KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS

In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone. In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13 7 13 pixels kernel with a simplified lin ear-feature representation of a cylinder is applied at different rotation angles (from 0\ub0 to 170\ub0 at 10\ub0 steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (mr), support vector machines (SVM) with linear kernel and random forests. The first two methods gave the best results. The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from 3c1.8 7 102 m3 to 3c1.2 7 104 m3. Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m3 and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow

An Approach to Model Resources Rationalisation in Hybrid Clouds through Users Activity Characterisation

In recent years, some strategies (e.g., server consolidation by means of virtualisation techniques) helped the managers of large Information Technology (IT) infrastructures to limit, when possible, the use of hardware resources in order to provide reliable services and to reduce the Total Cost of Ownership (TCO) of such infrastructures. Moreover, with the advent of Cloud computing, a resource usage rationalisation can be pursued also for the users applications, if this is compatible with the Quality of Service (QoS) which must be guaranteed. In this perspective, modern datacenters are “elastic”, i.e., able to shrink or enlarge the number of local physical or virtual resources from private/public Clouds. Moreover, many of large computing environments are integrated in distributed computing environment as the grid and cloud infrastructures. In this document, we report some advances in the realisation of a utility, we named Adaptive Scheduling Controller (ASC) which, interacting with the datacenter resource manager, allows an effective and efficient usage of resources, also by means of users jobs classification. Here, we focus both on some data mining algorithms which allows to classify the users activity and on the mathematical formalisation of the functional used by ASC to find the most suitable configuration for the datacenter’s resource manager. The presented case study concerns the SCoPE infrastructure, which has a twofold role: local computing resources provider for the University of Naples Federico II and remote resources provider for both the Italian Grid Infrastructure (IGI) and the European Grid Infrastructure (EGI) Federated Cloud

Studies on ''corky rugose wood'' of grapevine and on the diagnosis of grapevine virus B

Vines affected by corky rugose wood (CRW), a field syndrome characterized by pronounced cork production by the scion of several grapevine varieties just above the graft union, contain a number of filamentous and isometric phloem-limited viruses, such as grapevine leafroll-associated virus 1, 2, and 3 (GLRaV-1, GLRaV-2, GLRaV-3), grapevine virus A and B (GVA and GVB), and grapevine fleck virus (GFkV). However, the same viruses, with the exception of GVB, are widely represented also in vines with rugose wood without excessive corkyness. Although GVB was found in all vines indexing positive in LN 33 for corky bark disease, iis occurrence in CRW-affected vines was not consistent enough to suggest that it may have a determining role in the induction of this syndrome. Monoclonal antibodies to GVB raised previously were characterized and their possible use for reliable detection of GVB in field-grown vines investigated in detail. A triple antibody sandwich ELISA protocol that under our experimental conditions afforded consistent and repeatable results, was based on the use of crude cortical scraping extracts from mature canes collected in autumn, antibodies from a polyclonal antiserum for plate coating (trapping) and a monoclonal antibody for antigen detection

Embryo Culture, In Vitro Propagation, and Molecular Identification for Advanced Olive Breeding Programs

The high biodiversity of the olive tree is an important opportunity to develop sustainable plans to control Xylella fastidiosa (X) through breeding programs. Olive tree breeding activities have been limited due to various features of this species including the long time required for seed germination caused by the inhibition effect of the woody endocarp, the seed integument, and the endosperm. Starting from F1 seeds by cross-breeding, the embryo culture was compared with traditional seed germination, evaluating the effectiveness of in vitro multiplication of the plantlets for large-scale production. The isolated embryos were established on a new medium based on Rugini ‘84 macroelements, Murashige & Skoog ‘62 microelements, with Nitsch J. P. & Nitsch C. ‘69 vitamine and subcultured on Leva MSM modified. The results obtained confirmed that in vitro culture of olive embryos is a valid tool for increasing the percentage and speed of germination, helping to reduce the time of the olive breeding programs, offering the possibility to effectively propagate plantlets for further experiments

Assessment of Forest Biomass and Carbon Stocks at Stand Level Using Site-Specific Primary Data to Support Forest Management

To quantify and map woody biomass (WB) and forest carbon (C) stocks, several models were developed. They differ in terms of scale of application, details related to the input data required and outputs provided. Local Authorities, such as Mountain Communities, can be supported in sustainable forest planning and management by providing specific models in which the reference unit is the same as the one reported in the Forest Management Plans (FMP), i.e. the forest stand. In the Lombardy Region (Northern Italy), a few studies were performed to assess WB and forest C stocks, and they were generally based on data coming from regional\u2014or national\u2014forest inventories and remote sensing, without taking into account data collected in the FMPs. For this study, the first version of the stand-level model \u201cWOody biomass and Carbon ASsessment\u201d (WOCAS) for WB and C stocks calculation was improved into a second version (WOCAS v2) and preliminary results about its first application to 2019 forest stands of Valle Camonica District (Lombardy Region) are presented. Since the model WOCAS uses the growing stock as the main driver for the calculation, it can be applied in any other forest area where the same input data are available