Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps

The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d’Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification’s accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified

A priori choice of neuraxial labour analgesia and breastfeeding initiation success: A community-based cohort study in an Italian baby-friendly hospital

Objective To investigate whether the nature of the decision about receiving neuraxial labour analgesia is associated with breastfeeding initiation success (BIS), defined as exclusive breastfeeding until discharge associated with postnatal weight loss <7% at 60 hours from birth. Design Single-centre community-based cohort study. Setting An Italian baby-friendly hospital, from 1 July 2011 to 22 September 2015. Participants Inclusion criteria: women vaginally delivering singleton cephalic newborns and willing to breastfeed. Exclusion criteria: women who delivered in uterus-dead fetuses, were single or requested but did not receive neuraxial analgesia. Overall, 775 out of the 3628 enrolled women received neuraxial analgesia. Results Compared with women who tried to cope with labour pain, those who decided a priori to receive neuraxial analgesia had less BIS (planned vaginal birth: 2121/3421 (62.0%), vs 102/207 (49.3%; p<0.001; risk difference (RD), 12.7%); actual vaginal birth: 1924/2994 (64.3%), vs 93/189 (49.2%; p<0.001; RD, 15.1%)). Multivariable analyses with antelabour-only confounders confirmed both associations (planned vaginal birth: relative risk (RR), 0.65; 95% CI, 0.48 to 0.87; actual vaginal birth: RR, 0.59; 95% CI, 0.43 to 0.80). Although women who requested analgesia as a last resort had less BIS than did those successfully coping with labour pain in the bivariable analyses (planned vaginal birth: 1804/2853 (63.2%), vs 317/568 (55.8%; p=0.001; RD, 7.4%); actual vaginal birth: 1665/2546 (65.4%), vs 259/448 (57.8%; p=0.002; RD, 7.6%)), multivariable analyses with either antelabour-only or peripartum confounders did not confirm these associations (planned vaginal birth: RR, 0.99; 95% CI, 0.80 to 1.23; actual vaginal birth: RR, 0.90; 95% CI, 0.69 to 1.16). Conclusions Compared with trying to cope with labour pain, a priori choice of neuraxial analgesia is negatively associated with BIS. Conversely, compared with having successfully coped with pain, requesting neuraxial analgesia as a last resort is not negatively associated with BIS

HERASE: monitorare l’erosione del suolo nelle Alpi con tecniche Geomatiche

In Italia ci sono circa 4 milioni di ettari di terreno agricolo e forestale a rischio di erosione o frana e recenti stime del Ministero dell’Ambiente (2013) indicano che sarebbero necessari 40 miliardi di Euro per ridurre il rischio dovuto alla perdita di suolo sul territorio nazionale. Il progetto Hydrogeological modeling for Erosion Risk Assessment from SpacE (HERASE), finanziato da Fondazione Cariplo (Grant Nr.2016-0768), affronta questo tema nel bacino camuno del fiume Oglio, un’area alpina dell’Italia settentrionale. Scopo di HERASE è mettere a punto una metodologia di analisi basato sul Revised Universal Soil Loss Equation (RUSLE), reso dinamico dall’uso di mappe di copertura del suolo multi-temporali, per evidenziare le zone potenzialmente soggette a fenomeni erosivi e le dinamiche dei cambiamenti del territorio capaci di influenzarne l’entità. Misure in situ di erosione realizzate con un simulatore di pioggia permetteranno la caratterizzazione idrologica di zone rappresentative e la taratura del modello. Infine, le previsioni restituite dai modelli climatici saranno utilizzate per delineare possibili scenari di rischio futuro, in un contesto che vede il territorio montano, e quello alpino in particolare, soggetto a sempre più evidenti cambiamenti climatici. Il presente lavoro riporta alcuni risultati preliminari del progetto HERASE ottenuti sul sotto-bacino del torrente Arcanello (circa 21 km2), dove la stima preliminare dell’erosione è pari a 7,61 [t ha-1 a-1]. Tale risultato è concorde con il valore medio annuo a livello nazionale

Minimum noise fraction transform for improving the classification of airborne hyperspectral data: two case studies

This paper investigates the use of Minimum Noise Fraction (MNF) components to improve the spectral separability of two specific thematic classes in airborne hyperspectral imagery using Spectral Angle Mapper (SAM). Particularly, we compared trends on data distribution before and after MNF transform. Two different data sets recorded with the Multispectral Infrared Visible Imaging Spectrometer (MIVIS) were analyzed. In the first case study, the classification of MNF-transformed data led to an overall enhancement in mapping asbestos roofs. In the second case study, the classification of MNF-transformed data succeeded to distinguish between two different artificial lakes, whereas classification of original hyperspectral data failed. Overall, this study showed how the use of MNF as pre-processing could improve the capability to extract information from two different airborne hyperspectral data sets

Remote Sensing Urban Analysis

Satellite remote sensing is the process of collecting information about the Earth’s surface from the space through the measure of electromagnetic radiation. Nowadays, remote sensing is a mature technology used to extract, analyze and detect changes of geographic and thematic information over large areas, inacces-sible sites or where only limited knowledge is available. In this chapter we describe how satellite’s data collected over Multan (Pakistan) have been used for mapping and monitoring the dynamics of the urban area. A multi-scale approach allowed to evaluate the urban growth of the Municipality area occurred in the last 2 dec-ades with medium-resolution Landsat-5/TM time series. Urban green plots and in-frastructures (buildings and roads) have been mapped at the local scale of the his-toric Walled City with the state of the art GeoEye-1 and WorldView-2 very high-resolution multispectral imagery

High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification

This study shows a comparison between pixel-based and object-based approaches in data fusion of high-resolution multispectral GeoEye-1 imagery and high-resolution COSMO-SkyMed SAR data for land-cover/land-use classification. The per-pixel method consisted of a maximum likelihood classification of fused data based on discrete wavelet transform and a classification from optical images alone. Optical and SAR data were then integrated into an object-oriented environment with the addition of texture measurements from SAR and classified with a nearest neighbor approach. Results were compared with the classification of the GeoEye-1 data alone and the outcomes pointed out that per-pixel data fusion did not improve the classification accuracy, while the object-based data integration increased the overall accuracy from 73% to 89%. According to results, an object-based approach with the introduction of adjunctive information layers proved to be more performing in land-cover/land-use classification than standard pixel-based methods

Airborne remote sensing for mapping asbestos roofs in Aosta Valley

This paper describes the use of airborne hyperspectral remote sensing for mapping asbestos roofs in an orographic complex area in Northern Italy, the Aosta Valley. Using training samples collected during field surveys, thematic classification was able to detect the majority of asbestos surfaces. Considering the total amount of asbestos areas validation showed a correct detection of about 80%, while considering the number of asbestos roofs correctly detected this value decreased to 43%. This difference pointed out a clear relationship between data spatial resolution and asbestos roofs area. The study served as a first approach to an extensive use of the remote sensing technology for asbestos mapping over large areas and the encouraging results will support Public Administrations for decision making strategies and policies for their removal

D-RUSLE: a dynamic model to estimate potential soil erosion with satellite time series in the Italian Alps

Soil erosion is addressed as one of the main hydrogeological risks in the European Union. Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon

Large plot erosion simulations on Alpine area

We propose an experimental setup to measure soil erosion and related it with sediment transport using rainfall simulator on a large plots experimental area. Experiments were carried out in fulfilment of the HERASE project, aimed at investigating the seasonal variations of soil erosion in the Oglio basin, an Alpine and Pre-alpine watershed with an area of about 1800 km2, and maximum elevation of 3.538m a.s.l. Namely some rainfall simulation experiments were carried out to assess water erosion under three different scenarios, on in situ plots having same soil and slope, but with different soil coverage and initial moisture condition. In particular, two experiments were carried out on a grassland plot covering an area of 144m2, one considering an undisturbed initial soil moisture condition and the other on a wet soil. The third experiment plot, having the same soil and slope of the previous, and covering an area of 72m2, was set up in the pine forest underwood area. The duration of the three experiments were respectively of 18 minutes, 30 minutes and 30minutes and rainfall intensity was set equal to 70mm/h, accordingly to a 200 return period storm. The plots were properly designed to ensure a correct as possible measure of surface runoff, and transported sediments were collected at 1 minute steps. Suspended sediment, and particle size analysis were carried out ex-post. Our results show a different behaviour of the two analysed plots, in terms of runoff generation and sediment transport. The runoff peak on grassland ranged from 0.018l/s for the undisturbed condition, to 0.09l/s with wet initial condition, while the peak in sediment transport, slowly delayed with respect to the runoff peak, was 1,7 times bigger in wet conditions. In the third experiment, peak runoff was 0.9l/s (10 times bigger than the peak runoff on wet grassland), and constant until the end of simulated rainfall, and took five minutes to run out (concentration time). The analysis on sediment shows an initial slush, where pine needles composed the majority of sediment. Then, after the sediment reached the peak of 350mg/s after about 9 minutes from the beginning of the experiment, it decreased rapidly to an almost constant value of 50mg/s. Interpretations on experiment results with different formulations and a comparison with similar analyses carried out in the Alpine area of Italy are proposed

MODELLING SOIL EROSION IN THE ALPS WITH DYNAMIC RUSLE-LIKE MODEL AND SATELLITE OBSERVATIONS

Soil water erosion is a creeping natural phenomenon, mostly related to weather and climate, and one of the main hydrogeological risk in Europe. It causes nutrients loss and exposes the environment to landslides, with negative impacts on agriculture, ecosystem services and infrastructures. Conversely, several human activities induce environmental modifications which intensify pressure on soils, thus increasing their predisposition to erosion. This study describes the integration of satellite observations with a modified version of the well-known Revised Universal Soil Loss Equation (RUSLE) model for estimating soil erosion in an Italian Alpine river basin. Compared to traditional RUSLE formulation, in this study we assigned the cover management factor using a combination of DUSAF land cover classification and NDVI values computed from Landsat time series. Rainfall erosivity was estimated separating liquid precipitation (erosive) and solid precipitation (non-erosive) from hourly data. Soil erodibility for the study area was tuned combining soil maps with total organic carbon (TOC), acidity (pH) and texture (granulometry) from soil samples collected on site. Finally, the slope length and steepness factor was derived using a 30-meter spatial resolution digital elevation model. Integrating the RUSLE-like model with spectral indices derived from satellite data allows highlighting spatial patterns useful for understanding soil erosion dynamic and forcing. Thus, satellite-derived spectral information, that include both seasonal and long-term land cover changes, opens new ways for modelling the dynamics of soil erosion