR methods are less than 1.5 K.</span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span>