Vehicle Accident Databases: Correctness Checks for Accident Kinematic Data

(1) Background: Data collection procedures allow to obtain harmonization of in-depth road accident databases. Plausibility of calculable accident-related kinematic parameters depends on the constraints imposed on calculation, making their uncertainty degree higher than the one for measurable parameters (i.e., traces, airbag activation, etc.). Uncertainty translates in information loss, making the statistics based on databases analysis less consistent. Since kinematic parameters describe the global accident dynamics, their correctness assessment has a fundamental importance; (2) Methods: the paper takes as reference data collected in the Initiative for the GLobal harmonisation of Accident Data (IGLAD) database for vehicle-to-vehicle crashes. The procedure, however, has general nature and applies identically for other databases and multiple impacts between vehicles. To highlight issues which can arise in accident-related data collection, 3 different checks are proposed for parameters correctness assessment; (3) Results: by 4 examples, 1 with correct and 3 with incorrect parameters reported, the paper demonstrates that errors can go beyond simple calculation uncertainty, implying that a deeper analysis is desirable in data collection; (4) Conclusions: the step-by-step guidelines described in this paper will help in increasing goodness of collected data, providing for a methodology which can be used by each individual involved in accident data collection, both for collection itself and subsequent verification analysis

The new hyperspectral satellite prisma: Imagery for forest types discrimination

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups

Effects of forest management on beetle (Coleoptera) communities in beech forests (Fagus sylvatica) in the Apennines of Central Italy (Tuscany)

In European mountains most beech forest areas have been managed for timber production. This practice has reduced the availability of biomass for the whole forest-dwelling species assemblage and of deadwood for the saproxylic community. Despite most of Italy’s beech stands having a long history of management, its effects on forest species remain poorly understood. To address this gap, we studied beetle abundance and diversity in five beech-dominated forests with increasing management intensity in central Italy’s Apennines (Tuscany). We assessed if forests with similar management intensity exhibited comparable patterns in beetle diversity, abundance, and commonness versus rarity. Three forests were managed with even-aged shelterwood; one was managed with continuous cover forestry; and one was old-growth. We found 25 beetle families and 195 species across all sites with similar total abundance and richness. However, the representation of the most abundant families varied among sampling sites (ANOVA test: always significant for the total abundance of the most abundant families: F ≥ 2.77, d.f. = 4, p ≤ 0.038). The old-growth forest harbored more threatened species than managed sites. Saproxylic assemblages were similar between the recently cut site and the old-growth forest, and between shelterwood and continuous cover sites. While the similarity gradient among the whole species assemblages reflected geographical proximity, the similarity gradient among saproxylic assemblages reflected the successional proximity among forest management systems. Our research underscores the effects of management on beetle diversity, offering insights for sustainable forestry

random sequence for optimal low power laser generated ultrasound

Low-power laser generated ultrasounds are lately gaining importance in the research world, thanks to the possibility of investigating a mechanical component structural integrity through a non-contact and Non-Destructive Testing (NDT) procedure. The ultrasounds are, however, very low in amplitude, making it necessary to use pre-processing and post-processing operations on the signals to detect them. The cross-correlation technique is used in this work, meaning that a random signal must be used as laser input. For this purpose, a highly random and simple-to-create code called T sequence, capable of enhancing the ultrasound detectability, is introduced (not previously available at the state of the art). Several important parameters which characterize the T sequence can influence the process: the number of pulses Npulses , the pulse duration δ and the distance between pulses dpulses . A Finite Element FE model of a 3 mm steel disk has been initially developed to analytically study the longitudinal ultrasound generation mechanism and the obtainable outputs. Later, experimental tests have shown that the T sequence is highly flexible for ultrasound detection purposes, making it optimal to use high Npulses and δ but low dpulses . In the end, apart from describing all phenomena that arise in the low-power laser generation process, the results of this study are also important for setting up an effective NDT procedure using this technology

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019–October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE < 3725.3 ha, SE% < 9.69, OA > 89.7, PA and UA > 0.87, gmean > 0.83)

Estimating VAIA windstorm damaged forest area in Italy using time series Sentinel-2 imagery and continuous change detection algorithms

Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019 – October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE< 3725.3 ha, SE% < 9.69, OA > 89.7, PA and UA > 0.87, gmean > 0.83)