The ANDROID Case Study; Venice and its Territory: Identification of Hazards and Impact of Multi-hazard Scenarios☆

Abstract The objective of the paper is to review already published scientific papers and other relevant documents to identify hazards, their intensities and probability of occurrence in the Venice territory. In order to achieve the objective, the authors have selected relevant research papers and state of the art documents. Since the Venice and its territory are prone to various hazards, multi-hazard scenarios have been taken into consideration. Hazard impacts are the following: earthquake, tsunami and meteotsunami, flooding/"acqua alta", subsidence, coastal erosion, salt wedge intrusion, pollution. The paper classifies potential impacts and recognises possible combinations of hazards that may occur in case study territory. A multi-hazard scenarios analysis considers impacts which, either occurring at the same time or shortly following each other, are dependent from one another or because they are caused by the same triggering event or hazard, or merely threatening the same elements at risk (vulnerable or exposed elements) without chronological coincidence (EU, 2010). The research presented in the paper serves as a support for cross-border multi-hazard assessment in other North-Eastern Adriatic Sea areas

Integrated Use of Space, Geophysical and Hyperspectral Technologies Intended for Monitoring Water Leakages in Water Supply Networks

Remote sensing has been used for water management purposes over the years. This book describes the combination of satellite imagery, in-situ spectroradiometric data and radar techniques for the identification of water leakages in the water supply network in both rural and urban areas in Cyprus. This book presents a holistic approach combining new technologies for a complete system of water distrib..

The capacity of European Higher Educational Institutions to address threats imposed by natural hazards

The main aim of this work was to investigate the availability and capacity of educational programmes at different levels at European Higher Educational Institutions (HEI) that are directly related to disaster resilience. This investigation was important since education in disaster resilience for citizens, communities, public administrators and decision makers is vital for improving preparedness, mitigation, resistance, absorption and recovery as a result of natural disasters. For this purpose, a study on the capacity of HEIs across Europe to address the threats imposed by natural hazards has been carried out. The study was performed by using a web-based survey tool which ran for 1 year with participants from 28 European countries. Findings of this work showed a great lack of educational programmes in disaster resilience across Europe. However, in recent years, there has been an increase in such programmes mainly at postgraduate level. At undergraduate level, there is only a very limited number of such programmes, which might be related to the multidisciplinary nature of these programmes. The cohort size of the ongoing programmes indicates that they are in good health despite their young age, suggesting the potential and need for many more. The results of this study are valuable for HEIs, policy makers, decision makers and governments as it provides an insight into the existing situation across Europe with regard to educational programmes directly related to disaster resilience which can help them to focus, direct and promote education in disaster resilience according to the needs at national and European level.Web of Science8131466144

A simple method to estimate vegetation indices and crop canopy factors using field spectroscopy for solanum tuberosum during the whole phenological cycle

Field spectroscopy is a part of the remote sensing techniques and very important for studies in agriculture. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data of the spring potatoes for estimating spectral vegetation indices (SVI's). A field campaign was undertaken from September to the end of November 2012 for the collection of spectro-radiometric measurements. The study area was in the Mandria Village in Paphos district in Cyprus. This paper demonstrates how crop canopy factors can be statistically related to remotely sensed data, namely vegetation indices. The paper is a part of an EU cofounded project regarding estimating crop water requirements using remote sensing techniques and informing the farmers through 3G smart telephony

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

In this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans’ canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters