Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 3rd World Congress on GIS and Remote Sensing Charlotte, North Carolina, USA.

Day 1 :

Keynote Forum

Yong Wang

East Carolina University, USA

Keynote: Spaceborne InSAR and Urban Subsidence: from Theory to Practice
GIS 2017 International Conference Keynote Speaker Yong Wang photo
Biography:

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.

Abstract:

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.

Keynote Forum

Monica Wachowicz

University of New Brunswick, Canada

Keynote: Beyond GIS in the era of the Internet of Things
GIS 2017 International Conference Keynote Speaker Monica Wachowicz photo
Biography:

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.

Abstract:

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

GIS 2017 International Conference Keynote Speaker Wataru Takeuchi  photo
Biography:

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.

Abstract:

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
Speaker
Biography:

Kentaro Kuwata has developed an expertise in processing and analyzing satellite data in monitoring agriculture. He had experience of working at a Japanese private company to utilize various types of satellite data for agriculture and disaster management for two and half years. His recent research work for doctoral thesis was applying corn yield estimation model developed with satellite data and deep learning to a new type of agricultural insurance. His special ability is processing terra-byte class satellite data with machine learning and apply to new applications. After he received Dr. engineering at the University of Tokyo, he joined FarmX to build an innovative data science platform to help growers become more able to cope with increasingly divergent climates.

Abstract:

California is one of the biggest agricultural regions in the United States and a major producer of fruits, vegetables and nuts. However, there was record breaking drought during the winter seasons from 2011 through 2015. Despite above average rainfall in the winter of 2016, groundwater levels are still much lower than in past decades.  Over the long term, California's increasingly arid climate has made farmers aware that water resources should be used effectively because the limited amount of water depends on an unpredictable climate condition. FarmX, an agricultural technology company in Silicon Valley, has developed a data platform to reduce irrigation to over-watered areas while ensuring plant health and permanent crop sustainability. Our platform has various types of sensors measuring weather, evapo-transpiration, soil moisture and conditions related to crop growth, allowing for frequent observations of rich field data. Such data is gathered on our cloud platform and visualized in a graph and map style on web browsers allowing growers to make better decisions. In addition, by utilizing satellite data, FarmX allows growers to monitor macro responses to sensor driven control with a lagging indicator related to crop health. Satellite remote sensing is effective to observe the entire area of fields and contributes to early detection of anomalous areas that may indicate problems related to growth. Growers can access details of their fields quantitatively through this data platform and plan more precise irrigation to benefit their yields, given limited resources. FarmX is developing a cycle of observation (Figure 1) that provides real-time farmland information to growers from the cloud-based platform. By analyzing big data from our sensors and satellite with machine learning, we develop an automatic recommendation tool of irrigation to support more efficient and highly accurate farming activity, and contribute to sustainable development of the agricultural industry.

Speaker
Biography:

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.

Abstract:

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.

Speaker
Biography:

Dr. Keshav D Singh is a postdoctoral fellow in the College of Agricultural and Environmental Sciences, University of California, Davis CA. He has an expertise in Hyperspectral Imaging (HSI) system. He is working on applied use of remote sensing technologies in studies of host selection by selected insects and abilities to assess crop health via reflectance profiling (detection of crop responses to biotic and abiotic stressors). To assist with novel insight into improved calibration and processing of airborne remote sensing data to enhance classification accuracy and to promote the use of emerging UAS (unmanned aerial systems) technology in precision agricultural applications. Prior to joining UC Davis, he was an Assistant Professor at JECRC University, Jaipur, India where he taught "Engineering Physics" at Graduate level (B.Tech.), and "Remote Sensing, Astrophysics, GTR and Cosmology" at postgraduate level (M.Sc.). Prior to this, he also worked on “Hyperspectral Remote Sensing” as a Ph.D. graduate student at the Indian Institute of Technology Bombay, Mumbai. He did his Master of Technology (MS) in Engineering Physics and Bachelors in Physics (Hons.) with specialization in Astrophysics.

Abstract:

Hyperspectral imaging (HSI), an emerging technology developed in recent years, integrates conventional imaging and spectroscopy knowledge to attain both spectral and spatial information from an object. Imaging spectroscopy provide detailed signatures (such as reflectance) of the biological samples due to interaction between the electromagnetic radiation and contact material. It is a powerful tool in studies of host selection by selected insects and abilities to assess crop health via reflectance profiling (detection of crop responses to biotic stressors for precision agriculture). Abiotic stresses are drought (water deficit), excessive watering (waterlogging/flooding), extreme temperatures (cold, frost and heat), salinity (sodicity) and mineral (metal and metalloid) toxicity negatively impacts growth, development, yield and seed quality of crop and other plants. For this study, the hyperspectral data of various agricultural plants are acquired using an airborne “true push-broom” hyperspectral camera [OCI Imager (OCI-UAV-D1000), BaySpec Inc.; 116 bands from 450-970nm] mounted on a drone (S1000 Premium Octocopter). The acquired images are generally affected by ground reflectance and atmospheric conditions, so the bidirectional reflectance were corrected using Radiative Transfer Equation (RTE) based Hapke’s model, addressing non-linear factors arises due to multiple scattering. The hypercube data were elaborated and analyzed by an algorithm coded under MATLAB environment. The final classified images show that it is possible to pinpoint the areas covered by stress plants prior to pesticide spray over whole agriculture field. It reduces the time, cost of spray and poisonousness in our biosphere.
 

Speaker
Biography:

Dr. Anji Reddy Mareddy having more than 28 years of teaching and research experience in Remote Sensing and GIS, Geoinformatics for Environmental Management, is presently working as Professor and Director in JNTUH, Hyderabad India.  He has executed number of research projects in remote sensing and GIS in environmental management and planning and E I A. Principle investigator of Development of 3D city models and its application in urban planning. His expertise includes Remote Sensing and GIS, Geoinformatics for Environmental Management, Digital Image Processing and Environmental Science and Technology and EIA: Theory and Practice. He is the National expert committee member for number of operational developmental projects. For his outstanding contribution in Environmental problem solving, pollution control, health and safety, GIS and Remote sensing applications for water quality, transportation planning, assessment of sedimentation distribution pattern, EIA, Socio- economic development through scientific means, he won number of national and international awards.

Abstract:

The 3D technology has been playing the key research tool in the geospatial application fields like military, environmental geological, Civil engineering urban sprawl, planning and management. Such technologies provide the urban planners to develop various three dimensional models which can simulate the real world environment. The 3D city model is one of the predominant models developed using geospatial technology which represents the objects on the surface of the earth and simulation of such models helps in decision making process and also urban environmental planning. The development of geospatial 3D models involve the three main steps namely, data collection, 3D model development and 3D projection. The data collection encompasses the entire data necessary in generating a replica of the physical ground reality which may include building heights, dimensions topography and physiography as it is urban environment.  The sources of data includes videos, topo maps digital photos and the digital data derived from Global Positing System (GPS) and Light Defection and Ranging (LiDAR).  The second step is generation of 3D model using these data products, the technologies and relevant GIS softwares.  The third step involves the displaying and demonstrating the 3D model depicting all the earth surface features. Geographical Information System (GIS), Virtual reality (VR), Computer Aided Design (CAD) are used in this study. The results of this study are very much essential for urban and environmental planners to understand various earth surface patterns generated in each step.  This provides an overview of the potential of geospatial technologies, development of 3D model and their application in planning and decision making process.

Speaker
Biography:

Samuel is a PhD candidate in infectious disease epidemiology at Division of Epidemiology and Biostatistics, UQ School of Public Health. Samuel has a background in Public Health and Epidemiology. Before commencing his PhD, Samuel worked as a senior lecturer at Haramaya University, Ethiopia. Samuel has also been conducting a research with the Department of Ecology and Conservation of antural resources, and Department of Biological Sciences at Haramaya University under the supervision of Associate Professor Meseret Chimdessa (2013-2014). Samuel’s PhD research focuses on investigating the role of socioenvironmental factors in the spatiotemporal variation of malaria transmission. Samuel’s PhD supervisory team includes Dr Yuming Guo and Professor Gail Williams.

Abstract:

Statement of the problem: Despite the declining burden of malaria in China, the disease remains a significant public health problem with periodic outbreaks and spatial variation across the country. Few studies have reported spatial and temporal clustering of malaria in some provinces, but limited evidence is available at nationwide. A better understanding of the spatial and temporal characteristics of malaria is essential for consolidating the disease elimination programme. The purpose of this study is to understand the spatial and spatiotemporal distribution of Plasmodium vivax and Plasmodium falciparum malaria in China during 2005-2009. Methodology: Global Moran’s I statistics was applied in the Geographic Information System Software (ArcGIS) to explore spatial autocorrelation of county-level P. falciparum and P. vivax malaria. Spatial and space-time scan statistics were applied to detect spatial and spatiotemporal clusters, respectively and mapped in ArcGIS. Findings: Both P. vivax and P. falciparum malaria showed spatial autocorrelation. The most-likely spatial cluster of P. vivax was detected in northern Anhui province between 2005 and 2009, and western Yunnan province between 2010 and 2014. For P. falciparum, the clusters included several counties of western Yunnan province from 2005 to 2011, Guangxi from 2012 to 2013, and Anhui in 2014. The most likely space-time clusters of P. vivax malaria and P. falciparum malaria were detected in northern Anhui province and western Yunnan province, respectively, during 2005-2009. Conclusion: The spatial and space-time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Contrary to P. vivax, the high-risk areas for P. falciparum malaria shifted from the southwest to the southeast of China. Further studies are required to examine the spatial changes in risk of malaria transmission and identify the underlying causes of elevated risk in the high-risk areas.

Speaker
Biography:

Yousef Erfanifard has her expertise in evaluation of remote sensing and GIS in studying spatial ecology of plants in arid and semi-arid woodlands. His studies in spatial pattern analysis of plants have explored new aspects of ecological interactions among main plants of Zagros woodlands (including Persian oak, wild pistachio, wild almond, Montpellier maple) with one another and with their environment.

Abstract:

Statement of the Problem: Estimating quantitative and qualitative characteristics of large forest and woodland sites on remotely sensed datasets has been a focal point in vegetation management. This is of increasing interest in arid and semi-arid regions since it is possible to observe individuals on the datasets. Despite previous studies, it still is vague for some scientists how to measure the characteristics of plants in open woodlands on UltraCam-D (UCD) imagery with spatial resolution of 6 cm. The objective of this study was to develop a methodology for measurement of canopy density of trees on very high spatial resolution (VHSR) airborne UCD imagery in Zagros woodlands, Iran. Methodology & Theoretical Orientation: A 30 ha plot (500 × 600 m2) fully covered with Persian oak (Quercus persica) was selected for this research. The UCD imagery was classified by kNN classification algorithm using different nearest neighbours including k=1 to 10 and the results were tested comparing to the true canopy density of the study area to find out the most suitable one. Findings: Comparing the results of different k nearest neighbours (1 to 10), it was concluded that k=4 is the most suitable one with no significant difference between the observed and estimated canopy density (t=0.04 at 0.05 level). The results also showed that the canopy density map with 30 Ar cell size (80% overall accuracy and 0.61 KHAT coefficient) was the most suitable map. Conclusion & Significance: It was also concluded that the UCD VHSR imagery could produce a precise map of the canopy density in the study area due to its very high spatial resolution. One of the advantages of the proposed method was that users can choose different maps according to their needs. It helps users make management decisions and regularly monitor changes in woodlands more efficiently. 

Speaker
Biography:

Desmond Appiah has completed his masters in Geophysics and  currently undertaking PhD studies in China. Within my experience as PHD Research Project Assistant at Kwame Nkrumah University of Science and Technology,KNUST, Kumasi, I had a lot of practical works

 

Abstract:

Ghana formerly called gold coast has been the hub of the precious rock called gold. This rock has been mined over centuries therefore the need to use new technologies methods to identify and maximize potential yields. The Chirano gold deposit is hosted in Paleoproterozoic rocks within the Sefwi Birimian metavolcanic belt which has been metamorphosed regionally to greenschist facies. Chirano which falls within the Sefwi gold belt in the southwestern part of Ghana remains an area that can be examined to ascertain its worth of gold. Aeromagnetic and airborne radiometric datasets were used to examine the Chirano area. Very important information (lithology and geological features) were acquired from the datasets. These data (aeromagnetic and airborne radiometric) were enhanced to improve on the data quality to help locate the geological boundaries and features which may be of economical importance. First vertical derivative, analytic signal and reduction to the pole were some of the mathematical algorithms used in enhancing the magnetic data. These enhancements aided in locating the folds, fractures and faults which may entrap hydrothermal fluid (deposits).  The Birimian metasedimentary and metavolcanic rocks which are eminent in hosting gold mineralization and other metal ores were mapped as well in the Belt. The values recorded from the radiometric survey gave the amount of the uranium (U), potassium (K) and thorium (Th) which were very useful in mapping the Birimian metavolcanics, metasediments, zones of extreme deformation (altered zones) found in the lithology and contact zones between the main geological formations (lithological boundaries). The metasediments and the Belt-type granitoid (B1) were delineated to have high K, Th and U. The high resolution airborne radiometric and magnetic datasets of the study area (Chirano) gave an improved description of the major rock sequences, lithological boundaries and geological structures. This research demonstrates the worth of sets of data from geophysical surveys in mapping the possible geological structures which control the mineralization of hydrothermal gold.

Speaker
Biography:

Ajay Kumar Patel was born in Madhya Pradesh, India, in 1988. He received his B.E. degree in Electronics and Communication Engineering from the Rajiv Gandhi Technical University Bhopal, Madhya Pradesh, India in 2009. He received his Master of Technology degree in Geo-informatics and Natural Resources Engineering from the India Institute of Technology Bombay, India in 2013.  Also he has been worked as a project student at National Atmospheric Research Laboratory (NARL) (ISRO), Tirupati, Andhra Pradesh, India. Currently he is pursuing his doctoral program in Civil Engineering Department (Geomatics Group) from Indian Institute of Technology Roorkee. His research interest is in Hyperspectral Remote Sensing, Satellite Image Processing and Atmospheric Remote Sensing Using Lidar Technology

Abstract:

Hyperspectral imaging has obtained successful results in information extraction for discrimination and mapping of earth materials. The large amount of spectral data produced by hyperspectral remote sensing constrain the development of automated mapping algorithms that interpret mixed pixels imagery accurately. Mixed pixels spectral mapping techniques may be studied like multi-step object detection and one of the most applied strategy for pure pixel identification is the use of some spectral similarity measures with the reference spectra for various applications. Spectral similarity measures are effective in endmember extraction because they can reduce illumination-change effects. Many spectral matching algorithms, ranging from the conventional methods to the recent automated matching algorithms, have evolved. In this study, we analyzed various conventional spectral matching algorithms to classify mixed pixels spectra. These similarity measures algorithms are the euclidian distance (ED), the spectral angle mapper (SAM), the Pearson spectral correlation angle (SCA), the spectral similarity value (SSV) and the spectral information divergence (SID). In along with, we have implemented a constrained energy minimizing (CEM) technique, for finding the most similar pixels on our hyperspectral data set. These techniques are applied a data set which were taken with the Earth Observing-1 Hyperion sensor over the Jamda-Koira valley of Kendujhar district, Orissa (India) including iron ore site bounded by latitude 210 45 to 220 00 N and longitude 850 15 to 850 30 E occupying an area of approximately 770 sq. Km. The analysis of the conventional spectral similarity measures and the advanced automated spectral matching algorithms indicates that, for better performance of pure signature spectra detection, there is a need for combining two or more spectral matching algorithms. Well-built spectral library improves accuracy in vegetation species identification and health monitoring, mineral and soil mapping. Each method has own merits and demerits, a combined technique is used to benefit from all the strong points and ignores the weak points of the methods. Results show that combination approach may enhance the discrimination capability of mixed spectra; however, the conventional algorithms are important and are useful for pure pixel targets.

Speaker
Biography:

Salomon Cesar Nguemhe Fils is Researcher at the Image Processing Laboratory (LTI) of the Institute of Geological and Mining Research (IRGM), Nkolbisson,Yaounde. Graduate from the University of Yaounde1 in Cameroon and an intern of the 8th training session in Remote Sensing and Geographic Information System in the African Regional Centre for Science and Technology Space in French Language (CRASTE-LF) affiliated to the UN at Rabat in Morocco. I received during my university study and internship, a sharp training in fundamental geology and later on I specialized in soil sciences (Tropical Pedology); followed by an acquisition of techniques and methods of processing images from the Earth Observation (EO) and spatial analysis using Geographical Information System (GIS) - tools that are nowadays increasingly used for the study and understanding of the dynamics of surface phenomena.

Abstract:

Douala,   the   most   important   metropolis   of Cameroon, is a sub-Saharan wet coastal environment of which the anarchic urbanization is a socio-economic and environmental problem, significantly influencing the local climate. In this study, three Landsat images  from 1986 (TM), 2007 (ETM?) and 2016 (LDCM), were utilized to investigate the effect of this urbanization on the increasing land surface temperature (LST) between these dates. Thus, the urban indices (UI), determined from the Landsat Vis- ible and NIR channels were used to identify impervious areas (Urban Fabric and bare soil) of urban area. It has been  shown from the UI images  that,  impervious areas have been increased from 1986 to 2016. The LST images derived have a continual expansion of zones and points of heat throughout these dates. The correlation analysis of LST and UI, at the pixel-scale, indicated the positive relationship between these parameters, which could show a real impact of urbanization on the increasing temperature in the area. These correlations are fairly low in 1986 (maximum R-square value is about 0.35) and in 2007 (maximum R-square value is about 0.44. In 2016, a high positive  correlation  (maximum  R-square  value  is  about 0.77)  confirm that,  the  impervious  areas  strengthen the temperature and the Urban Heat Island effect in Douala urban zone. Overall, the earth observation images and the geographic information system techniques were effective approaches for aiming at environment monitoring and analyzing urban growth patterns and evaluating their impacts on urban climates.

Speaker
Biography:

Hamid Reza Moradi received his PhD in Natural Geography in 2001 from Tarbiat Modares University. He is a faculty member of the water resource management engineering at TMU. His expertise relies in applied GIS and remote sensing in natural resources and drought and Climate Change.  Became an associate professor and won the distinguished researcher at TMU in 2011. He supervised about 10 PhD and 25 master’s students and published over 60 papers in different national and international peer reviewed journals throughout his career at TMU.

Abstract:

Climate change is one of the most challenging issues which have affected all living matters on the Earth. This research aimed to assess the impacts of climate change on soil and water resources of Gorganroud river basin, in north of Iran, and its application in watershed management options. For this purpose, the hydrologic model Soil and Water Assessment Tool (SWAT) in combination with Sequential Uncertainty Fitting program (SUFI2) in SWAT-CUP package were used for calibration, validation and uncertainty analysis. Future climate scenarios for period of 2010–2100 were generated from three GCMs (CGCM1, HadCM3 and SCIRO) for scenarios A1F1, A2, and B1, which were downscaled. The hydrologic model was then applied to simulate the effects of climate change. Soil and water conservation options including range management, soil conservation in agriculture lands and sediment control in streams were proposed to evaluate the effectiveness of watershed management measures for adapting to climate change. Study results indicated a high sensitivity of sediment yield to climate change so that the increase in annual stream discharges were 5.8%, 2.8 % and 9.5% and in sediment yield were 47.7%, 44.5% and 35.9% for different emission scenarios for 2040-2069 period. Implementation of proposed adaptation measures in hydrological model of the watershed showed decrease of 1.1%, 6.9% 7.9% in sediment yield at watershed scale, for A2 scenario whereas in sub basin scale were 7.1%, 20.4% and 23.4 %. These results highlighted the likely impacts of climate change on hydrologic cycle and watershed management options.

Speaker
Biography:

Contribution a l’étude du Parc National du Diawling, eaux, sols, végétation. Contribution to study of the Diawling National Park water-soils-vegetation, 155 p.

Abstract:

Because of its remarkable geographical position, the Diawling National Park (PND) corresponds to a narrow triangle delimited by the Ocean to the west and the Senegal River to the east in the extreme south-west of Mauritania. During the 1990s, after the creation of the Diama salt barrier, it has also promoted an unexpected development of invasive plants such as Typha. To monitors and quantifies changes in the use of the land in the NDP by this plant. We used remote sensing, and we resorted to the use of satellite images taken on separate dates and included the analysis of Landsat scenes (TM, ETM + OLI and TIRS). The results highlight the rapid evolution of Typha acreage during the last three decades, with the most marked changes in the Gambar basin now covered 70% by this plant. Cattail has strongly changed the climax of the area.