Day 2 :
- Track 01: GIS
Track 02: Geoinformatics
Track 03: Remote Sensing
Session Introduction
Kentaro Kuwata
FarmX, USA
Title: Precise agricultural monitoring based on sensor network and satellite remote sensing: Saving water usage of orchards in California
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.
Aruna Saxena
Maulana Azad National Institute of Technology, India
Title: Environmental planning and management of urban lake using fuzzy logics and geospatial technology- case study Bhopal, MP, India
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.
Keshav D Singh
University of California Davis, USA
Title: Hyperspectral Remote Sensing in Detection of Biotic and Abiotic Stresses in Plants for Precision Agriculture
Biography:
Abstract:
Anji Reddy Mareddy
JNT University Hyderabad, India
Title: Hyperspectral remote sensing of water quality parameters in lakes: a case study of Hyderabad city, Telangana state, India
Biography:
Dr. Anji Reddy Mareddy has more than 28 years of teaching and research experience in Remote Sensing and GIS, Geoinformatics for Environmental Management. He is presently working as a 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. He has been the 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 has won a number of national and international awards.
Abstract:
The proposed research work is intended for a comprehensive water quality modeling for predicting five optical water quality parameters in typical inland lake environments (Hussain sagar, Shamirpet, Miralam tank and Umda sagar) using hyperspectral remote sensing technique. The five water quality parameters are chlorophyll (a), turbidity, secchi depth, total suspended solids and total phosphorus which are estimated through regression models by combining the field spectro-radiometer reflectance values with concurrent in situ ground data (analytical) collected in the study area and correlated and validated with the available hyperspectral data (hyperion). The optical indicators efficiently indicate the lake water quality in a very cost-effective manner over spatial and temporal variability. The formulation of these five band ratio models was based on data collected and processed from sample locations. The trained set of the pixels, extracted from the hyperspectral data for pure spectra is processed for preparing the water quality distribution maps. When subjected to multi-variant statistical tests of significance, the models have seen to yield satisfactory R2 values. The model versus in situ analysis results, demonstrated 0.79% correlation and that of model versus satellite data exhibited 0.65% mean efficiency. Study of in situ spectra for the lakes in the study area reveals a few important spectral characteristics of quality parameters. It is seen that all the parameters SD-710/550 nm, Chl-a-710/670 nm, TSS-850/550 nm, turbidity-710/740 nm, TP-467/560 nm studied has dominant absorption bands respectively. The most appropriate bands for algorithms were selected based on the correlation analysis. Evaluation results indicated that the methods of reflectance ratio were highly correlated (R2=0.79) with the measured quality parameter's concentrations. Thus, this study on the application of hyperspectral techniques proved to be more convenient and better approach in estimating the optical parameters of water quality in inland waters than the scope of traditional empirical methods. The present research work also orients the researchers to explore hyperspectral remote sensing further, more widely for inland water quality monitoring and modeling.
Samuel H. Hundessa
The University of Queensland, Australia
Title: Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014.
Biography:
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.
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.
Desmond Appiah
National Research Center of Pumps Zhenjiang, China
Title: Characterization of the Au (Gold) pathways within Chirano area of the Sefwi Gold belt using geophysical datasets.
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.
Ajay Kumar Patel
Indian Institute of Technology Roorkee, India
Title: ANALYSIS OF MIXED PIXELS USING SPECTRAL MATCHING ALGORITHMS FOR EO-1 HYPERION DATA
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.
Salomon Cesar
Geological and Mining Research (IRGM), Yaounde
Title: TM/ETM+/LDCM Images for Studying Land Surface Temperature (LST) Interplay with Impervious Surfaces Changes over Time Within the Douala Metropolis, Cameroon
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.
Hamid Reza Moradi
Tarbiat Modares University, Iran.
Title: Assessment of Climate Change Impacts on Soil and Water Resources Management using GIS
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.
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.
- Track 04: Remote Sensing Applications
Track 05: Geodynamics
Track 06: GIS in Mapping
Track 07: GPS & Photogrammetry
Session Introduction
Omid M. Rouhani
McGill University,United Kingdom
Title: Perceptions and Preferences about energy and environmental costs: the case of montreal transport users
Biography:
Omid Rouhani is an Assistant Professor at McGill University, Department of Civil Engineering and Applied Mechanics. Prior to this, he was a post-doctoral researcher at Cornell University. He finished his PhD studies in Civil and Environmental Engineering Department at the University of California, Davis. His expertise is in the areas of transportation systems analysis, transportation economics and modeling, and energy policy. Dr. Rouhani has been involved in a range of local, national, and international research projects related to these topics. His research has been published in several academic journals, including Sustainability, Renewable and Sustainable Energy Reviews, Journal of Transport Geography, Environmental Modeling and Software, Energies,
Abstract:
Providing travel-related energy and environmental information to transport users is becoming increasingly relevant. However, the impact of providing such information on users’ travel behavior is yet to be determined. This research examines the perceptions and preferences of the fuel consumption costs, GHG social costs, and health-related air pollution costs, and the influence such information have on travel behavior. Examining the case of transport users of Montreal, Canada, with a pilot survey, we found that the respondents are generally unaware of the energy and environmental footprints of their travel. Approximately, 70% of the respondents are not able to estimate GHG social costs and health-related air pollution costs across different modes. The respondents who could provide their perceived costs generally overestimate these costs and interestingly perceive higher environmental costs for public transport (metro) compared to cars. They also prefer to receive such information in monetary units rather than in their own units (e.g., grams of emissions) and they are more comfortable in receiving the information through mobile applications over other tools/means (GPS devices, radio and so on). The research also finds that energy and environmental information can influence respondents’ travel decisions especially their route choices. Finally, the respondents are willing to pay an average of $6/month in exchange for obtaining the information.
Gervais W. Tabopda
Georgia Institute of Technology, USA
Title: Urban growth and fuelwood distribution in dry tropical Africa: a next challenge for sustainable management of protected areas
Biography:
Abstract:
Protected areas in tropical dry Africa appear to be excellent support structures for the sustainable management of natural resources. However, they are increasingly subject to anthropogenic pressures linked to the dual factor of urban growth and domestic energy needs. It is the case of the Laf-Madjam Forest Reserve (5000 ha) in Cameroon, located sixty kilometers south of the city of Maroua (350,000 inhabitants). Since the late 1980s, this forest reserve has become the main area for the exploitation of fuelwood for households in the city of Maroua, where two forest reserves disappeared in the 1970s, under the pressure of timber harvesting. The decline of woody vegetation following the timber harvesting, and the lack of public policy strategy for reforestation and management of cuts in Laf-Madjam Forest Reserve, profoundly disturb the ecosystem of this protected area, which would represent both a climate regulator and a shelter of the vegetal cover in the whole of the Diamare plain.
This paper, which focuses on the interactions between urban areas and protected areas in relation to energy needs in the Far North region of Cameroon, presents a dual thematic and methodological concern. First, it aims at a better understanding of the difficult integration of conservation policies in urban functions in sub-Saharan Africa. And secondly, to implement a study approach adapted to the analysis of the relationships between cities and protected areas in a context of obvious environmental vulnerability.
The remote sensing data enabled us to highlight the dynamics of vegetation cover in this reserve between 1986 and 2015, using GIS analysis. Multispectral Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) satellite images from 1986, 2001 and 2010 were acquired and pre-processed. Multidate hybrid classification of the images was performed and four land use/land cover classes (woody savanna, shrub savanna, crops, little bare ground vegetation) was derived. The post-classification change detection techniques was performed to characterize and quantify changes in land cover and land use. The results show a decrease of the vegetation cover since 1986, with a rate of 0.51% per year. Thus, between 1986 and 2015, the clear forest and the savannah, mainly woody species, have experienced a considerable decline. The dynamics of the woods characterized by satellite image processing and GIS tools, is the consequence of the ever increasing demand for fuelwood in the surrounding localities. Moreover, field observations and analyzes of fuelwood distribution flows in the city of Maroua, enable us to highlight the contribution of the protected areas in this activity. More than 40% of the fuel wood distributed in the city of Maroua comes from protected areas (i.e. around 700 m3 of volume per year), with an estimated average consumption of 0.9 kg per household per day.
Jagbir Singh
University of Delhi, India
Title: Spatial technology & disaster management in India: some challenges
Biography:
Abstract:
India has been traditionally vulnerable to natural disasters on account of its unique geo-climatic conditions. Floods, droughts, cyclones, earthquakes and landslides have been recurrent phenomena. About 60% of the landmass is prone to earthquakes of various intensities; over 40 million hectares is prone to floods; about 8% of the total area is prone to cyclones and 68% of the area is susceptible to drought. There is an increasing trend in disasters both in frequency as well as damage caused in terms of human casualties, economic and environmental. Growth in the use of spatial technologies has secured acceptance for geospatial technology as an effective decision-making tool even by the government agencies. They have realized that this technology can provide them the much-needed tool to address the ever increasing demand for data availability. The technology today is used in integrated land information systems, land reform offices, education sector, urban planning, etc. In many states of our country, basic information for disaster reduction (technical studies, geographical data, etc) usually exists, but is not readily available to local authorities and other stakeholders. The information is hardly available in a form that facilitates sound decision-making. With the use of GIS and remote sensing the possible effects of natural phenomena like floods, drought, earthquakes, landslides, volcanic eruptions and fires on buildings, population, infrastructure etc. can be modelled and made visible in a spatial and interactive manner. If this is done in a proper way, GIS and remote sensing can be used as a powerful tool for analysis of hazard, vulnerability and risk, resulting in the development of different scenarios and concrete measures for disaster prevention. The low-cost GIS systems allow local authorities to properly plan the areas under their jurisdiction, and to incorporate the local knowledge and ensure community participation, combined with modeling results from experts. The GIS combines layers of information on various themes to enable the disaster managers to take the most appropriate decisions under the given circumstances. The GIS technology is new thing in India, and new things often arrive with added baggage. Questions arise about rates of adoption and participation across India. Is there equity of access? GIS and remote sensing application software require high end computers with high end graphics cards etc, which at the moment are comparatively expensive in India. GIS awareness and education levels are still low in India. The emergency preparedness and response application challenge is mainly concerned with the interaction between humans and their environment under conditions thought to be hazardous either to life or habitat. This application challenge is not only multifaceted as its title implies but also covers a wide range of disasters, many with fundamentally different underlying processes (such as earthquakes, cyclones, and fires). Therefore, the geo information data and tools like Remote Sensing, GIS and GPS have increasingly been used world over in pre, during and post disaster phases for generating updated maps, integrating information, visualizing scenarios and identifying and planning effective solutions. Lack of adequate data, lack of high accuracy & understanding of layman is big challenge in India.
RITA
University of Science and Research, TEHRAN, IRAN
Title: Develop a spatial method for locating wind turbines using methods fuzzy in gis
Biography:
Abstract:
Obtaining electricity from the wind energy is the way was considered by the human from ancient years and it is increasingly improving. Therefore, the main reasons for usage of wind turbines is producing non-polluting energy, producing economical energy from this energy and possibility of using the lands locating beneath the installed wind turbines.
However, it is of great importance to select the best sites for installing wind turbines to have the best effectiveness. TO determine the best site, it is required to consider several parameters and factors. Regarding that most of the factors are spatial ones, GIS as the science of managing spatial data, can help to select the best site. The main purpose of this study is to provide an appropriate model for site selection of wind turbines in SIAHPOUSH village of Qazvin Province.
For this purpose, criteria maps were firstly prepared for the study area making use of analysis in three different groups: access, geographical and technical criteria. Then, method used in the study: Fuzzy functions used to classification of criteria and according to the obtained results, proper regions for establishing wind turbines were defined that are mainly located in the north of SIAHPOUSH village.
It was recognized that Fuzzy Method with Gama=0.9 will have better results
Masood Bari
University of Karachi, Pakistan
Title: Finding the closest route and service area with the help of network analysis and web application with arcgis api for javascript.
Biography:
Masood Bari has expertise in Geographical information system and Remote sensing.Has belong with various organization in Pakistan for work as an Gis services such as in K electric and ECIL. Has done master in Geography from University of Karachi and got certificate of zero semester in Rs and Gis from Institute of Space and Technology
Abstract:
ArcGIS NetworkAnalysis provide the most efficient travel route,locating the closest facility,defining service areas.This study analyze the network analysis in defining the optimal service area of different services such as hospitals, schools of Karachi city. Google earth image of Karachi city has been used for this study.Digitization was carried by using GDB generated for different analysis.The network analysis tool was used to measure the efficiency of services in terms of time and distance and in the end the web application will be made through the help of arcgis api for javascript.
A.G. Koppad
College of Forestry, Sirsi, India
Title: Assessment of carbon sequestration in forest soils and their mapping through remote sensing and GIS techniques
Biography:
Abstract:
The study was taken up in Uttara Kannada districts during the year 2012-2015. Uttara Kannada district lies between 13.9220o N to 15.5252o N latitude and 74.0852o E to 75.0999o E longitude and covers an area of 10,215 km2. It extends from north to south about 180 km, and from west to east a maximum width of 110 km. The Indian satellite IRS P6 LISS-III imageries were procured from NRSC, Hyderabad India and topo-sheets of the study area was procured from Survey of India. The satellite imagery of the study area is clipped with district boundary shape file. The shape file of the district boundary was created from Arc GIS software. The field survey was taken up in the district to collect the ground truth data with GPS location as per land use land cover. The image was classified for different land use and land cover with the help of ground truth data as training sites and classified using supervised classification in ERDAS-11 software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. As per the supervised classification for estimating the area under different LU/LC classes, it was found that there are 9 LULC classes were found, Among them, dense forest covered an area of 63.32 % (6468.70 sq km) followed by agriculture 12.88 % (1315.31 sq. km), sparse forest 10.59 % (1081.37 sq. km), open land 6.09 % (622.37 sq. km), horticulture plantation and least was found in forest plantation 1.07 %. Settlement, stony land and water body covers about 4.26 percent of the area.
The soil samples at one meter depth in all land use classes were collected and soil organic carbon (SOC) was estimated. The result indicated that the SOC in soils of different land use classes are significantly different. The SOC percentage of Forest soils under different LU/LC in different talukas of UK district indicated that the average SOC % at 0-100 cm was found same in dense forest and horticulture plantation followed by forest plantation (1.06 %), sparse forest (1.04 %) and least was found in agriculture land (0.55 %). The average SOC in Uttara Kannada district was 121.23 t/ha. The average SOC according LULC in Dense Forest was 158.15 t/ha, Sparse Forest was 132.18 t/ha, Horticulture Plantation was 148.73 t/ha, Forest Plantation was 132.12 t/ha, Open Land was 89.29 t/ha, and least was in Agriculture (68.14 t/ha). The total SOC pool in UK district was 135.284 million tonnes (Mt) out of which 102.302 Mt sequester in dense forest followed by sparse forest (14.293 Mt). The remaining LU/LC classes sequester 8.962 Mt, 5.557 Mt, 2.722 Mt and 1.448 Mt in agriculture land, open land, horticulture plantation and forest plantation respectively. When we work out the carbon dioxide mitigation potential it was found that dense forest sequester 2.32 times more than agriculture land followed by horticulture plantation (2.18 times), sparse forest, forest plantation (1.94 times) and open land (1.31times).
Nguyen Hao Quang
The University of Tokyo, Japan
Title: Study on variation in turbidity in cam ranh bay and thuy trieu lagoon, Vietnam
Biography:
Abstract:
Turbidity, an indicator of water pollution, is an important water quality parameter directly related to underwater light penetration and thus affects the primary productivity in a water body. This study aims to investigate the variation of turbidity in Cam Ranh Bay and Thuy Trieu Lagoon as well as major factors affecting its spatiotemporal patterns by using remote sensing data. The algorithm for turbidity retrieval was developed based on the correlation between in situ measurements and red band of Landsat 8 OLI with R2 = 0.84, p < 0.05, Root Mean Square Error (RMSE) = 0.28 and Scatter Index (SI) = 0.22. Simulating WAves Nearshore (SWAN) model was used to compute bed shear stress, major factor affecting turbidity in shallow waters. In addition, the relationships between turbidity and bed shear stress, rainfall and tidal regime in study area were also analyzed, and found that: (1) During the dry season, turbidity was low in middle of the Bay but high in shallow waters and near coastlines. Resuspension of bed sediment was the major factor controlling turbidity in the time without raining. (2) During the rainy season as well as short time after raining in the dry season, turbidity was high due to the large amount of run-off entering into the study area. (3) Under tidal condition especially the flood tide regime, clear open ocean water entered into the Bay and diluted highly turbid waters resulted in decreasing turbidity. In deep waters, tidal regime combined with rainfall was the significant cause of highly turbid waters.
Abolfazl Abdollahi
Kharazmi University of Tehran, Iran
Title: Roads data conflation using update high resolution satellite images
Biography:
Abstract:
By the rapid advances of spatial data collection techniques and growth of the Internet, a lot of geographic data are now easily available on the web. But the widespread volume of map data available with low-resolution techniques have been developed in previous years. While a large amount of descriptive characteristics over time have been attributed to the map data, their geometry are not precisely fit the needs of the day. However, new techniques have been developed to obtain geometric data with higher accuracy. In this regard, the opportunity to enhance the geometric accuracy of the vector data map maintains the descriptive data associated with it is needed. In fact, the integration of spatial data in GIS is one of the main issues. In this study, a new method conflating vector data with image is presented. This method uses a variety of algorithms for automatic extraction of image pixels, identify the intersection and the end and beginning of points, match points and eventually Rubber-Sheeting method for transmission of vector data on new control points on were found on image then the conflating is used. By examining the RMSE error amounts, can realized to a major role Rubber-Sheeting can reduce the error rate and the proper functioning of the conflated vector data with image. So that the RMSE error before conflating process 839/11, and then reduced to the 124/4 meters. This shows that the algorithm used to conflating process improve the accuracy and have high-performance in conflating process.
Biography:
Nataliya A Rybnikova is a PhD student. She has her expertise in testing and empirical validating a possibility that missing data on geographic concentrations of economic activities can be reconstructed using remote techniques, that is, satellite imagery of artificial light-at-night, provided by US-DMSP, VIIRS-DNB and ISS.
Boris A Portnov is Professor at the University of Haifa. His current research covers interrelated aspects of environmental studies, population geography, and urban & regional planning, such as Environmental Epidemiology, Environmental Factors of Real Estate Appraisal, Urban Clustering, Internal Migration, Interregional Inequality and Sustainability of Urban Growth in Peripheral Areas.
Abstract:
Statement of the Problem: Educational and research activities (R&EAs) are major forces behind modern economic growth. However data on geographic location of such activities are poorly reported, which complicates a comparative analysis of their patterns and forces behind their geographic concentrations. The purpose of this study is to check the hypothesis, whether intensities and spectral properties of artificial light-at-night (ALAN) could be used for effective identification of different economic activities on the ground, due to the unique light "signature" of each economic activity. Methodology & Theoretical Orientation: In order to develop activity identification models, in situ measurements of ALAN intensities and spectral properties were carried out at the locations of different economic activities in the Greater Haifa Metropolitan Area. For this task we used an illuminance spectrophotometer CL-500A portable device, measuring the total and spectral irradiance of ALAN, incremented by a 1-ηm pitch, from 360 to 780 ηm. The total number of measurements was 610, including 148 measurements, carried out near four research institutions, located in the City of Haifa. Findings: As our analysis shows, ALAN intensities, emitted by different economic activities at peak wavelengths, help with their identification. In particular, logistic regressions, incorporating ALAN intensities at the peak or near-peak wavelengths, and geographical attributes of the sites as controls, succeeded to predict correctly 98.6% of the actual locations of existing R&EAs. A multispectral image of the Haifa bay area, obtained from the Astronaut Photography Database, was used for the model's validation. Conclusion & Significance: The current study is apparently the first one which uses ALAN spectral properties to identify on-ground economic activities, using R&EAs as a test case, and the proposed approach may be used in future studies for the identification of various on-ground EAs, access to which is restricted or information unavailable, by using remote sensing tools.
Biography:
Muluken Nega is received a BSc. degree in Forestry from Hawassa University, Ethiopia in 2010. He received MSc. degree in Environmental Science (Energy and Climate Change) from Addis Ababa University, Ethiopia in 2014. He also received MSc. degree in Geo-information Science and Earth Observation from University of Twente, Netherlands in 2017. Currently, he is working as Lecturer and Researcher, Dilla University, Ethiopia since 2010. He is expertise in application of earth observation data in natural resources. Current research interest is the application of LiDAR technologies and Unnamed Aerial Vehicle (UAV) in tropical forests parameter measurements and monitoring specifically to support climate change mitigation inputs. Recently, he develop methods for accurate measurement of the complex tropical rainforest carbon stocks through integrating Airborne LiDAR Scanning (ALS) and Terrestrial Laser Scanning (TLS) data. This approach is considered more accurate measurement way than traditional field-based method which could offer reliable data for carbon monetary
Abstract:
Parameters of individual trees can be measured from Airborne LiDAR scanner (ALS) point cloud data provided that the laser point is dense enough and trees in multiple canopy layers are visible from the top. However, retrieving tree parameters in a complex biophysical environment of tropical forests using single LiDAR technology could still be inadequate. This paper presents new approaches of acquiring tree parameters for estimating above-ground biomass (AGB) through integrating ALS and Terrestrial laser scanner (TLS). ALS data was used to detect and extract upper canopy tree parameters while TLS complemented for tree stems and lower canopy trees height measurements. Initially, multi-resolution segmentation of ALS canopy height model (CHM) was executed to delineate individual tree crowns of upper canopy trees. The result showed segmentation accuracy of 73% and 1:1 correspondence of 78% with the reference tree crowns. About 62% of trees were delineated from ALS-CHM while the remaining lower canopy trees were identified from TLS data. ALS detected trees were then co-registered and linked with the corresponding tree stems detected by TLS for diameter at breast height (DBH) use; 93.5% of the field recorded trees were recognized from TLS data for DBH measurements. DBH derived from TLS was validated using manually measured-field DBH, and coefficient of determination (R2) of 0.989 and root mean square error (RMSE) of 1.30 cm (6.52%) were achieved. Two-way tree height validations were implemented; upper and lower canopies tree heights. The R2 and RMSE between field and ALS-measured upper canopy trees height were found to be 0.61 and 3.24 m (20.18%), respectively. R2 of 0.69 and RMSE of 1.45 m (14.77%) were achieved between field and TLS-based lower canopy trees height. The AGB or carbon regression model that the relationship between AGB derived from remote sensing (ALS + TLS) and traditional field method at the plot level resulted in R2 of 0.97 and RMSE of 0.62 Mg (7.64%) where field method underestimates with the bias of –0.289 (–3.53%) Mg
Chizumba Shepande
University of Zambia, Zambia
Title: Designing a geospatial analysis tool for inventory and mapping of soils
Biography:
Abstract:
A soil inventory of the Chongwe region of Zambia was prepared using computer aided analysis of Landsat 7 ETM to determine the feasibility of Landsat data in soil mapping and how accurate soil spectral maps produced by digital analysis of satellite imagery can be and how such maps can improve the quality of soil survey in developing countries like Zambia. Thirteen spectral classes were produced. Overall, the work shows that there is a good agreement between Landsat Spectral data and field observation data with a classification accuracy of 72%. This is an indication that there is a definite relationship between Landsat imagery and soil types.
This work shows that visual interpretation and digital analysis of Landsat 7 ETM images have the capacity to map soils with reasonable accuracy. It also demonstrates the capability of Landsat 7 data to delineate soil patterns, especially when acquired during the dry season when there are long periods of cloud-free skies, low soil moisture and minimum vegetation cover.
The work also attempted to determine how the soil spectral data produced by digital analysis of Landsat 7 imagery compared with field observation data. Spectral classes of soil are correlated with individual soil types at sub-group level for all mapping units in the study area of interest. In situations where there is poor agreement between Landsat 7 data and field observation data, possible causes of such discrepancies are determined and explained.
Ashok Aswal
Uttarakhand Space Application Centre, India
Title: Geo-environmental studies in dehradun district with special reference to hazards
Biography:
Abstract:
Geo-environmental deals with every issue that affects a living organism in the world. It is essentially a multidisciplinary approach that brings about an appreciation of our natural world and human impact on its integrity. Since last few decades the human life is being severely affected by natural hazard, therefore, it looks that the studies related to geo-environmental and hazard need to studies in details (Rawat et.al.2015) One of such approaches is to prepare regional geo-environmental appraisal for identification of areas subject to natural hazard and environmental degradation. To address a geo-environmental problem like landslide hazard one have to use different geo-environmental parameters like geology, geomorphology, rainfall, soil, surface water, ground water, land use / land cover of the study area. Indian Himalayan Region (IHR) has one of the most rugged mountain topography in the Himalaya, which is geologically one of the youngest mountain range in the world and geodynamically still active, with vast wealth of natural resources. IHR occupies 18% geographical area with 6% resident population of the country. It stretches over 2400 km from East to West and varies in width from 220 to 300 kms in North to South disposition (Agrawal et al., 1997).
As the world heads toward its urban future, the environmental problems related to municipal solid waste, one of the byproducts of an urban lifestyle became glaring. The municipal waste generally includes domestic waste (garbage, rubbish, sewage, vegetable waste etc.), commercial waste and waste from building mater ial, dead animal skeleton etc. I n order to keep the urban environment clean, solid waste management is one among the basic essential services provided by municipal authorities. However, it is among the most poorly rendered services in the basket. Disposal of waste in open have created the unpredictable and variable behavior of the geo-environment which ultimately affects the planning and management of geo-resources. Dumping of industrial and municipal wastes causes toxic substances to be leached and seep into the soil and affects the ground water course,
This State experiences the problems of landslides and other mass movements at its various locations. This is due to a combination of factors like, dominantly geological with fragile rock formation as well as unconsolidated soil material coupled with high intensity annual precipitation and steep slopes. The monsoon months play a critical role in triggering the landslide events along the roads as well as other locations of human habitations. During monsoon months (particularly June, July, August) landslides are widespread; causing collapsed, loss of life and property in the coming year’s Monsoon month receives average monthly rainfall of approximately 300-600mm. The relative humidity also remain quite high providing enough moisture to the formation particularly with clay rich soils and rocks allowing them to swell, causing unpredictable landslides at places almost round the year.
Fatemeh Sadat Sharifi
Islamic Azad University, Tehran, Iran
Title: A comparison between the results of hormuz strait wave Simulations using WAVEWATCH-III and satellite altimetry observations
Biography:
Abstract:
In the present study, the capabilities of WAVEWATCH-III model for predicting wind-induced wave characteristics in the Hormoz Strait area are investigated. The input wind data were extracted from GFS (Global Forecast System) and introduced to the model with 5° spatial resolution and 6 hours time steps. The bathymetry data, introduced to the model with 2 arc-minute spatial resolution, also were derived from the ETOPO1. After the model was setup using aforementioned wind and bathymetry data, the effect of surface layer instability on wave growth was studied through considering the monthly averaged air-sea temperature difference. Results show that, during cold months when the surface layer is unstable, taking the air-sea temperature difference into account, the accuracy of model is enhanced in predicting significant wave height. A comparison between satellite altimetry observations and numerical simulation results suggest that, in January, the surface layer instability effects in the numerical simulation leads to higher correlation between significant wave height predicted by the numerical model and that obtained from satellite altimetry observations. It should be noted that the air-sea temperature difference, during the surface layer stability period, leads to no considerable changes in numerical simulation results. In the present study, the effect of air-sea temperature difference on numerical simulation of wind-wave in the Hormoz strait region was examined. All numerical simulations were carried out using the WAVEWATCH-III, using the GFS wind data and ETOPO1 bathymetry data. The model is forced by the Global Forecast System (GFS) data. Bathymetry data is also taken from the ETOPO1. Results of the numerical simulations suggest that in the periods of time when 
and consequently the surface layer is unstable, the effect of air sea temperature difference leads to a better agreement between the numerical simulation results and satellite altimetry observations.