Land cover (LC) is a scientific landscape classification based on physical properties of earth materials. This information is usually retrieved through remote sensing techniques (e.g. forest cover, urban, clay content, among others). In contrast, Land use (LU) is defined from an anthropocentric point of view. It describes how a specific area is used (e.g. it is usual to indicate whether a territory supports an intensive, extensive use or it is unused). Both geospatial layers are essential inputs in many socio-economic and environmental studies. The INSPIRE directive provides technical data specifications for harmonization and sharing of voluminous LU/ LC datasets across all countries of the EU. The INSPIRE initiative proposes Object-Oriented Modelling as a data modelling methodology. However, the most used Geographic Information Systems (GIS) are built upon relational databases. This may jeopardize LU/LC data usability, since GIS practitioners will eventually face the object-relational impedance mismatch. In this paper, the authors introduce the SIOSE database (Spanish Land Cover and Land Use Information System), which was the first implementation of an object-oriented land cover and Land-use datamodel, in line with the recommendation of the INSPIRE Directive, separating both themes. SIOSE data can be downloaded as relational database files, where information describing each single LU/LC object is divided among several related tables, so database queries can be complex and time consuming. The authors show these technical complexities through a computational experience, comparing SQL and NoSQL databases for querying spatial data downloaded from SIOSE. Finally, the authors conclude that NoSQL geodatabases deserve to be further explored because they could scale for LU/LC data, both horizontally and vertically, better than relational geodatabases, improving usability and making the most of the EU harmonization efforts. Keywords: Land Use, Land Cover, document store, SIOSE, geodatabase, PostgreSQL, jsonb LAND USE AND LAND COVER DATABASES IN THE EULand is a limited resource and its mismanagement is one of the main drivers of global change, with significant effects on ecosystem functions, goods and services [1]. There are complex environmental problems such as the over-exploitation of natural resources, biodiversity loss or climate change that require a long-term management perspective of natural resources. Many studies agree that these problems can be aggravated by land-use changes, so it is mandatory to monitor and apply long-term management policies at different scales [2].Land use (LU) and land cover (LC) information at national and regional level has been historically recorded in many EU Member States because of their environmental and territorial management's needs and requirements. In addition to Corine Land Cover (CLC) 1990 databases, many EU countries have been producing LC databases to manage and satisfy their requirements on environmental, agricultural, forest and land planning issues. As a conse...

Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project.

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Official information on Land Use Land Cover is essential for mapping wildland–urban interface (WUI) zones. However, these resources do not always provide the geometrical or thematic accuracy required to delimit buildings that are easily exposed to risk of wildfire at the appropriate scale. This research shows that the integration of active remote sensing and official Land Use Land Cover (LULC) databases, such as the Spanish Land Use Land Cover information system (SIOSE), creates the synergy capable of achieving this. An automated method was developed to detect WUI zones by the massive geoprocessing of data from official and open repositories of the Spanish national plan for territory observation (PNOT) of the Spanish national geographic institute (IGN), and it was tested in the most important metropolitan zones in Spain: Barcelona and Madrid. The processing of trillions of LiDAR data and their integration with thousands of SIOSE polygons were managed in a Linux environment, with libraries for geographic processing and a PostgreSQL database server. All this allowed the buildings that are exposed to wildfire risk with a high level of accuracy to be obtained with a methodology that can be applied anywhere in the Spanish territory.