Geomatics Engineering Research Articles

Winter mixing impacts gene expression in marine microbial populations in the Gulf of Aqaba

AME Aquatic Microbial Ecology Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials AME 80:223-242 (2017) - DOI: https://doi.org/10.3354/ame01854 Winter mixing impacts gene expression in marine microbial populations in the Gulf of Aqaba D. Miller1, U. Pfreundt2,3, S. Hou2, S. C. Lott2, W. R. Hess2, I. Berman-Frank1,* 1Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel 2Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany 3Present address: ETH Zürich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland *Corresponding author: ilana.berman-frank@biu.ac.il ABSTRACT: In aquatic systems, changes in temperature and irradiance fundamentally characterize the water column and regulate microbial population structure and function. In systems with stable thermal stratification, the warm surface mixed layer is typically nutrient impoverished, limiting biological production. In periods of destratification, convective mixing of the water column exposes the microorganisms inhabiting these mixed systems to rapid variations in light availability and spectra. We explored the impact of winter deep-mixing (500 m deep mixed layer) on microbial communities from the surface (2.5 m) and the aphotic waters (440 m) in the Gulf of Aqaba by examining changes in both population composition and function via DNA and RNA sequencing. The greatest fraction of 16S sequences was assigned to Euryarchaeota, while metatranscriptomes were dominated by Synechococcus transcripts. Community composition was highly similar at both depths, yet transcription profiles differed. Phototrophic organisms found at the photic surface overexpressed genes related to catabolism and energy metabolism, while genes affiliated with biosynthesis were overexpressed at the aphotic depth. Similar transcriptional trends were observed in the non-photoautotrophs SAR11, Euryarchaeota, and Thaumarchaeota, with niche partitioning based on differential utilization of nitrogen and phosphorus occurring between the 2 archaeal groups. We did not detect upregulated expression of cyanobacterial genes indicative of mixotrophy or glycogen metabolism in the aphotic zone, suggesting they survive the aphotic period by utilizing photosynthates produced in the photic zone. Indications for a mixotrophic lifestyle were observed for prasinophytes, with genes related to phagocytosis overexpressed at the aphotic depth compared with the surface. KEY WORDS: Deep mixing · Red Sea · Metatranscriptomics · Cyanobacteria · SAR11 · Archaea · Prasinophytes Full text in pdf format Supplement 1Supplement 2 PreviousNextCite this article as: Miller D, Pfreundt U, Hou S, Lott SC, Hess WR, Berman-Frank I (2017) Winter mixing impacts gene expression in marine microbial populations in the Gulf of Aqaba. Aquat Microb Ecol 80:223-242. https://doi.org/10.3354/ame01854 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in AME Vol. 80, No. 3. Online publication date: November 24, 2017 Print ISSN: 0948-3055; Online ISSN: 1616-1564 Copyright © 2017 Inter-Research.

DIGITAL PRESERVATION OF THE QUON SANG LUNG LAUNDRY BUILDING, FORT MACLEOD, ALBERTA

Abstract. This paper describes the results of an emergency recording and archiving of a historic structure in Southern Alberta and explores the lessons learned. Digital recording of the Quon Sang Lung Laundry building in Fort Macleod, Alberta, was a joint initiative between Alberta Culture and Tourism and the University of Calgary. The Quon Sang Lung Laundry was a boomtown-style wood structure situated in the Fort Macleod Provincial Historic Area, Alberta. Built in the mid-1800s, the structure was one of the four buildings comprising Fort Macleod’s Chinatown. Its association with Chinese immigration, settlement, and emergence of Chinese-owned businesses in early twentieth-century Alberta, made the Quon Sang Lung Laundry a unique and very significant historic resource. In recent years, a condition assessment of the structure indicated that the building was not safe and that the extent of the instability could lead to a sudden collapse. In response, Alberta Culture and Tourism engaged the Departments of Anthropology and Archaeology and Geomatics Engineering from the University of Calgary, to digitally preserve the laundry building. A complete survey including the laser scanning of all the remaining elements of the original structure, was undertaken. Through digital modeling, the work guarantees that a three-dimensional representation of the building is available for future use. This includes accurate 3D renders of the exterior and interior spaces and a collection of architectural drawings comprising floor plans, sections, and elevations.

THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK)

Abstract. Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

Two-Dimensional Boundary Element Method Application for Surface Deformation Modeling around Lembang and Cimandiri Fault, West Java

Lembang and Cimandiri fault are active faults in West Java that thread people near the faults with earthquake and surface deformation risk. To determine the deformation, GPS measurements around Lembang and Cimandiri fault was conducted then the data was processed to get the horizontal velocity at each GPS stations by Graduate Research of Earthquake and Active Tectonics (GREAT) Department of Geodesy and Geomatics Engineering Study Program, ITB. The purpose of this study is to model the displacement distribution as deformation parameter in the area along Lembang and Cimandiri fault using 2-dimensional boundary element method (BEM) using the horizontal velocity that has been corrected by the effect of Sunda plate horizontal movement as the input. The assumptions that used at the modeling stage are the deformation occurs in homogeneous and isotropic medium, and the stresses that acted on faults are in elastostatic condition. The results of modeling show that Lembang fault had left-lateral slip component and divided into two segments. A lineament oriented in southwest-northeast direction is observed near Tangkuban Perahu Mountain separating the eastern and the western segments of Lembang fault. The displacement pattern of Cimandiri fault shows that Cimandiri fault is divided into the eastern segment with right-lateral slip component and the western segment with left-lateral slip component separated by a northwest-southeast oriented lineament at the western part of Gede Pangrango Mountain. The displacement value between Lembang and Cimandiri fault is nearly zero indicating that Lembang and Cimandiri fault are not connected each other and this area is relatively safe for infrastructure development.

Enhancement of parcel valuation with adaptive artificial neural network modeling

This study targets a research on the application of artificial neural network (ANN) and multiple regression analysis (MRA) approaches in Geomatics Engineering science to land valuation process. The prediction capability was investigated and evaluated using three ANN models constructed with different activation functions (sigmoid, tangent hyperbolic and adaptive activation function) and MRA was used as a reference approach. These four methodologies were applied to land valuation in order to model the unit market value with various inputs based on essential criteria. All approaches were investigated with their estimation level in training and testing data. It was observed that adaptive ANN performed noticeably higher predicting the values with the highest accuracy and giving the smallest RMSE value in validation process, although other methodologies approximated to the raw data at a promising level for further valuation-based applications.

English

Least squares estimation, a regression technique based on minimisation of residuals, has been invaluable in bringing the best fit solutions to parameters in science and engineering. However, in dynamic environments such as in Geomatics Engineering, formation of these equations can be very challenging. And these constraints are ported and apparent in most program models, requiring users at ease with the subject matter. This paper reviews the methods of least squares approximation and examines a one-step automated approach, with error analysis, through the instrumentality of frames, object oriented programming.

Capacity Building in Geo-information Sector: A Case of Kathmandu University

Capacity building in Geo information is the empowerment that encompasses the ability, skills to initiate, manage, undertake, organize and evaluate Geoinformation activities. The capacity building includes major elements such as education, training, research, informal approaches such as networking, refresher courses, seminar/ conference. This paper aims to highlight the role of Kathmandu University (KU) for capacity building in Geo information sector in Nepal. It is committed to develop a center of excellence in Geoinformation sector by providing quality education, research, training etc. Kathmandu University, Geomatics Engineering program has collaboration with academic and nonacademic organizations to strengthen the quality of geoinformation education, research and internship. KU has also planned for the capacity building of faculty and students through its faculty development plan and student exchange program with other collaborating Universities. Geo spatial lab and other important advanced survey; Geodesy, remote sensing, photogrammetry lab etc. will be developed in near future at KU premises. LMTC and KU are also jointly establishing the necessary infrastructure required for the Geomatics Engineering program in the vicinity of these two organizations.

GEOSPATIAL ANALYSIS USING REMOTE SENSING IMAGES: CASE STUDIES OF ZONGULDAK TEST FIELD

Abstract. Inclined topographies are one of the most challenging problems for geospatial analysis of air-borne and space-borne imageries. However, flat areas are mostly misleading to exhibit the real performance. For this reason, researchers generally require a study area which includes mountainous topography and various land cover and land use types. Zonguldak and its vicinity is a very suitable test site for performance investigation of remote sensing systems due to the fact that it contains different land use types such as dense forest, river, sea, urban area; different structures such as open pit mining operations, thermal power plant; and its mountainous structure. In this paper, we reviewed more than 120 proceeding papers and journal articles about geospatial analysis that are performed on the test field of Zonguldak and its surroundings. Geospatial analysis performed with imageries include elimination of systematic geometric errors, 2/3D georeferencing accuracy assessment, DEM and DSM generation and validation, ortho-image production, evaluation of information content, image classification, automatic feature extraction and object recognition, pan-sharpening, land use and land cover change analysis and deformation monitoring. In these applications many optical satellite images are used i.e. ASTER, Bilsat-1, IKONOS, IRS-1C, KOMPSAT-1, KVR-1000, Landsat-3-5-7, Orbview-3, QuickBird, Pleiades, SPOT-5, TK-350, RADARSAT-1, WorldView-1-2; as well as radar data i.e. JERS-1, Envisat ASAR, TerraSAR-X, ALOS PALSAR and SRTM. These studies are performed by Departments of Geomatics Engineering at Bülent Ecevit University, at İstanbul Technical University, at Yıldız Technical University, and Institute of Photogrammetry and GeoInformation at Leibniz University Hannover. These studies are financially supported by TÜBİTAK (Turkey), the Universities, ESA, Airbus DS, ERSDAC (Japan) and Jülich Research Centre (Germany).

GEOSPATIAL ANALYSIS USING REMOTE SENSING IMAGES: CASE STUDIES OF ZONGULDAK TEST FIELD

Inclined topographies are one of the most challenging problems for geospatial analysis of air-borne and space-borne imageries. However, flat areas are mostly misleading to exhibit the real performance. For this reason, researchers generally require a study area which includes mountainous topography and various land cover and land use types. Zonguldak and its vicinity is a very suitable test site for performance investigation of remote sensing systems due to the fact that it contains different land use types such as dense forest, river, sea, urban area; different structures such as open pit mining operations, thermal power plant; and its mountainous structure. In this paper, we reviewed more than 120 proceeding papers and journal articles about geospatial analysis that are performed on the test field of Zonguldak and its surroundings. Geospatial analysis performed with imageries include elimination of systematic geometric errors, 2/3D georeferencing accuracy assessment, DEM and DSM generation and validation, ortho-image production, evaluation of information content, image classification, automatic feature extraction and object recognition, pan-sharpening, land use and land cover change analysis and deformation monitoring. In these applications many optical satellite images are used i.e. ASTER, Bilsat-1, IKONOS, IRS-1C, KOMPSAT-1, KVR-1000, Landsat-3-5-7, Orbview-3, QuickBird, Pleiades, SPOT-5, TK-350, RADARSAT-1, WorldView-1-2; as well as radar data i.e. JERS-1, Envisat ASAR, TerraSAR-X, ALOS PALSAR and SRTM. These studies are performed by Departments of Geomatics Engineering at Bülent Ecevit University, at İstanbul Technical University, at Yıldız Technical University, and Institute of Photogrammetry and GeoInformation at Leibniz University Hannover. These studies are financially supported by TÜBİTAK (Turkey), the Universities, ESA, Airbus DS, ERSDAC (Japan) and Jülich Research Centre (Germany).

The content analysis on doctoral thesies in geomatic engineering in turkey

The content analysis on doctoral thesies in geomatic engineering in turkey

Extracting Accurate Building Information from Off-Nadir VHR Images

GIScience 2016 Short Paper Proceedings Extracting Accurate Building Information from Off-Nadir VHR Images A. Suliman, Y. Zhang, R. Al-Tahir Department of Geodesy and Geomatics Engineering, University of New Brunswick, Canada, E3B 5A3 Email: {a.suliman; yunzhang; riad.altahir}@unb.ca 1. Introduction Since buildings are the most prominent class in urban areas, updated building information plays an important role in urban planning and management applications. The most cost- effective and broadly available geo-data for mapping building information is the very high resolution (VHR) satellite images. Thus, building detection in VHR images has been an active research area for the remote sensing community during the last two decades (Doxani, Karantzalos, and Tsakiri-Strati 2015; Khosravi, Momeni, and Rahnemoonfar 2014). VHR optical images are the 2D perspective projection of the real surface. Hence, they lack the elevation information. Since buildings are elevated objects, image and elevation data co-registration is required for accurate and reliable detection results. However, several problems are introduced when such data types are integrated (Dowman 2004). Elevation data are typically generated by the photogrammetric approaches and LiDAR (light detection and ranging) technology. Both of these sources provide the height information of the tops of surfaces such as buildings or trees. Hence, they result in digital surface models (DSMs). DSMs are generated as an orthographic projection (nadir view), while VHR images are usually acquired as perspective projections with across-track/along- track angle (off-nadir view). This projection difference makes accurate co-registration very difficult to achieve. The co-registering methods of image and elevation data are extensively reviewed in our previous work (Suliman and Zhang 2015). In that work, we introduced the line-of-sight DSM (LoS-DSM) solution to overcome most of the limitations in the reviewed methods and provide pixel-by-pixel co-registration. In our recent work, some modifications have been made to the original algorithm where an improved efficient algorithm based on disparity information has been developed (Suliman and Zhang 2016). In this study, we demonstrate the applicability of the recently developed disparity-based LoS-DSM solution for building detection in off-nadir VHR satellite images using stereo- based elevation data. The elevation data generated by the improved algorithm are of minimized terrain-relief effects. Hence, the need for calculating the aboveground building heights should be reduced in the dense urban areas that are characterized by moderate terrain- relief variations. This paper is organized as follows: the proposed method is briefly described in Section 2, the optical data and the achieved results are provided in Section 3, and finally the conclusions are drawn in Section 4. 2. The Proposed Method The method proposed is flowcharted in Figure 1. The method has four steps that are described as follows:

Estimation and Mapping of Carbon Stocks in Bosomkese Forest Reserve

A thesis submitted to the Department of Geomatic Engineering in partial fulfillment of the requirements for the award of Master of Science in Geomatic Engineering, 2016

A novel method for probabilistic coverage estimation of sensor networks based on 3D vector representation in complex urban environments

GIScience 2016 Short Paper Proceedings A novel method for probabilistic coverage estimation of sensor networks based on 3D vector representation in complex urban environments A. Afghantoloee 1 , F. Karimipour 2 , M. A. Mostafavi 1 Center for Research in Geomatics, Department of Geomatics, Universite Laval, Quebec, Canada Email: ali.afghantoloee.1@ulaval.ca;mir-abolfazl.mostafavi@scg.ulaval.ca Dept. of Geomatic Engineering, University of Tehran, Amir-abad Street, Tehran, Iran Email: fkarimipr@ut.ac.ir Abstract Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment. 1. Introduction Nowadays, WSNs have found various applications in industry, security, agriculture, military and disaster management. Efficient monitoring and management of dynamic phenomena in the real world necessitates its efficient and accurate coverage. The efficiency of the coverage of a sensor network depends on optimal position of each sensor node within the network. An individual sensor covers only a limited area, which depends on its sensing capacity, range of communication as well as the environment’s complexity. The total area covered by a WSN is obtained from the union of the regions covered by individual sensors. Therefore, efficient deployment of sensors in a WSN is a critical issue that affects the coverage as well as communication between sensors. Several optimization methods (i.e., global or local, deterministic or stochastic, etc.) have been proposed to detect and eliminate coverage holes and hence increase the coverage of sensor networks (Argany et al. 2015). One of the key issues of all deployment optimization algorithms is accurate estimation of the coverage of an individual sensor. Sensing model of individual sensors ─which could be binary or probabilistic, omnidirectional or directional─ has significant impact on the precise coverage estimation of a sensor network using diverse optimization algorithms. Most of the sensor coverage estimation methods use a raster representation of the environment (Akbarzadeh et al. 2013) for optimization purposes that limits their precision and efficiency. This is because raster representations are constrained by their spatial resolution, and their regular shapes result in redundant data for unoccupied areas. Few vector-based optimization algorithms are proposed in the literature, which are mostly based on 2D vector-based representation of the environment and do not adequately consider the presence of manmade and natural obstacles in the sensing areas (Wang and Cao 2011). To overcome these limitations, in this paper we propose a probabilistic sensor coverage estimation method based on precise 3D vector-base representation of the environment and we present some results of an ongoing research project that aims at better optimization of a sensor network in a 3D complex urban area.

Development of Landslide Deformation Monitoring and Analysis System: A Proof of Concept Study

This study introduces a novel landslide monitoring and analysis system, LANSDEMAS. LANSDEMAS is a kinematic deformation analysis model, developed using Kalman filtering procedures and implemented on MATLAB. This study describes the first deformation monitoring and analysis experiment conducted to validate the performance of LANSDEMAS. The experiment was conducted in the 3-D Measurement Laboratory, Department of Geomatic Engineering, Universiti Teknologi Malaysia using Leica TCA2003 Robotic Total Station. In the simulated deformation monitoring experiment, different magnitudes of millimeter-level artificial deformations were applied to four monitored points. The results of the computed movement parameters show that LANSDEMAS accurately detected the introduced deformations and has demonstrated the potential to meet the requirements for landslide deformation monitoring and analysis.

Adaptive method of speckle reduction based on curvelet transform and thresholding neural network in synthetic aperture radar images

K.N. Toosi University of Technology, Department of Geomatics Engineering, No. 1346,ValiAsr Street, Mirdamad Cross, Tehran, IranAbstract. Because of the effect of speckles in synthetic aperture radar (SAR) images, its reduc-tionhasbeenconsideredbymanyresearcherstoobtainreliableinformation.Thispaperproposesa method based on the curvelet transform to reduce speckles in SAR images. This study is basedonthethresholdingneural network(TNN)technique,which hasbeenpreviouslyusedinwavelettransformation. In addition, an adaptive learning TNN with remarkable time saving was intro-duced.Comparingtheobtainedresultsfrom themethodwithconventionalspecklefilterssuchasLee, Kuan, Frost, and Gamma filters, curvelet-based, nonadaptive despeckling, wavelet-basedTNN despeckling, and curvelet-based particle swarm optimization show better achievement ofthe proposed algorithm. For instance, noise mean value, noise standard deviation, mean squaredifference, equivalent number of looks, and β (an edge-preserving criterion) improved 2%, 9%,21%, 35%, and 9%, respectively.

Geoinformation postgraduate education at Universiti Teknologi Malaysia – towards a centre of high quality postgraduate education and research

Abstract. Studying at Universiti Teknologi Malaysia (UTM) will ensure academic and technological excellence. The Faculty of Geoinformation and Real Estate (FGHT), established in 1972, focus on education and research for undergraduate as well as postgraduate programs in the related disciplines such as geomatic engineering, geoinformatics, remote sensing, property management and land administration & development. FGHT strives to be a leading academic center in geoinformation and real estate in Southeast Asia. Graduates and alumni form major strong professional societies and work force in the related industries. Many of our graduates end up with good jobs not just in Malaysia but also in other countries (Asian, Middle East, Africa and Europe). The strong team and knowledgeable academic members in this faculty provide excellent ingredients for the success of the programs (i.e. with the relevant and up-to-date curriculum and syllabus). FGHT is continuously working to provide and offer first-class geoinformation and real estate education and research in the country and be at a par with other leading institutions in other parts of the globe. The Department of Geoinformation at FGHT runs a Bachelor of Engineering in Geomatic and a Bachelor of Science in Geoinformatics. At the postgraduate levels, namely M.Sc. and PhD programs, the offered disciplines are Geomatic Engineering, Geoinformatics and Remote Sensing. In the following, the state of the art of FGHT’s postgraduate education in Geoinformation is presented, including a comparison with other universities in Malaysia, program content and curriculum information, alumni statistics as well as future strategies.

EVALUATION OF MATCHING STRATEGIES FOR IMAGE-BASED MOBILE MAPPING

Abstract. The paper presents the implementation of a dense multi-view stereo matching pipeline for the evaluation of image sequences from a camera-based mobile mapping system. For this purpose the software system SURE is taken as a basis. Originally this system was developed to provide 3D point clouds or DEM from standard airborne and terrestrial image blocks. Since mobile mapping scenarios typically include stereo configurations with camera motion predominantly in viewing direction, processing steps like image rectification and structure computation of the existing processing pipeline had to be adapted. The presented investigations are based on imagery captured by the mobile mapping system of the Institute of Geomatics Engineering in the city center of Basel, Switzerland. For evaluation, reference point clouds from terrestrial laser scanning are used. Our first results already demonstrate a considerable increase in reliability and completeness of both depth maps and point clouds as result of the matching process.

La géomatique au service de la collecte de sang

To achieve blood supply, EFS organizes mobile blood collection sessions (80% of whole blood supply), close to target blood donors. Mobile sessions planning is usually developed in partnership with organizations of voluntary blood donors, based on the experience of previous sessions. However, population moving to urban areas and lifestyles changing force to focus on objective demographic analysis to determine a prospective vision of potentialities to ensure blood supply. MethodFor blood supply management, EFS recently implemented innovative methods of mapping, through Geographic Information System software (GIS) that allow a spatial representation of different indicators. Geomatics engineering gathers and processes spatially referenced information about blood collections and blood donors, combining data from EFS and data from IGN or INSEE. Results and conclusionThe authors present some examples of geomatics applied to blood supply and a feedback on the implementation of these tools in a French region. These innovative tools enhance expertise and increase the mastery of analysis for blood supply strategies.

On ill-conditioning in 3D Similarity Transformation

3D similarity transformations are often used for datum transformation in Geomatic Engineering. The transformations are routinely performed between the point coordinates evaluated from GNSS (Global Navigation Satellite Systems) observations and the point coordinates given in national datum. A mathematical model established among two different 3D coordinate systems contains tree type datum parameters which are translations, rotations and a scale. Coefficients of the parameters reflect large variations from each other’s. So, the unknown coefficient matrix in normal equations is to be ill-conditioned. In this study, it is indicated how the ill-condition structure is reduced to acceptable level by means of choosing at different unit of unknown parameters. Finally, the transformation from Kocaeli Metropolitan Municipality GNSS network coordinates to Turkish Geodetic Datum is used as a numerical example.

Observations and Modeling of the Climatic Impact of Land-Use Changes 2014

1 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China 2Department of Geography, Delhi School of Economics, University of Delhi, Delhi 110007, India 3School of Soil and Water Conservation, Beijing Forestry University, Beijing 100038, China 4Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA 5Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431-0991, USA