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The biological diversity of forests and of other natural or semi-natural environments is threatened by human induced habitat loss and fragmentation. These changes to forest spatial patterns may impact on their ecological functions and alter their ability to resist and respond to external disturbing factors. A number of indicators have been proposed to assess changes to the spatial patterns of forests but relatively few evaluate the ecological impact of such changes. This paper present results from a project to link and harmonise the analyses of forests spatial patterns at European, National and Regional scales, and improve the ecological evaluation of forests vulnerability and resilience. The work is based on the use of GIS fuzzy Multi Criteria Evaluation (MCE) modelling to relate changes to forest spatial patterns with the ecological functions. The results are presented for seven study areas from five different biogeographical regions across Europe, evaluating the impact of changes to forest patterns on five species specific models (one butterfly: Apatura ilia; one small mammal: Martes martes; one bird: Picoides minor; and two large mammals: Canis lupus and Capreolus capreolus), on one species-unspecific model and for two spatial scales. The results demonstrate that the proposed method may be operationally applied. In general, the aggregation of the species-specific models tend to have the same temporal trend as the unspecific model and for this reason it may be considered as a simple model for an operative application at pan-European level. However, the results are scale-dependent and demonstrates the necessaity for high resolution multi-temporal forest maps. Moreover, further speciesspecific information is need to support ecological modeling to effectively evaluate the impact of changes to the spatial pattern of forests on target species., JRC.DDG.H.7-Land management and natural hazards

The European Commission published in 2015 “Energy Union Strategy” to make energy production safer, more sustainable, competitive, and economical. To allow citizens of a community to cooperate to consume and exchange renewable energy they produce, the use of blockchain technology seems natural. In this note, we will start from a concrete case study to see what the technological challenges (and not only) are to be overcome. Furthermore, in the last sections, it will be shown how the creation of the IT infrastructure necessary for the exchange of energy produced can also be used for other purposes. That is according to the smart city paradigm to create a community able to make the most of the touristic, environmental, and social resources of the territory in which it lives.

Data uncertainty due to spatial gaps and heterogeneity is a fundamental problem in conservation and environmental planning. Thus, investigation of issues related to data uncertainty contributes to more efficient conservation plans. We evaluated the uncertainty of data related to forest diversity descriptors using a diffusion-based cartogram approach that visually displays how data information change in function with respect to degree of uncertainty. We used ground vegetation data for 3093 plots collected as part of the BioSoil project through the ICP Forests Level I network and stored in the LI-BioDiv database. For each plot, we assigned an uncertainty value based on the survey season and the mean monthly temperature for the survey period. The density-equalizing map or cartogram highlights that data collected in Spain, the United Kingdom and the German federal states of Berlin and Brandenburg have smaller values of species richness corresponding to larger values of uncertainty. We found that an awareness of the negative relationship between the survey period and species richness can lead to improved data handling and analysis. We demonstrated that cartograms are efficient tools for evaluating and managing uncertainty and can strengthen the results of data analysis by providing alternative perspectives and interpretations of spatial phenomena.

Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is over- all true when considering global scales, since human-induced global change is expected to make signicant changes on the Earth's biota. Biodiversity management and planning is mainly based on eld ob- servations related to community diversity, considering dierent taxa. However, such methods are time and cost demanding and does not allow in most cases to get temporal replicates. In this view, remote sensing can provide for a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly taking into account dierences in a neighborhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coecient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be benecial in order to plan more ecient conser- vation practices starting from spectral diversity hotspots in space andtime. In this paper we delivered a highly reproducible approach to cal- culate spatio-temporal diversity in a robust and straightforward man- ner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers. &nbsp