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 2 :

GIS 2017 International Conference Keynote Speaker Seyed Kazem Alavi Panah photo
Biography:

Prof. Dr. Seyed Kazem Alavipanah is a faculty member of Remote Sensing and GIS department in University of Tehran. He has received his PhD in Remote Sensing and GIS (soil) in University of Ghent, Belgium, in 1997. He has received about 10 awards and honors and issued 10 books mainly on Remote Sensing and GIS, on art and humanities, and also published more than 250 articles and conducted more than 20 projects. He has membership in national institute of Iranian elites and editorial board in many Remote Sensing and Earth Sciences journals. He was a member of Intergovernmental Panel on Soils (ITPS), and coordinator of World Soil Report of North East and North Africa, FAO-UN from 2013 to 2015. He presented the theory of “Heat as an Indicator for Intelligence of the World” (relation of time, space, and thermal remote sensing).

Abstract:

Earth is an open system with a large incoming energy from the sun. So, according to the thermodynamic laws, it tries to reduce this gradient using all available physical and chemical processes. Meteorological and oceanographic circulations are of these efforts. “Life” is also another means of dissipating the solar heat. From this viewpoint, life should be viewed as the most sophisticated end in the continuum of development of natural dissipative structures from physical to chemical to autocatalytic to living systems.

All Human activities also alter heat balance in ecosystems. Respiration, bearing, production, construction etc. change entropy and temperature as well. The Earth smartly resists to these changes. This resistance also causes further temperature increase resulting in global warming. Global warming is subsequently the cause of some disasters such as climate change, drought, dust storms and water crisis. Falling level of Lake Urmia in Iran and occurrence of dust storm in MENA region are examples of such disasters.

Briefly, heat affects the life creation, living activities also affect the heat balance. Heat reciprocally influences the life conditions and these mutual interactions will be continued.

The application of TIR remote sensing in different fields such as air, water and soil, and the severe problems of desertification, deforestation, wind and water erosion, dust storms, drought, air and water pollutions have been recently investigated, but the key role of TIR remote sensing for the monitoring these changes has not been widely discussed. Therefore, the potential and constraints of some remote sensing methods and disciplines in Iranian deserts will be discussed. Furthermore, some important case studies such as Lut desert for thermal band calibration will be generally evaluated.

In future when highly precise measuring of heat and detection of thermal spectral signatures, in the Nano scale, will be possible, many of the world’s mysteries will be discovered. Therefore, a thermal remote sensing data may be simulated based on increasing the spectral and radiometric resolutions and higher ability for recording emitted energy leading to more accurate recognition of materials. NASA HyspIRI mission is an important step towards reaching this goal.

  • Track 04: Remote Sensing Applications
    Track 05: Geodynamics
    Track 06: GIS in Mapping
    Track 07: GPS & Photogrammetry
Speaker
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. 

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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. 

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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
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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 

Speaker
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.

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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).  

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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.

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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.

Speaker
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.

Speaker
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

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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. 

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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. 

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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.