The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems.

Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract-Wireless Sensor Networks (WSN) are often composed of a wide range of sensor nodes, which may vary greatly in their type of hardware platform, as well as their sensing and mobility capabilities. The ability of a sensor to move is a particularly important feature in dynamic scenarios, since mobile sensors can fill the gap caused by the failures of those that are stationary, and thus extend the lifetime and span of a WSN. However, there remains the problems of intersensory communication in the field when integrating mobile sensors into the Sensor Web in dynamic scenarios since it does not have the necessary interoperability for automatically managing the different types of sensor data and activities involved in such scenarios. This paper tackles this problem by adopting an approach consisting of an enhanced messaging protocol and a dynamic sensor management component. In validating the proposal, two different realistic scenarios were simulated to evaluate the achieved results in terms of interoperability and performance. The results provided evidence that the proposal complies with Sensor Web standards as well as being suitable for near real-time data publication, and is thus able to support applications in dynamic scenarios.

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Earth observation images are a powerful source of data about changes in our planet. Given the magnitude of global environmental changes taking place, it is important that Earth Science researchers have access to spatiotemporal reasoning tools. One area of particular interest is land-use change. Using data obtained from images, researchers would like to express abstractions such as 'land abandonment', 'forest regrowth', and 'agricultural intensification'. These abstractions are specific types of land-use trajectories, defined as multi-year paths from one land cover into another. Given this need, this paper introduces a spatiotemporal calculus for reasoning about land-use trajectories. Using Allen's interval logic as a basis, we introduce new predicates that express cases of recurrence, conversion and evolution in land-use change. The proposed predicates are sufficient and necessary to express different kinds of land-use trajectories. Users can build expressions that describe how humans modify Earth's terrestrial surface. In this way, scientists can better understand the environmental and economic effects of land-use change. ARTICLE HISTORY

Flood risk management requires updated and accurate information about the overall situation in vulnerable areas. Social media messages are considered to be as a valuable additional source of information to complement authoritative data (e.g. in situ sensor data). In some cases, these messages might also help to complement unsuitable or incomplete sensor data, and thus a more complete description of a phenomenon can be provided. Nevertheless, it remains a difficult matter to identify information that is significant and trustworthy. This is due to the huge volume of messages that are produced and which raises issues regarding their authenticity, confidentiality, trustworthiness, ownership and quality. In light of this, this paper adopts an approach for on-the-fly prioritization of social media messages that relies on sensor data (esp. water gauges). A proof-of-concept application of our approach is outlined by means of a hypothetical scenario, which uses social media messages from Twitter as well as sensor data collected through hydrological stations networks maintained by Pegelonline in Germany. The results have shown that our approach is able to prioritize social media messages and thus provide updated and accurate information for supporting tasks carried out by decision-makers in flood risk management.