1,721,023 research outputs found
Possibile uso dell’etilene da ferita, prodotto dalle foglie di Cistus monspeliensis e Pistacia lentiscus, come indicatore biochimico dello stress idrico per la vegetazione mediterranea in condizioni naturali
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Dynamics of the anthocyanins in "Cannonau" grapevine as related to the environmental conditions
No full textTerrestrial Laser Scanning (TLS) for tree structure studies: a review of methods for wood-leaf classifications from 3D point clouds
No full textIn the last decades new remote sensing and proximal sensing techniques have been developed for forest monitoring and vegetation mapping application. In this context LiDAR systems are considered one of the most accurate remote/proximal sensing technologies. Ground-based LiDAR systems (Terrestrial Laser Scanner-TLS) have emerged, in the last twenty years, as valid alternatives to traditional ground-based forest inventory techniques. It represents a non-destructive approach to estimate canopy and stem volume and biomass-related parameters with a degree of detail greater than that achievable by traditional fieldwork. A particular application of the TLS in forestry studies concerns the identification and separation of wood components from non-wood parts of trees because it is a fundamental and essential step to correctly estimate several attributes of trees and crowns. Over the past decade, thanks to significant advances in technology and improved analysis methods, several approaches have been proposed to separate different materials within the same TLS point cloud: methods that rely on geometric features of the TLS data; methods that rely on radiometric features of the scanned points; and methods that exploit both geometric and intensity features. This article analyses the methods developed over the last twenty years to classify woody and leaf components from TLS data. The study highlights advances in TLS technology and the evolution of analytical methods, describes main characteristics of each approach, and give information on their main application in forestry studies. In general, the most recent approaches are based on the geometric characteristics of point clouds. Recently, the use of machine learning techniques has become widespread and has proven effective in separating leaves from wood in TLS data. In recent years, we have observed an increase in the use of classifiers based on artificial neural networks that seem to be able to achieve high-precision separation results
Correlazioni tra le caratteristiche biochimiche ed ammezzimento nel germoplasma del pero in Sardegna
No full textMorphological and physiological analysis of prickly pear (Opuntia ficus-indica Mill.) pollen
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An optimal Cellular Automata algorithm for simulating wildfire spread
No full textRaster-based methods for simulating wildfire spread are computationally more efficient than vectorbased
approaches. In spite of this, their success has been limited by the distortions that affect the fire
shapes. This work presents a Cellular Automata (CA) approach that is able to mitigate the problem of
distorted fire shapes thanks to a redefinition of the spread velocity, where the equations generally used
in vector-based approaches are modified by means of some correction factors. A numerical optimization
approach is used to find the optimal values for the correction factors. The results are compared to the
ones given by two Cellular Automata simulators from the literature under homogeneous conditions.
According to this work, the proposed approach provides better results, in terms of accuracy, at a comparable
computational cost. The proposed approach has then been compared to Farsite, a vector-based
fire-spread simulator, under realistic slope and wind conditions, producing equivalent results in a
reduced computational time
I consultori familiari e la relazione di aiuto
No full textIl contributo presenta i consultori familiari come servizi di sostegno alla famiglia e alle sue funzioni educative, mettendo in luce le caratteristiche principali di una qualificata relazione di aiuto
A phenological model to predict the time of maximum concentration of olive airborne pollen in the air. :
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