Ad-hoc combination and analysis of heterogeneous and distributed spatial data for environmental monitoring – design and prototype of a web-based solution

Wiemann, Stefan; Karrasch, Pierre; Bernard, Lars

doi:10.1080/17538947.2017.1326984

Cited by 10 publications

(12 citation statements)

References 41 publications

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“…They derive relevant and timely information from (big) data in efficient and reusable manner, which serves the objectives of Digital Earth. Wiemann et al (2018) focus on the assessment of water body quality based on the integration of data available in SDIs to date; the data types considered are feature objects and raster data. Their work introduced a new concept of geoprocessing patterns that suggest the application of processing functionality based on input data selected by the user of the application.…”

Section: Online Geoprocessing Applications In the Context Of Digital mentioning

confidence: 99%

Geospatial Information Processing Technologies

Gui

Hofer

et al. 2019

Manual of Digital Earth

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The increasing availability of geospatial data offers great opportunities for advancing scientific discovery and practices in society. Effective and efficient processing of geospatial data is essential for a wide range of Digital Earth applications such as climate change, natural hazard prediction and mitigation, and public health. However, the massive volume, heterogeneous, and distributed nature of global geospatial data pose challenges in geospatial information processing and computing. This chapter introduces three technologies for geospatial data processing: highperformance computing, online geoprocessing, and distributed geoprocessing, with each technology addressing one aspect of the challenges. The fundamental concepts, principles, and key techniques of the three technologies are elaborated in detail, followed by examples of applications and research directions in the context of Digital Earth. Lastly, a Digital Earth reference framework called discrete global grid system (DGGS) is discussed.

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Section: Online Geoprocessing Applications In the Context Of Digital mentioning

confidence: 99%

Geospatial Information Processing Technologies

Gui

Hofer

et al. 2019

Manual of Digital Earth

View full text Add to dashboard Cite

show abstract

“…Indeed, the arguments behind choosing a given SAS depend on the purpose of the application, as follows. There are spatial applications that process ad hoc spatial queries 31 . For these applications, the chosen system should provide support for processing different types of spatial operations, such as topological predicates (eg, contains, inside, and meet), geometric set operations (eg, union, intersection), and numerical operations (eg, area, distance).…”

Section: Introductionmentioning

confidence: 99%

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

2020

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Summary Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system‐centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user‐centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user‐centric comparison of the following SASs based on Hadoop and Spark: Hadoop‐GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real‐world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.

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“…State of the art research approaches and scientific measurement methods are limited to large-scale measurement campaigns carried out by scientific or governmental institutions. Due to the high methodical effort and the relatively low spatiotemporal coverage, such approaches are not yet feasible enough to provide a monitoring solution to address the heterogeneity and dynamics of aquatic ecosystems in an appropriate manner (Wiemann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Mobile Monitoring—Open-Source Based Optical Sensor System for Service-Oriented Turbidity and Dissolved Organic Matter Monitoring

et al. 2019

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The protection and sustainable use of aquatic resources require a better understanding of fresh water sources, limnic ecosystems, and oceans. The effects of global change, intensive use of natural resources and the complex interactions between humans and the environment show different effects at different scales. Current research approaches are not sufficient to appropriately take account of the heterogeneity and dynamics of aquatic ecosystems. A major challenge in applied environmental research is to extend methods for holistic monitoring and long-term observation technologies with enhanced resolution over both space and time. In this study, turbidity and the content of dissolved organic matter (DOM) are key parameters, as they are of importance for assessing the health of aquatic ecosystems and the state of ecosystem services (e.g., the provision of drinking water). Photonics and optical sensors as well as integrated circuits and open-source based components open interesting possibilities to overcome the current lack of adaptive and service-oriented sensor systems. An open source based optical sensor system was developed, which enables a user-specific, modular and adaptive in-situ monitoring of the turbidity and the dissolved organic substance content almost in real time. Quantification is based on attenuation or transmission measurements with two narrowband LEDs and corresponding detectors in the ultraviolet (DOM content) and infrared range (turbidity) of the electromagnetic spectrum. The developed in-situ sensor system shows a very high agreement with the results obtained using a laboratory photometer but with less methodological effort. First tests carried out in the area close to the city of Leipzig (Saxony, Germany) show promising results. The in-situ sensor system is able to acquire the optical attenuation with a sampling rate up to 0.1 Hz. Due to the fact that data is visualized directly with the help of web services, even the quality of data collection can be improved by assisting the selection of sampling points or a direct spatio-temporal data feedback. What this approach illustrates is the fact that open-source technologies and microelectronics can now be used to implement resilient and promising sensor systems that can set new standards in terms of performance and usability within applied environmental research.

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Ad-hoc combination and analysis of heterogeneous and distributed spatial data for environmental monitoring – design and prototype of a web-based solution

Cited by 10 publications

References 41 publications

Geospatial Information Processing Technologies

Geospatial Information Processing Technologies

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Mobile Monitoring—Open-Source Based Optical Sensor System for Service-Oriented Turbidity and Dissolved Organic Matter Monitoring

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