Abstract
Rapid advancements in Earth-observing sensor systems have led to the generation of large amounts of remote sensing data that can be used for the dynamic monitoring and analysis of hydrological disasters. The management and analysis of these data could take advantage of distributed information infrastructure technologies such as Web service and Sensor Web technologies, which have shown great potential in facilitating the use of observed big data in an interoperable, flexible and on-demand way. However, it remains a challenge to achieve timely response to hydrological disaster events and to automate the geoprocessing of hydrological disaster observations. This article proposes a Sensor Web and Web service-based approach to support active hydrological disaster monitoring. This approach integrates an event-driven mechanism, Web services, and a Sensor Web and coordinates them using workflow technologies to facilitate the Web-based sharing and processing of hydrological hazard information. The design and implementation of hydrological Web services for conducting various hydrological analysis tasks on the Web using dynamically updating sensor observation data are presented. An application example is provided to demonstrate the benefits of the proposed approach over the traditional approach. The results confirm the effectiveness and practicality of the proposed approach in cases of hydrological disaster.
Highlights
In the big data era, Earth observation technologies provide powerful capabilities to obtain enormous amounts of diverse geospatial data in an on-demand and continuous fashion [1]
Based on the results of the experiments, it can clearly be concluded that the proposed approach exhibits superior performance compared with the traditional method in terms of saving time and effort and shortening the wait times for endpoint consumers of hydrological disaster information
The performance was further improved when the hydrological analysis services were migrated to high-performance computing servers
Summary
In the big data era, Earth observation technologies provide powerful capabilities to obtain enormous amounts of diverse geospatial data in an on-demand and continuous fashion [1]. Hundreds of Earth-observing satellites are currently in orbit and performing various observation tasks These satellites, such as Landsat, MODIS, and the GF series, play an important role in monitoring regional water resources by collecting many spatial, spectral, radiation, and time-scale observation products that reflect chlorophyll, suspended solids, and turbidity in the water [3]. The Geospatial Data Abstraction Library (GDAL) is widely used to access and process raster and vector geospatial data [4]. It is an open-source geospatial library that supports the translation and processing of data in common geospatial formats such as GeoTIFF, Arc/Info ASCII Grid, and ESRI Shapefile. The integration of the enhanced GDAL with geographic information systems (GIS) can facilitate the processing of Earth observation data [5]. Large volumes of data and powerful computing resources are encapsulated as services with standard interfaces and protocols to enable Web-based sharing and automatic access, significantly enhancing the ability to use online/near-line data over the Web and allowing the widespread automation of data analysis and computation [8]
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