Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale.

Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest. We generated point clouds and multispectral orthomosaics from UAS through photogrammetric processes. These were used to automatically delineate individual tree crowns and calculate vegetation indices such as the normalized difference vegetation index (NDVI) and excess green index (ExG) to objectively quantify defoliation of trees previously identified. Overall, our research suggests that UAS imagery and its derived products enable robust estimation of tree crowns with acceptable accuracy and the assessment of tree defoliation by classifying trees along a gradient from completely defoliated to non-defoliated automatically with 81.8% overall accuracy. The promising results presented in this work should inspire further research and applications involving a combination of methods allowing the scaling up of the results on multispectral imagery by integrating satellite remote sensing information in the assessments over large spatial scales.

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.

According to the European Data Strategy established in 2020, the European Open Science Cloud (EOSC) is described as “the basis for a science, research and innovation data space… and will be connected and articulated with the sectoral data spaces”, being one of the 14 European Common Data Spaces. While current cross-sectoral interactions with the EOSC are realised through the association’s Health Data Task Force, advancements in other EU priorities, such as the Green Deal and the Digital Strategy, should be accelerated in the green and digital transitions and integrated by reinforcing each other to be climate-neutral by 2050. With this motivation, this paper systematically reviews data spaces and digital twins (DTs) within the context of research and innovation. Specifically, focusing on the relevance of the EOSC to the Green Deal Data Space (GDDS) and DTs of the Earth, the relationship between them is explored using a topic search with various keyword combinations in the Web of Science and CORDIS databases. Based on the selected scientific articles and projects, collaboration opportunities are mapped to connect relevant stakeholders. Furthermore, existing and developing service components that could contribute to technical building blocks for the GDDS are identified. In summary, key findings are highlighted, addressing the current gaps and opportunities among the GDDS initiatives presented in this review.

The increasing involvement of citizens in scientific projects over the last decade is another critical factor that has encouraged Open and FAIR data. Citizen science is in fact one of the eight priorities of the European Open Science Agenda (2018), along with the establishment of the European Open Science Cloud (EOSC) enabling a federation of multidisciplinary research infrastructures.Until now, citizen science projects and platforms, also known as Citizen Observatories (COs) in Europe, are yet to be considered among the research sector in the EOSC ecosystem. With the ambition of overcoming this challenge, the Cos4Cloud (Co-designed Citizen Observatories Services for the EOS-Cloud) project was the first ‘Enabling an operational, open and FAIR EOSC ecosystem (INFRAEOSC)’ project to include citizen science as a core part of research infrastructure.Specifically, the Cos4Cloud aimed to integrate citizen science in the EOSC through co-designing innovative services to support widely used COs in biodiversity and environmental monitoring. To make COs interoperable, the services adopted internationally recognized standards such as SensorThings API and Darwin Core. As a result, over 30 interoperability experiments have been reported in various combinations among the new services and existing COs during the project period; some of which are now offered in the EOSC Marketplace following open and FAIR principles. The Cos4Colud has also demonstrated that the services combined with AI technologies and robust algorithms could improve COs by leveraging the data quality to the research grade.We thus expect more resources and services derived from COs to be reused in the EOSC ecosystem, eventually enabling to establish its own thematic cluster for citizen science in the research infrastructure as well as facilitate the reuse of data by other researchers. We conclude with the key role of such diverse scientific communities enriched in the EOSC that may create a bridge among researchers, citizens and decision-makers. (This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 863463.)