Abstract
An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain) representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme.
Highlights
A total of 327 disastrous events were reported around the world in 2016
This study introduced an observation task chain (OTChain) representation model with four segments and eight-tuple metadata components that are developed based on the observation requirements of disasters
This model was applied to the remote-sensing satellite sensor observation planning of a long-term process-owned observation task
Summary
A total of 327 disastrous events were reported around the world in 2016 These disasters collectively resulted in USD 175 billion worth of economic losses and 11,000 human fatalities [1]. The disaster management paradigm is currently shifting from a static pattern to dynamic process monitoring [2,3]. In this case, disaster monitoring can be assigned to an observation task chain (OTChain) that comprises time-series observation tasks [2,4] with dynamic observation themes, large-scale observation time and space windows, and other personalized observation characteristics. Process-owned observation tasks must be efficiently and effectively expressed to ensure accurate disaster monitoring management [5]
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