Air surface temperature is an important parameter for a wide range of applications such as agriculture, hydrology and
climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations,
providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help
overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of
air surface temperature at both regional and global scales. Land Surface (skin) Temperatures (LST) derived from
Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide
spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to groundbased
near surface air (Tair) measurements obtained from 14 observational stations during 2011 to 2015, covering
coastal, mountainous and urban areas over Cyprus. Combining Terra and Aqua LST-8 Day and Night acquisitions into a
mean monthly value, provide a large number of LST observations and a better overall agreement with Tair. Comparison
between mean monthly LSTs and mean monthly Tair for all sites and all seasons pooled together yields a very high
correlation and biases. In addition, the presented high standard deviation can be explained by the influence of surface
heterogeneity within MODIS 1km2 grid cells, the presence of undetected clouds and the inherent difference between
LST and Tair. However, MODIS LST data proved to be a reliable proxy for surface temperature and mostly for studies
requiring temperature reconstruction in areas with lack of observational stations.
The temperature of the sea surface has been identified as an important parameter of the natural environment, governing processes that occur in the upper ocean. This paper focuses on the analysis of the Sea Surface Temperature (SST) anomalies at the greater area of Cyprus. For that, SST data derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instrument on board both Aqua and Terra sun synchronous satellites were used. A four year period was chosen as a first approach to address and describe this phenomenon. Geographical Information Systems (GIS) has been used as an integrated platform of analysis and presentation in addition of the support of MATLAB®. The methodology consists of five steps: (i) Collection of MODIS SST imagery, (ii) Development of the digital geo-database; (iii) Model and run the methodology in GIS as a script; (iv) Calculation of SST anomalies; and (v) Visualization of the results.
The SST anomaly values have presented a symmetric distribution over the study area with an increase trend through the years of analysis. The calculated monthly and annual average SST anomalies (ASST) make more obvious this trend, with negative and positive SST changes to be distributed over the study area. In terms of seasons, the same increase trend presented during spring, summer, autumn and winter with 2013 to be the year with maximum ASST observed values. Innovative aspects comprise of straightforward integration and modeling of available tools, providing a versatile platform of analysis and semi-automation of the operation. In addition, the fine resolution maps that extracted from the analysis with a wide spatial coverage, allows the detail representation of SST and ASST respectively in the region.
Land-cover change is considered one of the central components in current strategies for managing natural resources and monitoring environmental changes. It is important to manage land resources in a sustainable manner which targets at compacting and consolidating urban development. From 2005 to 2015,urban growth in Kyrenia has been quite dramatic, showing a wide and scattered pattern, lacking proper plan. As a result of this unplanned/unorganized expansion, agricultural areas, vegetation and water bodies have been lost in the region. Therefore, it has become a necessity to analyze the results of this urban growth and compare the losses between land-cover changes. With this goal in mind, a case study of Kyrenia region has been carried out using a supervised image classification method and Landsat TM images acquired in 2005 and 2015 to map and extract land-cover changes. This paper tries to assess urban-growth changes detected in the region by using Remote Sensing and GIS. The study monitors the changes between different land cover types. Also, it shows the urban occupation of primary soil loss and the losses in forest areas, open areas, etc.
Traffic accidents are causing major deaths in urban environments, so analyzing locations of the traffic accidents
and their reasons is crucial. In this manner, patterns of accidents and hotspot distribution are analyzed by using
geographic information technology. Locations of the traffic accidents in the years 2011, 2012 and 2013 are
combined to generate the kernel distribution map of Kyrenia City. This analysis aims to find high dense
intersections and segments within the city. Additionally, spatial autocorrelation methods Local Morans I and
Getis-Ord Gi are employed . The results are discussed in detail for further analysis. Finally, required changes for
numerous intersections are suggested to decrease potential risks of high dense accident locations.
KEYWORDS: Geographic information systems, Standards development, Databases, Roads, Geodesy, Information technology, Remote sensing, Environmental sensing, Current controlled current source, Excel
It is well known that green spaces are vital for increasing the quality of life within the urban environment. World Health
Organization states that it should be 9 square meters per person at least. European Environment Agency defines that 5000
square meters of green space should be accessible within 300 meters distance from households. Green structure in
Northern Cyprus is not sufficient and effective in this manner. In Northern Cyprus, they have neglected the urban planning
process and they have started to lose significance and importance. The present work analyzes the accessibility of green
spaces in Northern Cyprus cities. Kioneli, Famagusta, Kyrenia and the northern part of Nicosia are analyzed in this
manner. To do that, green space structure is analyzed by using digital data. Additionally, accessibility of the green space is
measured by using 300-meter buffers for each city. Euclidean distance is used from each building and accessibility maps
are generated. Kyrenia and Famagusta have shortage in green space per capita. The amount of green space in these cities is
less than 4 square meters. The factors affecting the accessibility and utilization of public spaces are discussed to present
better solutions to urban planning.
KEYWORDS: Mobile devices, Sensors, Sensor networks, Data modeling, Computing systems, Systems modeling, Data fusion, Data processing, Information fusion, Data integration
Mobile device use has vastly increased in the last few years. Many people use many mobile devices in their daily lives.
Context-aware computing is the main feature of pervasive and ubiquitous computing. Context awareness is also an
important topic that becomes more available with ubiquitous computing. As the sensors increase, the data collected via
mobile device sensors and sensor networks do not have much value because of the difficulty in analysis and understanding
the data. Context-aware computing helps us store contextual information and use or search it by mobile devices when we
want to see or analyze it. Contextual data can be made more meaningful by context-aware processing. There are different
types of data and context information that must be considered. By combining spatial and contextual data, we obtain more
meaningful data based on the entities. Contextual data is any information that can be used to characterize the situation of
the entity. The entity is a person, place, or object considered relevant to the interaction between the user and an application,
including the users and the applications. Using contextual data and good integration to mobile devices adds great value to
this data, and combining these with our other data sets will allow us to obtain more useful information and analysis.
KEYWORDS: Sensors, Mobile devices, Geographic information systems, Sensor networks, Data storage, Visualization, Databases, Internet, Data processing, Computing systems
Spatial data has become a critical issue in recent years. In the past years, nearly more than three quarters of databases, were related directly or indirectly to locations referring to physical features, which constitute the relevant aspects. Spatial data is necessary to identify or calculate the relationships between spatial objects when using spatial operators in programs or portals. Originally, calculations were conducted using Geographic Information System (GIS) programs on local computers. Subsequently, through the Internet, they formed a geospatial web, which is integrated into a discoverable collection of geographically related web standards and key features, and constitutes a global network of geospatial data that employs the World Wide Web to process textual data. In addition, the geospatial web is used to gather spatial data producers, resources, and users. Standards also constitute a critical dimension in further globalizing the idea of the geospatial web. The sensor web is an example of the real time service that the geospatial web can provide. Sensors around the world collect numerous types of data. The sensor web is a type of sensor network that is used for visualizing, calculating, and analyzing collected sensor data. Today, people use smart devices and systems more frequently because of the evolution of technology and have more than one mobile device. The considerable number of sensors and different types of data that are positioned around the world have driven the production of interoperable and platform-independent sensor web portals. The focus of such production has been on further developing the idea of an interoperable and interdependent sensor web of all devices that share and collect information. The other pivotal idea consists of encouraging people to use and send data voluntarily for numerous purposes with the some level of credibility. The principal goal is to connect mobile and non-mobile device in the sensor web platform together to operate for serving and collecting information from people.
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