The paper 1 deals with the problem of answering users queries searching for convenient paths to sequentially visit a number or relevant retrieved territorial resources and services. An exemplification is described by illustrating a Smart community-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) that collects and manages heterogeneous georeferenced data about both authoritative resources and services offered by volunteers. Paths through relevant retrieved resources are automatically generated and ranked based by considering personal priorities.

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

While spatial information quality is an established discipline in traditional scientific geographical information (GI), standards and protocols for representing and assessing the quality of geographic contributions generated by volunteers or by the generic 'web crowd' are still missing.This work offers an analysis of strategies for quality control and describes a simple representation of the components of the quality in crowdsourced GI.In this framework, and based on the research carried out in Criscuolo et al. (2014), we also introduce a methodology for quality assessment, based on the given representation, which goes beyond the limitations of previous methods in the literature defined for a specific purpose, being able to deal with many quality features, GI categories, and types of application.The method is designed as a decision making approach, so flexible as to take into account the purpose of GI analysis, and so transparent as to make explicit the criteria driving to quality evaluation, namely the quality features (e.g. the credibility of the volunteers, or the accuracy of the spatial features, etc.) and their relevance.

Initiatives that rely upon the contributions of volunteers to reach a specific goal are growing more and more with the success of Web 2.0–interactive applications. Also scientific projects are testing and exploiting volunteers' collaboration, but the quality of information obtained with this approach is often puzzling. This paper offers a rich overview of many scientific projects where geographic contributions are committed to volunteers, to the aim of defining strategies to improve information quality. By describing real examples of Volunteer Geographic Information (VGI), the contribution establishes a categorization based on the characteristics of the information, tasks, and scopes of the projects. After a discussion on the relationships of categories and VGI quality, the paper analyses techniques to improve the quality of volunteered information according to the moment of its assessment (i.e., ex ante, ex post, or both with respect to information creation). The paper outlines the main limitations of the different approaches and indicates some guidelines for future developments.

Despite Volunteered Geographic Information (VGI) activities are now extremely helpful in a number of scientific applications, researchers and decision makers oppose some resistance to the usage of volunteered contributions, due to quality issues. Several methods and workflows have been proposed to face quality issues in different VGI projects, usually built ad-hoc for specific datasets, thus resulting neither extensible nor transferable. In order to overcome this weakness, the authors propose to perform an user-driven assessment on VGI items in order to filter only those that satisfy minimally acceptable quality levels defined according to their specific quality requirements and project goals. In the present work the users, i.e., information consumers, are seen as decision makers and are allowed to set the minimum acceptable quality levels Thus the approach proposes a user driven assessment of the fitness for use of VGI items. The paper first briefly presents a view on VGI components and suitable quality indices, then it describes a logic architecture for managing them and for enabling a querying mechanism to the datasets. The approach is finally exemplified with a case study simulation.

The paper illustrates the potentials of geospatial data to access a historical digital atlas for landscape analysis and territorial government. The experience of a historical geo-portal, the "Atl@nte dei Catasti Storici," in the management of geo-referenced and non-geo-referenced maps-ancient cadastral and topographic maps of the Lombardy Region-can be considered a case study with common aspects to many European regions with an extensive cartographic heritage. The development of downstream Web-based services enables integration with other data sources (current maps, satellite and Unmanned Aerial Vehicle [UAV] airborne photogrammetry, and multi-spectral images and derived products). This provides new scenarios for retrieving geospatial knowledge in support of more sustainable management and governance of the territory.

The paper analyses the challenges and problems posed by the use of Volunteered Geographic Information (VGI) in citizen science and a proposal is formulated for assessing VGI quality based on a linguistic decision making approach so as to allow its feasible use for scientific purposes. VGI quality is represented by indicators at distinct levels of granularity which take into account the distinct components of the VGI items. The quality indicators represent both the extrinsic quality, depending on the characteristics and reputation of the sources of information; the intrinsic quality, depending on the distinct accuracy and precision of information; and, last but not least, the pragmatic quality, depending on the user needs and intended purposes. In order to assess the pragmatic quality of VGI items, a linguistic decision making approach is defined that allows users to rank and finally filter the VGI items based on the satisfaction of distinct criteria expressed by means of both linguistic terms, defining soft constraints on the distinct quality indicators, and linguistic aggregators, defining fuzzy operators which combine the satisfaction degrees of the soft constraints at distinct hierarchical levels to yield the final satisfaction of the VGI items. Finally, an example of quality assessment in a glaciological citizen science project is discussed.

Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree-day-based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow-covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE-SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume. Copyright (C) 2011 John Wiley & Sons, Ltd.

The paper illustrates the potentials of geospatial data and services to access historical digital atlas for landscape analysis and territorial government. The experience of a historical geo-portal, the 'Atl@nte dei Catasti Storici', in the management of geo-referenced and non-geo-referenced maps - ancient cadastral and topographic maps of Lombardy Region - can be considered a case study with common aspects to many European regions having an extensive cartographic heritage. The development of downstream web based services to integrate other data sources (current maps, satellite and UAV airborne photogrammetry, multi-spectral images and derived products) provides new scenarios for retrieving geospatial knowledge of territory, bridging the gap in supporting a sustainable management of the territory.

Spatial Data Infrastructures (SDI) allow users connected to the Internet to share and access remote and distributed heterogeneous geodata that are managed by their providers at their own Web sites. In SDIs, available geodata can be found via standard discovery geo-services that makes available query facilities of a metadata catalog. By expressing precise selection conditions on the values of the metadata collected in the catalog, the user can discover interesting and relevant geodata and then access them by means of the services of the SDI. An important dimension of geodata that often concerns such users’ requests is the temporal information that can have multiple semantics. Current practice to perform geodata discovery in SDIs is inadequate for several reasons. First of all, with respect to the temporal characterization, available recommendations for metadata specification, for example, the INSPIRE Directive of the European community do not consider the multiple semantics of the temporal metadata. To this aim, this chapter proposes to enrich the current temporal metadata with the possibility to indicate temporal metadata related to both the observations, i.e., the geodata, the observed event, i.e., the objects in the geodata, and the temporal resolution of observations, i.e., their timestamps. The chapter introduces also a proposal to manage temporal series of geodata observed at different dates. Moreover, in order to represent the uncertain and incomplete knowledge of the time information on the available geodata, the chapter proposes a representation for imperfect temporal metadata within the fuzzy set framework. Another issue that is faced in this chapter is the inadequacy of current discovery service query facilities: in order to obtain a list of geodata results, corresponding values of metadata must exactly match the query conditions. To allow more flexibility, the chapter proposes to adopt the framework of fuzzy databases to allow expressing soft selection conditions, i.e., tolerant to under-satisfaction, so as to retrieve geodata in decreasing order of relevance to the user needs. The chapter illustrates this proposal by an example.

This work presents a simple, cost-effective, and operational approach to monitor crop water requirements at the regional scale for water management and monitoring purposes. The recommended Food and Agricultural Organization of the United Nations methodology (FAO-56) calculates crop evapotranspiration using crop-specific coefficients (K-c), which vary according to the crop type, health, and phenological stage. This approach, though widely applied for irrigation planning, cannot always match the appropriate crop coefficient with the actual crop phenological stage and health condition, especially in anomalous situations. Previous research demonstrated that crop coefficients and spectral vegetation indexes are correlated. Recent studies have used this relationship with high-resolution satellite data from different sensors to provide information to irrigation advisory services. However, high-resolution data are not feasible for an operational and routine monitoring of water consumption and needs. This paper tests the usefulness of time series of coarse resolution satellite data such as those collected by the moderate-resolution imaging spectroradiometer (MODIS) sensor, to monitor crop coefficients temporal and spatial variability and therefore crop water needs at the regional scale taking advantage of the peculiar characteristics offered by MODIS in terms of high temporal resolution and preprocessed products availability. The outlined methodology takes into account the actual growing stage of the crops and nearly real-time vegetation variations, overcoming some limitations of the traditional FAO approach while preserving the maximum operability. The analysis was carried out in the South Milan agricultural area on data referring to 2003 and 2004. The results agreed with those of other studies and proved to be able to account for the anomalous conditions of the summer in 2003. These results were then compared with those obtained using the traditional FAO crop coefficient curves built with data collected during field campaigns in the same years in rice fields. Constraints, limitations, and possible uses are discussed.