Technological advances have driven all aspects of Earth observation data, including improvements realized in sensor characteristics and capabilities, global data processing, near realtime monitoring, value-added products, and the distribution of global products. In particular, the growth of the World Wide Web is contributing to an increase in the global user base. The synergy of remote sensing, geographic information systems (GIS), Internet, and mobile phone technologies is revolutionizing the way in which satellite-derived information is archived and distributed to users. The Fire Information for Resource Management System (FIRMS), a NASA-funded application, is just one of many examples that illustrate the increasing ease with which Earth observation data are accessible to a broad range of users. This paper describes how the delivery of satellite-derived fire information has evolved over the last six years. By understanding user requirements and taking advantage of recent developments in areas such as information management, search, access, visualization, and enabling technologies, FIRMS has expanded the number and range of users that are able to access and utilize satellite-derived fire information. Specifically, we describe how satellite remote sensing and GIS technologies have been integrated to deliver MODIS active fire data to natural resource managers using Internet mapping services and customized e-mail alerts to users in more than 90 countries. We also describe how this web-based desktop application has been transitioned to a mobile service in South Africa to deliver fire information to field staff to warn of fires that may be potentially damaging to both natural resources and infrastructure.
A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.
A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.
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