Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data

Wagemann, Julia; Clements, D. Oliver; Figuera, Ramiro Marco; Rossi, A. P.; Mantovani, Simone

doi:10.1080/17538947.2017.1351583

Cited by 35 publications

(33 citation statements)

References 22 publications

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“…In the context of geo-data analysis, a wide range of various web services are already available, such as those provided by the Open Geospatial Consortium. There are also several other web-based systems that provide tools to search, order, and download RS data; these include the USGS Earth Explorer [228] for data analysis, the National Climatic Data Center-NCDC Climate WebGIS (http://www.climate.gov/#dataServices), the NASA Giovanni [229] or the Eearth Observation Data Service (http://www.earthserver.eu/services/eods) for the RS community, the PlanetServer for the Planetary science community [230], and single web services and free software solutions for automatically generated 3D point clouds for UAVs [231]. Eberle et al [232] demonstrated how different web services can be embedded to generate a geo-data toolbox for monitoring forest in Siberia using RS data.…”

Section: Handling Petabyte-scale Rs Data With Cloud and Web Servicesmentioning

confidence: 99%

“…Eberle et al [232] demonstrated how different web services can be embedded to generate a geo-data toolbox for monitoring forest in Siberia using RS data. Other novel developments of geospatial web services are oriented toward a server-based on-demand access and processing of big Earth data [230]. These web services are suitable for monitoring platforms such as tablets for better managing forest health inventory data, where on-demand RS and geo-data are essential.…”

Section: Handling Petabyte-scale Rs Data With Cloud and Web Servicesmentioning

confidence: 99%

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Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

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Section: Handling Petabyte-scale Rs Data With Cloud and Web Servicesmentioning

confidence: 99%

Section: Handling Petabyte-scale Rs Data With Cloud and Web Servicesmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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“…The term Big Earth Data (Guo, Wang, Chen, & Liang, 2014;Guo, Wang, & Liang, 2016) has been used to refer to large volumes of Earth observation data collected from satellites. This term has also been used to refer to any available geospatial data, including ground-based sensors and large computational model systems such as Numerical Weather Prediction (NWP) (Wagemann, Clements, Figuera, Rossi, & Mantovani, 2017). Platforms serving Big Earth Data face the added challenge of not only having to store, manage and process large collections of data, but they also need to provide interfaces for users to access the data.…”

Section: Introductionmentioning

confidence: 99%

GSio: A programmatic interface for delivering Big Earth data-as-a-service

et al. 2017

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In this paper, we present GSio, a software system for serving geospatial raster or gridded Big Earth Data at scale. GSio allows different scientific communities to consume geospatial analysis ready data. It provides a generic interface to the data, which removes the need to interact with individual files, and can interoperate with existing geospatial collections hosted on data centres and public clouds. A distributed compute model is used to read and transform the data in parallel using a cluster of compute nodes for delivering data as a service to users. Several use cases are presented demonstrating different scenarios where this service has been used.

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“…We start with the paper entitled 'Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data'. Wagemann et al (2018) advocate in their paper a transfer of traditional data analysis workflows into service-oriented workflows. They demonstrate that web services have the potential to mitigate limitations of Big Earth Data analyses caused by restricted storage space and processing power at the user side.…”

Section: Innovation In Geoprocessing For a Digital Earthmentioning

confidence: 99%

“…They demonstrate that web services have the potential to mitigate limitations of Big Earth Data analyses caused by restricted storage space and processing power at the user side. Wagemann et al (2018) show four applications using standardized web services, more specifically Web Coverage Services (WCS) 2.0 and Web Coverage Processing Services, in marine, climate, planetary and Earth observation sciences. They complement the demonstration of the applications with a discussion of benefits and challenges for data providers, technical users and end users or decision makers.…”

Section: Innovation In Geoprocessing For a Digital Earthmentioning

confidence: 99%

Innovation in geoprocessing for a Digital Earth

Hofer

Granell

Bernard

2017

International Journal of Digital Earth

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Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data

Abstract: (2018) Geospatial web services pave new ways for server-based on-demand access and processing of

Cited by 35 publications

References 22 publications

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

GSio: A programmatic interface for delivering Big Earth data-as-a-service

Innovation in geoprocessing for a Digital Earth

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