Geo-processing workflow driven wildfire hot pixel detection under sensor web environment

  • Authors:

  • Nengcheng Chen;Liping Di;Genong Yu;Jianya Gong

  • Affiliations:

  • Center for Spatial Information Science and Systems (CSISS), George Mason University, 6301 Ivy Lane, Suite 620, Greenbelt, MD 20770, USA and State Key Lab for Information Engineering in Surveying, ...;Center for Spatial Information Science and Systems (CSISS), George Mason University, 6301 Ivy Lane, Suite 620, Greenbelt, MD 20770, USA;Center for Spatial Information Science and Systems (CSISS), George Mason University, 6301 Ivy Lane, Suite 620, Greenbelt, MD 20770, USA;State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, 129 Luoyu Road, Wuhan 430079, China

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2010

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Abstract

Integrating Sensor Web Enablement (SWE) services with Geo-Processing Workflows (GPW) has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. This paper presents a common GPW framework for Sensor Web data service as part of the NASA Sensor Web project. This abstract framework includes abstract GPW model construction, GPW chains from service combination, and data retrieval components. The concrete framework consists of a data service node, a data processing node, a data presentation node, a Catalogue Service node, and a BPEL engine. An abstract model designer is used to design the top level GPW model, a model instantiation service is used to generate the concrete Business Process Execution Language (BPEL), and the BPEL execution engine is adopted. This framework is used to generate several kinds of data: raw data from live sensors, coverage or feature data, geospatial products, or sensor maps. A prototype, including a model designer, model instantiation service, and GPW engine-BPELPower is presented. A scenario for an EO-1 Sensor Web data service for wildfire hot pixel detection is used to test the feasibility of the proposed framework. The execution time and influences of the EO-1 live Hyperion data wildfire classification service framework are evaluated. The benefits and high performance of the proposed framework are discussed. The experiments of EO-1 live Hyperion data wildfire classification service show that this framework can improve the quality of services for sensor data retrieval and processing.