Antonio Garzón

Pan-European Grassland Mapping Using Seasonal Statistics From Multisensor Image Time Series

Grasslands cover approximately 40% of the Earths surface. Low-cost tools for inventory, management, and monitoring are needed because of their great expanse, diversity, and the importance for environmental processes. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and large-scale grassland management. In the context of “GIO land” (Copernicus Initial Operations land program), which is currently contracted by the European Environment Agency, a high-resolution grassland layer of 39 European countries is being created with an overall classification accuracy of better than 80%. Since grassland canopy density, chlorophyll status, and ground cover (GC) are highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use image time series to characterize the phenological dynamics of grasslands throughout the year in order to discriminate between grasslands and other vegetation with similar spectral responses. This paper describes an operational approach based on a multisensor concept that employs optical multitemporal and multiscale satellite imagery to generate the high-resolution pan-European grassland layer. The approach is based on the supervised decision tree classifier C5.0 in combination with previous image segmentation and seasonal statistics for various vegetation indices (VIs). Results from the grassland classification for Hungary are presented. The accuracy assessment for this classification was carried out using 328 independent sample points derived from a ground-based European field survey program (LUCAS) and current CORINE Land Cover data. The grassland classification approach is explained in detail on the example of Hungary where an overall accuracy of 92.2% has been reached.

The European Urban Atlas

The origin of the Urban Atlas answers to a European Commission need for detailed and comparable information related to cities. The urban information is so far provided by local and national statistics or using data extracted from land cover maps as CORINE Land Cover. CORINE Land Cover provides a resolution of 1:100,000 (25 ha) and is clearly unsatisfactory for the demand of the Commission services. As in Europe, urban areas accommodate more than three-quarters of the population and these areas have grown rapidly in recent decades, there is an urgent need for pan-European, reliable and inter-comparable urban planning data. The Urban Atlas, developed as part of GMES (Global Monitoring for Environment and Security) brings exactly that.

Impacts of the 4.5 and 8.5 RCP global climate scenarios on urban meteorology and air quality

Climate change is expected to influence urban living conditions and challenge the ability of cities to adapt and mitigate climate change. This paper describes a new modelling system for climate change impact assessments on urban climate and air quality with feasible computational costs (the expected CPU time is too large for actual supercomputer platforms). The system takes the outputs from a global climate model, which are injected into a dynamical regional climate model (WRF-Chem) with the nested capability activated, with 25 km spatial resolution. In addition, the system uses a diagnostic meteorological model (CALMET) to produce urban detailed information (with 200 m spatial resolution) using this downscaling procedure. At the city level, a simplified chemical-transport model (based on CMAQ and using linear chemistry) is used to map the spatial distribution of the pollutants. The system is applied to five European cities: Madrid, Antwerp, Milan, Helsinki and London (Kensington-Chelsea area). The modelling system was used to simulate the climate and air quality for present year (2011) and future years (2030, 2050 and 2100) using 2011 emissions as control run, because we want to investigate the effects on the global climate on the actual (2011) cities. Effects on temperature, precipitation, and ozone are also considered. We compare the climate and air concentrations in future years 2030, 2050 and 2100 with the control year (2011). Comparison of simulations for present situation (using NNRP reanalysis 2011 data sets) shows acceptable agreement with measurements which give us strong confidence on the results for the RCP IPCC climate future simulations for 4.5 and 8.5 scenarios. Impacts of global climate on urban scale are showed for 2030, 2050 and 2100 for 4.5 and 8.5 RCP IPCC climate scenarios.Dynamical and diagnostic downscaling processes are properly combined.Two RCP scenarios are considered: 4.5 and 8.5.Present (2011) and future (2030, 2050 and 210) years are simulated.

Using Space-Based Downstream Services for Urban Management in Smart Cities

Data collected through remote sensing (for instance geo-satellite) provides necessary stimulus for developing smart solutions for climate change & public health, energy efficiency and land monitoring in an urban environment. The velocity, variety, volume and veracity of high resolution data produced by geo-satellites provide big opportunity for planning and decision making in a smart city context. However, processing and integrating remote sensing data with auxiliary data sources require proper data management and elastic computational resources to derive necessary information intelligence (or knowledge) for decision making. This paper presents prototype of selected Decumanus services and highlights strengths & weaknesses of climate change, energy efficiency and land monitoring applications for the different European cities. The analysis of the early results indicate that the amount of computation resources required to process data for above applications make cloud computing a suitable technology but also face challenges in adopting it due to its recency, impact on green computing and reluctance to transform from legacy computing systems to new paradigms like cloud computing. We critically discuss these challenges and suggest possible solutions.

Comparison between different dynamical downscaling methods using WRF-Chem for urban applications: Madrid case study

The European Centre for Medium-Range Weather Forecasts is used to provide boundary conditions for the mesoscale model WRF-Chem that has been run over Europe with 23 km spatial resolution. We have used a full one-way nesting approach to produce simulations centred over the city of Madrid (Spain) with 4.6 km spatial resolution, 0.92 km spatial resolution and 0.184 km spatial resolution. In last level, we have run the CMAQ (full chemistry) model to produce chemical pollution data. This is called the control reference simulation. The simplified and faster downscaling procedure used in this experiment is the CALMET-CMAQL (linear chemistry) model. Both downscaling techniques are compared using meteorological and air pollution monitoring station. The comparison between both downscaling techniques shows that the CALMET-CMAQL model is much faster and computationally cheap; the results are good enough to consider this tool for climate purposes.

Climate Downscaling Processes Using WRF/Chem and CALMET Modelling Systems: Madrid Case Study

Downscaling techniques are very important to assure the robustness and credibility of climate modelling exercises. Regional climate simulations use boundary conditions and initial conditions from global climate and meteorological models. The regional climate simulations (WRF/chem model) have much higher spatial resolution and using nesting approaches can be used to derive climate indicators at urban level. Dynamical nesting approaches -- also known as dynamical downscaling procedures -- use a substantial amount of computer power, particularly for urban applications, other alternatives such as CALMET diagnostic model (for meteorological applications) and CMAQ model (with linear chemistry) produce results faster and can be used for climate applications with reasonable required computer power. In this contribution, we are using the European Centre for Medium-Range Weather Forecasts (ECMWF) model data sets to provide boundary conditions for the mesoscale model WRF/Chem (NOAA, US) that has been ran over Europe with 23 km spatial resolution and 33 vertical levels up to 50 hPa. We have used the full nesting approach defined into WRF model to produce simulations centered over the city of Madrid (Spain) with 4.6 km spatial resolution (nesting level 1, l1), 0.92 km spatial resolution (nesting level 2, l2) and 0.184 km spatial resolution (nesting level 3, l3). In l3, we have run the CMAQ (full chemistry) model (EPA, US) to produce chemical pollution data. We have applied both downscaling techniques over Madrid area using Retiro meteorological and air pollution monitoring station as observational station. The comparison between both downscaling techniques shows that CALMET-CMAQL (linear chemistry) model is much faster and the results are good enough (compared with other simulations results) to consider this tool, when the number of simulations for climate purposes is very high (due to many years and several climate scenarios) and the application of the WRF/chem model (dynamical downscaling) is prohibited computationally.