Geospatial data harmonization from regional level to european level: a usa case in forest fire data

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Geospatial data harmonization is becoming more and more important to increase interoperability of heterogeneous data derived from various sources in spatial data infrastructures. To address this harmonization issue we present the current status of data availability among different communities, languages, and administrative scales from regional to national and European levels. With a use case in forest data models in Europe, interoperability of burned area data derived from Europe and Valencia Community in Spain were tested and analyzed on the syntactic, schematic and semantic level. We suggest approaches for achieving a higher chance of data interoperability to guide forest domain experts in forest fire analysis. For testing syntactic interoperability, a common platform in the context of formats and web services was examined. We found that establishing OGC standard web services in a combination with GIS software applications that support various formats and web services can increase the chance of achieving syntactic interoperability between multiple geospatial data derived from different sources. For testing schematic and semantic interoperability, the ontology-based schema mapping approach was taken to transform a regional data model to a European data model on the conceptual level. The Feature Manipulation Engine enabled various types of data transformation from source to target attributes to achieve schematic interoperability. Ontological modelling in Protégé helped identify a common concept between the source and target data models, especially in cases where matching attributes were not found at the schematic level. Establishment of the domain ontology was explored to reach common ground between application ontologies and achieve a higher level of semantic interoperability

Riego inteligente: proceso de captura de datos basado en gestión del conocimiento

This paper presents the process of acquiring environmental data that feed an intelligent irrigation control system, which based on the calculation of the evapotranspiration of a crop manages to calculate the water needs of the crop to supply them. It presents the problem of irrigation because a solution based on the Internet of Things (IoT) is considered satisfactory, specifying the variables involved in the process and the characteristics of the data produced by the sensors. After this, it develops the process of capturing data on an IoT architecture based on knowledge management and with the sensing, communication, and analytical phases, referring to the R software components that have been developed to carry out this process, culminating with the projections of irrigation analytics. As irrigation is the main aspect of crop yield, a need inherent to the field sector that is not yet automated and that seeks solutions to the conditions of the Colombian countryside is supplied.El presente artículo describe el proceso de adquisición de datos medioambientales que alimentan un sistema de control de riego inteligente el cual, basado en el cálculo de la evapotranspiración de un cultivo, logra calcular las necesidades hídricas del mismo para suplirlas. Luego de plantearse la problemática del riego, y la justificación de una solución basada en Internet de las Cosas (IoT) como satisfactoria, se precisan las variables que intervienen en el proceso y las características de los datos que producen los sensores; se desarrolla el proceso de captura de datos sobre una arquitectura IoT basada en gestión del conocimiento con las fases de: sensado, comunicación y analítica, refiriendo los componentes del software R que se han implementado para realizar este proceso, culminando con las proyecciones de analítica del riego. Al ser el riego el aspecto principal del rendimiento de un cultivo se concluye que se suple una necesidad inherente al sector del campo -que aún no está automatizado- proponiéndose una solución para las condiciones específicas del campo colombiano

Monitoring Patagonian Rangelands: The MARAS System

Rangelands in Patagonia have been managed with a lack of regulation since the introduction of sheep in the late 1880’s. Most rangelands are under private freehold in ‘estancias’ and the rest are public and managed by small subsistence-type farmers. Natural resources of rangelands are property of the provincial states according to the Constitution, but they have no mandate or resources to monitor their condition and public support funds have not been tied to proper management. Government agencies designed in the 1990’s two range evaluation systems hat have been applied extensively but they focus on short time - scale processes to allocate forage resources at the “estancia” scale. The need of a regional monitoring system to evaluate rangeland condition and trend is slowly being recognized by governments and farmers, and scientists of Patagonia have been discussing a possible unified methodology. The system has been named MARAS and is based on Australia’s WARMS (Holm 1998) and other similar methods. Lines of point interception and frequency samplers are used to evaluate herbaceous vegetation, Camfield lines to evaluate shrubs and patches, surface sampling, and estimates of soil condition. Ground observation points would be set one per cadastral unit (about 20.000 ha) and evaluated at five-year intervals. State funding has been obtained in 2004 to put in place the first monitors, and to design a web data base that would give selective access to the information to federal, provincial, or non-governmental institutions. The main challenge of the system is to assure funding through decades in order to assess long time-scale processes. We expect that ecocertification market requirements will induce farmers to support it, and that the new trend of public funding included in a new sheep promotion law will allow the system to continue.EEA Santa CruzFil: Oliva, Gabriel Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Oliva, Gabriel Esteban. Universidad Nacional de la Patagonia Austral. Unidad Académica Río Gallegos; Argentina.Fil: Escobar, Juan Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina.Fil: Siffredi, Guillermo Lorenzo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina.Fil: Salomone, Jorge Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina.Fil: Buono, Gustavo Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

AGROVOC: The linked data concept hub for food and agriculture

Newly acquired, aggregated and shared data are essential for innovation in food and agriculture to improve the discoverability of research. Since the early 1980′s, the Food and Agriculture Organization of the United Nations (FAO) has coordinated AGROVOC, a valuable tool for data to be classified homogeneously, facilitating interoperability and reuse. AGROVOC is a multilingual and controlled vocabulary designed to cover concepts and terminology under FAO's areas of interest. It is the largest Linked Open Data set about agriculture available for public use and its highest impact is through facilitating the access and visibility of data across domains and languages. This chapter has the aim of describing the current status of one of the most popular thesaurus in all FAO’s areas of interest, and how it has become the Linked Data Concept Hub for food and agriculture, through new procedures put in plac

Making research data discoverable: an outreach activity of Datacite

Objective The enormous growth in research data generated today has highlighted the value of data management (RDM) to make research FAIR (Findable, Accessible, Interconnected and Reusable). Appropriate data instructs researchers to use and reuse that data within appropriate citations and attribute it to the author. And Data citation refers to the process of presenting a reference to data in the same way as a bibliographic reference to printed resources is regularly provided by researchers. In this regard, the objective of this paper is to investigate the activities of the Datacite website in managing research data. Methodology The study approached the Datacite website, a non-profit organization that provides analysis with persistent identifiers (DOIs). The research examines the Statistics systems and other critical resources. Registrations by the Collective group and most involved repositories are included in the statistical approaches. The basic resources include top executives, OAI-PMH, DataCite Public Roadmap, DataCite Commons, DataCite/ORCID Auto-update and Service Providers. The outcomes were analysed by MS Excel. Results It is noted that there were 293 members of the registry from different countries. The USA was at the top of the 137 members according to registration, while at least one was located in India, Finland, Spain, etc. Germany was listed as the top member and most of the repository holding companies. Datafirst is the only server found in an Indian context. DataCite Commons found as a discovery tool which allows simple searches by works, individuals and organisations, while providing users with a detailed overview of the relationships between the entities in the research setting. Using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), the DataCite service exposes metadata stored in the DataCite Metadata Store (MDS). Datacite Auto-update unambiguously categorises researchers and provides tools to automate the link between researchers and their creative work

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems. © 2023 Marquez Torres et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The authors would like to thank Joan Busqué who created and shared the original Puerto model and the team lead by José Barquín at the Hydrological Institute of Cantabria (IHC). Special thanks to Simone Langhans and Ken Bagstad who suggested revisions to the article. Robinson et al. (2014) for logistic support for EarthEnv-DEM90

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features¿scalability, portability, interoperability, and usability¿that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.This research was partially supported by the Intelligent Energy Europe (IEE) program and the Ministerio de Economía y Competitividad under contract TIN2017-84553-C2-2-R, by the European Union FEDER (CAPAP-H6 network TIN2016-81840-REDT) and the demonstration activity financed by the Operation 01.02.01 of Technological Transfer from the Program of Rural Development in Catalunya 2014–2020 cofinanced by DARP and FEDER

Technological undercurrents and global information circulation

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