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The Faculty of Arts is home to one of the most multidisciplinary academic communities on campus. From neuroscience, through ancient languages to choreography and music and drama composition, our researchers and students lead critical and creative research inquiry that engages communities and fosters innovation, leadership and creative practice. Composed of 12 departments and two schools, our faculty fosters a culture of critical and creative inquiry, debate, imagination, discovery and entrepreneurial thinking. Our vision for energizing arts is to engage, inspire, discover. Continue reading to find out more about research in the Faculty of Arts.
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Browsing Arts by Department "Geomatics Engineering"
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Item Open Access Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review(Multidisciplinary Digital Publishing Institute, 2015-01-05) Mosleh, Mostafa K.; Hassan, Quazi K.; Chowdhury, Ehsan H.Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World’s arable land area during 2012. Rice provided ~19% of the global dietary energy in recent times and its annual average consumption per capita was ~65 kg during 2010–2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations.Item Open Access Bayesian filtering for indoor localization and tracking in wireless sensor networks(Springer Open, 2012-01) Dhital, Anup; Closas, Pau; Fernández-Prades, CarlesItem Open Access Combined Acquisition and Tracking Methods for GPS L1 C/A and L1C Signals(Hindawi Publishing Corporation, 2010-09-18) Macchi-Gernot, Florence; Petovello, Mark; Lachapelle, GérardItem Open Access Complete Triaxis Magnetometer Calibration in the Magnetic Domain(Hindawi Publishing Corporation, 2010-10-26) Renaudin, Valérie; Afzal, Muhammad Haris; Lachapelle, GérardItem Open Access Design of Short Synchronization Codes for Use in Future GNSS System(Hindawi Publishing Corporation, 2008) Shanmugam, Surendran K.; Mongrédien, Cécile; Nielsen, John; Lachapelle, GérardItem Open Access Detection Performance of Polarization and Spatial Diversities for Indoor GNSS Applications(Hindawi Publishing Corporation, 2012-01-10) Zaheri, Mohammadreza; Broumandan, Ali; Dehghanian, Vahid; Lachapelle, GérardItem Open Access Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data(MDPI, 2015-03-02) Chowdhury, Ehsan; Hassan, QuaziForest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing data and implement it over the northern part of Canadian province of Alberta during 2009–2011 fire seasons. The daily-scale FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived four-input variables, i.e., 8-day composite of surface temperature (TS), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The TS, NMDI, and NDVI variables were calculated during i period and PW during j day and then integrated to forecast fire danger conditions in five categories (i.e., extremely high, very high, high, moderate, and low) during j + 1 day. Our findings revealed that overall 95.51% of the fires fell under “extremely high” to “moderate” danger classes. Therefore, FFDFS has potential to supplement operational meteorological-based forecasting systems in between the observed meteorological stations and remote parts of the landscape.Item Open Access Development of a Remote Sensing-Based “Boro” Rice Mapping System(Multidisciplinary Digital Publishing Institute, 2014-03-03) Mosleh, Mostafa K.; Hassan, Quazi K.Item Open Access Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada(Multidisciplinary Digital Publishing Institute, 2014-12-24) Veiga, Victor B.; Hassan, Quazi K.; He, JianxunRiver flow forecasting is critical for flood forecasting, reservoir operations, and water resources management. However, flow forecasting can be difficult, challenging and time consuming due to the spatial and temporal variability of climatic conditions and watershed characteristics. From a practical point of view, a simple and intuitive approach might be more preferable than a complex modeling approach. In this study, our objective was to develop short-term (i.e., daily) flow forecasting models in the Bow River at the city of Calgary, Alberta, Canada. Here, we evaluated the performance of several regression models, along with a newly proposed “base difference” model, by using antecedent daily river flow values from three gauge stations (i.e., Banff, Seebe, and Calgary). Our analyses revealed that using a multivariable linear regression formulated as a function of upstream gauge stations (i.e., Banff or Seebe) and the station of interest (i.e., Calgary) using antecedent flows demonstrated strong relationships (i.e., having r2 (coefficient of determination) and RMSE (root-mean-square deviation) of approximately 0.93 and 14 m3/s, respectively). As such, we opted to suggest that the use of Banff and Calgary stations in forecasting the flows at Calgary could be considered as it would require a relatively lower number of gauge stations.Item Open Access Diversity Gain through Antenna Blocking(Hindawi Publishing Corporation, 2011-10-19) Dehghanian, Vahid; Nielsen, John; Lachapelle, GérardItem Open Access Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions(MDPI Publishing, 2010-05) Sekhon, Navdeep S.; Hassan, Quazi K.; Sleep, Robert W.Item Open Access GPS Vulnerability to Spoofing Threats and a Review of Antispoofing Techniques(Hindawi Publishing Corporation, 2012-05-29) Jafarnia-Jahromi, Ali; Broumandan, Ali; Nielsen, John; Lachapelle, GérardItem Open Access GPS/Reduced IMU with a Local Terrain Predictor in Land Vehicle Navigation(Hindawi Publishing Corporation, 2008) Sun, Debo; Petovello, Mark G.; Cannon, M. ElizabethItem Open Access Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods(MDPI, 2013-05-31) Chow, Jacky C. K.; Lichti, Derek D.; Glennie, Craig; Hartzell, PrestonItem Open Access Optimization of preventive health care facility locations(BioMed Central, 2010-03-18) Gu, Wei; Wang, Xin; McGregor, S. ElizabethItem Open Access Performance of Narrowband Signal Detection under Correlated Rayleigh Fading Based on Synthetic Array(Hindawi Publishing Corporation, 2009-07-27) Broumandan, Ali; Nielsen, John; Lachapelle, GérardItem Open Access Simplifying the Performance Analysis of the SPRT for GPS Acquisition(Hindawi Publishing Corporation, 2011-02-13) O'Mahony, Niamh; Lachapelle, Gérard; Murphy, Colin C.Item Open Access Spatial Characterization of GNSS Multipath Channels(Hindawi Publishing Corporation, 2012-02-07) Keshvadi, Hatef; Broumandan, Ali; Lachapelle, GérardItem Open Access Sub-carrier shaping for BOC modulated GNSS signals(Springer Open, 2011-11-12) Anantharamu, Pratibha B.; Borio, Daniele; Lachapelle, GérardItem Open Access Use of remote sensing-derived variables in developing a forest fire danger forecasting system(Springer Netherlands, 2013-01-26) Chowdhury, Ehsan; Hassan, QuaziOur aim was to develop a remote sensing-based forest fire danger forecasting system (FFDFS) and its implementation in forecasting 2011 fire season in the Canadian province of Alberta. The FFDFS used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 8-day composites of surface temperature, normalized multiband drought index, and normalized difference vegetation index as input variables. In order to eliminate the data gaps in the input variables, we propose a gap-filling technique by considering both of the spatial and temporal dimensions. These input variables were calculated during the i period and then integrated to forecast the fire danger conditions into four categories (i.e., very high, high, moderate, and low) during the i ? 1 period. It was observed that 98.19 % of the fire fell under ‘‘very high’’ to ‘‘moderate’’ danger classes. The performance of this system was also demonstrated its ability to forecast the worst fires occurred in Slave Lake and Fort McMurray region during mid-May 2011. For example, 100 and 94.0 % of the fire spots fell under ‘‘very high’’ to ‘‘high’’ danger categories for Slave Lake and Fort McMurray regions, respectively.