Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery

Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all facilities with flood detection is expensive. Therefore, methods are being sought to ensure that these facilities are monitored at a minimum cost. One of the possibilities is using remote sensing, especially radar data regularly scanned by satellites. A significant challenge in this area was the launch of Sentinel-1 providing free-of-charge data with adequate spatial resolution and relatively high revisit time. This paper presents a developed automatic processing chain for flood detection in the open landscape from Sentinel-1 data. Flood detection can be started on-demand; however, it mainly focuses on autonomous near real-time monitoring. It is based on a combination of algorithms for multi-temporal change detection and histogram thresholding open-water detection. The solution was validated on five flood events in four European countries by comparing its results with flood delineation derived from reference datasets. Long-term tests were also performed to evaluate the potential for a false positive occurrence. In the statistical classification assessments, the mean value of user accuracy (producer accuracy) for open-water class reached 83% (65%). The developed solution typically provided flooded polygons in the same areas as the reference dataset, but of a smaller size. This fact is mainly attributed to the use of universal sensitivity parameters, independent of the specific location, which ensure almost complete successful suppression of false alarms

Limitations of conventional drinking water technologies in pollutant removal

This chapter gives an overview of the more traditional drinking water treatment from ground and surface waters. Water is treated to meet the objectives of drinking water quality and standards. Water treatment and water quality are therefore closely connected. The objectives for water treatment are to prevent acute diseases by exposure to pathogens, to prevent long-term adverse health effects by exposure to chemicals and micropollutants, and finally to create a drinking water that is palatable and is conditioned in such a way that transport from the treatment works to the customer will not lead to quality deterioration. Traditional treatment technologies as described in this chapter are mainly designed to remove macro parameters such as suspended solids, natural organic matter, dissolved iron and manganese, etc. The technologies have however only limited performance for removal of micropollutants. Advancing analytical technologies and increased and changing use of compounds however show strong evidence of new and emerging threats to drinking water quality. Therefore, more advanced treatment technologies are required.</p

The possibilities of flash floods prediction

Flash floods are very important natural phenomenon today, which is devoted considerable attention in the general public, the media, the public administration system in the professional community The main characteristic of flash floods from the perspective of crisis management is the short time between the cause (torrential precipitation) and the result (flooding of the territory and the emergence of major damages or even loss of life). Right the shortness of time implies that to save lives it is necessary to obtain a warning of impending danger in the shortest possible time after the cause. To do this, a new methodology that enables to identify streams, which may be the subject of intense water runoff and thus manifestations of flash floods was created (Rapant, et al., 2015)

A health risk assessment for fluoride in Central Europe

Like many elements, fluorine (which generally occurs in nature as fluoride) is beneficial to human health in trace amounts, but can be toxic in excess. The links between low intakes of fluoride and dental protection are well known; however, fluoride is a powerful calcium-seeking element and can interfere with the calcified structure of bones and teeth in the human body at higher concentrations causing dental or skeletal fluorosis. One of the main exposure routes is via drinking water and the World Health Organisation currently sets water quality guidelines for the element. In Central Europe, groundwater resources that exceed the guideline value of 1.5 mg l-1 are widespread and effects on health of high fluoride in water have been reported. The aim of the current project was to develop a geographic information system (GIS) to aid the identification of areas where high-fluoride waters and fluorosis may be a problem; hence, where water treatment technologies should be targeted. The development of the GIS was based upon the collation and digitisation of existing information relevant to fluoride risk in Ukraine, Moldova, Hungary and Slovakia assembled for the first time in a readily accessible form. In addition, geochemistry and health studies to examine in more detail the relationships between high-fluoride drinking waters and health effects in the population were carried out in Moldova and Ukraine demonstrating dental fluorosis prevalence rates of 60–90% in adolescents consuming water containing 2–7 mg l-1 fluoride

Geographic data as personal data in four EU member states

The EU Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data aims at harmonising data protection legislation in the European Union. This should promote the free flow of products and services within the EU. This research found a wide variety of interpretations of the application of data protection legislation to geographic data. The variety was found among the different EU Member States, the different stakeholders and the different types ofgeographic data. In the Netherlands, the Data Protection Authority (DPA) states that panoramic images of streets are considered personal data. While Dutch case law judges that the data protection legislation does not apply if certain features are blurred and no link to an address is provided. The topographic datasets studied in the case studies do not contain personal data, according to theDutch DPA, while the German DPA and the Belgian DPA judge that topographic maps of a large scale can contain personal data, and impose conditions on the processing of topographic maps. The UK DPA does consider this data outside of the scope of legal definition of personal data. The patchwork of differences in data protection legislation can be harmonised by using a traffic lightmodel. This model focuses on the context in which the processing of the data takes place and has four categories of data: (1) sensitive personal data, (2) personal data, (3), data that can possibly lead to identification, and (4) non-personal data. For some geographic data, for example factual data that does not reveal sensitive information about a person, can be categorised in the thirdcategory giving room to opening up data under the INSPIRE Directive