Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively

ARMONIZZAZIONE E CONDIVISIONE INTEROPERABILE DI DATI GEOSPAZIALI MULTI TEMPORALI PER LA GESTIONE DEL PAESAGGIO RURALE

Recently, practitioners and researchers dealing with rural landscape analysis and characterization are facing the challenge of managing and sharing huge amount of geospatial data and information. Thanks to the internet diffusion and speed, it is easier to share data in the World Wide Web. It is worth highlighting that the data sharing process can improve participatory planning processes. Moreover, is also allows an easy comparison among different landscape areas. Sharing can be done with varying degrees of interoperability and different software tools, proprietary as well as free and open source (FOSS). A widespread way to share geospatial data and metadata is by Spatial Data Infrastructures (SDIs) taking advantages on the use of Open Geospatial Consortium (OGC) standards. By the way, data sharing through OGC services lack in data harmonization and in semantic enablement, thus making difficult compare, search and analyze data given by different sources. Different data schemas and linguistic barrier hinder the usefulness of data obtained from different sources. To overcome these limitations, in this study we show a novel data workflow implemented for sharing in an interoperable, harmonized and semantically enriched way, multi-temporal land cover (LC) datasets collected in a previous landscape characterization researches

ARMONIZZAZIONE E CONDIVISIONE INTEROPERABILE DI DATI GEOSPAZIALI MULTI TEMPORALI PER LA GESTIONE DEL PAESAGGIO RURALE

Recently, practitioners and researchers dealing with rural landscape analysis and characterization are facing the challenge of managing and sharing huge amount of geospatial data and information. Thanks to the internet diffusion and speed, it is easier to share data in the World Wide Web. It is worth highlighting that the data sharing process can improve participatory planning processes. Moreover, is also allows an easy comparison among different landscape areas. Sharing can be done with varying degrees of interoperability and different software tools, proprietary as well as free and open source (FOSS). A widespread way to share geospatial data and metadata is by Spatial Data Infrastructures (SDIs) taking advantages on the use of Open Geospatial Consortium (OGC) standards. By the way, data sharing through OGC services lack in data harmonization and in semantic enablement, thus making difficult compare, search and analyze data given by different sources. Different data schemas and linguistic barrier hinder the usefulness of data obtained from different sources. To overcome these limitations, in this study we show a novel data workflow implemented for sharing in an interoperable, harmonized and semantically enriched way, multi-temporal land cover (LC) datasets collected in a previous landscape characterization researches. DOI: http://dx.medra.org/10.19254/LaborEst.17.0

Sensor-based pavement diagnostic using acoustic signature for moduli estimation

AbstractThe diffusion of smart infrastructures for smart cities provides new opportunities for the improvement of both road infrastructure monitoring and maintenance management.Often pavement management is based on the periodic assessment of the elastic modulus of the bound layers (i.e., asphalt concrete layers) by means of traditional systems, such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD). Even if these methods are reliable, well-known, and widespread, they are quite complex, expensive, and are not able to provide updated information about the evolving structural health condition of the road pavement. Hence, more advanced, effective, and economical monitoring systems can be used to solve the problems mentioned above.Consequently, the main objective of the study presented in this paper is to present and apply an innovative solution that can be used to make smarter the road pavement monitoring. In more detail, an innovative Non-Destructive Test (NDT)-based sensing unit was used to gather the vibro-acoustic signatures of road pavements with different deterioration levels (e.g. with and without fatigue cracks) of an urban road. Meaningful features were extracted from the aforementioned acoustic signature and the correlation with the elastic modulus defined using GPR and FWD data was investigated.Results show that some of the features have a good correlation with the elastic moduli of the road section under investigation. Consequently, the innovative solution could be used to evaluate the variability of elastic modulus of the asphalt concrete layers, and to monitor with continuity the deterioration of road pavements under the traffic loads

TELERILEVAMENTO MULTISPETTRALE DA DRONE PER IL MONITORAGGIO DELLE COLTURE IN AGRICOLTURA DI PRECISIONE. UN’APPLICAZIONE ALLA CIPOLLA ROSSA DI TROPEA

Recently, the International Society for Precision Agriculture (ISPA) defined Precision Agriculture (PA) as ‘a management strategy that gathers, processes and analyses temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production’. In the framework of PA the present paper shows a specific case study applied to the red onion of Tropea (Cipolla Rossa di Tropea) crop. The aim was the monitoring of fields using multispectral imagery acquired by a fixed-wings UAV, and through the use of different vegetation indexes. Multitemporal surveys were carried out using the eBee fixed-wing UAV, equipped with a multispectral camera Sequoia Parrot (R-G-RedEdge-NIR). UAV MS imagery were calibrated using a panel with known reflectance and verified with spectroradiometer measurements using the Apogee Ps-300 on bare soil and vegetation. The UAV monitoring has been implemented on three surveys carried out from November 2018 to January 2019. The results of the analysis of the three datasets showed a high correlation of GNDVI and NDVI vegetation indexes with SAVI. Therefore, the latter was chosen to analyse the vegetative vigour by applying the VI to onion crop’s masks extracted after segmentation and classification of the three images by a geographical object-based image classification (GEOBIA). The obtained results are promising although additional experiments are expected

ANALISI DI LUNGO PERIODO DELLA TRASFORMAZIONE DEL PAESAGGIO FORESTALE NELL’AREA METROPOLITANA DI ROMA CAPITALE A SUPPORTO DELLA GOVERNANCE DEL TERRITORIO PER LA TRANSIZIONE ECOLOGICA

Restoring the forest ecosystem’s functionality is as an urgent action for biodiversity conservation and carbon mitigation as well as for achieving the 2030 Agenda of United Nations sustainability goals. By developing a landscape dynamics framework to guide future management and planning policies we characterised the historical trend of forest area changes from 1936 to 2010 in the Metropolitan City of Rome Capital (Italy). Remote sensing-based products and historical forest maps, coupled with landscape pattern metrics and fragmentation analysis have been implemented. Two main forest landscape dynamics were reconstructed: I) the increase of forest cover fragmentation in the lowland areas; (II) the rise in forest area by recently established forest in the interior sectors of the mountain landscape, mainly within protected areas. Results revealed the urgent need to establish new protected areas and rewilding spaces. The proposed framework can be used for testing the effectiveness of environmental planning and management in other forest landscapes to achieve the Agenda 2030 goals and EU 2030 Biodiversity Strategy

Il trattamento del gozzo immerso. La nostra esperienza

Gli Autori, dopo avere accennato alla storia naturale del gozzo immerso, riferiscono la loro esperienza di tale patologia maturata negli ultimi 5 anni, sottolineando i caratteri della complessa sintoma - tologia osservata nei vari casi, la condotta terapeutica seguìta, i buoni risultati ottenuti. Si soffermano quindi ad elencare le molteplici classificazioni, via via proposte. Illustrano le complesse situazioni sia di ordine emodina - mico che respiratorio di particolare interesse anestesiologico. Discutono, infine, sulla diagnostica e soprattutto sul corretto atteggiamento terapeutico il cui obiettivo è duplice: risolvere la sinto - matologia prodotta dalla massa mediastinica ed escludere la possibi - lità di recidiv

Isolated liver disease in a patient with a CFTR genotype F508del/12TG-5T and 470MV: A new face of an old disease

Today the knowledge of genotype-phenotype correlation in cystic fibrosis is enriched by the growing discoveries of new mutations of the CFTR gene. Although the combination of two severe mutations usually leads to the classic disease (pulmonary and pancreatic insufficiency, sterility, nasal polyposis), the presence of a complex genotype characterized by severe and milder mutations or polymorphism can cause a hidden disease, which is often asymptomatic at early ages. We report on a case of a 15 years old boy, in whom the only clinical signs of CF were chronic hypertransaminasemia and hyperbilirubinemia, and in whom it was demonstrated the presence of the mutations F508del associated with TG11-9T-470M in one allele and TG12-5T-470V in the other allele. Although a clear genotype-phenotype correlation for liver disease is still missing for CF patients, it is possible to state that this isolated clinical presentation could represent an unusual phenotype of CF, related to a complex genotype characterized by a severe mutation and one (or more) polymorphism

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively

A Multitemporal Fragmentation-Based Approach for a Dynamics Analysis of Agricultural Terraced Systems: The Case Study of Costa Viola Landscape (Southern Italy)

Among landscapes, rural landscapes are important because they simultaneously present functional and cultural aspects. Rural landscapes are often created to modify the Earth’s surface according to different human needs and, among these modifying activities, terracing has significant anthropogenic effect. A multitemporal approach that covers a significant time interval is crucial for monitoring the integrity and cultural value of historical rural landscapes. The present research aims to study the fragmentation dynamics detectable in an active agricultural terraced system of historical and cultural relevance over a considerable time interval, by conducting a morphological spatial pattern analysis (MSPA). We analysed a period of about 60 years, from 1955 to 2014, considering five intermediate years (1976, 1989, 1998, 2008, and 2012) and investigated the dynamics that occurred. We detected a trend of abandonment of agricultural terraces, with a reduction in area from 813.25 ha (in 1955) to 118.79 ha (in 2014). The MSPA results showed a decrease in core areas, the most stable pattern, and an increase in the relative importance of other less stable classes. Moreover, we highlighted two different fragmentation dynamics, i.e., one between 1955 and 1976 and the other between 1998 and 2008