Probability inundation map for a scenario combining storm surge and sea-level rise

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Pan-European land cover/use map for 2009

GlobCorine demonstrated an automatic service that can generate in a consistent way land cover / land use maps and land change indicators, based on a CLC-compatible legend. CLC is derived from a visual identification and classification of landscape objects using high resolution images. This methodology provides high thematic accuracy but limits the update rate since it is time-consuming. Therefore, the project evaluated the use of MERIS FR time series, processed automatically to provide a more frequent update of CLC-compatible maps. GlobCorine built upon the experience and resources available through the GlobCover project, to tune the classification chain and adapt it to the EEA needs, covering the pan-European area (including the Mediterranean basin and the European Russia), although the system could be potentially extendable globally. The project delivered two CLC-compatible pan-European land cover maps in less than two years, demonstrating efficient and quick production. The first map is based on Envisat MERIS fine resolution (300m) mode data acquired between end 2004 and mid 2006, while the second used full-year 2009 data. GlobCorine is an initiative of ESA with the partnership of EEA and is implemented by Universite' catholique de Louvain - UCL.

Bathymetric map of Heron Reef, Australia, derived from airborne hyperspectral data at 1 m resolution

A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.

Map of North African dust emission

Changes in the emission, transport and deposition of aeolian dust have profound effects on regional climate, so that characterizing the lifecycle of dust in observations and improving the representation of dust in global climate models is necessary. A fundamental aspect of characterizing the dust cycle is quantifying surface dust fluxes, yet no spatially explicit estimates of this flux exist for the World's major source regions. Here we present a novel technique for creating a map of the annual mean emitted dust flux for North Africa based on retrievals of dust storm frequency from the Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the relationship between dust storm frequency and emitted mass flux derived from the output of five models that simulate dust. Our results suggest that 64 (±16)% of all dust emitted from North Africa is from the Bodélé depression, and that 13 (±3)% of the North African dust flux is from a depression lying in the lee of the Aïr and Hoggar Mountains, making this area the second most important region of emission within North Africa.