Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires

We would like to thank the ReCAS Computing Center of the University of Bari, and, particularly, Stefano Nicotri and Giacinto Donvito for the use of facilities; in particular their Jupiter online access to the virtual environment for computation. The manuscript was proofread by Lena Rettori. We would like to thanks the contribution of the two anonymous reviewers.Vegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2.We would like to acknowledge the support of H2020 Ecopotential project with Grant Agreement No. 641762 for the discussion and the set up of a first version of the algorithm not shown in this paperGeoessential an ERA-PLANET project, an action from ERA-NET-Cofund Grant, with Grant Agreement No. 689443 for the actual development of the algorithm and the writing of the paper

<i>Ailanthus altissima</i> mapping from multi-temporal very high resolution satellite images

This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ?91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User\u27s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision

An Interactive WebGIS Framework for Coastal Erosion Risk Management

The Italian coastline stretches over about 8350 km, with 3600 km of beaches, representing a significant resource for the country. Natural processes and anthropic interventions keep threatening its morphology, moulding its shape and triggering soil erosion phenomena. Thus, many scholars have been focusing their work on investigating and monitoring shoreline instability. Outcomes of such activities can be largely widespread and shared with expert and non-expert users through Web mapping. This paper describes the performances of a WebGIS prototype designed to disseminate the results of the Italian project Innovative Strategies for the Monitoring and Analysis of Erosion Risk, known as the STIMARE project. While aiming to include the entire national coastline, three study areas along the regional coasts of Puglia and Emilia Romagna have already been implemented as pilot cases. This WebGIS was generated using Free and Open-Source Software for Geographic information systems (FOSS4G). The platform was designed by combining Apache http server, Geoserver, as open-source server and PostgreSQL (with PostGIS extension) as database. Pure javascript libraries OpenLayers and Cesium were implemented to obtain a hybrid 2D and 3D visualization. A user-friendly interactive interface was programmed to help users visualize and download geospatial data in several formats (pdf, kml and shp), in accordance with the European INSPIRE directives, satisfying both multi-temporal and multi-scale perspectives. © 2021 by the authors. Licensee MDPI, Basel, Switzerlan

Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy

Grassland ecosystems can provide a variety of services for humans, such as carbon storage, food production, crop pollination and pest regulation. However, grasslands are today one of the most endangered ecosystems due to land use change, agricultural intensification, land abandonment as well as climate change. The present study explores the performance of a knowledge-driven GEOgraphic-Object—based Image Analysis (GEOBIA) learning scheme to classify Very High Resolution(VHR)imagesfornaturalgrasslandecosystemmapping. Theclassificationwasappliedto a Natura 2000 protected area in Southern Italy. The Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme was instantiated in the learning phase of the algorithm. Four multi-temporal WorldView-2 (WV-2) images were classified by combining plant phenology and agricultural practices rules with prior-image spectral knowledge. Drawing on this knowledge, spectral bands and entropy features from one single date (Post Peak of Biomass) were firstly used for multiple-scale image segmentation into Small Objects (SO) and Large Objects (LO). Thereafter, SO were labelled by considering spectral and context-sensitive features from the whole multi-seasonal data set available together with ancillary data. Lastly, the labelled SO were overlaid to LO segments and, in turn, the latter were labelled by adopting FAO-LCCS criteria about the SOs presence dominance in each LO. Ground reference samples were used only for validating the SO and LO output maps. The knowledge driven GEOBIA classifier for SO classification obtained an OA value of 97.35% with an error of 0.04. For LO classification the value was 75.09% with an error of 0.70. At SO scale, grasslands ecosystem was classified with 92.6%, 99.9% and 96.1% of User’s, Producer’s Accuracy and F1-score, respectively. The findings reported indicate that the knowledge-driven approach not only can be applied for (semi)natural grasslands ecosystem mapping in vast and not accessible areas but can also reduce the costs of ground truth data acquisition. The approach used may provide different level of details (small and large objects in the scene) but also indicates how to design and validate local conservation policies

Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 x 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 x 5 patch sizes are used and then ConvNet performance starts decreasing

Posterolateral arthrodesis in lumbar spine surgery using autologous platelet-rich plasma and cancellous bone substitute: an osteoinductive and osteoconductive effect

Study Design Prospective cohort study. Objectives To analyze the effectiveness and practicality of using cancellous bone substitute with platelet-rich plasma (PRP) in posterolateral arthrodesis. Methods Twenty consecutive patients underwent posterolateral arthrodesis with implantation of cancellous bone substitute soaked with PRP obtained directly in the operating theater on the right hemifield and cancellous bone substitute soaked with saline solution on the right. Results Computed tomography scans at 6 and 12 months after surgery were performed in all patients. Bone density was investigated by comparative analysis of region of interest. The data were analyzed with repeated-measures variance analyses with value of density after 6 months and value of density after 12 months, using age, levels of arthrodesis, and platelet count as covariates. The data demonstrated increased bone density using PRP and heterologous cancellous block resulting in an enhanced fusion rate during the first 6 months after surgery. Conclusions PRP used with cancellous bone substitute increases the rate of fusion and bone density joining osteoinductive and osteoconductive effect

A frailty index predicts post-liver transplant morbidity and mortality in HIV-positive patients

Background: We hypothesized that frailty acts as a measure of health outcomes in the context of LT. The aim of this study was to explore frailty index across LT, as a measure of morbidity and mortality. This was a retrospective observational study including all consecutive 47 HIV+patients who received LT in Modena, Italy from 2003 to June 2015. Methods: frailty index (FI) was constructed from 30 health variables. It was used both as a continuous score and as a categorical variable, defining 'most frail' a FI > 0.45. FI change across transplant (deltaFI, \uce\u94FI) was calculated as the difference between year 1 FI (FI-Y1) and pre-transplant FI (FI-t0). The outcomes measures were mortality and "otpimal LT" (defined as being alive without multi-morbidity). Results: Median value of FI-t0 was 0.48 (IQR 0.42-0.52), FI-Y1 was 0.31 (IQR 0.26-0.41). At year five mortality rate was 45%, "optimal transplant" rate at year 1 was 38%. All the patients who died in the post-LT were most frail in the pre-LT. \uce\u94FI was a predictor of mortality after correction for age and MELD (HR = 1.10, p = 0.006) and was inversely associated with optimal transplant after correction for age (HR = 1.04, p = 0.01). Conclusions: We validated FI as a valuable health measure in HIV transplant. In particular, we found a relevant correlation between FI strata at baseline and mortality and a statistically significant correlation between, \uce\u94FI and survival rate

Advances in the monitoring of geo-structure subjected to climate loading

The paper presents results achieved within the project MAGIC, a project funded by the European Commission under the Marie-Curie Industry Academia Partnerships and Pathways (IAPP) scheme. The project MAGIC aims to advance the state-of-the art in the monitoring of geo-structures subjected to climate loading by filling some of the gaps in current monitoring technologies. The project involves a partnership between academic and industrial partners to boost knowledge transfer and promote the development of ‘industrial’ instruments and services. The paper presents developments concerning the measurement of pore-water tension (suction in excess of 100 kPa) and the integration of geotechnical and geophysical monitoring