The Research On Model And Method Of Sensor Resource Sharing Management Under The Earth Observation Web Environment

With the changes of the Earth environment, the demands of Earth Observation (EO) data are becoming increasingly complex, the EO systems are showing greatly diverse and thus the concept of EO web are urgently developing. EO web is a geospatial observation web that implements the event-aware task to the global environment. EO sensors are used to detect the Earth’s surface and lower atmosphere for obtaining the data/information of interest to the user. As we all know, the integration management and collaboration observation of the EO sensors is one of the major scientific problems under the EO web environment.EO sensor is the source of geospatial data acquisition. However, due to the characteristics (such as:diverse observation mechanism, huge number, isolation and autonomous condition) of the EO sensors, when facing with one specific epicontinental event, the EO sensor allocation is always inaccurate, incomplete and time-lagging. The direct result is inefficiency to the sensor planning and dispatch. Therefore, this paper aims to the sharing management of those distributed, massive and heterogeneous EO sensors which facilitate the timely, accurate and comprehensive scheduling sensors, thus improving the manageability and application for the EO sensors.According to the analysis of sensor resources above and the inefficient response to the epicontinental disaster event under the EO web, this paper points out that the sensor sharing management is a basic problem for sensor integration management and collaboration. To solve this basic problem, this paper carries out the in-depth analysis for the sensor sharing management framework, including sensor sharing management model and methodology.Firstly, the paper starts with the summary to the existing sensor information exchange standards and related metadata standards and the analysis to the sensor resources’sharing requirements, through expansing and reusing the existing sensor-related metadata standards, this paper proposes a sensor sharing eight-tuple metadata model. Based on the MOF metamodel theory, this paper standardly defines sensor resource description metamodel and establishes the sharing metadata-nested sensor resource description model, which plays a basic information model for the achievement of registry, discovery, collaborative planning, and dynamic visualization to the EO sensors.Secondly, in order to achieve the organic association between the specific epicontiental event and EO sensors, this paper presents an epicontinental event-centric association method. This method defines the association rules between the event and EO sensors, and designs the Multi-level Matching Algorithm based on Time and Location Constraints (MM_TL). In this manner, the scientific association between event and sensors has been realized, and it changes the past situation of that sensor scheduling and planning mainly relies on passive and imperfect expertise. This association method can provide the trust sensor collection for the sensor integration management and collaborative observation. Thirdly, on the basis of Open GIS Consortium (OGC) Catalog Service for Web (CSW) specification, this paper proposes a standard sensor register mechanism, including the extending of CSW core information model for supporting sensor registry, designation of mapping relationship between sensor resource description model and sensor registry information model. Then, the OGC/CSW-based sensor register center can be established, which lays the foundation for sensor discovery in the following.Fourthly, depending on the OGC/CSW sensor register center mentioned above, this paper designs the fine-grained multi-level sensor metadata query items. By adopting the OGC/CSW discovery interface, the registered EO sensor resources can be discovered on demand. Through using virtual reality simulation technology, this paper integratively visualizes the retrieved sensor resources facilitating the sensor inquirers’intuitive grasp and understanding of sensor basic information and temporal-spatial dynamic abilities, thus aiding the required sensor planning and dispatch decision-making.Finally, this paper builds the EO sensor sharing management platform framework. By using the hydrological environment monitoring sensor resources in the downstream section of the Yangtze River basin and the waterlogging type flood event as the experimental resources, the application of proposed sharing management model and method has been examplified.