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The soil microbiota is recognized as key player in both diversity and productivity of terrestrial ecosystems and it is mandatory to include its role in soil quality evaluation. The bacterial communities in soils vulnerable to desertification can undergo perturbations by various human activities, such as unsustainable land use and management, and by climate change. Alteration in microbial diversity and composition are sentinels of the impact of environmental changes on soil quality. Within the LIFE Desert-Adapt project we analyze the soil bacterial diversity and composition across European Soils threatened by desertification in the southern Mediterranean regions (Italy, Spain and Portugal) and evaluate the influences on soil bacteria communities of five different land use type (Broad-leaved agroforestry, conifer forest, cropland, pasture-grassland, shurubland). Bacterial diversity was analyzed by the Automated Ribosomal Intergenic Spacer Analysis (ARISA), Microbial Biomass was estimated as total extracted dsDNA. The Illumina MiSeq sequencing technology was applied to determine the community composition. The bacterial diversity was related with land-use, Richness and Shannon index varied across land uses, although only croplands and shrublands significantly differed. The microbial biomass was also affected by land use and were highest in the conifer forests land and lowest in the cropland soils. Actinobacteria, Proteobacteria Acidobacteria, Firmicutes, Bacteroidetes, Chloroflexi and Verrucomicrobia phyla were detected in all countries with relative abundance variable across land use type. The firmicutes was abundant in cropland and grassland soil with low C and N content and neutral pH 7, the Chloroflexi was most abundant in shrubland soil with high C content and acid pH. Actinobacteria was abundant in conifer, shrubland and cropland with basic pH, and a high CEC and carbon content, while the Proteobacteria was abundant in all land use type. Within the same phyla, the genera abundance was influenced differently by soil chemical- physical variables. We analyze relationships between land use and the soil microbiota, and create the baseline for the monitoring of European soils facing climate change and desertification phenomena. The identification of soil management-sensitive bacterial taxa could have great potential as bioindicators for soil quality and loss of functionality associated with land use.

Increasing scientific evidences indicate that agroforestry is a land use that can support economic, social and environmental sustainability, thus promoting resistance and resilience towards ongoing climate changes. The implementation and management of agroforestry systems still face social, economic and political barriers. Mediterranean agroecosystems suffer from increasingly negative climate change effects and urgent measures are needed to improve their rural economy while protecting soils and the environment. In this context, the LIFE Project Desert-Adapt: “Preparing desertification areas for increased climate change” (http://www.desert-adapt.it), aims to demonstrate the feasibility of innovative climate adaptation strategies and measures, based on the agroforestry concept, over 1000 hectares at risk of desertification in Italy, Spain and Portugal. The core of the project is the Desert Adaptation Model (DAM), an integrative development model fitted to the specific requirements of each farm which guide the implementation of sustainable agroforestry systems (e.g. montado, dehesa). The results will be evaluated through an array of multidisciplinary social, economic and environmental indicators. The project will deal with the most common conceptual, technical, and legal constraints encountered along the implementation and management of agroforestry systems in marginal lands and its results will fill the gap between farmers’ needs and policy makers at local and global level.

In the cultural heritage field, the identification within point clouds or meshes of various architectural elements rather than the distinction of materials or states of preservation can become valuable tools for studying the objects with different purposes. However, if we consider the wide variety and complexity that heritage case studies feature, the application of automatic classification procedures becomes a really challenging task. Within this research landscape, the main goal of the PhD was to develop, test and validate reliable and automatic procedures for the classification of architectural and archaeological 3D data (point clouds or polygonal mesh models coming from photogrammetric processing or laser scanning surveying). To achieve this goal, two different approaches have been developed based on texture or geometric information. They allow to: • characterize different constructing techniques; • detect existing restoration evidence; • quantify different states of conservation and materials; • identify and distinguish structural and decorative architectural elements.

Through this White Paper, which gathers contributions from experts of 3D data as well as professionals concerned with the interoperability and sustainability of 3D research data, the PARTHENOS project aims at highlighting some of the current issues they have to face, with possible specific points according to the discipline, and potential practices and methodologies to deal with these issues.During the workshop, several tools to deal with these issues have been introduced and confronted with the participants experiences, this White Paper now intends to go further by also integrating participants feedbacks and suggestions of potential improvements.Therefore, even if the focus is put on specific tools, the main goal is to contribute to the development of standardized good practices related to the sharing, publication, storage and long-term preservation of 3D data.

Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

A study was started aiming at assessing the suitability of zeolitized tuff as optimal mineral Human Transported Materials (HTMs) in pedotechnologies for quarry restoration.

A model has been developed for the interpretation of the complexity of pedological systems; this is referred to as “pedocharacter”. The main aim of the model was to reduce the variables able to define soils and their relationships with the environment through the following quali-quantitative approach: i) definition of a fair number of qualitative characters; and ii) development of an analytic function, defined as “Land Relevance of the Factor”.

A research was started aiming at evaluating the possible use of natural zeolites as exchange conditioners to improve and make durable the soil resilience against the adverse effects of the use of anomalous wastewater, for irrigation purposes. To satisfy such aims, two zeolitized tuffs (ZTs), viz. a Neapolitan yellow tuff (NYT) and a clinoptilolite bearing tuff (ZCL), were tested as pedotechnical materials to improve soil resilience against the impact of treatment by a ‘dirty’ municipal wastewater (DMW).