Search

Materials and Methods The IRIP© hydrological geomatics mapping model, or "Indicator of Intense Pluvial Runoff", is confronted with past extreme events for which rainfall radar measurements were acquired and damage maps were derived from multispectral bi-temporal satellite imagery (Sentinel-1 and 2) and machine learning (ML) supervised classification algorithms. Results Study areas Six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 2.000 km 2 of rural and periurban areas during three flash-flood events (2018-2020).

International audience; This work focuses on the contribution of modelling for the interpretation of multi- or hyperspectral optical images for the detection, characterisation and quantification of oil spills. Many parameters contribute to the spectral signature of an oil layer on the sea surface: the optical properties of the water column and of the oil, the film thickness, the surface roughness, the atmospheric radiance reaching the surface (direct and diffuse components), the geometry of observation and illumination. The number of these contributors and their combinations make the analysis of the spectral variability of oil signatures at the sea surface complex. Modelling approaches allow us to consider all those parameters and can then provide useful information to improve the interpretation of optical images. The model presented in this paper simulates the radiance of an oil layer from visible to short wave infrared spectral domains, taking into account all the above-mentioned parameters. The damping influence of the oil layer on sea surface waves is also considered. Comparisons of the simulations with in situ measurements shows a good overall agreement despite the lack of knowledge of some input parameters of the model. In combination with laboratory and in-the-field measurements, the model is then used to assess the expected contrast between water and oil and to estimate oil slick volume.; Ce travail se concentre sur la contribution de la modélisation pour l'interprétation des des images optiques hyperspectrales pour la détection, la caractérisation et la quantification des nappes de pétroles. De nombreux paramètres contribuent à la signature spectrale d'une couche d'hydrocarbures à la surface de la mer : les propriétés optiques de la colonne d'eau et des hydrocarbures, l'épaisseur du film, la rugosité de la surface, le rayonnement atmosphérique atteignant la surface (composantes directe et diffuse), la géométrie de l'observation et de l'illumination. Le nombre de ces facteurs et leurs combinaisons rendent complexe l'analyse de la variabilité spectrale des signatures d'hydrocarbures à la surface de la mer. Les approches de modélisation permettent de prendre en compte tous ces paramètres et peuvent ensuite fournir des informations utiles pour améliorer l'interprétation des images optiques. Le modèle présenté dans cet article simule la luminance d'une couche de pétrole du visible au SWIR en tenant compte tous les paramètres mentionnés ci-dessus. L'influence de la couche de pétrole sur l'amortissement des vagues est également pris en compte. La comparaison des simulations avec les mesures in situ montre un bon accord global malgré la méconnaissance de certains paramètres d'entrée du modèle. Combiné à des mesures terrain et en laboratoire , le modèle est ensuite utilisé pour évaluer le contraste attendu entre l'eau et le pétrole et pour estimer le volume de la nappe de pétrole.

Roof materials can be a significant source of pollution for the environment and can have negative health effects. Analyses of runoff water revealed high levels of metal traces but also polycyclic aromatic hydrocarbons and phthalates. This contamination would result from corrosion and alteration of roof materials. Similarly, the alteration or combustion of asbestos contained in certain types of roofs may allow the emission and dispersion of asbestos fibres into the environment. Therefore, acquiring information on roof materials is of great interest to decrease runoff water pollution, and to improve air and environmental quality around our homes. To this end, remote sensing is a particularly relevant tool since it allows semi-automatic mapping of roof materials using multispectral or hyperspectral data. The CASMATTELE project aims to develop a semi-automatic identification tool of roofing materials over the Liege area using remote-sensing and machine learning for public authorities., info:eu-repo/semantics/published

International audience; To date, estimating oil thickness on the sea surface remains a challenge in most cases. When oil thickness estimation using optical data is limited by the absorption properties of the target, a solution consists in combining experimental and airborne hyperspectral data. We developed a method to identify thickness classes from hyperspectral data which, combined with realistic thickness values derived from a pool experiment, allows to estimate slick volume. Hyperspectral images of the same oil emulsion were acquired over a pool and at sea, under real conditions. From the pool data, we derived two classes: the sheen and the thick pixels, along with their respective thickness. These classes are then identified on the airborne images acquired during the NOFO campaign by generating a detection mask and using two classification approaches based on spectral indices. The proposed method allows to correctly identify the two thickness classes and, combined with the data from the pool experiment, provides a total slick volume larger than the one derived for the Bonn Agreement Oil Appearance Code.

Most of the methods used to retrieve land surface temperature (LST) from thermal infrared (TIR) satellite data in urban areas do not take into account the complexity of the surface. Cities are characterized by high surface roughness and one of the main constraints to estimate LST over those areas is the difficulty to define an effective emissivity for a given pixel at a given scale. When working with mixed pixels, the emissivity used to estimate the LST is an effective emissivity composed of the emissivities of each basic element constituting the pixel. In urban areas, the surface geometry has a strong impact on this effective emissivity. Its estimation from TIR satellite data must be carried out considering multiple surface reflections and diffusions within the urban canopy in order to retrieve accurate LST values. The objective of this study is then to evaluate the impact of the surface geometry within the pixel on effective emissivity estimation and to propose a method to derive an effective emissivity corrected for those effects. Emissivity can be derived at 90 m of spatial resolution from the TIR data acquired by ASTER. To evaluate the impact of the geometry at the scale of an ASTER pixel, several urban canyon configurations are designed to develop and test the correction method. The basic principle behind the method is to accurately estimate the downwelling TIR radiation received by a pixel integrating contributions from both the atmosphere and the scene inside this pixel and then derive the corrected effective emissivity from ASTER data using the TES (temperature emissivity separation) algorithm. First, the total downwelling TIR radiation is estimated from the geometric characteristics of the scene, using morphological indicators and integrating the non-isothermal behavior of the pixel thanks to 3D thermo-radiative model simulations. The validation of those estimations for each canyon configuration provides a maximum RMSE (Root Mean Square Error) value of 2.2 W·m−2. The validation performed over a district extracted from the 3D numerical model of Strasbourg (France) shows a RMSE of 2.5 W·m−2. Once the method to estimate the total downwelling TIR radiation is validated, LSE and LST maps are retrieved from an ASTER image over three districts of Strasbourg, showing that accounting for the surface geometry highlights thermal behavior differences inside districts, and that the impact of the geometry seems more influenced by building height than street width or building density.

Regions with specific territorial features have received increasing attention in recent years, most notably in article 174 of the Treaty on the Functioning of the European Union (TFEU) and the Green Paper on Territorial Cohesion. These key policy documents identify certain territories – cross-border, island, mountain, Outermost and sparsely populated regions – in two ways: as having particular challenges, and as having particular assets, many of benefit to Europe as a whole. Two other types of such ‘geographic specificities' have also been recognised: coastal areas and inner peripheries. While there have been a number of studies of groups of these areas, or individual types of territories (e.g., coasts, mountains) at the European scale, GEOSPECS is the first comprehensive study of all of these particular types of territories.

Territorial specificities are mostly described as geographic, i.e. sparsely populated, insular, border, and mountainous regions, and generally result in economic and social performance levels around or below European averages. However, other territorial specificities are also found in Europe, in which the socio-economic characteristics prevail above the geographic ones. These specificities include ‘areas affected by industrial transition', as mentioned in article 174 of the Treaty on the Functioning of the European Union among ‘the regions concerned that should be paid particular attention'. Some countries and regions have identified these peripheral areas ‘out of the socio-economic loop' as ‘Inner Peripheries' (IP). No policy documents at European level address these explicitly, illustrating that the concept of IP as such is new in the European policy arena.