Exploring forest infrastructures equipment through multivariate analysis: complementarities, gaps and overlaps in the Mediterranean basin

The countries of the Mediterranean basin face several challenges regarding the sustainability of forest ecosystems and the delivery of crucial goods and services that they provide in a context of rapid global changes. Advancing scientific knowledge and foresting innovation is essential to ensure the sustainable management of Mediterranean forests and maximize the potential role of their unique goods and services in building a knowledge-based bioeconomy in the region. In this context, the European project FORESTERRA ("Enhancing FOrest RESearch in the MediTERRAnean through improved coordination and integration”) aims at reinforcing the scientific cooperation on Mediterranean forests through an ambitious transnational framework in order to reduce the existing research fragmentation and maximize the effectiveness of forest research activities. Within the FORESTERRA project framework, this work analyzed the infrastructures equipment of the Mediterranean countries belonging to the project Consortium. According to the European Commission, research infrastructures are facilities, resources and services that are used by the scientific communities to conduct research and foster innovation. To the best of our knowledge, the equipment and availability of infrastructures, in terms of experimental sites, research facilities and databases, have only rarely been explored. The aim of this paper was hence to identify complementarities, gaps and overlaps among the different forest research institutes in order to create a scientific network, optimize the resources and trigger collaborations

An Active Exoskeleton Called P.I.G.R.O. Designed for Unloaded Robotic Neurorehabilitation Training

The development of innovative robotic devices allows the design of exoskeletons for robotic neurorehabilitation training. This paper presents the active exoskeleton called pneumatic interactive gait rehabilitation orthosis (P.I.G.R.O.), developed by the authors. The main innovative characteristic of this prototype is its design for fully unloaded robotic neurorehabilitation training, specific for brain-injured patients. It has six degrees of freedom (DOF) in the sagittal plane, an active ankle joint (removable if it is required); a wide range of anthropometric regulations, both for men and for women; a useful human machine interface (HMI); and an innovative harness system for the patient for the unloaded training. It is realized using light and strong materials, and it is electropneumatically controlled. In particular the authors also studied and defined some innovative input control curves useful for the unloaded training. In this paper, the main characteristics and innovations of P.I.G.R.O. are presented

PRELIMINARY STRUCTURAL ANALYSIS OF AN ACTIVE EXOSKELETON FOR ROBOTIC NEURO-REHABILITATION

In this paper the exoskeleton P.I.G.R.O. (Pneumatic Interactive Gait Rehabilitation Orthosis), developed in the Department of Mechanical and Aerospace Engineering (DIMEAS) Politecnico di Torino with the important co-operation with doctors, is presented. It was preliminary designed for a completely unloaded walking gait cycle in order to treat the first steps of the neurorehabilitation trainings. An initial FEM evaluation of P.I.G.R.O. structure is here presented. It underlines a lot of important aspects and techniques to analyse the structural characteristics of P.I.G.R.O. legs rigid parts using a commercial software but analysing both the actions of the pneumatic actuators and of the patients muscles and/or movements. The results obtained are good and allow to verify the P.I.G.R.O. legs structure and to establish a procedure to study its characteristics also with the presence of the patien

Monitoring the last Apennine glacier: recent in situ campaigns and modelling of Calderone glacial apparatus

The Calderone glacier is at present the most southern glacier in Europe (42° 28' 15’’ N). The little apparatus (about 20.000 m2 in surface area) has been giving an interesting response both to short- and long-term climatic variations which resulted in a considerable reduction in surface area and volume. The glacial apparatus is split into two ice bodies (glacierets) since 2000. The two glacierets are located in a deep northward valley below the top of the Corno Grande (2912 m asl) in the centre of the Gran Sasso d’Italia mountain range (Central Italy). Such glacial apparatus has been subjected to a strong reduction, with a loss of total surface area of about 50% and thickness of about 65%with respect to the hypothetical size (about 105.00 m2 and 55 m at the Little Ice Age). Since early 90s the Calderone glacier has been subjected to several multidisciplinary field campaigns to monitor and evaluate its role as an environmental indicator in the framework of global warming. Starting from historical series related to more than a century of records, the variability of the different glacier properties has been estimated by using classical geomorphologic methods as well as in situ and remote sensing techniques. In particular, the last field campaigns, in 2015, 2016 and 2019, have been carried out using Ground Penetrating Radar equipped with different antenna frequencies, drone-based survey, snow pit measurements and chemical-physical sampling. The measurement campaigns have been complemented by a regional climate analysis, spanning the last fifty years, and snowpack modelling initialized with microphysical snow data (e.g., snow density, crystal shape and size, hardness). The snowpack chemical analyses include the main and trace elements, soluble inorganic and organic ions, EC/OC and PAH, with different spatial resolution depending on the analytes. We present here the methodological approach used and some preliminary results

Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly di°cult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979-2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network

On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library

The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using “NDVI” AND “LAI” AND “crop” as the search string, focusing on the period 2017–2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion

On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library

The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using “NDVI” AND “LAI” AND “crop” as the search string, focusing on the period 2017–2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion