Day 1 :
East Carolina University, USA
Yong Wang received his Ph.D. degree from the University of California, Santa Barbara, California, USA, in 1992 focusing on synthetic aperture radar (SAR) and its application. His current research interests include the application of remotely sensed and geospatial datasets to the study of environments and natural hazards, and the algorithm development in SAR imaging and information extraction and in InSAR (Interferometric SAR) data analysis and application.
The traffic congestion in a major city is a nuisance. One approach to ease the congestion is to develop a subway system. In the subway construction, the beneath surface excavation for the tunnel and station is routine. Although the reinforcement piles are created, subsidence of surrounding land surface can occur. If the subsidence is beyond the predetermined safety threshold, disastrous events can happen. Thus, there is urgent need to assess the subsidence along the subway. InSAR (interferometric synthetic aperture radar) technique is one widely-used approach to quantify the land surface deformation. With appropriate multi-temporal SAR datasets and InSAR analysis algorithms, one can assess surface variation at millimeter-scale through time. In this study, we quantified surface change and linked the change to Metro Line 1 of Chengdu, China using multi-temporal SAR datasets and the small baseline subset (SBaS) algorithm. Metro Line 1 constructed between 2005 and 2010 has been in operation since 2010. Eleven PALSAR FBS (fine bean single) L-HH and 10 FBD (fine beam dual) L-HH SAR data were acquired from 2007 to 2011. The study area was 9km×4 km along the metro line. Surface subsidence was revealed. The rate ranged from 0 to 33mm/year with the dominant rate of 0–11mm/year. The subsidence was attributed to the construction and operation of the metro. The time-series analyses consisting of 21 datasets at four locations were conducted. At two locations above two underground stations, large subsidence rates were observed. Patterns of rate changes from both stations were very similar. Small subsidence rates were quantified at two locations above subway tunnels. Patterns of rate changes were almost identical. In comparison with the low surface subsidence rate above the tunnel, the large open space and high traffic volume of passengers at the stations were attributed to the causes for the high subsidence rate.
University of New Brunswick, Canada
Dr. Monica Wachowicz is Associate Professor and the Cisco Innovation Chair in Big Data Analytics and the NSERC/Cisco Industrial Research Chair in Real-Time Mobility Analytics at the University of New Brunswick, Canada. She is also the Director of the People in Motion Laboratory, a centre of expertise in the application of Internet of Things (IoT) to smart cities. She works at the intersection of (1) Streaming data analytics for analyzing massive data from the Internet of Things in search of valuable spatio-temporal patterns in real-time; and (2) Art, Cartography, and Representations of human mobility behavior for making the maps of the future which will be culturally and linguistically designed to provide a greater “sense of people” in motion. Founding member of the IEEE Big Data Initiative and the International Journal of Big Data Intelligence, she is also joint Editor-in-Chief of the Cartographica Journal. Her pioneering work in multidisciplinary teams from government, industry and research organizations is fostering the next generation of data scientists for geospatial innovation.
Society has a very ambitious vision of large scale, digital and connected cities where anything can theoretically become part of the Internet of Things, allowing sensing, connectivity, and communication to take place without human intervention. This increase in the ability to create, transmit and analyze data raises new issues about whether the complexity of the data can best be exploited in GIS or there is a need to go beyond GIS in the belief that intelligence functions are required so that emerging technologies such as the Internet of Things can best improve the efficiency and competitiveness of the environment in which cities operate. This paper explores the main issues by advances in Internet of Things in solving the key problems of cities that require a greater understanding of how citizens can effectively interact with the Internet of Things and what kind of big data analytics is crucial to generate intelligence that entails the creation of any value and appreciated services for citizens. We are in the midst of an exciting paradigm shift in which the future of GIS may not be a GIS
The University of Tokyo Japan
Dr. Wataru Takeuchi is currently an Associate Professor at Institute of Industrial Science (IIS), University of Tokyo, Japan. He obtained Bachelor degree in 1999, Master degree in 2001, and PhD degree in 2004 at Department of Civil Engineering, Faculty of Engineering, The University of Tokyo, Japan. He has worked at IIS since 2004 as a Post doctoral fellow. Dr. Wataru Takeuchi was a Visiting Assistant Professor at Asian Institute of Technology (AIT), Thailand from 2007 to 2009. He was also Director of Japan Society for Promotion of Science (JSPS), Bangkok office, Thailand during the period 2010-2012. His current research interests consist of Remote sensing and GIS, Global land cover and land use change, Global carbon cycling, and Management and policy for terrestrial ecosystems.
Forest fire has become a global social issue. Originally, forest fills the role of preventing the global warming by photosynthesis. Once it is burned, however, it merely becomes the emission source of carbon dioxide. Besides this one, forest plays a multifunctional role, so wildfire destroying forest has serious effect on global environment and society. For this reason, the damage caused by forest fire must be minimized, and it is necessary to detect forest fire spreading accurately. In this study, the new generation Japanese satellite “Himawari-8” was focused on and forest fire detection was carried out. It is carrying Advanced Himawari-8 Imager (AHI) and the sensor composes of 16 observation bands. We present an approach to evaluate a wildfire duration time with 10 minutes temporal resolution because This extremely high temporal resolution is quite advantageous to understand forest fire spreading. AHI onboard Japanese geostationary satellite imagery is quite powerful to obtain the duration time of rapid fire events such as a grass land fire that cannot be detected with the frequency of Landsat nor MODIS. Research areas are evergreen needleleaf forest in Far-east Russia and evergreen broadleaf forest in Indonesia. Our approach is based on a model that the temperature of the pixel becomes higher than the non-fire pixels if there is some wildfire in the pixel. As a result, it is found that fire duration time is detected by comparing the fire pixel which contains hotspots with a non-fire pixel around it. This technique is useful to detect wildfire duration time even land coverage is evergreen needleleaf forests or evergreen broadleaf forests. We can conclude that an-hourly based monitoring provides us with a sufficient time resolution and plays an important role to monitor wild fire duration time with 10 minutes temporal resolution despite a lower spatial resolution in 2 kilometer than that of MODIS in 1 kilometer.
- Track 01: GIS
Track 02: Geoinformatics
Track 03: Remote Sensing
Professor & Head, Maulana Azad National Institute of Technology, India
Dr. Aruna Saxena obtained B.Arch. (Architecture) in 1992 and MUP (Urban Planning) from School of planning & Architecture, New Delhi in 1994. She did Ph.D. in Architecture using Remote Sensing & GIS technology in 2002. She did Specialization in Advance Remote Sensing and GIS from International Institute of Aerospace Survey & Earth Sciences (ITC), Enschede, the Netherlands in 2006. She has traveled to USA, South Africa, Spain, Switzerland, Germany, Italy, Netherlands, France, Singapore, Dubai,Egpyt, Australia for academic purpose.
Dr. Saxena has published about 70 research papers, guided 5 Ph.D. Thesis, 12 M.Tech. Thesis authored one Textbook on GIS and spatial data published in July 2008 Presently, Dr. Aruna Saxena is Professor & Head, Training, Placement & Students Welfare Cell of MANIT,Bhopal.
India is home to a wide range of water impoundments located in a diversity of climates, stretching from mountain conditions near the Himalayas in the north, to tropical conditions in the south. The impoundments include natural lakes, wetlands and coastal lagoons, as well as constructed reservoirs and tanks. This paper provides an overview of the urban lake management in Bhopal, India, focusing on use of geospatial and fuzzy logic techniques. Bhopal upper lake is exhibiting varying degrees of environmental degradation caused by encroachments, eutrophication (from domestic and industrial effluents) and siltation. The high population density ensures that this water body is under severe and direct pressure from anthropogenic activities in their catchments. Actions to control and prevent these problems are addressed. In this study, a noble concept of a fuzzy logic for lake water quality analysis is proposed. The aim of this evaluation on upper lake water quality is not only for decision support system (DSS) for environmental planning & management but also to facilitates urban planner, environmental experts and the community towards the conservation of water bodies.