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ABSTRACT:Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in a Service Oriented Architecture (SOA) platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

ABSTRACT:Environmental monitoring systems deal with time-sensitive issues which require quick responses in emergency situations. Handling the sensor observations in near real-time and obtaining valuable information is challenging issues in these systems from a technical and scientific point of view. The ever-increasing population growth in urban areas has caused certain problems in developing countries, which has direct or indirect impact on human life. One of applicable solution for controlling and managing air quality by considering real time and update air quality information gathered by spatially distributed sensors in mega cities, using sensor web technology for developing monitoring and early warning systems. Urban air quality monitoring systems using functionalities of geospatial information system as a platform for analysing, processing, and visualization of data in combination with Sensor Web for supporting decision support systems in disaster management and emergency situations. This system uses Sensor Web Enablement (SWE) framework of the Open Geospatial Consortium (OGC), which offers a standard framework that allows the integration of sensors and sensor data into spatial data infrastructures. SWE framework introduces standards for services to access sensor data and discover events from sensor data streams as well as definition set of standards for the description of sensors and the encoding of measurements. The presented system provides capabilities to collect, transfer, share, process air quality sensor data and disseminate air quality status in real-time. It is possible to overcome interoperability challenges by using standard framework. In a routine scenario, air quality data measured by in-situ sensors are communicated to central station where data is analysed and processed. The extracted air quality status is processed for discovering emergency situations, and if necessary air quality reports are sent to the authorities. This research proposed an architecture to represent how integrate air quality sensor data stream into geospatial data infrastructure to present an interoperable air quality monitoring system for supporting disaster management systems by real time information. Developed system tested on Tehran air pollution sensors for calculating Air Quality Index (AQI) for CO pollutant and subsequently notifying registered users in emergency cases by sending warning E-mails. Air quality monitoring portal used to retrieving and visualize sensor observation through interoperable framework. This system provides capabilities to retrieve SOS observation using WPS in a cascaded service chaining pattern for monitoring trend of timely sensor observation.

In order to analyze the dynamic processes of the Earth interior and the effect of the propagation of the seismic waves to the surface, a comprehensive study of the Earth crust kinematics is necessary. Although the Global Positing System (GPS) is a powerful method to measure ground displacements and velocities both horizontally and vertically as well as to infer the tectonic stress regime generated by the subsurface processes (from local fault systems to huge tectonic plate movements and active volcanoes), the complexity of the deformation pattern generated during such movements is not always easy to be interpreted. Therefore, it is necessary to work on new methodologies and modifying the previous approaches in order to improve the current methods and better understand the crustal movements. In this paper, we focus on western Alaska area, where many complex faults and active volcanoes exist. In particular, we analyze the data acquired each 30 seconds by three GPS stations located in western Alaska (AC31, AB09 and AB11) from January 1, 2012 to December 31, 2012 in order to compute their displacements in horizontal and vertical components by vectorial summation of the average daily and annual velocities components. Furthermore, we design non-parametric DMeyer and Haar wavelets for horizontal and vertical velocities directions in order to identify significant and homogenous displacements during the year 2012. Finally, the non-parametric decomposition of total horizontal and vertical normalized velocities based on level 1 and level 2 coefficients have been applied to compute normal and cumulative probability histograms related to the accuracy and statistical evolution of each applied wavelet. The results present a very good agreement between the designed non-parametric wavelets and their decomposition functions for each of the three above mentioned GPS stations displacements and velocities during the year 2012.