EDI is a general purpose, template-driven metadata editor for creating XML-based descriptions. Originally aimed at defining rich and standard metadata for geospatial resources, It can be easily customised in order to comply with a broad range of schemata and domains. EDI creates HTML5 [9] metadata forms with advanced assisted editing capabilities and compiles them into XML files. The examples included in the distribution implement profiles of the ISO 19139 standard for geographic information [14], such as core INSPIRE metadata [10], as well as the OGC [8] standard for sensor description, SensorML [11]. Templates (the blueprints for a specific metadata format) drive form behaviour by element data types and provide advanced features like codelists1 underlying combo boxes or autocompletion functionalities. Virtually, the editing of any metadata format can be supported by creating a specific template. EDI is stored on GitHub at https://github.com/SP7-Ritmare/EDI-NG_client and https://github.com/SP7-Ritmare/EDI-NG_server

The monitoring of the environment's status at continental scale involves the integration of information derived by the analysis of multiple, complex, multidisciplinary, and large‐scale phenomena. Thus, there is a need to define synthetic Environmental Indicators (EIs) that concisely represent these phenomena in a manner suitable for decision‐making. This research proposes a flexible system to define EIs based on a soft fusion of contributing environmental factors derived from multi‐source spatial data (mainly Earth Observation data). The flexibility is twofold: the EI can be customized based on the available data, and the system is able to cope with a lack of expert knowledge. The proposal allows a soft quantifier‐guided fusion strategy to be defined, as specified by the user through a linguistic quantifier such as ‘most of’. The linguistic quantifiers are implemented as Ordered Weighted Averaging operators. The proposed approach is applied in a case study to demonstrate the periodical computation of anomaly indicators of the environmental status of Africa, based on a 7‐year time series of dekadal Earth Observation datasets. Different experiments have been carried out on the same data to demonstrate the flexibility and robustness of the proposed method.

The conceptual framework of Essential Biodiversity Variables (EBVs) aims to capture the major dimensions of biodiversity change by structuring biodiversity monitoring and by ruling data collection amongst different providers. Amongst the research infrastructures adopting and implementing the EBV framework, LTER-Europe - the European node of ILTER (International Long-Term Ecological Research) - follows the approach to compare site-based biodiversity observations within and across its networks. However, a synoptic overview of their contributions with EBVs-relevant data is still missing, since data are not made available for several reasons. In this paper, we assess the capacity of LTER-Italy, one of the richest and heterogeneous networks of LTER sites in Europe, to provide data to “Species Distribution” and “Species Abundance” EBVs without inspecting and downloading their contents. To this aim, we mine the EBVs information which is publicly structured and shared by LTER site managers through DEIMS-SDR, the LTER-Europe online metadata repository. We classify the sites according to two types of contributions: (i) the actual contribution, based on metadata of datasets and (ii) the potential contribution, based on metadata of sites. Through these assessments, we investigate if LTER-Italy monitoring activities can provide EBVs measures and which sites currently provide datasets. By comparing the two contributions, we pinpoint the factors hampering the accessibility of LTER-Italy data and suggest solutions to increase the discoverability and reusability of LTER-Italy EBVs measurements. The research provides the first overview of EBVs monitored in LTER-Italy and the corresponding data management practices, as well as an evaluation of the interoperability of this network with respect to other research organisations for legal and technical aspects.

The adoption of Sensor Web Enablement (SWE) practices by sensor maintainers is hampered by the inherent complexity of the Sensor Model Language (SensorML), its high expressiveness, and the scarce availability of editing tools. To overcome these issues, the Earth Observation (EO) community often recurs to SensorML profiles narrowing the range of admitted metadata structures and value ranges. Unfortunately, profiles frequently fall short of providing usable editing tools and comprehensive validation criteria, particularly for the difficulty of checking value ranges in the multi-tenanted domain of the Web of Data. In this paper, we provide an updated review of current practices, techniques, and tools for editing SensorML in the perspective of profile support and introduce our solution for effective profile definition. Beside allowing for formalization of a broad range of constraints that concur in defining a metadata profile, our proposal closes the gap between profile definition and actual editing of the corresponding metadata by allowing for ex-ante validation of the metadata that is produced. On this basis, we suggest the notion of Semantic Web SensorML profiles, characterized by a new family of constraints involving Semantic Web sources. We also discuss implementation of SensorML profiles with our tool and pinpoint the benefits with respect to the existing ex-post validation facilities provided by schema definition languages.

This work presents the development of an efficient tool for managing, visualizing, analysing, and integrating with other data sources, the deformation time-series obtained by applying the advanced differential interferometric synthetic aperture radar (DInSAR) techniques. To implement such a tool we extend the functionalities of GeoNode, which is a web-based platform providing an open source framework based on the Open Geospatial Consortium (OGC) standards, that allows development of Geospatial Information Systems (GIS) and Spatial Data Infrastructures (SDI). In particular, our efforts have been dedicated to enable the GeoNode platform to effectively analyze and visualize the spatio/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. The examples we present, based on Sentinel-1 DInSAR results relevant to Italy, demonstrate the effectiveness of the extended version of the GeoNode platform.